Steven A. Sloman

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Distinctions have been proposed between systems of reasoning for centuries. This article distills properties shared by many of these distinctions and characterizes the resulting systems in light of recent findings and theoretical developments. One system is associative because its computations reflect similarity structure and relations of temporal(More)
We argue that it is important to distinguish between categorization as object recognition and as naming because the relation between the two may not be as straightforward as has often been assumed. We present data from speakers of English, Chinese, and Spanish that support this contention. Speakers of the three languages show substantially different(More)
A normative framework for modeling causal and counterfactual reasoning has been proposed by Spirtes, Glymour, and Scheines (1993; cf. Pearl, 2000). The framework takes as fundamental that reasoning from observation and intervention differ. Intervention includes actual manipulation as well as counterfactual manipulation of a model via thought. To represent(More)
The phenomenon of base-rate neglect has elicited much debate. One arena of debate concerns how people make judgments under conditions of uncertainty. Another more controversial arena concerns human rationality. In this target article, we attempt to unpack the perspectives in the literature on both kinds of issues and evaluate their ability to explain(More)
An influential study by Rips (1989) provides the strongest evidence available that categorization cannot be reduced to similarity. In Rips's study, subjects were presented a sparse description of an object that mentioned only a value on a single dimension (e.g., "an object 3 inches in diameter"), followed by two categories (e.g., pizza and quarter), where(More)
Can people learn causal structure more effectively through intervention rather than observation? Four studies used a trial-based learning paradigm in which participants obtained probabilistic data about a causal chain through either observation or intervention and then selected the causal model most likely to have generated the data. Experiment 1(More)
How do people learn causal structure? In 2 studies, the authors investigated the interplay between temporal-order, intervention, and covariational cues. In Study 1, temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2, both temporal order and intervention contributed to(More)