The concept of the receptive field, first articulated by Hartline, is central to visual neuroscience. The receptive field of a neuron encompasses the spatial and temporal properties of stimuli that activate the neuron, and, as Hubel and Wiesel conceived of it, a neuron’s receptive field is static. This makes it possible to build models of neural circuits and to build up more complex receptive fields out of simpler ones. Recent work in visual neurophysiology is providing evidence that the classical receptive (...) field is an inaccurate picture. The receptive field seems to be a dynamic feature of the neuron. In particular, the receptive field of neurons in V1 seems to be dependent on the properties of the stimulus. In this paper, we review the history of the concept of the receptive field and the problematic data. We then consider a number of possible theoretical responses to these data. (shrink)

Neuroeconomics illustrates our deepening descent into the details of individual cognition. This descent is guided by the implicit assumption that “individual human” is the important “agent” of neoclassical economics. I argue here that this assumption is neither obviously correct, nor of primary importance to human economies. In particular I suggest that the main genius of the human species lies with its ability to distribute cognition across individuals, and to incrementally accumulate physical and social cognitive artifacts that largely obviate the innate (...) biological limitations of individuals. If this is largely why our economies grow, then we should be much more interested in distributed cognition in human groups, and correspondingly less interested in individual cognition. We should also be much more interested in the cultural accumulation of cognitive artefacts: computational devices and media, social structures and economic institutions. (shrink)

When people want to identify the causes of an event, assign credit or blame, or learn from their mistakes, they often reflect on how things could have gone differently. In this kind of reasoning, one considers a counterfactual world in which some events are different from their real-world counterparts and considers what else would have changed. Researchers have recently proposed several probabilistic models that aim to capture how people do (or should) reason about counterfactuals. We present a new model and (...) show that it accounts better for human inferences than several alternative models. Our model builds on the work of Pearl (2000), and extends his approach in a way that accommodates backtracking inferences and that acknowledges the difference between counterfactual interventions and counterfactual observations. We present six new experiments and analyze data from four experiments carried out by Rips (2010), and the results suggest that the new model provides an accurate account of both mean human judgments and the judgments of individuals. (shrink)

Intelligent systems are faced with the problem of securing a principled (ideally, veridical) relationship between the world and its internal representation. I propose a unified approach to visual representation, addressing both the needs of superordinate and basic-level categorization and of identification of specific instances of familiar categories. According to the proposed theory, a shape is represented by its similarity to a number of reference shapes, measured in a high-dimensional space of elementary features. This amounts to embedding the stimulus in a (...) low-dimensional proximal shape space. That space turns out to support representation of distal shape similarities which is veridical in the sense of Shepard's (1968) notion of second-order isomorphism (i.e., correspondence between distal and proximal similarities among shapes, rather than between distal shapes and their proximal representations). Furthermore, a general expression for similarity between two stimuli, based on comparisons to reference shapes, can be used to derive models of perceived similarity ranging from continuous, symmetric, and hierarchical, as in the multidimensional scaling models (Shepard, 1980), to discrete and non-hierarchical, as in the general contrast models (Tversky, 1977; Shepard and Arabie, 1979). (shrink)

Executive functioning has been said to bear on a range of traditional philosophical topics, such as consciousness, thought, and action. Surprisingly, philosophers have not much engaged with the scientific literature on executive functioning. This lack of engagement may be due to several influential criticisms of that literature by Daniel Dennett, Alan Allport, and others. In this paper I argue that more recent research on executive functioning shows that these criticisms are no longer valid. The paper clears the way to a (...) more fruitful philosophical engagement with findings on the central executive system. (shrink)

We can infer moral conclusions from nonmoral evidence using a three-step procedure. First, we distinguish the processes generating belief so that their reliability in generating true belief is statistically predictable. Second, we assess the processes’ reliability, perhaps by observing how frequently they generate true nonmoral belief or logically inconsistent beliefs. Third, we adjust our credence in moral propositions in light of the truth ratios of the processes generating beliefs in them. This inferential route involves empirically discovering truths of the form (...) “Process P, which generates belief in moral proposition M, has truth ratio T", and using them to discover probabilities for moral propositions. The inferential route is noncircular, and progress along it is driven fundamentally by induction. (shrink)

The distinction between perception and cognition frames countless debates in philosophy and cognitive science. But what, if anything, does this distinction actually amount to? In this introductory article, we summarize recent work on this question. We first briefly consider the possibility that a perception-cognition border should be eliminated from our scientific ontology, and then introduce and critically examine five positive approaches to marking a perception–cognition border, framed in terms of phenomenology, revisability, modularity, format, and stimulus-dependence.

Experiments are presented showing that visual search for Mueller-Lyer (ML) stimuli is based on complete configurations, rather than component segments. Segments easily detected in isolation were difficult to detect when embedded in a configuration, indicating preemption by low-level groups. This preemption—which caused stimulus components to become inaccessible to rapid search—was an all-or-nothing effect, and so could serve as a powerful test of grouping. It is shown that these effects are unlikely to be due to blurring by simple spatial filters at (...) early visual levels. It is proposed instead that they are due to more sophisticated processes that rapidly bind contour fragments into spatially-extended assemblies. These results support the view that rapid visual search cannot access the primitives formed at the earliest stages of visual processing; rather, it can access only higher-level, more ecologically-relevant structures. (shrink)

Abstract:When I throw a ball at you, do you see it as catch‐able? Do we perceive objects as edible, climbable or Q‐able in general? One could argue that it is just a manner of speaking to say so: we do not reallyseean object as edible, we only infer on the basis of its other properties that it is. I argue that whether or not an object is edible or climbable is indeed represented perceptually: weseeobjects as edible, and do not just (...) believe that they are. My argument proceeds in two steps. First, I argue that in order to perform an action Q with respect to an object, we need to represent this object as Q‐able and, second, I argue that we represent objects as having these propertiesperceptually. (shrink)

Abstract: When I throw a ball at you, do you see it as catch-able? Do we perceive objects as edible, climbable or Q-able in general? One could argue that it is just a manner of speaking to say so: we do not really see an object as edible, we only infer on the basis of its other properties that it is. I argue that whether or not an object is edible or climbable is indeed represented perceptually: we see objects as (...) edible, and do not just believe that they are. My argument proceeds in two steps. First, I argue that in order to perform an action Q with respect to an object, we need to represent this object as Q-able and, second, I argue that we represent objects as having these properties perceptually. (shrink)

The term attention has been used to mean so many different things that some have despaired of it being useful at all. This paper is devoted to bringing a modicum of order to the chaos through the time-honored device of categorization. The chief purpose of this paper is to introduce a comprehensive descriptive taxonomy of the nuanced ways the term attention may be employed. It is presented in table form, followed by elucidations and illustrations of each of its items. But (...) first, I offer reasons why a taxonomy of attention is needed and explore some of its possible benefits. After presenting the taxonomy, I sketch by way of example how it might be applied to two interesting questions: is the umbrella term attention still useful?; and, what is it that ties the subdefinitions of attention together yet distinguishes them from other kinds of cognition? (shrink)

The familiar Cartesian sceptical arguments all involve an explanation of our experiences. An account of the persuasive power of the sceptical arguments should explain why this is so. This supports a diagnosis of the error in Cartesian sceptical arguments according to which they mislead us into regarding our perceptual beliefs as if they were justified as inferences to the best explanation. I argue that they have instead a perceptual justification that does not involve inference to the best explanation and that (...) should not be put in doubt by the sceptical hypotheses. (shrink)

Assuming a radical stance on embodied cognition, according to which the information ac- quired through basic cognitive processes is not contentful (Hutto and Myin, 2013), and as- suming that perception is a source of rationally grounded knowledge (Pritchard, 2012), a pluralistic account of perceptual knowledge is developed. The paper explains: (i) how the varieties of perceptual knowledge fall under the same broader category; (ii) how they are subject to the same kind of normative constraints; (iii) why there could not be (...) a conflict between the different varieties of perceptual knowledge; and (iv) why the traditional episte- mological inquiry is inclined to overestimate the role of propositional perceptual knowledge. (shrink)

This chapter argues that olfactory experiences represent either everyday objects or ad hoc olfactory objects as having primitive olfactory properties, which happen to be uninstantiated. On this picture, olfactory experiences reliably misrepresent: they falsely represent everyday objects or ad hoc objects as having properties they do not have, and they misrepresent in the same way on multiple occasions. One might worry that this view is incompatible with the plausible claim that olfactory experiences at least sometimes justify true beliefs about the (...) world. This chapter argues that there is no such incompatibility. Since olfactory experiences reliably misrepresent, they can lead to true and justified beliefs about putatively smelly objects. (shrink)

In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are (...) semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary. (shrink)

The essay is critically examines the conceptual problems with the influential modularity model of mind. We shall see that one of the essential characters of modules, namely informational encapsulation, is not only inessential, it ties a knot at a crucial place blocking the solution to the problem of understanding the formation of concepts from percepts (nodes of procedural knowledge). Subsequently I propose that concept formation takes place by modulation of modules leading to cross-representations, which were otherwise prevented by encapsulation. It (...) must be noted that the argument is not against modular architecture, but a variety of an architecture that prevents interaction among modules. This is followed by a brief argument demonstrating that module without modularization, i.e. without developmental history, is impossible. Finally the emerging picture of cognitive development is drawn in the form of the layers in the fabric of mind, with a brief statement of the possible implications. (shrink)

Recent advances in our understanding of visual perception have shown it to be a far more complex and counterintuitive process than previously believed. Several important consequences follow from this. First, the design of an effective statistical graphics system is unlikely to succeed based on intuition alone; instead, it must rely on a more sophisticated, systematic approach. The basic elements of such an approach are outlined here, along with several design principles. An overview is then given of recent advances in our (...) understanding of visual perception, including rapid perception, visual attention, and scene perception. It then is argued that the mechanisms involved can be successfully harnessed to allow data to be displayed more effectively than at present. Several directions of development are discussed, including effective use of visual attention, the display of dynamic information, and the effective use of nonattentional and nonconscious perceptual systems. (shrink)

Summary In this contribution, I discuss the perceptual potential of certain genres of experimental and contemporary music, commonly grouped under the label “sound-based music”. The sonic patterns typical of this music are mostly associated, during listening, with visual and tactile sensory qualities and can evoke mental representations as shapes in motion. These are the result of physical-acoustic energies organized according to a perceptual grammar whose organization follows a series of Gestalt and kinaesthetic principles. The paper explores the nature of the (...) relationship between sound patterns of sound-based music and their mental images. Based on morphodynamic theory, it is proposed the emergence of cognitive image schemas, which are at the centre of this relationship. The image schemas depict the forces and tensions of our experience of the world (e.g., figure-background, near-far, superimposition, compulsion, blockage), as being the cognitive and experiential response to the incoming sound patterns. The sense of this music activates precisely the basic structures of sensorimotor experience by which we encounter a world that we can understand and act within, leading to a rich series of high-level associations and responses. (shrink)

Let ‘semantic relevance’ be the thesis that the wide semantic properties of representational mental states (like beliefs and desires) are causally relevant to behavior. A popular way of arguing for semantic relevance runs as follows: start with a sufficient counterfactual condition for causal or explanatory relevance, and show that wide semantic properties meet it with respect to behavior (e.g., Loewer & Lepore (1987,1989), Rescorla (2014), Yablo (2003)).This paper discusses an in‐principle limitation of this strategy: even the most sophisticated counterfactual criteria (...) systematically misclassify irrelevant properties as relevant when they stand in certain kinds of modal co‐variation or ‘entanglement’ relations to genuinely relevant properties. This entanglement problem, I argue, is more general and more serious than proponents of the counterfactual strategy have recognized: it threatens recent interventionist arguments for semantic relevance, and is not easily solved by appeal to proportionality or naturalness. I end by suggesting that proponents of semantic relevance may need to shift their attention from patterns of counterfactuals to the lawful psychological generalizations that explain them. (shrink)

This is an amended version of material that first appeared in A. Clark, Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing (MIT Press, Cambridge, MA, 1989), Ch. 1, 2, and 6. It appears in German translation in Metzinger,T (Ed) DAS LEIB-SEELE-PROBLEM IN DER ZWEITEN HELFTE DES 20 JAHRHUNDERTS (Frankfurt am Main: Suhrkamp. 1999).

Although seemingly irrational choice abounds, the rules governing these mis‐steps that might provide hints about the factors limiting normative behavior are unclear. We consider three experimental tasks, which probe different aspects of non‐normative choice under uncertainty. We argue for systematic statistical, algorithmic, and implementational sources of irrationality, including incomplete evaluation of long‐run future utilities, Pavlovian actions, and habits, together with computational and statistical noise and uncertainty. We suggest structural and functional adaptations that minimize their maladaptive effects.

The aim of this paper is to clarify and critically assess the methods of evolutionary psychology, and offer a sketch of an alternative methodology. My thesis is threefold. (1) The methods of inquiry unique to evolutionary psychology rest upon the claim that the discovery of theadaptive functions of ancestral psychological capacities leads to the discovery of thepsychological functions of those ancestral capacities. (2) But this claim is false; in fact, just the opposite is true. We first must discover the psychological (...) functions of our psychological capacities in order to discover their adaptive functions. Hence the methods distinctive of evolutionary psychology are idle in our search for the mechanisms of the mind. (3) There are good reasons for preferring an alternative to the methods of evolutionary psychology, an alternative that aims to discover the functions of our psychological capacities by appeal to the concept of awhole psychology. (shrink)

In this paper, I define tacit knowledge as a kind of causal-explanatory structure, mirroring the derivational structure in the theory that is tacitly known. On this definition, tacit knowledge does not have to be explicitly represented. I then take the notion of a modular theory, and project the idea of modularity to several different levels of description: in particular, to the processing level and the neurophysiological level. The fundamental description of a connectionist network lies at a level between the processing (...) level and the physiological level. At this level, connectionism involves a characteristic departure from modularity, and a correlative absence of syntactic structure. This is linked to the fact that tacit knowledge descriptions of networks are only approximately true. A consequence is that strict causal systematicity in cognitive processes poses a problem for the connectionist programme. (shrink)

Research from ethology and evolutionary biology indicates the following about the evolution of reasoning capacity. First, solving problems of social competition and cooperation have direct impact on survival rates and reproductive success. Second, the social structure that evolved from this pressure is the dominance hierarchy. Third, primates that live in large groups with complex dominance hierarchies also show greater neocortical development, and concomitantly greater cognitive capacity. These facts suggest that the necessity of reasoning effectively about dominance hierarchies left an indelible (...) mark on primate reasoning architectures, including that of humans. In order to survive in a dominance hierarchy, an individual must be capable of (a) making rank discriminations, (b) recognizing what is forbidden and what is permitted based one's rank, and (c) deciding whether to engage in or refriin from activities that will allow one to move up in rank. The first problem is closely tied to the capacity for transitive reasoning, while the second and third are intimately related to the capacity for deontic reasoning. I argue that the human capacity for these types of reasoning have evolutionary roots that reach deeper into our ancestral past than the emergence of the hominid line, and the operation of these evolutionarily primitive reasoning systems can be seen in the development of human reasoning and domain-specific effects in adult reasoning. (shrink)

We argue that neuroeconomics should be a mechanistic science. We defend this view as preferable both to a revolutionary perspective, according to which classical economics is eliminated in favour of neuroeconomics, and to a classical economic perspective, according to which economics is insulated from facts about psychology and neuroscience. We argue that, like other mechanistic sciences, neuroeconomics will earn its keep to the extent that it either reconfigures how economists think about decision-making or how neuroscientists think about brain mechanisms underlying (...) behaviour. We discuss some ways that the search for mechanisms can bring about such top-down and bottom-up revision, and we consider some examples from the recent neuroeconomics literature of how varieties of progress of this sort might be achieved. (shrink)

Modularity is one of the most important concepts used to articulate a theory of cognitive architecture. Over the last 30 years, the debate in many areas of the cognitive sciences and in philosophy of psychology about what modules are, and to what extent our cognitive architecture is modular, has made little progress. After providing a diagnosis of this lack of progress, this article suggests a remedy. It argues that the theoretical framework of network science can be brought to bear on (...) the traditional modularity debate, facilitating our progress in articulating a good theory of the human cognitive architecture. (shrink)

A recent article in this journal (Patterson & Plaut, 2009) argued that cognitive neuropsychology has told us very little over the past 30 or 40 years about “how the brain accomplishes its cognitive business.” This may well be true, but it is not important, because the principal aim of cognitive neuropsychology is not to learn about the brain. Its principal aim is instead to learn about the mind, that is, to elucidate the functional architecture of cognition. I show that this (...) is so (a) via extensive quotations from leading figures in this field and (b) by analysis of the subject matter of articles in the leading journal in the field, Cognitive Neuropsychology. Recent reviews of the past 25 years of work in this field (Coltheart & Caramazza, 2006) have concluded that cognitive neuropsychology has told us much about the functional architecture of cognition in a variety of cognitive domains. Patterson and Plaut (2009) did not consider this aim of cognitive neuropsychology. Therefore, their conclusions that cognitive neuropsychology has not been successful, and that this is because the particular methods it uses are flawed, are not justified. (shrink)

There continues to be significant confusion about the goals, scope, and nature of modelling practice in neuroeconomics. This article aims to dispel some such confusion by using one of the most recent critiques of neuroeconomic modelling as a foil. The article argues for two claims. First, currently, for at least some economic model of choice behaviour, the benefits derivable from neurally informing an economic model do not involve special tractability costs. Second, modelling in neuroeconomics is best understood within Marr’s three-level (...) of analysis framework and in light of a co-evolutionary research ideology. The first claim is established by elucidating the relationship between the tractability of a model, its descriptive accuracy, and its number of variables. The second claim relies on an explanation of what it can take to neurally inform an economic model of choice behaviour. 1 Introduction2 Neurally Informed Models of Choice: A Case Study2.1 A case study on risk-sensitive choice2.1.1 Target and modelling framework2.1.2 Research question and hypotheses2.1.3 Competitive models of risk-sensitive behaviour2.1.4 Model-based fMRI: from economics to brains and back2.2 Neurally informed modelling3 Tractability: When Does Size Matter?4 Neural Integration and the Co-evolutionary Research Ideology5 Conclusion. (shrink)

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian (...) models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ( [1982] ). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms. (shrink)

A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are best (...) explained within the Bayesian framework. As a forewarning, progress in cognitive science may be hindered if too many scientists continue to focus their efforts on Bayesian modelling, which risks to monopolize scientific resources that may be better allocated to alternative approaches. (shrink)

Cognitive Science, Volume 46, Issue 7, July 2022.

Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large – it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that present such mappings we call the class of “type-2 problems.” Type-1 problems, by (...) contrast, present tractable problems of search insofar as the relevant regularities can be found by sampling the input data as originally coded. Type-2 problems, we suggest, present neither rare nor pathological cases. They are rife in biologically realistic settings and in domains ranging from simple animat behaviors to language acquisition. Not only are such problems rife – they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases, does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of recoding biases so as to reduce specific type-2 problems to type-1 mappings. Such a heavy-duty nativism is no doubt sometimes plausible. But we believe there are other, more general mechanisms also at work. Such mechanisms provide general strategies for managing problems of type-2 complexity. Several such mechanisms are investigated. At the heart of each is a fundamental ploy – namely, the maximal exploitation of states of representation already achieved by prior, simpler learning so as to reduce the amount of subsequent computational search. Such exploitation both characterizes and helps make unitary sense of a diverse range of mechanisms. These include simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription”. In addition, the most distinctive features of human cognition – language and culture – may themselves be viewed as adaptations enabling this representation/computation trade-off to be pursued on an even grander scale. (shrink)

Recent studies such as Thelen and Smith, Kelso, Van Gelder, Beer, and others have presented a forceful case for a dynamical systems approach to understanding cognition and adaptive behavior. These studies call into question some foundational assumptions concerning the nature of cognitive scientific explanation and the role of notions such as internal representation and computation. These are exciting and important challenges. But they must be handled with care. It is all to easy in this debate to lose sight of the (...) explanatorily important issues and to talk at cross purposes, courtesy of the various ways in which different theorists often concieve key terms. (shrink)

A suitable project for the new Millenium is to radically reconfigure our image of human rationality. Such a project is already underway, within the Cognitive Sciences, under the umbrellas of work in Situated Cognition, Distributed and De-centralized Cogition, Real-world Robotics and Artificial Life1. Such approaches, however, are often criticized for giving certain aspects of rationality too wide a berth. They focus their attention on on such superficially poor cousins as.

It is often claimed that pre-attentive vision has an ‘iconic’ format. This is seen to explain pre-attentive vision's characteristically high processing capacity and to make sense of an overlap in the mechanisms of early vision and mental imagery. But what does the iconicity of pre-attentive vision amount to? This paper considers two prominent ways of characterising pre-attentive visual icons and argues that neither is adequate: one approach renders the claim ‘pre-attentive vision is iconic’ empirically false while the other obscures its (...) ability to do the explanatory work, which motivates positing pre-attentive visual icons in the first place. With this noted, I introduce the notion of an ‘Analogue Map’ and argue that it provides a superior characterisation of pre-attentive vision's iconicity. I then argue that this forces a reassessment of debates which have traditionally presupposed the iconicity of pre-attentive vision, emphasising ramifications for the viability of a format-based perception-thought border. (shrink)