Information processing, computation, and cognition

PubMed Central

Scarantino, Andrea

2010-01-01

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism, connectionism, and computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects. PMID:22210958

Cognition and asynchronous distribution between human and machine building accidents.

PubMed

Martins, Edgard; Soares, Marcelo; Augusto, Lia; Laura, Laura

2012-01-01

The creation of meaning in communication is a trading activity, resulting from the construction that is born of the interaction between subjects. That is, the meaning is not inherent to the relationship between words, signs and symbols that arise from negotiating a necessary and unavoidable. As the concepts of sense as discrete and static representations imply a notion of classical computing and design of a cognitive system corresponding conceptions of meaning construction as located and shared among agents implies notions of different computing and cognition. Several efforts have been developed to meet these demands. Among them are the Connectionism (also known as neural networks. Records on aspects of mental health and stress of flight professionals are present in the official reports of the organs of investigation of aviation accidents worldwide since its inception. Problems related to health physical and mental health of pilots (fatigue, stress, physiological and psychosocial problems) account for 19% of causal factors in aircraft accidents. The training seems a paradox when we know that these professionals receive regular training, have high education and technical training of high level. However, problems arise related to the implementation of learning that can be influenced to reduce their cognitive capacity, making it in practice, relatively unable to exercise its functions effectively and safely.

Categorial Compositionality: A Category Theory Explanation for the Systematicity of Human Cognition

PubMed Central

Phillips, Steven; Wilson, William H.

2010-01-01

Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe—replaced by the relationships between the maps that transform them. PMID:20661306

Categorial compositionality: a category theory explanation for the systematicity of human cognition.

PubMed

Phillips, Steven; Wilson, William H

2010-07-22

Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe--replaced by the relationships between the maps that transform them.

AI techniques in geomagnetic storm forecasting

NASA Astrophysics Data System (ADS)

Lundstedt, Henrik

This review deals with how geomagnetic storms can be predicted with the use of Artificial Intelligence (AI) techniques. Today many different Al techniques have been developed, such as symbolic systems (expert and fuzzy systems) and connectionism systems (neural networks). Even integrations of AI techniques exist, so called Intelligent Hybrid Systems (IHS). These systems are capable of learning the mathematical functions underlying the operation of non-linear dynamic systems and also to explain the knowledge they have learned. Very few such powerful systems exist at present. Two such examples are the Magnetospheric Specification Forecast Model of Rice University and the Lund Space Weather Model of Lund University. Various attempts to predict geomagnetic storms on long to short-term are reviewed in this article. Predictions of a month to days ahead most often use solar data as input. The first SOHO data are now available. Due to the high temporal and spatial resolution new solar physics have been revealed. These SOHO data might lead to a breakthrough in these predictions. Predictions hours ahead and shorter rely on real-time solar wind data. WIND gives us real-time data for only part of the day. However, with the launch of the ACE spacecraft in 1997, real-time data during 24 hours will be available. That might lead to the second breakthrough for predictions of geomagnetic storms.

High level cognitive information processing in neural networks

NASA Technical Reports Server (NTRS)

Barnden, John A.; Fields, Christopher A.

1992-01-01

Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

Emergent latent symbol systems in recurrent neural networks

NASA Astrophysics Data System (ADS)

Monner, Derek; Reggia, James A.

2012-12-01

Fodor and Pylyshyn [(1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2), 3-71] famously argued that neural networks cannot behave systematically short of implementing a combinatorial symbol system. A recent response from Frank et al. [(2009). Connectionist semantic systematicity. Cognition, 110(3), 358-379] claimed to have trained a neural network to behave systematically without implementing a symbol system and without any in-built predisposition towards combinatorial representations. We believe systems like theirs may in fact implement a symbol system on a deeper and more interesting level: one where the symbols are latent - not visible at the level of network structure. In order to illustrate this possibility, we demonstrate our own recurrent neural network that learns to understand sentence-level language in terms of a scene. We demonstrate our model's learned understanding by testing it on novel sentences and scenes. By paring down our model into an architecturally minimal version, we demonstrate how it supports combinatorial computation over distributed representations by using the associative memory operations of Vector Symbolic Architectures. Knowledge of the model's memory scheme gives us tools to explain its errors and construct superior future models. We show how the model designs and manipulates a latent symbol system in which the combinatorial symbols are patterns of activation distributed across the layers of a neural network, instantiating a hybrid of classical symbolic and connectionist representations that combines advantages of both.

[Conceptual Development in Cognitive Science. Part II].

PubMed

Fierro, Marco

2012-03-01

Cognitive science has become the most influential paradigm on mental health in the late 20(th) and the early 21(st) centuries. In few years, the concepts, problem approaches and solutions proper to this science have significantly changed. Introduction and discussion of the fundamental concepts of cognitive science divided in four stages: Start, Classic Cognitivism, Connectionism, and Embodying / Enacting. The 2(nd) Part of the paper discusses the above mentioned fourth stage and explores the clinical setting, especially in terms of cognitive psychotherapy. The embodying/enacting stage highlights the role of the body including a set of determined evolutionary movements which provide a way of thinking and exploring the world. The performance of cognitive tasks is considered as a process that uses environmental resources that enhances mental skills and deploys them beyond the domestic sphere of the brain. On the other hand, body and mind are embedded in the world, thus giving rise to cognition when interacting, a process known as enacting. There is a close connection between perception and action, hence the interest in real-time interactions with the world rather than abstract reasoning. Regarding clinics, specifically the cognitive therapy, there is little conceptual discussion maybe due to good results from practice that may led us to consider that theoretical foundations are firm and not problem-raising. Copyright © 2012 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

The molecular turn in psychiatry: a philosophical analysis.

PubMed

Rudnick, Abraham

2002-06-01

Biological psychiatry has been dominated by a psychopharmacologically-driven neurotransmitter dysfunction paradigm. The objective of this paper is to explore a reductionist assumption underlying this paradigm, and to suggest an improvement on it. The methods used are conceptual analysis with a comparative approach, particularly using illustrations from the history of both biological psychiatry and molecular biology. The results are that complete reduction to physicochemical explanations is not fruitful, at least in the initial stages of research in the medical and life sciences, and that an appropriate (non-reducible) integrative principle--addressing a property of the whole system under study--is required for each domain of research. This is illustrated in Pauling's use of a topological integrative principle for the discovery of the functioning of proteins and in Watson and Crick's use of the notion of a genetic code as an integrative principle for the discovery of the structure of genes. The neurotransmitter dysfunction paradigm addresses single molecules and their neural pathways, yet their interactions within the CNS as a whole seem most pertinent to mental disorders such as schizophrenia. The lack within biological psychiatry of an integrative principle addressing a property of the CNS as a whole may be responsible for the empirical failure of orthomolecular psychiatry, as well as for the central role that serendipity has played in the study of mental disorders, which is dominated by the neurotransmitter paradigm. The conclusion is that research in biological psychiatry may benefit from using, at least initially, some integrative principle(s) addressing a property of the CNS as a whole, such as connectionism or a hierarchical notion.

Template-based procedures for neural network interpretation.

PubMed

Alexander, J A.; Mozer, M C.

1999-04-01

Although neural networks often achieve impressive learning and generalization performance, their internal workings are typically all but impossible to decipher. This characteristic of the networks, their opacity, is one of the disadvantages of connectionism compared to more traditional, rule-oriented approaches to artificial intelligence. Without a thorough understanding of the network behavior, confidence in a system's results is lowered, and the transfer of learned knowledge to other processing systems - including humans - is precluded. Methods that address the opacity problem by casting network weights in symbolic terms are commonly referred to as rule extraction techniques. This work describes a principled approach to symbolic rule extraction from standard multilayer feedforward networks based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressions. With an appropriate choice of representation, we show how template parameters may be efficiently identified and instantiated to yield the optimal match to the actual weights of a unit. Depending on the requirements of the application domain, the approach can accommodate n-ary disjunctions and conjunctions with O(k) complexity, simple n-of-m expressions with O(k(2)) complexity, or more general classes of recursive n-of-m expressions with O(k(L+2)) complexity, where k is the number of inputs to an unit and L the recursion level of the expression class. Compared to other approaches in the literature, our method of rule extraction offers benefits in simplicity, computational performance, and overall flexibility. Simulation results on a variety of problems demonstrate the application of our procedures as well as the strengths and the weaknesses of our general approach.

On the necessity of U-shaped learning.

PubMed

Carlucci, Lorenzo; Case, John

2013-01-01

A U-shaped curve in a cognitive-developmental trajectory refers to a three-step process: good performance followed by bad performance followed by good performance once again. U-shaped curves have been observed in a wide variety of cognitive-developmental and learning contexts. U-shaped learning seems to contradict the idea that learning is a monotonic, cumulative process and thus constitutes a challenge for competing theories of cognitive development and learning. U-shaped behavior in language learning (in particular in learning English past tense) has become a central topic in the Cognitive Science debate about learning models. Antagonist models (e.g., connectionism versus nativism) are often judged on their ability of modeling or accounting for U-shaped behavior. The prior literature is mostly occupied with explaining how U-shaped behavior occurs. Instead, we are interested in the necessity of this kind of apparently inefficient strategy. We present and discuss a body of results in the abstract mathematical setting of (extensions of) Gold-style computational learning theory addressing a mathematically precise version of the following question: Are there learning tasks that require U-shaped behavior? All notions considered are learning in the limit from positive data. We present results about the necessity of U-shaped learning in classical models of learning as well as in models with bounds on the memory of the learner. The pattern emerges that, for parameterized, cognitively relevant learning criteria, beyond very few initial parameter values, U-shapes are necessary for full learning power! We discuss the possible relevance of the above results for the Cognitive Science debate about learning models as well as directions for future research. Copyright © 2013 Cognitive Science Society, Inc.

[The current mind-brain theories in analytical philosophy of mind and their epistemic significance for psychiatry].

PubMed

Schäfer, M L

2005-03-01

This article begins with an orientational survey of the historical evolution of analytical philosophy of mind (APM) which was formulated in the last 40 years as "philosophy of mind" in the Anglo-Saxon scientific-cultural world and which, in the meantime, dominates to a great extent contemporary German philosophy. Then there follows a discussion of the currently most popular mind-brain theories in philosophy. In comparison to the more marginal dualist variants (interactionism, epiphenomenalism, parallelism), it is mainly the monistic positions of non-reductive, reductive and eliminative materialism and the materialist functionalism underlying it, which determines analytical philosophy of mind and its influence on psychopathology and psychiatry. Under the additional influence of modern brain research methods, particularly neuroimaging, it is progressively developing into a subdiscipline of neuroscience, a complex and increasingly more firmly established scientific discipline which comprises the totality of all sciences dealing with neuronal functions, including the close epistemic associations of APM and neuroimaging. This is the effective epistemic central idea determining the theory of the neuronal network which, in the form of a connectionist psychopathology, is intended to make possible a fundamentally new access to the comprehension of psychiatric forms of illness. In this respect it is evident, however, that the perception of the naturality of the mind as the fundamental thesis of APM and thus of connectionism cannot be followed through, since, up to now, neither from the phenomenality of the mind (especially the quality of senses, "Qualia") nor from intentionality of the mind (i. e. the ability to act intentionally, free from the constraint of the causality of nature and thus in self-responsible fashion) has proved it possible to reconstruct a generally accepted naturalist theory. Furthermore, it has not been possible to reformulate it in an exclusively physical, i. e. non-phenomenological concept and terminology which is, above all, free from the intentionality idiom. The consequence of this, however, is that a connectionist psychopathology can only represent a subpersonal, i. e., subhuman area and that in order to establish a personal psychopathology, naturalistic unreduced theories of experience-qualities and intentional acts of completeness are absolutely essential. The neuroscientific-connectionist paradigm of psychopathology must therefore - at least for the present - be supplement by the paradigm of a non-natural (e. g. phenomenological-hermeneutic) psychopathology. This result can only encourage the relinquishing of epistemically one-sided materialist and other monistic mind-brain theories of APM in favour of an epistemically open pragmatic interactionist dualism as the scientific position which best represents the current state of knowledge.