This commentary is divided into two parts. The first considers a possible role for Gray's predictor plus comparator mechanism in human episodic recognition memory. It draws on the computational specifications of recognition outlined in Humphreys et al. to demonstrate how the logically necessary components of recognition tasks might be mapped onto the mechanism. The second part demonstrates how the mechanism outlined by Gray might be implicated in a form of imitative learning suitable for the acquisition of complex tasks.

Gray mistakenly thinks I have rejected the sort of theoretical enterprise he is undertaking, because, according to him, I think that "more data" is all that is needed to resolve all the issues. Not at all. My stalking horse was the bizarre (often pathetic) claim that no amount of empirical, "third-person point-of-view" science (data plus theory) could ever reduce the residue of mystery about consciousness to zero. This "New Mysterianism" (Flanagan, 1991) is one that he should want to combat as (...) vigorously as I have done. (shrink)

Consciousness and experience, William G. Lycan. Cambridge, MA: MIT Press, 1996. ISBN 0–262–12197–2 (hc)Mind as motion: explorations in the dynamics of cognition, Robert Port & Timothy Van Gelder (Eds). Cambridge, MA: MIT/Bradford, 1995. ISBN 0–262–16150–8Perception, Kathleen Akins (Ed.). New York: Oxford University Press, 1996. ISBN 0–19–508461 (hbk); 0–19–508462–4 (pbk)Context and consciousness, B. Nardi (Ed.). Cambridge, MA: MIT Press, 1996. ISBN: 0–262–14058–6Catching ourselves in the act, Horst Hendriks‐Jansen. Cambridge, MA: MIT Press, 1996. ISBN 0–262–08246–2The search for mind. A new foundation for (...) cognitive science, Sean O'Nuallain. Norwood, NJ: Ablex, 1995Mindblindness: an essay on autism and theory of mind, Simon Baron‐Cohen. Cambridge, MA: MIT Press, 1995. ISBN 0–262–02384–9. (shrink)

This paper presents and argues for an account of objectual understanding that aims to do justice to the full range of cases of scientific understanding, including cases in which one does not have an explanation of the understood phenomenon. According to the proposed account, one understands a phenomenon just in case one grasps a sufficiently accurate and comprehensive model of the ways in which it or its features are situated within a network of dependence relations; one’s degree of understanding is (...) proportional to the comprehensiveness and accuracy of such a model. I compare this account with accounts of scientific understanding that explicate understanding in terms of having an explanation of the understood phenomenon. I discuss three distinct types of cases in which scientific understanding does not amount to possessing an explanation of any kind, and argue that the proposed model-based account can accommodate these cases while still retaining a strong link between understanding and explanation. (shrink)

Robust theories concerning the connection between consciousness and brain function should derive not only from empirical evidence but also from a well grounded inind-body ontology. In the case of the comparator hypothesis, Gray develops his ideas relying extensively on empirical evidence, but he bounces irresolutely among logically incompatible metaphysical theses which, in turn, leads him to excessively skeptical conclusions concerning the naturalization of consciousness.

Segmentalized consciousness in schizophrenia reflects a loss of the normal Gestalt organization and contextualization of perception. Grays model explains such segmentalization in terms of septohippocampal dysfunction, which is consistent with known neuropsychological impairment in schizophrenia. However, other considerations suggest that everyday perception and its failure in schizophrenia also involve prefrontal executive mechanisms, which are only minimally elaborated by Gray.

In this paper I start from a definition of “culture of the artificial” which might be stated by referring to the background of philosophical, methodological, pragmatical assumptions which characterizes the development of the information processing analysis of mental processes and of some trends in contemporary cognitive science: in a word, the development of AI as a candidate science of mind. The aim of this paper is to show how (with which plausibility and limitations) the discovery of the mentioned background might (...) be dated back to a period preceding the cybernetic era, the decade 1930–1940 at least. Therefore a somewhat detailed analysis of Hull's “robot approach” is given, as well as of some of its independent and future developments. -/- Reprinted in R.L. Chrisley (ed.), Artificial Intelligence: Critical Concepts in Cognitive Science, vol. 1, Routledge, London and New York, 2000, pp. 301-326. (shrink)

What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity. Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems—even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology (...) and computational neuroscience, we cash these distinctions out in terms of different kinds of generative models, and the representational and uncertainty-resolving capacities they afford. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual inference. (shrink)

Explanation is a central concept in human psychology. Drawing upon philosophical theories of explanation, psychologists have recently begun to examine the relationship between explanation, probability and causality. Our study advances this growing literature in the intersection of psychology and philosophy of science by systematically investigating how judgments of explanatory power are affected by the prior credibility of a potential explanation, the causal framing used to describe the explanation, the generalizability of the explanation, and its statistical relevance for the evidence. Collectively, (...) the results of our five experiments support the hypothesis that the prior credibility of a causal explanation plays a central role in explanatory reasoning: first, because of the presence of strong main effects on judgments of explanatory power, and second, because of the gate-keeping role it has for other factors. Highly credible explanations were not susceptible to causal framing effects. Instead, highly credible hypotheses were sensitive to the effects of factors which are usually considered relevant from a normative point of view: the generalizability of an explanation, and its statistical relevance for the evidence. These results advance current literature in the philosophy and psychology of explanation in three ways. First, they yield a more nuanced understanding of the determinants of judgments of explanatory power, and the interaction between these factors. Second, they illuminate the close relationship between prior beliefs and explanatory power. Third, they clarify the relationship between abductive and probabilistic reasoning. (shrink)

The target article sketched and explored a mechanism (action-oriented predictive processing) most plausibly associated with core forms of cortical processing. In assessing the attractions and pitfalls of the proposal we should keep that element distinct from larger, though interlocking, issues concerning the nature of adaptive organization in general.

The functional capabilities that consciousness seems to provide to biological systems can supply valuable principles in the design of more autonomous and robust technical systems. These functional concepts keep a notable similarity to those underlying the notion of operating system in software engineering, which allows us to specialize the computer metaphor for the mind into that of the operating system metaphor for consciousness. In this article, departing from these ideas and a model-based theoretical framework for cognition, we present an architectural (...) proposal for machine consciousness, called the Operative Mind. According to it, machine consciousness could be implemented as a set of services, in an operative system fashion, based on modeling of the own control architecture, that supervise the adequacy of the system architectural structure to the current objectives, triggering and managing adaptativity mechanisms. (shrink)

Cognitive scientists often describe the mind as constructing and using models of aspects of the environment, but it is not obvious what makes something a model as opposed to a mere representation....

Intentionality is characteristic of many psychological phenomena. It is commonly held by philosophers that intentionality cannot be ascribed to purely physical systems. This view does not merely deny that psychological language can be reduced to physiological language. It also claims that the appropriateness of some psychological explanation excludes the possibility of any underlying physiological or causal account adequate to explain intentional behavior. This is a thesis which I do not accept. I shall argue that physical systems of a specific sort (...) will show the characteristic features of intentionality. Psychological subjects are, under an alternative description, purely physical systems of a certain sort. The intentional description and the physical description are logically distinct, and are not intertranslatable. Nevertheless, the features of intentionality may be explained by a purely causal account, in the sense that they may be shown to be totally dependent upon physical processes. (shrink)

Cognitive science is an interdisciplinary research endeavor focusing on human cognitive phenomena such as memory, language use, and reasoning. It emerged in the second half of the 20th century and is charting new directions at the beginning of the 21st century. This chapter begins by identifying the disciplines that contribute to cognitive science and reviewing the history of the interdisciplinary engagements that characterize it. The second section examines the role that mechanistic explanation plays in cognitive science, while the third focuses (...) on the importance of mental representations in specifically cognitive explanations. The fourth section considers the interdisciplinary nature of cognitive science and explores how multiple disciplines can contribute to explanations that exceed what any single discipline might accomplish. The conclusion sketches some recent developments in cognitive science and their implications for philosophers. (shrink)

This paper is a commentary on the target article by Michael Arbib, “Levels of modeling of mechanisms of visually guided behavior”, in the same issue of the journal, pp. 407–465. -/- I focus on the importance of the inclusion of an ability of a system to entertain, at a given time, multiple instantiations of a given schema (situation template, frame, script, action plan, etc.), and complications introduced into neural/connectionist network systems by such inclusion.

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

In this paper, we aim at explaining the relevance of thought experiments in philosophy and the history of science by describing them as particular instances of two categories of creative thinking: metaphorical reasoning and abductive cognition. As a result of this definition, we will claim that TEs hold an ignorance-preserving trait that is evidenced in both TEs inferential structure and in the process of scenario creation they presuppose. Elaborating this thesis will allow us to explain the wonder that philosophers of (...) science have consistently shown for TEs, as well as the high functionality of TEs in the creative aspects of scientific and philosophical praxis. (shrink)

Intermediate constructs are required as bridges between complex behaviors and realistic models of neural circuitry. For cognitive scientists in general, schemas are the appropriate functional units; brain theorists can work with neural layers as units intermediate between structures subserving schemas and small neural circuits.After an account of different levels of analysis, we describe visuomotor coordination in terms of perceptual schemas and motor schemas. The interest of schemas to cognitive science in general is illustrated with the example of perceptual schemas in (...) high-level vision and motor schemas in the control of dextrous hands.Rana computatrix, the computational frog, is introduced to show how one constructs an evolving set of model families to mediate flexible cooperation between theory and experiment. Rana computatrix may be able to do for the study of the organizational principles of neural circuitry what Aplysia has done for the study of subcellular mechanisms of learning. Approach, avoidance, and detour behavior in frogs and toads are analyzed in terms of interacting schemas. Facilitation and prey recognition are implemented as tectal-pretectal interactions, with the tectum modeled by an array of tectal columns. We show how layered neural computation enters into models of stereopsis and how depth schemas may involve the interaction of accommodation and binocular cues in anurans. (shrink)