It is shown that the Fodor's interpretation of the frame problem is the central indication that his version of the Modularity Thesis is incompatible with computationalism. Since computationalism is far more plausible than this thesis, the latter should be rejected.

6. Seeing With the Mind’s Eye 1: The Puzzle of Mental Imagery .................................................6-1 6.1 What is the puzzle about mental imagery?..............................................................................6-1 6.2 Content, form and substance of representations ......................................................................6-6 6.3 What is responsible for the pattern of results obtained in imagery studies?.................................6-8..

Delusional beliefs have sometimes been considered as rational inferences from abnormal experiences. We explore this idea in more detail, making the following points. Firstly, the abnormalities of cognition which initially prompt the entertaining of a delusional belief are not always conscious and since we prefer to restrict the term “experience” to consciousness we refer to “abnormal data” rather than “abnormal experience”. Secondly, we argue that in relation to many delusions (we consider eight) one can clearly identify what the abnormal cognitive (...) data are which prompted the delusion and what the neuropsychological impairment is which is responsible for the occurrence of these data; but one can equally clearly point to cases where this impairments is present but delusion is not. So the impairment is not sufficient for delusion to occur. A second cognitive impairment, one which impairs the ability to evaluate beliefs, must also be present. Thirdly (and this is the main thrust of our chapter) we consider in detail what the nature of the inference is that leads from the abnormal data to the belief. This is not deductive inference and it is not inference by enumerative induction; it is abductive inference. We offer a Bayesian account of abductive inference and apply it to the explanation of delusional belief. (shrink)

The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and outcomes in decision situations, and so they are (...) incomplete. Second, it will be argues that when we attempt to incorporate the knowledge of such causal connections into Bayesian decision theory, a substantial technical problem arises for which there is no currently available solution that does not suffer from some damning objection or other. From a broader perspective, this then throws into question the use of decision theory as a model of human or machine planning. (shrink)

The paper begins by providing a game-theoretic reconstruction of Gilbert’s (1989) philosophical critique of Lewis (1969) on the role of salience in selecting conventions. Gilbert’s insight is reformulated thus: Nash equilibrium is insufficiently powerful as a solution concept to rationalize conventions for unboundedly rational agents if conventions are solutions to the kinds of games Lewis supposes. Both refinements to NE and appeals to bounded rationality can plug this gap, but lack generality. As Binmore (this issue) argues, evolutive game theory readily (...) explains the origin of conventional behavior, but that is not Lewis’s project. Gilbert’s critique is generalized by reference to Bacharach’s (2006) work on team reasoning in games. The paper then argues that although Lewis’s account of the rationalization of conventions is shown by the reconstruction of Gilbert’s critique to be incomplete, Gilbert is wrong to conclude that classical (‘eductive’) game theory lacks the resources to explain conformity to conventions among people. A game-theoretic account of the dynamics of socialization, based on Ross’s (2005, 2006) idea of ‘game determination’, rationalizes choices of conventional strategies in overlapping generations contexts, provided agents are products of evolutionary selection and know that other players are also such products. (shrink)

Chiappe and Kukla argue that relevance theory fails to solve the frame problem as defined by Fodor. They are right. They are wrong, however, to take Fodor’s frame problem too seriously. Fodor’s concerns, on the other hand, even though they are wrongly framed, are worth addressing. We argue that Relevance thoery helps address them.

Following Nancy Cartwright and others, I suggest that most (if not all) theories incorporate, or depend on, one or more idealizing assumptions. I then argue that such theories ought to be regimented as counterfactuals, the antecedents of which are simplifying assumptions. If this account of the logic form of theories is granted, then a serious problem arises for Bayesians concerning the prior probabilities of theories that have counterfactual form. If no such probabilities can be assigned, the the posterior probabilities will (...) be undefined, as the latter are defined in terms of the former. I argue here that the most plausible attempts to address the problem of probabilities of conditionals fail to help Bayesians, and, hence, that Bayesians are faced with a new problem. In so far as these proposed solutions fail, I argue that Bayesians must give up Bayesianism or accept the counterintuitive view that no theories that incorporate any idealizations have ever really been confirmed to any extent whatsoever. Moreover, as it appears that the latter horn of this dilemma is highly implausible, we are left with the conclusion that Bayesianism should be rejected, at least as it stands. (shrink)

In The Mind Doesn’t Work that Way, Jerry Fodor argues that mental representations have context sensitive features relevant to cognition, and that, therefore, the Classical Computational Theory of Mind (CTM) is mistaken. We call this the Globality Argument. This is an in principle argument against CTM. We argue that it is self-defeating. We consider an alternative argument constructed from materials in the discussion, which avoids the pitfalls of the official argument. We argue that it is also unsound and that, while (...) it is an empirical issue whether context sensitive features of mental representations are relevant to cognition, it is empirically implausible. (shrink)

To surmount the notorious difficulties of defining life, we should evaluate theories of life not by whether they provide necessary and sufficient conditions for our current preconceptions about life but by how well they explain living phenomena and how satisfactorily they resolve puzzles about life. On these grounds, the theory of life as supple adaptation (Bedau 1996) gets support from its natural and compelling resolutions of the following four puzzles: (1) How are different forms of life at different levels of (...) the vital hierarchy related? (2) Is there a continuum between life and non-life? (3) Does life essentially concern a living entity’s material composition or its form? (4) Are life and mind intrinsically connected? (shrink)

Imitation, deliberation, and mindreading are characteristically human sociocognitive skills. Research on imitation and its role in social cognition is flourishing across various disciplines; it is here surveyed under headings of behavior, subpersonal mechanisms, and functions of imitation. A model is then advanced within which many of the developments surveyed can be located and explained. The shared circuits model explains how imitation, deliberation, and mindreading can be enabled by subpersonal mechanisms of control, mirroring and simulation. It is cast at a middle, (...) functional level of description, between the level of neural implementation and the level of conscious perceptions and intentional actions. The shared circuits model connects shared informational dynamics for perception and action with shared informational dynamics for self and other, while also showing how the action/perception, self/other and actual/possible distinctions can be overlaid on these shared informational dynamics. It avoids the common conception of perception and action as separate and peripheral to central cognition. Rather, it contributes to the situated cognition movement by showing how mechanisms for perceiving action can be built on those for active perception. (shrink)

The frame problem is widely reputed among philosophers to be one of the deepest and most difficult problems of cognitive science. This paper discusses three recent attempts to display this problem: Dennett's problem of ignoring obviously irrelevant knowledge, Haugeland's problem of efficiently keeping track of salient side effects, and Fodor's problem of avoiding the use of kooky concepts. In a negative vein, it is argued that these problems bear nothing but a superficial similarity to the frame problem of AI, so (...) that they do not provide reasons to disparage standard attempts to solve it. More positively, it is argued that these problems are easily solved by slight variations on familiar AI themes. Finally, some discussion is devoted to more difficult problems confronting AI. (shrink)

This paper investigates connectionism's potential to solve the frame problem. The frame problem arises in the context of modelling the human ability to see the relevant consequences of events in a situation. It has been claimed to be unsolvable for classical cognitive science, but easily manageable for connectionism. We will focus on a representational approach to the frame problem which advocates the use of intrinsic representations. We argue that although connectionism's distributed representations may look promising from this perspective, doubts can (...) be raised about the potential of distributed representations to allow large amounts of complexly structured information to be adequately encoded and processed. It is questionable whether connectionist models that are claimed to effectively represent structured information can be scaled up to a realistic extent. We conclude that the frame problem provides a difficulty to connectionism that is no less serious than the obstacle it constitutes for classical cognitive science. (shrink)

We argue that A. Damasio’s (1994) Somatic Marker hypothesis can explain why humans don’t generally suffer from the frame problem, arguably the greatest obstacle facing the Computational Theory of Mind. This involves showing how humans with damaged emotional centers are best understood as actually suffering from the frame problem. We are then able to show that, paradoxically, these results provide evidence for the Computational Theory of Mind, and in addition call into question the very distinction between easy and hard problems (...) in the contemporary philosophy of mind. (shrink)

Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical underdetermination of the theory (...) by the data. Level t1 is clearly too underdetermined, T2 is vulnerable to a counterexample (Searle's Chinese Room Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3 is the appropriate target level for cognitive science. When it is reached, however, there will still remain more unanswerable questions than when Physics reaches its Grand Unified Theory of Everything (GUTE), because of the mind/body problem and the other-minds problem, both of which are inherent in this empirical domain, even though Turing hardly mentions them. (shrink)

Theories of rational decision making often abstract away from computational and other resource limitations faced by real agents. An alternative approach known as resource rationality puts such matters front and center, grounding choice and decision in the rational use of finite resources. Anticipated by earlier work in economics and in computer science, this approach has recently seen rapid development and application in the cognitive sciences. Here, the theory of rationality plays a dual role, both as a framework for normative assessment (...) and as a source of scientific hypotheses about how mental processes in fact work. The latter project, often called rational analysis, depends for its success on a fine-grained characterization of the computational problem facing a decision maker, which may in turn depend on realistic assumptions about what the relevant agent is like. As a consequence, resource rationality involves a delicate, but often fruitful interplay between the normative and the descriptive. (shrink)

Hubert Dreyfus has argued recently that the frame problem, discussion of which has fallen out of favour in the AI community, is still a deal breaker for the majority of AI projects, despite the fact that the logical version of it has been solved. (Shanahan 1997, Thielscher 1998). Dreyfus thinks that the frame problem will disappear only once we abandon the Cartesian foundations from which it stems and adopt, instead, a thoroughly Heideggerian model of cognition, in particular one that does (...) not appeal to representations. I argue that Dreyfus is too hasty in his condemnation of all representational views; the argument he provides licenses only a rejection of disembodied models of cognition. In casting his net too broadly, Dreyfus circumscribes the cognitive playing field so closely that one is left wondering how his Heideggerian alternative could ever provide a foundation explanatorily robust enough for a theory of cognition. As a consequence, he dilutes the force of his legitimate conclusion, that disembodied cognitive models will not work, and this conclusion needs to be heard. By disentangling the ideas of embodiment and representation, at least with respect to Dreyfus‘ frame problem argument, the real locus of the general polemic between traditional computational/representational cognitive science and the more recent embodied approaches is revealed. From this, I hope that productive debate will ensue. (shrink)

I want to suggest that the major influence of classical arguments for embodiment like "The Embodied Mind" by Varela, Thomson & Rosch (1991) has been a changing of positions rather than a refutation: Cognitivism has found ways to retreat and regroup at positions that have better fortification, especially when it concerns theses about artificial intelligence or artificial cognitive systems. For example: a) Agent-based cognitivism' that understands humans as taking in representations of the world, doing rule-based processing and then acting on (...) them (sense-plan-act) is often limited to conscious decision processes; and b) Purely syntactic cognition is compatible with embodiment, or supplemented by embodiment (e.g. for 'grounding'). While the empirical thesis of embodied cognition ('embodied cognitive science') is true and the practical engineering thesis ('morphological computation', 'cheap design') is often true, the conceptual thesis ('embodiment is necessary for cognition') is likely false - syntax is often enough for cognition, unless grounding is really necessary. I conclude that it has become more sensible to integrate embodiment with traditional approaches rather than "fight for embodiment" or "against cognitivism". (shrink)

Descartes’s uncompromising rejection of the possibility of animal intelligence was among his most controversial theses. That rejection is based on (1) his commitment to the doctrine of animal automatism and (2) two tests that he takes to be sufficient indicators of thought (the action and language tests). Of these two tests, only the language test is truly definitive, and Descartes is firmly of the view that no animal could demonstrate the capacity to use signs to convey meaning in “all the (...) circumstances of life.” The topic is fascinating for forcing us to ponder what exactly reason is for Descartes and the role it plays in everyday life. This article explores the tensions in Descartes’s arguments produced by an over reliance on the analogy between animals and clocks, including the question of what to make of Descartes’s recognition of the need to posit representational and information-processing subsystems in the brain. (shrink)

Unlike human soldiers, autonomous weapons systems are unaffected by psychological factors that would cause them to act outside the chain of command. This is a compelling moral justification for their development and eventual deployment in war. To achieve this level of sophistication, the software that runs AWS will have to first solve two problems: the frame problem and the representation problem. Solutions to these problems will inevitably involve complex software. Complex software will create security risks and will make AWS critically (...) vulnerable to hacking. I claim that the political and tactical consequences of hacked AWS far outweigh the purported advantages of AWS not being affected by psychological factors and always following orders. Therefore, one of the moral justifications for the deployment of AWS is undermined. (shrink)

FramesIn 1934 the Belgian artist René Magritte painted a room with a view. On an easel in front of the window stands a painting depicting the very piece of landscape blocked from sight. Magritte named his painting La Condition Humaine (“The Human Condition”), which is quite apt, for the life of everyone is determined by windows, doors, mirrors and many other frames. Indeed, to avert anxiety, one actually cultivates social behavior born of the fear of being “outside the frame.” Because (...) of this apprehension, we organize all reality into a series of preestablished frames. (shrink)

One of Thomas Kuhn's profoundest arguments is introduced in the 1970 “Postscript” to The Structure of Scientific Revolutions . Kuhn is discussing the idea of a “disciplinary matrix” as a more adequate articulation of the “paradigm” notion he'd introduced in the first, 1962, edition of his famous work . He notes that one “element” of disciplinary matrices is likely to be common to most or even all such matrices, unlike the other elements which serve to distinguish specific disciplines and sub-disciplines (...) from one another. This is the element which he calls “values”, which, as he notes , being common to a number of otherwise distinct disciplinary matrices, “do much to provide a sense of community to natural scientists as a whole”. On the other hand, they also do much, and crucially in Kuhn's view, to promote and sustain a healthy diversity among the practitioners who share any specific disciplinary matrix. In particular, Kuhn claims that “individual variability in the application of shared values may serve functions essential to science.”. (shrink)

Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...) a residual role for logic in understanding reasoning; and others put forward alternative formalisms for uncertain reasoning, or raise specific technical, methodological, or empirical challenges. In responding to these points, we aim to clarify the scope and limits of probability and logic in cognitive science; explore the meaning of the explanation of cognition; and re-evaluate the empirical case for Bayesian rationality. (shrink)

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we (...) explore fast and frugal heuristics – simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data – that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program. Key Words: adaptive toolbox; bounded rationality; decision making; elimination models; environment structure; heuristics; ignorance-based reasoning; limited information search; robustness; satisficing; simplicity. (shrink)

The theory-theory claims that the explanation and prediction of behavior works via the application of a theory, while the simulation theory claims that explanation works by putting ourselves in others' places and noting what we would do. On either account, in order to develop a prediction or explanation of another person's behavior, one first needs to have a characterization of that person's current or recent actions. Simulation requires that I have some grasp of the other person's behavior to project myself (...) upon; whereas theorizing requires a subject matter to theorize about. The frame problem shows that multiple, true characterizations are possible for any behavior or situation. However, only one or a few of these characterizations are relevant to explaining or predicting behavior. Since different characterizations of a behavior lead to different predictions or explanations, much of the work of interpersonal interpretation is done in the process of finding this characterization - that is, prior to either theorizing or simulating. Moreover, finding this characterization involves extensive knowledge of the physical, cultural, and social worlds of the persons involved. (shrink)

I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by (...) Pat Hayes, the problem is entertaining, but incapable of resolution. The solution to the frame problem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frame problem and the many other problems for artificial epistemology. I present the solution to the frame problem: an adequate theory and method for the machine induction of causal structure. Whereas this solution is clearly satisfactory in principle, and in practice real progress has been made in recent years in its application, its ultimate implementation is in prospect only for future generations of AI researchers. (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 is an expository article about the solution to the frame problem proposed in 1980 by Raymond Reiter. For years, his “frame default” remained untested and suspect. But developments in some seemingly unrelated areas of computer science—logic programming and satisfiability solvers—eventually exonerated the frame default and turned it into a basis for important applications.

I distinguish between being cognisant and being able to perform intelligent operations. The former, but not the latter, minimally involves the capacity to make adequate judgements about one's relation to objects in the environment. The referential nature of cognisance entails that the mental states of cognisant systems must be inter-related holistically, such that an individual thought becomes possible because of its relation to a system of potential thoughts. I use Gareth Evans' 'Generality Constraint' as a means of describing how the (...) reference and holism of mental states in cognisant systems are mutually dependent. Next, I describe attempts to deny the relevance of holism and reference by positing a mentalese. These attempts fail because the meanings of symbols are under determined, with there being no principled means of distinguishing between the mental tokening of a symbol and its disambiguation. I argue that the connectionist meta-theory does not encounter this problem because it is able to encompass the holism of the mental. Recent attempts to show that symbol processing theories of thought must be preferred to connectionist theories do not work. Despite appearances to the contrary, the Generality Constraint favours connectionist not symbol-processing theories. (shrink)

Prominent evolutionary psychologists have argued that our innate psychological endowment consists of numerous domainspecific cognitive resources, rather than a few domaingeneral ones. In the light of some conceptual clarification, we examine the central inprinciple arguments that evolutionary psychologists mount against domaingeneral cognition. We conclude (a) that the fundamental logic of Darwinism, as advanced within evolutionary psychology, does not entail that the innate mind consists exclusively, or even massively, of domainspecific features, and (b) that a mixed innate cognitive economy of domainspecific (...) and domaingeneral resources remains a genuine conceptual possibility. However, an examination of evolutionary psychology's 'grain problem' reveals that there is no way of establishing a principled and robust distinction between domainspecific and domaingeneral features. Nevertheless, we show that evolutionary psychologists can and do live with this grain problem without their whole enterprise being undermined. (shrink)

Recent accounts of pretense have been underdescribed in a number of ways. In this paper, we present a much more explicit cognitive account of pretense. We begin by describing a number of real examples of pretense in children and adults. These examples bring out several features of pretense that any adequate theory of pretense must accommodate, and we use these features to develop our theory of pretense. On our theory, pretense representations are contained in a separate mental workspace, a Possible (...) World Box which is part of the basic architecture of the human mind. The representations in the Possible World Box can have the same content as beliefs. Indeed, we suggest that pretense representations are in the same representational ``code'' as beliefs and that the representations in the Possible World Box are processed by the same inference and UpDating mechanisms that operate over real beliefs. Our model also posits a Script Elaborator which is implicated in the embellishment that occurs in pretense. Finally, we claim that the behavior that is seen in pretend play is motivated not from a ``pretend desire'', but from a real desire to act in a way that ®ts the description being constructed in the Possible World Box. We maintain that this account can accommodate the central features of pretense exhibited in the examples of pretense, and we argue that the alternative accounts either can't accommodate or fail to address entirely some of the central features of pretense. q 2000 Elsevier Science B.V. All rights reserved. (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)

According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic – the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining (...) the end-point of cognitive development; and contemporary psychology of reasoning has focussed on comparing human reasoning against logical standards. (shrink)

The frame problem is the difficulty of explaining how non-magical systems think and act in ways that are adaptively sensitive to context-dependent relevance. Influenced centrally by Heideggerian phenomenology, Hubert Dreyfus has argued that the frame problem is, in part, a consequence of the assumption (made by mainstream cognitive science and artificial intelligence) that intelligent behaviour is representation-guided behaviour. Dreyfus' Heideggerian analysis suggests that the frame problem dissolves if we reject representationalism about intelligence and recognize that human agents realize the property (...) of thrownness (the property of being always already embedded in a context). I argue that this positive proposal is incomplete until we understand exactly how the properties in question may be instantiated in machines like us. So, working within a broadly Heideggerian conceptual framework, I pursue the character of a representation-shunning thrown machine. As part of this analysis, I suggest that the frame problem is, in truth, a two-headed beast. The intra-context frame problem challenges us to say how a purely mechanistic system may achieve appropriate, flexible and fluid action within a context. The inter-context frame problem challenges us to say how a purely mechanistic system may achieve appropriate, flexible and fluid action in worlds in which adaptation to new contexts is open-ended and in which the number of potential contexts is indeterminate. Drawing on the field of situated robotics, I suggest that the intra-context frame problem may be neutralized by systems of special-purpose adaptive couplings, while the inter-context frame problem may be neutralized by systems that exhibit the phenomenon of continuous reciprocal causation. I also defend the view that while continuous reciprocal causation is in conflict with representational explanation, special-purpose adaptive coupling, as well as its associated agential phenomenology, may feature representations. My proposal has been criticized recently by Dreyfus, who accuses me of propagating a cognitivist misreading of Heidegger, one that, because it maintains a role for representation, leads me seriously astray in my handling of the frame problem. I close by responding to Dreyfus' concerns. (shrink)

Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning (...) leads to a radicalreinterpretation of empirical results which are typically viewed asdemonstrating human irrationality. (shrink)

Some have claimed that since machines lack emotional “qualia”, or conscious experiences of emotion, machine intelligence will fall short of human intelligence. I examine this objection, ultimately finding it unpersuasive. I first discuss recent work on emotion that suggests that emotion plays various roles in cognition. I then raise the following question: are phenomenal experiences of emotion an essential or necessary component of the performance of these cognitive abilities? I then sharpen the question by distinguishing between four possible positions one (...) might take. I reject one of these four positions largely on empirical grounds. But the remaining three positions all suggest that even if emotional qualia play an important role in human cognition, emotional qualia are not essential to the performance of these cognitive abilities in principle, so, e.g., a machine that lacks emotional qualia might still be able to perform them. (shrink)

This article selectively surveys recent work touching consciousness. It discusses some recent arguments and positions with a view to throwing light on a working principle of much influential philosophical psychology, namely that the first?person point of view is theoretically redundant. The discussion is divided under a number of headings corresponding to specific functions that have been attributed to the first?person viewpoint, from the experience of something it is like to undergo physical processes, to the presence of selfhood, mental substance, meaning, (...) understanding, and value. Arguments indicating limitations of the working principle are adduced, as well as arguments indicating possible exploitations of those limitations by first?personalist positions. Although some of the latter also have limitations, the direction in which the examination tends is that of a progressive widening of the concept of consciousness. (shrink)

A discussion is going on in cognitive science about the use of representations to explain how intelligent behavior is generated. In the traditional view, an organism is thought to incorporate representations. These provide an internal model that is used by the organism to instruct the motor apparatus so that the adaptive and anticipatory characteristics of behavior come about. So-called interactionists claim that this representational specification of behavior raises more problems than it solves. In their view, the notion of internal representational (...) models is to be dispensed with. Instead, behavior is to be explained as the intricate interaction between an embodied organism and the specific make up of an environment. The problem with a non-representational interactive account is that it has severe difficulties with anticipatory, future oriented behavior. The present paper extends the interactionist conceptual framework by drawing on ideas derived from the study of morphogenesis. This extended interactionist framework is based on an analysis of anticipatory behavior as a process which involves multiple spatio-temporal scales of neural, bodily and environmental dynamics. This extended conceptual framework provides the outlines for an explanation of anticipatory behavior without involving a representational specification of future goal states. (shrink)

Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.

Current psychological research on causal inference is dominated by two basic approaches: the covariation approach and the mechanism approach. This article reviews these two approaches, evaluates the contributions and limitations of each approach, and suggests how these approaches might be integrated into a more comprehensive framework. Covariation theorists assume that cognizers infer causal relations from conditional probabilities computed over samples of multiple events, but they do not provide an adequate account of how cognizers constrain their search for candidate causes and (...) relevant evidence. Mechanism theorists assume that cognizers use their knowledge of potential mechanisms to infer the causes of individual events, but they do not account for the origins of this kind of knowledge. Theorists might integrate these approaches into a framework that overcomes these limitations by (1) examining important relations between cognizers' beliefs about the nature of causality, the logic of causal inference, and the processes cognizers use to make causal inferences, and (2) providing a more complete account of cognizers' conceptions of causality and the origins of those conceptions. (shrink)