Confidence and certainty: Distinct probabilistic quantities for different goals

Alexandre Pouget, Jan Drugowitsch, Adam Kepecs

Research output: Contribution to journalReview articlepeer-review

165 Scopus citations

Abstract

When facing uncertainty, adaptive behavioral strategies demand that the brain performs probabilistic computations. In this probabilistic framework, the notion of certainty and confidence would appear to be closely related, so much so that it is tempting to conclude that these two concepts are one and the same. We argue that there are computational reasons to distinguish between these two concepts. Specifically, we propose that confidence should be defined as the probability that a decision or a proposition, overt or covert, is correct given the evidence, a critical quantity in complex sequential decisions. We suggest that the term certainty should be reserved to refer to the encoding of all other probability distributions over sensory and cognitive variables. We also discuss strategies for studying the neural codes for confidence and certainty and argue that clear definitions of neural codes are essential to understanding the relative contributions of various cortical areas to decision making.

Original languageEnglish
Pages (from-to)366-374
Number of pages9
JournalNature neuroscience
Volume19
Issue number3
DOIs
StatePublished - Feb 23 2016

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