The correspondence metaphor is useful in developing functional models of memory. However, the storehouse metaphor is still useful in developing structural and process models of memory. Traditional research techniques explore the structure of memory; everyday techniques explore the function of memory. We illustrate this point with two examples: amnesia and metamemory. In each phenomenon, both metaphors are useful.

The correspondence view is consistent with a theory of direct remembering that assumes continuity between perception and memory. Two implications of direct remembering for correspondence are suggested. It is assumed that forgetting is exponential, and that remembering at one time is independent of factors influencing remembering at another. Elaboration of the correspondence view in the same terms as perception offers a novel approach to the study of memory.

Koriat & Goldsmith are right in their claim that the “ecological” and “traditional” approaches to memory rely on different metaphors. But the underlying ecological metaphor is notcorrespondence(which in any case is not a metaphorical notion): it isaction. Remembering is a kind of doing; like most other forms of action it is purposive, personal, and particular.

The novel correspondence metaphor outlined by Koriat & Goldsmith offers important advantages for studying critical issues of memory-accuracy. It also fits well with the current emphasis on the reconstructive nature of memory and on the role of cognitive, metacognitive, and motivational factors in memory performance. These positive features notwithstanding, the storehouse/correspondence framework faces potential perils having to do with its implied linkage to the laboratory/real-life controversy and its proposal of studying correspondence issues in isolation from memory phenomena captured by the (...) storehouse paradigm. (shrink)

Koriat & Goldsmith raise important questions about memory, but there is need for caution: first, if we define accuracy by output measures, there is a danger that a perfectly accurate memory can be nearly useless. Second, when we focus on correspondence, there is a danger that syntactic correspondence will be mistaken for historical truth.

Research on moral judgment has been dominated by rationalist models, in which moral judgment is thought to be caused by moral reasoning. The author gives 4 reasons for considering the hypothesis that moral reasoning does not cause moral judgment; rather, moral reasoning is usually a post hoc construction, generated after a judgment has been reached. The social intuitionist model is presented as an alternative to rationalist models. The model is a social model in that it deemphasizes the private reasoning done (...) by individuals and emphasizes instead the importance of social and cultural influences. The model is an intuitionist model in that it states that moral judgment is generally the result of quick, automatic evaluations (intuitions). The model is more consistent than rationalist models with recent findings in social, cultural, evolutionary, and biological psychology, as well as in anthropology and primatology. (shrink)

(1997). Semantic fields and meaning: A bridge between mind and matter. World Futures: Vol. 48, The Concept of Collective Consiousness: Research Perspectives, pp. 161-170.

This paper proposes a causal-dispositional account of rule-following as it occurs in reasoning and intentional agency. It defends this view against Kripke’s (1982) objection to dispositional accounts of rule-following, and it proposes a solution to the problem of deviant causal chains. In the first part, I will outline the causal-dispositional approach. In the second part, I will follow Martin and Heil’s (1998) realist response to Kripke’s challenge. I will propose an account that distinguishes between two kinds of rule-conformity and two (...) kinds of rule-following, and I will defend the realist approach against two challenges that have recently been raised by Handfield and Bird (2008). In the third part, I will turn to the problem of deviant causal chains, and I will propose a new solution that is partly based on the realist account of rule-following. (shrink)

Taking inspiration from developments in neurocomputational modeling, Paul Church-land develops his positions in the philosophy of mind and the philosophy of science. Concerning the former, Churchland relaxes his eliminativism at various points and seems to endorse a traditional identity account of sensory qualia. Although he remains unsympathetic to folk psychology, he no longer seeks the elimination of normative epistemology, but rather its transformation to a philosophical enterprise informed by current developments in the relevant sciences. Churchland supplies suggestive discussions of the (...) character of knowledge, simplicity, explanation, theory, and conceptual change. Many of his treatments turn on his prototype activation model of neural representation, which looks to the notion of a 'prototype' as it is employed in the psychological literature on concept representation, however, this and other features of Churchland's neurocomputational program do not square well with some of his views about cross-scientific relations. (shrink)

In this discussion, the author asks the question if Oakeshott’s famous depiction of a practice might be understood in relation to contemporary cognitive science, in particular connectionism (the contemporary cognitive science approach concerned with the problem of skills and skilled knowing) and in terms of the now conventional view of "normativity" in Anglo-American philosophy. The author suggests that Oakeshott meant to contrast practices to an alternative "Kantian" model of a shared tacit mental frame or set of rules. If cognitive science, (...) in its connectionist forms, allows us to give a naturalistic though nonreductive sense to his words, Oakeshott, like other philosophers who have employed the concept of tradition, expanded his discussion into a broader reconsideration of the nature of theorizing, a metaphilosophy. And this extension can be understood in relation to such recent thinkers as McDowell and, in particular, to the problem of the acquisition of the normative. Key Words: idealism • Oakeshott • connectionism • normativity. (shrink)

Two standard epistemological accounts are conflated in Dienes & Perner's account of knowledge, and this conflation requires the rejection of their four conditions of knowledge. Because their four metarepresentations applied to the explicit-implicit distinction are paired with these conditions, it follows by modus tollens that if the latter are inadequate, then so are the former. Quite simply, their account misses the link between true reasoning and knowledge.

We focus on Karmiloff-Smith's Representational redescription model, arguing that it poses some problems concerning the architecture of a redescribing system. To discuss the topic, we consider the implicit/explicit dichotomy and the relations between natur al language and the language of thought. We argue that the model regards how knowledge is employed rather than how it is represented in the system.

[This is an earlier (1997), much longer and more detailed version of my entry on LOTH in the _Stanford Encyclopedia of Philosophy_] The Language of Thought Hypothesis (LOTH) is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that (...) has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)

In this article the question is raised whether artificial intelligence has any psychological relevance, i.e. contributes to our knowledge of how the mind/brain works. It is argued that the psychological relevance of artificial intelligence of the symbolic kind is questionable as yet, since there is no indication that the brain structurally resembles or operates like a digital computer. However, artificial intelligence of the connectionist kind may have psychological relevance, not because the brain is a neural network, but because connectionist networks (...) exhibit operating characteristics which mimic operant behavior. Finally it is concluded that, since most of the work done so far in AI and Law is of the symbolic kind, it has as yet contributed little to our understanding of the legal mind. (shrink)

A critical aspect of much human behaviour is the generation and regulation of sequential activities. Such behaviour is seen in both naturalistic settings such as routine action and language production and laboratory tasks such as serial recall and many reaction time experiments. There are a variety of computational mechanisms that may support the generation and regulation of sequential behaviours, ranging from those underlying Turing machines to those employed by recurrent connectionist networks. This paper surveys a range of such mechanisms, together (...) with a range of empirical phenomena related to human sequential behaviour. It is argued that the empirical phenomena pose difficulties for most sequencing mechanisms, but that converging evidence from behavioural flexibility, error data arising from when the system is stressed or when it is damaged following brain injury, and between-trial effects in reaction time tasks, point to a hybrid symbolic activation-based mechanism for the generation and regulation of sequential behaviour. Some implications of this view for the nature of mental computation are highlighted. (shrink)

This paper reviews four significant advances on the feedforward architecture that has dominated discussions of connectionism. The first involves introducing modularity into networks by employing procedures whereby different networks learn to perform different components of a task, and a Gating Network determines which network is best equiped to respond to a given input. The second consists in the use of recurrent inputs whereby information from a previous cycle of processing is made available on later cycles. The third development involves developing (...) compressed representations of strings in which there is no longer an explicit encoding of the components but where information about the structure of the original string can be recovered and so is present functionally. The final advance entails using connectionist learning procedures not just to change weights in networks but to change the patterns used as inputs to the network. These advances significantly increase the usefulness of connectionist networks for modeling human cognitive performance by, among other things, providing tools for explaining the productivity and systematicity of some mental activities, and developing representations that are sensitive to the content they are to represent. (shrink)

The implicit-explicit distinction is applied to knowledge representations. Knowledge is taken to be an attitude towards a proposition which is true. The proposition itself predicates a property to some entity. A number of ways in which knowledge can be implicit or explicit emerge. If a higher aspect is known explicitly then each lower one must also be known explicitly. This partial hierarchy reduces the number of ways in which knowledge can be explicit. In the most important type of implicit knowledge, (...) representations merely reflect the property of objects or events without predicating them of any particular entity The dearest cases of explicit knowledge of a fact are representations of one's own attitude of knowing that fact. These distinctions are discussed in their relationship to similar distinctions such as procedural-declarative, conscious unconscious, verbalizable-nonverbalizable, direct-indirect tests, and automatic voluntary control. This is followed by an outline of how these distinctions can be used to integrate and relate the often divergent uses of the implicit-explicit distinction in different research areas. We illustrate this for visual perception, memory, cognitive development, and artificial grammar learning. (shrink)

Many studies of language, whether in philosophy, linguistics, or psychology, have focused on highly developed human languages. In their highly developed forms, such as are employed in scientific discourse, languages have a unique set of properties that have been the focus of much attention. For example, descriptive sentences in a language have the property of being "true" or "false," and words of a language have senses and referents. Sentences in a language are structured in accord with complex syntactic rules. Theorists (...) focusing on language are naturally led to ask questions such as what constitutes the meanings of words and sentences and how are the principles of syntax encoded in the heads of language users. While there is an important function for inquiries into the highly developed forms of these cultural products (Abrahamsen, 1987), such a focus can be quite misleading when we want to explain how these products have arisen or the human capacity to use language. The problem is that focusing on its most developed forms makes linguistic ability seem to be a _sui generis_ phenomenon, not related to, and hence not explicable in terms of other cognitive capacities. Chomsky's (1980) postulation of a specific language module equipped with specialized resources needed to process language and possessed only by hum ans is not a surprising result. (shrink)

In a recent series of publications, dynamicist researchers have proposed a new conception of cognitive functioning. This conception is intended to replace the currently dominant theories of connectionism and symbolicism. The dynamicist approach to cognitive modeling employs concepts developed in the mathematical field of dynamical systems theory. They claim that cognitive models should be embedded, low-dimensional, complex, described by coupled differential equations, and non-representational. In this paper I begin with a short description of the dynamicist project and its role as (...) a cognitive theory. Subsequently, I determine the theoretical commitments of dynamicists, critically examine those commitments and discuss current examples of dynamicist models. In conclusion, I determine dynamicism's relation to symbolicism and connectionism and find that the dynamicist goal to establish a new paradigm has yet to be realized. (shrink)

In their critique of connectionist models Fodor and Pylyshyn (1988) dismiss such models as not being cognitive or psychological. Evaluating Fodor and Pylyshyn's critique requires examining what is required in characterizating models as 'cognitive'. The present discussion examines the various senses of this term. It argues the answer to the title question seems to vary with these different senses. Indeed, by one sense of the term, neither representa-tionalism nor connectionism is cognitive. General ramifications of such an appraisal are discussed and (...) alternative avenues for cognitive research are suggested. (shrink)

The reemergence of connectionism² has profoundly altered the philosophy of mind. Paul Churchland has argued that it should equally transform the philosophy of science. He proposes that connectionism offers radical and useful new ways of understanding theories and explanations.

Contemporary epistemology has assumed that knowledge is represented in sentences or propositions. However, a variety of extensions and alternatives to this view have been proposed in other areas of investigation. We review some of these proposals, focusing on (1) Ryle's notion of knowing how and Hanson's and Kuhn's accounts of theory-laden perception in science; (2) extensions of simple propositional representations in cognitive models and artificial intelligence; (3) the debate concerning imagistic versus propositional representations in cognitive psychology; (4) recent treatments of (...) concepts and categorization which reject the notion of necessary and sufficient conditions; and (5) parallel distributed processing (connectionist) models of cognition. This last development is especially promising in providing a flexible, powerful means of representing information nonpropositionally, and carrying out at least simple forms of inference without rules. Central to several of the proposals is the notion that much of human cognition might consist in pattern recognition rather than manipulation of rules and propositions. (shrink)

There is currently a debate over whether cognitive architecture is classical or connectionist in nature. One finds the following three comparisons between classical architecture and connectionist architecture made in the pro-connectionist literature in this debate: (1) connectionist architecture is neurally plausible and classical architecture is not; (2) connectionist architecture is far better suited to model pattern recognition capacities than is classical architecture; and (3) connectionist architecture is far better suited to model the acquisition of pattern recognition capacities by learning than (...) is classical architecture. If true, (1)–(3) would yield a compelling case against the view that cognitive architecture is classical, and would offer some reason to think that cognitive architecture may be connectionist. We first present the case for (1)–(3) in the very words of connectionist enthusiasts. We then argue that the currently available evidence fails to support any of (1)–(3). (shrink)

In this paper I assess the explanatory role of internal representations in connectionist models of cognition. Focusing on both the internal‘hidden’units and the connection weights between units, I argue that the standard reasons for viewing these components as representations are inadequate to bestow an explanatorily useful notion of representation. Hence, nothing would be lost from connectionist accounts of cognitive processes if we were to stop viewing the weights and hidden units as internal representations.

The relation between logic and thought has long been controversial, but has recently influenced theorizing about the nature of mental processes in cognitive science. One prominent tradition argues that to explain the systematicity of thought we must posit syntactically structured representations inside the cognitive system which can be operated upon by structure sensitive rules similar to those employed in systems of natural deduction. I have argued elsewhere that the systematicity of human thought might better be explained as resulting from the (...) fact that we have learned natural languages which are themselves syntactically structured. According to this view, symbols of natural language are external to the cognitive processing system and what the cognitive system must learn to do is produce and comprehend such symbols. In this paper I pursue that idea by arguing that ability in natural deduction itself may rely on pattern recognition abilities that enable us to operate on external symbols rather than encodings of rules that might be applied to internal representations. To support this suggestion, I present a series of experiments with connectionist networks that have been trained to construct simple natural deductions in sentential logic. These networks not only succeed in reconstructing the derivations on which they have been trained, but in constructing new derivations that are only similar to the ones on which they have been trained. (shrink)

Figure 1: A pr ototyp ical exa mple of a three-layer feed forward network, used by Plunkett and M archm an (1 991 ) to simulate learning the past-tense of En glish verbs. The inpu t units encode representations of the three phonemes of the present tense of the artificial words used in this simulation. Th e netwo rk is trained to produce a representation of the phonemes employed in the past tense form and the suffix (/d/, /ed/, or /t/) (...) used on regular verbs. To run the network, each input unit is assigned an activation value o f 0 or 1 , dep ending on whethe r the featu re is present or not. Eac h input unit is conne cted to each of the 30 hidden units by a we ighted conn ection and p rovid es an inp ut to each hidden unit equal to the product of the input unit's activation and the weight. Each hidd en unit's activation is then determined by summing ov er the va lues co ming fro m each inp ut unit to deter mine a netinput, and then applying a non-linear function (e.g., the logistic function 1/(1+e^netinput). Th is whole proced ure is. (shrink)

Fodor and Pylyshyn (1988), Fodor and McLaughlin (1990) and McLaughlin (1993) challenge connectionists to explain systematicity without simply implementing a classical architecture. In this paper I argue that what makes the challenge difficult for connectionists to meet has less to do with what is to be explained than with what is to count as an explanation. Fodor et al. are prepared to admit as explanatory, accounts of a sort that only classical models can provide. If connectionists are to meet the (...) challenge, they are going to have to insist on the propriety of changing what counts as an explanation of systematicity. Once that is done, there would seem to be as yet no reason to suppose that connectionists are unable to explain systematicity. (shrink)

In this paper I criticize the most significant recent examples of the practical knowledge analysis of knowledge-how in the philosophical literature: David Carr [1979, Mind, 88, 394–409; 1981a, American Philosophical Quarterly, 18, 53–61; 1981b, Journal of Philosophy of Education, 15(1), 87–96] and Stanley & Williamson [2001, Journal of Philosophy, 98(8), 411–444]. I stress the importance of know-how in our contemporary understanding of the mind, and offer the beginnings of a treatment of know-how capable of providing insight in to the use (...) of know-how in contemporary cognitive science. Specifically, I claim that Carr’s necessary conditions for know-how fail to capture the distinction he himself draws between ability and knowing-how. Moreover, Carr ties knowing-how to conscious intent, and to an explicit knowledge of procedural rules. I argue that both moves are mistakes, which together render Carr’s theory an inadequate account both of common ascriptions of knowledge-how and of widely accepted ascriptions of knowledge-how within explanations in cognitive science. Finally, I note that Carr’s conditions fail to capture intuitions (heshares) regarding the ascription of know-how to persons lacking ability. I then consider the position advocated by Stanley & Williamson (2001), which seems avoid Carr’s commitments to conscious intent and explicit knowledge while still maintaining that “knowledge-how is simply a species of knowledge-that" (Stanley & Williamson, 2001, p. 411). I argue that Stanley and Williamson’s attempt to frame a reductionist view that avoids consciously occurrent beliefs during exercises of knowledge-how and explicit knowledge of procedural rules is both empirically implausible and explanatorily vacuous. In criticizing these theories I challenge the presuppositions of the most pervasive response to Ryle in the philosophic literature, what might be described as “the received view." I also establish several facts about knowing-how. First, neither conscious intent nor explicit representation (much less conscious representation) of procedural rules are necessary for knowing-how given the theory of cognition current in cognitive science. I argue that the discussed analyses fail to capture the necessary conditions for knowledge-how because know-how requires the instantiation of an ability and of the capacities necessary for exploiting an ability—not conscious awareness of purpose or explicit knowledge of rules. Second, one must understand knowledge-how as task-specific, i.e., as presupposing certain underlying conditions. Conceiving of know-how as task-specific allows one to understand ascriptions of know-how in the absence of ability as counterfactual ascriptions based upon underlying competence. (shrink)

This paper attempts to clarify and critically examine Fodor's language of thought (LOT) hypothesis, focusing on his contention that the systematicity of language use provides a solid ground for the LOT hypothesis. (edited).

A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in (...) syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)

The question of how scientific hypotheses and theories should be evaluated in light of evidence has been a central question in philosophy of science. Far less attention has been given to the questions of how evidence is developed and is itself evaluated. From this neglect, one might assume that the processes by which scientists develop and evaluate evidence are unproblematic. An examination of the actual practice of experimental scientists, however, reveals that they are far from unproblematic. Much of the evidence (...) used to assess scientific theories is gathered with elaborate instruments. At a given time many instruments used in a particular science are noncontroversial. The techniques for using them are agreed upon so that, in a fairly routine way, scientists are able to generate evidence to settle empirical or theoretical disputes. To capture the fact that such instruments and techniques are not questioned, Latour (1987) refers to them as black boxes. (shrink)

We propose a non-representationalist framework for deep learning relying on a novel method computational phenomenology, a dialogue between the first-person perspective (relying on phenomenology) and the mechanisms of computational models. We thereby propose an alternative to the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities. This interpretation mainly relies on neuro-representationalism, a position that combines a strong ontological commitment towards scientific theoretical entities and the idea that the brain operates on symbolic (...) representations of these entities. We proceed as follows: after offering a review of cognitivism and neuro-representationalism in the field of deep learning, we first elaborate a phenomenological critique of these positions; we then sketch out computational phenomenology and distinguish it from existing alternatives; finally we apply this new method to deep learning models trained on specific tasks, in order to formulate a conceptual framework of deep-learning, that allows one to think of artificial neural networks’ mechanisms in terms of lived experience. (shrink)

In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one hand, all (...) mechanisms are more or less general, and on the other, many design principles are causal systems. (shrink)

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience must literally exist the system being modelled. Computational models are important tools to test a theory about how the collected data has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in (...) the model. While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling that combines dynamical systems theory mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science. (shrink)

Cognitive scientists have attributed the ubiquity of religious narratives partly to the favored recall of minimally counterintuitive concepts within those narratives. Yet, this memory bias is inconsistent, sometimes absent, and without a functional rationale. Here, we asked if MCI concepts are more fitness relevant than intuitive concepts, and if fitness relevance can explain the existence and variability of the observed memory bias. In three studies, participants rated the potential threat and potential opportunity afforded by agents with abilities that violated folk (...) psychology, physics, or biology. As in previous work, agents with MCI abilities were recalled better than those with intuitive abilities. Additionally, agents with MCI abilities were perceived as greater threats, and as providing greater opportunities, than agents with intuitive abilities, but this perceived fitness relevance only mediated the memory bias when MCI abilities were used to accomplish disproportionally consequential outcomes. Minimally counterintuitive abilities that violated folk psychology were rated more intuitive and more of an opportunity than violations of folk physics or biology, while folk physics violations were recalled best. Explanations for these effects and their relevance to the cognitive science of religion are discussed. (shrink)

In this paper, I argue that explaining cognitive behavior can be achieved through what I call hybrid explanatory inferences: inferences that posit mechanisms, but also draw on observed regularities. Moreover, these inferences can be used to achieve unification, in the sense developed by Allen Newel in his work on cognitive architectures. Thus, it seems that explanatory pluralism and unification do not rule out each other in cognitive science, but rather that the former represents a way to achieve the latter.

Hume is an experimental philosopher who attempts to understand why we think, feel, and act as we do. But how should we evaluate the adequacy of his proposals? This chapter examines Hume’s account from the perspective of interdisciplinary work in cognitive science.

In the search for the foundations of cognition philosophers often encounter a familiar problem - the problem of content. The problem of content is essentially the problem of how content, whether experiential or intentional, is possible. In practice providing a response to this problem involves providing an account of how an active self-consciousness is able to conceive/perceive, or in some way be consciousness ofx. The unique nature of this problem imposes significant constraints on the field of explanatory possibilities. Since the (...) x which is to be accounted for is essentially the possibility of absolutely any x there is no y which is not also an x. Hence, nothing remains outside of the explanandum which can be appealed to in order to account for it without, to some extent, presupposing that which needs to be explained. In many of the theories we will examine overcoming this problem involves appealing to a transcendental “structure of awareness” which more often than not is composed of “universal-like” transcendental “entities” of indeterminate nature and ontological status. A major appeal of transcendental entities is that they can at least appear to provide a way of supplying the power of objective “determinateness” necessary to account for the possibility of determination without themselves being determinates. The general strategy of appealing to such transcendental “entities” has, however, for some time been suspiciously regarded as it is unclear how such an appeal is able to avoid the aforementioned presupposition of content. But if the appeal to transcendental cognizing intermediates is to be ‘ dismissed we may be left to face up to the fact that content simply “happens”- that the process of determination, of “judgment”, is a mysterious talent that can neither be taught nor understood. Through a critical examination of both Western and Eastern philosophical approaches to the problem of content this dissertation identifies and describes, insofar as is possible, “that” through which content is rendered possible. In so doing it draws attention to previously neglected points of contact between major philosophical traditions and clarifies the central issues surrounding the problem. The dissertation supports the conclusion that, although there is a need to acknowledge a particular role for a transcendental “self- consciousness” in providing a coherent response to the problem of content, the attempt to articulate a mechanism through which this role is fulfilled most likely misguided. Although it appears to be possible both to know something about this “self-consciousness”, and even to know it more directly, it cannot be understood in the usual sense. (shrink)

I challenge the dominant understanding of what it means to say two thoughts are associated. The two views that dominate the current literature treat association as a kind of mechanism that drives sequences of thought. The first, which I call reductive associationism, treats association as a kind of neural mechanism. The second treats association as a feature of the kind of psychological mechanism associative processing. Both of these views are inadequate. I argue that association should instead be seen as a (...) highly abstract filler term, standing in for causal relations between representational states in a system. Associations, so viewed, could be implemented by many different mechanisms. I outline the role that this view gives associative models as part of a top-down characterization of psychological processes of any kind and of any complexity. (shrink)

For most philosophers of psychology and cognitive science, inquiry into human cognitive activity begins at the level of intrapersonal processes. A central question is whether these processes are sufficiently autonomous from more basic neurophysiological processes to be investigated in their own terms, or whether all explanations must be in neurophysiological terms. Some philosophers have insisted on the relative autonomy of the cognitive level. One currently quite popular view, eliminative materialism, however, holds that the explanations that have been advanced at the (...) intrapersonal cognitive level are misguided, and must be replaced by accounts developed from an understanding of the underlying neurophysiological processes. (shrink)