Journal Article

Confidence and certainty: distinct probabilistic quantities for different goals

Kepecs, Adam, Pouget, Alexandre, Drugowitsch, Jan

2016

Overview

The authors use recent probabilistic theories of neural computation to argue that confidence and certainty are not identical concepts. They propose precise mathematical definitions for both of these concepts and discuss putative neural representations. 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.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 631/378/116/1925 / 631/378/116/2394 / 631/378/116/2395 / Animal Genetics and Genomics / Animals / Behavioral Sciences / Biological Techniques / Biomedicine / Cerebral Cortex - physiology / Combinatorial probabilities / Computational biology / Confidence / Decision making / Decision Making - physiology / Geometric probabilities / Goals / Humans / Methods / Models, Neurological / Neurobiology / Neurosciences / perspective / Probabilities / Probability / Psychological aspects / Random variables / Statistical inference / Testing / Uncertainty