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)

It has been speculated, by Chomsky and others, that our capacity for scientific understanding is not only enabled but also limited by a biologically endowed science forming faculty . I look at two sorts of consideration for the SFF thesis and find both wanting. Firstly, it has been claimed that a problem‐mystery distinction militates for the SFF thesis. I suggest that the distinction can be coherently drawn for cases, but that the purported‘evidence’for even a fairly lose general demarcation of problems (...) and mysteries is not best explained by a SFF. Secondly, I consider in detail a range of cognitive considerations for the SFF thesis and contend that it is at best moot whether science can be so construed as to make it feasible that it is a faculty competence. (shrink)

Jerry Fodor argues that the massive modularity thesis – the claim that (human) cognition is wholly served by domain specific, autonomous computational devices, i.e., modules – is a priori incoherent, self-defeating. The thesis suffers from what Fodor dubs the input problem: the function of a given module (proprietarily understood) in a wholly modular system presupposes non-modular processes. It will be argued that massive modularity suffers from no such a priori problem. Fodor, however, also offers what he describes as a really (...) real input problem (i.e., an empirical one). It will be suggested that this problem is real enough, but it does not selectively strike down massive modularity – it is a problem for everyone. (shrink)

Multiple sciences have converged, in the past two decades, on a hitherto mostly unremarked question: what is observation? Here, I examine this evolution, focusing on three sciences: physics, especially quantum information theory, developmental biology, especially its molecular and “evo-devo” branches, and cognitive science, especially perceptual psychology and robotics. I trace the history of this question to the late 19th century, and through the conceptual revolutions of the 20th century. I show how the increasing interdisciplinary focus on the process of extracting (...) information from an environment provides an opportunity for conceptual unification, and sketch an outline of what such a unification might look like. (shrink)

As many philosophers agree, the frame problem is concerned with how an agent may efficiently filter out irrelevant information in the process of problem-solving. Hence, how to solve this problem hinges on how to properly handle semantic relevance in cognitive modeling, which is an area of cognitive science that deals with simulating human's cognitive processes in a computerized model. By "semantic relevance", we mean certain inferential relations among acquired beliefs which may facilitate information retrieval and practical reasoning under certain epistemic (...) constraints, e. g., the insufficiency of knowledge, the limitation of time budget, etc. However, traditional approaches to relevance—as for example, relevance logic, the Bayesian approach, as well as Description Logic—have failed to do justice to the foregoing constraints, and in this sense, they are not proper tools for solving the frame problem/relevance problem. As we will argue in this paper, Non-Axiomatic Reasoning System (NARS) can handle the frame problem in a more proper manner, because the resulting solution seriously takes epistemic constraints on cognition as a fundamental theoretical principle. (shrink)

How can abductive reasoning be physical, feasible, and reliable? This is Fodor’s riddle of abduction, and its apparent intractability is the cause of Fodor’s recent pessimism regarding the prospects for cognitive science. I argue that this riddle can be solved if we augment the computational theory of mind to allow for non-computational mental processes, such as those posited by classical associationists and contemporary connectionists. The resulting hybrid theory appeals to computational mechanisms to explain the semantic coherence of inference and associative (...) mechanisms to explain the efficient retrieval of relevant information from memory. The interaction of these mechanisms explains how abduction can be physical, feasible, and reliable. (shrink)

Paul Pietroski develops an iconoclastic account of linguistic meaning. Here, I invite him to say more about what it implies about the relations between language, truth, and conceptual content. Readers concerned with securing the objectivity of conceptual thought may be worried about his claims that typical concepts “have no extensions” and that they “fit one another better than they fit the world.” Others might applaud his anti‐extensionalism in natural‐language semantics but fear that his account re‐raises familiar problems about extensions at (...) the level of psychology. (shrink)

Inquiry, Volume 57, Issue 1, Page 55-96, February 2014.

We critically investigate and refine Dunn's relevant predication, his formalisation of the notion of a real property. We argue that Dunn's original dialectical moves presuppose some interpretation of relevant identity, though none is given. We then re-motivate the proposal in a broader context, considering the prospects for a classical formalisation of real properties, particularly of Geach's implicit distinction between real and ''Cambridge'' properties. After arguing against these prospects, we turn to relevance logic, re-motivating relevant predication with Geach's distinction in mind. (...) Finally we draw out some consequences of Dunn's proposal for the theory of identity in relevance logic. (shrink)

The conception of a modular structure of mind, which postulates that the mind is composed of more or less autonomous subsystems, is widespread in contemporary psychology. Proponents of a modular structure of the mind, such as Marshall (1980; 1984; 1985), Fodor (1983), Gardner (1983), and Shallice (1988), have linked their work to F. J. Gall's theory of organology or of the “functions of the brain”. This paper argues: (1) Gall's organology defends a view of the capacities of the mind that (...) relies on a specific relation between the organism and its organs. The organs of Gall stem from and originate “needs” so that they embody not so much processing capacities, but perception-for-action cycles. (2) Modules and organs arise at different levels of description that cannot be easily matched. (3) It is just this central distinction between organs and modules which explains the inherent relation between organology, comparative biology and differential psychology on the one hand and modularity and cognitive science on the other. (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)

This paper examines three cases of the clash between science and philosophy: Zeno’s paradoxes, the Frame Problem, and a recent attempt to experimentally refute skepticism. In all three cases, the relevant science claims to have resolved the purported problem. The sciences, construing the term broadly, are mathematics, artificial intelligence, and psychology. The goal of this paper is to show that none of the three scientific solutions work. The three philosophical problems remain as vibrant as ever in the face of robust (...) scientific attempts to dispel them. The paper concludes by examining some consequences of this persistence. (shrink)

This paper examines three cases of the clash between science and philosophy: Zeno’s paradoxes, the Frame Problem, and a recent attempt to experimentally refute skepticism. In all three cases, the relevant science claims to have resolved the purported problem. The sciences, construing the term broadly, are mathematics, artificial intelligence, and psychology. The goal of this paper is to show that none of the three scientific solutions work. The three philosophical problems remain as vibrant as ever in the face of robust (...) scientific attempts to dispel them. The paper concludes by examining some consequences of this persistence. (shrink)

The Frame Problem originated as an obstacle for classical, symbolic A.I. that was adopted, expanded, and reformulated by philosophers. The version of the problem that I focus on, the Holism Problem, points out the difficulty in programming systems to recognize and consider mostly relevant information, given that relevance is context-sensitive. My goal in this thesis is to determine whether the Global Workspace Theory (GWT) can solve the holism problem. GWT proposes that distributed parallel processing, global broadcast, and chaotic itinerancy can (...) solve the problem by providing a system with 1) the speed to search through information, 2) access to the information it needs to compute relevance, 3) the ability to synthesize information. I argue that GWT fails to enable a system to recognize any relevant information because it inadequately responds to the Epistemological Holism Problem, which requires successfully determining the norms by which a system can recognize relevance. (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)