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Predicting the Accuracy of a Decision: A Neural Mechanism of Confidence
Author(s) -
Christopher R. Fetsch,
Roozbeh Kiani,
Michael N. Shadlen
Publication year - 2014
Publication title -
cold spring harbor symposia on quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.615
H-Index - 77
eISSN - 1943-4456
pISSN - 0091-7451
DOI - 10.1101/sqb.2014.79.024893
Subject(s) - mechanism (biology) , computer science , microstimulation , neurophysiology , artificial intelligence , machine learning , psychology , neuroscience , philosophy , epistemology , stimulation
The quantitative study of decision-making has traditionally rested on three key behavioral measures: accuracy, response time, and confidence. Of these, confidence--defined as the degree of belief, prior to feedback, that a decision is correct-is least well understood at the level of neural mechanism, although recent years have seen a surge in interest in the topic among theoretical and systems neuroscientists. Here we review some of these developments and highlight a particular candidate mechanism for assigning confidence in a perceptual decision. The mechanism is appealing because it is rooted in the same decision-making framework--bounded accumulation of evidence--that successfully explains accuracy and reaction time in many tasks, and it is validated by neurophysiology and microstimulation experiments.

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