A distinction between the use of computational models in cognitive science and a philosophically inspired reductivist thesis is developed. PF is found questionable for phenomenal states, and, by analogy, dubious for the nonphenomenal introspectible mental states of common sense. PF is also shown to be threatened for the sub-cognitive theoretical states of cognitive science by the work of the so-called New Connectionists. CMM is shown to be less vulnerable to these criticisms.

Computational models of emotions have been thriving and increasingly popular since the 1990s. Such models used to be concerned with the emotions of individual agents when they interact with other agents. Out of the array of models for the emotions, we are going to devote special attention to the approach in Adamatzky’s Dynamics of Crowd-Minds. The reason it stands out, is that it considers the crowd, rather than the individual agent. It fits in computational intelligence. It works by mathematical simulation (...) on a crowd of simple artificial agents: by letting the computer program run, the agents evolve, and crowd behaviour emerges. Adamatzky’s purpose is to give an account of the emergence of allegedly “irrational” behaviour. This is not without problem, as the irrational to one person may seem entirely rational to another, and this in turn is an insight that, in the history of crowd psychology, has affected indeed the competition among theories of crowd dynamics. Quite importantly, Adamatzky’s book argues for the transition from individual agencies to a crowd’s or a mob’s coalesced mind as so, and at any rate for coalesced crowd’s agency. (shrink)

An important, but relatively neglected, aspect of human theory of mind is emotion inference: understanding how and why a person feels a certain why is central to reasoning about their beliefs, desires and plans. The authors review recent work that has begun to unveil the structure and determinants of emotion inference, organizing them within a unified probabilistic framework.

The purpose of this paper is to describe some limitations on scientific behaviorist and computational models of the mind. These limitations stem from the inability of either model to account for the integration of experience and behavior. Behaviorism fails to give an adequate account of felt experience, whereas the computational model cannot account for the integration of our behavior with the world. Both approaches attempt to deal with their limitations by denying that the domain outside their limits (...) is a part of psychology. These attempts to turn the shortcomings of the two models into virtues would be more convincing if their limitations were not diametrically opposed. I will argue that in each case the limitations are too restrictive unless the theories are augmented by physiology. (shrink)

I give a defense of the Massive Modularity hypothesis: the view that the mind is composed of discrete, encapsulated, informationally isolated computational structures dedicated to particular problem domains. This view contrasts with Psychological Rationalism: the view that mental structures take the form of unencapsulated representational items, all available as inputs to one domain-general computational processor. I argue that although Psychological Rationalism is in principle able to overcome the `intractability objection', the view must borrow many features of a massively modular (...) architecture in order to do so, that although it can, in principle, overcome the `optimality objection', the way it does so does not correlate with the way we think, and that although it can, in principle, respond to the `argument from biology', it cannot do so without advancing an unrealistic and unsupported account of cognitive evolution. (shrink)

How do we ensure that future generally intelligent AI share our values? This is the value-alignment problem. It is a weighty matter. After all, if AI are neutral with respect to our wellbeing, or worse, actively hostile toward us, then they pose an existential threat to humanity. Some philosophers have argued that one important way in which we can mitigate this threat is to develop only AI that shares our values or that has values that ‘align with’ ours. However, there (...) is nothing to guarantee that this policy will be universally implemented—in particular, ‘bad actors’ are likely to flout it. In this paper, I show how the predictive processing model of the mind, currently ascendant in cognitive science, may ameliorate the value-alignment problem. In essence, I argue that there are a plurality of reasons why any future generally intelligent AI will possess a predictive processing cognitive architecture (e.g. because we decide to build them that way; because it is the only possible cognitive architecture that can underpin general intelligence; because it is the easiest way to create AI.). I also argue that if future generally intelligent AI possess a predictive processing cognitive architecture, then they will come to share our pro-moral motivations (of valuing humanity as an end; avoiding maleficent actions; etc.), regardless of their initial motivation set. Consequently, these AI will pose a minimal threat to humanity. In this way then, I conclude, the value-alignment problem is significantly ameliorated under the assumption that future generally intelligent AI will possess a predictive processing cognitive architecture. (shrink)

Computational models of memory presented in this issue reflect varied empirical data and levels of representation. From mathematical models to neural and cognitive architectures, all aim to converge on a unified theory of the mind.

This author explores the intersection between cognitive science, as exemplified by the computational model of mind, and epistemologyó specifically, epistemic justification theory. Her analysis leads to the conclusion that some very specific and somewhat technical issues in epistemic justification theory can be at least partially resolved, if not entirely cleared up, by the use of the computational model. The third and fourth chapters of this work are devoted directly to that effort. Chapter one examines in detail epistemology (...) and cognitive sciences, while chapters two and three offer a thorough introduction to standard epistemic justification theory. Finally, chapter five is a critique of the computational model. (shrink)

Any analysis of the concept of computation as it occurs in the context of a discussion of the computational model of the mind must be consonant with the philosophic burden traditionally carried by that concept as providing a bridge between a physical and a psychological description of an agent. With this analysis in hand, one may ask the question: are connectionist-based systems consistent with the computational model of the mind? The answer depends upon which of several (...) versions of connectionism one presupposes: non-learning connectionist-based systems as simulated on digital computers are consistent with the computational model of the mind, whereas connectionist-based systems (/dynamical systems) qua analog systems are not. (shrink)

Through the concept of self-organizing consciousness (SOC), we posit that the dynamic of the mind stems from the recurrent interplay between the properties of conscious experiences and the properties of the world, hence making it unnecessary to postulate the existence of an unconscious mental level. In contrast, arguments are provided by commentators for the need for a functional level of organization located between the neural and the conscious. Other commentaries challenge us concerning the ability of our model to (...) account for specific phenomena in the domains of language, reasoning, incubation, and creativity. The possibility of unconscious semantic access and other alleged instances of adapted performance in the absence of any conscious counterpart are also put forth as evidence against our view. Our response emphasizes the fact that opponents to our model often present as factual, theory-free evidence which is in fact nothing more than the postulates underlying the classical computational framework. (shrink)

The paper describes a computational model that we have implemented in an experimental dialogue system. Communication in a natural language between two participants A and B is considered, where A has a communicative goal that his/her partner B will make a decision to perform an action D. A argues the usefulness, pleasantness, etc. of D, in order to guide B's reasoning in a desirable direction. A computational model of argumentation is developed, which includes reasoning. Our model is (...) based on the studies in the common-sense conception of how the human mind works in such situations. Theoretical considerations are followed by an analysis of Estonian spoken human–human dialogues. First, calls of clients to travel agencies are studied where a travel agent could use various arguments in order to persuade a client to book a trip. The analysis demonstrates that clients are primarily looking for information; argumentation occurs in a small number of dialogues. Secondly, calls of s... (shrink)

Is your mind the same thing as your brain, or are there aspects of mind beyond the brain's biology? This is the mind-body problem, and it has captivated curious minds since the dawn of human contemplation. Today many insist that the mind is completely reducible to the brain. But is that claim justified? In this stimulating anthology, twenty-five philosophers and scientists offer fresh insights into the mind-brain debate, drawing on psychology, neurology, philosophy, computer science, (...) and neurosurgery. Their provocative conclusion? The mind is indeed more than the brain. (shrink)

The aim of this paper is to address the question: Can an artificial neural network (ANN) model be used as a possible characterization of the power of the human mind? We will discuss what might be the relationship between such a model and its natural counterpart. A possible characterization of the different power capabilities of the mind is suggested in terms of the information contained (in its computational complexity) or achievable by it. Such characterization takes advantage (...) of recent results based on natural neural networks (NNN) and the computational power of arbitrary artificial neural networks (ANN). The possible acceptance of neural networks as the model of the human mind's operation makes the aforementioned quite relevant. (shrink)

In this paper, I argue for a modified version of what Devitt calls the Representational Thesis. According to RT, syntactic rules or principles are psychologically real, in the sense that they are represented in the mind/brain of every linguistically competent speaker/hearer. I present a range of behavioral and neurophysiological evidence for the claim that the human sentence processing mechanism constructs mental representations of the syntactic properties of linguistic stimuli. I then survey a range of psychologically plausible computational models of (...) comprehension and show that they are all committed to RT. I go on to sketch a framework for thinking about the nature of the representations involved in sentence processing. My claim is that these are best characterized not as propositional attitudes but, rather, as subpersonal states whose representational properties are determined by their functional role. Finally, I distinguish between explicit and implicit representations and argue that the latter can be drawn on as data by the algorithms that constitute our sentence processing routines. I conclude that skepticism concerning the psychological reality of grammars cannot be sustained. (shrink)

Recent years have witnessed rapidly increasing interests in developing quantum theoretical models of human cognition. Quantum mechanisms have been taken seriously to describe how the mind reasons and decides. Papers in this special issue report the newest results in the field. Here we discuss why the two levels of commitment, treating the human brain as a quantum computer and merely adopting abstract quantum probability principles to model human cognition, should be integrated. We speculate that quantum cognition models (...) gain greater modeling power due to a richer representation scheme. (shrink)

Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to assist us in (...) understanding emergent phenomena. (shrink)

This research aims to clarify, by constructing and testing a computer simulation, the use of multiple representations in problem solving, focusing on their role in visual reasoning. The model is motivated by extensive experimental evidence in the literature for the features it incorporates, but this article focuses on the system's structure. We illustrate the model's behavior by simulating the cognitive and perceptual processes of an economics expert as he teaches some well‐learned economics principles while drawing a graph (...) on a blackboard. Data in the experimental literature and concurrent verbal protocols were used to guide construction of a linked production system and parallel network, CaMeRa (Computation with Multiple Representations), that employs a “Mind's Eye” representation for pictorial information, consisting of a bitmap and associated node‐link structures. Propositional list structures are used to represent verbal information and reasoning. Small individual pieces from the different representations are linked on a sequential and temporary basis to form a reasoning and inferencing chain, using visually encoded information recalled to the Mind's Eye from long‐term memory and from cues recognized on an external display. CaMeRa, like the expert, uses the diagrammatic and verbal representations to complement one another, thus exploiting the unique advantages of each. (shrink)

In the early years of the 1990s, a number of philosophers and cognitive scientists became enthused about the idea that mental states are spatially and temporally distributed in the brain, and that this has significant consequences for philosophy of mind. Daniel Dennett (1991), for example, appealed to the spatial and temporal distribution of cognitive processes in the brain in order to argue that there is no unified place where or time when consciousness occurs in the brain. Dennett used this (...) interim conclusion as a premise in a series of arguments designed to undermine what he calls the “Cartesian Theater” view of consciousness, according to which there is an independent and fact of the matter about when and where conscious mental states occur. Most famously, Dennett argues that anyone who believes there are such facts of the matter must choose between “Orwellian” and “Stalinesque” theories of the timing of consciousness, in order to accommodate certain data about the spatial distribution of the timing of consciousness-related brain events. But, he claims, that distinction is a distinction without a difference: The choice between “Orwellian” and “Stalinesque” views is either nonsense, or at the very least one that we could never be justified in making. And this, Dennett takes it, is a reductio ad absurdum of any theory of consciousness that says there is a fact of the matter about when and where is occurs. (shrink)

In his famous 1982 paper, Allen Newell [22, 23] introduced the notion of knowledge level to indicate a level of analysis, and prediction, of the rational behavior of a cognitive arti cial agent. This analysis concerns the investigation about the availability of the agent knowledge, in order to pursue its own goals, and is based on the so-called Rationality Principle (an assumption according to which "an agent will use the knowledge it has of its environment to achieve its goals" [22, (...) p. 17]. By using the Newell's own words: "To treat a system at the knowledge level is to treat it as having some knowledge, some goals, and believing it will do whatever is within its power to attain its goals, in so far as its knowledge indicates" [22, p. 13]. In the last decades, the importance of the knowledge level has been historically and system- atically downsized by the research area in cognitive architectures (CAs), whose interests have been mainly focused on the analysis and the development of mechanisms and the processes governing human and (arti cial) cognition. The knowledge level in CAs, however, represents a crucial level of analysis for the development of such arti cial general systems and therefore deserves greater research attention [17]. In the following, we will discuss areas of broad agree- ment and outline the main problematic aspects that should be faced within a Common Model of Cognition [12]. Such aspects, departing from an analysis at the knowledge level, also clearly impact both lower (e.g. representational) and higher (e.g. social) levels. (shrink)

Computational approaches dominate contemporary cognitive science, promising a unified, scientific explanation of how the mind works. However, computational approaches raise major philosophical and scientific questions. In what sense is the mind computational? How do computational approaches explain perception, learning, and decision making? What kinds of challenges should computational approaches overcome to advance our understanding of mind, brain, and behaviour? The Routledge Handbook of the Computational Mind is an outstanding overview and exploration of these issues and the (...) first philosophical collection of its kind. Comprising thirty-five chapters by an international team of contributors from different disciplines, the Handbook is organised into four parts: History and future prospects of computational approaches Types of computational approach Foundations and challenges of computational approaches Applications to specific parts of psychology. Essential reading for students and researchers in philosophy of mind, philosophy of psychology, and philosophy of science, The Routledge Handbook of the Computational Mind will also be of interest to those studying computational models in related subjects such as psychology, neuroscience, and computer science. (shrink)

The phenomenon of consciousness has always been a central question for philosophers and scientists. Emerging in the past decade are new approaches to the understanding of consciousness in a scientific light. This book presents a series of essays by leading thinkers giving an account of the current ideas prevalent in the scientific study of consciousness. The value of the book lies in the discussion of this interesting though complex subject from different points of view ranging from physics, computer science (...) to the cognitive sciences. Reviews of controversial ideas related to the philosophy of mind from western and eastern sources including classical Indian first person methodologies provide a breadth of coverage that has seldom been attempted in a book before. Additionally, chapters relating to the new approaches in computational modelling of higher order cognitive function and consciousness are included. The book is of great value for established as well as young researchers from a wide cross-section of interdisciplinary scientific backgrounds, aiming to pursue research in this field, as well as an informed public. * Presents the latest developments in the scientific study of consciousness * Critically reviews different theoretical and philosophical explanations related to the subject * An important book for both students and researchers in designing research projects on consciousness. (shrink)

What is the mind? How does it work? How does it influence behavior? Some psychologists hope to answer such questions in terms of concepts drawn from computer science and artificial intelligence. They test their theories by modeling mental processes in computers. This book shows how computer models are used to study many psychological phenomena--including vision, language, reasoning, and learning. It also shows that computer modeling involves differing theoretical approaches. Computational psychologists disagree about some basic questions. For (...) instance, should the mind be modeled by digital computers, or by parallel-processing systems more like brains? Do computer programs consist of meaningless patterns, or do they embody (and explain) genuine meaning? (shrink)

Van Gelder (1995) has recently spearheaded a movement to challenge the dominance of connectionist and classicist models in cognitive science. The dynamical conception of cognition is van Gelder's replacement for the computation bound paradigms provided by connectionism and classicism. He relies on the Watt governor to fulfill the role of a dynamicist Turing machine and claims that the Motivational Oscillatory Theory (MOT) provides a sound empirical basis for dynamicism. In other words, the Watt governor is to be the theoretical exemplar (...) of the class of systems necessary for cognition and MOT is an empirical instantiation of that class. However, I shall argue that neither the Watt governor nor MOT successfully fulfill these prescribed roles. This failure, along with van Gelder's peculiar use of the concept of computation and his struggle with representationalism, prevent him from providing a convincing alternative to current cognitive theories. (shrink)

This paper proposes a model for an artificial autonomous moral agent (AAMA), which is parsimonious in its ontology and minimal in its ethical assumptions. Starting from a set of moral data, this AAMA is able to learn and develop a form of moral competency. It resembles an “optimizing predictive mind,” which uses moral data (describing typical behavior of humans) and a set of dispositional traits to learn how to classify different actions (given a given background knowledge) as morally (...) right, wrong, or neutral. When confronted with a new situation, this AAMA is supposedly able to predict a behavior consistent with the training set. This paper argues that a promising computational tool that fits our model is “neuroevolution,” i.e. evolving artificial neural networks. (shrink)

Penrose proved that a computational or formalizable theory of the brainís cognitive functioning is impossible, but suggested that a physical non-computational and non-formalizable one may be viable. Arguments as to why Penroseís program is unrealizable are presented. The main argument is that a non-formalizable theory should be verbal. However, verbal paradoxes based on Cantorís diagonal processes show the impossibility of a consistent verbal theory of the brain comprising its arithmetical cognition. It is suggested that comprehensive theories of the human brain (...) and of physical experience are Kantian rather than Platonic ideas. This suggestion is likewise based on arguments related to diagonal processes. (shrink)

This collection by a distinguished group of philosophers, psychologists, and physiologists reflects an interdisciplinary approach to the central question of cognitive science: how do we model the mind? Among the topics explored are the relationships (theoretical, reductive, and explanatory) between philosophy, psychology, computer science, and physiology; what should be asked of models in science generally, and in cognitive science in particular; whether theoretical models must make essential reference to objects in the environment; whether there are human competences (...) that are resistant, in principle, to modelling; whether simulated thinking and intentionality are really thinking and intentionality; how semantics can be generated from syntactics; the meaning of the terms "representations" and "modelling;" whether the nature of the "hardware" matters; and whether computer models of humans are "dehumanizing." Contributors include Donald Davidson, Daniel C. Dennett, Margaret A. Boden, Adam Morton, Dennis Noble, T. Poggio, Colin Blakemore, K.V. Wilkes, P.N. Johnson-Laird, and Jonathan St. B.T. Evans. (shrink)

This paper tries to express a critical point of view on the computational turn in philosophy by looking at a specific field of study: philosophy of science. The paper starts by briefly discussing the main contributions that information and communication technologies have given to the rising of computational philosophy of science, and in particular to the cognitive modelling approach. The main question then arises, concerning how computational models can cope with the presence of tacit knowledge in science. Would it be (...) possible to develop new ways of handling this specific type of knowledge, in order to incorporate it in computational models of scientific thinking? Or should tacit knowledge lead us to other approaches in using computer sciences to model scientific cognition? These questions are addressed by making reference to a detailed case study of a recent innovation development in the field of biotechnology. (shrink)

We offer philosophical motivations for a method we call Virtual World Cognitive Science (VW CogSci), in which researchers use virtual embodied agents that are embedded in virtual worlds to explore questions in the field of Cognitive Science. We focus on questions about mental and linguistic representation and the ways that such computational modeling can add rigor to philosophical thought experiments, as well as the terminology used in the scientific study of such representations. We find that this method forces researchers to (...) take a god’s-eye view when describing dynamical relationships between entities in minds and entities in an environment in a way that eliminates the need for problematic talk of belief and concept types, such as the belief that cats are silly, and the concept CAT, while preserving belief and concept tokens in individual cognizers’ minds. We conclude with some further key advantages of VW CogSci for the scientific study of mental and linguistic representation and for Cognitive Science more broadly. (shrink)

Extract from Hofstadter's revew in Bulletin of American Mathematical Society : http://www.ams.org/journals/bull/1980-02-02/S0273-0979-1980-14752-7/S0273-0979-1980-14752-7.pdf -/- "Aaron Sloman is a man who is convinced that most philosophers and many other students of mind are in dire need of being convinced that there has been a revolution in that field happening right under their noses, and that they had better quickly inform themselves. The revolution is called "Artificial Intelligence" (Al)-and Sloman attempts to impart to others the "enlighten- ment" which he clearly regrets not (...) having experienced earlier himself. Being somewhat of a convert, Sloman is a zealous campaigner for his point of view. Now a Reader in Cognitive Science at Sussex, he began his academic career in more orthodox philosophy and, by exposure to linguistics and AI, came to feel that all approaches to mind which ignore AI are missing the boat. I agree with him, and I am glad that he has written this provocative book. The tone of Sloman's book can be gotten across by this quotation (p. 5): "I am prepared to go so far as to say that within a few years, if there remain any philosophers who are not familiar with some of the main developments in artificial intelligence, it will be fair to accuse them of professional incom- petence, and that to teach courses in philosophy of mind, epistemology, aesthetics, philosophy of science, philosophy of language, ethics, metaphysics, and other main areas of philosophy, without discussing the relevant aspects of artificial intelligence will be as irresponsible as giving a degree course in physics which includes no quantum theory." -/- (The author now regrets the extreme polemical tone of the book.). (shrink)

This paper reports a computer program to generate novel designs for the arrangement of furniture within a simulated room. It is based on the engagement-reflection computer model of the creative processes. During engagement the system generates material in the form of sequences of actions (e.g. change the colours of the walls, include some furniture in the room, modify their colour, and so on) guided by content and knowledge constraints. During reflection, the system evaluates the composition produced so (...) far and, if it is necessary, modifies it. We discuss the implementation of the system and some of its most salient features, especially the use of a computational model for creativity in the terrain of design. We argue that this kind of model opens new possibilities for the simulation of the design processes as well as the development of tools. (shrink)

The increased interactivity and connectivity of computational devices along with the spreading of computational tools and computational thinking across the fields, has changed our understanding of the nature of computing. In the course of this development computing models have been extended from the initial abstract symbol manipulating mechanisms of stand-alone, discrete sequential machines, to the models of natural computing in the physical world, generally concurrent asynchronous processes capable of modelling living systems, their informational structures and dynamics on both symbolic and (...) sub-symbolic information processing levels. Present account of models of computation highlights several topics of importance for the development of new understanding of computing and its role: natural computation and the relationship between the model and physical implementation, interactivity as fundamental for computational modelling of concurrent information processing systems such as living organisms and their networks, and the new developments in logic needed to support this generalized framework. Computing understood as information processing is closely related to natural sciences; it helps us recognize connections between sciences, and provides a unified approach for modeling and simulating of both living and non-living systems. (shrink)

An underlying assumption in computational approaches in cognitive and brain sciences is that the nervous system is an input–output model of the world: Its input–output functions mirror certain relations in the target domains. I argue that the input–output modelling assumption plays distinct methodological and explanatory roles. Methodologically, input–output modelling serves to discover the computed function from environmental cues. Explanatorily, input–output modelling serves to account for the appropriateness of the computed function to the explanandum information-processing task. I compare very briefly (...) the modelling explanation to mechanistic and optimality explanations, noting that in both cases the explanations can be seen as complementary rather than contrastive or competing. (shrink)

The computational theory of mind construes the mind as an information-processor and cognitive capacities as essentially representational capacities. Proponents of the view claim a central role for representational content in computational models of these capacities. In this paper I argue that the standard view of the role of representational content in computational models is mistaken; I argue that representational content is to be understood as a gloss on the computational characterization of a cognitive process.Keywords: Computation; Representational content; Cognitive (...) capacities; Explanation. (shrink)

In this essay I analyse Wittgenstein’s criticism of several assumptions that are crucial for a large part of cognitive science. These involve the concepts of computational processes in the brain which cause mental states and processes, the algorithmic processing of information in the brain , the brain as a machine, psychophysical parallelism, the thinking machine, as well as the confusion of rule following with behaviour in accordance with the rule. In my opinion, the theorists of cognitive science have not yet (...) seriously considered Wittgenstein’s criticism so they, quite surprisingly, frequently confuse the question “how does it work?” with “what does it do?” But their most “deleterious” mistake is their confusion of the internal computational processes taking place in the brain with socially-based, everyday criteria of recognition and classification of, and knowledge about, the content of mental states. (shrink)

We don't yet have adequate theories of what the human mind is representing when it represents a social group. Worse still, many people think we do. This mistaken belief is a consequence of the state of play: Until now, researchers have relied on their own intuitions to link up the conceptsocial groupon the one hand and the results of particular studies or models on the other. While necessary, this reliance on intuition has been purchased at a considerable cost. When (...) looked at soberly, existing theories of social groups are either (i) literal, but not remotely adequate (such as models built atop economic games), or (ii) simply metaphorical (typically a subsumption or containment metaphor). Intuition is filling in the gaps of an explicit theory. This paper presents a computational theory of what, literally, a group representation is in the context of conflict: It is the assignment of agents to specific roles within a small number of triadic interaction types. This “mental definition” of a group paves the way for acomputational theory of social groups– in that it provides a theory of what exactly the information-processing problem of representing and reasoning about a group is. For psychologists, this paper offers a different way to conceptualize and study groups, and suggests that a non-tautological definition of a social group is possible. For cognitive scientists, this paper provides a computational benchmark against which natural and artificial intelligences can be held. (shrink)

Over the past thirty years, it is been common to hear the mind likened to a digital computer. This essay is concerned with a particular philosophical view that holds that the mind literally is a digital computer (in a specific sense of “computer” to be developed), and that thought literally is a kind of computation. This view—which will be called the “Computational Theory of Mind” (CTM)—is thus to be distinguished from other and broader attempts (...) to connect the mind with computation, including (a) various enterprises at modeling features of the mind using computational modeling techniques, and (b) employing some feature or features of production-model computers (such as the stored program concept, or the distinction between hardware and software) merely as a guiding metaphor for understanding some feature of the mind. This entry is therefore concerned solely with the Computational Theory of Mind (CTM) proposed by Hilary Putnam [1961] and developed most notably for philosophers by Jerry Fodor [1975, 1980, 1987, 1993]. The senses of ‘computer’ and ‘computation’ employed here are technical; the main tasks of this entry will therefore be to elucidate: (a) the technical sense of ‘computation’ that is at issue, (b) the ways in which it is claimed to be applicable to the mind, (c) the philosophical problems this understanding of the mind is claimed to solve, and (d) the major criticisms that have accrued to this view. (shrink)

A review of Manfred Spitzer's The mind within the net: Models of learning, thinking, and acting.

The nature of complex concepts has important implications for the computational modelling of the mind, as well as for the cognitive science of concepts. This paper outlines the way in which RVC – a Relational View of Concepts – accommodates a range of complex concepts, cases which have been argued to be non-compositional. RVC attempts to integrate a number of psychological, linguistic and psycholinguistic considerations with the situation-theoretic view that information-carrying relations hold only relative to background situations. The central (...) tenet of RVC is that the content of concepts varies systematically with perspective. The analysis of complex concepts indicates that compositionality too should be considered to be sensitive to perspective. Such a view accords with concepts and mental states being situated and the implications for theories of concepts and for computational models of the mind are discussed. (shrink)

This paper is devoted to some generalizations of explanatory potential of connectionist approaches to theoretical problems of the philosophy of mind. Are considered both strong, and weaknesses of neural network models. Connectionism has close methodological ties with modern neurosciences and neurophilosophy. And this fact strengthens its positions, in terms of empirical naturalistic approaches. However, at the same time this direction inherits weaknesses of computational approach, and in this case all system of anticomputational critical arguments becomes applicable to the connectionst (...) models of mind. The last developments in the field of deep learning gave rich empirical material for cognitive sciences. Multilayered networks, mathematical models of associative dynamics of learning, self-organizing neuronets and all that allow to explain the principles of human conceptual organizing and after this to emulate these processes in computer systems. At all engineering achievements of this technology there is a traditional criticism from representatives of cognitive psychology who cannot accept a thesis about learning ability of a neuronet on the basis of redistribution of scales. Process of learning of natural intelligence, according to cognitive models, happens due to attraction of knowledge broadcast in a symbolical form (mental representations, concepts) at the expense of the systems of output knowledge expressed in the propositional contents. Some philosophical aspects of «neural metaphor» in modern cognitive sciences create the problem field which demands comprehensive understanding, the first step towards which is taken in this work. (shrink)