In this article, we examine the role of category labels in inductive inference. Some leading research has suggested that information about category membership works just like any other feature in categorical inductions, whereas other research has proposed that the influence of category membership on induction goes beyond that of other features. To investigate these claims further, we developed an online measure of judgments that is akin to eyetracking. The judgment results and the mouse-tracking data jointly support the view that category labels do affect inductive inferences in a way distinct from that for feature information. When arbitrary labels conveyed category membership information, participants viewed these labels more often and earlier in a trial, in comparison with cases in which the same labels conveyed non-membership information. Our results suggest that category membership information works like a guide for inference. An ecological rationale for this induction strategy is also discussed.

Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our procedure combines image-processing techniques with a machinelearning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data.

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