Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I (...) differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users. (shrink)

Hubert Dreyfus once noted that it would be difficult to ascertain whether Edmund Husserl had a computational theory of mind. I provide evidence that he had one. Both Steven Pinker and Steven Horst think that the computational theory of mind must have two components: a representational-symbolic component and a causal component. Bearing this in mind, we proceed to a close-reading of the sections of “On the Logic of Signs” wherein Husserl presents, if I’m correct, his computational theory of mind embedded (...) in a language of thought. My argument goes like this: the computational theory of mind is the idea, following Haugeland, that the mind comes prepackaged as, or is endogenously constrained to be, an automatic formal system; this explains, according to Husserl, why automatic trains of thought without logical intent resemble arguments exhibiting deductive structure with logical intent. In general, an automatic formal system yields true results provided that the syntactic symbols with which they compute are univocal and are semantically evaluable, and the mechanized inferences they perform are valid and preserve truth. These two conditions describe a computational cognitive process: the first condition connects representations to syntax, and the second condition uses the syntax, in inauthentic judging, to arrive at true conclusions through blind causality. Now, in point of textual fact, these are the conditions which Husserl attributes to our “natural psychological mechanism of symbolic inference” which typically yields true results. Since a formal system attributed to the “internal structure” of the mind, and guided by blind causality, just is the computational theory of mind, it follows, I think, that Husserl had a computational theory of mind. This computational theory is, moreover, embedded in a language of thought, since Husserl attributes a language-like form to our thoughts so that they may be mechanically processed. I conclude with a discussion of my results. (shrink)

Formal theories of mental representation have receded from the importance they had in the early days of cognitive science. I argue that such theories are crucial in any mental domain, not just for their own sake, but to guide experimental inquiry, as well as to integrate the domain into the mind as a whole. To illustrate the criteria of adequacy for theories of mental representation, I compare two theoretical approaches to language: classical generative grammar (Chomsky, 1965, 1981, 1995) and the (...) parallel architecture (Jackendoff, 1997, 2002). The grounds for comparison include (a) the internal coherence of the theory across phonology, syntax, and semantics; (b) the relation of language to other mental faculties; (c) the relationship between grammar and lexicon; (d) relevance to theories of language processing; and (e) the possibility of languages with little or no syntax. (shrink)

The artificial intelligence models with machine learning that exhibit the best predictive accuracy, and therefore, the most powerful ones, are, paradoxically, those with the most opaque black-box architectures. At the same time, the unstoppable computerization of advanced industrial societies demands the use of these machines in a growing number of domains. The conjunction of both phenomena gives rise to a control problem on AI that in this paper we analyze by dividing the issue into two. First, we carry out an (...) epistemological examination of the AI’s opacity in light of the latest techniques to remedy it. And second, we evaluate the rationality of delegating tasks in opaque agents. (shrink)

Teresa believes in God. Maggie’s wife believes that the Earth is flat, and also that Maggie should be home from work by now. Anouk—a cat—believes it is dinner time. This dissertation is about what believing is: it concerns what, exactly, ordinary people are attributing to Teresa, Maggie’s wife, and Anouk when affirming that they are believers. Part I distinguishes the attitudes of belief that people attribute to each other (and other animals) in ordinary life from the cognitive states of belief (...) theoretically posited by (some) cognitive scientists. Part II defends the view that to have an attitude of belief is to live—to be disposed to act, react, think, and feel—in a pattern that an actual belief attributor identifies with taking the world to be some way. Drawing on scientific, scholarly, and literary sources of evidence, How Beliefs are like Colors provides a framework for research on belief across the humanities and sciences of the mind. (shrink)

It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the (...) components of the explanandum phenomenon and their causal organization. Moreover, each component in one level of mechanism is constituted and explained by components of an underlying level of mechanism. Hence, it seems, computational variables and functions cannot be mechanistically explained by the medium-dependent states and properties that implement them. How then, do the computational and the implementational integrate to create the mechanistic hierarchy? After explicating the general problem, we further demonstrate it through a concrete example, of reinforcement learning, in the cognitive and neural sciences. We then examine two possible solutions. On one solution, the mechanistic hierarchy embeds at the same levels computational and implementational properties. This picture fits with the view that computational explanations are mechanistic sketches. On the other solution, there are two separate hierarchies, one computational and another implementational, which are related by the implementation relation. This picture fits with the view that computational explanations are functional and autonomous explanations. It is less clear how these solutions fit with the view that computational explanations are full-fledged mechanistic explanations. Finally, we argue that both pictures are consistent with the reinforcement learning example, but that scientific practice does not align with the view that computational models are merely mechanistic sketches. (shrink)

Cognitive science has been beset for thirty years by foundational disputes about the nature and extension of cognition—e.g. whether cognition is necessarily representational, whether cognitive processes extend outside the brain or body, and whether plants or microbes have them. Whereas previous philosophical work aimed to settle these disputes, I aim to understand what conception of cognition scientists could share given that they disagree so fundamentally. To this end, I develop a number of variations on traditional conceptual explication, and defend a (...) novel explication of cognition called the sensitive management hypothesis. -/- Since expert judgments about the extension of “cognition” vary so much, I argue that there is value in explication that accurately models the variance in judgments rather than taking sides or treating that variance as noise. I say of explications that accomplish this that they are ecumenically extensionally adequate. Thus, rather than adjudicating whether, say, plants can have cognitive processes like humans, an ecumenically adequate explication should classify these cases differently: human cognitive processes as paradigmatically cognitive, and plant processes as controversially cognitive. -/- I achieve ecumenical adequacy by articulating conceptual explications with parameters, or terms that can be assigned a number of distinct interpretations based on the background commitments of participants in a discourse. For example, an explication might require that cognition cause “behavior,” and imply that plant processes are cognitive or not depending on whether anything plants do can be considered “behavior.” Parameterization provides a unified treatment of embattled concepts by isolating topics of disagreement in a small number of parameters. -/- I incorporate these innovations into an account on which cognition is the “sensitive management of organismal behavior.” The sensitive management hypothesis is ecumenically extensionally adequate, accurately classifying a broad variety of cases as paradigmatically or controversially cognitive phenomena. I also describe an extremely permissive version of the sensitive management hypothesis, arguing that it has the potential to explain several features of cognitive scientific discourse, including various facts about the way cognitive scientists ascribe representations to cognitive systems. (shrink)

A philosophical appraisal of historical positions on the nature of thought, mentality, and intelligence, this survey begins with the views of Descartes, Turing, and Newell and Simon, but includes the work of Haugeland, Fodor, Searle, and other major scholars. The underlying issues concern distinctions between syntax, semantics, and pragmatics, where physical computers seem to be best viewed as mark-manipulating or syntax-processing mechanisms. Alternative accounts have been advanced of what it takes to be a thinking thing, including being Turing machines, symbol (...) systems, semantic engines, and semiotic systems, which have the ability to use signs in the sense of the Charles S. Peirce. Reflections regarding the nature of representations and existence of mental algorithms suggest that the theory of minds as semiotic systems should be preferred to its alternatives, where digital computers can still qualify as “intelligent machines” even without minds. (shrink)

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

Intentional Mentalism is the view that mental intentionality is primary to non-mental intentionality and that the latter is derived from the former. In this article I examine three views which have been taken to motivate Intentional Mentalism: conventionalism as invoked by Searle, Gricean pragmatism, and the language of thought hypothesis. I argue that none of these views motivates Intentional Mentalism, and that, in fact, the former two imply its rejection.

Recent experiments have shown that when individuals with a substance use disorder are confronted with drug-related cues, they exhibit an automatically activated tendency to approach these cues. The strength of the drug approach bias has been associated with clinically relevant measures, such as increased drug craving and relapse, and activations in brain reward areas. Retraining the approach bias by means of cognitive bias modification has been demonstrated to decrease relapse rates in patients with an alcohol use disorder and to reduce (...) alcohol cue-evoked limbic activity. Here, we review empirical and theoretical literature on the drug approach bias and explore two distinct models of how the drug approach bias may be embodied. First, we consider the “biological meaning” hypothesis, which grounds the automatic approach bias in the natural meaning of the body. Second, we consider the “sensorimotor hypothesis,” which appeals to the specific sensorimotor loops involved in the instantiation of addictive behaviors as the basis of the automatic approach bias. In order to differentiate between the adequacies of these competing explanations, we present specific, predictions that each model should make. (shrink)

There is a prevalent notion among cognitive scientists and philosophers of mind that computers are merely formal symbol manipulators, performing the actions they do solely on the basis of the syntactic properties of the symbols they manipulate. This view of computers has allowed some philosophers to divorce semantics from computational explanations. Semantic content, then, becomes something one adds to computational explanations to get psychological explanations. Other philosophers, such as Stephen Stich, have taken a stronger view, advocating doing away with semantics (...) entirely. This paper argues that a correct account of computation requires us to attribute content to computational processes in order to explain which functions are being computed. This entails that computational psychology must countenance mental representations. Since anti-semantic positions are incompatible with computational psychology thus construed, they ought to be rejected. Lastly, I argue that in an important sense, computers are not formal symbol manipulators. (shrink)

Dramatic changes or revolutions in a field of science are often made by outsiders or 'trespassers,' who are not limited by the established, 'expert' approaches. Each essay in this diverse collection shows the fruits of intellectual trespassing and poaching among fields such as economics, Kantian ethics, Platonic philosophy, category theory, double-entry accounting, arbitrage, algebraic logic, series-parallel duality, and financial arithmetic.

Within the strong branch of artificial intelligence, which is aimed at creating thinking machines with intellectual powers like those of man, the most explored research program is symbolic aI, defined as the attempt to use electronic computers to replicate the human mind, either assuming a structural and functional similarity between them, or trying to replicate the behavior produced by the human mind through computational processes that also have an intentional structure but are only instrumentally equivalent. In this paper we show (...) that strong symbolic aI, in either of its two variants, is impossible, since formal systems, that is what all computer programs ultimately are, are not sufficient to replicate in an intentional way four intellectual faculties of man that are essential to intelligent behavior: sense of the situation, common sense, procedural skills and theory abduction. (shrink)

Recently, many philosophers have been inclined to ascribe mentality to animals on the main grounds that they possess certain complex computational abilities. In this paper I contend that this view is misleading, since it wrongly assumes that those computational abilities demand a psychological explanation. On the contrary, they can be just characterised from a computational level of explanation, which picks up a domain of computation and information processing that is common to many computing systems but is autonomous from the domain (...) of psychology. Thus, I propose that it is possible to conceive insects and other animals as mere computing agents, without having any commitment to ascribe mentality to them. I conclude by sketching a proposal about how to draw the line between mere computing and genuine mentality. (shrink)

A survey of contemporary philosophical and scientific literatures reveals that different authors employ the term ‘heuristic’ in ways that deviate from, and are sometimes inconsistent with, one another. Given its widespread use in philosophy and cognitive science generally, it is striking that there appears to be little concern for a clear account of what phenomena heuristics pick out or refer to. In response, I consider several accounts of ‘heuristic’, and I draw a number of distinctions between different sorts of heuristics (...) in order to make sense of various research programmes. I then develop a working characterization of ‘heuristic’ which is shown to be coherent and robust enough to serve as a kind within philosophy and cognitive science broadly. My intent is not to pursue a unified account of ‘heuristic’, but to highlight some features of certain kinds of heuristics that are important for theorizing about cognition in order to proceed with a science of the mind/brain. 1 Why We Need an Appropriate Characterization of ‘Heuristic’2 Inadequate Definitions2.1 A positive attempt2.2 Heuristics versus guaranteed correct outcomes3 A Taxonomy of Heuristics3.1 Stimulus-driven versus heuristic3.2 Perceptual versus cognitive heuristics3.3 Computational versus cognitive heuristics3.4 Methodological versus inferential heuristics3.5 Summary of distinctions4 Characterizing Cognitive Heuristics4.1 Heuristics as rules of thumb4.2 Beyond rules of thumb: exploiting information5 Concluding Remarks. (shrink)

This chapter probes the connections between the metaphysics of meaning and the investigation of human communication. It first argues that contemporary philosophy of mind has inherited most of its metaphysical questions from Brentano's puzzling definition of intentionality. Then it examines how intentionality came to occupy the forefront of pragmatics in three steps. By investigating speech acts, Austin and ordinary language philosophers pioneered the study of intentional actions performed by uttering sentences of natural languages. Based on his novel concept of speaker's (...) meaning and his inferential view of human communication as a cooperative and rational activity, Grice developed a three-tiered model of the meaning of utterances: the linguistic meaning of the uttered sentence; the explicit truth-conditional content of the utterance; the implicit content conveyed by the utterance. Finally, the new emerging truth-conditional trend in pragmatics urges that not only the implicit content conveyed by an utterance but its explicit content as well depends on the speaker's communicative intention. (shrink)

In this review of Hutto and Myin's Radicalizing Enactivism, I question the adequacy of a non-representational theory of mind. I argue first that such a theory cannot differentiate cognition from other bodily engagements such as wrestling with an opponent. Second, I question whether the simple robots constructed by Rodney Brooks are adequate as models of multimodal organisms. Last, I argue that Hutto and Myin pay very little attention to how semantically interacting representations are needed to give an account of choice (...) and action. (shrink)

Can animals think? In this paper I address the proposal that many animals, including insects such as honeybees, have genuine thoughts. I consider one prominent version of this view that claims that honeybees can represent and process information about their environments in a way that satisfies the main hallmarks of human conceptual thought. I shall argue, however, that this view fails to provide convincing grounds for accepting that animals possess concepts. More precisely, I suggest that two important aspects of conceptual (...) thought, viz., concept individuation and the generality constraint, are not satisfied. (shrink)

Understanding the role of ‘‘representations’’ in cognitive science is a fundamental problem facing the emerging framework of embodied, situated, dynamical cognition. To make progress, I follow the approach proposed by an influential representational skeptic, Randall Beer: building artificial agents capable of minimally cognitive behaviors and assessing whether their internal states can be considered to involve representations. Hence, I operationalize the concept of representing as ‘‘standing in,’’ and I look for representations in embodied agents involved in simple categorization tasks. In a (...) first experiment, no representation can be found, but the relevance of the task is undermined by the fact that agents with no internal states can reach high performance. A simple modification makes the task more “representationally hungry,” and in this case, agents’ internal states are found to qualify as representations. I conclude by discussing the benefits of reconciling the embodied-dynamical approach with the notion of representation. (shrink)

In the Fall of 1983, I offered a junior/senior-level course in Philosophy of Artificial Intelligence, in the Department of Philosophy at SUNY Fredonia, after returning there from a year’s leave to study and do research in computer science and artificial intelligence (AI) at SUNY Buffalo. Of the 30 students enrolled, most were computerscience majors, about a third had no computer background, and only a handful had studied any philosophy. (I might note that enrollments have subsequently increased in the Philosophy Department’s (...) AI-related courses, such as logic, philosophy of mind, and epistemology, and that several computer science students have added philosophy as a second major.) This article describes that course, provides material for use in such a course, and offers a bibliography of relevant articles in the AI, cognitive science, and philosophical literature. (shrink)

Controversies over the innateness of cognitive processes, mechanisms, and structures play a persistent role in driving research in philosophy as well as the cognitive sciences, but the appropriate way to understand the category of the innate remains subject to dispute. One venerable approach in philosophy and cognitive science merely contrasts innate features with those that are learned. In fact, Jerry Fodor has recently suggested that this remains our best handle on innateness.

O'Brien & Opie do not address the question of the psychotherapeutic role of unconscious representational states such as beliefs. A dilemma is proposed: if they accept the legitimacy of such states then they should modify what they say about dissociation, and if they do not, they owe us an account of why.

O'Brien & Opie's (O&O's) theory demands a view of unconscious processing that is incompatible with virtually all current PDP models of neural activity. Relative to the alternatives, the theory is closer to an AI than a parallel distributed processing (PDP) perspective, and its treatment of phenomenology is ad hoc. It raises at least one important question: Could features of network relaxation be the “switch” that turns an unconscious into a conscious network?

In this paper I defend an etiological theory of biological functions (according to which the proper function of a trait is the effect for which it was selected by natural selection) against three objections which have been influential. I argue, contrary to Millikan, that it is wrong to base our defense of the theory on a rejection of conceptual analysis, for conceptual analysis does have an important role in philosophy of science. I also argue that biology requires a normative notion (...) of a "proper function", and that a normative notion is not ahistorical. (shrink)

This paper contributes to the recently renewed debate over methodological individualism (MI) by carefully sorting out various individualist claims and by making use of recent work on reduction and explanation outside the social sciences. My major focus is on individualist claims about reduction and explanation. I argue that reductionist versions of MI fail for much the same reasons that mental predicates cannot be reduced to physical predicates and that attempts to establish reducibility by weakening the requirements for reduction also fail. (...) I consider and reject a number of explanatory theses, among them the claims that any adequate theory must refer only to individuals and that individualist theory suffices to explain fully. The latter claim, I argue, is not entailed by the supervenience of social facts on individual facts. Lastly, I argue that there is one individualist restriction on explanation which is far more plausible and significant than one would initially suspect. (shrink)

Der Kern des Leib-Seele-Problems besteht darin, dass mentale Phänomene (Ereignisse, Eigenschaften, Zustände) Merkmale zu haben scheinen, die es auf den ersten Blick unmöglich machen, diese Phänomene in ein naturalisti- sches Weltbild zu integrieren – sie mit physikalischen Phänomenen zu identifizieren oder auf physikalische Phänomene zu reduzieren.2 Heute stehen hauptsächlich zwei von in diesem Sinne kritischen Merkmalen im Mit- telpunkt des Interesses.3 Das erste ist das Merkmal intentionaler Zustände, einen repräsentationalen oder semantischen Inhalt zu besitzen. Das Problem der Naturalisierung dieser Zustände (...) möchte ich das ‘Problem der Intentiona- lität’ nennen. Das zweite kritische Merkmal ist die Eigenschaft von Wahr- nehmungseindrücken und körperlichen Empfindungen, einen qualitativen Aspekt zu besitzen. Dieses Merkmal beruht auf der Tatsache, dass es auf eine bestimmte Weise ist oder sich auf eine bestimmte Weise anfühlt, in diesen Zuständen zu sein. Das Problem der Naturalisierung dieser Zustände wird gewöhnlich das ‘Qualia-Problem’ genannt. (shrink)

I sketch an explanatory framework that fits a variety of contemporary research programs in cognitive science. I then investigate the scope and the implications of this framework. The framework emphasizes (a) the explanatory role played by the semantic content of cognitive representations, and (b) the important mechanistic, non-intentional dimension of cognitive explanations. I show how both of these features are present simultaneously in certain varieties of cognitive explanation. I also consider the explanatory role played by grounded representational content, that is, (...) content evaluated by appeal to its truth, falsity, accuracy, inaccuracy and other relational properties. (shrink)

The idea that human thinking and machine computing are "radically the same" provides the central theme for this marvelously lucid and witty book on...

I critically examine some provocative arguments that John Searle presents in his book The Rediscovery of Mind to support the claim that the syntactic states of a classical computational system are "observer relative" or "mind dependent" or otherwise less than fully and objectively real. I begin by explaining how this claim differs from Searle's earlier and more well-known claim that the physical states of a machine, including the syntactic states, are insufficient to determine its semantics. In contrast, his more recent (...) claim concerns the syntax, in particular, whether a machine actually has symbols to underlie its semantics. I then present and respond to a number of arguments that Searle offers to support this claim, including whether machine symbols are observer relative because the assignment of syntax is arbitrary, or linked to universal realizability, or linked to the sub-personal interpretive acts of a homunculus, or linked to a person's consciousness. I conclude that a realist about the computational model need not be troubled by such arguments. Their key premises need further support. (shrink)

What counts as a computation and how it relates to cognitive function are important questions for scientists interested in understanding how the mind thinks. This paper argues that pragmatic aspects of explanation ultimately determine how we answer those questions by examining what is needed to make rigorous the notion of computation used in the (cognitive) sciences. It (1) outlines the connection between the Church-Turing Thesis and computational theories of physical systems, (2) differentiates merely satisfying a computational function from true computation, (...) and finally (3) relates how we determine a true computation to the functional methodology in cognitive science. All of the discussion will be directed toward showing that the only way to connect formal notions of computation to empirical theory will be in virtue of the pragmatic aspects of explanation. (shrink)

There are different ways to present a Presidential Address to the APA; the one I have chosen is simply to report on work that I am doing right now, on work in progress. I am going to present some of my further explorations into the computational model of the mind.\**.

The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical (...) signals. I then indicate why computational theories have explanatory force with respect to these regularities:in a nutshell, they underscore correspondence relations between formal/mathematical properties of the electrical signals and formal/mathematical properties of the represented objects. I finally link my proposal to the philosophical thesis that content plays an essential role in computational taxonomy. (shrink)

There is a prevalent notion among cognitive scientists and philosophers of mind that computers are merely formal symbol manipulators, performing the actions they do solely on the basis of the syntactic properties of the symbols they manipulate. This view of computers has allowed some philosophers to divorce semantics from computational explanations. Semantic content, then, becomes something one adds to computational explanations to get psychological explanations. Other philosophers, such as Stephen Stich, have taken a stronger view, advocating doing away with semantics (...) entirely. This paper argues that a correct account of computation requires us to attribute content to computational processes in order to explain which functions are being computed. This entails that computational psychology must countenance mental representations. Since anti-semantic positions are incompatible with computational psychology thus construed, they ought to be rejected. Lastly, I argue that in an important sense, computers are not formal symbol manipulators. (shrink)

Cognitive science has been dominated by the computational conception that cognition is computation across representations. To the extent to which cognition as computation across representations is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from their interpretation by human users. Strictly speaking, computers as such — apart (...) from human users — are not only incapable of cognition, but even incapable of computation, properly construed. If we want to understand the nature of thought, then we have to study thinking, not computing, because they are not the same thing. (shrink)

Oscar is going to be the first artificial person — at any rate, he is going to be the first artificial person to be built in Tucson's Philosophy Department. Oscar's creator, John Pollock, maintains that once Oscar is complete he will experience qualia, will be self-conscious, will have desires, fears, intentions, and a full range of mental states (Pollock 1989, pp. ix–x). In this paper I focus on what seems to me to be the most problematical of these claims, viz., (...) that Oscar will experience qualia. I argue that we have not been given sufficient reasons to believe this bold claim. I doubt that Oscar will enjoy qualitative conscious phenomena and I maintain that it will be like nothing to be Oscar. (shrink)

This paper provides a survey of philosophical discussion of the "the Turing Test". In particular, it provides a very careful and thorough discussion of the famous 1950 paper that was published in Mind.

In this paper I review some leading developments in the empirical theory of affect. I argue that (1) affect is a distinct perceptual representation governed system, and (2) that there are significant modular factors in affect. The paper concludes with the observation thatfeeler (affective perceptual system) may be a natural kind within cognitive science. The main purpose of the paper is to explore some hitherto unappreciated connections between the theory of affect and the computational theory of mind.

Recent worries about possible epiphenomenalist consequences of nonreductive materialism are misplaced, not, as many have argued, because nonreductive materialism does not have epiphenomenalist implications but because the epiphenomenalist implications are actually virtues of the theory, rather than vices. It is only by showing how certain kinds of mental properties are causally impotent that cognitive scientific explanations of mentality as we know them are possible.