2013年12月17日火曜日

Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization

Ahmad T. Qamar, R. James Cotton, Ryan G. George, Jeffrey M. Beck, Eugenia Prezhdo, Allison Laudano, Andreas S. Tolias, and Wei Ji Ma
PNAS December 10, 2013 vol. 110 no. 50 20332-20337

ヒト、サル心理物理。
ヒト及びサルは刺激の不確実性(曖昧さ)に応じて、知覚のやり方を変えることができる。
具体的には、「知覚情報が溜まって行き、最終的に意思決定に至るまでの閾値」を調整する。

Categorization is a cornerstone of perception and cognition. Computationally, categorization amounts to applying decision boundaries in the space of stimulus features. We designed a visual categorization task in which optimal performance requires observers to incorporate trial-to-trial knowledge of the level of sensory uncertainty when setting their decision boundaries. We found that humans and monkeys did adjust their decision boundaries from trial to trial as the level of sensory noise varied, with some subjects performing near optimally. We constructed a neural network that implements uncertainty-based, near-optimal adjustment of decision boundaries. Divisive normalization emerges automatically as a key neural operation in this network. Our results offer an integrated computational and mechanistic framework for categorization under uncertainty.

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