A working assumption that processes of natural and cultural evolution have tailored the mind to fit the demands and structure of its environment begs the question: how are we to characterize the structure of cognitive environments? Decision problems faced by real organisms are not like simple multiplechoice examination papers. For example, some individual problems may occur much more frequently than others, whilst some may carry much more weight than others. Such considerations are not taken into account when (i) the performance of candidate cognitive mechanisms is assessed by employing a simple accuracy metric that is insensitive to the structure of the decision-maker's environment, and (ii) reason is defined as the adherence to internalist prescriptions of classical rationality. Here we explore the impact of frequency and significance structure on the performance of a range of candidate decision-making mechanisms. We show that the character of this impact is complex, since structured...

Humans perform actions to reach particular goals, that is, to intentionally create or modify personally relevant events---we move our eyes to learn more about a novel event, reach for a cup to quench our thirst, and move our lips to share our thoughts with someone else. Accordingly, even primitive actions must involve some kind of planning, some sort of anticipatory control. Indeed, there are at least three defining features that the simplest behavioral acts share with more complex ones. First, all of them are planned in terms of anticipated goal events. In particular, the first step of action planning consists in specifying the features the action is intended to possess; this is achieved by activating the appropriate action-effect codes, i.e., sensory-motor assemblies controlling the production of those features. Action-effect codes emerge through the perception of movement-effect contingencies, and they are acquired from the first months in life on. Besides action planning they are involved in the perception of both one's own actions and actions of others. Second, selected features of an intended action need to be integrated into a coherent, durable action plan, which is achieved by temporarily "binding" distributed feature codes. Third, planning an action turns the cognitive system into a kind of reflex machinery, which facilitates the proper execution of the plan under appropriate circumstances. This involves the implementation of automatic stimulus-response associations and the increase of the salience of action-related situational information, thereby delegating action control to the environment.

People and other animals are very adept at categorizing stimuli even when many features cannot be perceived. Many psychological models of categorization, on the other hand, assume that an entire set of features is known. We present a new model of categorization, called Categorization by Elimination, that uses as few features as possible to make an accurate category assignment. This algorithm demonstrates that it is possible to have a categorization process that is fast and frugal--using fewer features than other categorization methods--yet still highly accurate in its judgments. We show that Categorization by Elimination does as well as human subjects on a multi-feature categorization task, judging intention from animate motion, and that it does as well as other categorization algorithms on data sets from machine learning. Specific predictions of the Categorization by Elimination algorithm, such as the order of cue use during categorization and the time-course of these ...

Introduction: Characterizing the Questions of causal reasoning This chapter describes the mechanism approach to the study of causal reasoning. We will first offer a characterization of the central issues in human causal reasoning, and will discuss how the mechanism approach addresses these issues. In the course of this presentation, we will frequently compare the mechanism approach with alternative accounts based on analyses of covariation, or what is often termed the regularity view. The aims of this chapter are the following: to explain why covariation and mechanism are different, to discuss why such a distinction is actually a useful tool for our understanding of causal reasoning, and to explicate the complementary nature of the two views. Before presenting these two approaches, it is necessary first to offer a description of the domain or problem itself : namely, what are these alternative approaches to? Although there are a number of different ways of characterizing the study of

y be factored into the decision more heavily than is price. The execu- tive may choose to ride dow-ntown by taxi and then implement this decision by standing on line and taking a taxi to the hotel. To bring these sorts of decision situations into the laboratory, researchers commonly focused on the goal of obtaining money, which they assume is shared across people. In the prototypical task, subjects are given choice options that differ in probability and amount. The use of gambles enabled researchers to explore decision making under risk. Often, a number of different choices are made in a single experimental session, and the pattern of choices across sets is analyzed. For ample, people might be asked whether they prefer a 45% chance to win $200 or a 50% chance to win $150. Later in the same ses sion, they might be asked whether they prefer a 90% chance to win $200 or a 100% chance to win $150. At issue in studies like these is the consistency of people's choices. The anal- yses would in

We present an experimental design that allows us to investigate the representations and processes used in human multiple -cue judgment. We compare three ideal models of how knowledge is stored and applied in a judgment: A linear additive model (LAM), a heuristic model, Take-the-best (TTB) and a generic exemplar-based model (EBM). The results show that people adaptively change processing depending on what information is present in the learning phase and whether or not the learning situation is compatible with the test. Feedback on a continuous variable provides information sufficient to estimate a LAM that can be used both when learning is and is not compatible with the test. When only dichotomous feedback is provided, the processes differ depending on the learning-test compatibility. At high compatibility, the processing is best described by EBM, but at low compatibility heuristic processes such as TTB become more frequent alternatives to LAM.

v Declaration .................................... ix Acknowledgements................................ xi 1Prelude 1 TheVeryIdeaofRepresentation......................... 2 TypesofSimilarity ................................ 8 IsSimilarityIndeterminate? ........................... 11 TheRoleofSimilarityinCognition....................... 11 Summary&GeneralDiscussion......................... 14 2 Theories of Similarity 17 SimilarityDataSets................................ 17 SpatialRepresentation .............................. 21 FeaturalRepresentation.............................. 31 TreeRepresentation................................ 40 NetworkRepresentation ............................. 47 Alignment-BasedSimilarityModels....................... 48 TransformationalSimilarityModels ....................... 50 Summary&GeneralDiscussion......................... 54 i 3 On Representational Complexity 55 ApproachestoModelSelection ......................... 57 ChoosinganAdditiveClusteringRepresentation ................ 67 ChoosinganAdditiveTreeRepresentation ................... 82 ChoosingaSpatialRepresentation........................ 94 Summary&GeneralDiscussion......................... 95 4 Featural Representation 97 AMenagerieofFeaturalModels......................... 98 ClusteringModels.................................104 GeometricComplexityCriteria..........................106 AlgorithmsforFittingFeaturalModels .....................107 MonteCarloStudyI:DotheAlgorithmsWork? ................109 RepresentationsofKinshipTerms ........................117 MonteCarloStudyII:Complexity........................122 ExperimentI:Faces................................125 ExperimentII:Countries .............................1...

When researchers start doing a literature search,they often initially find an unmanageably large number of potentially relevant articles,and are forced to refine their searches. Faced with this information overload,prioritization o#ers an alternative to search refinement,by ordering the articles so that the ones most likely to be relevant are at the top of the list. E#ective prioritization relies on having a good model of human decision making that can learn from the way people decide whether articles are relevant,and make predictive decisions about which of the remaining articles will be relevant. In this paper,we develop and evaluate two psychological decision making models for prioritization. One is a `rational' model that weights and combines all of the available information to make decisions. The other is a `one reason' model that uses limited time and information. The models are evaluated in an experiment where users evaluated every article returned by PsycINFO for a number of di#erent research topics. The results show that both models achieve a level of prioritization that significantly improves upon the default ordering of PsycINFO. The one reason model is also shown to be superior to the rational model,especially when there are only a few relevant articles.

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