Natural statistics support a rational account of confidence biases

Taylor W. Webb (), Kiyofumi Miyoshi, Tsz Yan So, Sivananda Rajananda and Hakwan Lau ()
Additional contact information
Taylor W. Webb: University of California
Kiyofumi Miyoshi: Kyoto University
Tsz Yan So: The University of Hong Kong
Sivananda Rajananda: Harvard University
Hakwan Lau: RIKEN Center for Brain Science

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract Previous work has sought to understand decision confidence as a prediction of the probability that a decision will be correct, leading to debate over whether these predictions are optimal, and whether they rely on the same decision variable as decisions themselves. This work has generally relied on idealized, low-dimensional models, necessitating strong assumptions about the representations over which confidence is computed. To address this, we used deep neural networks to develop a model of decision confidence that operates directly over high-dimensional, naturalistic stimuli. The model accounts for a number of puzzling dissociations between decisions and confidence, reveals a rational explanation of these dissociations in terms of optimization for the statistics of sensory inputs, and makes the surprising prediction that, despite these dissociations, decisions and confidence depend on a common decision variable.

Date: 2023
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DOI: 10.1038/s41467-023-39737-2

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