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)

As illustrated by research Koehler himself cites (Dawes et al. 1993), the purpose of experiments is to choose between contrasting explanations of past observations – rather than to seek statistical generalizations about the prevalence of effects. True external validity results not from sampling various problems that are representative of “real world” decision making, but from reproducing an effect in the laboratory with minimal contamination (including from real world factors).

Hugo Mercier and Dan Sperber have proposed an “argumentative theory of rea-soning” in which the function of reasoning is to devise and evaluate arguments intended to persuade. Mercier and Sperber note that the theory does not work when we reason alone or with people who share our beliefs. However, the theory also fails in deliberations involving “framework beliefs”—beliefs that are only indirectly related to empirical evidence but that have a particular importance for the believer because of their centrality to a (...) dense web of other beliefs and their connections to social practices. Framework beliefs not only include many ethical, religious, and political beliefs, but also paradigmatic scientific beliefs. An alterna-tive theory of reasoning, derived from philosophical pragmatism, is presented that explains the rigidity of framework beliefs and is more optimistic about individual reasoning. A consequence of the pragmatist theory is that certain types of bias and polarization are functional. (shrink)

Replies to Hugo Mercier’s and Mike Oaksford’s comments on my paper “Why Do Humans Reason? A Pragmatic Supplement to an Argumentative Theory,” Thinking & Reasoning (August-November 2013) 472-487. http://dx.doi.org/10.1080/13546783.2013.802256.

The base rate fallacy is directly dependent on a particular judgment paradigm in which information may be unambiguously designated as either “base rate” or “individuating,” and in which subjects make two-stage sequential judgments. The paradigm may be a poor match for real world settings, and the fallacy may thus be undefined for natural ecologies of judgment.

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 Common Thread Of Induction JOHN H. HOLLAND, KEITH J. HOLYOAK, RICHARD E. NISBETT, and PAUL R. THAGARD [1986]: Induction: Processes of Inference, Learning, and Discovery, The MIT Press, Cambridge, Massachusetts: London, England, xii + 385 pp. ISBN 0-262-08160-1.

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)

Koehler's work will assist the effort to understand legal fact finding. It leaves two questions somewhat open: (i) the extent to which empirical research can measure correctness of fact-finding, a function that involves the resolution of normative questions and (ii) the standards judges should use in the absence of the research he advocates.

The study of causal inferences is an essential part of the study of other cultures. It is therefore crucial to describe the cognitive mechanisms whereby subjects are led to find specific causal explanations plausible and "natural." In the anthropological literature, specific causal connections are described as the result produced by applying a general "conception of causation" or some general "theories" to specific events; the essay aims to show that these answers are either trivial or false. The "naturalness" of explanations must (...) be examined in the context of concept acquisition and belief-fixation. On the basis of an ethnographic example, it is possible to show how certain presumptions (e.g., about the use of certain categories as natural kind terms) can be involved in the processes whereby certain explanations are made cognitively salient. (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)

A growing consensus in the philosophy and psychology of concepts is that while theories such as the prototype, exemplar, and theory theories successfully account for some instances of concept formation and application, none of them successfully accounts for all such instances. I argue against this ‘new consensus’ and show that the problem is, in fact, more severe: the explanatory force of each of these theories is limited even with respect to the phenomena often cited to support it, as each fails (...) to satisfy an important explanatory desideratum with respect to these phenomena. I argue that these explanatory shortcomings arise from a shared assumption on the part of these theories, namely, they take similarity judgements and application of causal knowledge to be discrete elements in a theory of concepts. I further propose that the same assumption carries over into alternative theories offered by proponents of the new consensus: pluralism, eliminativism, and hybrid theories. I put forth a sketch of an integrated model of concept formation and application, which rejects this shared assumption and satisfies the explanatory desiderata I discuss. I suggest that this model undermines the motivation for hybrid, pluralist, and eliminativist accounts of concepts. _1_ Introduction _2_ The Similarity-Based Approach and the Importance of Theory _2.1_ The similarity-based approach _2.2_ The selection desideratum _2.3_ Causal knowledge as satisfying the selection desideratum _3_ The Theory-Based Approach and the Importance of Similarity _3.1_ The theory-based approach _3.2_ The range desideratum _3.3_ Similarity as satisfying the range desideratum _4_ An Integrated Approach to Concepts _4.1_ An integrated model _4.2_ The integrated theory versus hybrid theories of concepts _5_ Conclusion. (shrink)

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.

Many of the mathematicians and scientists who guided the development of digital computers in the late 1940s, such as Alan Turing and John von Neumann, saw these new devices not just as tools for calculation but as devices that might employ the same principles as are exhibited in rational human thought. Thus, a subfield of what came to be called computer science assumed the label artificial intelligence. The idea of building artificial systems which could exhibit intelligent behavior comparable to that (...) of humans was a heady prospect, and the claims made on behalf of AI during the 1950s and 1960s were impossibly ambitious. Despite some theoretical and applied successes within the field, serious problems soon became evident. Instead of fulfilling the goal of quickly producing artificial intelligent agents which could compete with or outperform human beings, by the 1970s and 1980s AI had settled into a pattern of slower but real progress in modeling or simulating aspects of human intelligence. (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)

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)

Modularity has been the subject of intense debate in the cognitive sciences for more than 2 decades. In some cases, misunderstandings have impeded conceptual progress. Here the authors identify arguments about modularity that either have been abandoned or were never held by proponents of modular views of the mind. The authors review arguments that purport to undermine modularity, with particular attention on cognitive architecture, development, genetics, and evolution. The authors propose that modularity, cleanly defined, provides a useful framework for directing (...) research and resolving debates about individual cognitive systems and the nature of human evolved cognition. Modularity is a fundamental property of living things at every level of organization; it might prove indispensable for understanding the structure of the mind as well. (shrink)

We contend that empathy is best viewed as a kind of analogical thinking of the sort described in the multiconstraint theory of analogy proposed by Keith Holyoak and Paul Thagard (1995). Our account of empathy reveals the Theory-theory/Simulation theory debate to be based on a false assumption and formulated in terms too simple to capture the nature of mental state ascription. Empathy is always simulation, but may simultaneously include theory-application. By properly specifying the analogical processes of empathy and their constraints, (...) we are able to show how the amount of theory needed to empathize is determined. (shrink)

Cognitive algebra strongly disproved the representativeness heuristic almost before it was published; and therewith it also disproved the base rate fallacy. Cognitive algebra provides a theoretical foundation for judgment-decision theory through its joint solution to the two fundamental problems – true measurement of subjective values, and cognitive rules for integration of multiple determinants.

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)

I reject Lindquist et al.'s implicit claim that all emotion theories other than constructionist ones subscribe to a “brain locationist” approach. The neural mechanisms underlying relevance detection, reward, attention, conceptualization, or language use are consistent with many theories of emotion, in particular componential appraisal theories. I also question the authors' claim that the meta-analysis they report provides support for thespecificassumptions of constructionist theories.

Because of the complexity of human emotional responses, invariants must be sought not in the responses themselves, but in their generating mechanisms. Lindquist et al. show that functional locationism is a theoretical dead end; their proposed mechanistic framework is a first step toward better models of emotional behavior. We caution, however, that emotions may still be quasi-naturalperceptualtypes.

Lindquist et al. convincingly argue that the brain implements psychological operations that are constitutive of emotion rather than modules subserving discrete emotions. However, thenatureof such psychological operations is open to debate. I argue that considering appraisal theories may provide alternative interpretations of the neuroimaging data with respect to the psychological operations involved.

Commentators agree that simple conclusions about a general base rate fallacy are not appropriate. It is more constructive to identify conditions under which base rates are differentially weighted. Commentators also agree that improving the ecological validity of the research is desirable, although this is less important to those interested exclusively in psychological processes. The philosophers and ecologists among the commentators offer a kinder perspective on base rate reasoning than the psychologists. My own perspective is that the interesting questions (both psychological (...) and normative/prescriptive) are best answered through empirical studies that use real-world performance standards. (shrink)

We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. At the empirical level, a thorough examination of the base rate literature (including the famous lawyer–engineer problem) does not support the conventional wisdom that people routinely ignore base rates. Quite the contrary, the literature shows that base rates are almost always used and that their degree of use depends on task structure and representation. Specifically, base rates play a relatively larger role (...) in tasks where base rates are implicitly learned or can be represented in frequentist terms. Base rates are also used more when they are reliable and relatively more diagnostic than available individuating information. At the normative level, the base rate fallacy should be rejected because few tasks map unambiguously into the narrow framework that is held up as the standard of good decision making. Mechanical applications of Bayes's theorem to identify performance errors are inappropriate when (1) key assumptions of the model are either unchecked or grossly violated, and (2) no attempt is made to identify the decision maker's goals, values, and task assumptions. Methodologically, the current approach is criticized for its failure to consider how the ambiguous, unreliable, and unstable base rates of the real world are and should be used. Where decision makers' assumptions and goals vary, and where performance criteria are complex, the traditional Bayesian standard is insufficient. Even where predictive accuracy is the goal in commonly defined problems, there may be situations (e.g., informationally redundant environments) in which base rates can be ignored with impunity. A more ecologically valid research program is called for. This program should emphasize the development of prescriptive theory in rich, realistic decision environments. (shrink)

Base rates have no necessary relation to judgments that are not themselves probabilities. There is no logical imperative, for instance, that behavioral base rates must affect causal attributions or that base rate information should affect judgments of legal liability. Decision theorists should be cautious in arguing that base rates place normative constraints on judgments of anything other than posterior probabilities.

Abstract(1) The miscitations of seminal experiments in the base rate literature adds to the existing database of systematic miscitations of wellknown psychological experiments. These miscitations may be caused by a process of reconstructive remembering. (2) Representative design should be the methodological core of Koehler's call for ecologically valid research. This approach can benefit both basic and applied research.

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.

This commentary presents a self-assessment inventory that will allow readers to determine their own attitude toward the base rate fallacy and its literature. The inventory is scientifically valid but not Medicare/Medicaid reimbursable.

When base rates are learned and used in an experiential manner subjects show better base rate use, perhaps because the implicit learning system is engaged. A causal framework in which base rates are relevant might also be necessary. Humans might thus perform better on more ecologically valid tasks, which are likely to contain those three components.

Base rates are vital in predicting violent criminal recidivism. However, both lay people given simulated prediction tasks and professionals milking real life predictions appear insensitive to variations in the base rate of violent recidivism. Although there are techniques to help decision makers attend to base rates, increased decision accuracy is better sought in improved actuarial models as opposed to improved clinicians.

Consistent with Koehler's position, we propose a generalization of the base rate fallacy and earlier conservatism literatures. In studies using both traditional tasks and new tasks based on ecologically valid base rates, our subjects typically underweight individuating information at least as much as they underweight base rates. The implications of cue consistency for averaging heuristics are discussed.

Koehler is right that base rate information is used, to various degrees, both in laboratory tasks and in everyday life. However, it is not time to turn our backs on laboratory tasks and focus solely on ecologically valid decision making. Tightly controlled experimental data are still needed to understandhowbase rate information is used, and how this varies among groups.

Any instance (i.e., event, behavior, trait) belongs to infinitely many reference classes, hence there are infinitely many base rates from which to choose. People clearly do not entertain all possible reference classes, however, so something must be limiting the search space. We suggest some possible mechanisms that determine which reference class is evoked for the purpose of judgment and decision.

The fallacy beneath the base rate fallacy is that we know what a base rate is. We talk as if base rates and individuating information were two different kinds of information. From a Bayesian perspective, however, the only difference between base rate and individuating information is – which comes first.

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 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.

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.