The debate concerning the role of intuitions in philosophy has been characterized by a fundamental disagreement between two main camps. The first, the autonomists, hold that, due to the use in philosophical investigation of appeals to intuition, most of the central questions of philosophy can in principle be answered by philosophical investigation and argument without relying on the sciences. The second, the naturalists, deny the possibility of a priori knowledge and are skeptical of the role of intuition in providing evidence (...) in philosophical inquiry. In spite of the seemingly stark and deep disagreement between the autonomist and the naturalist, in this paper I will attempt to suggest that there is a way to mount a partial defense of intuition-based philosophical practice on naturalist grounds. As will be seen, doing so will require a number of concessions that will satisfy few autonomists while simultaneously disappointing those naturalists who might have thought that all notions of the a priori had been successfully laid to rest. However, I will argue that the account proposed here will preserve those features of traditional philosophical practice that matter most and better explain the history of successes and failures of that practice, thus answering the worries of the autonomist, and that the rejection of the No A Priori Thesis attendant with the account presented here is indeed demanded by the current theories of our best scientific understanding of the mind, thus pacifying the rabid naturalistic critic of any version of apriorism. (shrink)

In this paper I defend the classical computational account of reasoning against a range of highly influential objections, sometimes called relevance problems. Such problems are closely associated with the frame problem in artificial intelligence and, to a first approximation, concern the issue of how humans are able to determine which of a range of representations are relevant to the performance of a given cognitive task. Though many critics maintain that the nature and existence of such problems provide grounds for rejecting (...) classical computationalism, I show that this is not so. Some of these putative problems are a cause for concern only on highly implausible assumptions about the extent of our cognitive capacities, whilst others are a cause for concern only on similarly implausible views about the commitments of classical computationalism. Finally, some versions of the relevance problem are not really objections but hard research issues that any satisfactory account of cognition needs to address. I conclude by considering the diagnostic issue of why accounts of cognition in general—and classical computational accounts, in particular—have faired so poorly in addressing such research issues.Keywords: Computationalism; Frame problem; Relevance. (shrink)

Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.

Do accounts of scientific theory formation and revision have implications for theories of everyday cognition? We maintain that failing to distinguish between importantly different types of theories of scientific inference has led to fundamental misunderstandings of the relationship between science and everyday cognition. In this article, we focus on one influential manifestation of this phenomenon which is found in Fodor's well-known critique of theories of cognitive architecture. We argue that in developing his critique, Fodor confounds a variety of distinct claims (...) about the holistic nature of scientific inference. Having done so, we outline more promising relations that hold between theories of scientific inference and ordinary cognition. (shrink)

Two long-standing arguments in cognitive science invoke the assumption that holistic inference is computationally infeasible. The first is Fodor’s skeptical argument toward computational modeling of ordinary inductive reasoning. The second advocates modular computational mechanisms of the kind posited by Cosmides, Tooby and Sperber. Based on advances in machine learning related to Bayes nets, as well as investigations into the structure of scientific and ordinary information, I maintain neither argument establishes its architectural conclusion. Similar considerations also undermine Fodor’s decades-long diagnosis of (...) artificial intelligence research as confounded by an inability to circumscribe the amount of information relevant to inferential processes. This diagnosis is particularly inapposite with respect to Bayes nets, since one of their strengths as machine learning systems has been their capacity to reason probabilistically about large data sets whose size overwhelms the capacities of individual human reasoners. A general moral follows from these criticisms: Insights into artificial and human cognitive systems are likely to be cultivated by focusing greater attention on the structure and density of connections among items of information that are available to them. (shrink)

The frame problem was originally a problem for Artificial Intelligence, but philosophers have interpreted it as an epistemological problem for human cognition. As a result of this reinterpretation, however, specifying the frame problem has become a difficult task. To get a better idea of what the frame problem is, how it gives rise to more general problems of relevance, and how deep these problems run, I expound six guises of the frame problem. I then assess some proposed heuristic solutions to (...) the frame problem; I show that these proposals misunderstand, and fail to address, an important aspect of the frame problem. Finally, I argue that though human cognition does not solve the frame problem in its epistemological guise, human cognition avoids some of the epistemological worries. (shrink)

The concept of modularity has loomed large in philosophy of psychology since the early 1980s, following the publication of Fodor’s landmark book The Modularity of Mind (1983). In the decades since the term ‘module’ and its cognates first entered the lexicon of cognitive science, the conceptual and theoretical landscape in this area has changed dramatically. Especially noteworthy in this respect has been the development of evolutionary psychology, whose proponents adopt a less stringent conception of modularity than the one advanced by (...) Fodor, and who argue that the architecture of the mind is more pervasively modular than Fodor claimed. Where Fodor (1983, 2000) draws the line of modularity at the relatively low-level systems underlying perception and language, post-Fodorian theorists such as Sperber (2002) and Carruthers (2006) contend that the mind is modular through and through, up to and including the high-level systems responsible for reasoning, planning, decision making, and the like. The concept of modularity has also figured in recent debates in philosophy of science, epistemology, ethics, and philosophy of language—further evidence of its utility as a tool for theorizing about mental architecture. (shrink)

Heuristics are often invoked in the philosophical, psychological, and cognitive science literatures to describe or explain methodological techniques or "shortcut" mental operations that help in inference, decision-making, and problem-solving. Yet there has been surprisingly little philosophical work done on the nature of heuristics and heuristic reasoning, and a close inspection of the way(s) in which "heuristic" is used throughout the literature reveals a vagueness and uncertainty with respect to what heuristics are and their role in cognition. This dissertation seeks to (...) remedy this situation by motivating philosophical inquiry into heuristics and heuristic reasoning, and then advancing a theory of how heuristics operate in cognition. I develop a positive working characterization of heuristics that is coherent and robust enough to account for a broad range of phenomena in reasoning and inference, and makes sense of empirical data in a systematic way. I then illustrate the work this characterization does by considering the sorts of problems that many philosophers believe heuristics solve, namely those resulting from the so-called frame problem. Considering the frame problem motivates the need to gain a better understanding of how heuristics work and the cognitive structures over which they operate. I develop a general theory of cognition which I argue underwrites the heuristic operations that concern this dissertation. I argue that heuristics operate over highly organized systems of knowledge, and I offer a cognitive architecture to accommodate this view. I then provide an account of the systems of knowledge that heuristics are supposed to operate over, in which I suggest that such systems of knowledge are concepts. The upshot, then, is that heuristics operate over concepts. I argue, however, that heuristics do not operate over conceptual content, but over metainformational relations between activated and primed concepts and their contents. Finally, to show that my thesis is empirically adequate, I consider empirical evidence on heuristic reasoning and argue that my account of heuristics explains the data. (shrink)

Cognitive modules are internal mental structures. Some theorists and empirical researchers hypothesize that the human mind is either partially or massively comprised of structures that are modular in nature. Modules are also invoked to explain cognitive capacities associated with the performance of specific functional tasks. Jerry Fodor considered that modules are useful only for explaining relatively low-level systems. These are the systems involved in capacities like perception and language. For Fodor, the central systems of mind — those involved in capacities (...) like judgment, planning, decision-making, and so forth — are not explainable in terms of modular mechanisms. However, some other philosophers as well as proponents and practitioners of evolutionary psychology consider that it is sensible — and even necessary — to invoke modules for explaining these systems, too. Indeed, the debate over modularity is mainly a debate over the modularity of central systems. Admittedly, it is also possible to raise doubts about the modularity of peripheral systems, but this kind of skepticism is not widespread in the literature. In contrast, both the empirical and the theoretical cases in favor of central modularity are usually contested on multiple fronts and in tough terms. The case for the modularity of central systems is the core of the case for the massive modularity of mind hypothesis. Granted, the massive modularity of mind hypothesis is an empirical statement and, as such, its truth should be ultimately decided in an empirical way, not a priori. Nonetheless, much of the debate over massive modularity has taken place on theoretical grounds. This is due to an alleged underdetermination of the hypotheses regarding the modularity of central systems by data. In such conditions, the remaining open option has been to advance theoretical considerations in favor of the plausibility — and not directly in favor of the empirical truth or truthlikeness — of the massive modularity of mind generally, and of the modularity of some central systems in particular. These theoretical considerations are mostly based on an adaptationist view of evolution cum a classical computationalist approach to mind. They include arguments about the nature and evolution of hierarchically ordered complex systems, a presumption of optimality expressed in the apparent design of phenotypic traits shaped by natural selection and dismissing nonmodular mental architectures as computationally intractable. Is the massive modularity of mind hypothesis a cogent view about the ontological nature of human mind or is it, rather, an effective/ineffective adaptationist discovery heuristic for generating predictively successful hypothesis about both heretofore unknown psychological traits and unknown properties of already identified psychological traits? Considering the inadequacies of the case in favor of massive modularity as an ontological hypothesis, I suggest approaching and valuing massive modularity as an adaptationist discovery heuristic. (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)

I give a defense of the Massive Modularity hypothesis: the view that the mind is composed of discrete, encapsulated, informationally isolated computational structures dedicated to particular problem domains. This view contrasts with Psychological Rationalism: the view that mental structures take the form of unencapsulated representational items, all available as inputs to one domain-general computational processor. I argue that although Psychological Rationalism is in principle able to overcome the `intractability objection', the view must borrow many features of a massively modular architecture (...) in order to do so, that although it can, in principle, overcome the `optimality objection', the way it does so does not correlate with the way we think, and that although it can, in principle, respond to the `argument from biology', it cannot do so without advancing an unrealistic and unsupported account of cognitive evolution. (shrink)