Zenon Pylyshyn argues that cognitively driven attentional effects do not amount to cognitive penetration of early vision because such effects occur either before or after early vision. Critics object that in fact such effects occur at all levels of perceptual processing. We argue that Pylyshyn’s claim is correct—but not for the reason he emphasizes. Even if his critics are correct that attentional effects are not external to early vision, these effects do not satisfy Pylyshyn’s requirements that the effects be direct (...) and exhibit semantic coherence. In addition, we distinguish our defense from those found in recent work by Raftopoulos and by Firestone and Scholl, argue that attention should not be assimilated to expectation, and discuss alternative characterizations of cognitive penetrability, advocating a kind of pluralism. (shrink)

If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the.

Language, according to Jackendoff, is more than just an instrument of communication and cultural transmission. It is also a tool which helps us to think. It does so, he suggests, by expanding the range of our conscious contents and hence allowing processes of attention and reflection to focus on items which would not otherwise be available for scrutiny. I applaud Jackendoff s basic vision, but raise some doubts concerning the argument. In particular, I wonder what it is about public language (...) that uniquely fits it to play the functional role which Jackendoff isolates — why couldn't expression in a private inner code induce the same computational benefits? I suggest a weaker position in which the communicative role of public language moulds it into a suitably expressive resource, such that natural language emerges as the logically and technologically contingent filler of a functional role which could, in principle, be filled by other means. I also compare and contrast Jackendoff's position with some related ideas due to Daniel Dennett and others, concluding with a sketch of my own view of language as an external artifact whose computational properties complement those of the basic biological brain. (shrink)

An adequate theory of the nature of belief should help us explain the most obvious features of belief as we find it. Among these features are: guiding action and reasoning non-inferentially; varying in strength in ways that are spontaneously experience-sensitive; ‘aiming at truth’ in some sense and being evaluable in terms of correctness and warrant; possessing inertia across time and constancy across contexts; sustaining expectations in a manner mediated by propositional content; shaping the formation and execution of plans; generalizing spontaneously (...) projectively; and being independent of the will and resisting instrumentalization. Using the method of ‘creature construction’, I attempt to show how we can build an attitude with these features step-by-step from simpler components, thereby avoiding the problems of regress or circularity affecting a number of influential accounts of belief. (shrink)

Prominent models of attentional control assert a dichotomy between top-down and bottom-up control, with the former determined by current selection goals and the latter determined by physical salience. This theoretical dichotomy, however, fails to explain a growing number of cases in which neither current goals nor physical salience can account for strong selection biases. For example, equally salient stimuli associated with reward can capture attention, even when this contradicts current selection goals. Thus, although 'top-down' sources of bias are sometimes defined (...) as those that are not due to physical salience, this conception conflates distinct - and sometimes contradictory - sources of selection bias. We describe an alternative framework, in which past selection history is integrated with current goals and physical salience to shape an integrated priority map. (shrink)

Andy Clark is currently Professor of Philosophy and Director of the Philosophy/Neuroscience/Psychology program at Washington University in St. Louis, Missouri. He is the author of two books MICROCOGNITION (MIT Press/Bradford Books 1989) and ASSOCIATIVE ENGINES (MIT Press/Bradford Books, 1993) as well as numerous papers and four edited volumes. He is an ex- committee member of the British Society for the Philosophy of Science and of the Society for Artificial Intelligence and the Simulation of Behavior. Awards include a visiting Fellowship at (...) the Australia National University, Canberra in 1989, and an ESRC Senior Research Leave Fellowship in 1992. He is an Associate of BEHAVIORAL AND BRAIN SCIENCES, serves on the editorial boards of the journals. (shrink)

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...) the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?". (shrink)

Episodic counterfactual thoughts (CFT) and autobiographical memories (AM) involve the reactivation and recombination of episodic memory components into mental simulations. Upon reactivation, memories become labile and prone to modification. Thus, reactivating AM in the context of mentally generating CFT may provide an opportunity for editing processes to modify the content of the original memory. To examine this idea, this paper reports the results of two studies that investigated the effect of reactivating negative and positive AM in the context of either (...) imagining a better (i.e. upward CFT) or a worse (i.e. downward CFT) alternative to an experienced event, as opposed to attentively retrieving the memory without mental modification (i.e. remembering) or no reactivation. Our results suggest that attentive remembering was the best strategy to both reduce the negative affect associated with negative AM, and to prevent the decay of positive affect associated with positive AM. In addition, reactivating positive, but not negative, AM with or without CFT modification reduces the perceived arousal of the original memory over time. Finally, reactivating negative AM in a downward CFT or an attentive remembering condition increases the perceived detail of the original memory over time. (shrink)

This paper presents an empirical solution to the puzzle of weakness of will. Specifically, it presents a theory of action, grounded in contemporary cognitive neuroscientific accounts of decision making, that explains the phenomenon of weakness of will without resulting in a puzzle.

Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the “representation wars” of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing’s account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based (...) representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally, I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them. (shrink)

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous (...) signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. (shrink)

Embedded and embodied approaches to cognition urge that (1) complicated internal representations may be avoided by letting features of the environment drive behavior, and (2) environmental structures can play an enabling role in cognition, allowing prior cognitive processes to solve novel tasks. Such approaches are thus in a natural position to oppose the ‘thesis of linguistic structuring’: The claim that the ability to use language results in a wholesale recapitulation of linguistic structure in onboard mental representation. Prominent examples of researchers (...) adopting this critical stance include Andy Clark, Michael Wheeler, and Mark Rowlands. But is such opposition warranted? Since each of these authors advocate accounts of mental representation that are broadly connectionist, I survey research on formal language computation in artificial neural networks, and argue that results indicate a strong form of the linguistic structuring thesis is true: Internal representational systems recapitulate significant linguistic structure, even on a connectionist account of mental representation. I conclude by sketching how my conclusion can nonetheless be viewed as consistent with and complimentary to an embedded/embodied account of the role of linguistic structure in cognition. (shrink)

The core of the language of thought program is the claim that thinking is the manipulation of symbols according to rules. Yet LOT has said little about symbol natures, and existing accounts are highly controversial. This is a major flaw at the heart of the LOT program: LOT requires an account of symbol natures to naturalize intentionality, to determine whether the brain even engages in symbol manipulations, and to understand how symbols relate to lower-level neurocomputational states. This paper provides the (...) much-needed theory of symbols, and in doing so, alters the LOT program in significant respects. (shrink)

Language, according to Jackendoff, is more than just an instrument of communication and cultural transmission. It is also a tool which helps us to think. It does so, he suggests, by expanding the range of our conscious contents and hence allowing processes of attention and reflection to focus on items which would not otherwise be available for scrutiny. I applaud Jackendoff s basic vision, but raise some doubts concerning the argument. In particular, I wonder what it is about public language (...) that uniquely fits it to play the functional role which Jackendoff isolates — why couldn't expression in a private inner code induce the same computational benefits? I suggest a weaker position in which the communicative role of public language moulds it into a suitably expressive resource, such that natural language emerges as the logically and technologically contingent filler of a functional role which could, in principle, be filled by other means. I also compare and contrast Jackendoff's position with some related ideas due to Daniel Dennett and others, concluding with a sketch of my own view of language as an external artifact whose computational properties complement those of the basic biological brain. (shrink)

Over a century ago, William James outlined the first psychological constructionist model of emotion, arguing that emotions are phenomena constructed of more basic psychological parts. In this article, I outline a modern psychological constructionist model of emotion. I first explore the history of psychological construction to demonstrate that psychological constructionist models have historically emerged in an attempt to explain variability in emotion that cannot be accounted for by other approaches. I next discuss the modern psychological constructionist model of emotion that (...) I take in my own research, outlining its hypotheses, existing empirical support, and areas of future research. I conclude by arguing that psychological constructionist models can help scientists better understand the human mind. (shrink)

Natural language contains many expressions with underspecified emotive content. This paper proposes a way to resolve such underspecification. Nonmonotonic inference over a knowledge base is used to derive an expected interpretation for emotive expressions in a particular context. This ‘normal’ meaning is then taken to influence the hearer’s expectations about probable interpretations, and, because of these probable interpretations, the decisions of the speaker about when use of underspecified emotive terms is appropriate.

Natural language contains many expressions with underspecified emotive content. This paper proposes a way to resolve such underspecification. Nonmonotonic inference over a knowledge base is used to derive an expected interpretation for emotive expressions in a particular context. This ‘normal’ meaning is then taken to influence the hearer’s expectations about probable interpretations, and, because of these probable interpretations, the decisions of the speaker about when use of underspecified emotive terms is appropriate.