This paper addresses a fundamental line of research in neuroscience: the identification of a putative neural processing core of the cerebral cortex, often claimed to be “canonical”. This “canonical” core would be shared by the entire cortex, and would explain why it is so powerful and diversified in tasks and functions, yet so uniform in architecture. The purpose of this paper is to analyze the search for canonical explanations over the past 40 years, discussing the theoretical frameworks informing this research. (...) It will highlight a bias that, in my opinion, has limited the success of this research project, that of overlooking the dimension of cortical development. The earliest explanation of the cerebral cortex as canonical was attempted by David Marr, deriving putative cortical circuits from general mathematical laws, loosely following a deductive-nomological account. Although Marr’s theory turned out to be incorrect, one of its merits was to have put the issue of cortical circuit development at the top of his agenda. This aspect has been largely neglected in much of the research on canonical models that has followed. Models proposed in the 1980s were conceived as mechanistic. They identified a small number of components that interacted as a basic circuit, with each component defined as a function. More recent models have been presented as idealized canonical computations, distinct from mechanistic explanations, due to the lack of identifiable cortical components. Currently, the entire enterprise of coming up with a single canonical explanation has been criticized as being misguided, and the premise of the uniformity of the cortex has been strongly challenged. This debate is analyzed here. The legacy of the canonical circuit concept is reflected in both positive and negative ways in recent large-scale brain projects, such as the Human Brain Project. One positive aspect is that these projects might achieve the aim of producing detailed simulations of cortical electrical activity, a negative one regards whether they will be able to find ways of simulating how circuits actually develop. (shrink)

This volume is about the many ways we perceive. Contributors explore the nature of the individual senses, how and what they tell us about the world, and how they interrelate. They consider how the senses extract perceptual content from receptoral information. They consider what kinds of objects we perceive and whether multiple senses ever perceive a single event. They consider how many senses we have, what makes one sense distinct from another, and whether and why distinguishing senses may be useful. (...) They consider the extent to which the senses act in concert, rather than as discrete modalities, and whether this influence is epistemically pernicious, neutral, or beneficial. They explore both the familiar five sense modalities and other candidate sense modalities, including a sense for flavour, inner senses (e.g., a sense for pain), and various senses that may result from using sensory substitution devices. Contributors often represent competing views, and the methods they deploy sometimes differ from one essay to the next—with some drawing upon the sciences and engineering and others relying upon conceptual analysis. All contributors agree, nonetheless, that traditional theorizing about the senses is hampered by a neglect of the senses other than vision, and by the misconception that vision is a passive receptacle for an image thrown by a lens. And many contributors believe that it is unduly restrictive to think of perception as a collation of contents provided by individual sense modalities; they think that to understand perception properly, we need to build into our accounts the idea that the senses work together. The ambition of the volume is to begin to develop better paradigms for understanding the senses, and thereby to move toward a better understanding of perception. (shrink)

Advocates of dynamic systems have suggested that higher mental processes are based on continuous representations. In order to evaluate this claim, we first define the concept of representation, and rigorously distinguish between discrete representations and continuous representations. We also explore two important bases of representational content. Then, we present seven arguments that discrete representations are necessary for any system that must discriminate between two or more states. It follows that higher mental processes require discrete representations. We also argue that discrete (...) representations are more influenced by conceptual role than continuous representations. We end by arguing that the presence of discrete representations in cognitive systems entails that computationalism (i.e., the view that the mind is a computational device) is true, and that cognitive science should embrace representational pluralism. (shrink)

This chapter subsumes David Marr’s levels of analysis account of explanation in cognitive science under the framework of mechanistic explanation: Answering the questions that define each one of Marr’s three levels is tantamount to describing the component parts and operations of mechanisms, as well as their organization, behavior, and environmental context. By explicating these questions and showing how they are answered in several different cognitive science research programs, this chapter resolves some of the ambiguities that remain in Marr’s account, and (...) shows that many different areas and traditions of cognitive scientific research can be unified under the mechanistic framework. (shrink)

What is the role of the environment, and of the information it provides, in cognition? More specifically, may there be a role for certain artefacts to play in this context? These are questions that motivate "4E" theories of cognition (as being embodied, embedded, extended, enactive). In his take on that family of views, Hajo Greif first defends and refines a concept of information as primarily natural, environmentally embedded in character, which had been eclipsed by information-processing views of cognition. He continues (...) with an inquiry into the cognitive bearing of some artefacts that are sometimes referred to as 'intelligent environments'. Without necessarily having much to do with Artificial Intelligence, such artefacts may ultimately modify our informational environments. -/- With respect to human cognition, the most notable effect of digital computers is not that they might be able, or become able, to think but that they alter the way we perceive, think and act. (shrink)

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...) none of these kinds of evidence can be relevant to a choice among competing computational models unless there are in fact type-type correlations between the states postulated by computational models and brain states. Thus, I conclude, research into the computational procedures employed in human cognition must be conducted hand-in-hand with research into the brain processes which realize those procedures. (shrink)

The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...) none of these kinds of evidence can be relevant to a choice among competing computational models unless there are in fact type-type correlations between the states postulated by computational models and brain states. Thus, I conclude, research into the computational procedures employed in human cognition must be conducted hand-in-hand with research into the brain processes which realize those procedures. (shrink)

There is widespread belief that connectionist networks are dramatically different from classical or symbolic models. However, connectionists rarely test this belief by interpreting the internal structure of their nets. A new approach to interpreting networks was recently introduced by Berkeley et al. (1995). The current paper examines two implications of applying this method: (1) that the internal structure of a connectionist network can have a very classical appearance, and (2) that this interpretation can provide a cognitive theory that cannot be (...) dismissed as a mere implementation. (shrink)

Exposing the chimpanzee to language training appears to enhance the animal's ability to perform some kinds of tasks but not others. The abilities that are enhanced involve abstract judgment, as in analogical reasoning, matching proportions of physically unlike exemplars, and completing incomplete representations of action. The abilities that do not improve concern the location of items in space and the inferences one might make in attempting to obtain them. Representing items in space and making inferences about them could be done (...) with an imaginal code, but representing relations and judging the relations between them, as in analogies, requires a more abstract code. Language training cannot instill such an abstract code, but for species that have the code to start with, it may enhance the animal's ability to use it. (shrink)

Theories in cognitive science, and especially cognitive neuroscience, often claim that parts of cognitive systems are reused for different cognitive functions. Philosophical analysis of this concept, however, is rare. Here, I first provide a set of criteria for an analysis of reuse, and then I analyse reuse in terms of the functions of subsystems. I also discuss how cognitive systems execute cognitive functions, the relation between learning and reuse, and how to differentiate reuse from related concepts like multi-use, redundancy, and (...) duplication of parts. Finally, I illustrate how my account captures the reuse of dynamical subsystems as unveiled by recent research in cognitive neurobiology. This recent research suggests two different evolutionary justifications for reuse claims. _1_ Introduction _2_ Criteria for Analyses of Reuse _3_ Use and Reuse _4_ The Reuse of Dynamical Systems in Cognitive Systems _5_ Conclusion. (shrink)

Hebbian mechanisms are justified according to their functional utility in an evolutionary sense. The selective advantage of correlating content-contingent stimuli reflects the putative common cause of temporally or spatially contiguous inputs. The selective consequences of such correlations are discussed by using examples from the evolution of signal form in sexual selection and model-mimic coevolution. We suggest that evolutionary justification might be considered in addition to neurophysiology plansibility when constructing representational models.

The problem of epistemic opacity in Artificial Intelligence is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this claim, I first (...) contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system. While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties. (shrink)

In Section 5 of his interesting article, Goldman suggests that the consideration of imaginary cases can be valuable in the analysis of our psychological concepts. In particular, he argues that we can imagine a system that is isomorphic to us under any functional description, but which lacks qualitative mental states, such as pains and color sensations. Whether or not such a being is empirically possible, it certainly seems to be logically possible, or conceptually coherent. Goldman argues from this possibility to (...) the conclusion that our concepts of qualitative mental states cannot be analyzed entirely in functional terms. (shrink)

It is accepted that “primary awareness” may emerge from the integration of two classes of information. It is unclear, however, why this cannot take place within the comparator rather than in conjunction with feedback to the perceptual systems. The model has plausibility in relation to the continuity of conscious experience in the normal waking state and may be extended to encompass certain aspects of the “sense of self” which are frequently disrupted in psychotic patients.

Is there a cognitive faculty dedicated to the moral domain? Mark Johnson has developed a number of arguments against the existence of such a faculty. I claim that these arguments are not persuasive and that there may be a moral faculty.

In Gray's conjecture, mismatches in the subicular comparator and matches have equal prominence in consciousness. In rival cognitive views novelty and difficulty especially elicit more conscious modes of cognition and higher levels of self-regulation. The mismatch between Gray's conjecture and these views is discussed.

This aim of this paper is two-fold: it critically analyses and rejects accounts blending active inference as theory of mind and enactivism; and it advances an enactivist-dynamic understanding of social cognition that is compatible with active inference. While some social cognition theories seemingly take an enactive perspective on social cognition, they explain it as the attribution of mental states to other people, by assuming representational structures, in line with the classic Theory of Mind. Holding both enactivism and ToM, we argue, (...) entails contradiction and confusion due to two ToM assumptions widely known to be rejected by enactivism: that social cognition reduces to mental representation and social cognition is a hardwired contentful ‘toolkit’ or ‘starter pack’ that fuels the model-like theorising supposed in. The paper offers a positive alternative, one that avoids contradictions or confusion. After rejecting ToM-inspired theories of social cognition and clarifying the profile of social cognition under enactivism, that is without assumptions and, the last section advances an enactivist-dynamic model of cognition as dynamic, real-time, fluid, contextual social action, where we use the formalisms of dynamical systems theory to explain the origins of socio-cognitive novelty in developmental change and active inference as a tool to demonstrate social understanding as generalised synchronisation. (shrink)

Traditional views separate cognitive processes from sensory–motor processes, seeing cognition as amodal, propositional, and compositional, and thus fundamentally different from the processes that underlie perceiving and acting. These were the ideas on which cognitive science was founded 30 years ago. However, advancing discoveries in neuroscience, cognitive neuroscience, and psychology suggests that cognition may be inseparable from processes of perceiving and acting. From this perspective, this study considers the future of cognitive science with respect to the study of cognitive development.

Recently, there has been a growing interest, both within theoretical biology and the philosophy of biology, in the possibility and desirability of a theory of development. Among the many issues raised within this debate, the questions of the spatial and temporal boundaries of development have received particular attention. In this article, noting that so far the discussion has mostly centered on the processes of morphogenesis and organogenesis, we argue that an important missing element in the equation, namely the development of (...) language and cognition in general, may play an important role in settling the issue of temporal boundaries. After examining the idea that the development of language, cognition, and action are bona fide biological processes, we explore the consequences for a general theory of development of taking them into consideration. (shrink)

In trying to characterize the relationship between psychology and neuroscience, the trend has been to argue that reductionism does not work without suggesting a suitable substitute. I offer explanatory extension as a good model for elucidating the complex relationship among disciplines which are obviously connected but which do not share pragmatic explanatory features. Explanatory extension rests on the idea that one field can "illuminate" issues that were incompletely treated in another. In this paper, I explain how this "illumination" would work (...) between psychology and neuroscience. (shrink)

Stephen Stich has argued that psychological theories that instantiate his Syntactic Theory of Mind are to be preferred to content-based or representationalist theories, because the former can capture and explain a wider range of generalizations about cognitive processes than the latter. Stich's claims about the relative merits of the Syntactic Theory of Mind are unfounded. Not only is it false that syntactic theories can capture psychological generalizations that content-based theories cannot, but a large class of behavioral regularities, readily explained by (...) content-based theories, appear to be beyond their explanatory reach. (shrink)

The doctrine that the character of our perceptual knowledge is plastic, and can vary substantially with the theories embraced by the perceiver, has been criticized in a recent paper by Fodor. His arguments are based on certain experimental facts and theoretical approaches in cognitive psychology. My aim in this paper is threefold: to show that Fodor's views on the impenetrability of perceptual processing do not secure a theory-neutral foundation for knowledge; to show that his views on impenetrability are almost certainly (...) false; and to provide some additional arguments for, and illustrations of, the theoretical character of all observation judgments. (shrink)

One central debate in cognitive science is over imagery. Do images constitute, or constitute evidence for, a distinctive, depictive form of mental representation? The most sophisticated advocacy of this view has been developed by Kosslyn and his coworkers. This paper focuses on his position and argues (i) that though Kosslyn has not developed a satisfactory account of depiction, there is nothing in principle unintelligible about the idea of depictive neural representation, but (ii) Kosslyn's model of imagery rescues the intelligibility of (...) pictorialism at the cost of its explanatory power. (shrink)

Current computational psychology, especially as described by Fodor (1975, 1980, 1981), Pylyshyn (1980), and Stich (1983), is both a bold, promising program for cognitive science and an alternative to naturalistic psychology (Putnam 1975). Whereas naturalistic psychology depends on the general scientific framework to fix the meanings of general terms and, hence, the content of thoughts utilizing or expressed in those terms, computational cognitive theory banishes semantical considerations in psychological investigations, embracing methodological, not ontological, solipsism. I intend to argue that computational (...) psychology cannot individuate thoughts as it promises. For, semantics is fundamental in fixing an important subset of the computational relations that, according to the computational theory, are supposed both to obtain among thoughts and, thereby, to determine their identity conditions. If what I contend is correct, then contrary to what its advocates maintain, computational psychology is not preferable to naturalistic psychology as a research strategy in cognitive science. (shrink)

This paper addresses a recent argument of the Churchlands against the "linguistic-rationalist" tradition exemplified by current cognitive-computational psychology. Because of its commitment to methodological solipsism--the argument goes--computational psychology cannot provide an account of how organisms are able to represent and "hook up to" the world. First I attempt to determine the exact nature of this charge and its relation to the Churchlands' long-standing polemic against 'folk psychology' and the linguistic-rationalist methodology. I then turn my attention to the Churchlands' account of (...) what it is for computational psychology to be methodologically solipsistic. I argue that there is no reason to suppose that methodological solipsism commits one to a purely syntactic theory of the mind (of the kind that Stephen Stich has recently advocated): the formality constraints that methodological solipsism imposes on psychological explanation do not exclude 'essential' reference to the representational content of mental states, as long as this content is construed in the 'narrow' sense. I conclude by raising a problem for computational psychology that may provide some real cause for concern. (shrink)

One way in which philosophy of science can perform a valuable normative function for science is by showing characteristic errors made in scientific research programs and proposing ways in which such errors can be avoided or corrected. This paper examines two errors that have commonly plagued research in biology and psychology: 1) functional localization errors that arise when parts of a complex system are assigned functions which these parts are not themselves able to perform, and 2) vacuous functional explanations in (...) which one provides an analysis that does account for the inputs and outputs of a system but does not employ the same set of functions to produce this output as does the natural system. These two kinds of error usually arise when researchers limit their investigation to one type of evidence. Historically, correction of these errors has awaited researchers who have employed the opposite type of evidence. This paper explores the tendency to commit these errors by examining examples from historical and contemporary science and proposes a dialectical process through which researchers can avoid or correct such errors in the future. (shrink)

There is often substantial disparity between philosophical ideals and scientific practice. Philosophical reductionism is motivated by a drive for ontological austerity. The vehicle is conceptual parsimony: the fewer our conceptual primitives, the less are our ontological commitments. A general moral to be drawn from my “Functionalism and Reductionism” is that scientific reduction does not, and should not be expected to, facilitate conceptual economy; yet reduction it still is, and in the classical mold. Those who press for the irreducibility of a (...) functional psychology have been seduced by an inadequate account of scientific reduction. Patricia Kitcher is unhappy with my argument because it fails to live up to her philosophical ideals. “The claim that psychology is irreducible to neurophysiology,” she suggests, “rests to a large extent on the fact that, even in principle, neurophysiology is not capable of carrying out the explanatory work of a functional psychology”. (shrink)

Consciousness remains a mystery—“a phenomenon that people do not know how to think about—yet” (Dennett, , p. 21). Here, I consider how the connectionist perspective on information processing may help us progress toward the goal of understanding the computational principles through which conscious and unconscious processing differ. I begin by delineating the conceptual challenges associated with classical approaches to cognition insofar as understanding unconscious information processing is concerned, and to highlight several contrasting computational principles that are constitutive of the connectionist (...) approach. This leads me to suggest that conscious and unconscious processing are fundamentally connected, that is, rooted in the very same computational principles. I further develop a perspective according to which the brain continuously and unconsciously learns to redescribe its own activity itself based on constant interaction with itself, with the world, and with other minds. The outcome of such interactions is the emergence of internal models that are metacognitive in nature and that function so as to make it possible for an agent to develop a (limited, implicit, practical) understanding of itself. In this light, plasticity and learning are constitutive of what makes us conscious, for it is in virtue of our own experiences with ourselves and with other people that our mental life acquires its subjective character. The connectionist framework continues to be uniquely positioned in the Cognitive Sciences to address the challenge of identifying what one could call the “computational correlates of consciousness” (Mathis & Mozer, ) because it makes it possible to focus on the mechanisms through which information processing takes place. (shrink)

Perception is typically distinguished from cognition. For example, seeing is importantly different from believing. And while what one sees clearly influences what one thinks, it is debatable whether what one believes and otherwise thinks can influence, in some direct and non-trivial way, what one sees. The latter possible relation is the cognitive penetration of perception. Cognitive penetration, if it occurs, has implications for philosophy of science, epistemology, philosophy of mind, and cognitive science. This paper offers an analysis of the phenomenon, (...) its theoretical consequences, and a variety of experimental results and possible interpretations of them. The paper concludes by proposing some constraints for analyses and definitions of cognitive penetrability. (shrink)

: Stich, Nichols et al. assert that the process of deriving predictions by simulation must be cognitively impenetrable. Hence, they claim, the occurrence of certain errors in prediction provides empirical evidence against simulation theory. But it is false that simulation‐derived prediction must be cognitively impenetrable. Moreover the errors they cite, which are instances of irrationality, are not evidence against the version of simulation theory that takes the central domain of simulation to be intelligible transitions between states with content.

: Stich, Nichols et al. assert that the process of deriving predictions by simulation must be cognitively impenetrable. Hence, they claim, the occurrence of certain errors in prediction provides empirical evidence against simulation theory. But it is false that simulation‐derived prediction must be cognitively impenetrable. Moreover the errors they cite, which are instances of irrationality, are not evidence against the version of simulation theory that takes the central domain of simulation to be intelligible transitions between states with content.

There are good, even if inconclusive, reasons to think that cognitive penetration of perception occurs: that cognitive states like belief causally affect, in a relatively direct way, the contents of perceptual experience. The supposed importance of – indeed as it is suggested here, what is definitive of – this possible phenomenon is that it would result in important epistemic and scientific consequences. One interesting and intuitive consequence entirely unremarked in the extant literature concerns the perception of art. Intuition has it (...) that knowledge about art changes how one aesthetically evaluates artworks. A profound explanation of this intuitive fact is that perceptual experiences vary with artistic expertise. Cognitive penetration provides an explanatory mechanism for this latter effect. What one knows or otherwise thinks about art may affect, in one of two ways sketched below, how one perceives art. Differences in aesthetic evaluation may follow, either because high-level aesthetic properties can be perceptually represented or because they causally depend on low-level perceptible properties. All of this lends credence to the hypothesis that the expert better judges art because she better perceives art. And she better perceives art because she better knows art. (shrink)