Adaptive neurons compute confidence in a decision network

Abstract

Humans and many animals have the ability to assess the confidence of their decisions. However, little is known about the underlying neural substrate and mechanism. In this study we propose a computational model consisting of a group of ’confidence neurons’ with adaptation, which are able to assess the confidence of decisions by detecting the slope of ramping activities of decision neurons. The simulated activities of ’confidence neurons’ in our simple model capture the typical features of confidence observed in humans and animals experiments. Our results indicate that confidence could be online formed along with the decision formation, and the adaptation properties could be used to monitor the formation of confidence during the decision making.

Tracing the shadow of time

“…An alternative mechanistic account relies on the notion that “confidence neurons” detect the rate of temporal integration prior to responding and lead to confidence judgments when compared to the average (veridical) slope estimate ( 11 ) ( Fig. 1 ).…”