RESUMEN
When explaining other people's behavior, people generally find some explanations more satisfying than others. We propose that people judge behavior explanations based on two computational principles: simplicity and rational support-the extent to which an explanation makes the behavior "make sense" under the assumption that the person is a rational agent. Furthermore, we present a computational framework based on decision networks that can formalize both of these principles. We tested this account in a series of experiments in which subjects rated or generated explanations for other people's behavior. In Experiments 1 and 2, the explanations varied in what the other person liked and disliked. In Experiment 3, the explanations varied in what the other person knew or believed. Results from Experiments 1 and 2 supported the idea that people rely on both simplicity and rational support. However, Experiment 3 suggested that subjects rely only on rational support when judging explanations of people's behavior that vary in what someone knew.
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Emociones , Conducta Social , HumanosRESUMEN
Boyer & Petersen (B&P) argue that a "rudimentary exchange psychology" is responsible for many of people's folk-economic beliefs that are at odds with the consensus views of economists. However, they focus primarily on macroeconomic beliefs. I argue that the same rudimentary exchange psychology could be expected to produce fairly accurate microeconomic intuitions. Existing evidence supports this prediction.
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Evolución Biológica , IntuiciónRESUMEN
People are capable of learning other people's preferences by observing the choices they make. We propose that this learning relies on inverse decision-making-inverting a decision-making model to infer the preferences that led to an observed choice. In Experiment 1, participants observed 47 choices made by others and ranked them by how strongly each choice suggested that the decision maker had a preference for a specific item. An inverse decision-making model generated predictions that were in accordance with participants' inferences. Experiment 2 replicated and extended a previous study by Newtson (1974) in which participants observed pairs of choices and made judgments about which choice provided stronger evidence for a preference. Inverse decision-making again predicted the results, including a result that previous accounts could not explain. Experiment 3 used the same method as Experiment 2 and found that participants did not expect decision makers to be perfect utility-maximizers.
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Toma de Decisiones , Juicio , Aprendizaje , Comportamiento del Consumidor , Humanos , Modelos PsicológicosRESUMEN
The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference.
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Conducta de Elección , Toma de Decisiones , Teorema de Bayes , Humanos , Cadenas de Markov , Modelos Psicológicos , Conducta Social , PensamientoRESUMEN
Belief polarization occurs when 2 people with opposing prior beliefs both strengthen their beliefs after observing the same data. Many authors have cited belief polarization as evidence of irrational behavior. We show, however, that some instances of polarization are consistent with a normative account of belief revision. Our analysis uses Bayesian networks to characterize different kinds of relationships between hypotheses and data, and distinguishes between cases in which normative reasoners with opposing beliefs should both strengthen their beliefs, cases in which both should weaken their beliefs, and cases in which one should strengthen and the other should weaken his or her belief. We apply our analysis to several previous studies of belief polarization and present a new experiment that suggests that people tend to update their beliefs in the directions predicted by our normative account.
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Cultura , Toma de Decisiones , Juicio , Modelos Psicológicos , Teorema de Bayes , Función Ejecutiva , Humanos , Individualidad , Solución de ProblemasRESUMEN
Inductive inferences about objects, features, categories, and relations have been studied for many years, but there are few attempts to chart the range of inductive problems that humans are able to solve. We present a taxonomy of inductive problems that helps to clarify the relationships between familiar inductive problems such as generalization, categorization, and identification, and that introduces new inductive problems for psychological investigation. Our taxonomy is founded on the idea that semantic knowledge is organized into systems of objects, features, categories, and relations, and we attempt to characterize all of the inductive problems that can arise when these systems are partially observed. Recent studies have begun to address some of the new problems in our taxonomy, and future work should aim to develop unified theories of inductive reasoning that explain how people solve all of the problems in the taxonomy.
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Clasificación , Generalización Psicológica , Conocimiento , Semántica , Pensamiento , HumanosRESUMEN
People are capable of imagining and generating new category exemplars and categories. This ability has not been addressed by previous models of categorization, most of which focus on classifying category exemplars rather than generating them. We develop a formal account of exemplar and category generation which proposes that category knowledge is represented by probability distributions over exemplars and categories, and that new exemplars and categories are generated by sampling from these distributions. This sampling account of generation is evaluated in two pairs of behavioral experiments. In the first pair of experiments, participants were asked to generate novel exemplars of a category. In the second pair of experiments, participants were asked to generate a novel category after observing exemplars from several related categories. The results suggest that generation is influenced by both structural and distributional properties of the observed categories, and we argue that our data are better explained by the sampling account than by several alternative approaches.