Hybrid generative-discriminative human action recognition by combining spatiotemporal words with supervised topic models

NASA Astrophysics Data System (ADS)

Sun, Hao; Wang, Cheng; Wang, Boliang

2011-02-01

We present a hybrid generative-discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. A video sequence is represented as a collection of spatiotemporal words by extracting space-time interest points and describing these points using both shape and motion cues. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure that is most relevant to action categorization. The proposed algorithm retains most of the desirable properties of generative learning while increasing the classification performance though a discriminative setting. It has also been extended to exploit both labeled data and unlabeled data to learn human actions under a unified framework. We test our algorithm on three challenging data sets: the KTH human motion data set, the Weizmann human action data set, and a ballet data set. Our results are either comparable to or significantly better than previously published results on these data sets and reflect the promise of hybrid generative-discriminative learning approaches.

Action Learning, the Tool for Problem-Solving in Universities; Uganda Martyrs Nkozi, Makerere and Nkumba Universities

ERIC Educational Resources Information Center

Bwegyeme, Jacinta; Munene, John C.

2015-01-01

The article presents an account of how action learning principles were implemented to alleviate complex problems in universities. It focuses on the registrars and administrators under the academic Registrar's department. The Marquardt model of action learning was used in combination with the constructivist theories of learning, namely community of…

Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI.

PubMed

Colas, Jaron T; Pauli, Wolfgang M; Larsen, Tobias; Tyszka, J Michael; O'Doherty, John P

2017-10-01

Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.

Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Blended Learning in Action: A Practical Guide toward Sustainable Change

ERIC Educational Resources Information Center

Tucker, Catlin R.; Wycoff, Tiffany; Green, Jason T.

2017-01-01

Blended learning has the power to reinvent education, but transitioning to a blended model is challenging. Blended learning requires a fundamentally new approach to learning as well as a new skillset for both teachers and school leaders. Loaded with research, examples, and resources, "Blended Learning in Action" demonstrates the…

The role of efference copy in striatal learning.

PubMed

Fee, Michale S

2014-04-01

Reinforcement learning requires the convergence of signals representing context, action, and reward. While models of basal ganglia function have well-founded hypotheses about the neural origin of signals representing context and reward, the function and origin of signals representing action are less clear. Recent findings suggest that exploratory or variable behaviors are initiated by a wide array of 'action-generating' circuits in the midbrain, brainstem, and cortex. Thus, in order to learn, the striatum must incorporate an efference copy of action decisions made in these action-generating circuits. Here we review several recent neural models of reinforcement learning that emphasize the role of efference copy signals. Also described are ideas about how these signals might be integrated with inputs signaling context and reward. Copyright © 2014 Elsevier Ltd. All rights reserved.

Procuring a Sustainable Future: An Action Learning Approach to the Development and Modelling of Ethical and Sustainable Procurement Practices

ERIC Educational Resources Information Center

Boak, George; Watt, Peter; Gold, Jeff; Devins, David; Garvey, Robert

2016-01-01

This paper contributes to an understanding of the processes by which organisational actors learn how to affect positive and sustainable social change in their local region through action learning, action research and appreciative inquiry. The paper is based on a critically reflective account of key findings from an ongoing action research project,…

Dose Dependent Dopaminergic Modulation of Reward-Based Learning in Parkinson's Disease

ERIC Educational Resources Information Center

van Wouwe, N. C.; Ridderinkhof, K. R.; Band, G. P. H.; van den Wildenberg, W. P. M.; Wylie, S. A.

2012-01-01

Learning to select optimal behavior in new and uncertain situations is a crucial aspect of living and requires the ability to quickly associate stimuli with actions that lead to rewarding outcomes. Mathematical models of reinforcement-based learning to select rewarding actions distinguish between (1) the formation of stimulus-action-reward…

The role of first impression in operant learning.

PubMed

Shteingart, Hanan; Neiman, Tal; Loewenstein, Yonatan

2013-05-01

We quantified the effect of first experience on behavior in operant learning and studied its underlying computational principles. To that goal, we analyzed more than 200,000 choices in a repeated-choice experiment. We found that the outcome of the first experience has a substantial and lasting effect on participants' subsequent behavior, which we term outcome primacy. We found that this outcome primacy can account for much of the underweighting of rare events, where participants apparently underestimate small probabilities. We modeled behavior in this task using a standard, model-free reinforcement learning algorithm. In this model, the values of the different actions are learned over time and are used to determine the next action according to a predefined action-selection rule. We used a novel nonparametric method to characterize this action-selection rule and showed that the substantial effect of first experience on behavior is consistent with the reinforcement learning model if we assume that the outcome of first experience resets the values of the experienced actions, but not if we assume arbitrary initial conditions. Moreover, the predictive power of our resetting model outperforms previously published models regarding the aggregate choice behavior. These findings suggest that first experience has a disproportionately large effect on subsequent actions, similar to primacy effects in other fields of cognitive psychology. The mechanism of resetting of the initial conditions that underlies outcome primacy may thus also account for other forms of primacy. PsycINFO Database Record (c) 2013 APA, all rights reserved.

How and why do infants imitate? An ideomotor approach to social and imitative learning in infancy (and beyond).

PubMed

Paulus, Markus

2014-10-01

It has been proposed that already in infancy, imitative learning plays a pivotal role in the acquisition of knowledge and abilities. Yet the cognitive mechanisms underlying the acquisition of novel action knowledge through social learning have remained unclear. The present contribution presents an ideomotor approach to imitative learning (IMAIL) in infancy (and beyond) that draws on the ideomotor theory of action control and on recent findings of perception-action matching. According to IMAIL, the central mechanism of imitative and social learning is the acquisition of cascading bidirectional action-effect associations through observation of own and others' actions. First, the observation of the visual effect of own actions leads to the acquisition of first-order action-effect associations, linking motor codes to the action's typical visual effects. Second, observing another person's action leads to motor activation (i.e., motor resonance) due to the first-order associations. This activated motor code then becomes linked to the other salient effects produced by the observed action, leading to the acquisition of (second-order) action-effect associations. These novel action-effect associations enable later imitation of the observed actions. The article reviews recent behavioral and neurophysiological studies with infants and adults that provide empirical support for the model. Furthermore, it is discussed how the model relates to other approaches on social-cognitive development and how developmental changes in imitative abilities can be conceptualized.

Online learning and control of attraction basins for the development of sensorimotor control strategies.

PubMed

de Rengervé, Antoine; Andry, Pierre; Gaussier, Philippe

2015-04-01

Imitation and learning from humans require an adequate sensorimotor controller to learn and encode behaviors. We present the Dynamic Muscle Perception-Action(DM-PerAc) model to control a multiple degrees-of-freedom (DOF) robot arm. In the original PerAc model, path-following or place-reaching behaviors correspond to the sensorimotor attractors resulting from the dynamics of learned sensorimotor associations. The DM-PerAc model, inspired by human muscles, permits one to combine impedance-like control with the capability of learning sensorimotor attraction basins. We detail a solution to learn incrementally online the DM-PerAc visuomotor controller. Postural attractors are learned by adapting the muscle activations in the model depending on movement errors. Visuomotor categories merging visual and proprioceptive signals are associated with these muscle activations. Thus, the visual and proprioceptive signals activate the motor action generating an attractor which satisfies both visual and proprioceptive constraints. This visuomotor controller can serve as a basis for imitative behaviors. In addition, the muscle activation patterns can define directions of movement instead of postural attractors. Such patterns can be used in state-action couples to generate trajectories like in the PerAc model. We discuss a possible extension of the DM-PerAc controller by adapting the Fukuyori's controller based on the Langevin's equation. This controller can serve not only to reach attractors which were not explicitly learned, but also to learn the state/action couples to define trajectories.

Immunity to Transformational Learning and Change

ERIC Educational Resources Information Center

Bochman, David J.; Kroth, Michael

2010-01-01

Purpose: The purpose of this paper is to examine and synthesize Argyris and Schon's Theory of Action and Kegan and Lahey's theory of Immunity to Change in order to produce an integrated model. Design/methodology/approach: Literature discussing Argyris and Schon's Theory of Action (Model I and Model II), single and double-loop learning, espoused…

Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI

PubMed Central

Pauli, Wolfgang M.; Larsen, Tobias; Tyszka, J. Michael; O’Doherty, John P.

2017-01-01

Prediction-error signals consistent with formal models of “reinforcement learning” (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models—namely, “actor/critic” models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning. PMID:29049406

The organization of an autonomous learning system

NASA Technical Reports Server (NTRS)

Kanerva, Pentti

1988-01-01

The organization of systems that learn from experience is examined, human beings and animals being prime examples of such systems. How is their information processing organized. They build an internal model of the world and base their actions on the model. The model is dynamic and predictive, and it includes the systems' own actions and their effects. In modeling such systems, a large pattern of features represents a moment of the system's experience. Some of the features are provided by the system's senses, some control the system's motors, and the rest have no immediate external significance. A sequence of such patterns then represents the system's experience over time. By storing such sequences appropriately in memory, the system builds a world model based on experience. In addition to the essential function of memory, fundamental roles are played by a sensory system that makes raw information about the world suitable for memory storage and by a motor system that affects the world. The relation of sensory and motor systems to the memory is discussed, together with how favorable actions can be learned and unfavorable actions can be avoided. Results in classical learning theory are explained in terms of the model, more advanced forms of learning are discussed, and the relevance of the model to the frame problem of robotics is examined.

Grounding the Meanings in Sensorimotor Behavior using Reinforcement Learning

PubMed Central

Farkaš, Igor; Malík, Tomáš; Rebrová, Kristína

2012-01-01

The recent outburst of interest in cognitive developmental robotics is fueled by the ambition to propose ecologically plausible mechanisms of how, among other things, a learning agent/robot could ground linguistic meanings in its sensorimotor behavior. Along this stream, we propose a model that allows the simulated iCub robot to learn the meanings of actions (point, touch, and push) oriented toward objects in robot’s peripersonal space. In our experiments, the iCub learns to execute motor actions and comment on them. Architecturally, the model is composed of three neural-network-based modules that are trained in different ways. The first module, a two-layer perceptron, is trained by back-propagation to attend to the target position in the visual scene, given the low-level visual information and the feature-based target information. The second module, having the form of an actor-critic architecture, is the most distinguishing part of our model, and is trained by a continuous version of reinforcement learning to execute actions as sequences, based on a linguistic command. The third module, an echo-state network, is trained to provide the linguistic description of the executed actions. The trained model generalizes well in case of novel action-target combinations with randomized initial arm positions. It can also promptly adapt its behavior if the action/target suddenly changes during motor execution. PMID:22393319

Learning through Participatory Action Research for Community Ecotourism Planning.

ERIC Educational Resources Information Center

Guevara, Jose Roberto Q.

1996-01-01

Ecologically sound tourism planning and policy require an empowering community participation. The participatory action research model helps a community gain understanding of its social reality, learn how to learn, initiate dialog, and discover new possibilities for addressing its situation. (SK)

Oculomotor learning revisited: a model of reinforcement learning in the basal ganglia incorporating an efference copy of motor actions

PubMed Central

Fee, Michale S.

2012-01-01

In its simplest formulation, reinforcement learning is based on the idea that if an action taken in a particular context is followed by a favorable outcome, then, in the same context, the tendency to produce that action should be strengthened, or reinforced. While reinforcement learning forms the basis of many current theories of basal ganglia (BG) function, these models do not incorporate distinct computational roles for signals that convey context, and those that convey what action an animal takes. Recent experiments in the songbird suggest that vocal-related BG circuitry receives two functionally distinct excitatory inputs. One input is from a cortical region that carries context information about the current “time” in the motor sequence. The other is an efference copy of motor commands from a separate cortical brain region that generates vocal variability during learning. Based on these findings, I propose here a general model of vertebrate BG function that combines context information with a distinct motor efference copy signal. The signals are integrated by a learning rule in which efference copy inputs gate the potentiation of context inputs (but not efference copy inputs) onto medium spiny neurons in response to a rewarded action. The hypothesis is described in terms of a circuit that implements the learning of visually guided saccades. The model makes testable predictions about the anatomical and functional properties of hypothesized context and efference copy inputs to the striatum from both thalamic and cortical sources. PMID:22754501

Oculomotor learning revisited: a model of reinforcement learning in the basal ganglia incorporating an efference copy of motor actions.

PubMed

Fee, Michale S

2012-01-01

In its simplest formulation, reinforcement learning is based on the idea that if an action taken in a particular context is followed by a favorable outcome, then, in the same context, the tendency to produce that action should be strengthened, or reinforced. While reinforcement learning forms the basis of many current theories of basal ganglia (BG) function, these models do not incorporate distinct computational roles for signals that convey context, and those that convey what action an animal takes. Recent experiments in the songbird suggest that vocal-related BG circuitry receives two functionally distinct excitatory inputs. One input is from a cortical region that carries context information about the current "time" in the motor sequence. The other is an efference copy of motor commands from a separate cortical brain region that generates vocal variability during learning. Based on these findings, I propose here a general model of vertebrate BG function that combines context information with a distinct motor efference copy signal. The signals are integrated by a learning rule in which efference copy inputs gate the potentiation of context inputs (but not efference copy inputs) onto medium spiny neurons in response to a rewarded action. The hypothesis is described in terms of a circuit that implements the learning of visually guided saccades. The model makes testable predictions about the anatomical and functional properties of hypothesized context and efference copy inputs to the striatum from both thalamic and cortical sources.

The prefrontal cortex and hybrid learning during iterative competitive games.

PubMed

Abe, Hiroshi; Seo, Hyojung; Lee, Daeyeol

2011-12-01

Behavioral changes driven by reinforcement and punishment are referred to as simple or model-free reinforcement learning. Animals can also change their behaviors by observing events that are neither appetitive nor aversive when these events provide new information about payoffs available from alternative actions. This is an example of model-based reinforcement learning and can be accomplished by incorporating hypothetical reward signals into the value functions for specific actions. Recent neuroimaging and single-neuron recording studies showed that the prefrontal cortex and the striatum are involved not only in reinforcement and punishment, but also in model-based reinforcement learning. We found evidence for both types of learning, and hence hybrid learning, in monkeys during simulated competitive games. In addition, in both the dorsolateral prefrontal cortex and orbitofrontal cortex, individual neurons heterogeneously encoded signals related to actual and hypothetical outcomes from specific actions, suggesting that both areas might contribute to hybrid learning. © 2011 New York Academy of Sciences.

Learning of spatial relationships between observed and imitated actions allows invariant inverse computation in the frontal mirror neuron system.

PubMed

Oh, Hyuk; Gentili, Rodolphe J; Reggia, James A; Contreras-Vidal, José L

2011-01-01

It has been suggested that the human mirror neuron system can facilitate learning by imitation through coupling of observation and action execution. During imitation of observed actions, the functional relationship between and within the inferior frontal cortex, the posterior parietal cortex, and the superior temporal sulcus can be modeled within the internal model framework. The proposed biologically plausible mirror neuron system model extends currently available models by explicitly modeling the intraparietal sulcus and the superior parietal lobule in implementing the function of a frame of reference transformation during imitation. Moreover, the model posits the ventral premotor cortex as performing an inverse computation. The simulations reveal that: i) the transformation system can learn and represent the changes in extrinsic to intrinsic coordinates when an imitator observes a demonstrator; ii) the inverse model of the imitator's frontal mirror neuron system can be trained to provide the motor plans for the imitated actions.

The Relationship of Learning Traits, Motivation and Performance-Learning Response Dynamics

ERIC Educational Resources Information Center

Hwang, Wu-Yuin; Chang, Chen-Bin; Chen, Gan-Jung

2004-01-01

This paper proposes a model of learning dynamics and learning energy, one that analyzes learning systems scientifically. This model makes response to the learner action by means of some equations relating to learning dynamics, learning energy, learning speed, learning force, and learning acceleration, which is analogous to the notion of Newtonian…

Human reinforcement learning subdivides structured action spaces by learning effector-specific values

PubMed Central

Gershman, Samuel J.; Pesaran, Bijan; Daw, Nathaniel D.

2009-01-01

Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable, due to the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning – such as prediction error signals for action valuation associated with dopamine and the striatum – can cope with this “curse of dimensionality.” We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and BOLD activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to “divide and conquer” reinforcement learning over high-dimensional action spaces. PMID:19864565

Human reinforcement learning subdivides structured action spaces by learning effector-specific values.

PubMed

Gershman, Samuel J; Pesaran, Bijan; Daw, Nathaniel D

2009-10-28

Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable because of the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning-such as prediction error signals for action valuation associated with dopamine and the striatum-can cope with this "curse of dimensionality." We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and blood oxygen level-dependent (BOLD) activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to "divide and conquer" reinforcement learning over high-dimensional action spaces.

Action video game play facilitates the development of better perceptual templates.

PubMed

Bejjanki, Vikranth R; Zhang, Ruyuan; Li, Renjie; Pouget, Alexandre; Green, C Shawn; Lu, Zhong-Lin; Bavelier, Daphne

2014-11-25

The field of perceptual learning has identified changes in perceptual templates as a powerful mechanism mediating the learning of statistical regularities in our environment. By measuring threshold-vs.-contrast curves using an orientation identification task under varying levels of external noise, the perceptual template model (PTM) allows one to disentangle various sources of signal-to-noise changes that can alter performance. We use the PTM approach to elucidate the mechanism that underlies the wide range of improvements noted after action video game play. We show that action video game players make use of improved perceptual templates compared with nonvideo game players, and we confirm a causal role for action video game play in inducing such improvements through a 50-h training study. Then, by adapting a recent neural model to this task, we demonstrate how such improved perceptual templates can arise from reweighting the connectivity between visual areas. Finally, we establish that action gamers do not enter the perceptual task with improved perceptual templates. Instead, although performance in action gamers is initially indistinguishable from that of nongamers, action gamers more rapidly learn the proper template as they experience the task. Taken together, our results establish for the first time to our knowledge the development of enhanced perceptual templates following action game play. Because such an improvement can facilitate the inference of the proper generative model for the task at hand, unlike perceptual learning that is quite specific, it thus elucidates a general learning mechanism that can account for the various behavioral benefits noted after action game play.

Action video game play facilitates the development of better perceptual templates

PubMed Central

Bejjanki, Vikranth R.; Zhang, Ruyuan; Li, Renjie; Pouget, Alexandre; Green, C. Shawn; Lu, Zhong-Lin; Bavelier, Daphne

2014-01-01

The field of perceptual learning has identified changes in perceptual templates as a powerful mechanism mediating the learning of statistical regularities in our environment. By measuring threshold-vs.-contrast curves using an orientation identification task under varying levels of external noise, the perceptual template model (PTM) allows one to disentangle various sources of signal-to-noise changes that can alter performance. We use the PTM approach to elucidate the mechanism that underlies the wide range of improvements noted after action video game play. We show that action video game players make use of improved perceptual templates compared with nonvideo game players, and we confirm a causal role for action video game play in inducing such improvements through a 50-h training study. Then, by adapting a recent neural model to this task, we demonstrate how such improved perceptual templates can arise from reweighting the connectivity between visual areas. Finally, we establish that action gamers do not enter the perceptual task with improved perceptual templates. Instead, although performance in action gamers is initially indistinguishable from that of nongamers, action gamers more rapidly learn the proper template as they experience the task. Taken together, our results establish for the first time to our knowledge the development of enhanced perceptual templates following action game play. Because such an improvement can facilitate the inference of the proper generative model for the task at hand, unlike perceptual learning that is quite specific, it thus elucidates a general learning mechanism that can account for the various behavioral benefits noted after action game play. PMID:25385590

An Action Research Project by Teacher Candidates and Their Instructor into Using Math Inquiry: Learning about Relations between Theory and Practice

ERIC Educational Resources Information Center

Betts, Paul; McLarty, Michelle; Dickson, Krysta

2017-01-01

This paper reports on what two teacher candidates and their instructor learned from an action research project into the use of inquiry to teach mathematics. We use a model of the relation between theory and practice in teacher education to interpret what we learned about inquiry. This model describes three modes for teacher candidates to learn…

Too Good to be True? Ideomotor Theory from a Computational Perspective

PubMed Central

Herbort, Oliver; Butz, Martin V.

2012-01-01

In recent years, Ideomotor Theory has regained widespread attention and sparked the development of a number of theories on goal-directed behavior and learning. However, there are two issues with previous studies’ use of Ideomotor Theory. Although Ideomotor Theory is seen as very general, it is often studied in settings that are considerably more simplistic than most natural situations. Moreover, Ideomotor Theory’s claim that effect anticipations directly trigger actions and that action-effect learning is based on the formation of direct action-effect associations is hard to address empirically. We address these points from a computational perspective. A simple computational model of Ideomotor Theory was tested in tasks with different degrees of complexity. The model evaluation showed that Ideomotor Theory is a computationally feasible approach for understanding efficient action-effect learning for goal-directed behavior if the following preconditions are met: (1) The range of potential actions and effects has to be restricted. (2) Effects have to follow actions within a short time window. (3) Actions have to be simple and may not require sequencing. The first two preconditions also limit human performance and thus support Ideomotor Theory. The last precondition can be circumvented by extending the model with more complex, indirect action generation processes. In conclusion, we suggest that Ideomotor Theory offers a comprehensive framework to understand action-effect learning. However, we also suggest that additional processes may mediate the conversion of effect anticipations into actions in many situations. PMID:23162524

Analysis of a physics teacher's pedagogical `micro-actions' that support 17-year-olds' learning of free body diagrams via a modelling approach

NASA Astrophysics Data System (ADS)

Tay, Su Lynn; Yeo, Jennifer

2018-01-01

Great teaching is characterised by the specific actions a teacher takes in the classroom to bring about learning. In the context of model-based teaching (MBT), teachers' difficulty in working with students' models that are not scientifically consistent is troubling. To address this problem, the aim of this study is to identify the pedagogical micro-actions to support the development of scientific models and modelling skills during the evaluation and modification stages of MBT. Taking the perspective of pedagogical content knowing (PCKg), it identifies these micro-actions as an in-situ, dynamic transformation of knowledges of content, pedagogy, student and environment context. Through a case study approach, a lesson conducted by an experienced high-school physics teacher was examined. Audio and video recordings of the lesson contributed to the data sources. Taking a grounded approach in the analysis, eight pedagogical micro-actions enacted by the teacher were identified, namely 'clarification', 'evaluation', 'explanation', 'modification', 'exploration', 'referencing conventions', 'focusing' and 'meta-representing'. These micro-actions support students' learning related to the conceptual, cognitive, discursive and epistemological aspects of modelling. From the micro-actions, we identify the aspects of knowledges of PCKg that teachers need in order to competently select and enact these micro-actions. The in-situ and dynamic transformation of these knowledges implies that professional development should also be situated in the context in which these micro-actions are meaningful.

Model-based hierarchical reinforcement learning and human action control

PubMed Central

Botvinick, Matthew; Weinstein, Ari

2014-01-01

Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822

Intrinsically motivated action-outcome learning and goal-based action recall: a system-level bio-constrained computational model.

PubMed

Baldassarre, Gianluca; Mannella, Francesco; Fiore, Vincenzo G; Redgrave, Peter; Gurney, Kevin; Mirolli, Marco

2013-05-01

Reinforcement (trial-and-error) learning in animals is driven by a multitude of processes. Most animals have evolved several sophisticated systems of 'extrinsic motivations' (EMs) that guide them to acquire behaviours allowing them to maintain their bodies, defend against threat, and reproduce. Animals have also evolved various systems of 'intrinsic motivations' (IMs) that allow them to acquire actions in the absence of extrinsic rewards. These actions are used later to pursue such rewards when they become available. Intrinsic motivations have been studied in Psychology for many decades and their biological substrates are now being elucidated by neuroscientists. In the last two decades, investigators in computational modelling, robotics and machine learning have proposed various mechanisms that capture certain aspects of IMs. However, we still lack models of IMs that attempt to integrate all key aspects of intrinsically motivated learning and behaviour while taking into account the relevant neurobiological constraints. This paper proposes a bio-constrained system-level model that contributes a major step towards this integration. The model focusses on three processes related to IMs and on the neural mechanisms underlying them: (a) the acquisition of action-outcome associations (internal models of the agent-environment interaction) driven by phasic dopamine signals caused by sudden, unexpected changes in the environment; (b) the transient focussing of visual gaze and actions on salient portions of the environment; (c) the subsequent recall of actions to pursue extrinsic rewards based on goal-directed reactivation of the representations of their outcomes. The tests of the model, including a series of selective lesions, show how the focussing processes lead to a faster learning of action-outcome associations, and how these associations can be recruited for accomplishing goal-directed behaviours. The model, together with the background knowledge reviewed in the paper, represents a framework that can be used to guide the design and interpretation of empirical experiments on IMs, and to computationally validate and further develop theories on them. Copyright © 2012 Elsevier Ltd. All rights reserved.

Dopamine Dependence in Aggregate Feedback Learning: A Computational Cognitive Neuroscience Approach

PubMed Central

Valentin, Vivian V.; Maddox, W. Todd; Ashby, F. Gregory

2016-01-01

Procedural learning of skills depends on dopamine-mediated striatal plasticity. Most prior work investigated single stimulus-response procedural learning followed by feedback. However, many skills include several actions that must be performed before feedback is available. A new procedural-learning task is developed in which three independent and successive unsupervised categorization responses receive aggregate feedback indicating either that all three responses were correct, or at least one response was incorrect. Experiment 1 showed superior learning of stimuli in position 3, and that learning in the first two positions was initially compromised, and then recovered. An extensive theoretical analysis that used parameter space partitioning found that a large class of procedural-learning models, which predict propagation of dopamine release from feedback to stimuli, and/or an eligibility trace, fail to fully account for these data. The analysis also suggested that any dopamine released to the second or third stimulus impaired categorization learning in the first and second positions. A second experiment tested and confirmed a novel prediction of this large class of procedural-learning models that if the to-be-learned actions are introduced one-by-one in succession then learning is much better if training begins with the first action (and works forwards) than if it begins with the last action (and works backwards). PMID:27596541

Action Learning for Strategic Innovation in Mature Organizations: Key Cognitive, Design and Contextual Considerations

ERIC Educational Resources Information Center

Kuhn, Jeffrey S.; Marsick, Victoria J.

2005-01-01

This article lays out a model of action learning for catalyzing strategic innovation in mature organizations that are faced with a new competitive playing field. Central to this model is the development of a set of sophisticated cognitive capabilities--sensemaking, strategic thinking, critical thinking, divergent thinking, conceptual capacity and…

Information-theoretic approach to interactive learning

NASA Astrophysics Data System (ADS)

Still, S.

2009-01-01

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.

Learning robot actions based on self-organising language memory.

PubMed

Wermter, Stefan; Elshaw, Mark

2003-01-01

In the MirrorBot project we examine perceptual processes using models of cortical assemblies and mirror neurons to explore the emergence of semantic representations of actions, percepts and concepts in a neural robot. The hypothesis under investigation is whether a neural model will produce a life-like perception system for actions. In this context we focus in this paper on how instructions for actions can be modeled in a self-organising memory. Current approaches for robot control often do not use language and ignore neural learning. However, our approach uses language instruction and draws from the concepts of regional distributed modularity, self-organisation and neural assemblies. We describe a self-organising model that clusters actions into different locations depending on the body part they are associated with. In particular, we use actual sensor readings from the MIRA robot to represent semantic features of the action verbs. Furthermore, we outline a hierarchical computational model for a self-organising robot action control system using language for instruction.

Cross-Situational Learning with Bayesian Generative Models for Multimodal Category and Word Learning in Robots

PubMed Central

Taniguchi, Akira; Taniguchi, Tadahiro; Cangelosi, Angelo

2017-01-01

In this paper, we propose a Bayesian generative model that can form multiple categories based on each sensory-channel and can associate words with any of the four sensory-channels (action, position, object, and color). This paper focuses on cross-situational learning using the co-occurrence between words and information of sensory-channels in complex situations rather than conventional situations of cross-situational learning. We conducted a learning scenario using a simulator and a real humanoid iCub robot. In the scenario, a human tutor provided a sentence that describes an object of visual attention and an accompanying action to the robot. The scenario was set as follows: the number of words per sensory-channel was three or four, and the number of trials for learning was 20 and 40 for the simulator and 25 and 40 for the real robot. The experimental results showed that the proposed method was able to estimate the multiple categorizations and to learn the relationships between multiple sensory-channels and words accurately. In addition, we conducted an action generation task and an action description task based on word meanings learned in the cross-situational learning scenario. The experimental results showed that the robot could successfully use the word meanings learned by using the proposed method. PMID:29311888

Cognitive components underpinning the development of model-based learning.

PubMed

Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

2017-06-01

Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Implementation of Models-Based Practice in Physical Education through Action Research

ERIC Educational Resources Information Center

Casey, Ashley; Dyson, Ben

2009-01-01

The purpose of this study was to explore the use of action research as a framework to investigate cooperative learning and tactical games as instructional models in physical education (PE). The teacher/researcher taught a tennis unit using a combination of Cooperative Learning and Teaching Games for Understanding to three classes of boys aged…

Habits, action sequences, and reinforcement learning

PubMed Central

Dezfouli, Amir; Balleine, Bernard W.

2012-01-01

It is now widely accepted that instrumental actions can be either goal-directed or habitual; whereas the former are rapidly acquire and regulated by their outcome, the latter are reflexive, elicited by antecedent stimuli rather than their consequences. Model-based reinforcement learning (RL) provides an elegant description of goal-directed action. Through exposure to states, actions and rewards, the agent rapidly constructs a model of the world and can choose an appropriate action based on quite abstract changes in environmental and evaluative demands. This model is powerful but has a problem explaining the development of habitual actions. To account for habits, theorists have argued that another action controller is required, called model-free RL, that does not form a model of the world but rather caches action values within states allowing a state to select an action based on its reward history rather than its consequences. Nevertheless, there are persistent problems with important predictions from the model; most notably the failure of model-free RL correctly to predict the insensitivity of habitual actions to changes in the action-reward contingency. Here, we suggest that introducing model-free RL in instrumental conditioning is unnecessary and demonstrate that reconceptualizing habits as action sequences allows model-based RL to be applied to both goal-directed and habitual actions in a manner consistent with what real animals do. This approach has significant implications for the way habits are currently investigated and generates new experimental predictions. PMID:22487034

Towards tailored teaching: using participatory action research to enhance the learning experience of Longitudinal Integrated Clerkship students in a South African rural district hospital.

PubMed

von Pressentin, Klaus B; Waggie, Firdouza; Conradie, Hoffie

2016-03-08

The introduction of Stellenbosch University's Longitudinal Integrated Clerkship (LIC) model as part of the undergraduate medical curriculum offers a unique and exciting training model to develop generalist doctors for the changing South African health landscape. At one of these LIC sites, the need for an improvement of the local learning experience became evident. This paper explores how to identify and implement a tailored teaching and learning intervention to improve workplace-based learning for LIC students. A participatory action research approach was used in a co-operative inquiry group (ten participants), consisting of the students, clinician educators and researchers, who met over a period of 5 months. Through a cyclical process of action and reflection this group identified a teaching intervention. The results demonstrate the gaps and challenges identified when implementing a LIC model of medical education. A structured learning programme for the final 6 weeks of the students' placement at the district hospital was designed by the co-operative inquiry group as an agreed intervention. The post-intervention group reflection highlighted a need to create a structured programme in the spirit of local collaboration and learning across disciplines. The results also enhance our understanding of both students and clinician educators' perceptions of this new model of workplace-based training. This paper provides practical strategies to enhance teaching and learning in a new educational context. These strategies illuminate three paradigm shifts: (1) from the traditional medical education approach towards a transformative learning approach advocated for the 21(st) century health professional; (2) from the teaching hospital context to the district hospital context; and (3) from block-based teaching towards a longitudinal integrated learning model. A programme based on balancing structured and tailored learning activities is recommended in order to address the local learning needs of students in the LIC model. We recommend that action learning sets should be developed at these LIC sites, where the relevant aspects of work-place based learning are negotiated.

Cognitive Components Underpinning the Development of Model-Based Learning

PubMed Central

Potter, Tracey C.S.; Bryce, Nessa V.; Hartley, Catherine A.

2016-01-01

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. PMID:27825732

Analysis of a Physics Teacher's Pedagogical "Micro-Actions" That Support 17-Year-Olds' Learning of Free Body Diagrams via a Modelling Approach

ERIC Educational Resources Information Center

Tay, Su Lynn; Yeo, Jennifer

2018-01-01

Great teaching is characterised by the specific actions a teacher takes in the classroom to bring about learning. In the context of model-based teaching (MBT), teachers' difficulty in working with students' models that are not scientifically consistent is troubling. To address this problem, the aim of this study is to identify the pedagogical…

The emergence of learning-teaching trajectories in education: a complex dynamic systems approach.

PubMed

Steenbeek, Henderien; van Geert, Paul

2013-04-01

In this article we shall focus on learning-teaching trajectories ='successful' as well as 'unsuccessful' ones - as emergent and dynamic phenomena resulting from the interactions in the entire educational context, in particular the interaction between students and teachers viewed as processes of intertwining self-, other- and co-regulation. The article provides a review of the educational research literature on action regulation in learning and teaching, and interprets this literature in light of the theory of complex dynamic systems. Based on this reinterpretation of the literature, two dynamic models are proposed, one focusing on the short-term dynamics of learning-teaching interactions as they take place in classrooms, the other focusing on the long-term dynamics of interactions in a network of variables encompassing concerns, evaluations, actions and action effects (such as learning) students and teachers. The aim of presenting these models is to demonstrate, first, the possibility of transforming existing educational theory into dynamic models and, second, to provide some suggestions as to how such models can be used to further educational theory and practice.

Lack of Antidepressant Effects of (2R,6R)-Hydroxynorketamine in a Rat Learned Helplessness Model: Comparison with (R)-Ketamine

PubMed Central

Shirayama, Yukihiko

2018-01-01

Abstract Background (R)-Ketamine exhibits rapid and sustained antidepressant effects in animal models of depression. It is stereoselectively metabolized to (R)-norketamine and subsequently to (2R,6R)-hydroxynorketamine in the liver. The metabolism of ketamine to hydroxynorketamine was recently demonstrated to be essential for ketamine’s antidepressant actions. However, no study has compared the antidepressant effects of these 3 compounds in animal models of depression. Methods The effects of a single i.p. injection of (R)-ketamine, (R)-norketamine, and (2R,6R)-hydroxynorketamine in a rat learned helplessness model were examined. Results A single dose of (R)-ketamine (20 mg/kg) showed an antidepressant effect in the rat learned helplessness model. In contrast, neither (R)-norketamine (20 mg/kg) nor (2R,6R)-hydroxynorketamine (20 and 40 mg/kg) did so. Conclusions Unlike (R)-ketamine, its metabolite (2R,6R)-hydroxynorketamine did not show antidepressant actions in the rat learned helplessness model. Therefore, it is unlikely that the metabolism of ketamine to hydroxynorketamine is essential for ketamine’s antidepressant actions. PMID:29155993

Dopamine dependence in aggregate feedback learning: A computational cognitive neuroscience approach.

PubMed

Valentin, Vivian V; Maddox, W Todd; Ashby, F Gregory

2016-11-01

Procedural learning of skills depends on dopamine-mediated striatal plasticity. Most prior work investigated single stimulus-response procedural learning followed by feedback. However, many skills include several actions that must be performed before feedback is available. A new procedural-learning task is developed in which three independent and successive unsupervised categorization responses receive aggregate feedback indicating either that all three responses were correct, or at least one response was incorrect. Experiment 1 showed superior learning of stimuli in position 3, and that learning in the first two positions was initially compromised, and then recovered. An extensive theoretical analysis that used parameter space partitioning found that a large class of procedural-learning models, which predict propagation of dopamine release from feedback to stimuli, and/or an eligibility trace, fail to fully account for these data. The analysis also suggested that any dopamine released to the second or third stimulus impaired categorization learning in the first and second positions. A second experiment tested and confirmed a novel prediction of this large class of procedural-learning models that if the to-be-learned actions are introduced one-by-one in succession then learning is much better if training begins with the first action (and works forwards) than if it begins with the last action (and works backwards). Copyright © 2016 Elsevier Inc. All rights reserved.

Imitation learning based on an intrinsic motivation mechanism for efficient coding

PubMed Central

Triesch, Jochen

2013-01-01

A hypothesis regarding the development of imitation learning is presented that is rooted in intrinsic motivations. It is derived from a recently proposed form of intrinsically motivated learning (IML) for efficient coding in active perception, wherein an agent learns to perform actions with its sense organs to facilitate efficient encoding of the sensory data. To this end, actions of the sense organs that improve the encoding of the sensory data trigger an internally generated reinforcement signal. Here it is argued that the same IML mechanism might also support the development of imitation when general actions beyond those of the sense organs are considered: The learner first observes a tutor performing a behavior and learns a model of the the behavior's sensory consequences. The learner then acts itself and receives an internally generated reinforcement signal reflecting how well the sensory consequences of its own behavior are encoded by the sensory model. Actions that are more similar to those of the tutor will lead to sensory signals that are easier to encode and produce a higher reinforcement signal. Through this, the learner's behavior is progressively tuned to make the sensory consequences of its actions match the learned sensory model. I discuss this mechanism in the context of human language acquisition and bird song learning where similar ideas have been proposed. The suggested mechanism also offers an account for the development of mirror neurons and makes a number of predictions. Overall, it establishes a connection between principles of efficient coding, intrinsic motivations and imitation. PMID:24204350

Discovering and Articulating What Is Not yet Known: Using Action Learning and Grounded Theory as a Knowledge Management Strategy

ERIC Educational Resources Information Center

Pauleen, David J.; Corbitt, Brian; Yoong, Pak

2007-01-01

Purpose: To provide a conceptual model for the discovery and articulation of emergent organizational knowledge, particularly knowledge that develops when people work with new technologies. Design/methodology/approach: The model is based on two widely accepted research methods--action learning and grounded theory--and is illustrated using a case…

Blueprint for Incorporating Service Learning: A Basic, Developmental, K-12 Service Learning Typology

ERIC Educational Resources Information Center

Terry, Alice W.; Bohnenberger, Jann E.

2004-01-01

Citing the need for a basic, K-12 developmental framework for service learning, this article describes such a model. This model, an inclusive typology of service learning, distinguishes three levels of service learning: Community Service, Community Exploration, and Community Action. The authors correlate this typology to Piaget's cognitive…

Can model-free reinforcement learning explain deontological moral judgments?

PubMed

Ayars, Alisabeth

2016-05-01

Dual-systems frameworks propose that moral judgments are derived from both an immediate emotional response, and controlled/rational cognition. Recently Cushman (2013) proposed a new dual-system theory based on model-free and model-based reinforcement learning. Model-free learning attaches values to actions based on their history of reward and punishment, and explains some deontological, non-utilitarian judgments. Model-based learning involves the construction of a causal model of the world and allows for far-sighted planning; this form of learning fits well with utilitarian considerations that seek to maximize certain kinds of outcomes. I present three concerns regarding the use of model-free reinforcement learning to explain deontological moral judgment. First, many actions that humans find aversive from model-free learning are not judged to be morally wrong. Moral judgment must require something in addition to model-free learning. Second, there is a dearth of evidence for central predictions of the reinforcement account-e.g., that people with different reinforcement histories will, all else equal, make different moral judgments. Finally, to account for the effect of intention within the framework requires certain assumptions which lack support. These challenges are reasonable foci for future empirical/theoretical work on the model-free/model-based framework. Copyright © 2016 Elsevier B.V. All rights reserved.

Perceptual learning during action video game playing.

PubMed

Green, C Shawn; Li, Renjie; Bavelier, Daphne

2010-04-01

Action video games have been shown to enhance behavioral performance on a wide variety of perceptual tasks, from those that require effective allocation of attentional resources across the visual scene, to those that demand the successful identification of fleetingly presented stimuli. Importantly, these effects have not only been shown in expert action video game players, but a causative link has been established between action video game play and enhanced processing through training studies. Although an account based solely on attention fails to capture the variety of enhancements observed after action game playing, a number of models of perceptual learning are consistent with the observed results, with behavioral modeling favoring the hypothesis that avid video game players are better able to form templates for, or extract the relevant statistics of, the task at hand. This may suggest that the neural site of learning is in areas where information is integrated and actions are selected; yet changes in low-level sensory areas cannot be ruled out. Copyright © 2009 Cognitive Science Society, Inc.

Modeling Healthy Behavior: Actions and Attitudes in Schools.

ERIC Educational Resources Information Center

Berryman, Judy C.; Breighner, Kathryn W.

This book notes that much of what children and adolescents know about life they learn from watching adult role models: teachers, parents, coaches, and clergy members. It was written to help adults examine their health-related beliefs and actions and evaluate how they model these beliefs and actions, consciously and unconsciously, to children. The…

Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection.

PubMed

Wang, Haoran; Yuan, Chunfeng; Hu, Weiming; Ling, Haibin; Yang, Wankou; Sun, Changyin

2014-02-01

In this paper, we propose using high-level action units to represent human actions in videos and, based on such units, a novel sparse model is developed for human action recognition. There are three interconnected components in our approach. First, we propose a new context-aware spatial-temporal descriptor, named locally weighted word context, to improve the discriminability of the traditionally used local spatial-temporal descriptors. Second, from the statistics of the context-aware descriptors, we learn action units using the graph regularized nonnegative matrix factorization, which leads to a part-based representation and encodes the geometrical information. These units effectively bridge the semantic gap in action recognition. Third, we propose a sparse model based on a joint l2,1-norm to preserve the representative items and suppress noise in the action units. Intuitively, when learning the dictionary for action representation, the sparse model captures the fact that actions from the same class share similar units. The proposed approach is evaluated on several publicly available data sets. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.

Relations between EFL Teachers' Formal Knowledge of Grammar and Their In-Action Mental Models of Children's Minds and Learning

ERIC Educational Resources Information Center

Haim, O.; Strauss, S.; Ravid, D.

2004-01-01

We studied the relations between English as a foreign language teachers' grammar knowledge and their in-action mental models (MMs) of children's minds and learning. The grammar knowledge we examined was English wh-constructions. A total of 74 teachers completed an assessment task and were classified to have deep, intermediate or shallow knowledge.…

Action selection performance of a reconfigurable basal ganglia inspired model with Hebbian–Bayesian Go-NoGo connectivity

PubMed Central

Berthet, Pierre; Hellgren-Kotaleski, Jeanette; Lansner, Anders

2012-01-01

Several studies have shown a strong involvement of the basal ganglia (BG) in action selection and dopamine dependent learning. The dopaminergic signal to striatum, the input stage of the BG, has been commonly described as coding a reward prediction error (RPE), i.e., the difference between the predicted and actual reward. The RPE has been hypothesized to be critical in the modulation of the synaptic plasticity in cortico-striatal synapses in the direct and indirect pathway. We developed an abstract computational model of the BG, with a dual pathway structure functionally corresponding to the direct and indirect pathways, and compared its behavior to biological data as well as other reinforcement learning models. The computations in our model are inspired by Bayesian inference, and the synaptic plasticity changes depend on a three factor Hebbian–Bayesian learning rule based on co-activation of pre- and post-synaptic units and on the value of the RPE. The model builds on a modified Actor-Critic architecture and implements the direct (Go) and the indirect (NoGo) pathway, as well as the reward prediction (RP) system, acting in a complementary fashion. We investigated the performance of the model system when different configurations of the Go, NoGo, and RP system were utilized, e.g., using only the Go, NoGo, or RP system, or combinations of those. Learning performance was investigated in several types of learning paradigms, such as learning-relearning, successive learning, stochastic learning, reversal learning and a two-choice task. The RPE and the activity of the model during learning were similar to monkey electrophysiological and behavioral data. Our results, however, show that there is not a unique best way to configure this BG model to handle well all the learning paradigms tested. We thus suggest that an agent might dynamically configure its action selection mode, possibly depending on task characteristics and also on how much time is available. PMID:23060764

Action Control, Motivated Strategies, and Integrative Motivation as Predictors of Language Learning Affect and the Intention to Continue Learning French

ERIC Educational Resources Information Center

MacIntyre, Peter D.; Blackie, Rebecca A.

2012-01-01

The present study examines the relative ability of variables from three motivational frameworks to predict four non-linguistic outcomes of language learning. The study examines Action Control Theory with its measures of (1) hesitation, (2) volatility and (3) rumination. The study also examined Pintrich's expectancy-value model that uses measures…

Dual learning processes underlying human decision-making in reversal learning tasks: functional significance and evidence from the model fit to human behavior

PubMed Central

Bai, Yu; Katahira, Kentaro; Ohira, Hideki

2014-01-01

Humans are capable of correcting their actions based on actions performed in the past, and this ability enables them to adapt to a changing environment. The computational field of reinforcement learning (RL) has provided a powerful explanation for understanding such processes. Recently, the dual learning system, modeled as a hybrid model that incorporates value update based on reward-prediction error and learning rate modulation based on the surprise signal, has gained attention as a model for explaining various neural signals. However, the functional significance of the hybrid model has not been established. In the present study, we used computer simulation in a reversal learning task to address functional significance in a probabilistic reversal learning task. The hybrid model was found to perform better than the standard RL model in a large parameter setting. These results suggest that the hybrid model is more robust against the mistuning of parameters compared with the standard RL model when decision-makers continue to learn stimulus-reward contingencies, which can create abrupt changes. The parameter fitting results also indicated that the hybrid model fit better than the standard RL model for more than 50% of the participants, which suggests that the hybrid model has more explanatory power for the behavioral data than the standard RL model. PMID:25161635

Two-step adaptive management for choosing between two management actions

USGS Publications Warehouse

Moore, Alana L.; Walker, Leila; Runge, Michael C.; McDonald-Madden, Eve; McCarthy, Michael A

2017-01-01

Adaptive management is widely advocated to improve environmental management. Derivations of optimal strategies for adaptive management, however, tend to be case specific and time consuming. In contrast, managers might seek relatively simple guidance, such as insight into when a new potential management action should be considered, and how much effort should be expended on trialing such an action. We constructed a two-time-step scenario where a manager is choosing between two possible management actions. The manager has a total budget that can be split between a learning phase and an implementation phase. We use this scenario to investigate when and how much a manager should invest in learning about the management actions available. The optimal investment in learning can be understood intuitively by accounting for the expected value of sample information, the benefits that accrue during learning, the direct costs of learning, and the opportunity costs of learning. We find that the optimal proportion of the budget to spend on learning is characterized by several critical thresholds that mark a jump from spending a large proportion of the budget on learning to spending nothing. For example, as sampling variance increases, it is optimal to spend a larger proportion of the budget on learning, up to a point: if the sampling variance passes a critical threshold, it is no longer beneficial to invest in learning. Similar thresholds are observed as a function of the total budget and the difference in the expected performance of the two actions. We illustrate how this model can be applied using a case study of choosing between alternative rearing diets for hihi, an endangered New Zealand passerine. Although the model presented is a simplified scenario, we believe it is relevant to many management situations. Managers often have relatively short time horizons for management, and might be reluctant to consider further investment in learning and monitoring beyond collecting data from a single time period.

Two-step adaptive management for choosing between two management actions.

PubMed

Moore, Alana L; Walker, Leila; Runge, Michael C; McDonald-Madden, Eve; McCarthy, Michael A

2017-06-01

Adaptive management is widely advocated to improve environmental management. Derivations of optimal strategies for adaptive management, however, tend to be case specific and time consuming. In contrast, managers might seek relatively simple guidance, such as insight into when a new potential management action should be considered, and how much effort should be expended on trialing such an action. We constructed a two-time-step scenario where a manager is choosing between two possible management actions. The manager has a total budget that can be split between a learning phase and an implementation phase. We use this scenario to investigate when and how much a manager should invest in learning about the management actions available. The optimal investment in learning can be understood intuitively by accounting for the expected value of sample information, the benefits that accrue during learning, the direct costs of learning, and the opportunity costs of learning. We find that the optimal proportion of the budget to spend on learning is characterized by several critical thresholds that mark a jump from spending a large proportion of the budget on learning to spending nothing. For example, as sampling variance increases, it is optimal to spend a larger proportion of the budget on learning, up to a point: if the sampling variance passes a critical threshold, it is no longer beneficial to invest in learning. Similar thresholds are observed as a function of the total budget and the difference in the expected performance of the two actions. We illustrate how this model can be applied using a case study of choosing between alternative rearing diets for hihi, an endangered New Zealand passerine. Although the model presented is a simplified scenario, we believe it is relevant to many management situations. Managers often have relatively short time horizons for management, and might be reluctant to consider further investment in learning and monitoring beyond collecting data from a single time period. © 2017 by the Ecological Society of America.

Alterations in choice behavior by manipulations of world model.

PubMed

Green, C S; Benson, C; Kersten, D; Schrater, P

2010-09-14

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) "probability matching"-a consistent example of suboptimal choice behavior seen in humans-occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning.

Alterations in choice behavior by manipulations of world model

PubMed Central

Green, C. S.; Benson, C.; Kersten, D.; Schrater, P.

2010-01-01

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) “probability matching”—a consistent example of suboptimal choice behavior seen in humans—occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning. PMID:20805507

Learning to use working memory: a reinforcement learning gating model of rule acquisition in rats

PubMed Central

Lloyd, Kevin; Becker, Nadine; Jones, Matthew W.; Bogacz, Rafal

2012-01-01

Learning to form appropriate, task-relevant working memory representations is a complex process central to cognition. Gating models frame working memory as a collection of past observations and use reinforcement learning (RL) to solve the problem of when to update these observations. Investigation of how gating models relate to brain and behavior remains, however, at an early stage. The current study sought to explore the ability of simple RL gating models to replicate rule learning behavior in rats. Rats were trained in a maze-based spatial learning task that required animals to make trial-by-trial choices contingent upon their previous experience. Using an abstract version of this task, we tested the ability of two gating algorithms, one based on the Actor-Critic and the other on the State-Action-Reward-State-Action (SARSA) algorithm, to generate behavior consistent with the rats'. Both models produced rule-acquisition behavior consistent with the experimental data, though only the SARSA gating model mirrored faster learning following rule reversal. We also found that both gating models learned multiple strategies in solving the initial task, a property which highlights the multi-agent nature of such models and which is of importance in considering the neural basis of individual differences in behavior. PMID:23115551

Lack of Antidepressant Effects of (2R,6R)-Hydroxynorketamine in a Rat Learned Helplessness Model: Comparison with (R)-Ketamine.

PubMed

Shirayama, Yukihiko; Hashimoto, Kenji

2018-01-01

(R)-Ketamine exhibits rapid and sustained antidepressant effects in animal models of depression. It is stereoselectively metabolized to (R)-norketamine and subsequently to (2R,6R)-hydroxynorketamine in the liver. The metabolism of ketamine to hydroxynorketamine was recently demonstrated to be essential for ketamine's antidepressant actions. However, no study has compared the antidepressant effects of these 3 compounds in animal models of depression. The effects of a single i.p. injection of (R)-ketamine, (R)-norketamine, and (2R,6R)-hydroxynorketamine in a rat learned helplessness model were examined. A single dose of (R)-ketamine (20 mg/kg) showed an antidepressant effect in the rat learned helplessness model. In contrast, neither (R)-norketamine (20 mg/kg) nor (2R,6R)-hydroxynorketamine (20 and 40 mg/kg) did so. Unlike (R)-ketamine, its metabolite (2R,6R)-hydroxynorketamine did not show antidepressant actions in the rat learned helplessness model. Therefore, it is unlikely that the metabolism of ketamine to hydroxynorketamine is essential for ketamine's antidepressant actions. © The Author 2017. Published by Oxford University Press on behalf of CINP.

Case-Based Modeling for Learning: Socially Constructed Skill Development

ERIC Educational Resources Information Center

Lyons, Paul; Bandura, Randall P.

2018-01-01

Purpose: Grounded on components of experiential learning theory (ELT) and self-regulation of learning (SRL) theory, augmented by elements of action theory and script development, the purpose of this paper is to demonstrate the case-based modeling (CBM) instructional approach that stimulates learning in groups or teams. CBM is related to individual…

From creatures of habit to goal-directed learners: Tracking the developmental emergence of model-based reinforcement learning

PubMed Central

Decker, Johannes H.; Otto, A. Ross; Daw, Nathaniel D.; Hartley, Catherine A.

2016-01-01

Theoretical models distinguish two decision-making strategies that have been formalized in reinforcement-learning theory. A model-based strategy leverages a cognitive model of potential actions and their consequences to make goal-directed choices, whereas a model-free strategy evaluates actions based solely on their reward history. Research in adults has begun to elucidate the psychological mechanisms and neural substrates underlying these learning processes and factors that influence their relative recruitment. However, the developmental trajectory of these evaluative strategies has not been well characterized. In this study, children, adolescents, and adults, performed a sequential reinforcement-learning task that enables estimation of model-based and model-free contributions to choice. Whereas a model-free strategy was evident in choice behavior across all age groups, evidence of a model-based strategy only emerged during adolescence and continued to increase into adulthood. These results suggest that recruitment of model-based valuation systems represents a critical cognitive component underlying the gradual maturation of goal-directed behavior. PMID:27084852

A Functional Model of the Digital Extensor Mechanism: Demonstrating Biomechanics with Hair Bands

ERIC Educational Resources Information Center

Cloud, Beth A.; Youdas, James W.; Hellyer, Nathan J.; Krause, David A.

2010-01-01

The action of muscles about joints can be explained through analysis of their spatial relationship. A functional model of these relationships can be valuable in learning and understanding the muscular action about a joint. A model can be particularly helpful when examining complex actions across multiple joints such as in the digital extensor…

Learning the ShamWow: Creating Infomercials to Teach the AIDA Model

ERIC Educational Resources Information Center

Lee, Seung Hwan; Hoffman, K. Douglas

2015-01-01

The AIDA Model (Attention-Interest-Desire-Action) is one of the classical promotional theories in marketing. Through active-learning techniques and peer critiques, we use infomercials as an innovative educational tool to instruct the four components of the AIDA model. Student evaluations regarding this active-learning assignment reveal that the…

Toddlers' imitative learning in interactive and observational contexts: the role of age and familiarity of the model.

PubMed

Shimpi, Priya M; Akhtar, Nameera; Moore, Chris

2013-10-01

Three experiments examined the effects of age and familiarity of a model on toddlers' imitative learning in observational contexts (Experiments 1, 2, and 3) and interactive contexts (Experiments 2 and 3). Experiment 1 (N=112 18-month-old toddlers) varied the age (child vs. adult) and long-term familiarity (kin vs. stranger) of the person who modeled the novel actions. Experiment 2 (N=48 18-month-olds and 48 24-month-olds) and Experiment 3 (N=48 24-month-olds) varied short-term familiarity with the model (some or none) and learning context (interactive or observational). The most striking findings were that toddlers were able to learn a new action from observing completely unfamiliar strangers who did not address them and were far less likely to imitate an unfamiliar model who directly interacted with them. These studies highlight the robustness of toddlers' observational learning and reveal limitations of learning from unfamiliar models in interactive contexts. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Organizational Learning Post Catastrophic Events: A Descriptive Case Study Exploring NASA's Learning over Time Following Two Catastrophic Shuttle Accidents Using the Schwandt's Organizational Learning System Model

ERIC Educational Resources Information Center

Castro, Edgar Oscar

2013-01-01

A 30-year contribution of the Space Shuttle Program is the evolution of NASA's social actions through organizational learning. This study investigated how NASA learned over time following two catastrophic accidents. Schwandt's (1997) organizational Learning System Model (OLSM) characterized the learning in this High Reliability…

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans.

PubMed

Ikegami, Tsuyoshi; Ganesh, Gowrishankar

2017-01-01

The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants' ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert's abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert's self-estimation is explained only by considering a change in the individual's forward model, showing that an improvement in an expert's ability to predict outcomes of observed actions affects the individual's forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.

Professional Learning Communities: Concepts in Action in a Principal Preparation Program, an Elementary School Team, a Leadership Team, and a Business Partnership

ERIC Educational Resources Information Center

Servais, Kristine; Derrington, Mary Lynne; Sanders, Kellie

2009-01-01

The Professional Learning Community (PLC) model has moved to the forefront in the field of education as one of the most effective frameworks to improve student achievement and overall school success. The research conducted for this paper provides evidence for systemic and action based improvement using the PLC model in four diverse venues:…

Work-based learning: a leadership development example from an action research study of shared governance implementation.

PubMed

Williamson, Tracey

2005-11-01

An empowering action research study was undertaken to evaluate and strengthen the implementation of shared governance. One aim was to identify factors that acted as aids or barriers to effective decision-making by clinical leaders. As a work-based learning approach, action research was expected to lead to integration of learning into practice by researcher and participants alike. Shared governance replaces traditional hierarchies and requires and develops clinical leaders. Strategies are needed to maximize learning from introduction of such initiatives at the individual, group and organizational level. Participant-observations and interviews were undertaken with shared governance council members from one model in north-west England. Leadership skills and knowledge and shared governance practices were significantly enhanced. Preparation for council roles was considered inadequate. Increased structured time for reflection and action planning was indicated. Implementation of shared governance has succeeded in developing leadership capacity. Evaluation findings have led to improvements in the overall shared governance model. Action research has been found to have great utility at optimizing work-based learning. Nurse Managers need to develop their coaching and facilitating skills and recognize there is no "quick fix" for developing clinical leaders. Implications include the need to support learners in identifying and implementing changes arising from work-based learning activities, the significant resource implications and the need to optimize the organizational climate if work-based learning approaches to leadership and management development are to succeed.

Problem-Based Learning Associated by Action-Process-Object-Schema (APOS) Theory to Enhance Students' High Order Mathematical Thinking Ability

ERIC Educational Resources Information Center

Mudrikah, Achmad

2016-01-01

The research has shown a model of learning activities that can be used to stimulate reflective abstraction in students. Reflective abstraction as a method of constructing knowledge in the Action-Process-Object-Schema theory, and is expected to occur when students are in learning activities, will be able to encourage students to make the process of…

The Soft-Skills Learning Triangle: A Learning Model for Supporting Online Management & Leadership Development

ERIC Educational Resources Information Center

Adams, Jean

2010-01-01

The purpose of this paper is to present the Soft-skills Learning Triangle (SLT)--a model created to help coaches, mentors, and educators understand how web-technologies can be used to support management learning and soft-skills development. SLT emerged as part of a larger action-learning research project--the NewMindsets Management Education…

A neural model of hierarchical reinforcement learning.

PubMed

Rasmussen, Daniel; Voelker, Aaron; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.

Personalized summarization using user preference for m-learning

NASA Astrophysics Data System (ADS)

Lee, Sihyoung; Yang, Seungji; Ro, Yong Man; Kim, Hyoung Joong

2008-02-01

As the Internet and multimedia technology is becoming advanced, the number of digital multimedia contents is also becoming abundant in learning area. In order to facilitate the access of digital knowledge and to meet the need of a lifelong learning, e-learning could be the helpful alternative way to the conventional learning paradigms. E-learning is known as a unifying term to express online, web-based and technology-delivered learning. Mobile-learning (m-learning) is defined as e-learning through mobile devices using wireless transmission. In a survey, more than half of the people remarked that the re-consumption was one of the convenient features in e-learning. However, it is not easy to find user's preferred segmentation from a full version of lengthy e-learning content. Especially in m-learning, a content-summarization method is strongly required because mobile devices are limited to low processing power and battery capacity. In this paper, we propose a new user preference model for re-consumption to construct personalized summarization for re-consumption. The user preference for re-consumption is modeled based on user actions with statistical model. Based on the user preference model for re-consumption with personalized user actions, our method discriminates preferred parts over the entire content. Experimental results demonstrated successful personalized summarization.

Partial Planning Reinforcement Learning

DTIC Science & Technology

2012-08-31

Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS Reinforcement Learning, Bayesian Optimization, Active ... Learning , Action Model Learning, Decision Theoretic Assistance Prasad Tadepalli, Alan Fern Oregon State University Office of Sponsored Programs Oregon State

Dorsolateral Striatum Engagement Interferes with Early Discrimination Learning.

PubMed

Bergstrom, Hadley C; Lipkin, Anna M; Lieberman, Abby G; Pinard, Courtney R; Gunduz-Cinar, Ozge; Brockway, Emma T; Taylor, William W; Nonaka, Mio; Bukalo, Olena; Wills, Tiffany A; Rubio, F Javier; Li, Xuan; Pickens, Charles L; Winder, Danny G; Holmes, Andrew

2018-05-22

In current models, learning the relationship between environmental stimuli and the outcomes of actions involves both stimulus-driven and goal-directed systems, mediated in part by the DLS and DMS, respectively. However, though these models emphasize the importance of the DLS in governing actions after extensive experience has accumulated, there is growing evidence of DLS engagement from the onset of training. Here, we used in vivo photosilencing to reveal that DLS recruitment interferes with early touchscreen discrimination learning. We also show that the direct output pathway of the DLS is preferentially recruited and causally involved in early learning and find that silencing the normal contribution of the DLS produces plasticity-related alterations in a PL-DMS circuit. These data provide further evidence suggesting that the DLS is recruited in the construction of stimulus-elicited actions that ultimately automate behavior and liberate cognitive resources for other demands, but with a cost to performance at the outset of learning. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Using the Cognitive Apprenticeship Model with a Chat Tool to Enhance Online Collaborative Learning

ERIC Educational Resources Information Center

Rodríguez-Bonces, Mónica; Ortiz, Kris

2016-01-01

In Colombia, many institutions are in the firm quest of virtual learning environments to improve instruction, and making the most of online tools is clearly linked to offering quality learning. Thus, the purpose of this action research was to identify how the Cognitive Apprenticeship Model enhances online collaborative learning by using a chat…

Implementing a New Model for Teachers' Professional Learning in Papua New Guinea

ERIC Educational Resources Information Center

Honan, Eileen; Evans, Terry; Muspratt, Sandy; Paraide, Patricia; Reta, Medi; Baroutsis, Aspa

2012-01-01

This article reports on a study that investigates the possibilities of developing a professional learning model based on action research that could lead to sustained improvements in teaching and learning in schools in remote areas of Papua New Guinea. The issues related to the implementation of this model are discussed using a critical lens that…

A continuous-time neural model for sequential action.

PubMed

Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard

2014-11-05

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Model-Based and Model-Free Pavlovian Reward Learning: Revaluation, Revision and Revelation

PubMed Central

Dayan, Peter; Berridge, Kent C.

2014-01-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation. PMID:24647659

Model-based and model-free Pavlovian reward learning: revaluation, revision, and revelation.

PubMed

Dayan, Peter; Berridge, Kent C

2014-06-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.

Exploration and Exploitation During Sequential Search

PubMed Central

Dam, Gregory; Körding, Konrad

2012-01-01

When we learn how to throw darts we adjust how we throw based on where the darts stick. Much of skill learning is computationally similar in that we learn using feedback obtained after the completion of individual actions. We can formalize such tasks as a search problem; among the set of all possible actions, find the action that leads to the highest reward. In such cases our actions have two objectives: we want to best utilize what we already know (exploitation), but we also want to learn to be more successful in the future (exploration). Here we tested how participants learn movement trajectories where feedback is provided as a monetary reward that depends on the chosen trajectory. We mathematically derived the optimal search policy for our experiment using decision theory. The search behavior of participants is well predicted by an ideal searcher model that optimally combines exploration and exploitation. PMID:21585479

Professional Learning with Action Research in Innovative Middle Schools

ERIC Educational Resources Information Center

Netcoh, Steven; Olofson, Mark W.; Downes, John M.; Bishop, Penny A.

2017-01-01

This article illustrates how action research can be used as a model for professional development with middle grades educators in rapidly changing and technology-intensive schools. Drawing upon ten years of using this model, the authors present three examples of educator action research to highlight five characteristics of effective projects: (1)…

A Reinforcement Learning Model Equipped with Sensors for Generating Perception Patterns: Implementation of a Simulated Air Navigation System Using ADS-B (Automatic Dependent Surveillance-Broadcast) Technology.

PubMed

Álvarez de Toledo, Santiago; Anguera, Aurea; Barreiro, José M; Lara, Juan A; Lizcano, David

2017-01-19

Over the last few decades, a number of reinforcement learning techniques have emerged, and different reinforcement learning-based applications have proliferated. However, such techniques tend to specialize in a particular field. This is an obstacle to their generalization and extrapolation to other areas. Besides, neither the reward-punishment (r-p) learning process nor the convergence of results is fast and efficient enough. To address these obstacles, this research proposes a general reinforcement learning model. This model is independent of input and output types and based on general bioinspired principles that help to speed up the learning process. The model is composed of a perception module based on sensors whose specific perceptions are mapped as perception patterns. In this manner, similar perceptions (even if perceived at different positions in the environment) are accounted for by the same perception pattern. Additionally, the model includes a procedure that statistically associates perception-action pattern pairs depending on the positive or negative results output by executing the respective action in response to a particular perception during the learning process. To do this, the model is fitted with a mechanism that reacts positively or negatively to particular sensory stimuli in order to rate results. The model is supplemented by an action module that can be configured depending on the maneuverability of each specific agent. The model has been applied in the air navigation domain, a field with strong safety restrictions, which led us to implement a simulated system equipped with the proposed model. Accordingly, the perception sensors were based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology, which is described in this paper. The results were quite satisfactory, and it outperformed traditional methods existing in the literature with respect to learning reliability and efficiency.

A Reinforcement Learning Model Equipped with Sensors for Generating Perception Patterns: Implementation of a Simulated Air Navigation System Using ADS-B (Automatic Dependent Surveillance-Broadcast) Technology

PubMed Central

Álvarez de Toledo, Santiago; Anguera, Aurea; Barreiro, José M.; Lara, Juan A.; Lizcano, David

2017-01-01

Over the last few decades, a number of reinforcement learning techniques have emerged, and different reinforcement learning-based applications have proliferated. However, such techniques tend to specialize in a particular field. This is an obstacle to their generalization and extrapolation to other areas. Besides, neither the reward-punishment (r-p) learning process nor the convergence of results is fast and efficient enough. To address these obstacles, this research proposes a general reinforcement learning model. This model is independent of input and output types and based on general bioinspired principles that help to speed up the learning process. The model is composed of a perception module based on sensors whose specific perceptions are mapped as perception patterns. In this manner, similar perceptions (even if perceived at different positions in the environment) are accounted for by the same perception pattern. Additionally, the model includes a procedure that statistically associates perception-action pattern pairs depending on the positive or negative results output by executing the respective action in response to a particular perception during the learning process. To do this, the model is fitted with a mechanism that reacts positively or negatively to particular sensory stimuli in order to rate results. The model is supplemented by an action module that can be configured depending on the maneuverability of each specific agent. The model has been applied in the air navigation domain, a field with strong safety restrictions, which led us to implement a simulated system equipped with the proposed model. Accordingly, the perception sensors were based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology, which is described in this paper. The results were quite satisfactory, and it outperformed traditional methods existing in the literature with respect to learning reliability and efficiency. PMID:28106849

Predictive representations can link model-based reinforcement learning to model-free mechanisms.

PubMed

Russek, Evan M; Momennejad, Ida; Botvinick, Matthew M; Gershman, Samuel J; Daw, Nathaniel D

2017-09-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.

Predictive representations can link model-based reinforcement learning to model-free mechanisms

PubMed Central

Botvinick, Matthew M.

2017-01-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation. PMID:28945743

Potentiation in young infants: The origin of the prior knowledge effect?

PubMed Central

Barr, Rachel; Rovee-Collier, Carolyn; Learmonth, Amy

2011-01-01

In two experiments with 6-month-old infants, we found that prior learning of an operant task (remembered for 2 weeks) mediated new learning of a modeling event (remembered for only 1 day) and increased its recall. Infants first learned to associate lever pressing with moving a toy train housed in a large box. One or 2 weeks later, three target actions were modeled on a hand puppet while the train box (a retrieval cue) was in view. Merely retrieving the train memory strengthened it, and simultaneously pairing its retrieved memory with the modeled actions potentiated their learning and recall. When paired 1 week later, deferred imitation increased from 1 day to 4 weeks; when paired 2 weeks later, it increased from 1 day to 6 weeks. The striking parallels between potentiated learning in infants and the prior knowledge effect in adults suggests that the prior knowledge effect originates in early infancy. PMID:21264602

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other’s Actions by Humans

PubMed Central

Ganesh, Gowrishankar

2017-01-01

Abstract The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants’ ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert’s abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert’s self-estimation is explained only by considering a change in the individual’s forward model, showing that an improvement in an expert’s ability to predict outcomes of observed actions affects the individual’s forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions. PMID:29340300

"Model age-based" and "copy when uncertain" biases in children's social learning of a novel task.

PubMed

Wood, Lara A; Harrison, Rachel A; Lucas, Amanda J; McGuigan, Nicola; Burdett, Emily R R; Whiten, Andrew

2016-10-01

Theoretical models of social learning predict that individuals can benefit from using strategies that specify when and whom to copy. Here the interaction of two social learning strategies, model age-based biased copying and copy when uncertain, was investigated. Uncertainty was created via a systematic manipulation of demonstration efficacy (completeness) and efficiency (causal relevance of some actions). The participants, 4- to 6-year-old children (N=140), viewed both an adult model and a child model, each of whom used a different tool on a novel task. They did so in a complete condition, a near-complete condition, a partial demonstration condition, or a no-demonstration condition. Half of the demonstrations in each condition incorporated causally irrelevant actions by the models. Social transmission was assessed by first responses but also through children's continued fidelity, the hallmark of social traditions. Results revealed a bias to copy the child model both on first response and in continued interactions. Demonstration efficacy and efficiency did not affect choice of model at first response but did influence solution exploration across trials, with demonstrations containing causally irrelevant actions decreasing exploration of alternative methods. These results imply that uncertain environments can result in canalized social learning from specific classes of model. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

A blended learning approach for teaching computer programming: design for large classes in Sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Bayu Bati, Tesfaye; Gelderblom, Helene; van Biljon, Judy

2014-01-01

The challenge of teaching programming in higher education is complicated by problems associated with large class teaching, a prevalent situation in many developing countries. This paper reports on an investigation into the use of a blended learning approach to teaching and learning of programming in a class of more than 200 students. A course and learning environment was designed by integrating constructivist learning models of Constructive Alignment, Conversational Framework and the Three-Stage Learning Model. Design science research is used for the course redesign and development of the learning environment, and action research is integrated to undertake participatory evaluation of the intervention. The action research involved the Students' Approach to Learning survey, a comparative analysis of students' performance, and qualitative data analysis of data gathered from various sources. The paper makes a theoretical contribution in presenting a design of a blended learning solution for large class teaching of programming grounded in constructivist learning theory and use of free and open source technologies.

Experiential Learning Theory as One of the Foundations of Adult Learning Practice Worldwide

ERIC Educational Resources Information Center

Dernova, Maiya

2015-01-01

The paper presents the analysis of existing theory, assumptions, and models of adult experiential learning. The experiential learning is a learning based on a learning cycle guided by the dual dialectics of action-reflection and experience-abstraction. It defines learning as a process of knowledge creation through experience transformation, so…

Can we (control) Engineer the degree learning process?

NASA Astrophysics Data System (ADS)

White, A. S.; Censlive, M.; Neilsen, D.

2014-07-01

This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process.

Developmental Approach for Behavior Learning Using Primitive Motion Skills.

PubMed

Dawood, Farhan; Loo, Chu Kiong

2018-05-01

Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet the discussion on the origin of primitive imitative abilities is often neglected, referring instead to the possibility of its innateness. This paper presents a developmental model of imitation learning based on the hypothesis that humanoid robot acquires imitative abilities as induced by sensorimotor associative learning through self-exploration. In designing such learning system, several key issues will be addressed: automatic segmentation of the observed actions into motion primitives using raw images acquired from the camera without requiring any kinematic model; incremental learning of spatio-temporal motion sequences to dynamically generates a topological structure in a self-stabilizing manner; organization of the learned data for easy and efficient retrieval using a dynamic associative memory; and utilizing segmented motion primitives to generate complex behavior by the combining these motion primitives. In our experiment, the self-posture is acquired through observing the image of its own body posture while performing the action in front of a mirror through body babbling. The complete architecture was evaluated by simulation and real robot experiments performed on DARwIn-OP humanoid robot.

An Action Research Study from Implementing the Flipped Classroom Model in Primary School History Teaching and Learning

ERIC Educational Resources Information Center

Aidinopoulou, Vasiliki; Sampson, Demetrios G.

2017-01-01

The benefits of the flipped classroom (FC) model in students' learning are claimed in many recent studies. These benefits are typically accounted to the pedagogically efficient use of classroom time for engaging students in active learning. Although there are several relevant studies for the deployment of the FC model in Science, Technology,…

From Creatures of Habit to Goal-Directed Learners: Tracking the Developmental Emergence of Model-Based Reinforcement Learning.

PubMed

Decker, Johannes H; Otto, A Ross; Daw, Nathaniel D; Hartley, Catherine A

2016-06-01

Theoretical models distinguish two decision-making strategies that have been formalized in reinforcement-learning theory. A model-based strategy leverages a cognitive model of potential actions and their consequences to make goal-directed choices, whereas a model-free strategy evaluates actions based solely on their reward history. Research in adults has begun to elucidate the psychological mechanisms and neural substrates underlying these learning processes and factors that influence their relative recruitment. However, the developmental trajectory of these evaluative strategies has not been well characterized. In this study, children, adolescents, and adults performed a sequential reinforcement-learning task that enabled estimation of model-based and model-free contributions to choice. Whereas a model-free strategy was apparent in choice behavior across all age groups, a model-based strategy was absent in children, became evident in adolescents, and strengthened in adults. These results suggest that recruitment of model-based valuation systems represents a critical cognitive component underlying the gradual maturation of goal-directed behavior. © The Author(s) 2016.

Mayan Children's Creation of Learning Ecologies by Initiative and Cooperative Action.

PubMed

de León, Lourdes

2015-01-01

This chapter examines Mayan children's initiatives in creating their own learning environments in collaboration with others as they engage in culturally relevant endeavors of family and community life. To this end, I carry out a fine-grained ethnographic and linguistic analysis of the interactional emergence of learning ecologies. Erickson defines learning ecology as a socioecological system where participants mutually influence one another through verbal and nonverbal actions, as well as through other forms of semiotic communication (2010, 254). In analyzing learning ecologies, I adopt a "theory of action" approach, taking into account multimodal communication (e.g., talk, gesture, gaze, body positioning), participants' sociospatial organization, embodied action, objects, tools, and other culturally relevant materials brought together to build action (Goodwin, 2000, 2013; Hutchins, 1995). I use microethnographic analysis (Erickson, 1992) to bring to the surface central aspects of children's agentive roles in learning through "cooperative actions" (Goodwin, 2013) and "hands-on" experience (Ingold, 2007) the skills of competent members of their community. I examine three distinct Learning Ecologies created by children's initiatives among the Mayan children that I observed: (i) children requesting guidance to collaborate in a task, (ii) older children working on their own initiative with subsequent monitoring and correction from competent members, and (iii) children with near competence in a task with occasional monitoring and no guidance. I argue that these findings enrich and add power to models of family- and community-based learning such as Learning by Observing and Pitching In (Rogoff, 2014). © 2015 Elsevier Inc. All rights reserved.

A Biologically Plausible Architecture of the Striatum to Solve Context-Dependent Reinforcement Learning Tasks.

PubMed

Shivkumar, Sabyasachi; Muralidharan, Vignesh; Chakravarthy, V Srinivasa

2017-01-01

Basal ganglia circuit is an important subcortical system of the brain thought to be responsible for reward-based learning. Striatum, the largest nucleus of the basal ganglia, serves as an input port that maps cortical information. Microanatomical studies show that the striatum is a mosaic of specialized input-output structures called striosomes and regions of the surrounding matrix called the matrisomes. We have developed a computational model of the striatum using layered self-organizing maps to capture the center-surround structure seen experimentally and explain its functional significance. We believe that these structural components could build representations of state and action spaces in different environments. The striatum model is then integrated with other components of basal ganglia, making it capable of solving reinforcement learning tasks. We have proposed a biologically plausible mechanism of action-based learning where the striosome biases the matrisome activity toward a preferred action. Several studies indicate that the striatum is critical in solving context dependent problems. We build on this hypothesis and the proposed model exploits the modularity of the striatum to efficiently solve such tasks.

A Biologically Plausible Architecture of the Striatum to Solve Context-Dependent Reinforcement Learning Tasks

PubMed Central

Shivkumar, Sabyasachi; Muralidharan, Vignesh; Chakravarthy, V. Srinivasa

2017-01-01

Basal ganglia circuit is an important subcortical system of the brain thought to be responsible for reward-based learning. Striatum, the largest nucleus of the basal ganglia, serves as an input port that maps cortical information. Microanatomical studies show that the striatum is a mosaic of specialized input-output structures called striosomes and regions of the surrounding matrix called the matrisomes. We have developed a computational model of the striatum using layered self-organizing maps to capture the center-surround structure seen experimentally and explain its functional significance. We believe that these structural components could build representations of state and action spaces in different environments. The striatum model is then integrated with other components of basal ganglia, making it capable of solving reinforcement learning tasks. We have proposed a biologically plausible mechanism of action-based learning where the striosome biases the matrisome activity toward a preferred action. Several studies indicate that the striatum is critical in solving context dependent problems. We build on this hypothesis and the proposed model exploits the modularity of the striatum to efficiently solve such tasks. PMID:28680395

Learners' Preferences in Using Online Learning Resources

ERIC Educational Resources Information Center

Li, Sha; Leh, Amy; Fu, Yujian; Zhao, Xiang

2009-01-01

This article describes an action research in a graduate educational technology class. The study employed the Online Top-Down Modeling Model (Li & Liu, 2005) as a case in which the students used the learning resources from the course website to perform various learning activities. The findings of this research identify the students' eight…

A Computer Model of Simple Forms of Learning.

ERIC Educational Resources Information Center

Jones, Thomas L.

A basic unsolved problem in science is that of understanding learning, the process by which people and machines use their experience in a situation to guide future action in similar situations. The ideas of Piaget, Pavlov, Hull, and other learning theorists, as well as previous heuristic programing models of human intelligence, stimulated this…

Action Research in Professional Work: Developing New Practices through Design, Dialogue or Learning?

ERIC Educational Resources Information Center

Lahn, Leif Chr.

This paper examines action research that has been carried out in organizations consisting of predominantly highly educated personnel. The paper revolves around discussion of the Scandinavian model of action research, asking to what degree this model, which has been developed within the framework of industrial democracy, might also serve as a…

A whole school approach: collaborative development of school health policies, processes, and practices.

PubMed

Hunt, Pete; Barrios, Lisa; Telljohann, Susan K; Mazyck, Donna

2015-11-01

The Whole School, Whole Community, Whole Child (WSCC) model shows the interrelationship between health and learning and the potential for improving educational outcomes by improving health outcomes. However, current descriptions do not explain how to implement the model. The existing literature, including scientific articles, programmatic guidance, and publications by national agencies and organizations, was reviewed and synthesized to describe an overview of interrelatedness of learning and health and the 10 components of the WSCC model. The literature suggests potential benefits of applying the WSCC model at the district and school level. But, the model lacks specific guidance as to how this might be made actionable. A collaborative approach to health and learning is suggested, including a 10-step systematic process to help schools and districts develop an action plan for improving health and education outcomes. Essential preliminary actions are suggested to minimize the impact of the challenges that commonly derail systematic planning processes and program implementation, such as lack of readiness, personnel shortages, insufficient resources, and competing priorities. All new models require testing and evidence to confirm their value. District and schools will need to test this model and put plans into action to show that significant, substantial, and sustainable health and academic outcomes can be achieved. © 2015 The Authors. Journal of School Health published by Wiley Periodicals, Inc. on behalf of American School Health Association.

Moral Learning: Conceptual foundations and normative relevance.

PubMed

Railton, Peter

2017-10-01

What is distinctive about a bringing a learning perspective to moral psychology? Part of the answer lies in the remarkable transformations that have taken place in learning theory over the past two decades, which have revealed how powerful experience-based learning can be in the acquisition of abstract causal and evaluative representations, including generative models capable of attuning perception, cognition, affect, and action to the physical and social environment. When conjoined with developments in neuroscience, these advances in learning theory permit a rethinking of fundamental questions about the acquisition of moral understanding and its role in the guidance of behavior. For example, recent research indicates that spatial learning and navigation involve the formation of non-perspectival as well as ego-centric models of the physical environment, and that spatial representations are combined with learned information about risk and reward to guide choice and potentiate further learning. Research on infants provides evidence that they form non-perspectival expected-value representations of agents and actions as well, which help them to navigate the human environment. Such representations can be formed by highly-general mental processes such as causal and empathic simulation, and thus afford a foundation for spontaneous moral learning and action that requires no innate moral faculty and can exhibit substantial autonomy with respect to community norms. If moral learning is indeed integral with the acquisition and updating of casual and evaluative models, this affords a new way of understanding well-known but seemingly puzzling patterns in intuitive moral judgment-including the notorious "trolley problems." Copyright © 2016 The Author. Published by Elsevier B.V. All rights reserved.

Vicarious reinforcement learning signals when instructing others.

PubMed

Apps, Matthew A J; Lesage, Elise; Ramnani, Narender

2015-02-18

Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.

Community Based Learning and Civic Engagement: Informal Learning among Adult Volunteers in Community Organizations

ERIC Educational Resources Information Center

Mundel, Karsten; Schugurensky, Daniel

2008-01-01

Many iterations of community based learning employ models, such as consciousness raising groups, cultural circles, and participatory action research. In all of them, learning is a deliberate part of an explicit educational activity. This article explores another realm of community learning: the informal learning that results from volunteering in…

Doing Poverty: Learning Outcomes among Students Participating in the Community Action Poverty Simulation Program

ERIC Educational Resources Information Center

Steck, Laura West; Engler, Jennifer N.; Ligon, Mary; Druen, Perri B.; Cosgrove, Erin

2011-01-01

This article discusses an application of the Lewinian/Kolb experiential learning model in the context of undergraduate participation in the Missouri Community Action Poverty Simulation (CAPS) program. CAPS is designed to simulate common, everyday experiences among people living in poverty as participants take on the roles of family members working…

Project CAPABLE: Model Unit.

ERIC Educational Resources Information Center

Madawaska School District, ME.

Project CAPABLE (Classroom Action Program: Aim: Basic Learning Effectiveness) is a classroom approach which integrates the basic learning skills with content. The goal of the project is to use basic learning skills to enhance the learning of content and at the same time use the content to teach basic learning skills. This manual illustrates how…

Cross-View Action Recognition via Transferable Dictionary Learning.

PubMed

Zheng, Jingjing; Jiang, Zhuolin; Chellappa, Rama

2016-05-01

Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

A neural model of hierarchical reinforcement learning

PubMed Central

Rasmussen, Daniel; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain’s general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model’s behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions. PMID:28683111

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection.

PubMed

Baston, Chiara; Ursino, Mauro

2015-01-01

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.

Projective simulation for artificial intelligence

NASA Astrophysics Data System (ADS)

Briegel, Hans J.; de Las Cuevas, Gemma

2012-05-01

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.

Projective simulation for artificial intelligence

PubMed Central

Briegel, Hans J.; De las Cuevas, Gemma

2012-01-01

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation. PMID:22590690

Managing and learning with multiple models: Objectives and optimization algorithms

USGS Publications Warehouse

Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.

2011-01-01

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

Professional Learning Communities: A Middle School Model

ERIC Educational Resources Information Center

Gentile, David N.

2010-01-01

This research project explored the transition from a traditional model to a Professional Learning Community model in a NJ Middle School. The administration overcame obstacles during the transition such as scheduling conflicts, teacher apathy, and resistance. This action research study gathered data to determine how to best structure the…

Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

PubMed

Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

2016-10-05

Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning

PubMed Central

Konovalov, Arkady; Krajbich, Ian

2016-01-01

Organisms appear to learn and make decisions using different strategies known as model-free and model-based learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Here using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset. In contrast, model-free subjects tend to ignore model-based aspects of the task and instead seem to treat the decision problem as a simple comparison process between two differentially valued items, consistent with previous work on sequential-sampling models of decision making. These findings illustrate a problem with assuming that experimental subjects make their decisions at the same prescribed time. PMID:27511383

Trial-and-error copying of demonstrated actions reveals how fledglings learn to ‘imitate’ their mothers

PubMed Central

Lotem, Arnon

2017-01-01

Understanding how humans and other animals learn to perform an act from seeing it done has been a major challenge in the study of social learning. To determine whether this ability is based on ‘true imitation’, many studies have applied the two-action experimental paradigm, examining whether subjects learn to perform the specific action demonstrated to them. Here, we show that the insights gained from animals' success in two-action experiments may be limited, and that a better understanding is achieved by monitoring subjects' entire behavioural repertoire. Hand-reared house sparrows that followed a model of a mother demonstrator were successful in learning to find seeds hidden under a leaf, using the action demonstrated by the mother (either pushing the leaf or pecking it). However, they also produced behaviours that had not been demonstrated but were nevertheless related to the demonstrated act. This finding suggests that while the learners were clearly influenced by the demonstrator, they did not accurately imitate her. Rather, they used their own behavioural repertoire, gradually fitting it to the demonstrated task solution through trial and error. This process is consistent with recent views on how animals learn to imitate, and may contribute to a unified process-level analysis of social learning mechanisms. PMID:28228516

Improving Language Learning Strategies and Performance of Pre-Service Language Teachers through a CALLA-TBLT Model

ERIC Educational Resources Information Center

Guapacha Chamorro, Maria Eugenia; Benavidez Paz, Luis Humberto

2017-01-01

This paper reports an action-research study on language learning strategies in tertiary education at a Colombian university. The study aimed at improving the English language performance and language learning strategies use of 33 first-year pre-service language teachers by combining elements from two models: the cognitive academic language…

Model-based choices involve prospective neural activity

PubMed Central

Doll, Bradley B.; Duncan, Katherine D.; Simon, Dylan A.; Shohamy, Daphna; Daw, Nathaniel D.

2015-01-01

Decisions may arise via “model-free” repetition of previously reinforced actions, or by “model-based” evaluation, which is widely thought to follow from prospective anticipation of action consequences using a learned map or model. While choices and neural correlates of decision variables sometimes reflect knowledge of their consequences, it remains unclear whether this actually arises from prospective evaluation. Using functional MRI and a sequential reward-learning task in which paths contained decodable object categories, we found that humans’ model-based choices were associated with neural signatures of future paths observed at decision time, suggesting a prospective mechanism for choice. Prospection also covaried with the degree of model-based influences on neural correlates of decision variables, and was inversely related to prediction error signals thought to underlie model-free learning. These results dissociate separate mechanisms underlying model-based and model-free evaluation and support the hypothesis that model-based influences on choices and neural decision variables result from prospection. PMID:25799041

A computational model of Dopamine and Acetylcholine aberrant learning in Basal Ganglia.

PubMed

Baston, Chiara; Ursino, Mauro

2015-01-01

Basal Ganglia (BG) are implied in many motor and cognitive tasks, such as action selection, and have a central role in many pathologies, primarily Parkinson Disease. In the present work, we use a recently developed biologically inspired BG model to analyze how the dopamine (DA) level can affect the temporal response during action selection, and the capacity to learn new actions following rewards and punishments. The model incorporates the 3 main pathways (direct, indirect and hyperdirect) working in BG functioning. The behavior of 2 alternative networks (the first with normal DA levels, the second with reduced DA) is analyzed both in untrained conditions, and during training performed in different epochs. The results show that reduced DA causes delayed temporal responses in the untrained network, and difficult of learning during training, characterized by the necessity of much more epochs. The results provide interesting hints to understand the behavior of healthy and dopamine depleted subjects, such as parkinsonian patients.

Active Player Modeling in the Iterated Prisoner's Dilemma

PubMed Central

Park, Hyunsoo; Kim, Kyung-Joong

2016-01-01

The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player's behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent's behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent's behavior than when the data were collected through random actions. PMID:26989405

Active Player Modeling in the Iterated Prisoner's Dilemma.

PubMed

Park, Hyunsoo; Kim, Kyung-Joong

2016-01-01

The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player's behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent's behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent's behavior than when the data were collected through random actions.

Synchrony and motor mimicking in chimpanzee observational learning

PubMed Central

Fuhrmann, Delia; Ravignani, Andrea; Marshall-Pescini, Sarah; Whiten, Andrew

2014-01-01

Cumulative tool-based culture underwrote our species' evolutionary success, and tool-based nut-cracking is one of the strongest candidates for cultural transmission in our closest relatives, chimpanzees. However the social learning processes that may explain both the similarities and differences between the species remain unclear. A previous study of nut-cracking by initially naïve chimpanzees suggested that a learning chimpanzee holding no hammer nevertheless replicated hammering actions it witnessed. This observation has potentially important implications for the nature of the social learning processes and underlying motor coding involved. In the present study, model and observer actions were quantified frame-by-frame and analysed with stringent statistical methods, demonstrating synchrony between the observer's and model's movements, cross-correlation of these movements above chance level and a unidirectional transmission process from model to observer. These results provide the first quantitative evidence for motor mimicking underlain by motor coding in apes, with implications for mirror neuron function. PMID:24923651

Synchrony and motor mimicking in chimpanzee observational learning.

PubMed

Fuhrmann, Delia; Ravignani, Andrea; Marshall-Pescini, Sarah; Whiten, Andrew

2014-06-13

Cumulative tool-based culture underwrote our species' evolutionary success, and tool-based nut-cracking is one of the strongest candidates for cultural transmission in our closest relatives, chimpanzees. However the social learning processes that may explain both the similarities and differences between the species remain unclear. A previous study of nut-cracking by initially naïve chimpanzees suggested that a learning chimpanzee holding no hammer nevertheless replicated hammering actions it witnessed. This observation has potentially important implications for the nature of the social learning processes and underlying motor coding involved. In the present study, model and observer actions were quantified frame-by-frame and analysed with stringent statistical methods, demonstrating synchrony between the observer's and model's movements, cross-correlation of these movements above chance level and a unidirectional transmission process from model to observer. These results provide the first quantitative evidence for motor mimicking underlain by motor coding in apes, with implications for mirror neuron function.

The Implementation of Collaborative Learning Model "Find Someone Who and Flashcard Game" to Enhance Social Studies Learning Motivation for the Fifth Grade Students

ERIC Educational Resources Information Center

Nurhaniyah, Binti; Soetjipto, Budi Eko; Hanurawan, Fattah

2015-01-01

The aims of this classroom action research are to describe: (1) the implementation of cooperative learning model "find someone who and flashcard game" to boost students' motivation to learn social studies for the fifth grade students; (2) the response of the fifth grade students at SDN Klanderan, Kediri, East Java on the implementation…

The Application of Carousel Feedback and Round Table Cooperative Learning Models to Improve Student's Higher Order Thinking Skills (HOTS) and Social Studies Learning Outcomes

ERIC Educational Resources Information Center

Yusmanto, Harry; Soetjipto, Budi Eko; Djatmika, Ery Tri

2017-01-01

This Classroom Action Research aims to improve students' HOTS (High Order Thinking Skills) and Social Studies learning outcomes through the application of Carousel Feedback and Round Table cooperative learning methods. This study was based on a model proposed by Elliott and was implemented for three cycles. The subjects were 30 female students of…

Beyond adaptive-critic creative learning for intelligent mobile robots

NASA Astrophysics Data System (ADS)

Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

2001-10-01

Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it permits the discovery of the unknown problems, ones that are not yet recognized but may be critical to survival or success.

A unified probabilistic framework for spontaneous facial action modeling and understanding.

PubMed

Tong, Yan; Chen, Jixu; Ji, Qiang

2010-02-01

Facial expression is a natural and powerful means of human communication. Recognizing spontaneous facial actions, however, is very challenging due to subtle facial deformation, frequent head movements, and ambiguous and uncertain facial motion measurements. Because of these challenges, current research in facial expression recognition is limited to posed expressions and often in frontal view. A spontaneous facial expression is characterized by rigid head movements and nonrigid facial muscular movements. More importantly, it is the coherent and consistent spatiotemporal interactions among rigid and nonrigid facial motions that produce a meaningful facial expression. Recognizing this fact, we introduce a unified probabilistic facial action model based on the Dynamic Bayesian network (DBN) to simultaneously and coherently represent rigid and nonrigid facial motions, their spatiotemporal dependencies, and their image measurements. Advanced machine learning methods are introduced to learn the model based on both training data and subjective prior knowledge. Given the model and the measurements of facial motions, facial action recognition is accomplished through probabilistic inference by systematically integrating visual measurements with the facial action model. Experiments show that compared to the state-of-the-art techniques, the proposed system yields significant improvements in recognizing both rigid and nonrigid facial motions, especially for spontaneous facial expressions.

A path less traveled: A self-guided action science inquiry among a small group of adult learners

NASA Astrophysics Data System (ADS)

Folkman, Daniel Vance

This dissertation provides an analysis of the dialogue that occurred among a small group of adult learners who engaged in a self-guided action science inquiry into their own practice. The following pages describe how this group of five practitioners ventured into a critical, self-reflective inquiry into their own values, feelings, and intentions in search of personal and professional growth. It is a deeply revealing story that shows how, through group dialogue, the members gradually unravel the interconnections between their values, feelings, and intention. They uncover surprising and unanticipated patterns in their reasoning-in-action that reflect lessons from present day experiences as well as childhood axioms about what constitutes appropriate behavior. They push their learning further to recognize emotional triggers that are useful in confronting old habits of mind that must be overcome if new Model II strategies are to be learned and internalized. They conclude that becoming Model II requires a centering on basic values, a personal commitment to change, a willingness to persist in the face of resistance, and the wisdom to act with deliberate caution. The transformative power of this insight lies in the realization of what it takes personally and collectively to make the world a truly respectful, productive, democratic, and socially just place in which to live and work. The action science literature holds the assumption that a trained facilitator is needed to guide such an inquiry and the learning of Model II skills. Unfortunately, there are few educator-trainers available to facilitate the learning of Model II proficiencies over the months and years that may be required. The data presented here show that it is possible for a group of highly motivated individuals to initiate and sustain their own action science inquiry without the aid of a highly skilled facilitator. A model of the group dialogue is presented that highlights the salient characteristics of an action science discourse and seventeen heuristics are offered as guidelines for others who wish to undertake their own self-guided action science inquiry.

Learning and exploration in action-perception loops.

PubMed

Little, Daniel Y; Sommer, Friedrich T

2013-01-01

Discovering the structure underlying observed data is a recurring problem in machine learning with important applications in neuroscience. It is also a primary function of the brain. When data can be actively collected in the context of a closed action-perception loop, behavior becomes a critical determinant of learning efficiency. Psychologists studying exploration and curiosity in humans and animals have long argued that learning itself is a primary motivator of behavior. However, the theoretical basis of learning-driven behavior is not well understood. Previous computational studies of behavior have largely focused on the control problem of maximizing acquisition of rewards and have treated learning the structure of data as a secondary objective. Here, we study exploration in the absence of external reward feedback. Instead, we take the quality of an agent's learned internal model to be the primary objective. In a simple probabilistic framework, we derive a Bayesian estimate for the amount of information about the environment an agent can expect to receive by taking an action, a measure we term the predicted information gain (PIG). We develop exploration strategies that approximately maximize PIG. One strategy based on value-iteration consistently learns faster than previously developed reward-free exploration strategies across a diverse range of environments. Psychologists believe the evolutionary advantage of learning-driven exploration lies in the generalized utility of an accurate internal model. Consistent with this hypothesis, we demonstrate that agents which learn more efficiently during exploration are later better able to accomplish a range of goal-directed tasks. We will conclude by discussing how our work elucidates the explorative behaviors of animals and humans, its relationship to other computational models of behavior, and its potential application to experimental design, such as in closed-loop neurophysiology studies.

Modeling Learner Situation Awareness in Collaborative Mobile Web 2.0 Learning

ERIC Educational Resources Information Center

Norman, Helmi; Nordin, Norazah; Din, Rosseni; Ally, Mohamed

2016-01-01

The concept of situation awareness is essential in enhancing collaborative learning. Learners require information from different awareness aspects to deduce a learning situation for decision-making. Designing learning environments that assist learners to understand situation awareness via monitoring actions and reaction of other learners has been…

Effect of Information Load and Time on Observational Learning

ERIC Educational Resources Information Center

Breslin, Gavin; Hodges, Nicola J.; Williams, A. Mark

2009-01-01

We examined whether altering the amount of and moment when visual information is presented affected observational learning for participants practicing a bowling skill. On Day 1, four groups practiced a cricket bowling action. Three groups viewed a full-body point-light model, the model's bowling arm, or between-limb coordination of the model's…

Efficient model learning methods for actor-critic control.

PubMed

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

PubMed

Gopnik, Alison; Wellman, Henry M

2012-11-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.

Learning Designs Using Flipped Classroom Instruction (Conception d'apprentissage à l'aide de l'instruction en classe inversée)

ERIC Educational Resources Information Center

Mazur, Amber D.; Brown, Barbara; Jacobsen, Michele

2015-01-01

The flipped classroom is an instructional model that leverages technology-enhanced instruction outside of class time in order to maximize student engagement and learning during class time. As part of an action research study, the authors synthesize reflections about how the flipped classroom model can support teaching, learning and assessment…

How Is the Learning Environment in Physics Lesson with Using 7E Model Teaching Activities

ERIC Educational Resources Information Center

Turgut, Umit; Colak, Alp; Salar, Riza

2017-01-01

The aim of this research is to reveal the results in the planning, implementation and evaluation of the process for learning environments to be designed in compliance with 7E learning cycle model in physics lesson. "Action research", which is a qualitative research pattern, is employed in this research in accordance with the aim of the…

Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions

PubMed Central

Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

2017-01-01

An important characteristic of human language is compositionality. We can efficiently express a wide variety of real-world situations, events, and behaviors by compositionally constructing the meaning of a complex expression from a finite number of elements. Previous studies have analyzed how machine-learning models, particularly neural networks, can learn from experience to represent compositional relationships between language and robot actions with the aim of understanding the symbol grounding structure and achieving intelligent communicative agents. Such studies have mainly dealt with the words (nouns, adjectives, and verbs) that directly refer to real-world matters. In addition to these words, the current study deals with logic words, such as “not,” “and,” and “or” simultaneously. These words are not directly referring to the real world, but are logical operators that contribute to the construction of meaning in sentences. In human–robot communication, these words may be used often. The current study builds a recurrent neural network model with long short-term memory units and trains it to learn to translate sentences including logic words into robot actions. We investigate what kind of compositional representations, which mediate sentences and robot actions, emerge as the network's internal states via the learning process. Analysis after learning shows that referential words are merged with visual information and the robot's own current state, and the logical words are represented by the model in accordance with their functions as logical operators. Words such as “true,” “false,” and “not” work as non-linear transformations to encode orthogonal phrases into the same area in a memory cell state space. The word “and,” which required a robot to lift up both its hands, worked as if it was a universal quantifier. The word “or,” which required action generation that looked apparently random, was represented as an unstable space of the network's dynamical system. PMID:29311891

Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions.

PubMed

Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

2017-01-01

An important characteristic of human language is compositionality. We can efficiently express a wide variety of real-world situations, events, and behaviors by compositionally constructing the meaning of a complex expression from a finite number of elements. Previous studies have analyzed how machine-learning models, particularly neural networks, can learn from experience to represent compositional relationships between language and robot actions with the aim of understanding the symbol grounding structure and achieving intelligent communicative agents. Such studies have mainly dealt with the words (nouns, adjectives, and verbs) that directly refer to real-world matters. In addition to these words, the current study deals with logic words, such as "not," "and," and "or" simultaneously. These words are not directly referring to the real world, but are logical operators that contribute to the construction of meaning in sentences. In human-robot communication, these words may be used often. The current study builds a recurrent neural network model with long short-term memory units and trains it to learn to translate sentences including logic words into robot actions. We investigate what kind of compositional representations, which mediate sentences and robot actions, emerge as the network's internal states via the learning process. Analysis after learning shows that referential words are merged with visual information and the robot's own current state, and the logical words are represented by the model in accordance with their functions as logical operators. Words such as "true," "false," and "not" work as non-linear transformations to encode orthogonal phrases into the same area in a memory cell state space. The word "and," which required a robot to lift up both its hands, worked as if it was a universal quantifier. The word "or," which required action generation that looked apparently random, was represented as an unstable space of the network's dynamical system.

Catecholaminergic challenge uncovers distinct Pavlovian and instrumental mechanisms of motivated (in)action.

PubMed

Swart, Jennifer C; Froböse, Monja I; Cook, Jennifer L; Geurts, Dirk Em; Frank, Michael J; Cools, Roshan; den Ouden, Hanneke Em

2017-05-15

Catecholamines modulate the impact of motivational cues on action. Such motivational biases have been proposed to reflect cue-based, 'Pavlovian' effects. Here, we assess whether motivational biases may also arise from asymmetrical instrumental learning of active and passive responses following reward and punishment outcomes. We present a novel paradigm, allowing us to disentangle the impact of reward and punishment on instrumental learning from Pavlovian response biasing. Computational analyses showed that motivational biases reflect both Pavlovian and instrumental effects: reward and punishment cues promoted generalized (in)action in a Pavlovian manner, whereas outcomes enhanced instrumental (un)learning of chosen actions. These cue- and outcome-based biases were altered independently by the catecholamine enhancer melthylphenidate. Methylphenidate's effect varied across individuals with a putative proxy of baseline dopamine synthesis capacity, working memory span. Our study uncovers two distinct mechanisms by which motivation impacts behaviour, and helps refine current models of catecholaminergic modulation of motivated action.

Reasoning, learning, and creativity: frontal lobe function and human decision-making.

PubMed

Collins, Anne; Koechlin, Etienne

2012-01-01

The frontal lobes subserve decision-making and executive control--that is, the selection and coordination of goal-directed behaviors. Current models of frontal executive function, however, do not explain human decision-making in everyday environments featuring uncertain, changing, and especially open-ended situations. Here, we propose a computational model of human executive function that clarifies this issue. Using behavioral experiments, we show that unlike others, the proposed model predicts human decisions and their variations across individuals in naturalistic situations. The model reveals that for driving action, the human frontal function monitors up to three/four concurrent behavioral strategies and infers online their ability to predict action outcomes: whenever one appears more reliable than unreliable, this strategy is chosen to guide the selection and learning of actions that maximize rewards. Otherwise, a new behavioral strategy is tentatively formed, partly from those stored in long-term memory, then probed, and if competitive confirmed to subsequently drive action. Thus, the human executive function has a monitoring capacity limited to three or four behavioral strategies. This limitation is compensated by the binary structure of executive control that in ambiguous and unknown situations promotes the exploration and creation of new behavioral strategies. The results support a model of human frontal function that integrates reasoning, learning, and creative abilities in the service of decision-making and adaptive behavior.

Reasoning, Learning, and Creativity: Frontal Lobe Function and Human Decision-Making

PubMed Central

Collins, Anne; Koechlin, Etienne

2012-01-01

The frontal lobes subserve decision-making and executive control—that is, the selection and coordination of goal-directed behaviors. Current models of frontal executive function, however, do not explain human decision-making in everyday environments featuring uncertain, changing, and especially open-ended situations. Here, we propose a computational model of human executive function that clarifies this issue. Using behavioral experiments, we show that unlike others, the proposed model predicts human decisions and their variations across individuals in naturalistic situations. The model reveals that for driving action, the human frontal function monitors up to three/four concurrent behavioral strategies and infers online their ability to predict action outcomes: whenever one appears more reliable than unreliable, this strategy is chosen to guide the selection and learning of actions that maximize rewards. Otherwise, a new behavioral strategy is tentatively formed, partly from those stored in long-term memory, then probed, and if competitive confirmed to subsequently drive action. Thus, the human executive function has a monitoring capacity limited to three or four behavioral strategies. This limitation is compensated by the binary structure of executive control that in ambiguous and unknown situations promotes the exploration and creation of new behavioral strategies. The results support a model of human frontal function that integrates reasoning, learning, and creative abilities in the service of decision-making and adaptive behavior. PMID:22479152

With you or against you: social orientation dependent learning signals guide actions made for others.

PubMed

Christopoulos, George I; King-Casas, Brooks

2015-01-01

In social environments, it is crucial that decision-makers take account of the impact of their actions not only for oneself, but also on other social agents. Previous work has identified neural signals in the striatum encoding value-based prediction errors for outcomes to oneself; also, recent work suggests that neural activity in prefrontal cortex may similarly encode value-based prediction errors related to outcomes to others. However, prior work also indicates that social valuations are not isomorphic, with social value orientations of decision-makers ranging on a cooperative to competitive continuum; this variation has not been examined within social learning environments. Here, we combine a computational model of learning with functional neuroimaging to examine how individual differences in orientation impact neural mechanisms underlying 'other-value' learning. Across four experimental conditions, reinforcement learning signals for other-value were identified in medial prefrontal cortex, and were distinct from self-value learning signals identified in striatum. Critically, the magnitude and direction of the other-value learning signal depended strongly on an individual's cooperative or competitive orientation toward others. These data indicate that social decisions are guided by a social orientation-dependent learning system that is computationally similar but anatomically distinct from self-value learning. The sensitivity of the medial prefrontal learning signal to social preferences suggests a mechanism linking such preferences to biases in social actions and highlights the importance of incorporating heterogeneous social predispositions in neurocomputational models of social behavior. Published by Elsevier Inc.

With you or against you: Social orientation dependent learning signals guide actions made for others

PubMed Central

Christopoulos, George I.; King-Casas, Brooks

2014-01-01

In social environments, it is crucial that decision-makers take account of the impact of their actions not only for oneself, but also on other social agents. Previous work has identified neural signals in the striatum encoding value-based prediction errors for outcomes to oneself; also, recent work suggests neural activity in prefrontal cortex may similarly encode value-based prediction errors related to outcomes to others. However, prior work also indicates that social valuations are not isomorphic, with social value orientations of decision-makers ranging on a cooperative to competitive continuum; this variation has not been examined within social learning environments. Here, we combine a computational model of learning with functional neuroimaging to examine how individual differences in orientation impact neural mechanisms underlying ‘other-value’ learning. Across four experimental conditions, reinforcement learning signals for other-value were identified in medial prefrontal cortex, and were distinct from self-value learning signals identified in striatum. Critically, the magnitude and direction of the other-value learning signal depended strongly on an individual’s cooperative or competitive orientation towards others. These data indicate that social decisions are guided by a social orientation-dependent learning system that is computationally similar but anatomically distinct from self-value learning. The sensitivity of the medial prefrontal learning signal to social preferences suggests a mechanism linking such preferences to biases in social actions and highlights the importance of incorporating heterogeneous social predispositions in neurocomputational models of social behavior. PMID:25224998

Cognitive-Motivational Determinants of Residents’ Civic Engagement and Health (Inequities) in the Context of Noise Action Planning: A Conceptual Model

PubMed Central

Riedel, Natalie; van Kamp, Irene; Köckler, Heike; Scheiner, Joachim; Loerbroks, Adrian; Claßen, Thomas; Bolte, Gabriele

2017-01-01

The Environmental Noise Directive expects residents to be actively involved in localising and selecting noise abatement interventions during the noise action planning process. Its intervention impact is meant to be homogeneous across population groups. Against the background of social heterogeneity and environmental disparities, however, the impact of noise action planning on exposure to traffic-related noise and its health effects is unlikely to follow homogenous distributions. Until now, there has been no study evaluating the impact of noise action measures on the social distribution of traffic-related noise exposure and health outcomes. We develop a conceptual (logic) model on cognitive-motivational determinants of residents’ civic engagement and health (inequities) by integrating arguments from the Model on household’s Vulnerability to the local Environment, the learned helplessness model in environmental psychology, the Cognitive Activation Theory of Stress, and the reserve capacity model. Specifically, we derive four hypothetical patterns of cognitive-motivational determinants yielding different levels of sustained physiological activation and expectancies of civic engagement. These patterns may help us understand why health inequities arise in the context of noise action planning and learn how to transform noise action planning into an instrument conducive to health equity. While building on existing frameworks, our conceptual model will be tested empirically in the next stage of our research process. PMID:28556813

Cognitive-Motivational Determinants of Residents' Civic Engagement and Health (Inequities) in the Context of Noise Action Planning: A Conceptual Model.

PubMed

Riedel, Natalie; van Kamp, Irene; Köckler, Heike; Scheiner, Joachim; Loerbroks, Adrian; Claßen, Thomas; Bolte, Gabriele

2017-05-30

The Environmental Noise Directive expects residents to be actively involved in localising and selecting noise abatement interventions during the noise action planning process. Its intervention impact is meant to be homogeneous across population groups. Against the background of social heterogeneity and environmental disparities, however, the impact of noise action planning on exposure to traffic-related noise and its health effects is unlikely to follow homogenous distributions. Until now, there has been no study evaluating the impact of noise action measures on the social distribution of traffic-related noise exposure and health outcomes. We develop a conceptual (logic) model on cognitive-motivational determinants of residents' civic engagement and health (inequities) by integrating arguments from the Model on household's Vulnerability to the local Environment, the learned helplessness model in environmental psychology, the Cognitive Activation Theory of Stress, and the reserve capacity model. Specifically, we derive four hypothetical patterns of cognitive-motivational determinants yielding different levels of sustained physiological activation and expectancies of civic engagement. These patterns may help us understand why health inequities arise in the context of noise action planning and learn how to transform noise action planning into an instrument conducive to health equity. While building on existing frameworks, our conceptual model will be tested empirically in the next stage of our research process.

The Ongoing Development of an Effective Model of Action Learning for Use by the Busy GP Veterinary Surgeon

ERIC Educational Resources Information Center

Shuttleworth, Sue

2005-01-01

I passionately believe that reflective practice is an essential competency for the busy GP veterinary surgeon to develop throughout their career. Action learning sets would appear to offer a way of promoting this while at the same time helping the GP veterinary surgeon find a way forward with professional issues. In this article I reflect on my…

Improving Mathematics Achievement of Indonesian 5th Grade Students through Guided Discovery Learning

ERIC Educational Resources Information Center

Yurniwati; Hanum, Latipa

2017-01-01

This research aims to find information about the improvement of mathematics achievement of grade five student through guided discovery learning. This research method is classroom action research using Kemmis and Taggart model consists of three cycles. Data used in this study is learning process and learning results. Learning process data is…

Artist-Teachers' In-Action Mental Models While Teaching Visual Arts

ERIC Educational Resources Information Center

Russo-Zimet, Gila

2017-01-01

Studies have examined the assumption that teachers have previous perceptions, beliefs and knowledge about learning (Cochran-Smith & Villegas, 2015). This study presented the In-Action Mental Model of twenty leading artist-teachers while teaching Visual Arts in three Israeli art institutions of higher Education. Data was collected in two…

Integrating Learning and Performance.

ERIC Educational Resources Information Center

1998

This document contains four papers from a symposium on integrating learning and performance in human resource development (HRD). "Action Imperatives that Impact Knowledge Performance and Financial Performance in the Learning Organization: An Exploratory Model" (Gary L. Selden, Karen E. Watkins, Thomas Valentine, Victoria J. Marsick)…

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection

PubMed Central

Baston, Chiara

2015-01-01

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments. PMID:26640481

Robot learning and error correction

NASA Technical Reports Server (NTRS)

Friedman, L.

1977-01-01

A model of robot learning is described that associates previously unknown perceptions with the sensed known consequences of robot actions. For these actions, both the categories of outcomes and the corresponding sensory patterns are incorporated in a knowledge base by the system designer. Thus the robot is able to predict the outcome of an action and compare the expectation with the experience. New knowledge about what to expect in the world may then be incorporated by the robot in a pre-existing structure whether it detects accordance or discrepancy between a predicted consequence and experience. Errors committed during plan execution are detected by the same type of comparison process and learning may be applied to avoiding the errors.

The Reflective Teacher Leader: An Action Research Model

ERIC Educational Resources Information Center

Furtado, Leena; Anderson, Dawnette

2012-01-01

This study presents four teacher reflections from action research projects ranging from kindergarten to adult school improvements. A teacher leadership matrix guided participants to connect teaching and learning theory to best practices by exploring uncharted territory within an iterative cycle of research and action. Teachers developed the…

Deferred imitation in 18-month-olds from two cultural contexts: the case of Cameroonian Nso farmer and German-middle class infants.

PubMed

Borchert, Sonja; Lamm, Bettina; Graf, Frauke; Knopf, Monika

2013-12-01

Imitative learning has been described in naturalistic studies for different cultures, but lab-based research studying imitative learning across different cultural contexts is almost missing. Therefore, imitative learning was assessed with 18-month-old German middle-class and Cameroonian Nso farmer infants - representing two highly different eco-cultural contexts associated with different cultural models, the psychological autonomy and the hierarchical relatedness - by using the deferred imitation paradigm. Study 1 revealed that the infants from both cultural contexts performed a higher number of target actions in the deferred imitation than in the baseline phase. Moreover, it was found that German middle-class infants showed a higher mean imitation rate as they performed more target actions in the deferred imitation phase compared with Cameroonian Nso farmer infants. It was speculated that the opportunity to manipulate the test objects directly after the demonstration of the target actions could enhance the mean deferred imitation rate of the Cameroonian Nso farmer infants which was confirmed in Study 2. Possible explanations for the differences in the amount of imitated target actions of German middle-class and Cameroonian Nso farmer infants are discussed considering the object-related, dyadic setting of the imitation paradigm with respect to the different learning contexts underlying the different cultural models of learning. Copyright © 2013 Elsevier Inc. All rights reserved.

Goal Directed Model Inversion: A Study of Dynamic Behavior

NASA Technical Reports Server (NTRS)

Colombano, Silvano P.; Compton, Michael; Raghavan, Bharathi; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome 0 "would have been right if the outcome had been the desired one." The algorithm then proceeds as follows: (1) store the action that produced the wrong outcome as a "target" (2) redefine the wrong outcome as a desired goal (3) submit the new desired goal to the system (4) compare the new action with the target action and modify the system by using a suitable algorithm for credit assignment (Back propagation in our example) (5) resubmit the original goal. Prior publications by our group in this area focused on demonstrating empirical results based on the inverse kinematic problem for a simulated robotic arm. In this paper we apply the inversion process to much simpler analytic functions in order to elucidate the dynamic behavior of the system and to determine the sensitivity of the learning process to various parameters. This understanding will be necessary for the acceptance of GDMI as a practical tool.

The Ghost Condition: Imitation Versus Emulation in Young Children's Observational Learning.

ERIC Educational Resources Information Center

Thompson, Doreen E.; Russell, James

2004-01-01

Although observational learning by children may occur through imitating a modeler's actions, it can also occur through learning about an object's dynamic affordances- a process that M. Tomasello (1996) calls "emulation." The relative contributions of imitation and emulation within observational learning were examined in a study with 14- to…

Toxin-Induced Experimental Models of Learning and Memory Impairment

PubMed Central

More, Sandeep Vasant; Kumar, Hemant; Cho, Duk-Yeon; Yun, Yo-Sep; Choi, Dong-Kug

2016-01-01

Animal models for learning and memory have significantly contributed to novel strategies for drug development and hence are an imperative part in the assessment of therapeutics. Learning and memory involve different stages including acquisition, consolidation, and retrieval and each stage can be characterized using specific toxin. Recent studies have postulated the molecular basis of these processes and have also demonstrated many signaling molecules that are involved in several stages of memory. Most insights into learning and memory impairment and to develop a novel compound stems from the investigations performed in experimental models, especially those produced by neurotoxins models. Several toxins have been utilized based on their mechanism of action for learning and memory impairment such as scopolamine, streptozotocin, quinolinic acid, and domoic acid. Further, some toxins like 6-hydroxy dopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and amyloid-β are known to cause specific learning and memory impairment which imitate the disease pathology of Parkinson’s disease dementia and Alzheimer’s disease dementia. Apart from these toxins, several other toxins come under a miscellaneous category like an environmental pollutant, snake venoms, botulinum, and lipopolysaccharide. This review will focus on the various classes of neurotoxin models for learning and memory impairment with their specific mechanism of action that could assist the process of drug discovery and development for dementia and cognitive disorders. PMID:27598124

Toxin-Induced Experimental Models of Learning and Memory Impairment.

PubMed

More, Sandeep Vasant; Kumar, Hemant; Cho, Duk-Yeon; Yun, Yo-Sep; Choi, Dong-Kug

2016-09-01

Animal models for learning and memory have significantly contributed to novel strategies for drug development and hence are an imperative part in the assessment of therapeutics. Learning and memory involve different stages including acquisition, consolidation, and retrieval and each stage can be characterized using specific toxin. Recent studies have postulated the molecular basis of these processes and have also demonstrated many signaling molecules that are involved in several stages of memory. Most insights into learning and memory impairment and to develop a novel compound stems from the investigations performed in experimental models, especially those produced by neurotoxins models. Several toxins have been utilized based on their mechanism of action for learning and memory impairment such as scopolamine, streptozotocin, quinolinic acid, and domoic acid. Further, some toxins like 6-hydroxy dopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and amyloid-β are known to cause specific learning and memory impairment which imitate the disease pathology of Parkinson's disease dementia and Alzheimer's disease dementia. Apart from these toxins, several other toxins come under a miscellaneous category like an environmental pollutant, snake venoms, botulinum, and lipopolysaccharide. This review will focus on the various classes of neurotoxin models for learning and memory impairment with their specific mechanism of action that could assist the process of drug discovery and development for dementia and cognitive disorders.

A spiking neural network based on the basal ganglia functional anatomy.

PubMed

Baladron, Javier; Hamker, Fred H

2015-07-01

We introduce a spiking neural network of the basal ganglia capable of learning stimulus-action associations. We model learning in the three major basal ganglia pathways, direct, indirect and hyperdirect, by spike time dependent learning and considering the amount of dopamine available (reward). Moreover, we allow to learn a cortico-thalamic pathway that bypasses the basal ganglia. As a result the system develops new functionalities for the different basal ganglia pathways: The direct pathway selects actions by disinhibiting the thalamus, the hyperdirect one suppresses alternatives and the indirect pathway learns to inhibit common mistakes. Numerical experiments show that the system is capable of learning sets of either deterministic or stochastic rules. Copyright © 2015 Elsevier Ltd. All rights reserved.

Model I & II Organizations: Examining Organizational Learning in Institutions Participating in the Academy for the Assessment of Student Learning

ERIC Educational Resources Information Center

Haywood, Antwione Maurice

2012-01-01

The Academy was an assessment enhancement program created by the HLC to help institutions strengthen and improve the assessment of student learning. Using a multiple case study approach, this study applies Argyis and Schon's (1976) Theory of Action to explore the espoused values and existence of Model I and II behavior characteristics. Argyis…

The Future of Pedagogical Action Research in Psychology

ERIC Educational Resources Information Center

Cormack, Sophie; Bourne, Victoria; Deuker, Charmaine; Norton, Lin; O'Siochcru, Cathal; Watling, Rosamond

2014-01-01

Psychology lecturers are well-qualified to carry out action research which would contribute to the theoretical understanding of learning as well as having practical benefits for students. Pedagogical action research demonstrates how knowledge of psychology can be applied to solve practical problems, providing role models of psychological literacy…

'Proactive' use of cue-context congruence for building reinforcement learning's reward function.

PubMed

Zsuga, Judit; Biro, Klara; Tajti, Gabor; Szilasi, Magdolna Emma; Papp, Csaba; Juhasz, Bela; Gesztelyi, Rudolf

2016-10-28

Reinforcement learning is a fundamental form of learning that may be formalized using the Bellman equation. Accordingly an agent determines the state value as the sum of immediate reward and of the discounted value of future states. Thus the value of state is determined by agent related attributes (action set, policy, discount factor) and the agent's knowledge of the environment embodied by the reward function and hidden environmental factors given by the transition probability. The central objective of reinforcement learning is to solve these two functions outside the agent's control either using, or not using a model. In the present paper, using the proactive model of reinforcement learning we offer insight on how the brain creates simplified representations of the environment, and how these representations are organized to support the identification of relevant stimuli and action. Furthermore, we identify neurobiological correlates of our model by suggesting that the reward and policy functions, attributes of the Bellman equitation, are built by the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC), respectively. Based on this we propose that the OFC assesses cue-context congruence to activate the most context frame. Furthermore given the bidirectional neuroanatomical link between the OFC and model-free structures, we suggest that model-based input is incorporated into the reward prediction error (RPE) signal, and conversely RPE signal may be used to update the reward-related information of context frames and the policy underlying action selection in the OFC and ACC, respectively. Furthermore clinical implications for cognitive behavioral interventions are discussed.

The lifecycle of e-learning course in the adaptive educational environment

NASA Astrophysics Data System (ADS)

Gustun, O. N.; Budaragin, N. V.

2017-01-01

In the article we have considered the lifecycle model of the e-learning course in the electronic educational environment. This model consists of three stages and nine phases. In order to implement the adaptive control of the learning process we have determined the actions which are necessary to undertake at different phases of the e-learning course lifecycle. The general characteristics of the SPACEL-technology is given for creating adaptive educational environments of the next generation.

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

PubMed

Wang, Quan; Rothkopf, Constantin A; Triesch, Jochen

2017-08-01

The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.

Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

NASA Technical Reports Server (NTRS)

Lima, Pedro; Beard, Randal

1992-01-01

The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a lookup table for the world model and implement the Q function with a neural net. Time limitations prevented the combination of these two approaches. The final section discusses the results and gives clues for future work.

Serotonin affects association of aversive outcomes to past actions.

PubMed

Tanaka, Saori C; Shishida, Kazuhiro; Schweighofer, Nicolas; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2009-12-16

Impairment in the serotonergic system has been linked to action choices that are less advantageous in a long run. Such impulsive choices can be caused by a deficit in linking a given reward or punishment with past actions. Here, we tested the effect of manipulation of the serotonergic system by tryptophan depletion and loading on learning the association of current rewards and punishments with past actions. We observed slower associative learning when actions were followed by a delayed punishment in the low serotonergic condition. Furthermore, a model-based analysis revealed a positive correlation between the length of the memory trace for aversive choices and subjects' blood tryptophan concentration. Our results suggest that the serotonergic system regulates the time scale of retrospective association of punishments to past actions.

Approximate reasoning-based learning and control for proximity operations and docking in space

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Jani, Yashvant; Lea, Robert N.

1991-01-01

A recently proposed hybrid-neutral-network and fuzzy-logic-control architecture is applied to a fuzzy logic controller developed for attitude control of the Space Shuttle. A model using reinforcement learning and learning from past experience for fine-tuning its knowledge base is proposed. Two main components of this approximate reasoning-based intelligent control (ARIC) model - an action-state evaluation network and action selection network are described as well as the Space Shuttle attitude controller. An ARIC model for the controller is presented, and it is noted that the input layer in each network includes three nodes representing the angle error, angle error rate, and bias node. Preliminary results indicate that the controller can hold the pitch rate within its desired deadband and starts to use the jets at about 500 sec in the run.

From TPACK-in-Action Workshops to Classrooms: CALL Competency Developed and Integrated

ERIC Educational Resources Information Center

Tai, Shu-Ju Diana

2015-01-01

This study investigated the impact of a CALL teacher education workshop guided by the TPACK-in-Action model (Tai, 2013). This model is framed within Technological Pedagogical Content Knowledge (TPACK, Mishra & Koehler, 2006) and advocates a learning-by-doing approach (Chapelle & Hegelheimer, 2004) to understand how English teachers develop…

A Neural Basis of Facial Action Recognition in Humans

PubMed Central

Srinivasan, Ramprakash; Golomb, Julie D.

2016-01-01

By combining different facial muscle actions, called action units, humans can produce an extraordinarily large number of facial expressions. Computational models and studies in cognitive science and social psychology have long hypothesized that the brain needs to visually interpret these action units to understand other people's actions and intentions. Surprisingly, no studies have identified the neural basis of the visual recognition of these action units. Here, using functional magnetic resonance imaging and an innovative machine learning analysis approach, we identify a consistent and differential coding of action units in the brain. Crucially, in a brain region thought to be responsible for the processing of changeable aspects of the face, multivoxel pattern analysis could decode the presence of specific action units in an image. This coding was found to be consistent across people, facilitating the estimation of the perceived action units on participants not used to train the multivoxel decoder. Furthermore, this coding of action units was identified when participants attended to the emotion category of the facial expression, suggesting an interaction between the visual analysis of action units and emotion categorization as predicted by the computational models mentioned above. These results provide the first evidence for a representation of action units in the brain and suggest a mechanism for the analysis of large numbers of facial actions and a loss of this capacity in psychopathologies. SIGNIFICANCE STATEMENT Computational models and studies in cognitive and social psychology propound that visual recognition of facial expressions requires an intermediate step to identify visible facial changes caused by the movement of specific facial muscles. Because facial expressions are indeed created by moving one's facial muscles, it is logical to assume that our visual system solves this inverse problem. Here, using an innovative machine learning method and neuroimaging data, we identify for the first time a brain region responsible for the recognition of actions associated with specific facial muscles. Furthermore, this representation is preserved across subjects. Our machine learning analysis does not require mapping the data to a standard brain and may serve as an alternative to hyperalignment. PMID:27098688

Towards Actionable Learning Analytics Using Dispositions

ERIC Educational Resources Information Center

Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan

2017-01-01

Studies in the field of learning analytics (LA) have shown students' demographics and learning management system (LMS) data to be effective identifiers of "at risk" performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students…

Mirror neuron system and observational learning: behavioral and neurophysiological evidence.

PubMed

Lago-Rodriguez, Angel; Lopez-Alonso, Virginia; Fernández-del-Olmo, Miguel

2013-07-01

Three experiments were performed to study observational learning using behavioral, perceptual, and neurophysiological data. Experiment 1 investigated whether observing an execution model, during physical practice of a transitive task that only presented one execution strategy, led to performance improvements compared with physical practice alone. Experiment 2 investigated whether performing an observational learning protocol improves subjects' action perception. In experiment 3 we evaluated whether the type of practice performed determined the activation of the Mirror Neuron System during action observation. Results showed that, compared with physical practice, observing an execution model during a task that only showed one execution strategy does not provide behavioral benefits. However, an observational learning protocol allows subjects to predict more precisely the outcome of the learned task. Finally, intersperse observation of an execution model with physical practice results in changes of primary motor cortex activity during the observation of the motor pattern previously practiced, whereas modulations in the connectivity between primary and non primary motor areas (PMv-M1; PPC-M1) were not affected by the practice protocol performed by the observer. Copyright © 2013 Elsevier B.V. All rights reserved.

Doing It Differently: The Ups and Downs of Peer Group Learning

ERIC Educational Resources Information Center

Belward, Shaun; Balatti, Jo

2012-01-01

Peer group learning is the name we have given to a particular type of collaborative learning that has been implemented as part of an action research project designed to improve teaching and learning of first year university mathematics at James Cook University. Using an innovation-decision process model we analysed the response of academics to the…

An Approach to Improving the Learning Experience for First Year Accounting Curriculum

ERIC Educational Resources Information Center

Kirkham, Ross

2013-01-01

The purpose of this paper is to present a theoretical model to address design and assessment of accounting practice sets that will enhance learning and provide clearer learning outcomes for first year accounting students. The paper explores extant literature in developing an action plan that can be followed to maximise learning outcomes for first…

Action Learning: Avoiding Conflict or Enabling Action

ERIC Educational Resources Information Center

Corley, Aileen; Thorne, Ann

2006-01-01

Action learning is based on the premise that action and learning are inextricably entwined and it is this potential, to enable action, which has contributed to the growth of action learning within education and management development programmes. However has this growth in action learning lead to an evolution or a dilution of Revan's classical…

Hierarchical extreme learning machine based reinforcement learning for goal localization

NASA Astrophysics Data System (ADS)

AlDahoul, Nouar; Zaw Htike, Zaw; Akmeliawati, Rini

2017-03-01

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning (RL) method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model. This long learning time results from the weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. In other words, hidden weights are generated randomly and output weights are calculated analytically. H-ELM algorithm was used in this work to find good features for effective representation. This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB.

Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

PubMed Central

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739

Behavior learning in differential games and reorientation maneuvers

NASA Astrophysics Data System (ADS)

Satak, Neha

The purpose of this dissertation is to apply behavior learning concepts to incomplete- information continuous time games. Realistic game scenarios are often incomplete-information games in which the players withhold information. A player may not know its opponent's objectives and strategies prior to the start of the game. This lack of information can limit the player's ability to play optimally. If the player can observe the opponent's actions, it can better optimize its achievements by taking corrective actions. In this research, a framework to learn an opponent's behavior and take corrective actions is developed. The framework will allow a player to observe the opponent's actions and formulate behavior models. The developed behavior model can then be utilized to find the best actions for the player that optimizes the player's objective function. In addition, the framework proposes that the player plays a safe strategy at the beginning of the game. A safe strategy is defined in this research as a strategy that guarantees a minimum pay-off to the player independent of the other player's actions. During the initial part of the game, the player will play the safe strategy until it learns the opponent's behavior. Two methods to develop behavior models that differ in the formulation of the behavior model are proposed. The first method is the Cost-Strategy Recognition (CSR) method in which the player formulates an objective function and a strategy for the opponent. The opponent is presumed to be rational and therefore will play to optimize its objective function. The strategy of the opponent is dependent on the information available to the opponent about other players in the game. A strategy formulation presumes a certain level of information available to the opponent. The previous observations of the opponent's actions are used to estimate the parameters of the formulated behavior model. The estimated behavior model predicts the opponent's future actions. The second method is the Direct Approximation of Value Function (DAVF) method. In this method, unlike the CSR method, the player formulates an objective function for the opponent but does not formulates a strategy directly; rather, indirectly the player assumes that the opponent is playing optimally. Thus, a value function satisfying the HJB equation corresponding to the opponent's cost function exists. The DAVF method finds an approximate solution for the value function based on previous observations of the opponent's control. The approximate solution to the value function is then used to predict the opponent's future behavior. Game examples in which only a single player is learning its opponent's behavior are simulated. Subsequently, examples in which both players in a two-player game are learning each other's behavior are simulated. In the second part of this research, a reorientation control maneuver for a spinning spacecraft will be developed. This will aid the application of behavior learning and differential games concepts to the specific scenario involving multiple spinning spacecraft. An impulsive reorientation maneuver with coasting will be analytically designed to reorient the spin axis of the spacecraft using a single body fixed thruster. Cooperative maneuvers of multiple spacecraft optimizing fuel and relative orientation will be designed. Pareto optimality concepts will be used to arrive at mutually agreeable reorientation maneuvers for the cooperating spinning spacecraft.

Instructional Models in Methods Courses. Occasional Paper No. 7.

ERIC Educational Resources Information Center

Clubok, Arthur, Ed.

Instructional models are distinct sets of sequenced teaching actions created to promote student achievement of selected learning outcomes. They identify: (1) the type of information to be presented to students; (2) the sequence in which it should be presented; (3) the teaching tactics that stimulate necessary cognitive learning processes; and (4)…

Working for the Common Good: Concepts and Models for Service-Learning in Management. AAHE's Series on Service-Learning in the Disciplines.

ERIC Educational Resources Information Center

Godfrey, Paul C., Ed.; Grasso, Edward T., Ed.

The articles in this volume, 15th in a series of monographs on service learning and the academic disciplines, show how student learning can be enhanced by joining management theory with experience and management analysis with action. Service learning prepares business students to see new dimensions of relevance in their coursework, and it provides…

Catecholaminergic challenge uncovers distinct Pavlovian and instrumental mechanisms of motivated (in)action

PubMed Central

Swart, Jennifer C; Froböse, Monja I; Cook, Jennifer L; Geurts, Dirk EM; Frank, Michael J; Cools, Roshan; den Ouden, Hanneke EM

2017-01-01

Catecholamines modulate the impact of motivational cues on action. Such motivational biases have been proposed to reflect cue-based, ‘Pavlovian’ effects. Here, we assess whether motivational biases may also arise from asymmetrical instrumental learning of active and passive responses following reward and punishment outcomes. We present a novel paradigm, allowing us to disentangle the impact of reward and punishment on instrumental learning from Pavlovian response biasing. Computational analyses showed that motivational biases reflect both Pavlovian and instrumental effects: reward and punishment cues promoted generalized (in)action in a Pavlovian manner, whereas outcomes enhanced instrumental (un)learning of chosen actions. These cue- and outcome-based biases were altered independently by the catecholamine enhancer melthylphenidate. Methylphenidate’s effect varied across individuals with a putative proxy of baseline dopamine synthesis capacity, working memory span. Our study uncovers two distinct mechanisms by which motivation impacts behaviour, and helps refine current models of catecholaminergic modulation of motivated action. DOI: http://dx.doi.org/10.7554/eLife.22169.001 PMID:28504638

Minimalist Social-Affective Value for Use in Joint Action: A Neural-Computational Hypothesis

PubMed Central

Lowe, Robert; Almér, Alexander; Lindblad, Gustaf; Gander, Pierre; Michael, John; Vesper, Cordula

2016-01-01

Joint Action is typically described as social interaction that requires coordination among two or more co-actors in order to achieve a common goal. In this article, we put forward a hypothesis for the existence of a neural-computational mechanism of affective valuation that may be critically exploited in Joint Action. Such a mechanism would serve to facilitate coordination between co-actors permitting a reduction of required information. Our hypothesized affective mechanism provides a value function based implementation of Associative Two-Process (ATP) theory that entails the classification of external stimuli according to outcome expectancies. This approach has been used to describe animal and human action that concerns differential outcome expectancies. Until now it has not been applied to social interaction. We describe our Affective ATP model as applied to social learning consistent with an “extended common currency” perspective in the social neuroscience literature. We contrast this to an alternative mechanism that provides an example implementation of the so-called social-specific value perspective. In brief, our Social-Affective ATP mechanism builds upon established formalisms for reinforcement learning (temporal difference learning models) nuanced to accommodate expectations (consistent with ATP theory) and extended to integrate non-social and social cues for use in Joint Action. PMID:27601989

"Learning-in-Action" and "Learning Inaction": Advancing the Theory and Practice of Critical Action Learning

ERIC Educational Resources Information Center

Vince, Russ

2008-01-01

This paper seeks to improve our understanding of the emotional and political dynamics that are generated (and too often avoided) in action learning. The idea at the centre of the paper is a distinction between "learning-in-action" and "learning inaction". The phrase "learning-in-action" represents the value of action…

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning.

PubMed

Franklin, Nicholas T; Frank, Michael J

2015-12-25

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments.

Recognizing human actions by learning and matching shape-motion prototype trees.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2012-03-01

A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.

Conceptual Framework of Modes of Problem Solving Action (MPSA): Implications for Internet Teaching and Learning.

ERIC Educational Resources Information Center

Lai, Su-Huei

The conceptual framework of the Modes of Problem Solving Action (MPSA) model integrates Dewey's pragmatism, critical science theory, and theory regarding the three modes of inquiry. The MPSA model is formulated in the shape of a matrix. Horizontally, there are the following modes: technical, interpretive, and emancipating. Vertically, there are…

The Effects of the Coordination Support on Shared Mental Models and Coordinated Action

ERIC Educational Resources Information Center

Kim, Hyunsong; Kim, Dongsik

2008-01-01

The purpose of this study was to examine the effects of coordination support (tool support and tutor support) on the development of shared mental models (SMMs) and coordinated action in a computer-supported collaborative learning environment. Eighteen students were randomly assigned to one of three conditions, including the tool condition, the…

The Role and Mechanisms of Action of Glucocorticoid Involvement in Memory Storage

PubMed Central

Sandi, Carmen

1998-01-01

Adrenal steroid hormones modulate learning and memory processes by interacting with specific glucocorticoid receptors at different brain areas. In this article, certain components of the physiological response to stress elicited by learning situations are proposed to form an integral aspect of the neurobiological mechanism underlying memory formation. By reviewing the work carried out in different learning models in chicks (passive avoidance learning) and rats (spatial orientation in the Morris water maze and contextual fear conditioning), a role for brain corticosterone action through the glucocorticoid receptor type on the mechanisms of memory consolidation is hypothesized. Evidence is also presented to relate post-training corticosterone levels to the strength of memory storage. Finally, the possible molecular mechanisms that might mediate the influences of glucocorticoids in synaptic plasticity subserving long-term memory formation are considered, mainly by focusing on studies implicating a steroid action through (i) glutamatergic transmission and (ii) cell adhesion molecules. PMID:9920681

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

NASA Astrophysics Data System (ADS)

Boucenna, Sofiane; Cohen, David; Meltzoff, Andrew N.; Gaussier, Philippe; Chetouani, Mohamed

2016-02-01

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning.

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

PubMed Central

Boucenna, Sofiane; Cohen, David; Meltzoff, Andrew N.; Gaussier, Philippe; Chetouani, Mohamed

2016-01-01

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning. PMID:26844862

Dopaminergic control of motivation and reinforcement learning: a closed-circuit account for reward-oriented behavior.

PubMed

Morita, Kenji; Morishima, Mieko; Sakai, Katsuyuki; Kawaguchi, Yasuo

2013-05-15

Humans and animals take actions quickly when they expect that the actions lead to reward, reflecting their motivation. Injection of dopamine receptor antagonists into the striatum has been shown to slow such reward-seeking behavior, suggesting that dopamine is involved in the control of motivational processes. Meanwhile, neurophysiological studies have revealed that phasic response of dopamine neurons appears to represent reward prediction error, indicating that dopamine plays central roles in reinforcement learning. However, previous attempts to elucidate the mechanisms of these dopaminergic controls have not fully explained how the motivational and learning aspects are related and whether they can be understood by the way the activity of dopamine neurons itself is controlled by their upstream circuitries. To address this issue, we constructed a closed-circuit model of the corticobasal ganglia system based on recent findings regarding intracortical and corticostriatal circuit architectures. Simulations show that the model could reproduce the observed distinct motivational effects of D1- and D2-type dopamine receptor antagonists. Simultaneously, our model successfully explains the dopaminergic representation of reward prediction error as observed in behaving animals during learning tasks and could also explain distinct choice biases induced by optogenetic stimulation of the D1 and D2 receptor-expressing striatal neurons. These results indicate that the suggested roles of dopamine in motivational control and reinforcement learning can be understood in a unified manner through a notion that the indirect pathway of the basal ganglia represents the value of states/actions at a previous time point, an empirically driven key assumption of our model.

A cooperative inquiry into action learning and praxis development in a community nursing module.

PubMed

Jenkins, Emrys R; Mabbett, Gaynor M; Surridge, Andrea G; Warring, Joanna; Gwynn, Elizabeth D

2009-09-01

As nurse lecturers we investigated practice development and action learning approaches aimed at enabling postregistration bachelor's- and master's-level nursing students (Community Health Studies, Nursing in the Home) to advance practice in the context of policy and professional developments. A patchwork text was used to assess summatively what students achieved (practice change/development) and how this was informed critically, via an extended epistemology. First-person inquiry supplemented by cooperative inquiry postcourse completion (including reflective discussions with 16 students and 16 practice mentors) were used to assist coresearcher constructions of meaning. A relational, tripartite approach to learning and assessment (students', teachers', and practice mentors' collective contributions) depends on continuing reflective attention. Action learning enhances interrelation of experience with dialectic thinking. The patchwork text functions to promote creative writing, evaluative thinking, and praxis development. Role modeling by all, being genuine and not just "talking" genuine, is challenging yet crucial if people are to function as mutual resources for learning.

Development of models for classification of action between heat-clearing herbs and blood-activating stasis-resolving herbs based on theory of traditional Chinese medicine.

PubMed

Chen, Zhao; Cao, Yanfeng; He, Shuaibing; Qiao, Yanjiang

2018-01-01

Action (" gongxiao " in Chinese) of traditional Chinese medicine (TCM) is the high recapitulation for therapeutic and health-preserving effects under the guidance of TCM theory. TCM-defined herbal properties (" yaoxing " in Chinese) had been used in this research. TCM herbal property (TCM-HP) is the high generalization and summary for actions, both of which come from long-term effective clinical practice in two thousands of years in China. However, the specific relationship between TCM-HP and action of TCM is complex and unclear from a scientific perspective. The research about this is conducive to expound the connotation of TCM-HP theory and is of important significance for the development of the TCM-HP theory. One hundred and thirty-three herbs including 88 heat-clearing herbs (HCHs) and 45 blood-activating stasis-resolving herbs (BAHRHs) were collected from reputable TCM literatures, and their corresponding TCM-HPs/actions information were collected from Chinese pharmacopoeia (2015 edition). The Kennard-Stone (K-S) algorithm was used to split 133 herbs into 100 calibration samples and 33 validation samples. Then, machine learning methods including supported vector machine (SVM), k-nearest neighbor (kNN) and deep learning methods including deep belief network (DBN), convolutional neutral network (CNN) were adopted to develop action classification models based on TCM-HP theory, respectively. In order to ensure robustness, these four classification methods were evaluated by using the method of tenfold cross validation and 20 external validation samples for prediction. As results, 72.7-100% of 33 validation samples including 17 HCHs and 16 BASRHs were correctly predicted by these four types of methods. Both of the DBN and CNN methods gave out the best results and their sensitivity, specificity, precision, accuracy were all 100.00%. Especially, the predicted results of external validation set showed that the performance of deep learning methods (DBN, CNN) were better than traditional machine learning methods (kNN, SVM) in terms of their sensitivity, specificity, precision, accuracy. Moreover, the distribution patterns of TCM-HPs of HCHs and BASRHs were also analyzed to detect the featured TCM-HPs of these two types of herbs. The result showed that the featured TCM-HPs of HCHs were cold, bitter, liver and stomach meridians entered, while those of BASRHs were warm, bitter and pungent, liver meridian entered. The performance on validation set and external validation set of deep learning methods (DBN, CNN) were better than machine learning models (kNN, SVM) in sensitivity, specificity, precision, accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. The deep learning classification methods owned better generalization ability and accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. Besides, the methods of deep learning would help us to improve our understanding about the relationship between herbal property and action, as well as to enrich and develop the theory of TCM-HP scientifically.

Improvement of Learning Process and Learning Outcomes in Physics Learning by Using Collaborative Learning Model of Group Investigation at High School (Grade X, SMAN 14 Jakarta)

ERIC Educational Resources Information Center

Astra, I. Made; Wahyuni, Citra; Nasbey, Hadi

2015-01-01

The aim of this research is to improve the quality of physics learning through application of collaborative learning of group investigation at grade X MIPA 2 SMAN 14 Jakarta. The method used in this research is classroom action research. This research consisted of three cycles was conducted from April to May in 2014. Each cycle consists of…

Action Learning in Undergraduate Engineering Thesis Supervision

ERIC Educational Resources Information Center

Stappenbelt, Brad

2017-01-01

In the present action learning implementation, twelve action learning sets were conducted over eight years. The action learning sets consisted of students involved in undergraduate engineering research thesis work. The concurrent study accompanying this initiative investigated the influence of the action learning environment on student approaches…

The Clinical Learning Spiral: A Model to Develop Reflective Practitioners.

ERIC Educational Resources Information Center

Stockhausen, Lynette

1994-01-01

The Clinical Learning Spiral incorporates reflective processes into undergraduate nursing education. It entails successive cycles of four phases: preparative (briefing, planning), constructive (practice development), reflective (debriefing), and reconstructive (planning for change and commitment to action). (SK)

Mechanisms of Hierarchical Reinforcement Learning in Corticostriatal Circuits 1: Computational Analysis

PubMed Central

Badre, David

2012-01-01

Growing evidence suggests that the prefrontal cortex (PFC) is organized hierarchically, with more anterior regions having increasingly abstract representations. How does this organization support hierarchical cognitive control and the rapid discovery of abstract action rules? We present computational models at different levels of description. A neural circuit model simulates interacting corticostriatal circuits organized hierarchically. In each circuit, the basal ganglia gate frontal actions, with some striatal units gating the inputs to PFC and others gating the outputs to influence response selection. Learning at all of these levels is accomplished via dopaminergic reward prediction error signals in each corticostriatal circuit. This functionality allows the system to exhibit conditional if–then hypothesis testing and to learn rapidly in environments with hierarchical structure. We also develop a hybrid Bayesian-reinforcement learning mixture of experts (MoE) model, which can estimate the most likely hypothesis state of individual participants based on their observed sequence of choices and rewards. This model yields accurate probabilistic estimates about which hypotheses are attended by manipulating attentional states in the generative neural model and recovering them with the MoE model. This 2-pronged modeling approach leads to multiple quantitative predictions that are tested with functional magnetic resonance imaging in the companion paper. PMID:21693490

Developing a Professional Learning Community among Preservice Teachers

ERIC Educational Resources Information Center

Bond, Nathan

2013-01-01

This action research study examined the development of a professional learning community (PLC) among 20 preservice secondary teachers as they met regularly during a semester-long, field-based education course to share artifacts of learning from their professional portfolios. The PLC model described by Hord and Tobia (2012) served as a framework…

A Framework for Structuring Learning Assessment in a Online Educational Game: Experiment Centered Design

ERIC Educational Resources Information Center

Conrad, Shawn; Clarke-Midura, Jody; Klopfer, Eric

2014-01-01

Educational games offer an opportunity to engage and inspire students to take interest in science, technology, engineering, and mathematical (STEM) subjects. Unobtrusive learning assessment techniques coupled with machine learning algorithms can be utilized to record students' in-game actions and formulate a model of the students' knowledge…

Model-Based Reinforcement Learning under Concurrent Schedules of Reinforcement in Rodents

ERIC Educational Resources Information Center

Huh, Namjung; Jo, Suhyun; Kim, Hoseok; Sul, Jung Hoon; Jung, Min Whan

2009-01-01

Reinforcement learning theories postulate that actions are chosen to maximize a long-term sum of positive outcomes based on value functions, which are subjective estimates of future rewards. In simple reinforcement learning algorithms, value functions are updated only by trial-and-error, whereas they are updated according to the decision-maker's…

Zoology Students' Experiences of Collaborative Enquiry in Problem-Based Learning

ERIC Educational Resources Information Center

Harland, Tony

2002-01-01

This paper presents an action-research case study that focuses on experiences of collaboration in a problem-based learning (PBL) course in Zoology. Our PBL model was developed as a research activity in partnership with a commercial organisation. Consequently, learning was grounded in genuine situations of practice in which a high degree of…

The Use of Cooperative Round Robin Discussion Model to Improve Students' Holistic Ability in TEFL Class

ERIC Educational Resources Information Center

Asari, Slamet; Ma'rifah, Ulfatul; Arifani, Yudhi

2017-01-01

This classroom action research is carried out within two cycles to breed a strategy on how a" Round Robin Discussion Learning Model" enhance students' critical thinking, presentation skills, confidence, and independent learning in Teaching English as a Foreign Language (TEFL) class. Pop-up quiz, teacher made-tests, classroom…

A Theory of the Measurement of Knowledge Content, Access, and Learning.

ERIC Educational Resources Information Center

Pirolli, Peter; Wilson, Mark

1998-01-01

An approach to the measurement of knowledge content, knowledge access, and knowledge learning is developed. First a theoretical view of cognition is described, and then a class of measurement models, based on Rasch modeling, is presented. Knowledge access and content are viewed as determining the observable actions selected by an agent to achieve…

The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

PubMed

Orr, Mark G; Thrush, Roxanne; Plaut, David C

2013-01-01

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual's pre-existing belief structure and the beliefs of others in the individual's social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.

The Theory of Reasoned Action as Parallel Constraint Satisfaction: Towards a Dynamic Computational Model of Health Behavior

PubMed Central

Orr, Mark G.; Thrush, Roxanne; Plaut, David C.

2013-01-01

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual’s pre-existing belief structure and the beliefs of others in the individual’s social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics. PMID:23671603

Action Learning at Work.

ERIC Educational Resources Information Center

Mumford, Alan, Ed.

This book contains 34 papers examining the theory, process, and outcomes of action learning at work. The following papers are included: "An Introduction to the Text" (Alan Mumford); "The Learning Equation" (Reg Revans); "Action Learning as a Vehicle for Learning" (Alan Mumford); "Placing Action Learning and…

The role of consensus and culture in children's imitation of inefficient actions.

PubMed

DiYanni, Cara J; Corriveau, Kathleen H; Kurkul, Katelyn; Nasrini, Jad; Nini, Deniela

2015-09-01

A significant body of work has demonstrated children's imitative abilities when learning novel actions. Although some research has examined the role of cultural background in children's imitation of inefficient actions, to our knowledge no research has explored how culture and conformity interact when engaging in imitation. In Study 1, 87 Caucasian American and Chinese American preschoolers were presented with either one model or three models performing an inefficient action. Whereas there were no cultural differences in imitation in the Single Model condition, Chinese Americans were significantly more likely to copy the model's preference for an inefficient tool in the Consensus condition. Children's tool choice was associated with their justification for their choice as well as their memory for the model's action. Study 2 explored the impact of immigration status on the cultural differences in children's tool choice by including 16 first-generation Caucasian American children. When comparing the findings with the rates from Study 1, both groups of Caucasian American preschoolers imitated at rates significantly lower than the Chinese American preschoolers. We suggest that the tool choices of Caucasian American children relate to a tendency to engage in a perceptually driven mode of learning, whereas the choices of the Chinese American children reflect a greater likelihood to use a socially driven mode. Copyright © 2015 Elsevier Inc. All rights reserved.

Notes toward a Philosophy of Action Learning Research

ERIC Educational Resources Information Center

Coghlan, David; Coughlan, Paul

2010-01-01

The philosophical foundations of action learning research have not received a great deal of attention. In the context of action learning postgraduate and professional programmes in universities, articulation of a philosophy of action learning research seems timely and appropriate. This article explores a philosophy of action learning research,…

Novel associative-memory-based self-learning neurocontrol model

NASA Astrophysics Data System (ADS)

Chen, Ke

1992-09-01

Intelligent control is an important field of AI application, which is closely related to machine learning, and the neurocontrol is a kind of intelligent control that controls actions of a physical system or a plant. Linear associative memory model is a good analytic tool for artificial neural networks. In this paper, we present a novel self-learning neurocontrol on the basis of the linear associative memory model to support intelligent control. Using our self-learning neurocontrol model, the learning process is viewed as an extension of one of J. Piaget's developmental stages. After a particular linear associative model developed by us is presented, a brief introduction to J. Piaget's cognitive theory is described as the basis of our self-learning style control. It follows that the neurocontrol model is presented, which usually includes two learning stages, viz. primary learning and high-level learning. As a demonstration of our neurocontrol model, an example is also presented with simulation techniques, called that `bird' catches an aim. The tentative experimental results show that the learning and controlling performance of this approach is surprisingly good. In conclusion, future research is pointed out to improve our self-learning neurocontrol model and explore other areas of application.

Do domestic dogs learn words based on humans' referential behaviour?

PubMed

Tempelmann, Sebastian; Kaminski, Juliane; Tomasello, Michael

2014-01-01

Some domestic dogs learn to comprehend human words, although the nature and basis of this learning is unknown. In the studies presented here we investigated whether dogs learn words through an understanding of referential actions by humans rather than simple association. In three studies, each modelled on a study conducted with human infants, we confronted four word-experienced dogs with situations involving no spatial-temporal contiguity between the word and the referent; the only available cues were referential actions displaced in time from exposure to their referents. We found that no dogs were able to reliably link an object with a label based on social-pragmatic cues alone in all the tests. However, one dog did show skills in some tests, possibly indicating an ability to learn based on social-pragmatic cues.

A simple computational algorithm of model-based choice preference.

PubMed

Toyama, Asako; Katahira, Kentaro; Ohira, Hideki

2017-08-01

A broadly used computational framework posits that two learning systems operate in parallel during the learning of choice preferences-namely, the model-free and model-based reinforcement-learning systems. In this study, we examined another possibility, through which model-free learning is the basic system and model-based information is its modulator. Accordingly, we proposed several modified versions of a temporal-difference learning model to explain the choice-learning process. Using the two-stage decision task developed by Daw, Gershman, Seymour, Dayan, and Dolan (2011), we compared their original computational model, which assumes a parallel learning process, and our proposed models, which assume a sequential learning process. Choice data from 23 participants showed a better fit with the proposed models. More specifically, the proposed eligibility adjustment model, which assumes that the environmental model can weight the degree of the eligibility trace, can explain choices better under both model-free and model-based controls and has a simpler computational algorithm than the original model. In addition, the forgetting learning model and its variation, which assume changes in the values of unchosen actions, substantially improved the fits to the data. Overall, we show that a hybrid computational model best fits the data. The parameters used in this model succeed in capturing individual tendencies with respect to both model use in learning and exploration behavior. This computational model provides novel insights into learning with interacting model-free and model-based components.

How Can Action Learning Contribute to Social Capital?

ERIC Educational Resources Information Center

Pedler, Mike; Attwood, Margaret

2011-01-01

This paper explores the contribution that action learning can make to the formation of social capital via experiences of action learning projects in NHS Pathology Services in the UK. The paper describes the development of action learning practice in recent years, reviews the notion of social capital and considers how action learning might…

Acquisition of automatic imitation is sensitive to sensorimotor contingency.

PubMed

Cook, Richard; Press, Clare; Dickinson, Anthony; Heyes, Cecilia

2010-08-01

The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror system plasticity is sensitive to contingency (i.e., the extent to which activation of one representation predicts activation of another). In Experiment 1, residual automatic imitation was measured following incompatible training in which the action stimulus was a perfect predictor of the response (contingent) or not at all predictive of the response (noncontingent). A contingency effect was observed: There was less automatic imitation indicative of more learning in the contingent group. Experiment 2 replicated this contingency effect and showed that, as predicted by associative learning theory, it can be abolished by signaling trials in which the response occurs in the absence of an action stimulus. These findings support the view that mirror system development depends on associative learning and indicate that this learning is not purely Hebbian. If this is correct, associative learning theory could be used to explain, predict, and intervene in mirror system development.

Action, outcome, and value: a dual-system framework for morality.

PubMed

Cushman, Fiery

2013-08-01

Dual-system approaches to psychology explain the fundamental properties of human judgment, decision making, and behavior across diverse domains. Yet, the appropriate characterization of each system is a source of debate. For instance, a large body of research on moral psychology makes use of the contrast between "emotional" and "rational/cognitive" processes, yet even the chief proponents of this division recognize its shortcomings. Largely independently, research in the computational neurosciences has identified a broad division between two algorithms for learning and choice derived from formal models of reinforcement learning. One assigns value to actions intrinsically based on past experience, while another derives representations of value from an internally represented causal model of the world. This division between action- and outcome-based value representation provides an ideal framework for a dual-system theory in the moral domain.

Young children overimitate in third-party contexts.

PubMed

Nielsen, Mark; Moore, Chris; Mohamedally, Jumana

2012-05-01

The exhibition of actions that are causally unnecessary to the outcomes with which they are associated is a core feature of human cultural behavior. To enter into the world(s) of their cultural in-group, children must learn to assimilate such unnecessary actions into their own behavioral repertoire. Past research has established the habitual tendency of children to adopt the redundant actions of adults demonstrated directly to them. Here we document how young children will do so even when such actions are modeled to a third person regardless of whether children are presented with the test apparatus by the demonstrating, and assumedly expert, adult or by the observing, and assumedly naive, adult (Experiment 1), whether or not children had opportunity to discover how the apparatus works prior to modeling (Experiment 1), and whether or not children's attention was drawn to the demonstration while they were otherwise occupied (Experiment 2). These results emphasize human children's readiness to acquire behavior that is in keeping with what others do, regardless of the apparent efficiency of the actions employed, and in so doing to participate in cultural learning. Copyright © 2012 Elsevier Inc. All rights reserved.

Feature and Region Selection for Visual Learning.

PubMed

Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

2016-03-01

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Managerial Action and Sensemaking in E-Learning Implementation in Brazilian Business Schools

ERIC Educational Resources Information Center

de Freitas, Angilberto Sabino; Bandeira-de-Mello, Rodrigo

2012-01-01

The existing literature on e-learning implementation is either descriptive or normative and falls short on explaining how managers act in introducing and disseminating e-learning projects in school settings. In this paper, we follow a symbolic approach in order to offer a grounded model for explaining how managerial framing of the introduction of…

Implication of Dopaminergic Modulation in Operant Reward Learning and the Induction of Compulsive-Like Feeding Behavior in "Aplysia"

ERIC Educational Resources Information Center

Bedecarrats, Alexis; Cornet, Charles; Simmers, John; Nargeot, Romuald

2013-01-01

Feeding in "Aplysia" provides an amenable model system for analyzing the neuronal substrates of motivated behavior and its adaptability by associative reward learning and neuromodulation. Among such learning processes, appetitive operant conditioning that leads to a compulsive-like expression of feeding actions is known to be associated…

Preparing Scholars of Teaching and Learning Using a Model of Collaborative Peer Consulting and Action Research

ERIC Educational Resources Information Center

Waterman, Margaret; Weber, Janet; Pracht, Carl; Conway, Kathleen; Kunz, David; Evans, Beverly; Hoffman, Steven; Smentkowski, Brian; Starrett, David

2010-01-01

The Scholarship of Teaching and Learning (SoTL) Fellows Program at Southeast Missouri State University supports an annual cohort of 10 faculty Fellows to evaluate, through individual research projects, the effect of teaching on student learning of two or more of the university's General Education objectives. Designed around practical action…

Leadership development through action learning sets: an evaluation study.

PubMed

Walia, Surinder; Marks-Maran, Di

2014-11-01

This article examines the use of action learning sets in a leadership module delivered by a university in south east England. An evaluation research study was undertaking using survey method to evaluate student engagement with action learning sets, and their value, impact and sustainability. Data were collected through a questionnaire with a mix of Likert-style and open-ended questions and qualitative and quantitative data analysis was undertaken. Findings show that engagement in the action learning sets was very high. Action learning sets also had a positive impact on the development of leadership knowledge and skills and are highly valued by participants. It is likely that they would be sustainable as the majority would recommend action learning to colleagues and would consider taking another module that used action learning sets. When compared to existing literature on action learning, this study offers new insights as there is little empirical literature on student engagement with action learning sets and even less on value and sustainability. Copyright © 2014 Elsevier Ltd. All rights reserved.

Action learning across the decades.

PubMed

Eason, Ken

2017-05-02

Purpose The purpose of this paper is to explore how action learning concepts were used in two healthcare projects undertaken many decades apart. The specific purpose in both cases was to examine how action learning can contribute to shared learning across key stakeholders in a complex socio-technical system. In each case study, action learning supported joint design programmes and the sharing of perspectives about the complex system under investigation. Design/methodology/approach Two action learning projects are described: first, the Hospital Internal Communications (HIC) project led by Reg Revans in the 1960s. Senior staff in ten London hospitals formed action learning teams to address communication issues. Second, in the Better Outcomes for People with Learning Disabilities: Transforming Care (BOLDTC) project, videoconferencing equipment enabled people with learning disabilities to increase their opportunities to communicate. A mutual learning process was established to enable stakeholders to explore the potential of the technical system to improve individual care. Findings The HIC project demonstrated the importance of evidence being shared between team members and that action had to engage the larger healthcare system outside the hospital. The BOLDTC project confirmed the continuing relevance of action learning to healthcare today. Mutual learning was achieved between health and social care specialists and technologists. Originality/value This work draws together the socio-technical systems tradition (considering both social and technical issues in organisations) and action learning to demonstrate that complex systems development needs to be undertaken as a learning process in which action provides the fuel for learning and design.

Prefrontal involvement in imitation learning of hand actions: effects of practice and expertise.

PubMed

Vogt, Stefan; Buccino, Giovanni; Wohlschläger, Afra M; Canessa, Nicola; Shah, N Jon; Zilles, Karl; Eickhoff, Simon B; Freund, Hans-Joachim; Rizzolatti, Giacomo; Fink, Gereon R

2007-10-01

In this event-related fMRI study, we demonstrate the effects of a single session of practising configural hand actions (guitar chords) on cortical activations during observation, motor preparation and imitative execution. During the observation of non-practised actions, the mirror neuron system (MNS), consisting of inferior parietal and ventral premotor areas, was more strongly activated than for the practised actions. This finding indicates a strong role of the MNS in the early stages of imitation learning. In addition, the left dorsolateral prefrontal cortex (DLPFC) was selectively involved during observation and motor preparation of the non-practised chords. This finding confirms Buccino et al.'s [Buccino, G., Vogt, S., Ritzl, A., Fink, G.R., Zilles, K., Freund, H.-J., Rizzolatti, G., 2004a. Neural circuits underlying imitation learning of hand actions: an event-related fMRI study. Neuron 42, 323-334] model of imitation learning: for actions that are not yet part of the observer's motor repertoire, DLPFC engages in operations of selection and combination of existing, elementary representations in the MNS. The pattern of prefrontal activations further supports Shallice's [Shallice, T., 2004. The fractionation of supervisory control. In: Gazzaniga, M.S. (Ed.), The Cognitive Neurosciences, Third edition. MIT Press, Cambridge, MA, pp. 943-956] proposal of a dominant role of the left DLPFC in modulating lower level systems and of a dominant role of the right DLPFC in monitoring operations.

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

PubMed Central

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-01-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

PubMed

Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

2013-04-01

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Reinforcement learning in professional basketball players

PubMed Central

Neiman, Tal; Loewenstein, Yonatan

2011-01-01

Reinforcement learning in complex natural environments is a challenging task because the agent should generalize from the outcomes of actions taken in one state of the world to future actions in different states of the world. The extent to which human experts find the proper level of generalization is unclear. Here we show, using the sequences of field goal attempts made by professional basketball players, that the outcome of even a single field goal attempt has a considerable effect on the rate of subsequent 3 point shot attempts, in line with standard models of reinforcement learning. However, this change in behaviour is associated with negative correlations between the outcomes of successive field goal attempts. These results indicate that despite years of experience and high motivation, professional players overgeneralize from the outcomes of their most recent actions, which leads to decreased performance. PMID:22146388

Looking Good versus Doing Good: Which Factors Take Precedence when Children Learn about New Tools?

ERIC Educational Resources Information Center

DiYanni, Cara; Nini, Deniela; Rheel, Whitney

2011-01-01

We present two experiments exploring whether individuals would be persuaded to imitate the intentional action of an adult model whose actions suggest that the correct way to complete a task is with an inefficient tool. In Experiment 1, children ages 5-10 years and a group of adults watched an adult model reject an efficient tool in favor of one…

Hierarchically organized behavior and its neural foundations: A reinforcement-learning perspective

PubMed Central

Botvinick, Matthew M.; Niv, Yael; Barto, Andrew C.

2009-01-01

Research on human and animal behavior has long emphasized its hierarchical structure — the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask sequences, which in turn are built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely considered to reflect prefrontal cortical functions. In this paper, we reexamine behavioral hierarchy and its neural substrates from the point of view of recent developments in computational reinforcement learning. Specifically, we consider a set of approaches known collectively as hierarchical reinforcement learning, which extend the reinforcement learning paradigm by allowing the learning agent to aggregate actions into reusable subroutines or skills. A close look at the components of hierarchical reinforcement learning suggests how they might map onto neural structures, in particular regions within the dorsolateral and orbital prefrontal cortex. It also suggests specific ways in which hierarchical reinforcement learning might provide a complement to existing psychological models of hierarchically structured behavior. A particularly important question that hierarchical reinforcement learning brings to the fore is that of how learning identifies new action routines that are likely to provide useful building blocks in solving a wide range of future problems. Here and at many other points, hierarchical reinforcement learning offers an appealing framework for investigating the computational and neural underpinnings of hierarchically structured behavior. PMID:18926527

Evaluation of linearly solvable Markov decision process with dynamic model learning in a mobile robot navigation task.

PubMed

Kinjo, Ken; Uchibe, Eiji; Doya, Kenji

2013-01-01

Linearly solvable Markov Decision Process (LMDP) is a class of optimal control problem in which the Bellman's equation can be converted into a linear equation by an exponential transformation of the state value function (Todorov, 2009b). In an LMDP, the optimal value function and the corresponding control policy are obtained by solving an eigenvalue problem in a discrete state space or an eigenfunction problem in a continuous state using the knowledge of the system dynamics and the action, state, and terminal cost functions. In this study, we evaluate the effectiveness of the LMDP framework in real robot control, in which the dynamics of the body and the environment have to be learned from experience. We first perform a simulation study of a pole swing-up task to evaluate the effect of the accuracy of the learned dynamics model on the derived the action policy. The result shows that a crude linear approximation of the non-linear dynamics can still allow solution of the task, despite with a higher total cost. We then perform real robot experiments of a battery-catching task using our Spring Dog mobile robot platform. The state is given by the position and the size of a battery in its camera view and two neck joint angles. The action is the velocities of two wheels, while the neck joints were controlled by a visual servo controller. We test linear and bilinear dynamic models in tasks with quadratic and Guassian state cost functions. In the quadratic cost task, the LMDP controller derived from a learned linear dynamics model performed equivalently with the optimal linear quadratic regulator (LQR). In the non-quadratic task, the LMDP controller with a linear dynamics model showed the best performance. The results demonstrate the usefulness of the LMDP framework in real robot control even when simple linear models are used for dynamics learning.

Implications of Modeling Uncertainty for Water Quality Decision Making

NASA Astrophysics Data System (ADS)

Shabman, L.

2002-05-01

The report, National Academy of Sciences report, "Assessing the TMDL Approach to Water Quality Management" endorsed the "watershed" and "ambient water quality focused" approach" to water quality management called for in the TMDL program. The committee felt that available data and models were adequate to move such a program forward, if the EPA and all stakeholders better understood the nature of the scientific enterprise and its application to the TMDL program. Specifically, the report called for a greater acknowledgement of model prediction uncertinaity in making and implementing TMDL plans. To assure that such uncertinaity was addressed in water quality decision making the committee called for a commitment to "adaptive implementation" of water quality management plans. The committee found that the number and complexity of the interactions of multiple stressors, combined with model prediction uncertinaity means that we need to avoid the temptation to make assurances that specific actions will result in attainment of particular water quality standards. Until the work on solving a water quality problem begins, analysts and decision makers cannot be sure what the correct solutions are, or even what water quality goals a community should be seeking. In complex systems we need to act in order to learn; adaptive implementation is a concurrent process of action and learning. Learning requires (1) continued monitoring of the waterbody to determine how it responds to the actions taken and (2) carefully designed experiments in the watershed. If we do not design learning into what we attempt we are not doing adaptive implementation. Therefore, there needs to be an increased commitment to monitoring and experiments in watersheds that will lead to learning. This presentation will 1) explain the logic for adaptive implementation; 2) discuss the ways that water quality modelers could characterize and explain model uncertinaity to decision makers; 3) speculate on the implications of the adaptive implementation for setting of water quality standards, for design of watershed monitoring programs and for the regulatory rules governing the TMDL program implementation.

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

PubMed Central

Grossberg, Stephen

2009-01-01

An intimate link exists between the predictive and learning processes in the brain. Perceptual/cognitive and spatial/motor processes use complementary predictive mechanisms to learn, recognize, attend and plan about objects in the world, determine their current value, and act upon them. Recent neural models clarify these mechanisms and how they interact in cortical and subcortical brain regions. The present paper reviews and synthesizes data and models of these processes, and outlines a unified theory of predictive brain processing. PMID:19528003

Neural computations underlying inverse reinforcement learning in the human brain

PubMed Central

Pauli, Wolfgang M; Bossaerts, Peter; O'Doherty, John

2017-01-01

In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred food outcomes with varying probabilities, through observing the repeated slot choices of agents with similar and dissimilar food preferences. Using formal model comparison, we found that participants implemented inverse RL as opposed to a simple imitation strategy, in which the actions of the other agent are copied instead of inferring the underlying reward structure of the decision problem. Our computational fMRI analysis revealed that anterior dorsomedial prefrontal cortex encoded inferences about action-values within the value space of the agent as opposed to that of the observer, demonstrating that inverse RL is an abstract cognitive process divorceable from the values and concerns of the observer him/herself. PMID:29083301

Switching Reinforcement Learning for Continuous Action Space

NASA Astrophysics Data System (ADS)

Nagayoshi, Masato; Murao, Hajime; Tamaki, Hisashi

Reinforcement Learning (RL) attracts much attention as a technique of realizing computational intelligence such as adaptive and autonomous decentralized systems. In general, however, it is not easy to put RL into practical use. This difficulty includes a problem of designing a suitable action space of an agent, i.e., satisfying two requirements in trade-off: (i) to keep the characteristics (or structure) of an original search space as much as possible in order to seek strategies that lie close to the optimal, and (ii) to reduce the search space as much as possible in order to expedite the learning process. In order to design a suitable action space adaptively, we propose switching RL model to mimic a process of an infant's motor development in which gross motor skills develop before fine motor skills. Then, a method for switching controllers is constructed by introducing and referring to the “entropy”. Further, through computational experiments by using robot navigation problems with one and two-dimensional continuous action space, the validity of the proposed method has been confirmed.

Altered Connectivity and Action Model Formation in Autism Is Autism

PubMed Central

Mostofsky, Stewart H.; Ewen, Joshua B.

2014-01-01

Internal action models refer to sensory-motor programs that form the brain basis for a wide range of skilled behavior and for understanding others’ actions. Development of these action models, particularly those reliant on visual cues from the external world, depends on connectivity between distant brain regions. Studies of children with autism reveal anomalous patterns of motor learning and impaired execution of skilled motor gestures. These findings robustly correlate with measures of social and communicative function, suggesting that anomalous action model formation may contribute to impaired development of social and communicative (as well as motor) capacity in autism. Examination of the pattern of behavioral findings, as well as convergent data from neuroimaging techniques, further suggests that autism-associated action model formation may be related to abnormalities in neural connectivity, particularly decreased function of long-range connections. This line of study can lead to important advances in understanding the neural basis of autism and, more critically, can be used to guide effective therapies targeted at improving social, communicative, and motor function. PMID:21467306

Improving Theory Application among Pre-Service Teachers

ERIC Educational Resources Information Center

Jones, Anne

2009-01-01

This article describes the process of implementing Inter-collegially Supported Learning (Tigchelaar and Melief, 1997) and reflection using the ALACT [action, looking back, awareness of essential aspects, creating alternative methods of action, and trial] model (Korthhagen, 1985, 1988) an Elementary Masters in Teaching Program. This study takes a…

Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks.

PubMed

Hinaut, Xavier; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

2014-01-01

One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot's execution of these events in time. Second, this learning mechanism functions in the inverse sense, i.e., in a language production mode, where rather than executing commanded actions, the robot will describe the results of human generated actions. Finally, we collect data from naïve subjects who interact with the robot via spoken language, and demonstrate significant learning and generalization results. This allows us to conclude that such a neural language learning system not only helps to characterize and understand some aspects of human language acquisition, but also that it can be useful in adaptive human-robot interaction.

Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks

PubMed Central

Hinaut, Xavier; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

2014-01-01

One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot's execution of these events in time. Second, this learning mechanism functions in the inverse sense, i.e., in a language production mode, where rather than executing commanded actions, the robot will describe the results of human generated actions. Finally, we collect data from naïve subjects who interact with the robot via spoken language, and demonstrate significant learning and generalization results. This allows us to conclude that such a neural language learning system not only helps to characterize and understand some aspects of human language acquisition, but also that it can be useful in adaptive human-robot interaction. PMID:24834050

Outdoor Experiences and Sustainability

ERIC Educational Resources Information Center

Prince, Heather E.

2017-01-01

Positive outdoor teaching and learning experiences and sound pedagogical approaches undoubtedly have contributed towards an understanding of environmental sustainability but it is not always clear how, and to what extent, education can translate into action. This article argues, with reference to social learning theory, that role modelling,…

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning

PubMed Central

Franklin, Nicholas T; Frank, Michael J

2015-01-01

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments. DOI: http://dx.doi.org/10.7554/eLife.12029.001 PMID:26705698

Neural correlates of forward planning in a spatial decision task in humans

PubMed Central

Simon, Dylan Alexander; Daw, Nathaniel D.

2011-01-01

Although reinforcement learning (RL) theories have been influential in characterizing the brain’s mechanisms for reward-guided choice, the predominant temporal difference (TD) algorithm cannot explain many flexible or goal-directed actions that have been demonstrated behaviorally. We investigate such actions by contrasting an RL algorithm that is model-based, in that it relies on learning a map or model of the task and planning within it, to traditional model-free TD learning. To distinguish these approaches in humans, we used fMRI in a continuous spatial navigation task, in which frequent changes to the layout of the maze forced subjects continually to relearn their favored routes, thereby exposing the RL mechanisms employed. We sought evidence for the neural substrates of such mechanisms by comparing choice behavior and BOLD signals to decision variables extracted from simulations of either algorithm. Both choices and value-related BOLD signals in striatum, though most often associated with TD learning, were better explained by the model-based theory. Further, predecessor quantities for the model-based value computation were correlated with BOLD signals in the medial temporal lobe and frontal cortex. These results point to a significant extension of both the computational and anatomical substrates for RL in the brain. PMID:21471389

The minimalist grammar of action

PubMed Central

Pastra, Katerina; Aloimonos, Yiannis

2012-01-01

Language and action have been found to share a common neural basis and in particular a common ‘syntax’, an analogous hierarchical and compositional organization. While language structure analysis has led to the formulation of different grammatical formalisms and associated discriminative or generative computational models, the structure of action is still elusive and so are the related computational models. However, structuring action has important implications on action learning and generalization, in both human cognition research and computation. In this study, we present a biologically inspired generative grammar of action, which employs the structure-building operations and principles of Chomsky's Minimalist Programme as a reference model. In this grammar, action terminals combine hierarchically into temporal sequences of actions of increasing complexity; the actions are bound with the involved tools and affected objects and are governed by certain goals. We show, how the tool role and the affected-object role of an entity within an action drives the derivation of the action syntax in this grammar and controls recursion, merge and move, the latter being mechanisms that manifest themselves not only in human language, but in human action too. PMID:22106430

Do Domestic Dogs Learn Words Based on Humans’ Referential Behaviour?

PubMed Central

Tempelmann, Sebastian; Kaminski, Juliane; Tomasello, Michael

2014-01-01

Some domestic dogs learn to comprehend human words, although the nature and basis of this learning is unknown. In the studies presented here we investigated whether dogs learn words through an understanding of referential actions by humans rather than simple association. In three studies, each modelled on a study conducted with human infants, we confronted four word-experienced dogs with situations involving no spatial-temporal contiguity between the word and the referent; the only available cues were referential actions displaced in time from exposure to their referents. We found that no dogs were able to reliably link an object with a label based on social-pragmatic cues alone in all the tests. However, one dog did show skills in some tests, possibly indicating an ability to learn based on social-pragmatic cues. PMID:24646732

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning

PubMed Central

Zhu, Lusha; Mathewson, Kyle E.; Hsu, Ming

2012-01-01

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents’ beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs. PMID:22307594

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

PubMed

Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

2012-01-31

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

Joint object and action recognition via fusion of partially observable surveillance imagery data

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex L.

2017-05-01

Partially observable group activities (POGA) occurring in confined spaces are epitomized by their limited observability of the objects and actions involved. In many POGA scenarios, different objects are being used by human operators for the conduct of various operations. In this paper, we describe the ontology of such as POGA in the context of In-Vehicle Group Activity (IVGA) recognition. Initially, we describe the virtue of ontology modeling in the context of IVGA and show how such an ontology and a priori knowledge about the classes of in-vehicle activities can be fused for inference of human actions that consequentially leads to understanding of human activity inside the confined space of a vehicle. In this paper, we treat the problem of "action-object" as a duality problem. We postulate a correlation between observed human actions and the object that is being utilized within those actions, and conversely, if an object being handled is recognized, we may be able to expect a number of actions that are likely to be performed on that object. In this study, we use partially observable human postural sequences to recognition actions. Inspired by convolutional neural networks (CNNs) learning capability, we present an architecture design using a new CNN model to learn "action-object" perception from surveillance videos. In this study, we apply a sequential Deep Hidden Markov Model (DHMM) as a post-processor to CNN to decode realized observations into recognized actions and activities. To generate the needed imagery data set for the training and testing of these new methods, we use the IRIS virtual simulation software to generate high-fidelity and dynamic animated scenarios that depict in-vehicle group activities under different operational contexts. The results of our comparative investigation are discussed and presented in detail.

The Power of Social Networks: A Model for Weaving the Scholarship of Teaching and Learning into Institutional Culture

ERIC Educational Resources Information Center

Williams, Andrea L.; Verwood, Roselynn; Beery, Theresa A.; Dalton, Helen; McKinnon, James; Strickland, Karen; Pace, Jessica; Poole, Gary

2013-01-01

This paper offers a guide for those seeking to integrate the Scholarship of Teaching and Learning (SoTL) into higher education institutions to improve the quality of student learning. The authors posit that weaving SoTL into institutional cultures requires the coordinated actions of individuals working in linked social networks rather than…

Service Learning and Its Influenced to Pre-Service Teachers: Social Responsibility and Self-Efficacy Study

ERIC Educational Resources Information Center

Prasertsang, Parichart; Nuangchalerm, Prasart; Pumipuntu, Chaloey

2013-01-01

The purpose of the research was to study pre-service teachers on social responsibility and self-efficacy through service learning. The mixed methodology included two major procedures (i) the actual use of a developed service learning instructional model by means of action research principles and qualitative research and (ii) the study into the…

Jobs to Manufacturing Careers: Work-Based Courses. Work-Based Learning in Action

ERIC Educational Resources Information Center

Kobes, Deborah

2016-01-01

This case study, one of a series of publications exploring effective and inclusive models of work-based learning, finds that work-based courses bring college to the production line by using the job as a learning lab. Work-based courses are an innovative way to give incumbent workers access to community college credits and degrees. They are…

Entrepreneurial Learning through Action: A Case Study of the Six-Squared Program

ERIC Educational Resources Information Center

Pittaway, Luke; Missing, Caroline; Hudson, Nigel; Maragh, Dean

2009-01-01

This paper explores the role of "action" in entrepreneurial learning and illustrates how programs designed to support action learning can enhance management development in entrepreneurial businesses. The paper begins by exploring action learning and the way "action" is conceived in different types of program. In the second part, the paper details…

Enhancing Postgraduate Learning and Development: A Participatory Action Learning and Action Research Approach through Conferences

ERIC Educational Resources Information Center

Wood, Lesley; Louw, Ina; Zuber-Skerritt, Ortrun

2017-01-01

As supervisors who advocate the transformational potential of research both to generate theory and practical and emancipatory outcomes, we practice participatory action learning and action research (PALAR). This paper offers an illustrative case of how supervision practices based on action learning can foster emancipatory and lifelong learning…

The Implementation of Cooperative Learning Model "Number Heads Together" ("NHT") in Improving the Students' Ability in Reading Comprehension

ERIC Educational Resources Information Center

Maman, Mayong; Rajab, Andi Aryani

2016-01-01

The study aimed at describing the implementation of cooperative learning model of (NHT) at student of SMPN 2 Maros. The method used was a classroom action research in two cycles. Data were collected using the test for the quantitative and non-test for the qualitative by employing observation, field note, student's workbook, student's reflection…

Medial prefrontal cortex as an action-outcome predictor.

PubMed

Alexander, William H; Brown, Joshua W

2011-09-18

The medial prefrontal cortex (mPFC) and especially anterior cingulate cortex is central to higher cognitive function and many clinical disorders, yet its basic function remains in dispute. Various competing theories of mPFC have treated effects of errors, conflict, error likelihood, volatility and reward, using findings from neuroimaging and neurophysiology in humans and monkeys. No single theory has been able to reconcile and account for the variety of findings. Here we show that a simple model based on standard learning rules can simulate and unify an unprecedented range of known effects in mPFC. The model reinterprets many known effects and suggests a new view of mPFC, as a region concerned with learning and predicting the likely outcomes of actions, whether good or bad. Cognitive control at the neural level is then seen as a result of evaluating the probable and actual outcomes of one's actions. © 2011 Nature America, Inc. All rights reserved.

Medial prefrontal cortex as an action-outcome predictor

PubMed Central

Alexander, William H.; Brown, Joshua W.

2011-01-01

The medial prefrontal cortex (mPFC) and especially anterior cingulate cortex (ACC) is central to higher cognitive function and numerous clinical disorders, yet its basic function remains in dispute. Various competing theories of mPFC have treated effects of errors, conflict, error likelihood, volatility, and reward, based on findings from neuroimaging and neurophysiology in humans and monkeys. To date, no single theory has been able to reconcile and account for the variety of findings. Here we show that a simple model based on standard learning rules can simulate and unify an unprecedented range of known effects in mPFC. The model reinterprets many known effects and suggests a new view of mPFC, as a region concerned with learning and predicting the likely outcomes of actions, whether good or bad. Cognitive control at the neural level is then seen as a result of evaluating the probable and actual outcomes of one's actions. PMID:21926982

Imitation, empathy, and mirror neurons.

PubMed

Iacoboni, Marco

2009-01-01

There is a convergence between cognitive models of imitation, constructs derived from social psychology studies on mimicry and empathy, and recent empirical findings from the neurosciences. The ideomotor framework of human actions assumes a common representational format for action and perception that facilitates imitation. Furthermore, the associative sequence learning model of imitation proposes that experience-based Hebbian learning forms links between sensory processing of the actions of others and motor plans. Social psychology studies have demonstrated that imitation and mimicry are pervasive, automatic, and facilitate empathy. Neuroscience investigations have demonstrated physiological mechanisms of mirroring at single-cell and neural-system levels that support the cognitive and social psychology constructs. Why were these neural mechanisms selected, and what is their adaptive advantage? Neural mirroring solves the "problem of other minds" (how we can access and understand the minds of others) and makes intersubjectivity possible, thus facilitating social behavior.

Could Intelligent Tutors Anticipate Successfully User Reactions?

NASA Astrophysics Data System (ADS)

Kalisz, Eugenia; Florea, Adina Magda

2006-06-01

Emotions have been shown to have an important impact on several human processes such as decision-making, planning, cognition, and learning. In an e-learning system, an artificial tutor capable of effectively understanding and anticipating the student emotions during learning will have a significantly enhanced role. The paper presents a model of an artificial tutor endowed with synthesized emotions according to the BDE model, previously developed by the authors. It also analyzes possible student reactions while interacting with the learning material and the way the artificial tutor could anticipate and should respond to these reactions, with adequate actions.

Green Action through Education: A Model for Fostering Positive Attitudes about STEM

ERIC Educational Resources Information Center

Wheland, Ethel R.; Donovan, William J.; Dukes, J. Thomas; Qammar, Helen K.; Smith, Gregory A.; Williams, Bonnie L.

2013-01-01

This paper describes an innovative collaboration between instructors of non-STEM (science, technology, engineering, and mathematics) courses and scientists who teach STEM courses in the GATE (Green Action Through Education) learning community. The scientists in this project presented engaging science--in such diverse locations as a sewage…

Using Action Research to Improve Teaching and Learning

ERIC Educational Resources Information Center

Ahrens, Christie L.; Brant, Mary Ellen; Lee, E. Suzanne

2007-01-01

Saint Xavier University in Chicago, Illinois offers a Master of Arts in Teaching and Leadership (MATL) degree program for certified teachers in the state of Illinois. This professional development program is provided through a partnership with Pearson Achievement Solutions. The program employs an action research model to guide teachers in…

Toward Self-Referential Autonomous Learning of Object and Situation Models.

PubMed

Damerow, Florian; Knoblauch, Andreas; Körner, Ursula; Eggert, Julian; Körner, Edgar

2016-01-01

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach.

Faster Teaching via POMDP Planning

ERIC Educational Resources Information Center

Rafferty, Anna N.; Brunskill, Emma; Griffiths, Thomas L.; Shafto, Patrick

2016-01-01

Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model…

Creating an Online Library To Support a Virtual Learning Community.

ERIC Educational Resources Information Center

Sandelands, Eric

1998-01-01

International Management Centres (IMC), an independent business school, and Anbar Electronic Intelligence (AEI), a database publisher, have created a virtual library for IMC's virtual business school. Topics discussed include action learning; IMC's partnership with AEI; the virtual university model; designing virtual library resources; and…

The role of social interaction and pedagogical cues for eliciting and reducing overimitation in preschoolers.

PubMed

Hoehl, Stefanie; Zettersten, Martin; Schleihauf, Hanna; Grätz, Sabine; Pauen, Sabina

2014-06-01

The tendency to imitate causally irrelevant actions is termed overimitation. Here we investigated (a) whether communication of a model performing irrelevant actions is necessary to elicit overimitation in preschoolers and (b) whether communication of another model performing an efficient action modulates the subsequent reduction of overimitation. In the study, 5-year-olds imitated irrelevant actions both when they were modeled by a communicative and pedagogical experimenter and when they were modeled by a non-communicative and non-pedagogical experimenter. However, children stopped using the previously learned irrelevant actions only when they were subsequently shown the more efficient way to achieve the goal by a pedagogical experimenter. Thus, communication leads preschoolers to adapt their imitative behavior but does not seem to affect overimitation in the first place. Results are discussed with regard to the importance of communication for the transmission of cultural knowledge during development. Copyright © 2014 Elsevier Inc. All rights reserved.

Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions.

PubMed

Yang, Yang; Saleemi, Imran; Shah, Mubarak

2013-07-01

This paper proposes a novel representation of articulated human actions and gestures and facial expressions. The main goals of the proposed approach are: 1) to enable recognition using very few examples, i.e., one or k-shot learning, and 2) meaningful organization of unlabeled datasets by unsupervised clustering. Our proposed representation is obtained by automatically discovering high-level subactions or motion primitives, by hierarchical clustering of observed optical flow in four-dimensional, spatial, and motion flow space. The completely unsupervised proposed method, in contrast to state-of-the-art representations like bag of video words, provides a meaningful representation conducive to visual interpretation and textual labeling. Each primitive action depicts an atomic subaction, like directional motion of limb or torso, and is represented by a mixture of four-dimensional Gaussian distributions. For one--shot and k-shot learning, the sequence of primitive labels discovered in a test video are labeled using KL divergence, and can then be represented as a string and matched against similar strings of training videos. The same sequence can also be collapsed into a histogram of primitives or be used to learn a Hidden Markov model to represent classes. We have performed extensive experiments on recognition by one and k-shot learning as well as unsupervised action clustering on six human actions and gesture datasets, a composite dataset, and a database of facial expressions. These experiments confirm the validity and discriminative nature of the proposed representation.

The Representation of Motor (Inter)action, States of Action, and Learning: Three Perspectives on Motor Learning by Way of Imagery and Execution

PubMed Central

Frank, Cornelia; Schack, Thomas

2017-01-01

Learning in intelligent systems is a result of direct and indirect interaction with the environment. While humans can learn by way of different states of (inter)action such as the execution or the imagery of an action, their unique potential to induce brain- and mind-related changes in the motor action system is still being debated. The systematic repetition of different states of action (e.g., physical and/or mental practice) and their contribution to the learning of complex motor actions has traditionally been approached by way of performance improvements. More recently, approaches highlighting the role of action representation in the learning of complex motor actions have evolved and may provide additional insight into the learning process. In the present perspective paper, we build on brain-related findings and sketch recent research on learning by way of imagery and execution from a hierarchical, perceptual-cognitive approach to motor control and learning. These findings provide insights into the learning of intelligent systems from a perceptual-cognitive, representation-based perspective and as such add to our current understanding of action representation in memory and its changes with practice. Future research should build bridges between approaches in order to more thoroughly understand functional changes throughout the learning process and to facilitate motor learning, which may have particular importance for cognitive systems research in robotics, rehabilitation, and sports. PMID:28588510

A new computational account of cognitive control over reinforcement-based decision-making: Modeling of a probabilistic learning task.

PubMed

Zendehrouh, Sareh

2015-11-01

Recent work on decision-making field offers an account of dual-system theory for decision-making process. This theory holds that this process is conducted by two main controllers: a goal-directed system and a habitual system. In the reinforcement learning (RL) domain, the habitual behaviors are connected with model-free methods, in which appropriate actions are learned through trial-and-error experiences. However, goal-directed behaviors are associated with model-based methods of RL, in which actions are selected using a model of the environment. Studies on cognitive control also suggest that during processes like decision-making, some cortical and subcortical structures work in concert to monitor the consequences of decisions and to adjust control according to current task demands. Here a computational model is presented based on dual system theory and cognitive control perspective of decision-making. The proposed model is used to simulate human performance on a variant of probabilistic learning task. The basic proposal is that the brain implements a dual controller, while an accompanying monitoring system detects some kinds of conflict including a hypothetical cost-conflict one. The simulation results address existing theories about two event-related potentials, namely error related negativity (ERN) and feedback related negativity (FRN), and explore the best account of them. Based on the results, some testable predictions are also presented. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation of a Theory of Instructional Sequences for Physics Instruction

NASA Astrophysics Data System (ADS)

Wackermann, Rainer; Trendel, Georg; Fischer, Hans E.

2010-05-01

The background of the study is the theory of basis models of teaching and learning, a comprehensive set of models of learning processes which includes, for example, learning through experience and problem-solving. The combined use of different models of learning processes has not been fully investigated and it is frequently not clear under what circumstances a particular model should be used by teachers. In contrast, the theory under investigation here gives guidelines for choosing a particular model and provides instructional sequences for each model. The aim is to investigate the implementation of the theory applied to physics instruction and to show if possible effects for the students may be attributed to the use of the theory. Therefore, a theory-oriented education programme for 18 physics teachers was developed and implemented in the 2005/06 school year. The main features of the intervention consisted of coaching physics lessons and video analysis according to the theory. The study follows a pre-treatment-post design with non-equivalent control group. Findings of repeated-measures ANOVAs show large effects for teachers' subjective beliefs, large effects for classroom actions, and small to medium effects for student outcomes such as perceived instructional quality and student emotions. The teachers/classes that applied the theory especially well according to video analysis showed the larger effects. The results showed that differentiating between different models of learning processes improves physics instruction. Effects can be followed through to student outcomes. The education programme effect was clearer for classroom actions and students' outcomes than for teachers' beliefs.

Soft Systems Methodology

NASA Astrophysics Data System (ADS)

Checkland, Peter; Poulter, John

Soft systems methodology (SSM) is an approach for tackling problematical, messy situations of all kinds. It is an action-oriented process of inquiry into problematic situations in which users learn their way from finding out about the situation, to taking action to improve it. The learning emerges via an organised process in which the situation is explored using a set of models of purposeful action (each built to encapsulate a single worldview) as intellectual devices, or tools, to inform and structure discussion about a situation and how it might be improved. This paper, written by the original developer Peter Checkland and practitioner John Poulter, gives a clear and concise account of the approach that covers SSM's specific techniques, the learning cycle process of the methodology and the craft skills which practitioners develop. This concise but theoretically robust account nevertheless includes the fundamental concepts, techniques, core tenets described through a wide range of settings.

Action Learning and Constructivist Grounded Theory: Powerfully Overlapping Fields of Practice

ERIC Educational Resources Information Center

Rand, Jane

2013-01-01

This paper considers the shared characteristics between action learning (AL) and the research methodology constructivist grounded theory (CGT). Mirroring Edmonstone's [2011. "Action Learning and Organisation Development: Overlapping Fields of Practice." "Action Learning: Research and Practice" 8 (2): 93-102] article, which…

Influence of action-effect associations acquired by ideomotor learning on imitation.

PubMed

Bunlon, Frédérique; Marshall, Peter J; Quandt, Lorna C; Bouquet, Cedric A

2015-01-01

According to the ideomotor theory, actions are represented in terms of their perceptual effects, offering a solution for the correspondence problem of imitation (how to translate the observed action into a corresponding motor output). This effect-based coding of action is assumed to be acquired through action-effect learning. Accordingly, performing an action leads to the integration of the perceptual codes of the action effects with the motor commands that brought them about. While ideomotor theory is invoked to account for imitation, the influence of action-effect learning on imitative behavior remains unexplored. In two experiments, imitative performance was measured in a reaction time task following a phase of action-effect acquisition. During action-effect acquisition, participants freely executed a finger movement (index or little finger lifting), and then observed a similar (compatible learning) or a different (incompatible learning) movement. In Experiment 1, finger movements of left and right hands were presented as action-effects during acquisition. In Experiment 2, only right-hand finger movements were presented during action-effect acquisition and in the imitation task the observed hands were oriented orthogonally to participants' hands in order to avoid spatial congruency effects. Experiments 1 and 2 showed that imitative performance was improved after compatible learning, compared to incompatible learning. In Experiment 2, although action-effect learning involved perception of finger movements of right hand only, imitative capabilities of right- and left-hand finger movements were equally affected. These results indicate that an observed movement stimulus processed as the effect of an action can later prime execution of that action, confirming the ideomotor approach to imitation. We further discuss these findings in relation to previous studies of action-effect learning and in the framework of current ideomotor approaches to imitation.

Influence of Action-Effect Associations Acquired by Ideomotor Learning on Imitation

PubMed Central

Bunlon, Frédérique; Marshall, Peter J.; Quandt, Lorna C.; Bouquet, Cedric A.

2015-01-01

According to the ideomotor theory, actions are represented in terms of their perceptual effects, offering a solution for the correspondence problem of imitation (how to translate the observed action into a corresponding motor output). This effect-based coding of action is assumed to be acquired through action-effect learning. Accordingly, performing an action leads to the integration of the perceptual codes of the action effects with the motor commands that brought them about. While ideomotor theory is invoked to account for imitation, the influence of action-effect learning on imitative behavior remains unexplored. In two experiments, imitative performance was measured in a reaction time task following a phase of action-effect acquisition. During action-effect acquisition, participants freely executed a finger movement (index or little finger lifting), and then observed a similar (compatible learning) or a different (incompatible learning) movement. In Experiment 1, finger movements of left and right hands were presented as action-effects during acquisition. In Experiment 2, only right-hand finger movements were presented during action-effect acquisition and in the imitation task the observed hands were oriented orthogonally to participants’ hands in order to avoid spatial congruency effects. Experiments 1 and 2 showed that imitative performance was improved after compatible learning, compared to incompatible learning. In Experiment 2, although action-effect learning involved perception of finger movements of right hand only, imitative capabilities of right- and left-hand finger movements were equally affected. These results indicate that an observed movement stimulus processed as the effect of an action can later prime execution of that action, confirming the ideomotor approach to imitation. We further discuss these findings in relation to previous studies of action-effect learning and in the framework of current ideomotor approaches to imitation. PMID:25793755

Community-Based Research: From Practice to Theory and Back Again.

ERIC Educational Resources Information Center

Stoecker, Randy

2003-01-01

Explores the theoretical strands being combined in community-based research--charity service learning, social justice service learning, action research, and participatory research. Shows how different models of community-based research, based in different theories of society and different approaches to community work, may combine or conflict. (EV)

Fostering Learning Opportunities through Employee Participation amid Organizational Change

ERIC Educational Resources Information Center

Valleala, Ulla Maija; Herranen, Sanna; Collin, Kaija; Paloniemi, Susanna

2015-01-01

Health care organizations are facing rapid changes, frequently involving modification of existing procedures. The case study reported here examined change processes and learning in a health care organization. The organizational change in question occurred in the emergency clinic of a Finnish central hospital where a new action model for…

Teachers' Perceptions of the Principal Role and Actions in Successful Professional Learning Communities

ERIC Educational Resources Information Center

Dilmar, Amy D.

2017-01-01

Despite millions of dollars spent on reform efforts, effective and sustainable improvement still eludes schools. The appropriate development of the professional learning community model, including five key dimensions, provides a structure for educational institutions to bring about sustainable improvements in student achievement. If principals do…

Satisfaction Analysis of Experiential Learning-Based Popular Science Education

ERIC Educational Resources Information Center

Dzan, Wei-Yuan; Tsai, Huei-Yin; Lou, Shi-Jer; Shih, Ru-Chu

2015-01-01

This study employed Kolb's experiential learning model-specific experiences, observations of reflections, abstract conceptualization, and experiment-action in activities to serve as the theoretical basis for popular science education planning. It designed the six activity themes of "Knowledge of the Ocean, Easy to Know, See the Large from the…

Digital Roundup

ERIC Educational Resources Information Center

Horn, Michael B.

2013-01-01

State policy is crucial to the spread of digital-learning opportunities at the elementary and secondary level. A review of recent legislative action reveals policies that are constantly in flux and differ quite markedly from one state to another. Some have hoped for model digital-learning legislation that could handle all the various issues…

Science Learning with Information Technologies as a Tool for "Scientific Thinking" in Engineering Education

ERIC Educational Resources Information Center

Smirnov, Eugeny; Bogun, Vitali

2011-01-01

New methodologies in science (or mathematics) learning process and scientific thinking in the classroom activity of engineer students with ICT (information and communication technology), including graphic calculator are presented: visual modelling with ICT, action research with graphic calculator, insight in classroom and communications and…

Inquiring into the Dilemmas of Implementing Action Learning. Innovative Session 6. [Concurrent Innovative Session at AHRD Annual Conference, 2000.

ERIC Educational Resources Information Center

Yorks, Lyle; Dilworth, Robert L.; Marquardt, Michael J.; Marsick, Victoria; O'Neil, Judy

Action learning is receiving increasing attention from human resource development (HRD) practitioners and the HRD management literature. Action learning has been characterized as follows: (1) working in small groups to take action on meaningful problems while seeking to learn from having taken the specified action lies at the foundation of action…

Proposal of Self-Learning and Recognition System of Facial Expression

NASA Astrophysics Data System (ADS)

Ogawa, Yukihiro; Kato, Kunihito; Yamamoto, Kazuhiko

We describe realization of more complicated function by using the information acquired from some equipped unripe functions. The self-learning and recognition system of the human facial expression, which achieved under the natural relation between human and robot, are proposed. The robot with this system can understand human facial expressions and behave according to their facial expressions after the completion of learning process. The system modelled after the process that a baby learns his/her parents’ facial expressions. Equipping the robot with a camera the system can get face images and equipping the CdS sensors on the robot’s head the robot can get the information of human action. Using the information of these sensors, the robot can get feature of each facial expression. After self-learning is completed, when a person changed his facial expression in front of the robot, the robot operates actions under the relevant facial expression.

Action Learning. A Guide for Professional, Management and Educational Development. Second Edition.

ERIC Educational Resources Information Center

McGill, Ian; Beaty, Liz

Action learning is a process of learning and reflection that happens with the support of a group of colleagues ("set") working with real problems with the intention of getting things done. This guide is for those who want to practice action learning. It can be used to introduce the concepts of action learning to others and as a manual…

Hunger at Home: A Higher Education Service Learning Course of Appraisal and Action in Community Food Security

ERIC Educational Resources Information Center

Ross, Nancy J.

2011-01-01

Service learning and civic engagement are playing an increasingly larger role in higher education. Unity College's Hunger at Home course could serve as a model for service learning in disciplines such as nutrition, sociology, and food and agriculture. The class worked with local partners to get a better understanding of hunger in the area, recent…

Sustaining Competitive Advantage: Mental Models and Organizational Learning for Future Marines

DTIC Science & Technology

2007-01-01

Soft Systems Methodology : Other Voices.” Systemic Practice and Action Research. 13, no. 6, (2000): 773. Larsen, Kai R. T., Claire McInerney...30. Mingers, John. “An Idea Ahead of Its Time: The History and Development of Soft Systems Methodology .” Systemic Practice and Action...Soft System Dynamics Methodology (SSDM): Combinging Soft Systems Methodology (SSM) and System Dynamics (SD).” Systemic Practice and Action

Developmental Changes in Learning: Computational Mechanisms and Social Influences

PubMed Central

Bolenz, Florian; Reiter, Andrea M. F.; Eppinger, Ben

2017-01-01

Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development. PMID:29250006

Embracing Complexity: Using Technology to Develop a Life-Long Learning Model for Non-Working Time in the Interdependent Homes for Adults with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Chiang, I-Tsun; Chen, Mei-Li

2011-01-01

The purpose of this study was to employ complexity theory as a theoretical framework and technology to facilitate the development of a life-long learning model for non-working time in the interdependent homes for adults with Autism Spectrum Disorders (ASD). A "Shining Star Sustainable Action Project" of the ROC Foundation for Autistic…

Action Learning for Professionals: A New Approach to Practice

ERIC Educational Resources Information Center

Abbott, Christine; Mayes, Cathy

2014-01-01

Following on from the article "Building Capacity in Social Care: An Evaluation of a National Programme of Action Learning Facilitator Development" (Abbott, C., L. Burtney, and C. Wall. 2013. "Action Learning: Research & Practice" 10 (2): 168--177), this article describes how action learning is being introduced in Cornwall…

Work-based learning using action learning sets.

PubMed

Rosser, Elizabeth

2016-10-27

Elizabeth Rosser, Deputy Dean (Education and Professional Practice) and Professor of Nursing at Bournemouth University reflects on the concept of action learning, and the benefits of being part of an action learning set.

Impact of CALL In-House Professional Development Training on Teachers' Pedagogy: An Evaluative Study

ERIC Educational Resources Information Center

Sulaimani, Amjjad Osama; Sarhandi, Pir Suhail Ahmed; Buledi, Majid Hussain

2017-01-01

This study examines the impact of computer-assisted language learning (CALL) in-house professional development trainings based on Technological Pedagogical Content Knowledge in-Action (TPACK-In-Action) model on female teachers' pedagogy at a Saudi Arabian university. Data were collected using survey questionnaires to gather participants'…

Promoting Engagement: Using Species Action Plans to Bring Together Students and Conservation Professionals

ERIC Educational Resources Information Center

Scott, Graham W.; Turnbull, Shona; Spencer, James

2008-01-01

We describe an exercise, the production of a species action plan, which utilises components of both transmission mode and experiential learning. This exercise brings together students and a professional role model to promote a stronger engagement with aspects of local biodiversity management. We outline perceived benefits and outcomes of the…

Ohio Vocational Consumer/Homemaking Curriculum Guide. Practical Action.

ERIC Educational Resources Information Center

Ohio State Univ., Columbus. Instructional Materials Lab.

This curriculum guide helps students learn the technical skills of the occupation of homemaking. It also uses the process model of practical reasoning to assist men and women in taking action regarding the perennial problems that face individuals and families living in the world society. The first section provides the philosophy, aim, student…

An Action Research Project: Development of a Pre-Licensure Examination Review Course for Emergency Medical Technician Program Graduates at a Rural Community College

ERIC Educational Resources Information Center

Boucher, Daryl

2013-01-01

This action research project examined how "Efficiency in Learning" ("EL") strategies, "Appreciative Inquiry" ("AI") and the "Interactive Model of Program Planning" ("IMPP") could be used to discern the content and preferred pedagogical approaches in the development of a pre-licensure…

Action Learning Research? Reflections from the Colloquium at the Third International Conference on Action Learning

ERIC Educational Resources Information Center

Coghlan, David

2013-01-01

The case for the notion of action learning research has been posed and explored in several publications over the past few years. There is no tradition within action learning of understanding it as an approach to research. Within some academic circles, there has been a focus on the "action turn," the development of the notion of actionable…

Action Learning: a new method to increase tractor rollover protective structure (ROPS) adoption.

PubMed

Biddle, Elyce Anne; Keane, Paul R

2012-01-01

Action Learning is a problem-solving process that is used in various industries to address difficult problems. This project applied Action Learning to a leading problem in agricultural safety. Tractor overturns are the leading cause of fatal injury to farmworkers. This cause of injury is preventable using rollover protective structures (ROPS), protective equipment that functions as a roll bar structure to protect the operator in the event of an overturn. For agricultural tractors manufactured after 1976 and employee operated, Occupational Safety and Health Administration (OSHA) regulation requires employers to equip them with ROPS and seat belts. By the mid-1980s, US tractor manufacturers began adding ROPS on all farm tractors over 20 horsepower sold in the United States (http://www.nasdonline.org/document/113/d001656/rollover-protection-for-farm-tractor-operators.html). However, many older tractors remain in use without ROPS, putting tractor operators at continued risk for traumatic injury and fatality. For many older tractor models ROPS are available for retrofit, but for a variety of reasons, tractor owners have not chosen to retrofit those ROPS. The National Institute for Occupational Safety and Health (NIOSH) attempted various means to ameliorate this occupational safety risk, including the manufacture of a low-cost ROPS for self-assembly. Other approaches address barriers to adoption. An Action Learning approach to increasing adoption of ROPS was followed in Virginia and New York, with mixed results. Virginia took action to increase the manufacturing and adoption of ROPS, but New York saw problems that would be insurmountable. Increased focus on team composition might be needed to establish effective Action Learning teams to address this problem.

Action Learning: A New Method to Increase Tractor Rollover Protective Structure (ROPS) Adoption

PubMed Central

Biddle, Elyce Anne; Keane, Paul R.

2016-01-01

Action Learning is a problem-solving process that is used in various industries to address difficult problems. This project applied Action Learning to a leading problem in agricultural safety. Tractor overturns are the leading cause of fatal injury to farmworkers. This cause of injury is preventable using rollover protective structures (ROPS), protective equipment that functions as a roll bar structure to protect the operator in the event of an overturn. For agricultural tractors manufactured after 1976 and employee operated, Occupational Safety and Health Administration (OSHA) regulation requires employers to equip them with ROPS and seat belts. By the mid-1980s, US tractor manufacturers began adding ROPS on all farm tractors over 20 horsepower sold in the United States (http://www.nasdonline.org/document/113/d001656/rollover-protection-for-farm-tractor-operators.html). However, many older tractors remain in use without ROPS, putting tractor operators at continued risk for traumatic injury and fatality. For many older tractor models ROPS are available for retrofit, but for a variety of reasons, tractor owners have not chosen to retrofit those ROPS. The National Institute for Occupational Safety and Health (NIOSH) attempted various means to ameliorate this occupational safety risk, including the manufacture of a low-cost ROPS for self-assembly. Other approaches address barriers to adoption. An Action Learning approach to increasing adoption of ROPS was followed in Virginia and New York, with mixed results. Virginia took action to increase the manufacturing and adoption of ROPS, but New York saw problems that would be insurmountable. Increased focus on team composition might be needed to establish effective Action Learning teams to address this problem. PMID:22994641

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

Action recognition in depth video from RGB perspective: A knowledge transfer manner

NASA Astrophysics Data System (ADS)

Chen, Jun; Xiao, Yang; Cao, Zhiguo; Fang, Zhiwen

2018-03-01

Different video modal for human action recognition has becoming a highly promising trend in the video analysis. In this paper, we propose a method for human action recognition from RGB video to Depth video using domain adaptation, where we use learned feature from RGB videos to do action recognition for depth videos. More specifically, we make three steps for solving this problem in this paper. First, different from image, video is more complex as it has both spatial and temporal information, in order to better encode this information, dynamic image method is used to represent each RGB or Depth video to one image, based on this, most methods for extracting feature in image can be used in video. Secondly, as video can be represented as image, so standard CNN model can be used for training and testing for videos, beside, CNN model can be also used for feature extracting as its powerful feature expressing ability. Thirdly, as RGB videos and Depth videos are belong to two different domains, in order to make two different feature domains has more similarity, domain adaptation is firstly used for solving this problem between RGB and Depth video, based on this, the learned feature from RGB video model can be directly used for Depth video classification. We evaluate the proposed method on one complex RGB-D action dataset (NTU RGB-D), and our method can have more than 2% accuracy improvement using domain adaptation from RGB to Depth action recognition.

Understanding the Causal Path between Action, Learning, and Solutions: Maximizing the Power of Action Learning to Achieve Great Results

ERIC Educational Resources Information Center

Leonard, H. Skipton

2015-01-01

Clients and practitioners alike are often confused about the ultimate purpose of action learning (AL). Because of the title of the method, many believe the primary goal of AL is to generate learning. This article clarifies the relationship between action, learning, and solutions. It also provides historical evidence to support the conclusion that…

HiTEC: a connectionist model of the interaction between perception and action planning.

PubMed

Haazebroek, Pascal; Raffone, Antonino; Hommel, Bernhard

2017-11-01

Increasing evidence suggests that perception and action planning do not represent separable stages of a unidirectional processing sequence, but rather emerging properties of highly interactive processes. To capture these characteristics of the human cognitive system, we have developed a connectionist model of the interaction between perception and action planning: HiTEC, based on the Theory of Event Coding (Hommel et al. in Behav Brain Sci 24:849-937, 2001). The model is characterized by representations at multiple levels and by shared representations and processes. It complements available models of stimulus-response translation by providing a rationale for (1) how situation-specific meanings of motor actions emerge, (2) how and why some aspects of stimulus-response translation occur automatically and (3) how task demands modulate sensorimotor processing. The model is demonstrated to provide a unitary account and simulation of a number of key findings with multiple experimental paradigms on the interaction between perception and action such as the Simon effect, its inversion (Hommel in Psychol Res 55:270-279, 1993), and action-effect learning.

"Should I or shouldn't I?" Imitation of undesired versus allowed actions from peer and adult models by 18- and 24-month-old toddlers.

PubMed

Seehagen, Sabine; Schneider, Silvia; Miebach, Kristin; Frigge, Katharina; Zmyj, Norbert

2017-11-01

Imitation is a common way of acquiring novel behaviors in toddlers. However, little is known about toddlers' imitation of undesired actions. Here we investigated 18- and 24-month-olds' (N=110) imitation of undesired and allowed actions from televised peer and adult models. Permissiveness of the demonstrated actions was indicated by the experimenter's response to their execution (angry or neutral). Analyses revealed that toddlers' imitation scores were higher after demonstrations of allowed versus undesired actions, regardless of the age of the model. In agreement with prior research, these results suggest that third-party reactions to a model's actions can be a powerful cue for toddlers to engage in or refrain from imitation. In the context of the present study, third-party reactions were more influential on imitation than the model's age. Considering the relative influence of different social cues for imitation can help to gain a fuller understanding of early observational learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Neuronal correlates of decisions to speak and act: Spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas

PubMed Central

Garagnani, Max; Pulvermüller, Friedemann

2013-01-01

The neural mechanisms underlying the spontaneous, stimulus-independent emergence of intentions and decisions to act are poorly understood. Using a neurobiologically realistic model of frontal and temporal areas of the brain, we simulated the learning of perception–action circuits for speech and hand-related actions and subsequently observed their spontaneous behaviour. Noise-driven accumulation of reverberant activity in these circuits leads to their spontaneous ignition and partial-to-full activation, which we interpret, respectively, as model correlates of action intention emergence and action decision-and-execution. Importantly, activity emerged first in higher-association prefrontal and temporal cortices, subsequently spreading to secondary and finally primary sensorimotor model-areas, hence reproducing the dynamics of cortical correlates of voluntary action revealed by readiness-potential and verb-generation experiments. This model for the first time explains the cortical origins and topography of endogenous action decisions, and the natural emergence of functional specialisation in the cortex, as mechanistic consequences of neurobiological principles, anatomical structure and sensorimotor experience. PMID:23489583

Impairments in action-outcome learning in schizophrenia.

PubMed

Morris, Richard W; Cyrzon, Chad; Green, Melissa J; Le Pelley, Mike E; Balleine, Bernard W

2018-03-03

Learning the causal relation between actions and their outcomes (AO learning) is critical for goal-directed behavior when actions are guided by desire for the outcome. This can be contrasted with habits that are acquired by reinforcement and primed by prevailing stimuli, in which causal learning plays no part. Recently, we demonstrated that goal-directed actions are impaired in schizophrenia; however, whether this deficit exists alongside impairments in habit or reinforcement learning is unknown. The present study distinguished deficits in causal learning from reinforcement learning in schizophrenia. We tested people with schizophrenia (SZ, n = 25) and healthy adults (HA, n = 25) in a vending machine task. Participants learned two action-outcome contingencies (e.g., push left to get a chocolate M&M, push right to get a cracker), and they also learned one contingency was degraded by delivery of noncontingent outcomes (e.g., free M&Ms), as well as changes in value by outcome devaluation. Both groups learned the best action to obtain rewards; however, SZ did not distinguish the more causal action when one AO contingency was degraded. Moreover, action selection in SZ was insensitive to changes in outcome value unless feedback was provided, and this was related to the deficit in AO learning. The failure to encode the causal relation between action and outcome in schizophrenia occurred without any apparent deficit in reinforcement learning. This implies that poor goal-directed behavior in schizophrenia cannot be explained by a more primary deficit in reward learning such as insensitivity to reward value or reward prediction errors.

Psychological Climates in Action Learning Sets: A Manager's Perspective

ERIC Educational Resources Information Center

Yeadon-Lee, Annie

2015-01-01

Action learning (AL) is often viewed as a process that facilitates professional learning through the creation of a positive psychological climate [Marquardt, M. J. 2000. "Action Learning and Leadership." "The Learning Organisation" 7 (5): 233-240; Schein, E. H. 1979. "Personal Change Through Interpersonal…

Statistical learning in social action contexts.

PubMed

Monroy, Claire; Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and-if so-whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together ('Joint' condition) or stated the intention to act alone ('Parallel' condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor's action reliably predicted the second actor's action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects.

Statistical learning in social action contexts

PubMed Central

Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and—if so—whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together (‘Joint’ condition) or stated the intention to act alone (‘Parallel’ condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor’s action reliably predicted the second actor’s action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects. PMID:28475619

Reconciling Market Requirements and Operations Resources: An Opportunity for Action Learning

ERIC Educational Resources Information Center

Coughlan, Paul; Coghlan, David

2009-01-01

This article brings together the fields of action learning and operations strategy. It presents a case of action learning focused on strategic operations improvement in the extended manufacturing enterprise. As the third article in the set of explorations in this journal within the fields of action learning, operations strategy and collaborative…

Action Learning. Symposium 21. [Concurrent Symposium Session at AHRD Annual Conference, 2000.

ERIC Educational Resources Information Center

2000

This document contains three papers from a symposium on action learning that was conducted as part of a conference on human resource development (HRD). "Searching for Meaning in Complex Action Learning Data: What Environments, Acts, and Words Reveal" (Verna J. Willis) analyzes complex action learning documents produced as course…

Transitioning to a More Sustainable Society: Unpacking the Role of the Learning-Action Nexus

ERIC Educational Resources Information Center

Moyer, Joanne M.; Sinclair, A. John; Quinn, Lisa

2016-01-01

In recent years, action on sustainability has been highly influential around the globe and many now recognize the importance of individual and social learning for inspiring action and achieving sustainability outcomes. Transformative learning theory has been criticized, however, for insufficient development of the link between learning and action.…

Action Learning: Developing Leaders and Supporting Change in a Healthcare Context

ERIC Educational Resources Information Center

Doyle, Louise

2014-01-01

This account of practice outlines how action learning was used as the key component of a leadership development initiative for managers in an acute hospital setting. It explains how the initiative was conceived, why action learning was chosen and how action learning principles were incorporated. Insights into the outcomes and considerations for…

Practicing What We Teach: Using Action Research to Learn about Teaching Action Research

ERIC Educational Resources Information Center

Brown, Barb; Dressler, Roswita; Eaton, Sarah Elaine; Jacobsen, Michele

2015-01-01

In this article, action research is explored as a process for instructor reflection, professional learning and collaboration. The context for the professional learning was the teaching of graduate level education courses in which action research, in conjunction with a cohort-based, collaboratory approach to learning, was used to facilitate…

Culture and Commitment: The Key to the Creation of an Action Learning Organization

ERIC Educational Resources Information Center

Hind, Matthew; Koenigsberger, John

2007-01-01

This article examines the introduction and practice of action learning into a highly volatile, commercial environment. During nine years of action learning projects, the impact on individuals, the action learning sets into which they were formed, the organization and its structure and the organizational culture were evaluated. The article…

Sound-Making Actions Lead to Immediate Plastic Changes of Neuromagnetic Evoked Responses and Induced β-Band Oscillations during Perception.

PubMed

Ross, Bernhard; Barat, Masihullah; Fujioka, Takako

2017-06-14

Auditory and sensorimotor brain areas interact during the action-perception cycle of sound making. Neurophysiological evidence of a feedforward model of the action and its outcome has been associated with attenuation of the N1 wave of auditory evoked responses elicited by self-generated sounds, such as talking and singing or playing a musical instrument. Moreover, neural oscillations at β-band frequencies have been related to predicting the sound outcome after action initiation. We hypothesized that a newly learned action-perception association would immediately modify interpretation of the sound during subsequent listening. Nineteen healthy young adults (7 female, 12 male) participated in three magnetoencephalographic recordings while first passively listening to recorded sounds of a bell ringing, then actively striking the bell with a mallet, and then again listening to recorded sounds. Auditory cortex activity showed characteristic P1-N1-P2 waves. The N1 was attenuated during sound making, while P2 responses were unchanged. In contrast, P2 became larger when listening after sound making compared with the initial naive listening. The P2 increase occurred immediately, while in previous learning-by-listening studies P2 increases occurred on a later day. Also, reactivity of β-band oscillations, as well as θ coherence between auditory and sensorimotor cortices, was stronger in the second listening block. These changes were significantly larger than those observed in control participants (eight female, five male), who triggered recorded sounds by a key press. We propose that P2 characterizes familiarity with sound objects, whereas β-band oscillation signifies involvement of the action-perception cycle, and both measures objectively indicate functional neuroplasticity in auditory perceptual learning. SIGNIFICANCE STATEMENT While suppression of auditory responses to self-generated sounds is well known, it is not clear whether the learned action-sound association modifies subsequent perception. Our study demonstrated the immediate effects of sound-making experience on perception using magnetoencephalographic recordings, as reflected in the increased auditory evoked P2 wave, increased responsiveness of β oscillations, and enhanced connectivity between auditory and sensorimotor cortices. The importance of motor learning was underscored as the changes were much smaller in a control group using a key press to generate the sounds instead of learning to play the musical instrument. The results support the rapid integration of a feedforward model during perception and provide a neurophysiological basis for the application of music making in motor rehabilitation training. Copyright © 2017 the authors 0270-6474/17/375948-12$15.00/0.

Prefrontal cortex as a meta-reinforcement learning system.

PubMed

Wang, Jane X; Kurth-Nelson, Zeb; Kumaran, Dharshan; Tirumala, Dhruva; Soyer, Hubert; Leibo, Joel Z; Hassabis, Demis; Botvinick, Matthew

2018-06-01

Over the past 20 years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine 'stamps in' associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. We now draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine system trains another part of the brain, the prefrontal cortex, to operate as its own free-standing learning system. This new perspective accommodates the findings that motivated the standard model, but also deals gracefully with a wider range of observations, providing a fresh foundation for future research.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Model learning for robot control: a survey.

PubMed

Nguyen-Tuong, Duy; Peters, Jan

2011-11-01

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

Intelligent flight control systems

NASA Technical Reports Server (NTRS)

Stengel, Robert F.

1993-01-01

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms.

Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

PubMed

Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

2015-11-01

The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

Action-based language: a theory of language acquisition, comprehension, and production.

PubMed

Glenberg, Arthur M; Gallese, Vittorio

2012-07-01

Evolution and the brain have done a marvelous job solving many tricky problems in action control, including problems of learning, hierarchical control over serial behavior, continuous recalibration, and fluency in the face of slow feedback. Given that evolution tends to be conservative, it should not be surprising that these solutions are exploited to solve other tricky problems, such as the design of a communication system. We propose that a mechanism of motor control, paired controller/predictor models, has been exploited for language learning, comprehension, and production. Our account addresses the development of grammatical regularities and perspective, as well as how linguistic symbols become meaningful through grounding in perception, action, and emotional systems. Copyright © 2011 Elsevier Srl. All rights reserved.

Action and Organizational Learning in an Elevator Company

ERIC Educational Resources Information Center

De Loo, Ivo

2006-01-01

Purpose: To highlight the relevance of management control in action learning programs that aim to foster organizational learning. Design/methodology/approach: Literature review plus case study. The latter consists of archival analysis and multiple interviews. Findings: When action learning programs are built around singular learning experiences,…

Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: A quasi-experimental study.

PubMed

Dubovi, Ilana; Dagan, Efrat; Sader Mazbar, Ola; Nassar, Laila; Levy, Sharona T

2018-02-01

Pharmacology is a crucial component of medications administration in nursing, yet nursing students generally find it difficult and self-rate their pharmacology skills as low. To evaluate nursing students learning pharmacology with the Pharmacology Inter-Leaved Learning-Cells environment, a novel approach to modeling biochemical interactions using a multiscale, computer-based model with a complexity perspective based on a small set of entities and simple rules. This environment represents molecules, organelles and cells to enhance the understanding of cellular processes, and combines these cells at a higher scale to obtain whole-body interactions. Sophomore nursing students who learned the pharmacology of diabetes mellitus with the Pharmacology Inter-Leaved Learning-Cells environment (experimental group; n=94) or via a lecture-based curriculum (comparison group; n=54). A quasi-experimental pre- and post-test design was conducted. The Pharmacology-Diabetes-Mellitus questionnaire and the course's final exam were used to evaluate students' knowledge of the pharmacology of diabetes mellitus. Conceptual learning was significantly higher for the experimental than for the comparison group for the course final exam scores (unpaired t=-3.8, p<0.001) and for the Pharmacology-Diabetes-Mellitus questionnaire (U=942, p<0.001). The largest effect size for the Pharmacology-Diabetes-Mellitus questionnaire was for the medication action subscale. Analysis of complex-systems component reasoning revealed a significant difference for micro-macro transitions between the levels (F(1, 82)=6.9, p<0.05). Learning with complexity-based computerized models is highly effective and enhances the understanding of moving between micro and macro levels of the biochemical phenomena, this is then related to better understanding of medication actions. Moreover, the Pharmacology Inter-Leaved Learning-Cells approach provides a more general reasoning scheme for biochemical processes, which enhances pharmacology learning beyond the specific topic learned. The present study implies that deeper understanding of pharmacology will support nursing students' clinical decisions and empower their proficiency in medications administration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Belief-desire reasoning as a process of selection.

PubMed

Leslie, Alan M; German, Tim P; Polizzi, Pamela

2005-02-01

Human learning may depend upon domain specialized mechanisms. A plausible example is rapid, early learning about the thoughts and feelings of other people. A major achievement in this domain, at about age four in the typically developing child, is the ability to solve problems in which the child attributes false beliefs to other people and predicts their actions. The main focus of theorizing has been why 3-year-olds fail, and only recently have there been any models of how success is achieved in false-belief tasks. Leslie and Polizzi (Inhibitory processing in the false-belief task: Two conjectures. Developmental Science, 1, 247-254, 1998) proposed two competing models of success, which are the focus of the current paper. The models assume that belief-desire reasoning is a process which selects a content for an agent's belief and an action for the agent's desire. In false belief tasks, the theory of mind mechanism (ToMM) provides plausible candidate belief contents, among which will be a 'true-belief.' A second process reviews these candidates and by default will select the true-belief content for attribution. To succeed in a false-belief task, the default content must be inhibited so that attention shifts to another candidate belief. In traditional false-belief tasks, the protagonist's desire is to approach an object. Here we make use of tasks in which the protagonist has a desire to avoid an object, about which she has a false-belief. Children find such tasks much more difficult than traditional tasks. Our models explain the additional difficulty by assuming that predicting action from an avoidance desire also requires an inhibition. The two processing models differ in the way that belief and desire inhibitory processes combine to achieve successful action prediction. In six experiments we obtain evidence favoring one model, in which parallel inhibitory processes cancel out, over the other model, in which serial inhibitions force attention to a previously inhibited location. These results are discussed in terms of a set of simple proposals for the modus operandi of a domain specific learning mechanism. The learning mechanism is in part modular--the ToMM--and in part penetrable--the Selection Processor (SP). We show how ToMM-SP can account both for competence and for successful and unsuccessful performance on a wide range of belief-desire tasks across the preschool period. Together, ToMM and SP attend to and learn about mental states.

The Industrial Manufacturing Technician Apprenticeship. Work-Based Learning in Action

ERIC Educational Resources Information Center

Scott, Geri

2016-01-01

This case study, one of a series of publications exploring effective and inclusive models of work-based learning, finds that entry-level occupations in manufacturing have historically been considered unskilled jobs for which little or no training is necessary. As a consequence, employers have experienced high turnover among new-hires, and…

An Inquiry into Flipped Learning in Fourth Grade Math Instruction

ERIC Educational Resources Information Center

D'addato, Teresa; Miller, Libbi R.

2016-01-01

The objective of this action research project was to better understand the impact of flipped learning on fourth grade math students in a socioeconomically disadvantaged setting. A flipped instructional model was implemented with the group of students enrolled in the researcher's class. Data was collected in the form of classroom observations,…

Applying Universal Design for Learning in Online Courses: Pedagogical and Practical Considerations

ERIC Educational Resources Information Center

Dell, Cindy Ann; Dell, Thomas F.; Blackwell, Terry L.

2015-01-01

Inclusion of the universal design for learning (UDL) model as a guiding set of principles for online curriculum development in higher education is discussed. Fundamentally, UDL provides the student with multiple means of accessing the course based on three overarching principles: presentation; action and expression; and engagement and interaction.…

How Teacher Leaders Influence Others and Understand Their Leadership

ERIC Educational Resources Information Center

Fairman, Janet C.; Mackenzie, Sarah V.

2015-01-01

This study elaborates the many ways that teachers lead work with colleagues to improve teaching and learning, and their understanding of their work as leadership. Through qualitative case studies of seven Maine schools and a review of the literature, the authors developed a conceptual model, Spheres of Teacher Leadership Action for Learning. They…

Modeling Social Activism and Teaching about Violence against Women through Theatre Education

ERIC Educational Resources Information Center

Pataki, Sherri P.; Mackenzie, Scott A.

2012-01-01

To inform students about global violence against women and to empower them to take action, the authors developed an interdisciplinary course focused on experiential learning and theatre education. Their article discusses the development of the course; the implementation of active learning strategies to develop critical thinking, empathy, and…

A Hybrid Approach for Supporting Adaptivity in E-Learning Environments

ERIC Educational Resources Information Center

Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco

2016-01-01

Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity…

A Framework for Action: Intervening to Increase Adoption of Transformative Web 2.0 Learning Resources

ERIC Educational Resources Information Center

Hughes, Joan E.; Guion, James M.; Bruce, Kama A.; Horton, Lucas R.; Prescott, Amy

2011-01-01

Web 2.0 tools have emerged as conducive for innovative pedagogy and transformative learning opportunities for youth. Currently,Web 2.0 is often adopted into teachers' practice to simply replace or amplify traditional instructional approaches rather than to promote or facilitate transformative educational change. Current models of innovation…

Action Planning and Recording Achievement.

ERIC Educational Resources Information Center

Green, Muriel

This document examines strategies and procedures that British further education (FE) colleges can use to develop and enhance systems and structures for guiding and supporting learners and learning. It is based on the findings of a field test of the Managing Learning model for planning and recording the process of FE students. First, the importance…

Ares I-X Thermal Model Correlation and Lessons Learned

NASA Technical Reports Server (NTRS)

Amundsen, Ruth M.

2010-01-01

The Ares I-X vehicle launched and flew successfully on October 28, 2009. This paper will describe the correlation of the vehicle thermal model to both ground testing and flight data. A main purpose of the vehicle model and ground testing was to ensure that the avionics within the vehicle were held within their thermal limits prior to launch and during flight. The correlation of the avionics box temperatures will be shown. Also, the lessons learned in the thermal discipline during the modeling, test, correlation to test, and flight of the Ares I-X flight test vehicle will be described. Lessons learned will cover thermal modeling, as well as management of the thermal discipline, thermal team, and thermal-related actions in design, testing, and flight.

Issues in Action Learning: A Critical Realist Interpretation

ERIC Educational Resources Information Center

Burgoyne, John

2009-01-01

The purpose of this paper is to argue that the perspective of "critical realism" has considerable potential for moving forward the theory and practice of action learning. The paper addresses three questions: (1) Does action learning emphasise the individual or the collective? (2) Can action learning be thought of as critical, but should it also be…

Defining Success in Action Learning: An International Comparison

ERIC Educational Resources Information Center

Bong, Hyeon-Cheol; Cho, Yonjoo

2017-01-01

Purpose: The purpose of this paper was to explore how the two groups of action learning experts (Korean and non-Korean experts) define success of action learning to see whether there are any cultural differences. To this end, the authors conducted a total of 44 interviews with action learning experts around the world. Research questions guiding…

Obstinate Actions-Oriented Behaviour towards Applying Theoractive Learning: An Ontology of Educational Learning and Leadership Theories in Practice

ERIC Educational Resources Information Center

Rajbhandari, Mani Man Singh

2018-01-01

Obstinate actions-oriented behaviour is the study of learning and practicing behaviour theoractively, which is acquired from the content based, process based learning and spawning critical reflexivity to the learnt theoretical phenomena into practical actions. Obstinate actions-oriented behaviour is a multi-faceted behaviour that is generally…

Safe or Unsafe? The Paradox of Action Learning

ERIC Educational Resources Information Center

Robertson, Jane; Bell, Diane

2017-01-01

Business Driven Action Learning (BDAL), as a learning philosophy that attempts to create real value for business is often used by executive education providers in their management development programmes. As the action learning facilitator, I found that the learning that took place during such a management development programme resulted in…

Grounding Action Words in the Sensorimotor Interaction with the World: Experiments with a Simulated iCub Humanoid Robot

PubMed Central

Marocco, Davide; Cangelosi, Angelo; Fischer, Kerstin; Belpaeme, Tony

2010-01-01

This paper presents a cognitive robotics model for the study of the embodied representation of action words. The present research will present how an iCub humanoid robot can learn the meaning of action words (i.e. words that represent dynamical events that happen in time) by physically interacting with the environment and linking the effects of its own actions with the behavior observed on the objects before and after the action. The control system of the robot is an artificial neural network trained to manipulate an object through a Back-Propagation-Through-Time algorithm. We will show that in the presented model the grounding of action words relies directly to the way in which an agent interacts with the environment and manipulates it. PMID:20725503

Transformative Learning and Professional Identity Formation During International Health Electives: A Qualitative Study Using Grounded Theory.

PubMed

Sawatsky, Adam P; Nordhues, Hannah C; Merry, Stephen P; Bashir, M Usmaan; Hafferty, Frederic W

2018-03-27

International health electives (IHEs) are widely available during residency and provide unique experiences for trainees. Theoretical models of professional identity formation and transformative learning may provide insight into residents' experiences during IHEs. The purpose of this study was to explore transformative learning and professional identity formation during resident IHEs and characterize the relationship between transformative learning and professional identity formation. The authors used a constructivist grounded theory approach, with the sensitizing concepts of transformative learning and professional identity formation to analyze narrative reflective reports of residents' IHEs. The Mayo International Health Program supports residents from all specialties across three Mayo Clinic sites. In 2015, the authors collected narrative reflective reports from 377 IHE participants dating from 2001-2014. Reflections were coded and themes were organized into a model for transformative learning during IHEs, focusing on professional identity. Five components of transformative learning were identified during IHEs: a disorienting experience; an emotional response; critical reflection; perspective change; and a commitment to future action. Within the component of critical reflection three domains relating to professional identity were identified: making a difference; the doctor-patient relationship; and medicine in its "purest form." Transformation was demonstrated through perspective change and a commitment to future action, including continued service, education, and development. IHEs provide rich experiences for transformative learning and professional identity formation. Understanding the components of transformative learning may provide insight into the interaction between learner, experiences, and the influence of mentors in the process of professional identity formation.

Cognitive Models for Learning to Control Dynamic Systems

DTIC Science & Technology

2008-09-26

1992 [47] G. F. Franklin and J. D. Powell, Feedback Control of Dynamic Systems, New Jersey: Pearson Prentice Hall 2006 [48] M . Fishbein and I . Ajzen ...the course of decision making, the valence of an action Vi ( i = A or M ) is defined as the subjective expected payoff for each action also fluctuates...research: The role of formal models, IEEE Transactions on Systems, Man, and Cybernetics 16, 1986, pp. 439–449. [54] M . I . Jordan, Constrained

Neuroscientific Model of Motivational Process

PubMed Central

Kim, Sung-il

2013-01-01

Considering the neuroscientific findings on reward, learning, value, decision-making, and cognitive control, motivation can be parsed into three sub processes, a process of generating motivation, a process of maintaining motivation, and a process of regulating motivation. I propose a tentative neuroscientific model of motivational processes which consists of three distinct but continuous sub processes, namely reward-driven approach, value-based decision-making, and goal-directed control. Reward-driven approach is the process in which motivation is generated by reward anticipation and selective approach behaviors toward reward. This process recruits the ventral striatum (reward area) in which basic stimulus-action association is formed, and is classified as an automatic motivation to which relatively less attention is assigned. By contrast, value-based decision-making is the process of evaluating various outcomes of actions, learning through positive prediction error, and calculating the value continuously. The striatum and the orbitofrontal cortex (valuation area) play crucial roles in sustaining motivation. Lastly, the goal-directed control is the process of regulating motivation through cognitive control to achieve goals. This consciously controlled motivation is associated with higher-level cognitive functions such as planning, retaining the goal, monitoring the performance, and regulating action. The anterior cingulate cortex (attention area) and the dorsolateral prefrontal cortex (cognitive control area) are the main neural circuits related to regulation of motivation. These three sub processes interact with each other by sending reward prediction error signals through dopaminergic pathway from the striatum and to the prefrontal cortex. The neuroscientific model of motivational process suggests several educational implications with regard to the generation, maintenance, and regulation of motivation to learn in the learning environment. PMID:23459598

Neuroscientific model of motivational process.

PubMed

Kim, Sung-Il

2013-01-01

Considering the neuroscientific findings on reward, learning, value, decision-making, and cognitive control, motivation can be parsed into three sub processes, a process of generating motivation, a process of maintaining motivation, and a process of regulating motivation. I propose a tentative neuroscientific model of motivational processes which consists of three distinct but continuous sub processes, namely reward-driven approach, value-based decision-making, and goal-directed control. Reward-driven approach is the process in which motivation is generated by reward anticipation and selective approach behaviors toward reward. This process recruits the ventral striatum (reward area) in which basic stimulus-action association is formed, and is classified as an automatic motivation to which relatively less attention is assigned. By contrast, value-based decision-making is the process of evaluating various outcomes of actions, learning through positive prediction error, and calculating the value continuously. The striatum and the orbitofrontal cortex (valuation area) play crucial roles in sustaining motivation. Lastly, the goal-directed control is the process of regulating motivation through cognitive control to achieve goals. This consciously controlled motivation is associated with higher-level cognitive functions such as planning, retaining the goal, monitoring the performance, and regulating action. The anterior cingulate cortex (attention area) and the dorsolateral prefrontal cortex (cognitive control area) are the main neural circuits related to regulation of motivation. These three sub processes interact with each other by sending reward prediction error signals through dopaminergic pathway from the striatum and to the prefrontal cortex. The neuroscientific model of motivational process suggests several educational implications with regard to the generation, maintenance, and regulation of motivation to learn in the learning environment.

Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task.

PubMed

Akam, Thomas; Costa, Rui; Dayan, Peter

2015-12-01

The recently developed 'two-step' behavioural task promises to differentiate model-based from model-free reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted its widespread adoption. Here, we analyse the interactions between a range of different strategies and the structure of transitions and outcomes in order to examine constraints on what can be learned from behavioural performance. The task involves a trade-off between the need for stochasticity, to allow strategies to be discriminated, and a need for determinism, so that it is worth subjects' investment of effort to exploit the contingencies optimally. We show through simulation that under certain conditions model-free strategies can masquerade as being model-based. We first show that seemingly innocuous modifications to the task structure can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions. We confirm the power of a suggested correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies that exploit correlations between where rewards are obtained and which actions have high expected value. These generate behaviour that appears model-based under these, and also more sophisticated, analyses. Exploiting the full potential of the two-step task as a tool for behavioural neuroscience requires an understanding of these issues.

Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task

PubMed Central

Akam, Thomas; Costa, Rui; Dayan, Peter

2015-01-01

The recently developed ‘two-step’ behavioural task promises to differentiate model-based from model-free reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted its widespread adoption. Here, we analyse the interactions between a range of different strategies and the structure of transitions and outcomes in order to examine constraints on what can be learned from behavioural performance. The task involves a trade-off between the need for stochasticity, to allow strategies to be discriminated, and a need for determinism, so that it is worth subjects’ investment of effort to exploit the contingencies optimally. We show through simulation that under certain conditions model-free strategies can masquerade as being model-based. We first show that seemingly innocuous modifications to the task structure can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions. We confirm the power of a suggested correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies that exploit correlations between where rewards are obtained and which actions have high expected value. These generate behaviour that appears model-based under these, and also more sophisticated, analyses. Exploiting the full potential of the two-step task as a tool for behavioural neuroscience requires an understanding of these issues. PMID:26657806

Turning the Table on Professional Development in Mathematics by Setting the Stage for Teacher-Led Inquiry: An Action Research Study

ERIC Educational Resources Information Center

McCullough, Sabrina D.

2016-01-01

This action research study investigated the change in professional development model in the acquisition of content knowledge for fourth-grade math teachers. The current professional development atmosphere is a traditional "sit and get" opportunity. However, research offers that teachers should be active participants in their learning.…

Pigeons and humans use action and pose information to categorize complex human behaviors.

PubMed

Qadri, Muhammad A J; Cook, Robert G

2017-02-01

The biological mechanisms used to categorize and recognize behaviors are poorly understood in both human and non-human animals. Using animated digital models, we have recently shown that pigeons can categorize different locomotive animal gaits and types of complex human behaviors. In the current experiments, pigeons (go/no-go task) and humans (choice task) both learned to conditionally categorize two categories of human behaviors that did not repeat and were comprised of the coordinated motions of multiple limbs. These "martial arts" and "Indian dance" action sequences were depicted by a digital human model. Depending upon whether the model was in motion or not, each species was required to engage in different and opposing responses to the two behavioral categories. Both species learned to conditionally and correctly act on this dynamic and static behavioral information, indicating that both species use a combination of static pose cues that are available from stimulus onset in addition to less rapidly available action information in order to successfully discriminate between the behaviors. Human participants additionally demonstrated a bias towards the dynamic information in the display when re-learning the task. Theories that rely on generalized, non-specific visual mechanisms involving channels for motion and static cues offer a parsimonious account of how humans and pigeons recognize and categorize behaviors within and across species. Copyright © 2016 Elsevier Ltd. All rights reserved.

Imitation by combination: preschool age children evidence summative imitation in a novel problem-solving task.

PubMed

Subiaul, Francys; Krajkowski, Edward; Price, Elizabeth E; Etz, Alexander

2015-01-01

Children are exceptional, even 'super,' imitators but comparatively poor independent problem-solvers or innovators. Yet, imitation and innovation are both necessary components of cumulative cultural evolution. Here, we explored the relationship between imitation and innovation by assessing children's ability to generate a solution to a novel problem by imitating two different action sequences demonstrated by two different models, an example of imitation by combination, which we refer to as "summative imitation." Children (N = 181) from 3 to 5 years of age and across three experiments were tested in a baseline condition or in one of six demonstration conditions, varying in the number of models and opening techniques demonstrated. Across experiments, more than 75% of children evidenced summative imitation, opening both compartments of the problem box and retrieving the reward hidden in each. Generally, learning different actions from two different models was as good (and in some cases, better) than learning from 1 model, but the underlying representations appear to be the same in both demonstration conditions. These results show that summative imitation not only facilitates imitation learning but can also result in new solutions to problems, an essential feature of innovation and cumulative culture.

How much to trust the senses: Likelihood learning

PubMed Central

Sato, Yoshiyuki; Kording, Konrad P.

2014-01-01

Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood. PMID:25398975

Action Learning.

ERIC Educational Resources Information Center

1996

These four papers were presented at a symposium on action learning moderated by Lex Dilworth at the 1996 conference of the Academy of Human Resource Development. "Developing an Infrastructure for Individual and Organizational Change: Transfer of Learning from an Action Reflection Learning (ARL) Program" (ARL Inquiry) reports findings…

CLEANing the Reward: Counterfactual Actions to Remove Exploratory Action Noise in Multiagent Learning

NASA Technical Reports Server (NTRS)

HolmesParker, Chris; Taylor, Mathew E.; Tumer, Kagan; Agogino, Adrian

2014-01-01

Learning in multiagent systems can be slow because agents must learn both how to behave in a complex environment and how to account for the actions of other agents. The inability of an agent to distinguish between the true environmental dynamics and those caused by the stochastic exploratory actions of other agents creates noise in each agent's reward signal. This learning noise can have unforeseen and often undesirable effects on the resultant system performance. We define such noise as exploratory action noise, demonstrate the critical impact it can have on the learning process in multiagent settings, and introduce a reward structure to effectively remove such noise from each agent's reward signal. In particular, we introduce Coordinated Learning without Exploratory Action Noise (CLEAN) rewards and empirically demonstrate their benefits

Student beliefs and learning environments: Developing a survey of factors related to conceptual change

NASA Astrophysics Data System (ADS)

Hanrahan, Mary

1994-12-01

This paper presents a model for the type of classroom environment believed to facilitate scientific conceptual change. A survey based on this model contains items about students' motivational beliefs, their study approach and their perceptions of their teacher's actions and learning goal orientation. Results obtained from factor analyses, correlations and analyses of variance, based on responses from 113 students, suggest that an empowering interpersonal teacher-student relationship is related to a deep approach to learning, a positive attitude to science, and positive self-efficacy beliefs, and may be increased by a constructivist approach to teaching.

Informal learning processes in support of clinical service delivery in a service-oriented community pharmacy.

PubMed

Patterson, Brandon J; Bakken, Brianne K; Doucette, William R; Urmie, Julie M; McDonough, Randal P

The evolving health care system necessitates pharmacy organizations' adjustments by delivering new services and establishing inter-organizational relationships. One approach supporting pharmacy organizations in making changes may be informal learning by technicians, pharmacists, and pharmacy owners. Informal learning is characterized by a four-step cycle including intent to learn, action, feedback, and reflection. This framework helps explain individual and organizational factors that influence learning processes within an organization as well as the individual and organizational outcomes of those learning processes. A case study of an Iowa independent community pharmacy with years of experience in offering patient care services was made. Nine semi-structured interviews with pharmacy personnel revealed initial evidence in support of the informal learning model in practice. Future research could investigate more fully the informal learning model in delivery of patient care services in community pharmacies. Copyright © 2016 Elsevier Inc. All rights reserved.

Action-learning collaboratives as a platform for community-based participatory research to advance obesity prevention.

PubMed

Bazos, Dorothy A; Schifferdecker, Karen E; Fedrizzi, Rudolph; Hoebeke, Jaime; Ruggles, Laural; Goldsberry, Yvonne

2013-01-01

Although process elements that define community-based participatory research (CBPR) are well articulated and provide guidance for bringing together researchers and communities, additional models to implement CBPR are needed. One potential model for implementing and monitoring CBPR is Action Learning Collaboratives (ALCs); short term, team-based learning processes that are grounded in quality improvement. Since 2010, the Prevention Research Center at Dartmouth (PRCD) has used ALCs with three communities as a platform to design, implement and evaluate CBPR. The first ALC provided an opportunity for academia and community leadership to strengthen their relationships and knowledge of respective assets through design and evaluation of community-based QI projects. Building on this work, we jointly designed and are implementing a second ALC, a cross-community research project focused on obesity prevention in vulnerable populations. An enhanced community capacity now exists to support CBPR activities with a high degree of sophistication and decreased reliance on external facilitation.

Computational Dysfunctions in Anxiety: Failure to Differentiate Signal From Noise.

PubMed

Huang, He; Thompson, Wesley; Paulus, Martin P

2017-09-15

Differentiating whether an action leads to an outcome by chance or by an underlying statistical regularity that signals environmental change profoundly affects adaptive behavior. Previous studies have shown that anxious individuals may not appropriately differentiate between these situations. This investigation aims to precisely quantify the process deficit in anxious individuals and determine the degree to which these process dysfunctions are specific to anxiety. One hundred twenty-two subjects recruited as part of an ongoing large clinical population study completed a change point detection task. Reinforcement learning models were used to explicate observed behavioral differences in low anxiety (Overall Anxiety Severity and Impairment Scale score ≤ 8) and high anxiety (Overall Anxiety Severity and Impairment Scale score ≥ 9) groups. High anxiety individuals used a suboptimal decision strategy characterized by a higher lose-shift rate. Computational models and simulations revealed that this difference was related to a higher base learning rate. These findings are better explained in a context-dependent reinforcement learning model. Anxious subjects' exaggerated response to uncertainty leads to a suboptimal decision strategy that makes it difficult for these individuals to determine whether an action is associated with an outcome by chance or by some statistical regularity. These findings have important implications for developing new behavioral intervention strategies using learning models. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Action Learning: Images and Pathways. Professional Practices in Adult Education and Lifelong Learning Series.

ERIC Educational Resources Information Center

Dilworth, Robert L.; Willis, Verna J.

This book provides information and strategies on how adult educators can integrate action learning concepts in their teaching practice. The book defines action learning as going beyond the traditional idea of "learn by doing" and applies it to various organizational cultures and educational contexts. Chapter 1 introduces the origins of action…

Its All Action, Its All Learning: Action Learning in SMEs

ERIC Educational Resources Information Center

Clarke, Jean; Thorpe, Richard; Anderson, Lisa; Gold, Jeff

2006-01-01

Purpose: The purpose of this paper is to argue that action learning (AL) may provide a means of successfully developing small to medium-sized enterprises (SMEs). Design/methodology/approach: The literature around SME learning suggests a number of processes are important for SME learning which similarity, it is argued, are encompassed in AL. AL may…

Exemplar-based human action pose correction.

PubMed

Shen, Wei; Deng, Ke; Bai, Xiang; Leyvand, Tommer; Guo, Baining; Tu, Zhuowen

2014-07-01

The launch of Xbox Kinect has built a very successful computer vision product and made a big impact on the gaming industry. This sheds lights onto a wide variety of potential applications related to action recognition. The accurate estimation of human poses from the depth image is universally a critical step. However, existing pose estimation systems exhibit failures when facing severe occlusion. In this paper, we propose an exemplar-based method to learn to correct the initially estimated poses. We learn an inhomogeneous systematic bias by leveraging the exemplar information within a specific human action domain. Furthermore, as an extension, we learn a conditional model by incorporation of pose tags to further increase the accuracy of pose correction. In the experiments, significant improvements on both joint-based skeleton correction and tag prediction are observed over the contemporary approaches, including what is delivered by the current Kinect system. Our experiments for the facial landmark correction also illustrate that our algorithm can improve the accuracy of other detection/estimation systems.

Developing a Contextual Consciousness: Learning to Address Gender, Societal Power, and Culture in Clinical Practice

ERIC Educational Resources Information Center

Esmiol, Elisabeth E.; Knudson-Martin, Carmen; Delgado, Sarah

2012-01-01

Despite the growing number of culturally sensitive training models and considerable literature on the importance of training clinicians in larger contextual issues, research examining how students learn to apply these issues is limited. In this participatory action research project, we systematically studied our own process as marriage and family…

Reinventing the High School Government Course: Rigor, Simulations, and Learning from Text

ERIC Educational Resources Information Center

Parker, Walter C.; Lo, Jane C.

2016-01-01

The high school government course is arguably the main site of formal civic education in the country today. This article presents the curriculum that resulted from a multiyear study aimed at improving the course. The pedagogic model, called "Knowledge in Action," centers on a rigorous form of project-based learning where the projects are…

Changing Practice in Malaysian Primary Schools: Learning from Student Teachers' Reports of Using Action, Reflection and Modelling (ARM)

ERIC Educational Resources Information Center

Dickerson, Claire; Thomas, Kit; Jarvis, Joy; Levy, Roger

2018-01-01

Curricular and pedagogical reforms are complex inter-linked processes such that curricular reform can only be enacted through teachers teaching differently. This article reports the perspective of emergent Malaysian primary teachers who were expected to implement a Government reform that promoted active learning. The 120 student teachers were…

Taking Action toward Inclusion: Organizational Change and the Inclusion of People with Disabilities in Museum Learning

ERIC Educational Resources Information Center

Reich, Christine A.

2014-01-01

This study examined organizational change in science museums toward practices that are inclusive of people with disabilities. Guided by two overarching frameworks, organizational learning and the social model of disability, this study sought to answer the following: What are the contexts and processes that facilitate, sustain, or impede a science…

When Are Workload and Workplace Learning Opportunities Related in a Curvilinear Manner? The Moderating Role of Autonomy

ERIC Educational Resources Information Center

van Ruysseveldt, Joris; van Dijke, Marius

2011-01-01

Building on theoretical frameworks like the Job Demands Control model and Action Theory we tested whether the relationship between workload and employees' experiences of opportunities for workplace learning is of an inverted u-shaped nature and whether autonomy moderates this relationship. We predicted that--at moderate levels of…

Investigating the Potential of the Flipped Classroom Model in K-12 ICT Teaching and Learning: An Action Research Study

ERIC Educational Resources Information Center

Kostaris, Christoforos; Sergis, Stylianos; Sampson, Demetrios G.; Giannakos, Michail N.; Pelliccione, Lina

2017-01-01

The emerging Flipped Classroom approach has been widely used to enhance teaching practices in many subject domains and educational levels, reporting promising results for enhancing student learning experiences. However, despite this encouraging body of research, the subject domain of Information and Communication Technologies (ICT) teaching at…

Using Multimodal Learning Analytics to Model Student Behaviour: A Systematic Analysis of Behavioural Framing

ERIC Educational Resources Information Center

Andrade, Alejandro; Delandshere, Ginette; Danish, Joshua A.

2016-01-01

One of the challenges many learning scientists face is the laborious task of coding large amounts of video data and consistently identifying social actions, which is time consuming and difficult to accomplish in a systematic and consistent manner. It is easier to catalog observable behaviours (e.g., body motions or gaze) without explicitly…

Instructional design affects the efficacy of simulation-based training in central venous catheterization.

PubMed

Craft, Christopher; Feldon, David F; Brown, Eric A

2014-05-01

Simulation-based learning is a common educational tool in health care training and frequently involves instructional designs based on Experiential Learning Theory (ELT). However, little research explores the effectiveness and efficiency of different instructional design methodologies appropriate for simulations. The aim of this study was to compare 2 instructional design models, ELT and Guided Experiential Learning (GEL), to determine which is more effective for training the central venous catheterization procedure. Using a quasi-experimental randomized block design, nurse anesthetists completed training under 1 of the 2 instructional design models. Performance was assessed using a checklist of central venous catheterization performance, pass rates, and critical action errors. Participants in the GEL condition performed significantly better than those in the ELT condition on the overall checklist score after controlling for individual practice time (F[1, 29] = 4.021, P = .027, Cohen's d = .71), had higher pass rates (P = .006, Cohen's d = 1.15), and had lower rates of failure due to critical action errors (P = .038, Cohen's d = .81). The GEL model of instructional design is significantly more effective than ELT for simulation-based learning of the central venous catheterization procedure, yielding large differences in effect size. Copyright © 2014 Elsevier Inc. All rights reserved.

Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning.

PubMed

Sebold, Miriam; Schad, Daniel J; Nebe, Stephan; Garbusow, Maria; Jünger, Elisabeth; Kroemer, Nils B; Kathmann, Norbert; Zimmermann, Ulrich S; Smolka, Michael N; Rapp, Michael A; Heinz, Andreas; Huys, Quentin J M

2016-07-01

Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, model-free systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus-action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovian-instrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction.

A neural network model of causative actions.

PubMed

Lee-Hand, Jeremy; Knott, Alistair

2015-01-01

A common idea in models of action representation is that actions are represented in terms of their perceptual effects (see e.g., Prinz, 1997; Hommel et al., 2001; Sahin et al., 2007; Umiltà et al., 2008; Hommel, 2013). In this paper we extend existing models of effect-based action representations to account for a novel distinction. Some actions bring about effects that are independent events in their own right: for instance, if John smashes a cup, he brings about the event of the cup smashing. Other actions do not bring about such effects. For instance, if John grabs a cup, this action does not cause the cup to "do" anything: a grab action has well-defined perceptual effects, but these are not registered by the perceptual system that detects independent events involving external objects in the world. In our model, effect-based actions are implemented in several distinct neural circuits, which are organized into a hierarchy based on the complexity of their associated perceptual effects. The circuit at the top of this hierarchy is responsible for actions that bring about independently perceivable events. This circuit receives input from the perceptual module that recognizes arbitrary events taking place in the world, and learns movements that reliably cause such events. We assess our model against existing experimental observations about effect-based motor representations, and make some novel experimental predictions. We also consider the possibility that the "causative actions" circuit in our model can be identified with a motor pathway reported in other work, specializing in "functional" actions on manipulable tools (Bub et al., 2008; Binkofski and Buxbaum, 2013).

Negotiating energy dynamics through embodied action in a materially structured environment

NASA Astrophysics Data System (ADS)

Scherr, Rachel E.; Close, Hunter G.; Close, Eleanor W.; Flood, Virginia J.; McKagan, Sarah B.; Robertson, Amy D.; Seeley, Lane; Wittmann, Michael C.; Vokos, Stamatis

2013-12-01

We provide evidence that a learning activity called Energy Theater engages learners with key conceptual issues in the learning of energy, including disambiguating matter flow and energy flow and theorizing mechanisms for energy transformation. A participationist theory of learning, in which learning is indicated by changes in speech and behavior, supports ethnographic analysis of learners’ embodied interactions with each other and the material setting. We conduct detailed analysis to build plausible causal links between specific features of Energy Theater and the conceptual engagement that we observe. Disambiguation of matter and energy appears to be promoted especially by the material structure of the Energy Theater environment, in which energy is represented by participants, while objects are represented by areas demarcated by loops of rope. Theorizing mechanisms of energy transformation is promoted especially by Energy Theater’s embodied action, which necessitates modeling the time ordering of energy transformations.

Reasoning about instrumental and communicative agency in human infancy.

PubMed

Gergely, György; Jacob, Pierre

2012-01-01

Theoretical rationality and practical rationality are, respectively, properties of an individual's belief system and decision system. While reasoning about instrumental actions complies with practical rationality, understanding communicative actions complies with the principle of relevance. Section 2 reviews the evidence showing that young infants can reason about an agent's instrumental action by representing her subjective motivations and the episodic contents of her epistemic states (including false beliefs). Section 3 reviews the evidence showing special sensitivity in young human infants to some ostensive behavioral signals encoding an agent's communicative intention. We also address the puzzle of imitative learning of novel means actions by 1-year olds and argue that it can be resolved only by assuming that the infant construes the model's demonstration as a communicative, not an instrumental, action. Section 4 reviews the evidence for natural pedagogy, a species-unique social communicative learning mechanism that exploits human infants' receptivity to ostensive-communicative signals and enables infants to acquire kind-wide generalizations from the nonverbal demonstrations of communicative agents. We argue that the essentialist bias that has been shown to be involved in children's concepts of natural kinds also applies to infants' concepts of artifacts. We further examine how natural pedagogy may also boost inductive learning in human infancy.

Improved probabilistic inference as a general learning mechanism with action video games.

PubMed

Green, C Shawn; Pouget, Alexandre; Bavelier, Daphne

2010-09-14

Action video game play benefits performance in an array of sensory, perceptual, and attentional tasks that go well beyond the specifics of game play [1-9]. That a training regimen may induce improvements in so many different skills is notable because the majority of studies on training-induced learning report improvements on the trained task but limited transfer to other, even closely related, tasks ([10], but see also [11-13]). Here we ask whether improved probabilistic inference may explain such broad transfer. By using a visual perceptual decision making task [14, 15], the present study shows for the first time that action video game experience does indeed improve probabilistic inference. A neural model of this task [16] establishes how changing a single parameter, namely the strength of the connections between the neural layer providing the momentary evidence and the layer integrating the evidence over time, captures improvements in action-gamers behavior. These results were established in a visual, but also in a novel auditory, task, indicating generalization across modalities. Thus, improved probabilistic inference provides a general mechanism for why action video game playing enhances performance in a wide variety of tasks. In addition, this mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. Copyright © 2010 Elsevier Ltd. All rights reserved.

Action Learning in China

ERIC Educational Resources Information Center

Marquardt, Michael J.

2015-01-01

Action learning was introduced into China less than 20 years ago, but has rapidly become a valuable tool for organizations seeking to solve problems, develop their leaders, and become learning organizations. This article provides an historical overview of action learning in China, its cultural underpinnings, and five case studies. It concludes…

Designing and Implementing Collaborative Improvement in the Extended Manufacturing Enterprise: Action Learning and Action Research (ALAR) in CO-IMPROVE

ERIC Educational Resources Information Center

Coghlan, David; Coughlan, Paul

2006-01-01

Purpose: The purpose of this article is to provide a design and implementation framework for ALAR (action learning action research) programme which aims to address collaborative improvement in the extended manufacturing enterprise. Design/methodology/approach: This article demonstrates the design of a programme in which action learning and action…

Staying Mindful in Action: The Challenge of "Double Awareness" on Task and Process in an Action Lab

ERIC Educational Resources Information Center

Svalgaard, Lotte

2016-01-01

Action Learning is a well-proven method to integrate "task" and "process", as learning about team and self (process) takes place while delivering on a task or business challenge of real importance (task). An Action Lab® is an intensive Action Learning programme lasting for 5 days, which aims at balancing and integrating…

Translating visual information into action predictions: Statistical learning in action and nonaction contexts.

PubMed

Monroy, Claire D; Gerson, Sarah A; Hunnius, Sabine

2018-05-01

Humans are sensitive to the statistical regularities in action sequences carried out by others. In the present eyetracking study, we investigated whether this sensitivity can support the prediction of upcoming actions when observing unfamiliar action sequences. In two between-subjects conditions, we examined whether observers would be more sensitive to statistical regularities in sequences performed by a human agent versus self-propelled 'ghost' events. Secondly, we investigated whether regularities are learned better when they are associated with contingent effects. Both implicit and explicit measures of learning were compared between agent and ghost conditions. Implicit learning was measured via predictive eye movements to upcoming actions or events, and explicit learning was measured via both uninstructed reproduction of the action sequences and verbal reports of the regularities. The findings revealed that participants, regardless of condition, readily learned the regularities and made correct predictive eye movements to upcoming events during online observation. However, different patterns of explicit-learning outcomes emerged following observation: Participants were most likely to re-create the sequence regularities and to verbally report them when they had observed an actor create a contingent effect. These results suggest that the shift from implicit predictions to explicit knowledge of what has been learned is facilitated when observers perceive another agent's actions and when these actions cause effects. These findings are discussed with respect to the potential role of the motor system in modulating how statistical regularities are learned and used to modify behavior.

Learning about causes from people and about people as causes: probabilistic models and social causal reasoning.

PubMed

Buchsbaum, Daphna; Seiver, Elizabeth; Bridgers, Sophie; Gopnik, Alison

2012-01-01

A major challenge children face is uncovering the causal structure of the world around them. Previous research on children's causal inference has demonstrated their ability to learn about causal relationships in the physical environment using probabilistic evidence. However, children must also learn about causal relationships in the social environment, including discovering the causes of other people's behavior, and understanding the causal relationships between others' goal-directed actions and the outcomes of those actions. In this chapter, we argue that social reasoning and causal reasoning are deeply linked, both in the real world and in children's minds. Children use both types of information together and in fact reason about both physical and social causation in fundamentally similar ways. We suggest that children jointly construct and update causal theories about their social and physical environment and that this process is best captured by probabilistic models of cognition. We first present studies showing that adults are able to jointly infer causal structure and human action structure from videos of unsegmented human motion. Next, we describe how children use social information to make inferences about physical causes. We show that the pedagogical nature of a demonstrator influences children's choices of which actions to imitate from within a causal sequence and that this social information interacts with statistical causal evidence. We then discuss how children combine evidence from an informant's testimony and expressed confidence with evidence from their own causal observations to infer the efficacy of different potential causes. We also discuss how children use these same causal observations to make inferences about the knowledge state of the social informant. Finally, we suggest that psychological causation and attribution are part of the same causal system as physical causation. We present evidence that just as children use covariation between physical causes and their effects to learn physical causal relationships, they also use covaration between people's actions and the environment to make inferences about the causes of human behavior.

Action-outcome learning and prediction shape the window of simultaneity of audiovisual outcomes.

PubMed

Desantis, Andrea; Haggard, Patrick

2016-08-01

To form a coherent representation of the objects around us, the brain must group the different sensory features composing these objects. Here, we investigated whether actions contribute in this grouping process. In particular, we assessed whether action-outcome learning and prediction contribute to audiovisual temporal binding. Participants were presented with two audiovisual pairs: one pair was triggered by a left action, and the other by a right action. In a later test phase, the audio and visual components of these pairs were presented at different onset times. Participants judged whether they were simultaneous or not. To assess the role of action-outcome prediction on audiovisual simultaneity, each action triggered either the same audiovisual pair as in the learning phase ('predicted' pair), or the pair that had previously been associated with the other action ('unpredicted' pair). We found the time window within which auditory and visual events appeared simultaneous increased for predicted compared to unpredicted pairs. However, no change in audiovisual simultaneity was observed when audiovisual pairs followed visual cues, rather than voluntary actions. This suggests that only action-outcome learning promotes temporal grouping of audio and visual effects. In a second experiment we observed that changes in audiovisual simultaneity do not only depend on our ability to predict what outcomes our actions generate, but also on learning the delay between the action and the multisensory outcome. When participants learned that the delay between action and audiovisual pair was variable, the window of audiovisual simultaneity for predicted pairs increased, relative to a fixed action-outcome pair delay. This suggests that participants learn action-based predictions of audiovisual outcome, and adapt their temporal perception of outcome events based on such predictions. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Multiple memory systems as substrates for multiple decision systems

PubMed Central

Doll, Bradley B.; Shohamy, Daphna; Daw, Nathaniel D.

2014-01-01

It has recently become widely appreciated that value-based decision making is supported by multiple computational strategies. In particular, animal and human behavior in learning tasks appears to include habitual responses described by prominent model-free reinforcement learning (RL) theories, but also more deliberative or goal-directed actions that can be characterized by a different class of theories, model-based RL. The latter theories evaluate actions by using a representation of the contingencies of the task (as with a learned map of a spatial maze), called an “internal model.” Given the evidence of behavioral and neural dissociations between these approaches, they are often characterized as dissociable learning systems, though they likely interact and share common mechanisms. In many respects, this division parallels a longstanding dissociation in cognitive neuroscience between multiple memory systems, describing, at the broadest level, separate systems for declarative and procedural learning. Procedural learning has notable parallels with model-free RL: both involve learning of habits and both are known to depend on parts of the striatum. Declarative memory, by contrast, supports memory for single events or episodes and depends on the hippocampus. The hippocampus is thought to support declarative memory by encoding temporal and spatial relations among stimuli and thus is often referred to as a relational memory system. Such relational encoding is likely to play an important role in learning an internal model, the representation that is central to model-based RL. Thus, insofar as the memory systems represent more general-purpose cognitive mechanisms that might subserve performance on many sorts of tasks including decision making, these parallels raise the question whether the multiple decision systems are served by multiple memory systems, such that one dissociation is grounded in the other. Here we investigated the relationship between model-based RL and relational memory by comparing individual differences across behavioral tasks designed to measure either capacity. Human subjects performed two tasks, a learning and generalization task (acquired equivalence) which involves relational encoding and depends on the hippocampus; and a sequential RL task that could be solved by either a model-based or model-free strategy. We assessed the correlation between subjects’ use of flexible, relational memory, as measured by generalization in the acquired equivalence task, and their differential reliance on either RL strategy in the decision task. We observed a significant positive relationship between generalization and model-based, but not model-free, choice strategies. These results are consistent with the hypothesis that model-based RL, like acquired equivalence, relies on a more general-purpose relational memory system. PMID:24846190

Critical Action Learning: Extending Its Reach

ERIC Educational Resources Information Center

Ram, Monder

2012-01-01

The trend to imbue action learning with an explicit conception of criticality appears to be gathering momentum. The idea of critical action learning (CAL) foregrounds the connection between power, emotion and organizing. How this triumvirate of forces relate to each other fundamentally shapes the scope for learning. Theoretical and empirical…

Action Learning and Executive Education: Achieving Credible Personal, Practitioner and Organisational Learning

ERIC Educational Resources Information Center

Stephens, Simon; Margey, Michael

2015-01-01

Action learning involves balancing the often conflicting forces between working knowledge and academic knowledge. This paper explores the experience of executive learners; academics and external contributors involved in action learning at the postgraduate level. The executive learners are members of cohorts on two masters programmes based in…

Action Learning: Towards a Framework in Inter-Organisational Settings

ERIC Educational Resources Information Center

Coughlan, Paul; Coghlan, David

2004-01-01

While much of the literature on action learning focuses on managers developing their capacity to learn and transform their own organizations, this article explores how action learning has been used in inter-organisational settings. Two settings are presented: the first an EU-funded management development programme called the National Action…

Microstimulation of the human substantia nigra alters reinforcement learning.

PubMed

Ramayya, Ashwin G; Misra, Amrit; Baltuch, Gordon H; Kahana, Michael J

2014-05-14

Animal studies have shown that substantia nigra (SN) dopaminergic (DA) neurons strengthen action-reward associations during reinforcement learning, but their role in human learning is not known. Here, we applied microstimulation in the SN of 11 patients undergoing deep brain stimulation surgery for the treatment of Parkinson's disease as they performed a two-alternative probability learning task in which rewards were contingent on stimuli, rather than actions. Subjects demonstrated decreased learning from reward trials that were accompanied by phasic SN microstimulation compared with reward trials without stimulation. Subjects who showed large decreases in learning also showed an increased bias toward repeating actions after stimulation trials; therefore, stimulation may have decreased learning by strengthening action-reward associations rather than stimulus-reward associations. Our findings build on previous studies implicating SN DA neurons in preferentially strengthening action-reward associations during reinforcement learning. Copyright © 2014 the authors 0270-6474/14/346887-09$15.00/0.

An Application of the Theory of Reasoned Action for Relating Attitude, Social Support and Behavioral Intention in an EFL Setting.

ERIC Educational Resources Information Center

Livesey, Daniel J.; And Others

A study investigated use of the Theory of Reasoned Action (TRA) for relating learner attitude toward engagement in English-as-a-Second-Language (ESL) learning behaviors, perceived social support for engagement in those behaviors, and behavioral intention. TRA proposes a sequence of dependencies underlying behavior. The model also relates attitude…

A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision-Making Models

ERIC Educational Resources Information Center

Stocco, Andrea

2018-01-01

Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of…

Visual variability affects early verb learning.

PubMed

Twomey, Katherine E; Lush, Lauren; Pearce, Ruth; Horst, Jessica S

2014-09-01

Research demonstrates that within-category visual variability facilitates noun learning; however, the effect of visual variability on verb learning is unknown. We habituated 24-month-old children to a novel verb paired with an animated star-shaped actor. Across multiple trials, children saw either a single action from an action category (identical actions condition, for example, travelling while repeatedly changing into a circle shape) or multiple actions from that action category (variable actions condition, for example, travelling while changing into a circle shape, then a square shape, then a triangle shape). Four test trials followed habituation. One paired the habituated verb with a new action from the habituated category (e.g., 'dacking' + pentagon shape) and one with a completely novel action (e.g., 'dacking' + leg movement). The others paired a new verb with a new same-category action (e.g., 'keefing' + pentagon shape), or a completely novel category action (e.g., 'keefing' + leg movement). Although all children discriminated novel verb/action pairs, children in the identical actions condition discriminated trials that included the completely novel verb, while children in the variable actions condition discriminated the out-of-category action. These data suggest that - as in noun learning - visual variability affects verb learning and children's ability to form action categories. © 2014 The British Psychological Society.

Action research and millennials: Improving pedagogical approaches to encourage critical thinking.

PubMed

Erlam, Gwen; Smythe, Liz; Wright-St Clair, Valerie

2018-02-01

This article examines the effects of intergenerational diversity on pedagogical practice in nursing education. While generational cohorts are not entirely homogenous, certain generational features do emerge. These features may require alternative approaches in educational design in order to maximize learning for millennial students. Action research is employed with undergraduate millennial nursing students (n=161) who are co-researchers in that they are asked for changes in current simulation environments which will improve their learning in the areas of knowledge acquisition, skill development, critical thinking, and communication. These changes are put into place and a re-evaluation of the effectiveness of simulation progresses through three action cycles. Millennials, due to a tendency for risk aversion, may gravitate towards more supportive learning environments which allow for free access to educators. This tendency is mitigated by the educator modeling expected behaviors, followed by student opportunity to repeat the behavior. Millennials tend to prefer to work in teams, see tangible improvement, and employ strategies to improve inter-professional communication. This research highlights the need for nurse educators working in simulation to engage in critical discourse regarding the adequacy and effectiveness of current pedagogy informing simulation design. Pedagogical approaches which maximize repetition, modeling, immersive feedback, and effective communication tend to be favored by millennial students. Copyright © 2017 Elsevier Ltd. All rights reserved.

Representation of aversive prediction errors in the human periaqueductal gray

PubMed Central

Roy, Mathieu; Shohamy, Daphna; Daw, Nathaniel; Jepma, Marieke; Wimmer, Elliott; Wager, Tor D.

2014-01-01

Pain is a primary driver of learning and motivated action. It is also a target of learning, as nociceptive brain responses are shaped by learning processes. We combined an instrumental pain avoidance task with an axiomatic approach to assessing fMRI signals related to prediction errors (PEs), which drive reinforcement-based learning. We found that pain PEs were encoded in the periaqueductal gray (PAG), an important structure for pain control and learning in animal models. Axiomatic tests combined with dynamic causal modeling suggested that ventromedial prefrontal cortex, supported by putamen, provides an expected value-related input to the PAG, which then conveys PE signals to prefrontal regions important for behavioral regulation, including orbitofrontal, anterior mid-cingulate, and dorsomedial prefrontal cortices. Thus, pain-related learning involves distinct neural circuitry, with implications for behavior and pain dynamics. PMID:25282614

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task.

PubMed

Sargent, Barbara; Reimann, Hendrik; Kubo, Masayoshi; Fetters, Linda

2015-06-01

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants' leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

Exploring the potential of a multi-level approach to improve capability for continuous organizational improvement and learning in a Swedish healthcare region.

PubMed

Nyström, M E; Höög, E; Garvare, R; Andersson Bäck, M; Terris, D D; Hansson, J

2018-05-24

Eldercare and care of people with functional impairments is organized by the municipalities in Sweden. Improving care in these areas is complex, with multiple stakeholders and organizations. Appropriate strategies to develop capability for continuing organizational improvement and learning (COIL) are needed. The purpose of our study was to develop and pilot-test a flexible, multilevel approach for COIL capability building and to identify what it takes to achieve changes in key actors' approaches to COIL. The approach, named "Sustainable Improvement and Development through Strategic and Systematic Approaches" (SIDSSA), was applied through an action-research and action-learning intervention. The SIDSSA approach was tested in a regional research and development (R&D) unit, and in two municipalities handling care of the elderly and people with functional impairments. Our approach included a multilevel strategy, development loops of five flexible phases, and an action-learning loop. The approach was designed to support systems understanding, strategic focus, methodological practices, and change process knowledge - all of which required double-loop learning. Multiple qualitative methods, i.e., repeated interviews, process diaries, and documents, provided data for conventional content analyses. The new approach was successfully tested on all cases and adopted and sustained by the R&D unit. Participants reported new insights and skills. The development loop facilitated a sense of coherence and control during uncertainty, improved planning and problem analysis, enhanced mapping of context and conditions, and supported problem-solving at both the individual and unit levels. The systems-level view and structured approach helped participants to explain, motivate, and implement change initiatives, especially after working more systematically with mapping, analyses, and goal setting. An easily understood and generalizable model internalized by key organizational actors is an important step before more complex development models can be implemented. SIDSSA facilitated individual and group learning through action-learning and supported systems-level views and structured approaches across multiple organizational levels. Active involvement of diverse organizational functions and levels in the learning process was facilitated. However, the time frame was too short to fully test all aspects of the approach, specifically in reaching beyond the involved managers to front-line staff and patients.

E-Learning as an Opportunity for the Public Administration

NASA Astrophysics Data System (ADS)

Casagranda, Milena; Colazzo, Luigi; Molinari, Andrea; Tomasini, Sara

In this paper we will describe the results of a learning project in the Public Administration, highlighting the methodological approach based on a blended training model in a context that has never experienced this type of activities. The observations contained in the paper will be focused on the evaluation results of this experience and the redesign elements in term of alternation between the classroom and distance training, methodologies, the value and use of the e-learning platform and learning evaluation. The elements that emerge will also provide the basis for the design of future teaching actions for this context (in which at this moment we are involved). The objective is to identify a "learning model", related also to the use of technological tools that are able to support lifelong learning and to define dynamics and process relating to facilitating learning activities of teachers and tutors.

Using Collaborative Action Learning Projects to Increase the Impact of Management Development

ERIC Educational Resources Information Center

Lyso, Ingunn Hybertsen; Mjoen, Kristian; Levin, Morten

2011-01-01

This article aims to contribute to the field of human resource development by exploring the conditions that influence the organizational impact of action learning projects. Many organizations use such projects as an integral part of their management development programs. Past research on action learning projects has shown how balancing action and…

Proceedings of the NATO IST-128 Workshop: Assessing Mission Impact of Cyberattacks Held in Istanbul, Turkey on 15-17 June 2015

DTIC Science & Technology

2015-12-01

combine satisficing behaviour with learning and adaptation through environmental feedback. This a sequential decision making with one alternative...next action that an opponent will most likely take in a strategic interaction. Also, cognitive models derived from instance- based learning theory (IBL... through instance- based learning . In Y. Li (Ed.), Lecture Notes in Computer Science (Vol. 6818, pp. 281-293). Heidelberg: Springer Berlin. Gonzalez, C

Family Involvement as a Priority Element for an Educational Action Based on Dialogic Learning

NASA Astrophysics Data System (ADS)

Valls, Mercè Pañellas; de Nicolás, Montserrat Alguacil; Torremorell, Maria Carme Boqué

In our society, there is a need for a critical reflection on education and the tasks to be developed by every agent. The family and school are the two main socializing settings of children and adolescents and, therefore, their joint responsibility in their education is a commitment that should be established in an atmosphere of confidence and harmony in order to tend towards a learning community model based on dialogic learning.

Action Learning in Postgraduate Executive Management Education: An Account of Practice

ERIC Educational Resources Information Center

Ruane, Meadbh

2016-01-01

The merits of action learning as a change tool and enabler of deep learning are well recognised. However, there is a gap in the literature of participants' stories regarding their experiences on accredited postgraduate executive programmes underpinned by an action learning philosophy. The following account of practice addresses this gap and…

Action Learning, Team Learning and Co-Operation in the Czech Republic

ERIC Educational Resources Information Center

Kubatova, Slava

2012-01-01

This account of practice presents two cases of the application of Action Learning (AL) communication methodology as described by Marquardt [2004. "Optimising the power of action learning". Mountain View, CA: Davies-Black Publishing]. The teams were Czech and international top management teams. The AL methodology was used to improve…

Action Learning--An Experiential Tool for Solving Organizational Issues

ERIC Educational Resources Information Center

Kinsey, Sharon B.

2011-01-01

Action Learning can be effectively used in both large and small businesses and organizations by employees, stakeholders, or volunteers through this "learning by doing" approach to evaluate an issue or issues of importance to the organization. First developed in the 1940s, Action Learning has increasingly been used as a method to explore questions…

Enabling Team Learning in Healthcare

ERIC Educational Resources Information Center

Boak, George

2016-01-01

This paper is based on a study of learning processes within 35 healthcare therapy teams that took action to improve their services. The published research on team learning is introduced, and the paper suggests it is an activity that has similarities with action research and with those forms of action learning where teams address collective…

Focusing on learning through constructive alignment with task-oriented portfolio assessment

NASA Astrophysics Data System (ADS)

Cain, A.; Grundy, J.; Woodward, C. J.

2018-07-01

Approaches to learning have been shown to have a significant impact on student success in technical units. This paper reports on an action research study that applied the principles of constructive alignment to improve student learning outcomes in programming units. The proposed model uses frequent formative feedback to engage students with unit material, and encourage them to adopt deep approaches to learning. Our results provide a set of guiding principles and a structured teaching approach that focuses students on meeting unit learning objectives, the goal of constructive alignment. The results are demonstrated via descriptions of the resulting teaching and learning environment, student results, and staff and student reflections.

Why do organizations not learn from incidents? Bottlenecks, causes and conditions for a failure to effectively learn.

PubMed

Drupsteen, Linda; Hasle, Peter

2014-11-01

If organizations would be able to learn more effectively from incidents that occurred in the past, future incidents and consequential injury or damage can be prevented. To improve learning from incidents, this study aimed to identify limiting factors, i.e. the causes of the failure to effectively learn. In seven organizations focus groups were held to discuss factors that according to employees contributed to the failure to learn. By use of a model of the learning from incidents process, the steps, where difficulties for learning arose, became visible, and the causes for these difficulties could be studied. Difficulties were identified in multiple steps of the learning process, but most difficulties became visible when planning actions, which is the phase that bridges the gap from incident investigation to actions for improvement. The main causes for learning difficulties, which were identified by the participants in this study, were tightly related to the learning process, but some indirect causes - or conditions - such as lack of ownership and limitations in expertise were also mentioned. The results illustrate that there are two types of causes for the failure to effectively learn: direct causes and indirect causes, here called conditions. By actively and systematically studying learning, more conditions might be identified and indicators for a successful learning process may be determined. Studying the learning process does, however, require a shift from learning from incidents to learning to learn. Copyright © 2014 Elsevier Ltd. All rights reserved.

Acting to gain information

NASA Technical Reports Server (NTRS)

Rosenchein, Stanley J.; Burns, J. Brian; Chapman, David; Kaelbling, Leslie P.; Kahn, Philip; Nishihara, H. Keith; Turk, Matthew

1993-01-01

This report is concerned with agents that act to gain information. In previous work, we developed agent models combining qualitative modeling with real-time control. That work, however, focused primarily on actions that affect physical states of the environment. The current study extends that work by explicitly considering problems of active information-gathering and by exploring specialized aspects of information-gathering in computational perception, learning, and language. In our theoretical investigations, we analyzed agents into their perceptual and action components and identified these with elements of a state-machine model of control. The mathematical properties of each was developed in isolation and interactions were then studied. We considered the complexity dimension and the uncertainty dimension and related these to intelligent-agent design issues. We also explored active information gathering in visual processing. Working within the active vision paradigm, we developed a concept of 'minimal meaningful measurements' suitable for demand-driven vision. We then developed and tested an architecture for ongoing recognition and interpretation of visual information. In the area of information gathering through learning, we explored techniques for coping with combinatorial complexity. We also explored information gathering through explicit linguistic action by considering the nature of conversational rules, coordination, and situated communication behavior.

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes

PubMed Central

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M.; Zimmermann, Ulrich S.; Schlagenhauf, Florian; Smolka, Michael N.; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities. PMID:28642696

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes.

PubMed

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M; Zimmermann, Ulrich S; Schlagenhauf, Florian; Smolka, Michael N; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities.

Observational Learning without a Model Is Influenced by the Observer's Possibility to Act: Evidence from the Simon Task

ERIC Educational Resources Information Center

Iani, Cristina; Rubichi, Sandro; Ferraro, Luca; Nicoletti, Roberto; Gallese, Vittorio

2013-01-01

We assessed whether observational learning in perceptual-motor tasks is affected by the visibility of an action producing perceived environmental effects and by the observer's possibility to act during observation. To this end, we conducted three experiments in which participants were required to observe a spatial compatibility task in which only…

Transforming a High School Media Center into a Library Learning Commons

ERIC Educational Resources Information Center

Chiara, Nancy A.

2014-01-01

This study outlines a planned action based research project focused on studying the transformation of an urban high school media center to a learning commons model. This study includes a descriptive account as well as the impact of steps taken to match the media center to the needs of the 21st century learner. The research focuses on shifting…

Assessment for Learning in Music Education in the Slovenian Context--From Punishment or Reward to Support

ERIC Educational Resources Information Center

Sicherl Kafol, Barbara; Kordeš, Urban; Holcar Brunauer, Ada

2017-01-01

This qualitative study used action research to provide an insight into how pupils experience and perceive assessment in music education. In collaboration with pupils we constructed an assessment for learning model aimed at involving pupils in the co-development of assessment criteria and in the processes of self and peer assessment. In addition we…

Translating Data into Action: A Data Team Model as the Seed of Comprehensive District Change

ERIC Educational Resources Information Center

Ruffner, Karen Blake

2010-01-01

Educational reform is not easy. As school leaders search for a format that leads to improvement on many fronts concurrently, data teams is one such promising practice. The data team design not only involves sensemaking of data as evidence of effective teaching and learning, but also builds a professional learning community, distributes leadership,…

Hands-On! Living in the Biosphere: Production, Pattern, Population, and Diversity. Developing Active Learning Module on the Human Dimensions of Global Change.

ERIC Educational Resources Information Center

Brown, Dwight

Biogeography examines questions of organism inventory and pattern, organisms' interactions with the environment, and the processes that create and change inventory, pattern, and interactions. This learning module uses time series maps and simple simulation models to illustrate how human actions alter biological productivity patterns at local and…

The Effects of Technology on the Community of Inquiry and Satisfaction with Online Courses

ERIC Educational Resources Information Center

Rubin, Beth; Fernandes, Ron; Avgerinou, Maria D.

2013-01-01

This paper extends the research on the Community of Inquiry (CoI) framework of understanding features of successful online learning to include the effects of the software used to support and facilitate it. This study examines how the Learning Management System (LMS) affords people the ability to take actions in an online course. A model is…

Enhancing Job Performance

ERIC Educational Resources Information Center

Devlin, Patricia

2011-01-01

The impact of the Self-Determined Career Development Model (hereafter called the Self-Determined Career Model) on the job performance of four adults with moderate intellectual disability employed in competitive work settings was examined. Employees learned to set work-related goals, develop an action plan, implement the plan, and adjust their…

Intensive Group Learning and On-Site Services to Improve Sexual and Reproductive Health Among Young Adults in Liberia: A Randomized Evaluation of HealthyActions.

PubMed

Firestone, Rebecca; Moorsmith, Reid; James, Simon; Urey, Marilyn; Greifinger, Rena; Lloyd, Danielle; Hartenberger-Toby, Lisa; Gausman, Jewel; Sanoe, Musa

2016-09-28

Young Liberians, particularly undereducated young adults, face substantial sexual and reproductive health (SRH) challenges, with low uptake of contraceptive methods, high rates of unintended pregnancy, and low levels of knowledge about HIV status. The purpose of this study was to assess the impact of a 6-day intensive group learning intervention combined with on-site SRH services (called HealthyActions) among out-of-school young adults, implemented through an existing alternative education program, on uptake of contraception and HIV testing and counseling (HTC). The intervention was implemented among young women and men ages 15-35 who were enrolled in alternative basic education learning sites in 5 counties of Liberia. We conducted a randomized evaluation to assess program impact. Baseline data were collected in January-March 2014, and endline data in June-July 2014. Key outcomes of condom use, contraceptive use, and HTC were estimated with difference-in-difference models using fixed effects. All analyses were conducted in Stata 13. We assessed outcomes for 1,157 learners at baseline and 1,052 learners at endline, across 29 treatment and 26 control sites. After adjusting for potential confounders, learners in the HealthyActions intervention group were 12% less likely to report never using a condom with a regular partner over the last month compared with the control group (P = .02). Female learners who received HealthyActions were 13% more likely to use any form of modern contraception compared with learners in control sites (P<.001), with the greatest increase in the use of contraceptive implants. Learners in HealthyActions sites were 45% more likely to have received HTC (P<.001). Providing intensive group learning in a supportive environment coupled with on-site health services improved SRH outcomes among participating learners. The focus of HealthyActions on participatory learning for low-literacy populations presents an adaptable solution for health programming across Liberia and the region. © Firestone et al.

Developing Online Course Portal to Improve Teachers’ Competency in Creating Action Research (CAR) Proposal Using Learning Management System (LMS) Moodle

NASA Astrophysics Data System (ADS)

Muhtar, A. A.

2017-02-01

Online course can offer flexible and easy way to improve teachers’ competency in conducting education research, especially in classroom action research (CAR). Teachers can attend the course without physically present in the class. This research aims to (1) develop online course portal to improve teachers’ competency in creating CAR proposal, and (2) produce proper online course portal validated and evaluated from four aspects: learning process, content, graphic user interface and programming. Online course in this research developed using Learning Management System (LMS) Moodle. The research model is using modified Borg & Gall Research and Development (R&D) started from preliminary studies, designing product, creating product, and evaluation. Product validated by three experts from three universities. Research subjects for field test are seven teachers as participants from different schools in several provinces in Indonesia. Based on expert validation and field test results, the product developed in this research categorized as “very good” in all aspects and it is suitable for teacher to improve their competency in creating CAR proposal. Online course portal produced in this research can be used as a proper model for online learning in creating CAR proposal.

Robots show us how to teach them: feedback from robots shapes tutoring behavior during action learning.

PubMed

Vollmer, Anna-Lisa; Mühlig, Manuel; Steil, Jochen J; Pitsch, Karola; Fritsch, Jannik; Rohlfing, Katharina J; Wrede, Britta

2014-01-01

Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction.

Robots Show Us How to Teach Them: Feedback from Robots Shapes Tutoring Behavior during Action Learning

PubMed Central

Vollmer, Anna-Lisa; Mühlig, Manuel; Steil, Jochen J.; Pitsch, Karola; Fritsch, Jannik; Rohlfing, Katharina J.; Wrede, Britta

2014-01-01

Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction. PMID:24646510

Action Prediction Allows Hypothesis Testing via Internal Forward Models at 6 Months of Age

PubMed Central

Gredebäck, Gustaf; Lindskog, Marcus; Juvrud, Joshua C.; Green, Dorota; Marciszko, Carin

2018-01-01

We propose that action prediction provides a cornerstone in a learning process known as internal forward models. According to this suggestion infants’ predictions (looking to the mouth of someone moving a spoon upward) will moments later be validated or proven false (spoon was in fact directed toward a bowl), information that is directly perceived as the distance between the predicted and actual goal. Using an individual difference approach we demonstrate that action prediction correlates with the tendency to react with surprise when social interactions are not acted out as expected (action evaluation). This association is demonstrated across tasks and in a large sample (n = 118) at 6 months of age. These results provide the first indication that infants might rely on internal forward models to structure their social world. Additional analysis, consistent with prior work and assumptions from embodied cognition, demonstrates that the latency of infants’ action predictions correlate with the infant’s own manual proficiency. PMID:29593600

40 CFR 85.535 - Liability, recordkeeping, and end of year reporting.

Code of Federal Regulations, 2011 CFR

2011-07-01

..., if we learn that your actions fall short of full compliance with applicable requirements we may... calendar year intermediate age conversions, outside useful life conversions, and the same conversion model...

Learning, Action and Solutions in Action Learning: Investigation of Facilitation Practice Using the Concept of Living Theories

ERIC Educational Resources Information Center

Sanyal, Chandana

2018-01-01

This paper explores the practice of action learning (AL) facilitation in supporting AL set members to address their 'messy' problems through a self-reflexive approach using the concept of 'living theory' [Whitehead, J., and J. McNiff. 2006. "Action Research Living Theory." London: Sage]. The facilitation practice is investigated through…

A computational neural model of goal-directed utterance selection.

PubMed

Klein, Michael; Kamp, Hans; Palm, Guenther; Doya, Kenji

2010-06-01

It is generally agreed that much of human communication is motivated by extra-linguistic goals: we often make utterances in order to get others to do something, or to make them support our cause, or adopt our point of view, etc. However, thus far a computational foundation for this view on language use has been lacking. In this paper we propose such a foundation using Markov Decision Processes. We borrow computational components from the field of action selection and motor control, where a neurobiological basis of these components has been established. In particular, we make use of internal models (i.e., next-state transition functions defined on current state action pairs). The internal model is coupled with reinforcement learning of a value function that is used to assess the desirability of any state that utterances (as well as certain non-verbal actions) can bring about. This cognitive architecture is tested in a number of multi-agent game simulations. In these computational experiments an agent learns to predict the context-dependent effects of utterances by interacting with other agents that are already competent speakers. We show that the cognitive architecture can account for acquiring the capability of deciding when to speak in order to achieve a certain goal (instead of performing a non-verbal action or simply doing nothing), whom to address and what to say. Copyright 2010 Elsevier Ltd. All rights reserved.

Implementation of Inquiry-Based Tutorials in AN Introductory Physics Course: the Role of the Graduate Teaching Assistant.

NASA Astrophysics Data System (ADS)

Thoresen, Carol Wiggins

1994-01-01

This study determined if the training provided physics teaching assistants was sufficient to accomplish the objectives of inquiry-based tutorials for an introductory physics course. Qualitative research methods were used: (1) to determine if the Physics by Inquiry method was modeled; (2) to describe the process from the teaching assistant perspective; (3) to determine TA opinions on training methods; (4) to develop a frame of reference to better understand the role of TA's as instructional support staff. The study determined that the teaching assistants verbalized appropriate instructional actions, but were observed to use a predominantly didactic teaching style. TA's held a variety of perceptions and beliefs about inquiry -based learning and how science is learned. They felt comfortable in the role of tutorial instructor. They were satisfied with the training methods provided and had few suggestions to change or improve training for future tutorial instructors. A concurrent theme of teacher action dependent on teacher beliefs was sustained throughout the study. The TA's actions, as tutorial instructors, reflected their educational beliefs, student background and learning experiences. TA's performance as tutorial instructors depended on what they think and believe about learning science. Practical implications exist for training teaching assistants to be tutorial instructors. Some recommendations may be appropriate for TA's required to use instructional methods that they have not experienced as students. Interview prospective teaching assistants to determine educational experience and beliefs. Employ inexperienced teaching assistants whose perspectives match the proposed instructional role and who might be more receptive to modeling. Incorporate training into staff meetings. Provide time for TA's to experience the instructional model with simulation or role play as students and as instructors, accompanied by conference discussion. Use strategies known to enhance adult learning and that are sensitive to the variability of adult learners. Educate for critical reflection; incorporate a system of peer coaching. Include a teaching assistant training component in group process and group management.

Does Lean Production Sacrifice Learning in a Manufacturing Environment? An Action Learning Case Study.

ERIC Educational Resources Information Center

Scott, Fiona M.; Butler, Jim; Edwards, John

2001-01-01

An action learning program was implemented by a manufacturer using lean production practices. Action learning practices were accommodated during times of stability, but abandoned in times of crisis. The meaning of work in this organizational culture excluded all practices, such as reflection, that were not visible and targeted at immediate…

Enacting Critical Learning: Power, Politics and Emotions at Work

ERIC Educational Resources Information Center

Trehan, Kiran; Rigg, Clare

2015-01-01

This article seeks to develop the understanding of critical action learning (CAL) and to make a contribution to its theory and practice. The article begins by conceptualising critical action learning and builds on the work of Revans (1982) to stimulate fresh thinking. It provides a different calibration of his coupling of action and learning. An…

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

PubMed Central

Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

2011-01-01

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

Action without Action Planning: The Potential of the Career Thinking Session in Enabling Transformational Career Learning and Development

ERIC Educational Resources Information Center

Bassot, Barbara

2017-01-01

This paper examines the potential of the Career Thinking Session (CTS) model to career guidance and counselling practice with young people. A qualitative research study is presented, focusing on the case study of a client involved in the transition to higher education. The setting for the research is described and the origins of the CTS are…

Integration of Temporal and Ordinal Information During Serial Interception Sequence Learning

PubMed Central

Gobel, Eric W.; Sanchez, Daniel J.; Reber, Paul J.

2011-01-01

The expression of expert motor skills typically involves learning to perform a precisely timed sequence of movements (e.g., language production, music performance, athletic skills). Research examining incidental sequence learning has previously relied on a perceptually-cued task that gives participants exposure to repeating motor sequences but does not require timing of responses for accuracy. Using a novel perceptual-motor sequence learning task, learning a precisely timed cued sequence of motor actions is shown to occur without explicit instruction. Participants learned a repeating sequence through practice and showed sequence-specific knowledge via a performance decrement when switched to an unfamiliar sequence. In a second experiment, the integration of representation of action order and timing sequence knowledge was examined. When either action order or timing sequence information was selectively disrupted, performance was reduced to levels similar to completely novel sequences. Unlike prior sequence-learning research that has found timing information to be secondary to learning action sequences, when the task demands require accurate action and timing information, an integrated representation of these types of information is acquired. These results provide the first evidence for incidental learning of fully integrated action and timing sequence information in the absence of an independent representation of action order, and suggest that this integrative mechanism may play a material role in the acquisition of complex motor skills. PMID:21417511

Action learning: a tool for the development of strategic skills for Nurse Consultants?

PubMed

Young, Sarah; Nixon, Eileen; Hinge, Denise; McFadyen, Jan; Wright, Vanessa; Lambert, Pauline; Pilkington, Carolyn; Newsome, Christine

2010-01-01

This paper will discuss the process of action learning and the outcomes of using action learning as a tool to achieve a more strategic function from Nurse Consultant posts. It is documented that one of the most challenging aspect of Nurse Consultant roles, in terms of leadership, is the strategic contribution they make at a senior corporate Trust level, often across organizations and local health economies. A facilitated action learning set was established in Brighton, England, to support the strategic leadership development of eight nurse consultant posts across two NHS Trusts. Benefits to patient care, with regard to patient pathways and cross-organizational working, have been evident outcomes associated with the nurse consultant posts involved in the action learning set. Commitment by organizational nurse leaders is essential to address the challenges facing nurse consultants to implement change at strategic levels. The use of facilitated action learning had been a successful tool in developing the strategic skills of Nurse Consultant posts within this setting. Action learning sets may be successfully applied to a range of senior nursing posts with a strategic remit and may assist post holders in achieving better outcomes pertinent to their roles.

Application of scl - pbl method to increase quality learning of industrial statistics course in department of industrial engineering pancasila university

NASA Astrophysics Data System (ADS)

Darmawan, M.; Hidayah, N. Y.

2017-12-01

Currently, there has been a change of new paradigm in the learning model in college, ie from Teacher Centered Learning (TCL) model to Student Centered Learing (SCL). It is generally assumed that the SCL model is better than the TCL model. The Courses of 2nd Industrial Statistics in the Department Industrial Engineering Pancasila University is the subject that belongs to the Basic Engineering group. So far, the applied learning model refers more to the TCL model, and field facts show that the learning outcomes are less satisfactory. Of the three consecutive semesters, ie even semester 2013/2014, 2014/2015, and 2015/2016 obtained grade average is equal to 56.0; 61.1, and 60.5. In the even semester of 2016/2017, Classroom Action Research (CAR) is conducted for this course through the implementation of SCL model with Problem Based Learning (PBL) methods. The hypothesis proposed is that the SCL-PBL model will be able to improve the final grade of the course. The results shows that the average grade of the course can be increased to 73.27. This value was then tested using the ANOVA and the test results concluded that the average grade was significantly different from the average grade value in the previous three semesters.

How to use coaching and action learning to support mentors in the workplace.

PubMed

Nash, Sue; Scammell, Janet

Using the example of mentoring preregistration student nurses, this article explores facilitation of learning in the workplace and examines the use of coaching and action learning to support mentors and the wider clinical team. A case study, where a mentor has difficulties with an underperforming student, is considered. Action learning and coaching are then explored, with the aim of maximising personal and team learning. These strategies can be easily transferred to other work based learning situations.

Associative vocabulary learning: development and testing of two paradigms for the (re-) acquisition of action- and object-related words.

PubMed

Freundlieb, Nils; Ridder, Volker; Dobel, Christian; Enriquez-Geppert, Stefanie; Baumgaertner, Annette; Zwitserlood, Pienie; Gerloff, Christian; Hummel, Friedhelm C; Liuzzi, Gianpiero

2012-01-01

Despite a growing number of studies, the neurophysiology of adult vocabulary acquisition is still poorly understood. One reason is that paradigms that can easily be combined with neuroscientfic methods are rare. Here, we tested the efficiency of two paradigms for vocabulary (re-) acquisition, and compared the learning of novel words for actions and objects. Cortical networks involved in adult native-language word processing are widespread, with differences postulated between words for objects and actions. Words and what they stand for are supposed to be grounded in perceptual and sensorimotor brain circuits depending on their meaning. If there are specific brain representations for different word categories, we hypothesized behavioural differences in the learning of action-related and object-related words. Paradigm A, with the learning of novel words for body-related actions spread out over a number of days, revealed fast learning of these new action words, and stable retention up to 4 weeks after training. The single-session Paradigm B employed objects and actions. Performance during acquisition did not differ between action-related and object-related words (time*word category: p = 0.01), but the translation rate was clearly better for object-related (79%) than for action-related words (53%, p = 0.002). Both paradigms yielded robust associative learning of novel action-related words, as previously demonstrated for object-related words. Translation success differed for action- and object-related words, which may indicate different neural mechanisms. The paradigms tested here are well suited to investigate such differences with neuroscientific means. Given the stable retention and minimal requirements for conscious effort, these learning paradigms are promising for vocabulary re-learning in brain-lesioned people. In combination with neuroimaging, neuro-stimulation or pharmacological intervention, they may well advance the understanding of language learning to optimize therapeutic strategies.

Imitation by combination: preschool age children evidence summative imitation in a novel problem-solving task

PubMed Central

Subiaul, Francys; Krajkowski, Edward; Price, Elizabeth E.; Etz, Alexander

2015-01-01

Children are exceptional, even ‘super,’ imitators but comparatively poor independent problem-solvers or innovators. Yet, imitation and innovation are both necessary components of cumulative cultural evolution. Here, we explored the relationship between imitation and innovation by assessing children’s ability to generate a solution to a novel problem by imitating two different action sequences demonstrated by two different models, an example of imitation by combination, which we refer to as “summative imitation.” Children (N = 181) from 3 to 5 years of age and across three experiments were tested in a baseline condition or in one of six demonstration conditions, varying in the number of models and opening techniques demonstrated. Across experiments, more than 75% of children evidenced summative imitation, opening both compartments of the problem box and retrieving the reward hidden in each. Generally, learning different actions from two different models was as good (and in some cases, better) than learning from 1 model, but the underlying representations appear to be the same in both demonstration conditions. These results show that summative imitation not only facilitates imitation learning but can also result in new solutions to problems, an essential feature of innovation and cumulative culture. PMID:26441782

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks.

PubMed

Cox, Louis Anthony Tony

2015-10-01

Decision biases can distort cost-benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well-documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk-cost-benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk-reducing regulations. Low-regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low-regret learning strategies using regulation of air pollutants with uncertain health effects as an example. © 2015 Society for Risk Analysis.

Making Entrepreneurship Education Work: The REAL Enterprises Model.

ERIC Educational Resources Information Center

Larson, Rick; King, Lisa; McGee, Mark; Shea, Brendon

This paper discusses the REAL (Rural Entrepreneurship through Action Learning) model as a necessary component of rural school-to-work (STW) programs. In rural areas where opportunities for traditional STW approaches (such as apprenticeships) are limited, entrepreneurial education teaches students to be job creators, not just job applicants. This…

Cesar Chavez--Grade Six Model Curriculum and Resources.

ERIC Educational Resources Information Center

California State Dept. of Education, Sacramento.

In this California state curriculum model for grade 6, "World History and Geography: Ancient Civilization," students learn that religious ideas have inspired and influenced the lives and actions of men and women, including Cesar Chavez. They see how his unselfishness, compassion for others, tolerance, and nonviolence have roots reaching…

Leadership and the Professional Learning Community

ERIC Educational Resources Information Center

Gaspar, Sandra

2010-01-01

The purpose of this study was to describe the transformation of one small, rural school district's professional development program. The study focused on the actions that school leaders took to replace a traditional, workshop-based program that was deemed ineffective with a new professional development model. The new model was designed to create…

Dynamic User Modeling within a Game-Based ITS

ERIC Educational Resources Information Center

Snow, Erica L.

2015-01-01

Intelligent tutoring systems are adaptive learning environments designed to support individualized instruction. The adaptation embedded within these systems is often guided by user models that represent one or more aspects of students' domain knowledge, actions, or performance. The proposed project focuses on the development and testing of user…

Pedagogical Content Knowledge in Mathematical Modelling Instruction

ERIC Educational Resources Information Center

Tan, Liang Soon; Ang, Keng Cheng

2012-01-01

This paper posits that teachers' pedagogical content knowledge in mathematical modelling instruction can be demonstrated in the crafting of action plans and expected teaching and learning moves via their lesson images (Schoenfeld, 1998). It can also be developed when teachers shape appropriate teaching moves in response to students' learning…

The Challenge of Evaluating Action Learning

ERIC Educational Resources Information Center

Edmonstone, John

2015-01-01

The paper examines the benefits claimed for action learning at individual, organisational and inter-organisational levels. It goes on to identify both generic difficulties in evaluating development programmes and action learning specifically. The distinction between formative and summative evaluation is considered and a summative evaluation…

How actions shape perception: learning action-outcome relations and predicting sensory outcomes promote audio-visual temporal binding

PubMed Central

Desantis, Andrea; Haggard, Patrick

2016-01-01

To maintain a temporally-unified representation of audio and visual features of objects in our environment, the brain recalibrates audio-visual simultaneity. This process allows adjustment for both differences in time of transmission and time for processing of audio and visual signals. In four experiments, we show that the cognitive processes for controlling instrumental actions also have strong influence on audio-visual recalibration. Participants learned that right and left hand button-presses each produced a specific audio-visual stimulus. Following one action the audio preceded the visual stimulus, while for the other action audio lagged vision. In a subsequent test phase, left and right button-press generated either the same audio-visual stimulus as learned initially, or the pair associated with the other action. We observed recalibration of simultaneity only for previously-learned audio-visual outcomes. Thus, learning an action-outcome relation promotes temporal grouping of the audio and visual events within the outcome pair, contributing to the creation of a temporally unified multisensory object. This suggests that learning action-outcome relations and the prediction of perceptual outcomes can provide an integrative temporal structure for our experiences of external events. PMID:27982063

How actions shape perception: learning action-outcome relations and predicting sensory outcomes promote audio-visual temporal binding.

PubMed

Desantis, Andrea; Haggard, Patrick

2016-12-16

To maintain a temporally-unified representation of audio and visual features of objects in our environment, the brain recalibrates audio-visual simultaneity. This process allows adjustment for both differences in time of transmission and time for processing of audio and visual signals. In four experiments, we show that the cognitive processes for controlling instrumental actions also have strong influence on audio-visual recalibration. Participants learned that right and left hand button-presses each produced a specific audio-visual stimulus. Following one action the audio preceded the visual stimulus, while for the other action audio lagged vision. In a subsequent test phase, left and right button-press generated either the same audio-visual stimulus as learned initially, or the pair associated with the other action. We observed recalibration of simultaneity only for previously-learned audio-visual outcomes. Thus, learning an action-outcome relation promotes temporal grouping of the audio and visual events within the outcome pair, contributing to the creation of a temporally unified multisensory object. This suggests that learning action-outcome relations and the prediction of perceptual outcomes can provide an integrative temporal structure for our experiences of external events.

Dissecting children's observational learning of complex actions through selective video displays.

PubMed

Flynn, Emma; Whiten, Andrew

2013-10-01

Children can learn how to use complex objects by watching others, yet the relative importance of different elements they may observe, such as the interactions of the individual parts of the apparatus, a model's movements, and desirable outcomes, remains unclear. In total, 140 3-year-olds and 140 5-year-olds participated in a study where they observed a video showing tools being used to extract a reward item from a complex puzzle box. Conditions varied according to the elements that could be seen in the video: (a) the whole display, including the model's hands, the tools, and the box; (b) the tools and the box but not the model's hands; (c) the model's hands and the tools but not the box; (d) only the end state with the box opened; and (e) no demonstration. Children's later attempts at the task were coded to establish whether they imitated the hierarchically organized sequence of the model's actions, the action details, and/or the outcome. Children's successful retrieval of the reward from the box and the replication of hierarchical sequence information were reduced in all but the whole display condition. Only once children had attempted the task and witnessed a second demonstration did the display focused on the tools and box prove to be better for hierarchical sequence information than the display focused on the tools and hands only. Copyright © 2013 Elsevier Inc. All rights reserved.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives.

PubMed

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives

PubMed Central

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context. PMID:24550798

Embodied learning of a generative neural model for biological motion perception and inference

PubMed Central

Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V.

2015-01-01

Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons. PMID:26217215

Embodied learning of a generative neural model for biological motion perception and inference.

PubMed

Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V

2015-01-01

Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

Inferring interventional predictions from observational learning data.

PubMed

Meder, Bjorn; Hagmayer, York; Waldmann, Michael R

2008-02-01

Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.

A model for the transfer of perceptual-motor skill learning in human behaviors.

PubMed

Rosalie, Simon M; Müller, Sean

2012-09-01

This paper presents a preliminary model that outlines the mechanisms underlying the transfer of perceptual-motor skill learning in sport and everyday tasks. Perceptual-motor behavior is motivated by performance demands and evolves over time to increase the probability of success through adaptation. Performance demands at the time of an event create a unique transfer domain that specifies a range of potentially successful actions. Transfer comprises anticipatory subconscious and conscious mechanisms. The model also outlines how transfer occurs across a continuum, which depends on the individual's expertise and contextual variables occurring at the incidence of transfer

Echoes on the motor network: how internal motor control structures afford sensory experience.

PubMed

Burgess, Jed D; Lum, Jarrad A G; Hohwy, Jakob; Enticott, Peter G

2017-12-01

Often, during daily experiences, hearing peers' actions can activate motor regions of the CNS. This activation is termed auditory-motor resonance (AMR) and is thought to represent an internal simulation of one's motor memories. Currently, AMR is demonstrated at the neuronal level in the Macaque and songbird, in conjunction with evidence on a systems level in humans. Here, we review evidence of AMR development from a motor control perspective. In the context of internal modelling, we consider data that demonstrates sensory-guided motor learning and action maintenance, particularly the notion of sensory comparison seen during songbird vocalisation. We suggest that these comparisons generate accurate sensory-to-motor inverse mappings. Furthermore, given reports of mapping decay after songbird learning, we highlight the proposal that the maintenance of these sensorimotor maps potentially explains why frontoparietal regions are activated upon hearing known sounds (i.e., AMR). In addition, we also recommend that activation of these types of internal models outside of action execution may provide an ecological advantage when encountering known stimuli in ambiguous conditions.

Exploring the Challenges in Scaling up the Delivery of Action Learning Facilitator Training within a Global Organisation

ERIC Educational Resources Information Center

Antell, Sonja; Heywood, John

2015-01-01

Action learning is often used as an element of leadership development programmes. The intention is to support classroom learning with an experiential thread which runs throughout the life of the programme. Action Learning Associates (ALA) has been working with an international organisation for three years to deliver the global "First Line…

To Act and Learn: A Bakhtinian Exploration of Action Learning

ERIC Educational Resources Information Center

Gold, Jeff; Anderson, Lisa; Clarke, Jean; Thorpe, Richard

2009-01-01

This paper considers the work of the Russian social philosopher and cultural theorist, Mikhail Mikhailovich Bakhtin as a source of understanding for those involved in action learning. Drawing upon data gathered over two years during the evaluation of 20 action learning sets in the north of England, we will seek to work with the ideas of Bakhtin to…

Student Accounts of Action Learning on a DBA Programme: Learning Inaction

ERIC Educational Resources Information Center

Mendonça, Roger; Parker, Anthony; Udo, Uwem; Groves, Catherine

2015-01-01

This account of practice sets out the action learning experience of three doctoral students on the same Doctoral Programme in Business Administration at a UK university. It also include the sense-making of a fourth member of the set. It explores the tension between their area of work and their engagement in the action learning process and, in so…

Action-based effects on music perception

PubMed Central

Maes, Pieter-Jan; Leman, Marc; Palmer, Caroline; Wanderley, Marcelo M.

2013-01-01

The classical, disembodied approach to music cognition conceptualizes action and perception as separate, peripheral processes. In contrast, embodied accounts of music cognition emphasize the central role of the close coupling of action and perception. It is a commonly established fact that perception spurs action tendencies. We present a theoretical framework that captures the ways in which the human motor system and its actions can reciprocally influence the perception of music. The cornerstone of this framework is the common coding theory, postulating a representational overlap in the brain between the planning, the execution, and the perception of movement. The integration of action and perception in so-called internal models is explained as a result of associative learning processes. Characteristic of internal models is that they allow intended or perceived sensory states to be transferred into corresponding motor commands (inverse modeling), and vice versa, to predict the sensory outcomes of planned actions (forward modeling). Embodied accounts typically refer to inverse modeling to explain action effects on music perception (Leman, 2007). We extend this account by pinpointing forward modeling as an alternative mechanism by which action can modulate perception. We provide an extensive overview of recent empirical evidence in support of this idea. Additionally, we demonstrate that motor dysfunctions can cause perceptual disabilities, supporting the main idea of the paper that the human motor system plays a functional role in auditory perception. The finding that music perception is shaped by the human motor system and its actions suggests that the musical mind is highly embodied. However, we advocate for a more radical approach to embodied (music) cognition in the sense that it needs to be considered as a dynamical process, in which aspects of action, perception, introspection, and social interaction are of crucial importance. PMID:24454299

Action-based effects on music perception.

PubMed

Maes, Pieter-Jan; Leman, Marc; Palmer, Caroline; Wanderley, Marcelo M

2014-01-03

The classical, disembodied approach to music cognition conceptualizes action and perception as separate, peripheral processes. In contrast, embodied accounts of music cognition emphasize the central role of the close coupling of action and perception. It is a commonly established fact that perception spurs action tendencies. We present a theoretical framework that captures the ways in which the human motor system and its actions can reciprocally influence the perception of music. The cornerstone of this framework is the common coding theory, postulating a representational overlap in the brain between the planning, the execution, and the perception of movement. The integration of action and perception in so-called internal models is explained as a result of associative learning processes. Characteristic of internal models is that they allow intended or perceived sensory states to be transferred into corresponding motor commands (inverse modeling), and vice versa, to predict the sensory outcomes of planned actions (forward modeling). Embodied accounts typically refer to inverse modeling to explain action effects on music perception (Leman, 2007). We extend this account by pinpointing forward modeling as an alternative mechanism by which action can modulate perception. We provide an extensive overview of recent empirical evidence in support of this idea. Additionally, we demonstrate that motor dysfunctions can cause perceptual disabilities, supporting the main idea of the paper that the human motor system plays a functional role in auditory perception. The finding that music perception is shaped by the human motor system and its actions suggests that the musical mind is highly embodied. However, we advocate for a more radical approach to embodied (music) cognition in the sense that it needs to be considered as a dynamical process, in which aspects of action, perception, introspection, and social interaction are of crucial importance.

Civil Society, Adult Learning and Action in India.

ERIC Educational Resources Information Center

Tandon, Rajesh

2000-01-01

Five case studies of individual and collective learning projects in India demonstrate that (1) the impetus for civic action arises from local conditions; (2) transformative action requires sustained adult learning; and (3) civil society is a complex concept reflecting diverse priorities and perspectives. (SK)

Integrating cortico-limbic-basal ganglia architectures for learning model-based and model-free navigation strategies

PubMed Central

Khamassi, Mehdi; Humphries, Mark D.

2012-01-01

Behavior in spatial navigation is often organized into map-based (place-driven) vs. map-free (cue-driven) strategies; behavior in operant conditioning research is often organized into goal-directed vs. habitual strategies. Here we attempt to unify the two. We review one powerful theory for distinct forms of learning during instrumental conditioning, namely model-based (maintaining a representation of the world) and model-free (reacting to immediate stimuli) learning algorithms. We extend these lines of argument to propose an alternative taxonomy for spatial navigation, showing how various previously identified strategies can be distinguished as “model-based” or “model-free” depending on the usage of information and not on the type of information (e.g., cue vs. place). We argue that identifying “model-free” learning with dorsolateral striatum and “model-based” learning with dorsomedial striatum could reconcile numerous conflicting results in the spatial navigation literature. From this perspective, we further propose that the ventral striatum plays key roles in the model-building process. We propose that the core of the ventral striatum is positioned to learn the probability of action selection for every transition between states of the world. We further review suggestions that the ventral striatal core and shell are positioned to act as “critics” contributing to the computation of a reward prediction error for model-free and model-based systems, respectively. PMID:23205006

Linking Action Learning and Inter-Organisational Learning: The Learning Journey Approach

ERIC Educational Resources Information Center

Schumacher, Thomas

2015-01-01

The article presents and illustrates the learning journey (LJ)--a new management development approach to inter-organisational learning based on observation, reflection and problem-solving. The LJ involves managers from different organisations and applies key concepts of action learning and systemic organisational development. Made up of…

Building High Performance Learning: A Focus on Career Results and the Bottom Line.

ERIC Educational Resources Information Center

Ingram, Hadyn; Sandelands, Eric; Teare, Richard

2001-01-01

Discusses how action learning can be targeted to business objectives and how electronically enabled action learning can increase productivity. Provides examples of personal learning aligned with organizational goals, including a certificate of management studies course, prior learning experiences, and an advanced diploma in virtual learning. (SK)

A Neurocomputational model of tonic and phasic dopamine in action selection: A comparison with cognitive deficits in Parkinson’s disease

PubMed Central

Guthrie, M.; Myers, C.E.; Gluck, M.A.

2015-01-01

The striatal dopamine signal has multiple facets; tonic level, phasic rise and fall, and variation of the phasic rise/fall depending on the expectation of reward/punishment. We have developed a network model of the striatal direct pathway using an ionic current level model of the medium spiny neuron that incorporates currents sensitive to changes in the tonic level of dopamine. The model neurons in the network learn action selection based on a novel set of mathematical rules that incorporate the phasic change in the dopamine signal. This network model is capable of learning to perform a sequence learning task that in humans is thought to be dependent on the basal ganglia. When both tonic and phasic levels of dopamine are decreased, as would be expected in unmedicated Parkinson’s disease (PD), the model reproduces the deficits seen in a human PD group off medication. When the tonic level is increased to normal, but with reduced phasic increases and decreases in response to reward and punishment respectively, as would be expected in PD medicated with L-Dopa, the model again reproduces the human data. These findings support the view that the cognitive dysfunctions seen in Parkinson’s disease are not solely due to either the decreased tonic level of dopamine or to the decreased responsiveness of the phasic dopamine signal to reward and punishment, but to a combination of the two factors that varies dependent on disease stage and medication status. PMID:19162084

Codifying Implementation Guidelines for a Collaborative Improvement Initiative

ERIC Educational Resources Information Center

Coughlan, Paul; Coghlan, David

2008-01-01

The application of action learning in inter-organizational settings is largely undeveloped. This article presents a description of and reflection on an action learning approach to enabling collaborative improvement in the extended manufacturing enterprise. The article focuses in particular on implementing the action learning approach. However, the…

Attitudes Regarding Action Learning: Undergraduate vs. Graduate Business Students

ERIC Educational Resources Information Center

Rosenstein, Alvin; Ashley, Allan; Gupta, Rakesh; Ulin, Kristin

2008-01-01

Previous research in our Action Learning Program demonstrated that although undergraduates preferred the Action Learning mode to the traditional lecture and discussion mode of instruction, they missed the familiar structure of the more traditional pedagogy. Consequently increased structure was implemented in both an undergraduate and graduate…

Reinforcement learning and episodic memory in humans and animals: an integrative framework

PubMed Central

Gershman, Samuel J.; Daw, Nathaniel D.

2018-01-01

We review the psychology and neuroscience of reinforcement learning (RL), which has witnessed significant progress in the last two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. However, the simplicity of these tasks misses important aspects of reinforcement learning in the real world: (i) State spaces are high-dimensional, continuous, and partially observable; this implies that (ii) data are relatively sparse: indeed precisely the same situation may never be encountered twice; and also that (iii) rewards depend on long-term consequences of actions in ways that violate the classical assumptions that make RL tractable. A seemingly distinct challenge is that, cognitively, these theories have largely connected with procedural and semantic memory: how knowledge about action values or world models extracted gradually from many experiences can drive choice. This misses many aspects of memory related to traces of individual events, such as episodic memory. We suggest that these two gaps are related. In particular, the computational challenges can be dealt with, in part, by endowing RL systems with episodic memory, allowing them to (i) efficiently approximate value functions over complex state spaces, (ii) learn with very little data, and (iii) bridge long-term dependencies between actions and rewards. We review the computational theory underlying this proposal and the empirical evidence to support it. Our proposal suggests that the ubiquitous and diverse roles of memory in RL may function as part of an integrated learning system. PMID:27618944

Extending the mirror neuron system model, II: what did I just do? A new role for mirror neurons.

PubMed

Bonaiuto, James; Arbib, Michael A

2010-04-01

A mirror system is active both when an animal executes a class of actions (self-actions) and when it sees another execute an action of that class. Much attention has been given to the possible roles of mirror systems in responding to the actions of others but there has been little attention paid to their role in self-actions. In the companion article (Bonaiuto et al. Biol Cybern 96:9-38, 2007) we presented MNS2, an extension of the Mirror Neuron System model of the monkey mirror system trained to recognize the external appearance of its own actions as a basis for recognizing the actions of other animals when they perform similar actions. Here we further extend the study of the mirror system by introducing the novel hypotheses that a mirror system may additionally help in monitoring the success of a self-action and may also be activated by recognition of one's own apparent actions as well as efference copy from one's intended actions. The framework for this computational demonstration is a model of action sequencing, called augmented competitive queuing, in which action choice is based on the desirability of executable actions. We show how this "what did I just do?" function of mirror neurons can contribute to the learning of both executability and desirability which in certain cases supports rapid reorganization of motor programs in the face of disruptions.

National Study of Excellence and Innovation in Physical Therapist Education: Part 2-A Call to Reform.

PubMed

Jensen, Gail M; Hack, Laurita M; Nordstrom, Terrence; Gwyer, Janet; Mostrom, Elizabeth

2017-09-01

This perspective shares recommendations that draw from (1) the National Study of Excellence and Innovation in Physical Therapist Education research findings and a conceptual model of excellence in physical therapist education, (2) the Carnegie Foundation's Preparation for the Professions Program (PPP), and (3) research in the learning sciences. The 30 recommendations are linked to the dimensions described in the conceptual model for excellence in physical therapist education: Culture of Excellence, Praxis of Learning, and Organizational Structures and Resources. This perspective proposes a transformative call for reform framed across 3 core categories: (1) creating a culture of excellence, leadership, and partnership, (2) advancing the learning sciences and understanding and enacting the social contract, and (3) implementing organizational imperatives. Similar to the Carnegie studies, this perspective identifies action items (9) that should be initiated immediately in a strategic and systematic way by the major organizational stakeholders in physical therapist education. These recommendations and action items provide a transformative agenda for physical therapist education, and thus the profession, in meeting the changing needs of society through higher levels of excellence. © 2017 American Physical Therapy Association.

From feedback- to response-based performance monitoring in active and observational learning.

PubMed

Bellebaum, Christian; Colosio, Marco

2014-09-01

Humans can adapt their behavior by learning from the consequences of their own actions or by observing others. Gradual active learning of action-outcome contingencies is accompanied by a shift from feedback- to response-based performance monitoring. This shift is reflected by complementary learning-related changes of two ACC-driven ERP components, the feedback-related negativity (FRN) and the error-related negativity (ERN), which have both been suggested to signal events "worse than expected," that is, a negative prediction error. Although recent research has identified comparable components for observed behavior and outcomes (observational ERN and FRN), it is as yet unknown, whether these components are similarly modulated by prediction errors and thus also reflect behavioral adaptation. In this study, two groups of 15 participants learned action-outcome contingencies either actively or by observation. In active learners, FRN amplitude for negative feedback decreased and ERN amplitude in response to erroneous actions increased with learning, whereas observational ERN and FRN in observational learners did not exhibit learning-related changes. Learning performance, assessed in test trials without feedback, was comparable between groups, as was the ERN following actively performed errors during test trials. In summary, the results show that action-outcome associations can be learned similarly well actively and by observation. The mechanisms involved appear to differ, with the FRN in active learning reflecting the integration of information about own actions and the accompanying outcomes.

Defining Learning Space in a Serious Game in Terms of Operative and Resultant Actions

NASA Technical Reports Server (NTRS)

Martin, Michael W.; Shen, Yuzhong

2012-01-01

This paper explores the distinction between operative and resultant actions in games, and proposes that the learning space created by a serious game is a function of these actions. Further, it suggests a possible relationship between these actions and the forms of cognitive load imposed upon the game player. Association of specific types of cognitive load with respective forms of actions in game mechanics also presents some heuristics for integrating learning content into serious games. Research indicates that different balances of these types of actions are more suitable for novice or experienced learners. By examining these relationships, we can develop a few basic principles of game design which have an increased potential to promote positive learning outcomes.

Transfer Learning beyond Text Classification

NASA Astrophysics Data System (ADS)

Yang, Qiang

Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. While much has been done in transfer learning in text classification and reinforcement learning, there has been a lack of documented success stories of novel applications of transfer learning in other areas. In this invited article, I will argue that transfer learning is in fact quite ubiquitous in many real world applications. In this article, I will illustrate this point through an overview of a broad spectrum of applications of transfer learning that range from collaborative filtering to sensor based location estimation and logical action model learning for AI planning. I will also discuss some potential future directions of transfer learning.

An adult learning perspective on disability and microfinance: The case of Katureebe.

PubMed

Nuwagaba, Ephraim L; Rule, Peter N

2016-01-01

Despite Uganda's progress in promoting affirmative action for persons with disabilities and its strategy of using microfinance to fight poverty, access to microfinance services by persons with disabilities is still problematic due to barriers, characterised by discrepancies between policies and practices. Regarding education, the affirmative action in favour of learners with disabilities has not translated into actual learning opportunities due to personal and environmental barriers. The study on which this article is based investigated the non-formal and informal adult learning practices regarding microfinance that persons with disabilities engaged in. This article seeks to illuminate the barriers that a person with a visual impairment encountered while learning about and engaging with microfinance and the strategies that he developed to overcome them. This was a case study, framed within the social model of disability and critical research paradigm. Data were collected through in-depth interviews of a person with visual impairment and observations of the environment in which adult learning and engagement with Savings and Credit Cooperative Organisations (SACCOs) occurred. Findings indicate that the person with a visual disability faced barriers to learning about microfinance services. He experienced barriers in an integrated manner and developed strategies to overcome these barriers. The barriers and strategies are theorised using the social model of disability. The case of a person with visual impairment suggests that persons with disabilities face multiple barriers regarding microfinance, including social, psychological and educational. However, his own agency and attitudes were also of importance as they influenced his learning. Viewing these barriers as blockades can lead to non-participation in learning and engagement with microfinance whereas viewing them as surmountable hurdles can potentially motivate participants to succeed in learning about and engaging with microfinance.

An adult learning perspective on disability and microfinance: The case of Katureebe

PubMed Central

Nuwagaba, Ephraim L.

2016-01-01

Background Despite Uganda’s progress in promoting affirmative action for persons with disabilities and its strategy of using microfinance to fight poverty, access to microfinance services by persons with disabilities is still problematic due to barriers, characterised by discrepancies between policies and practices. Regarding education, the affirmative action in favour of learners with disabilities has not translated into actual learning opportunities due to personal and environmental barriers. Objectives The study on which this article is based investigated the non-formal and informal adult learning practices regarding microfinance that persons with disabilities engaged in. This article seeks to illuminate the barriers that a person with a visual impairment encountered while learning about and engaging with microfinance and the strategies that he developed to overcome them. Methods This was a case study, framed within the social model of disability and critical research paradigm. Data were collected through in-depth interviews of a person with visual impairment and observations of the environment in which adult learning and engagement with Savings and Credit Cooperative Organisations (SACCOs) occurred. Results Findings indicate that the person with a visual disability faced barriers to learning about microfinance services. He experienced barriers in an integrated manner and developed strategies to overcome these barriers. The barriers and strategies are theorised using the social model of disability. Conclusion The case of a person with visual impairment suggests that persons with disabilities face multiple barriers regarding microfinance, including social, psychological and educational. However, his own agency and attitudes were also of importance as they influenced his learning. Viewing these barriers as blockades can lead to non-participation in learning and engagement with microfinance whereas viewing them as surmountable hurdles can potentially motivate participants to succeed in learning about and engaging with microfinance. PMID:28730047

Isolating Visual and Proprioceptive Components of Motor Sequence Learning in ASD.

PubMed

Sharer, Elizabeth A; Mostofsky, Stewart H; Pascual-Leone, Alvaro; Oberman, Lindsay M

2016-05-01

In addition to defining impairments in social communication skills, individuals with autism spectrum disorder (ASD) also show impairments in more basic sensory and motor skills. Development of new skills involves integrating information from multiple sensory modalities. This input is then used to form internal models of action that can be accessed when both performing skilled movements, as well as understanding those actions performed by others. Learning skilled gestures is particularly reliant on integration of visual and proprioceptive input. We used a modified serial reaction time task (SRTT) to decompose proprioceptive and visual components and examine whether patterns of implicit motor skill learning differ in ASD participants as compared with healthy controls. While both groups learned the implicit motor sequence during training, healthy controls showed robust generalization whereas ASD participants demonstrated little generalization when visual input was constant. In contrast, no group differences in generalization were observed when proprioceptive input was constant, with both groups showing limited degrees of generalization. The findings suggest, when learning a motor sequence, individuals with ASD tend to rely less on visual feedback than do healthy controls. Visuomotor representations are considered to underlie imitative learning and action understanding and are thereby crucial to social skill and cognitive development. Thus, anomalous patterns of implicit motor learning, with a tendency to discount visual feedback, may be an important contributor in core social communication deficits that characterize ASD. Autism Res 2016, 9: 563-569. © 2015 International Society for Autism Research, Wiley Periodicals, Inc. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Action observation versus motor imagery in learning a complex motor task: a short review of literature and a kinematics study.

PubMed

Gatti, R; Tettamanti, A; Gough, P M; Riboldi, E; Marinoni, L; Buccino, G

2013-04-12

Both motor imagery and action observation have been shown to play a role in learning or re-learning complex motor tasks. According to a well accepted view they share a common neurophysiological basis in the mirror neuron system. Neurons within this system discharge when individuals perform a specific action and when they look at another individual performing the same or a motorically related action. In the present paper, after a short review of literature on the role of action observation and motor imagery in motor learning, we report the results of a kinematics study where we directly compared motor imagery and action observation in learning a novel complex motor task. This involved movement of the right hand and foot in the same angular direction (in-phase movement), while at the same time moving the left hand and foot in an opposite angular direction (anti-phase movement), all at a frequency of 1Hz. Motor learning was assessed through kinematics recording of wrists and ankles. The results showed that action observation is better than motor imagery as a strategy for learning a novel complex motor task, at least in the fast early phase of motor learning. We forward that these results may have important implications in educational activities, sport training and neurorehabilitation. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

After-Action Reports: Capturing Lessons Learned and Identifying Areas for Improvement. Lessons Learned from School Crises and Emergencies. Volume 2, Issue 1, 2007

ERIC Educational Resources Information Center

US Department of Education, 2007

2007-01-01

"Lessons Learned" is a series of publications that are a brief recounting of actual school emergencies and crises. This issue of "Lessons Learned" addresses after-action reports, which are an integral part of the emergency preparedness planning continuum and support effective crisis response. After-action reports have a threefold purpose. They…

Parallel, but Dissociable, Processing in Discrete Corticostriatal Inputs Encodes Skill Learning.

PubMed

Kupferschmidt, David A; Juczewski, Konrad; Cui, Guohong; Johnson, Kari A; Lovinger, David M

2017-10-11

Changes in cortical and striatal function underlie the transition from novel actions to refined motor skills. How discrete, anatomically defined corticostriatal projections function in vivo to encode skill learning remains unclear. Using novel fiber photometry approaches to assess real-time activity of associative inputs from medial prefrontal cortex to dorsomedial striatum and sensorimotor inputs from motor cortex to dorsolateral striatum, we show that associative and sensorimotor inputs co-engage early in action learning and disengage in a dissociable manner as actions are refined. Disengagement of associative, but not sensorimotor, inputs predicts individual differences in subsequent skill learning. Divergent somatic and presynaptic engagement in both projections during early action learning suggests potential learning-related in vivo modulation of presynaptic corticostriatal function. These findings reveal parallel processing within associative and sensorimotor circuits that challenges and refines existing views of corticostriatal function and expose neuronal projection- and compartment-specific activity dynamics that encode and predict action learning. Published by Elsevier Inc.

Reinforcement learning of periodical gaits in locomotion robots

NASA Astrophysics Data System (ADS)

Svinin, Mikhail; Yamada, Kazuyaki; Ushio, S.; Ueda, Kanji

1999-08-01

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance- based reinforcement learning scheme, is used for sensory- motor control of an eight-legged mobile robot. Important feature of the classifier system is its ability to work with the continuous sensor space. The robot does not have a prior knowledge of the environment, its own internal model, and the goal coordinates. It is only assumed that the robot can acquire stable gaits by learning how to reach a light source. During the learning process the control system, is self-organized by reinforcement signals. Reaching the light source defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. Feasibility of the proposed self-organized system is tested under simulation and experiment. The control actions are specified at the leg level. It is shown that, as learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns.

The Evidence for the Effectiveness of Action Learning

ERIC Educational Resources Information Center

Leonard, H. Skipton; Marquardt, Michael J.

2010-01-01

For the past 50 years, organizations and individuals around the world have reported success in their use of action learning programs to solve problems, develop leaders, build teams and transform their corporate cultures. However, very little rigorous research has been conducted to determine the effectiveness of action learning. The authors…

Participatory Action Research and Environmental Learning: Implications for Resilient Forests and Communities

ERIC Educational Resources Information Center

Ballard, Heidi L.; Belsky, Jill M.

2010-01-01

How can a participatory approach to research promote environmental learning and enhance social-ecological systems resilience? Participatory action research (PAR) is an approach to research that its' supporters claim can foster new knowledge, learning, and action to support positive social and environmental change through reorienting the standard…

Perspective Taking Promotes Action Understanding and Learning

ERIC Educational Resources Information Center

Lozano, Sandra C.; Martin Hard, Bridgette; Tversky, Barbara

2006-01-01

People often learn actions by watching others. The authors propose and test the hypothesis that perspective taking promotes encoding a hierarchical representation of an actor's goals and subgoals-a key process for observational learning. Observers segmented videos of an object assembly task into coarse and fine action units. They described what…

Transformative and Restorative Learning: A Vital Dialectic for Sustainable Societies

ERIC Educational Resources Information Center

Lange, Elizabeth A.

2004-01-01

This study explores the potential of critical transformative learning for revitalizing citizen action, particularly action toward a sustainable society. Through an action research process with 14 university extension participants, it was found that a dialectic of transformative and restorative learning is vital for fostering active citizenship.…

Assessing the Value of Action Learning for Social Enterprises and Charities

ERIC Educational Resources Information Center

Smith, Sue; Smith, Laurie

2017-01-01

In this paper we evaluate action learning for leaders of social enterprises and charities. Based on ethnographic research including participant observation, facilitator reflective diary notes and in-depth, qualitative interviews with participants of two action learning sets undertaken over eight months, analysed using Wenger, Trayner, and de Laat…

Virtual Action Learning: Practices and Challenges

ERIC Educational Resources Information Center

Dickenson, Mollie; Burgoyne, John; Pedler, Mike

2010-01-01

This paper reports findings from research that set out to explore virtual action learning (VAL) as an emerging variety of action learning (AL). In bringing together geographically dispersed individuals within and across organizations, and possibly across time, VAL has obvious potential in both educational and commercial contexts. Whilst there is…

Action Learning in Virtual Higher Education: Applying Leadership Theory

ERIC Educational Resources Information Center

Curtin, Joseph

2016-01-01

This paper reports the historical foundation of Northeastern University's course, LDR 6100: Developing Your Leadership Capability, a partial literature review of action learning (AL) and virtual action learning (VAL), a course methodology of LDR 6100 requiring students to apply leadership perspectives using VAL as instructed by the author,…

Business Action Learning Tasmania (BALT)--An Account of Practice

ERIC Educational Resources Information Center

Cother, Genevieve; Cother, Robert F.

2017-01-01

Business Action Learning Tasmania's (BALT) mission is self-reliant industry development, with diverse companies co-operating to improve their profitability, develop their people and grow the local economy. This is achieved through collaborative action learning, with companies working together on projects of vital importance and sharing the…

A Framework for the Ethical Practice of Action Learning

ERIC Educational Resources Information Center

Johnson, Craig

2010-01-01

By tradition the action learning community has encouraged an eclectic view of practice. This involves a number of different permutations around a kernel of nebulous ideas. However, the disadvantages of such an open philosophy have never been considered. In particular consumer protection against inauthentic action learning experiences has been…

25 Action Learning Schools.

ERIC Educational Resources Information Center

National Association of Secondary School Principals, Reston, VA.

This booklet on action-learning reflects an interest in preparing youth for the world of real experiences. Arranged in two major parts, the first offers information on the background and development of action-learning. Included in this section are the conclusions of the Panel on Youth of the President's Science Advisory Committee, the National…

Action learning: an effective way to improve cancer-related pain management.

PubMed

Kasasbeh, Mohammed Ali Mohammed; McCabe, Catherine; Payne, Sheila

2017-11-01

To evaluate the efficacy of action learning for improving cancer related pain management in the acute healthcare settings. Despite the prevalent use of action learning in private, public, clinical and non-clinical settings, no studies were found in the literature that either examined cancer pain management or used action learning as an approach to improve patient care in acute healthcare settings. An intervention pre - posttest design was adopted using an action learning programme (ALPs) as the intervention. Healthcare professionals' knowledge, attitudes and practice were assessed and evaluated before and after the implementation of the six-month ALPs. A pre and post audit and survey were conducted for data collection. The data were collected from the entire population of 170 healthcare professionals in one healthcare organisation. The management of cancer related pain improved significantly following the intervention. Significant improvement were also seen in healthcare professionals' knowledge, attitudes with improved cancer related pain management as a consequence of this. Despite many organisational challenges to practice development and collaborative working in healthcare settings there is evidence that action learning can achieve positive outcomes for improving CRP and supporting collaborative working. Action learning needs to be considered as a strategy for achieving high quality standards. © 2016 John Wiley & Sons Ltd.

Promoting Shifts in Preservice Science Teachers' Thinking through Teaching and Action Research in Informal Science Settings

NASA Astrophysics Data System (ADS)

Wallace, Carolyn S.

2013-08-01

The purpose of this study was to investigate the influence of an integrated experiential learning and action research project on preservice science teachers' developing ideas about science teaching, learning, and action research itself. The qualitative, interpretive study examined the action research of 10 master's degree students who were involved in service learning with children in informal education settings. Results indicated that all of the participants enhanced their knowledge of children as diverse learners and the importance of prior knowledge in science learning. In-depth case studies for three of the participants indicated that two developed deeper understandings of science learners and learning. However, one participant was resistant to learning and gained more limited understandings.

Optimally designing games for behavioural research

PubMed Central

Rafferty, Anna N.; Zaharia, Matei; Griffiths, Thomas L.

2014-01-01

Computer games can be motivating and engaging experiences that facilitate learning, leading to their increasing use in education and behavioural experiments. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. In this framework, we use Markov decision processes to model players' actions within a game, and then make inferences about the parameters of a cognitive model from these actions. Using a variety of concept learning games, we show that in practice, this method can predict which games will result in better estimates of the parameters of interest. The best games require only half as many players to attain the same level of precision. PMID:25002821

Hemispheric Asymmetries in Striatal Reward Responses Relate to Approach-Avoidance Learning and Encoding of Positive-Negative Prediction Errors in Dopaminergic Midbrain Regions.

PubMed

Aberg, Kristoffer Carl; Doell, Kimberly C; Schwartz, Sophie

2015-10-28

Some individuals are better at learning about rewarding situations, whereas others are inclined to avoid punishments (i.e., enhanced approach or avoidance learning, respectively). In reinforcement learning, action values are increased when outcomes are better than predicted (positive prediction errors [PEs]) and decreased for worse than predicted outcomes (negative PEs). Because actions with high and low values are approached and avoided, respectively, individual differences in the neural encoding of PEs may influence the balance between approach-avoidance learning. Recent correlational approaches also indicate that biases in approach-avoidance learning involve hemispheric asymmetries in dopamine function. However, the computational and neural mechanisms underpinning such learning biases remain unknown. Here we assessed hemispheric reward asymmetry in striatal activity in 34 human participants who performed a task involving rewards and punishments. We show that the relative difference in reward response between hemispheres relates to individual biases in approach-avoidance learning. Moreover, using a computational modeling approach, we demonstrate that better encoding of positive (vs negative) PEs in dopaminergic midbrain regions is associated with better approach (vs avoidance) learning, specifically in participants with larger reward responses in the left (vs right) ventral striatum. Thus, individual dispositions or traits may be determined by neural processes acting to constrain learning about specific aspects of the world. Copyright © 2015 the authors 0270-6474/15/3514491-10$15.00/0.

A Playbook for Data: Real-Life Scenario Demonstrates Learning Forward's Data Standard in Action

ERIC Educational Resources Information Center

Hirsh, Stephanie; Hord, Shirley

2012-01-01

This article is an excerpt from "A Playbook for Professional Learning: Putting the Standards Into Action" (Learning Forward, 2012). Written by Learning Forward Executive Director Stephanie Hirsh and Scholar Laureate Shirley Hord, "A Playbook for Professional Learning" provides those who work in professional learning with readily accessible…

Modelling in Action. Examining How Students Approach Modelling Real Life Situations. Three Case Studies. Model of the Movement of an Elevator

ERIC Educational Resources Information Center

Rivas, Eugenia Marmolejo

2015-01-01

By means of three case studies, we will present two mathematical modelling activities that are suitable for students enrolled in senior high school and the first year of mathematics at university level. The activities have been designed to enrich the learning process and promote the formation of vital modelling skills. In case studies one and two,…

Modeling reality

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1990-01-01

Although powerful computers have allowed complex physical and manmade hardware systems to be modeled successfully, we have encountered persistent problems with the reliability of computer models for systems involving human learning, human action, and human organizations. This is not a misfortune; unlike physical and manmade systems, human systems do not operate under a fixed set of laws. The rules governing the actions allowable in the system can be changed without warning at any moment, and can evolve over time. That the governing laws are inherently unpredictable raises serious questions about the reliability of models when applied to human situations. In these domains, computers are better used, not for prediction and planning, but for aiding humans. Examples are systems that help humans speculate about possible futures, offer advice about possible actions in a domain, systems that gather information from the networks, and systems that track and support work flows in organizations.

Toward a dual-learning systems model of speech category learning

PubMed Central

Chandrasekaran, Bharath; Koslov, Seth R.; Maddox, W. T.

2014-01-01

More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions. PMID:25132827

The Psychodrama-Social Dramatics Separation.

ERIC Educational Resources Information Center

Klepac, Richard L.

Social dramatics is a therapeutic and educational program that can act as a mirror to reflect images of the self in action with others. It is the modality for experiential learning to correct social dysfunction by providing models for imitation, opportunities to practice and develop individual forms from that model, and risk free environments for…