According to rationalism regarding the psychology of moral judgment, people’s moral judgments are generally the result of a process of reasoning that relies on moral principles or rules. By contrast, intuitionist models of moral judgment hold that people generally come to have moral judgments about particular cases on the basis of gut-level, emotion-driven intuition, and do so without reliance on reasoning and hence without reliance on moral principles. In recent years the intuitionist model has been forcefully defended by Jonathan Haidt. (...) One important implication of Haidt’s model is that in giving reasons for their moral judgments people tend to confabulate – the reasons they give in attempting to explain their moral judgments are not really operative in producing those judgments. Moral reason-giving on Haidt’s view is generally a matter of post hoc confabulation. Against Haidt, we argue for a version of rationalism that we call ‘morphological rationalism.’ We label our version ‘morphological’ because according to it, the information contained in moral principles is embodied in the standing structure of a typical individual’s cognitive system, and this morphologically embodied information plays a causal role in the generation of particular moral judgments. The manner in which the principles play this role is via ‘proceduralization’ – such principles operate automatically. In contrast to Haidt’s intuitionism, then, our view does not imply that people’s moral reason-giving practices are matters of confabulation. In defense of our view, we appeal to what we call the ‘nonjarring’ character of the phenomenology of making moral judgments and of giving reasons for those judgments. (shrink)

We respond to the central concerns raised by our commentators to our book, The Epistemological Spectrum. Casullo believes that our account of what we term “low-grade a priori” justification provides important clarification of a kind of philosophical reflection. However he objects to calling such reflection a priori. We explain what we think is at stake. Along the way, we comment on his idea of that there may be an epistemic payoff to making a distinction between assumptions and presumptions. In the (...) book, we argued that an epistemically important form of nonaccidental reliability can be understood as a matter of processes being “transglobally reliable under modulational control.” Graham recommends another form of nonaccidental reliability, one rooted in evolutionary etiology. We explain why we think that the reliability of perceptual processes is best understood as turning of the kinds of modulational control that we highlight. We clarify how this approach represents a kind of reasonable epistemic patience—modulational control takes time, as it must turn on agents generating information about their own capacities and foibles. Lyons raises interesting questions regarding how (what we term) morphological content possessed by the agent can do the work that we set for it. We argue that it is necessary in order for agents to accommodate the background information that is relevant to many central problems of belief formation. We clarify how it can be expected to work. (shrink)

After a brief survey of the literature on ceteris paribus clauses and ceteris paribus laws (1), the problem of exceptions, which creates the need for cp laws, is discussed (2). It emerges that the so-called skeptical view of laws of nature does not apply to laws of any kind whatever. Only some laws of physics are plagued with exceptions, not THE laws (3). Cp clauses promise a remedy, which has to be located among the further reactions to the skeptical view (...) (4). After inspecting various translations of the Latin term 'ceteris paribus' (5), the paper arrives at the conclusion that, on the most reasonable translation, there are no such things as cp laws, for reasons of logical form. Cp clauses have an indexical content, so that they need singular propositions as their habitat, not general ones. Cp clauses and the universal generalizations they are supposed to modify are not fit for each other (6). (shrink)

Laws of nature take center stage in philosophy of science. Laws are usually believed to stand in a tight conceptual relation to many important key concepts such as causation, explanation, confirmation, determinism, counterfactuals etc. Traditionally, philosophers of science have focused on physical laws, which were taken to be at least true, universal statements that support counterfactual claims. But, although this claim about laws might be true with respect to physics, laws in the special sciences (such as biology, psychology, economics etc.) (...) appear to have—maybe not surprisingly—different features than the laws of physics. Special science laws—for instance, the economic law “Under the condition of perfect competition, an increase of demand of a commodity leads to an increase of price, given that the quantity of the supplied commodity remains constant” and, in biology, Mendel's Laws—are usually taken to “have exceptions”, to be “non-universal” or “to be ceteris paribus laws”. How and whether the laws of physics and the laws of the special sciences differ is one of the crucial questions motivating the debate on ceteris paribus laws. Another major, controversial question concerns the determination of the precise meaning of “ceteris paribus”. Philosophers have attempted to explicate the meaning of ceteris paribus clauses in different ways. The question of meaning is connected to the problem of empirical content, i.e., the question whether ceteris paribus laws have non-trivial and empirically testable content. Since many philosophers have argued that ceteris paribus laws lack empirically testable content, this problem constitutes a major challenge to a theory of ceteris paribus laws. (shrink)

The new kid on the block in cognitive science these days is dynamic systems. This way of thinking about the mind is, as usual, radically opposed to computationalism - - the hypothesis that thinking is computing. The use of dynamic systems is just the latest in a series of attempts, from Searle's Chinese Room Argument, through the weirdnesses of postmodernism, to overthrown computationalism, which as we all know is a perfectly nice hypothesis about the mind that never hurt anyone.

Phenomenology is intrinsically intentional for Brentano. Qualitative conscious experiences are individuated by their phenomenal space. Examples concerning the phenomenal take account of both experiential and physical spaces. As directedness at an object and reflexive directedness of the act at itself come interwoven, there is the intrinsic phenomenology of intentionality. Both intentionality of phenomenology and phenomenology of intentionality present the wholes with mutually pervading and only logically distinguishable parts. The above theses establish balance between phenomenology and intentionality, a balance disrupted in (...) both analytical and continental traditions to which Brentano was a precursor. (shrink)

One of the central points of contention in the epistemology of testimony concerns the uniqueness (or not) of the justification of beliefs formed through testimony--whether such justification can be accounted for in terms of, or 'reduced to,' other familiar sort of justification, e.g. without relying on any epistemic principles unique to testimony. One influential argument for the reductionist position, found in the work of Elizabeth Fricker, argues by appeal to the need for the hearer to monitor the testimony for credibility. (...) Fricker (1994) argues, first, that some monitoring for trustworthiness is required if the hearer is to avoid being gullible, and second, that reductionism but not anti-reductionism is compatible with ascribing an important role to the process of monitoring in the course of justifiably accepting observed testimony. In this paper we argue that such an argument fails. (shrink)

Abstract:Much of Western speculative metaphysics has subscribed to what has been called “explanatory rationalism,” which holds that there is a reason for everything that is and for the way everything is. Theodicies, or metaphysical attempts to solve the problem of evil, have relied on a special application of this principle of explanatory rationalism, namely, the principle of plenitude, which holds that the evil in the world is a necessary ingredient in the world's overall perfection or degree of reality. This essay (...) argues that the principle of plenitude is aesthetically motivated, and that only in art and perhaps in revealed religion can the demands of explanatory rationalism be satisfied. (shrink)

Morphological content is information that is implicitly embodied in the standing structure of a cognitive system and is automatically accommodated during cognitive processing without first becoming explicit in consciousness. We maintain that much belief-formation in human cognition is essentially morphological : i.e., it draws heavily on large amounts of morphological content, and must do so in order to tractably accommodate the holistic evidential relevance of background information possessed by the cognitive agent. We also advocate a form of experiential evidentialism concerning (...) epistemic justification—roughly, the view that the justification-status of an agent’s beliefs is fully determined by the character of the agent’s conscious experience. We have previously defended both the thesis that much belief-formation is essentially morphological, and also a version of evidentialism. Here we explain how experiential evidentialism can be smoothly and plausibly combined with the thesis that much of the cognitive processing that generates justified beliefs is essentially morphological. The leading idea is this: even though epistemically relevant morphological content does not become explicit in consciousness during the process of belief-generation, nevertheless such content does affect the overall character of conscious experience in an epistemically significant way: it is implicit in conscious experience, and is implicitly appreciated by the experiencing agent. (shrink)

We propose an approach to epistemic justification that incorporates elements of both reliabilism and evidentialism, while also transforming these elements in significant ways. After briefly describing and motivating the non-standard version of reliabilism that Henderson and Horgan call “transglobal” reliabilism, we harness some of Henderson and Horgan’s conceptual machinery to provide a non-reliabilist account of propositional justification (i.e., evidential support). We then invoke this account, together with the notion of a transglobally reliable belief-forming process, to give an account of doxastic (...) justification. (shrink)

Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate (...) the higher-order, systematic, relational capabilities of a physical symbol system (PSS) (Newell 1980). We show that this symbolic-relational discontinuity pervades nearly every domain of cognition and runs much deeper than even the spectacular scaffolding provided by language or culture alone can explain. We propose a representational-level specification as to where human and nonhuman animals' abilities to approximate a PSS are similar and where they differ. We conclude by suggesting that recent symbolic-connectionist models of cognition shed new light on the mechanisms that underlie the gap between human and nonhuman minds. (shrink)

This paper examines whether a classical model could be translated into a PDP network using a standard connectionist training technique called extra output learning. In Study 1, standard machine learning techniques were used to create a decision tree that could be used to classify 8124 different mushrooms as being edible or poisonous on the basis of 21 different Features (Schlimmer, 1987). In Study 2, extra output learning was used to insert this decision tree into a PDP network being trained on (...) the identical problem. An interpretation of the trained network revealed a perfect mapping from its internal structure to the decision tree, representing a precise translation of the classical theory to the connectionist model. In Study 3, a second network was trained on the mushroom problem without using extra output learning. An interpretation of this second network revealed a different algorithm for solving the mushroom problem, demonstrating that the Study 2 network was indeed a proper theory translation. (shrink)

Although Paul Churchland and Jerry Fodor both subscribe to the so-called theory-theory– the theory that folk psychology (FP) is an empirical theory of behavior – they disagree strongly about FP’s fate. Churchland contends that FP is a fundamentally flawed view analogous to folk biology, and he argues that recent advances in computational neuroscience and connectionist AI point toward development of a scientifically respectable replacement theory that will give rise to a new common-sense psychology. Fodor, however, wagers that FP will be (...) largely preserved and vindicated by scientific investigations of behavior. Recent findings by developmental psychologists, I argue, will push both Churchlandians and Fodorians toward the pessimistic view that FP is a misguided theory that will never be displaced, because it is, so to speak, built into our cognitive systems. I explore the possibility of preserving optimism by rejecting the theory-theory and adopting the simulation theory, a competing view developed by Robert Gordon, Alvin Goldman, and Jane Heal. According to simulationists, common-sense interpretation of behavior is accomplished by means of pretense-like operations that deploy the cognitive system’s own reasoning capabilities in a disengaged manner. Since on this view no theory-like set of principles would be needed, the simulation theory seems to enjoy a simplicity advantage over the theory-theory. Steven Stich and Shawn Nichols, however, contend that as the cognitive system would require special mechanisms for disengaged operation, the simplicity question cannot be resolved until suitable computational models are developed. I describe a set of models I have constructed to meet this need, and I discuss the contribution such models can make to determining FP’s fate. (shrink)

Over the past thirty years, it is been common to hear the mind likened to a digital computer. This essay is concerned with a particular philosophical view that holds that the mind literally is a digital computer (in a specific sense of “computer” to be developed), and that thought literally is a kind of computation. This view—which will be called the “Computational Theory of Mind” (CTM)—is thus to be distinguished from other and broader attempts to connect the mind with computation, (...) including (a) various enterprises at modeling features of the mind using computational modeling techniques, and (b) employing some feature or features of production-model computers (such as the stored program concept, or the distinction between hardware and software) merely as a guiding metaphor for understanding some feature of the mind. This entry is therefore concerned solely with the Computational Theory of Mind (CTM) proposed by Hilary Putnam [1961] and developed most notably for philosophers by Jerry Fodor [1975, 1980, 1987, 1993]. The senses of ‘computer’ and ‘computation’ employed here are technical; the main tasks of this entry will therefore be to elucidate: (a) the technical sense of ‘computation’ that is at issue, (b) the ways in which it is claimed to be applicable to the mind, (c) the philosophical problems this understanding of the mind is claimed to solve, and (d) the major criticisms that have accrued to this view. (shrink)

Traditional approaches to modeling cognitive systems are computational, based on utilizing the standard tools and concepts of the theory of computation. More recently, a number of philosophers have argued that cognition is too subtle or complex for these tools to handle. These philosophers propose an alternative based on dynamical systems theory. Proponents of this view characterize dynamical systems as (i) utilizing continuous rather than discrete mathematics, and, as a result, (ii) being computationally more powerful than traditional computational automata. Indeed, the (...) logical possibility of such super-powerful systems has been demonstrated in the form of analog artificial neural networks. In this paper I consider three arguments against the nomological possibility of these automata. While the first two arguments fail, the third succeeds. In particular, the presence of noise reduces the computational power of analog networks to that of traditional computational automata, and noise is a pervasive feature of information processing in biological systems. Consequently, as an empirical thesis, the proposed dynamical alternative is under-motivated: What is required is an account of how continuously valued systems could be realized in physical systems despite the ubiquity of noise. (shrink)

The notion of a "mental representation" is, arguably, in the first instance a theoretical construct of cognitive science. As such, it is a basic concept of the Computational Theory of Mind, according to which cognitive states and processes are constituted by the occurrence, transformation and storage (in the mind/brain) of information-bearing structures (representations) of one kind or another.

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

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)

This thesis contributes to a better conceptual understanding of how self-organized control works. I begin by analyzing the control problem and its solution space. I argue that the two prominent solutions offered by classical cognitive science (centralized control with rich commands, e.g., the Fodorian central systems) and embodied cognitive science (distributed control with simple commands, such as the subsumption architecture by Rodney Brooks) are merely two positions in a two-dimensional solution space. I outline two alternative positions: one is distributed control (...) with rich commands, defended by proponents of massive modularity hypothesis; the other is centralized control with simple commands. My goal is to develop a hybrid account that combines aspects of the second alternative position and that of the embodied cognitive science (i.e., centralized and distributed controls with simple commands). Before developing my account, I discuss the virtues and challenges of the first three. This discussion results in a set of criteria for successful neural control mechanisms. Then, I develop my account through analyzing neuroscientific models of decision-making and control with the theoretical lenses provided by formal decision and social choice theories. I contend that neural processes can be productively modeled as a collective of agents, and neural self-organization is analogous to democratic self-governance. In particular, I show that the basal ganglia, a set of subcortical structures, contribute to the production of coherent and intelligent behaviors through implementing “democratic" procedures. Unlike the Fodorian central system—which is a micro-managing “neural commander-in-chief”—the basal ganglia are a “central election commission.” They delegate control of habitual behaviors to other distributed control mechanisms. Yet, when novel problems arise, they engage and determine the result on the basis of simple information (the votes) from across the system with the principles of neurodemocracy, and control with simple commands of inhibition and disinhibition. By actively managing and taking advantage of the wisdom-of-the-crowd effect, these democratic processes enhance the intelligence and coherence of the mind’s final "collective" decisions. I end by defending this account from both philosophical and empirical criticisms and showing that it meets the criteria for successful solution. (shrink)

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly work as computational (...) models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order (classical) systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes. (shrink)

One of the best known arguments against the connectionist approach to artificial intelligence and cognitive science is that neural networks are black boxes, i.e., there is no understandable account of their operation. This difficulty has impeded efforts to explain how categories arise from raw sensory data. Moreover, it has complicated investigation about the role of symbols and language in cognition. This state of things has been radically changed by recent experimental findings in artificial deep learning research. Two kinds of artificial (...) deep learning networks, namely the convolutional neural network and the generative adversarial network have been found to possess the capability to build internal states that are interpreted by humans as complex visual categories, without any specific hints or any grammatical processing. This emergent ability suggests that those categories do not depend on human knowledge or the syntactic structure of language, while they do rely on their visual context. This supports a mild form of empiricism, while it does not assume that computational functionalism is true. Some consequences are extracted regarding the debate about amodal and grounded representations in the human brain. Furthermore, new avenues for research on cognitive science are open. (shrink)

Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes that (...) make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. (shrink)

Mind &Language, Volume 35, Issue 1, Page 90-114, February 2020.

This paper explores the nature of self-knowledge of beliefs by investigating the relationship between self-knowledge of beliefs and one's knowledge of other people's beliefs. It introduces and defends a new account of self-knowledge of beliefs according to which this type of knowledge is developmentally interconnected with and dependent on resources already used for acquiring knowledge of other people's beliefs, which is inferential in nature. But when these resources are applied to oneself, one attains and subsequently frequently uses a method for (...) acquiring knowledge of beliefs that is non-inferential in nature. The paper argues that this account is preferable to some of the most common empirically motivated theories of self-knowledge of beliefs and explains the origin of the widely discussed phenomenon that our own beliefs are often transparent to us in that we can determine whether we believe that p simply by settling whether p is the case. (shrink)

This paper surveys and evaluates the answers that philosophers and animal researchers have given to two questions. Do animals have thoughts? If so, are their thoughts conceptual? Along the way, special attention is paid to distinguish debates of substance from mere battles over terminology, and to isolate fruitful areas for future research.

Showcasing original arguments for well-defined positions, as well as clear and concise statements of sophisticated philosophical views, this volume is an ...

It seems that hope springs eternal for the cherished idea that norms (or normativeprinciples) explain actions or regularities in actions. But it also seems thatthere are many ways of going wrong when taking norms and normative principlesas explanatory. The author argues that neither norms nor normative principles—insofar as they are the sort of things with normative force—is explanatoryof what is done. He considers the matter using both erotetic and ontic models ofexplanation. He further considers various understandings of norms. Key Words: (...) explanation • norms • social science • rationality. (shrink)

This paper presents the leading idea of my doctoral dissertation and thus has been shaped by the reactions of all the members of my thesis committee: Charles Chastain, Walter Edelberg, W. Kent Wilson, Dorothy Grover, and Charles Marks. I am especially grateful for the help of Professors Chastain, Edelberg, and Wilson; each worked closely with me at one stage or another in the development of the ideas contained in the present work. Shorter versions of this paper were presented at the (...) 47th Annual Northwest Conference on Philosophy (1995), the 1996 Mid-South Philosophy Conference, the 1997 meeting of the Central Division of the American Philosophical Association, and at the University of Washington, Seattle; thanks to all audiences for their insightful comments and questions and also to my conference commentators, Eric Gampel, Jonathan Cohen, and Bruce Glymour, respectively, each of whom offered a thoughtful critique. Lastly, I extend my gratitude to anonymous referees, including two from Mind and Language, whose remarks led to significant improvements in the paper. Address for correspondence: 13416, 4th Ave. S., Seattle, WA 98168, USA E-mail: [email protected].. (shrink)

This paper explores a variety of different versions of the thesis that natural language is involved in human thinking. It distinguishes amongst strong and weak forms of this thesis, dismissing some as implausibly strong and others as uninterestingly weak. Strong forms dismissed include the view that language is conceptually necessary for thought (endorsed by many philosophers) and the view that language is _de facto_ the medium of all human conceptual thinking (endorsed by many philosophers and social scientists). Weak forms include (...) the view that language is necessary for the acquisition of many human concepts, and the view that language can serve to scaffold human thought processes. The paper also discusses the thesis that language may be the medium of _conscious_ propositional thinking, but argues that this cannot be its most fundamental cognitive role. The idea is then proposed that natural language is the medium for non-domain-specific thinking, serving to integrate the outputs of a variety of domain-specific conceptual faculties (or central-cognitive ‘quasi-modules’). Recent experimental evidence in support of this idea is reviewed, and the implications of the idea are discussed, especially for our conception of the architecture of human cognition. Finally, some further kinds of evidence which might serve to corroborate or refute the hypothesis are mentioned. The overall goal of the paper is to review a wide variety of accounts of the cognitive function of natural language, integrating a number of different kinds of evidence and theoretical consideration in order to propose and elaborate the most plausible candidate. (shrink)

The past decade has witnessed the emergence of a novel stance on semantic representation, and its relationship to context sensitivity. Connectionist-minded philosophers, including Clark and van Gelder, have espoused the merits of viewing hidden-layer, context-sensitive representations as possessing semantic content, where this content is partially revealed via the representations'' position in vector space. In recent work, Bodén and Niklasson have incorporated a variant of this view of semantics within their conception of semantic systematicity. Moreover, Bodén and Niklasson contend that they (...) have produced experimental results which not only satisfy a kind of context-based, semantic systematicity, but which, to the degree that reality permits, effectively deals with challenges posed by Fodor and Pylyshyn (1988), and Hadley (1994a). The latter challenge involved well-defined criteria for strong semantic systematicity. This paper examines the relevant claims and experiments of Bodén and Niklasson. It is argued that their case fatally involves two fallacies of equivocation; one concerning ''semantic content'' and the other concerning ''novel test sentences''. In addition, it is argued that their ultimate construal of context sensitive semantics contains serious confusions. These confusions are also found in certain publications dealing with "latent semantic analysis". Thus, criticisms presented here have relevance beyond the work of Bodén and Niklasson. (shrink)

According to the received view in philosophy of mind, mental states or properties are _realized_ by brain states or properties but are not identical to them. This view is often called _realization_ _physicalism_. Carl Gillett has recently defended a detailed formulation of the realization relation. However, Gillett’s formulation cannot be the relation that realization physicalists have in mind. I argue that Gillett’s “dimensioned” view of realization fails to apply to a textbook case of realization. I also argue Gillett counts as (...) realization some cases that should not count if realization physicalism is to be distinguished from its competitors in the usual ways. I conclude that the relation described by Gillett cannot be realization. (shrink)

Cognitive neuroscientists are turning to an increasingly rich array of neurodynamical systems to explain mental phenomena. In these explanations, cognitive capacities are decomposed into a set of functions, each of which is described mathematically, and then these descriptions are mapped on to corresponding mathematical descriptions of the dynamics of neural systems. In this paper, I outline a novel explanatory schema based on these explanations. I then argue that these explanations present a novel type of dynamicism for the philosophy of mind (...) and neuroscience, componential dynamicism, that focuses on the parts of cognitive systems that fill certain functional roles in producing cognitive phenomena. (shrink)

Some hold that beliefs arising out of certain sources such as perceptual experience enjoy a kind of entitlement—as one is entitled to believe what is thereby presented as true, at least unless further evidence undermines that entitlement. This is commonly understood to require that default epistemic entitlement is a non-evidential kind of epistemic warrant. Our project here is to challenge this common, non-evidential, conception of epistemic entitlement. We will argue that although there are indeed basic beliefs with default entitlement status, (...) typically the kind of default entitlement they possess is primarily a matter of the evidential support that accrues to them, both synchronically and diachronically, from wider mental states beyond the specific sensory-perceptual experiences that spawn them. We will call this status evidentially embedded epistemic entitlement—as distinct from entitlement as commonly understood in the literature, which we will call evidentially insular. Epistemic entitlement normally is characterized in the manner set forth in the first paragraph above, viz., as a form of default epistemic warrant that a given belief possesses independently of any other beliefs. We suggest that not all evidential support is managed at the level of belief. Thus, leaves room for the possibility of an epistemically embedded kind of entitlement. Here we develop the needed conception of entitlement drawing on Henderson and Horgan’s ideas of a kind of “iceberg epistemology.”. (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.

. Interpreted dynamical systems are dynamical systems with an additional interpretation mapping by which propositional formulas are assigned to system states. The dynamics of such systems may be described in terms of qualitative laws for which a satisfaction clause is defined. We show that the systems Cand CL of nonmonotonic logic are adequate with respect to the corresponding description of the classes of interpreted ordered and interpreted hierarchical systems, respectively. Inhibition networks, artificial neural networks, logic programs, and evolutionary systems are (...) instances of such interpreted dynamical systems, and thus our results entail that each of them may be described correctly and, in a sense, even completely by qualitative laws that obey the rules of a nonmonotonic logic system. (shrink)

While the research programs in early cognitive science and artificial intelligence aimed to articulate what cognition was in ideal terms, much research in contemporary computational neuroscience looks at how and why brains fail to function as they should ideally. This focus on impairment affects how we understand David Marr's hypothesized three levels of understanding. In this essay, we suggest some refinements to Marr's distinctions using a population activity model of cortico-striatal circuitry exploring impulsivity and behavioral inhibition as a case study. (...) In particular, we urge that Marr's computational level should be redefined to include a description of how systems break down. We also underscore that feed-forward processing, cognition disconnected from behavioral context, and representations do not always drive cognition in the way that Marr originally assumed. (shrink)

This paper challenges arguments that systematic patterns of intelligent behavior license the claim that representations must play a role in the cognitive system analogous to that played by syntactical structures in a computer program. In place of traditional computational models, I argue that research inspired by Dynamical Systems theory can support an alternative view of representations. My suggestion is that we treat linguistic and representational structures as providing complex multi-dimensional targets for the development of individual brains. This approach acknowledges the (...) indispensability of the intentional or representational idiom in psychological explanation without locating representations in the brains of intelligent agents. (shrink)

This study addresses the dynamical nature of a “representation‐hungry” cognitive task involving an imagined action. In our experiment, participants were handed rods that systematically increased or decreased in length on subsequent trials. Participants were asked to judge whether or not they thought they could reach for a distant object with the hand‐held rod. The results are in agreement with a dynamical model, extended from Tuller, Case, Ding, and Kelso (1994). The dynamical effects observed in this study suggest that predictive judgments (...) regarding the possibility or impossibility of a certain action can be understood in terms of dynamically evolving basins of attraction instead of as depending on representational structures. (shrink)

While Process Reliabilism has long been regarded by many as a version of Foundationalism, this paper argues that there is a version of Process Reliabilism that can also been seen as at least a partial vindication of Coherentism as well. The significance of this result lies in what it tells us both about the prospects for a plausible Process Reliabilism, but also about the old-school debate between Foundationalists and Coherentists.

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...) this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism on the other. We defend the relevance to cognitive science of both computation, in a generic sense that we fully articulate for the first time, and information processing, in three important senses of the term. Our account advances some foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects. (shrink)

Horgan and Tienson do a wonderful job of explicating the dynamical system perspective and contrasting that view with classical AI approaches. However, their arguments for replacing a classical conception of connectionism with system dynamics rely on philosophical distinctions that do not make a difference. In particular, (1) their generalized version of Man's three levels of analysis collapses into itself; (2) their description of attractor dynamics works better than their metaphor of forces; and (3) their versions of “soft laws” and physical (...) laws amount to the same thing. (shrink)

What is required for one thing to be a reason for another? Must the reason, more precisely, be or involve a principle? In this essay I target the idea that justification via reasons of one's beliefs (e.g., epistemic or moral) requires that the 'justifying reasons' be or involve (substantive and significant) principles. I identify and explore some potential sources of a principles requirement, and conclude that none of them (i.e., the normative function of reasons, the abstract structure of reasons, the (...) universalizability constraint on [moral] reasons, and even the pragmatic considerations that attenuate 'real-world' reason-giving) mandate that reasons be principles. I then explore implications of this conclusion, and note especially the resultant and paradoxical permissibility of justifying reasons that manage to be lawlike (a sine qua non for justifying reasons) even while consisting of highly-detailed and situation-specific proposition sets, e.g., novel-length narratives. (shrink)

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

Recently, connectionist models have been developed that seem to exhibit structuresensitive cognitive capacities without executing a program. This paper examines one such model and argues that it does execute a program. The argument proceeds by showing that what is essential to running a program is preserving the functional structure of the program. It has generally been assumed that this can only be done by systems possessing a certain temporalcausal organization. However, counterfactualpreserving functional architecture can be instantiated in other ways, for (...) example geometrically, which are realizable by connectionist networks. (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)