Consider how we evaluate how normal an object is. On the dual-nature hypothesis, a normality evaluation depends on the object’s goodness and frequency. On the single-nature hypothesis, the evaluation depends solely on either frequency or goodness. To assess these hypotheses, I ran four experiments. Study 1 shows that normality evaluations vary with both the goodness and the frequency assessment of the object. Study 2 shows that manipulating the goodness and the frequency dimension changes the normality evaluation. Yet, neither experiment rules (...) out that some people evaluate normality solely based on frequency, and the rest evaluate normality solely based on goodness. Whence two more experiments. Study 3 reveals that when scenarios are contrasted—presented one after another—only frequency matters. But, as study 4 shows, when scenarios are evaluated alone, both frequency and goodness influence normality evaluations in a single person, although the more a person is sensitive to one dimension, the less she’s sensitive to the other. The dual-nature hypothesis seems thus true of uncontrasted applications of the concept of normality, whereas the single-nature hypothesis seems true of contrasted applications. (shrink)

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...) inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition. (shrink)

Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been observed in existing studies. (...) More importantly, the measure predicts a new effect ("abnormal deflation"). Two studies show that people's judgments do, in fact, show this new effect. Taken together, the patterns of people's causal judgments thereby provide support for the proposed explanation. (shrink)

Subjective probability plays an increasingly important role in many fields concerned with human cognition and behavior. Yet there have been significant criticisms of the idea that probabilities could actually be represented in the mind. This paper presents and elaborates a view of subjective probability as a kind of sampling propensity associated with internally represented generative models. The resulting view answers to some of the most well known criticisms of subjective probability, and is also supported by empirical work in neuroscience and (...) behavioral psychology. The repercussions of the view for how we conceive of many ordinary instances of subjective probability, and how it relates to more traditional conceptions of subjective probability, are discussed in some detail. (shrink)

We present the results of four experiments concerning the evaluation people make of sentences involving “many”, showing that two sentences of the form “many As are Bs” vs. “many As are Cs” need not be equivalent when evaluated relative to a background in which B and C have the same cardinality and proportion to A, but in which B and C are predicates with opposite semantic and affective values. The data provide evidence that subjects lower the standard relevant to ascribe (...) “many” for the more negative predicate, and that judgments involving “many” are sensitive to moral considerations, namely to expectations involving a representation of the desirability as opposed to the mere probability of an outcome. We relate the results to similar semantic asymmetries discussed in the psychological literature, in particular to the Knobe effect and to framing effects. (shrink)