Seeing Patterns in Randomness: A Computational Model of Surprise

Topics in Cognitive Science 11 (1):103-118 (2019)
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Abstract

Much research has linked surprise to violation of expectations, but it has been less clear how one can be surprised when one has no particular expectation. This paper discusses a computational theory based on Algorithmic Information Theory, which can account for surprises in which one initially expects randomness but then notices a pattern in stimuli. The authors present evidence that a “randomness deficiency” heuristic leads to surprise in such cases.

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