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  1. Another Look at Looking Time: Surprise as Rational Statistical Inference.Zi L. Sim & Fei Xu - 2019 - Topics in Cognitive Science 11 (1):154-163.
    Surprise—operationalized as looking time—has a long history in developmental research, providing a window into the perception and cognition of infants. Recently, however, a number of developmental researchers have considered infants’ and children's surprise in its own right. This article reviews empirical evidence and computational models of complex statistical inferences underlying surprise, and discusses how these findings relate to the role that surprise appears to play as a catalyst for learning.
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  • Information foraging.Peter Pirolli & Stuart Card - 1999 - Psychological Review 106 (4):643-675.
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  • A rational analysis of the selection task as optimal data selection.Mike Oaksford & Nick Chater - 1994 - Psychological Review 101 (4):608-631.
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  • Object permanence in five-month-old infants.Elizabeth S. Spelke - 1985 - Cognition 20 (3):191-208.
    A new method was devised to test object permanence in young infants. Fivemonth-old infants were habituated to a screen that moved back and forth through a 180-degree arc, in the manner of a drawbridge. After infants reached habituation, a box was centered behind the screen. Infants were shown two test events: a possible event and an impossible event. In the possible event, the screen stopped when it reached the occluded box; in the impossible event, the screen moved through the space (...)
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  • A Rational Analysis of Rule‐Based Concept Learning.Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldman & Thomas L. Griffiths - 2008 - Cognitive Science 32 (1):108-154.
    This article proposes a new model of human concept learning that provides a rational analysis of learning feature‐based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space—a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well‐known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7‐feature concepts—a more natural setting in several (...)
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  • Reconciling novelty and complexity through a rational analysis of curiosity.Rachit Dubey & Thomas L. Griffiths - 2020 - Psychological Review 127 (3):455-476.
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  • Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model.Sebastian Bitzer, Hame Park, Felix Blankenburg & Stefan J. Kiebel - 2014 - Frontiers in Human Neuroscience 8.
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