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)

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)

Robot ethics encompasses ethical questions about how humans should design, deploy, and treat robots; machine morality encompasses questions about what moral capacities a robot should have and how these capacities could be computationally implemented. Publications on both of these topics have doubled twice in the past 10 years but have often remained separate from one another. In an attempt to better integrate the two, I offer a framework for what a morally competent robot would look like and discuss a number (...) of ethical questions about the design, use, and treatment of such moral robots in society. Instead of searching for a fixed set of criteria of a robot’s moral competence I identify the multiple elements that make up human moral competence and probe the possibility of designing robots that have one or more of these human elements, which include: moral vocabulary; a system of norms; moral cognition and affect; moral decision making and action; moral communication. Juxtaposing empirical research, philosophical debates, and computational challenges, this article adopts an optimistic perspective: if robotic design truly commits to building morally competent robots, then those robots could be trustworthy and productive partners, caretakers, educators, and members of the human community. Moral competence does not resolve all ethical concerns over robots in society, but it may be a prerequisite to resolve at least some of them. (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)

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.

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)

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 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)

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.

This paper discusses different approaches incognitive science and artificial intelligenceresearch from the perspective of radicalconstructivism, addressing especially theirrelation to the biologically based theories ofvon Uexküll, Piaget as well as Maturana andVarela. In particular recent work in New AI and adaptive robotics on situated and embodiedintelligence is examined, and we discuss indetail the role of constructive processes asthe basis of situatedness in both robots andliving organisms.

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)

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)

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)

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)

English: In this paper I indicate why I consider 'experimental philosophy' to be good news, though not as good as it could be, and not as all that new. I'll argue that there is no need to restrict experimental philosophy to eliciting intuitions through questionnaires. I'll indicate that good examples of experimental philosophy already exist in Artificial Intelligence and Cognitive Neuroscience. Dutch: Hieronder wil ik proberen aan te geven waarom ik de experimentele filosofie weliswaar als goed nieuws beschouw, maar niet (...) half zo goed als zou kunnen, en ook niet als echt nieuw. Qua filosofische experimenten beperkt de XF-beweging zich vooralsnog onnodig door haar focus op het eliciteren van intuïties, terwijl er reeds lang diverse goede voorbeelden van XF bestaan op het gebied van de kunstmatige intelligentie en de cognitieve neurowetenschap. (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)

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)

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)

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)

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)

The dispute between individualism and anti-individualism is about the individuation of psychological states, and individualism, on some accounts, is committed to the claim that psychological subjects together with their environments do not constitute integrated computational systems. Hence on this view the computational states that explain psychological states in computational accounts of mind will not involve the subject''s natural and social environment. Moreover, the explanation of a system''s interaction with the environment is, on this view, not the primary goal of computational (...) theorizing. Recent work in computational developmental psychology (by A. Karmiloff-Smith and J. Rutkowska) as well as artificial agents or embedded artificial systems (by L.P. Kaelbling, among others) casts doubt on these claims. In these computational models, the environment does not just trigger and sustain input for computational operations, but some computational operations actually involve environmental structures. (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)

In this paper, it is argued that Ferguson’s (2003, Argumentation 17, 335–346) recent proposal to reconcile monotonic logic with defeasibility has three counterintuitive consequences. First, the conclusions that can be derived from his new rule of inference are vacuous, a point that as already made against default logics when there are conflicting defaults. Second, his proposal requires a procedural “hack” to the break the symmetry between the disjuncts of the tautological conclusions to which his proposal leads. Third, Ferguson’s proposal amounts (...) to arguing that all everyday inferences are sound by definition. It is concluded that the informal logic response to defeasibility, that an account of the context in which inferences are sound or unsound is required, still stands. It is also observed that another possible response is given by Bayesian probability theory (Oaksford and Chater, in press, Bayesian Rationality: The Probabilistic Approach to Human Reasoning, Oxford University Press, Oxford, UK; Hahn and Oaksford, in press, Synthese). (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)

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)

The anticipation of the typical under the assumption of the non-occurrence of the atypical is the experiential schema governing the individuation of ordinary enduring objects and their properties. Against this background, a primitive form of “if-and-only-if” consciousness is implicit in our everyday perceptual intentions. The thematization of the fact that perception operates under this proto-tentative structure occurs at the level of reflection and is expressed by defeasible judgments of the form “if p, then q, unless r,” or “if p, then (...) q provided that not-r”. Here I will argue that what is made thematic by judgments containing such restrictive clauses is the structure of unpredictability inherent in what Husserl calls the horizon of latency and, indirectly, the world as background of normality. (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)

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)

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..

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)

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)

Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use (...) of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is necessary for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing. (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)

According to Darwinian thinking, organisms are designed by natural selection, and so are integrated collections of adaptations, where an adaptation is a phenotypic trait that is a specialized response to a particular selection pressure. For animals that make their living in the Arctic, one adaptive problem is how to maintain body temperature above a certain minimum level necessary for survival. Polar bears' thick coats are a response to that selection pressure . A thick coat makes a positive difference to a (...) polar bear's fitness, since polar bears with very thin coats left fewer offspring than those with thicker coats. The foundational idea of evolutionary psychology is that brains are no different from any other organ with an evolutionary function, insofar as brains too are systems shaped by natural selection to solve adaptive problems. Thus brains have a particular functional organization because their behavioural effects tend, or once tended, to help maintain or increase the fitness of organisms with those brains. Prominent evolutionary psychologists have endorsed the view that the last time any significant modifications were made by natural selection to the human brain's functional architecture, we were hunter-gatherers, inhabiting a world quite different from that which we now inhabit. That world was the Pleistocene epoch, between about 2 million years ago and 10 thousand years ago. On this view, then, the Pleistocene constitutes what evolutionary psychologists often call our environment of evolutionary adaptedness , and the information- processing structure and organization of our present-day cognitive architecture is no different from that of our recent huntergatherer ancestors. (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)

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)

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.

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)