A Unifying Normative Framework of Decision Confidence

Amelia Johnson, Michael A Buice, Koosha Khalvati

Keywords: Decision making, cognitive science, computational neuroscience, planning as inference, confidence

Abstract: Self-assessment of one’s choices, i.e., confidence, is the topic of many decision neuroscience studies. Computational models of confidence, however, are limited to specific scenarios such as between choices with the same value. Here we present a normative framework for modeling decision confidence that is generalizable to various tasks and experimental setups. We further drive the implications of our model from both theoretical and experimental points of view. Specifically, we show that our model maps to the planning as an inference framework where the objective function is maximizing the gained reward and information entropy of the policy. Moreover, we validate our model on two different psychophysics experiments and show its superiority over other approaches in explaining subjects' confidence reports.

Primary Area: Neuroscience and cognitive science (neural coding, brain-computer interfaces)

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Submission Number: 13298