# Behavioral and neural algorithms for decision confidence

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2022 · $432,947

## Abstract

Project Summary/Abstract
The long-term goal of our investigations is to understand how neural circuits in the frontal cortex support decision-
making. Orbitofrontal cortex (OFC) plays a key role in decision-making under uncertainty and is thought to enable
humans and other animals to make predictions about outcomes more accurately. The objective of this proposal
is to determine the algorithms responsible for confidence-guided behaviors and their neural basis in the
mammalian brain.
 Previous work from our laboratory has shown that `decision confidence', a cognitive variable, is encoded
in single OFC neurons and OFC inactivation specifically impairs a confidence-guided behavior, time investment.
The proposed experiments are designed to determine the computational and neural algorithms responsible for
two confidence-guided behaviors: time investment and choice strategy updating (learning). Our central
hypothesis is that OFC generates an abstract representation of decision confidence, independent of sensory
evidence, that supports multiple confidence-guided behaviors. To test this idea, we have designed a quantitative
psychophysical task for rats, adapted from human and primate work, that enable behavioral readouts of
confidence, as a post-decision temporal wager. Simply, after each perceptual decision (olfactory or auditory) rats
invest time waiting for a delayed, uncertain reward. These graded- duration time investments serve as a
behavioral report of confidence that the perceptual decision just made will result in the success of the perceptual
decision. First, we will develop computational algorithms to explain confidence-guided time investment and
learning, and second we will identify neural substrates of these algorithms in the OFC. Third, we will determine
if OFC representations are sensory-modality and behavioral-output general and test the causal role of OFC using
inactivations. Finally, we will map a sensory route for auditory information to inform OFC representations and
test the hypothesis that auditory cortex lesions lead to deaf-hearing, the ability to discriminate sounds without
perceptual confidence.
 These contributions are significant, in our opinion, because they will provide critical missing information
about the algorithmic and neural foundations of decision confidence, a key cognitive variable. Our approach is
innovative, chiefly because we have developed a computational and behavioral framework to study confidence
in rats. Beyond these mechanistic studies, the proposed work will advance knowledge about the frontal cortical
logic of cognitive variable representations and inform an improved framework for understanding how impairments
in a single brain area can lead to a wide range of psychiatric disorders, as seen in depression, obsessive-
compulsive and psychotic disorders.

## Key facts

- **NIH application ID:** 10400736
- **Project number:** 5R01MH097061-08
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Adam Kepecs
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $432,947
- **Award type:** 5
- **Project period:** 2014-06-02 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10400736

## Citation

> US National Institutes of Health, RePORTER application 10400736, Behavioral and neural algorithms for decision confidence (5R01MH097061-08). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10400736. Licensed CC0.

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