Eyetracking Reveals Multiple-Category Use in Induction

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Abstract

Category information is used to predict properties of new category members. When categorization is uncertain, people often rely on only one, most likely category to make predictions. Yet studies of perception and action often conclude that people combine multiple sources of information near-optimally. We present a perception-action analog of category-based induction using eye movements as a measure of prediction. The categories were objects of different shapes that moved in various directions. Experiment 1 found that people integrated information across categories in predicting object motion. The results of Experiment 2 suggest that the integration of information found in Experiment 1 were not a result of explicit strategies. Experiment 3 tested the role of explicit categorization, finding that making a categorization judgment, even an uncertain one, stopped people from using multiple categories in our eye-movement task. Experiment 4 found that induction was indeed based on category-level predictions rather than associations between object properties and directions.

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