期刊
TOPICS IN COGNITIVE SCIENCE
卷 15, 期 2, 页码 290-302出版社
WILEY
DOI: 10.1111/tops.12631
关键词
Decision-making; Learning; Bayesian modeling; Cognitive development
This article introduces a rational action model called RANCH that captures human decision-making on what to look at and for how long. The model is evaluated by comparing it with different baseline models and alternative linking hypotheses, and proves its effectiveness.
From birth, humans constantly make decisions about what to look at and for how long. Yet, the mechanism behind such decision-making remains poorly understood. Here, we present the rational action, noisy choice for habituation (RANCH) model. RANCH is a rational learning model that takes noisy perceptual samples from stimuli and makes sampling decisions based on expected information gain (EIG). The model captures key patterns of looking time documented in developmental research: habituation and dishabituation. We evaluated the model with adult looking time collected from a paradigm analogous to the infant habituation paradigm. We compared RANCH with baseline models (no learning model, no perceptual noise model) and models with alternative linking hypotheses (Surprisal, KL divergence). We showed that (1) learning and perceptual noise are critical assumptions of the model, and (2) Surprisal and KL are good proxies for EIG under the current learning context.
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