As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than (...) reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding. (shrink)

Jerry Fodor's argument for an innate language of thought continues to be a hurdle for researchers arguing that natural languages provide us with richer conceptual systems than our innate cognitive resources. I argue that because the logical/formal terms of natural languages are given a usetheory of meaning, unlike predicates, logical/formal terms might be learned without a mediating internal representation. In that case, our innate representational system might have less logical structure than a natural language, making it possible that we augment (...) our innate representational system and improve our ability to think by learning a natural language. (shrink)

This book consists of a dialogue between two interlocutors, Pablo and a student, who discuss a great range of issues in social philosophy and political theory, and in particular, the emergence, working properties and economic effects of institutions. It uses the dialogical form to make philosophy more accessible, but also to show how ideas develop through intellectual interaction. The fact that one of the interlocutors is the "student" in a place in the real world makes the dialogue quasi-fictive in character (...) and enables the active engagement of the reader. After all, we are all philosophers and we develop our own philosophy by exchanging views and arguments. The dialogue form is and should remain the principal form of philosophizing, since ideas, like butterflies, do not merely exist – they develop. This is certainly the case in actual philosophical interaction, and it can be the case in written philosophical exposition. Although the dialogue does not presuppose prior acquaintance with the respective philosophical and social scientific literature under discussion in this book, it makes arguments more accessible, and conveys the feeling that there are no definite solutions to philosophical problems. (shrink)

The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for (...) unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling. (shrink)

People’s beliefs about normality play an important role in many aspects of cognition and life (e.g., causal cognition, linguistic semantics, cooperative behavior). But how do people determine what sorts of things are normal in the first place? Past research has studied both people’s representations of statistical norms (e.g., the average) and their representations of prescriptive norms (e.g., the ideal). Four studies suggest that people’s notion of normality incorporates both of these types of norms. In particular, people’s representations of what is (...) normal were found to be influenced both by what they believed to be descriptively average and by what they believed to be prescriptively ideal. This is shown across three domains: people’s use of the word ‘‘normal” (Study 1), their use of gradable adjectives (Study 2), and their judgments of concept prototypicality (Study 3). A final study investigated the learning of normality for a novel category, showing that people actively combine statistical and prescriptive information they have learned into an undifferentiated notion of what is normal (Study 4). Taken together, these findings may help to explain how moral norms impact the acquisition of normality and, conversely, how normality impacts the acquisition of moral norms. (shrink)

I examine the phenomenon of implicit learning, the process by which knowledge about the rule-governed complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two seemingly disparate experimental paradigms of synthetic grammar learning and probability learning, is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: Implicit learning produces a tacit knowledge base that is abstract and representative of the structure of (...) the environment; such knowledge is optimally acquired independently of conscious efforts to learn; and it can be used implicitly to solve problems and make accurate decisions about novel stimulus circumstances. Various epistemological issues and related problems such as intuition, neuroclinical disorders of learning and memory, and the relationship of evolutionary processes to cognitive science are also discussed. (shrink)

Plant and animal species are information-processing entities of such complexity, integration, and adaptive competence that it may be scientifically fruitful to consider them intelligent. The possibility arises from the analogy between learning and evolution, and from recent developments in evolutionary science, psychology and cognitive science. Species are now described as spatiotemporally localized individuals in an expanded hierarchy of biological entities. Intentional and cognitive abilities are now ascribed to animal, human, and artificial intelligence systems that process information adaptively, and that manifest (...) problem-solving abilities. The structural and functional similarities between such systems and species are extensive, although they “are usually obscured by population-genetic metaphors that have nonetheless contributed much to our understanding of evolution.In this target article, I use Sewall Wright's notion of the “adaptive landscape” to compare the performances of evolving species to those of intelligent organisms. With regard to their adaptive achievements and the kinds of processes by which they are achieved, biological species compare very favorably to intelligent animals by virtue of interactions between populations and their environments, between ontogeny and phylogeny, and between natural, interdemic, organic, and species selection. Addressing the question of whether species are intelligent could help to refine our ideas about species, evolution, and intelligence, and could open new lines of empirical and theoretical inquiry in many disciplines. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information‐processing mechanisms, cognitive science needs an account of (...) how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher‐level mechanisms from their components and situating them in their environments. (shrink)

The species problem is often described as the abundance of conflicting definitions of _species_, such as the biological species concept and phylogenetic species concepts. But biologists understand the notion of species in a non-definitional as well as a definitional way. In this article I argue that when they understand _species_ without a definition in their mind, their understanding is often mediated by the notion of _good species_, or prototypical species, as the idea of ``prototype'' is explicated in cognitive psychology. This (...) distinction helps us make sense of several puzzling phenomena regarding biologists' dealing with species, such as the fact that in everyday research biologists often behave as if the species problem is solved, while they should be fully aware that it is not. I also briefly discuss implications of this finding, including that some extant attempts to answer what the nature of species is have an inadequate assumption about how the notion of species is represented in biologists' minds. (shrink)

In this article, we provide a broad overview of the interplay among cognition, belief systems, and institutions, and how they affect economic performance. We argue that a deeper understanding of institutions’ emergence, their working properties, and their effect on economic and political outcomes should begin from an analysis of cognitive processes. We explore the nature of individual and collective learning, stressing that the issue is not whether agents are perfectly or boundedly rational, but rather how human beings actually reason and (...) choose, individually and in collective settings. We then tie the processes of learning to institutional analysis, providing arguments in favor of what can be characterized as “cognitive institutionalism.” Besides, we show that a full treatment of the phenomenon of path dependence should start at the cognitive level, proceed at the institutional level, and culminate at the economic level. (shrink)

Since Freud, clinicians have understood that disturbing memories contribute to psychopathology and that new emotional experiences contribute to therapeutic change. Yet, controversy remains about what is truly essential to bring about psychotherapeutic change. Mounting evidence from empirical studies suggests that emotional arousal is a key ingredient in therapeutic change in many modalities. In addition, memory seems to play an important role but there is a lack of consensus on the role of understanding what happened in the past in bringing about (...) therapeutic change. The core idea of this paper is that therapeutic change in a variety of modalities, including behavioral therapy, cognitive-behavioral therapy, emotion-focused therapy, and psychodynamic psychotherapy, results from the updating of prior emotional memories through a process of reconsolidation that incorporates new emotional experiences. We present an integrated memory model with three interactive components – autobiographical (event) memories, semantic structures, and emotional responses – supported by emerging evidence from cognitive neuroscience on implicit and explicit emotion, implicit and explicit memory, emotion-memory interactions, memory reconsolidation, and the relationship between autobiographical and semantic memory. We propose that the essential ingredients of therapeutic change include: (1) reactivating old memories; (2) engaging in new emotional experiences that are incorporated into these reactivated memories via the process of reconsolidation; and (3) reinforcing the integrated memory structure by practicing a new way of behaving and experiencing the world in a variety of contexts. The implications of this new, neurobiologically grounded synthesis for research, clinical practice, and teaching are discussed. (shrink)

Speaking: from Intention to Articulation Willem J. M. Levelt, 1989 (1993 paperback) Cambridge, MA: MIT Press ISBN: 0–262–12137–9(hb), 0–262–62089–8(pb)Rules for Reasoning Richard E. Nisbett (Ed.), 1993 Hillsdale, NJ, Lawrence Erlbaum Associates ISBN: 0–8058–1256–3(hb), 0–8085–1257–1 (pb)Readings in Philosophy and Cognitive Science Alvin I. Goldman, 1993 Cambridge, MA, MIT Press ISBN: 0–262–07153–3(hb), 0–262–57100–5(pb)Language Comprehension in Ape and Child, Monographs of the Society for Research in Child Development, Serial No. 233, Vol. 58, Nos 3–4 Sue Savage‐Rumbaugh, Jeannine Murphy, Rose A. Sevcik, Karen E. (...) Brakke, Shelly L. Williams & Duane M. Rumbaugh, with commentary by Elizabeth Bates, and a reply by the authors, 1993 Chicago: University of Chicago Press ISBN: 0–226–73542–7Language, Music and Mind Diana Raffman, 1993 Cambridge, MIT Press/Bradford Books ISBN: 0–262–18150–9Psychology of Proof Lance J. Rips, 1994 Cambridge, MA, MIT Press ISBN: 0–262–18153–3Human and Machine Thinking P. Johnson‐Laird, 1993 Hillsdale, NJ, Erlbaum ISBN: 0–8058–0921‐XIntelligent Behavior in Animals and Robots David McFarland & Thomas Bosser, 1993 Cambridge, MA and London, UK: MIT Press/ Bradford Books ISBN: 0–262–13293–1The Eye and the Mind: Reflections on Perception and the Problem of Knowledge Charles Landesman, 1993 Dordrecht: Kluwer Academic Publishers ISBN: 0–7923–2586–9The Moral Self GilG. Noam & Thomas E. Wren (Eds), in cooperation with Gertud Nunner‐Winkler & Wolfgang Edelstein, 1993 Cambridge, MA: MIT Press ISBN: 0–262–14052–1The Logic of Discovery: A Theory of the Rationality of Scientific Research Scott A. Kleiner, 1993 Dordrecht: Kluwer Academic Publishers ISBN: 0–7923–2371–8Folk Psychology and the Philosophy of Mind S.M. Christenson & D.R. Turner (Eds), 1993 Hillsdale, NJ: Lawrence Erlbaum ISBN: 0–805–80931–7. (shrink)

What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with respect (...) to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (shrink)

Two distinct issues are sometimes confused in the base rate literature: Why do people make logical mistakes in the assessment of probabilities? and why do subjects not use base rates the way experimenters do? The latter problem may often reflect differences in an implicit reference class rather than a disinclination to update a base rate by Bayes' theorem. Also important are considerations concerning the interaction of several potentially relevant base rates.

Koehler's target article attempts a balanced view of the relevance of knowledge of base rates to judgments of subjective or credal probability, but he is not sensitive enough to the difference between requiring and permitting the equation of probability judgments with base rates, the interaction between precision of base rate and reference class information, and the possibility of indeterminate probability judgment.

Koehler is right to argue for more nuanced interpretation of base rate anomalies. These anomalies are best understood in relation to a broader class of cognitive anomalies, which are important for theory and practice. Recognizing a need for more nuanced analysis should not be taken as a license for treating the effects as “explained away.”.

Koehler's summary and critique of research on the base rate fallacy is cogent and persuasive. However, he may have overstated the case, and his suggestions for future research may be too restrictive. We agree that methodological approaches to this topic should be broadened, but we argue that experimental laboratory research and the Bayesian normative standard are useful and should not be abandoned.

Setting the two hypotheses of complete neglect and full use of base rates against each other is inappropriate. The proper question concerns the degree to which base rates are used (or neglected), and under what conditions. We outline alternative approaches and recommend regression analysis. Koehler's conclusion that we have been oversold on the base rate fallacy seems to be premature.

There is a familiar risk of antinomy if fromxisEand p(xisH/xisE) =rit is permissible to infer p(xisH) =r, and what Carnap (1950) called “The requirement of total evidence” will not prevent such antinomies satisfactorily. What is needed instead is a properly developed theory of evidential weight.

The argument by analogy for other minds is customarily rejected as a weak inference because the argument is based on a single instance. The current paper argues that this objection fundamentally misunderstands the inferential structure of analogies and so misrepresents the role analogy plays in the justifycation of belief in other minds. Arguments by be uniquely suited to draw inferences from single instances. This defense does not remove all difficulties faced by the argument by analogy for other minds.

Conceptual integration—“blending”—is a general cognitive operation on a par with analogy, recursion, mental modeling, conceptual categorization, and framing. It serves a variety of cognitive purposes. It is dynamic, supple, and active in the moment of thinking. It yields products that frequently become entrenched in conceptual structure and grammar, and it often performs new work on its previously entrenched products as inputs. Blending is easy to detect in spectacular cases but it is for the most part a routine, workaday process that (...) escapes detection except on technical analysis. It is not reserved for special purposes, and is not costly.In blending, structure from input mental spaces is projected to a separate, “blended” mental space. The projection is selective. Through completion and elaboration, the blend develops structure not provided by the inputs. Inferences, arguments, and ideas developed in the blend can have effect in cognition, leading us to modify the initial inputs and to change our view of the corresponding situations.Blending operates according to a set of uniform structural and dynamic principles. It additionally observes a set of optimality principles. (shrink)

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and (...) to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number‐based rules. The first two experiments used the convergent evidence of problem‐solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions. (shrink)

A computational theory of primate social intelligence is proposed in which primates represent social situations internally by discrete symbol structures, called scripts. Three well‐defined computational operations on scripts are sufficient to support social learning, planning, and prediction. This gives a formal, predictive model with which to analyse how primate social knowledge is acquired, as well as how it is used.The theory is compared with primate data, such as Cheney and Seyfarth's observations of vervet monkeys. It gives simple, understandable script‐based analyses (...) of many observed phenomena—such as the recognition and use of kin relations, learning of alarm calls, habituation to calls, knowledge of rank, tactical deception, and attachment behaviour.I argue that a tight, concise theory of social cognition, such as script theory, is needed to explain the rapid learning and social guile seen in primates. It also has the benefits of simplicity and testability. The extension of scripts to incorporate a primate theory of mind is described in a subsequent paper. (shrink)

In adversarial problem solving (APS), one must anticipate, understand and counteract the actions of an opponent. Military strategy, business, and game playing all require an agent to construct a model of an opponent that includes the opponent's model of the agent. The cognitive mechanisms required for such modeling include deduction, analogy, inductive generalization, and the formation and evaluation of explanatory hypotheses. Explanatory coherence theory captures part of what is involved in APS, particularly in cases involving deception.

In this article, I use a mental models computational account of representation to illustrate some details of my previously presented inferential model of scientific understanding. The hope is to shed some light on possible mechanisms behind the notion of scientific understanding. I argue that if mental models are a plausible approach to modelling cognition, then understanding can best be seen as the coupling of specific rules. I present our beliefs as ?ordinary? conditional rules, and the coupling process as one where (...) the consequent of one ordinary rule (OR) matches and activates the antecedent of the rule to which it is coupled in virtue of the activation of an intermediate ?inference? rule. I argue that on this approach knowledge of an explanation is the activation of ORs in a cognitive hierarchy, while understanding is achieved when those activated ORs are also coupled via correct inference rules. I do not directly address issues regarding the plausibility of mental models themselves. This article should therefore be seen as an exercise in refining the inferential model within an already presupposed computational setting, not one of arguing for the psychological adequacy of computational approaches. (shrink)

The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, (...) we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase.Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model. (shrink)

Although philosophy has been only a minor contributor to cognitive science to date, this paper describes two projects in naturalistic philosophy of mind and one in naturalistic philosophy of science that have been pursued during the past 30 years and that can make theoretical and methodological contributions to cognitive science. First, stances on the mind–body problem (identity theory, functionalism, and heuristic identity theory) are relevant to cognitive science as it negotiates its relation to neuroscience and cognitive neuroscience. Second, analyses of (...) mental representations address both their vehicles and their contents; new approaches to characterizing how representations have content are particularly relevant to understanding the relation of cognitive agents to their environments. Third, the recently formulated accounts of mechanistic explanation in philosophy of science both provide perspective on the explanatory project of cognitive science and may offer normative guidance to cognitive science (e.g., by providing perspective on how multiple disciplinary perspectives can be integrated in understanding a given mechanism). (shrink)

This target article presents a new computational theory of explanatory coherence that applies to the acceptance and rejection of scientific hypotheses as well as to reasoning in everyday life, The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions. A hypothesis coheres with propositions that it explains, or that explain it, or that participate with it in explaining other propositions, or that offer analogous explanations. Propositions are incoherent with each other if they (...) are contradictory, Propositions that describe the results of observation have a degree of acceptability on their own. An explanatory hypothesis is acccpted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which treats hypothesis evaluation as a constraint satisfaction problem. Inputs about the explanatory relations are used to create a network of units representing propositions, while coherende and incoherence relations are encoded by excitatory and inbihitory links. ECHO provides an algorithm for smoothly integrating theory evaluation based on considerations of explanatory breadth, simplicity, and analogy. It has been applied to such important scientific cases as Lovoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy. (shrink)

A close scrutiny of the psychological literature reveals that many psychologists favor a 'segregative' approach to theory development. One theory is pitted against another, and the one that accounts for the data most successfully is deemed the theory of choice. However, an examination of the theoretical debates in which the segregative approach has been pursued reveals a variety of weaknesses to the approach, namely, masking an underlying theoretical indistinguishability of theoretical predictions, causing psychologists to focus unknowingly on different aspects of (...) the same phenomemon, and locking the theorist into a particular way of looking at a phenomemon. Although recent work in the philosophy of science has sought to develop a more satisfactory approach to theory development, this work also suffers from a variety of shortcomings. Work on intentionality and constructionism by philosophers of mind suggests an alternative to the segregative approach to theory development. We call this alternative the 'integrative' approach. In this approach one integrates the best aspects of a set of given theories with one's own ideas regarding the domain under investigation. Instead of emphasising those features that discriminate among theories, this approach seeks to identify those facets of competing theories that can provide a unified explanation of a given problem area. (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 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)

Neuroeconomics is the newest of the economic sciences with a focus on how the embodied human brain interacts with its institutional and social environment to make economic decisions. This paper presents an overview of neuroeconomics methods and reviews a number of results in this emerging field of study.

The nature of cultural organization remains an open anthropological question. Although we eschew any simplistic global reductionism, here we argue that three organizational features of culture, its systematicity; the recurrence of distinctions across semantic, conceptual and practical boundaries; and the 'bleeding' of properties between associated concepts, may find their origin in fundamental operating principles of the human mind: respectively, the cognitive principle of relevance, the decompositionality of cognitive processing and the network structure of semantic memory. The reframing of some features (...) of culture in cognitive terms may open up some ethnographic observations, so far resistant to anthropological explanation, to new avenues for theory and relevant data from other disciplines. (shrink)

Currently, there is widespread skepticism that higher cognitive processes, given their apparent flexibility and globality, could be carried out by specialized computational devices, or modules. This skepticism is largely due to Fodor’s influential definition of modularity. From the rather flexible catalogue of possible modular features that Fodor originally proposed has emerged a widely held notion of modules as rigid, informationally encapsulated devices that accept highly local inputs and whose opera- tions are insensitive to context. It is a mistake, however, to (...) equate such features with computational devices in general and therefore to assume, as Fodor does, that higher cognitive processes must be non-computational. Of the many possible non-Fodorean architectures, one is explored here that offers possible solutions to computational problems faced by conventional modular systems: an ‘enzymatic’ architecture. Enzymes are computational devices that use lock-and-key template matching to iden- tify relevant information (substrates), which is then operated upon and returned to a common pool for possible processing by other devices. Highly specialized enzymes can operate together in a common pool of information that is not pre-sorted by information type. Moreover, enzymes can use molecular ‘tags’ to regulate the operations of other devices and to change how particular substrates are construed and operated upon, allowing for highly interactive, context-specific processing. This model shows how specialized, modular processing can occur in an open system, and suggests that skepti- cism about modularity may largely be due to failure to consider alternatives to the standard model. (shrink)