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Abstract:
Meta-cognition, our ability to assess the quality of our own decisions, is important for regulating choices and has been extensively studied through assessing and modeling reports of confidence. However, these studies focus on immediate choices rather than sequential ones. The latter pose particular problems for meta-cognitive efficiency assessments, because the underlying difficulty of decision-making changes as the problems evolve. Here, we focus on sensitivity and bias of meta-cognitive judgments in learning/decision-making tasks in which outcome values must be learned across trials. We repurposed the central idea underlying the M-ratio, a popular meta-cognitive assessment measure in perceptual decision-making. We built a Forward model of confidence, characterizing the subjects’ choices and generating ‘first order’ confidence from the modelled probability of being correct; and a Backward model of confidence, which generates choices whose first-order confidence best matches the subjects’ confidence reports. The performance of Forward and Backward models play the roles of d’ and meta-d’ in our measure of meta-cognitive efficiency, MetaRL-Ratio. We found that the value of the MetaRL-Ratio was consistent with previous measures of meta-cognitive sensitivity and differentiated simulated low and high meta-cognitive competence. This study suggests that MetaRL-Ratio is a promising tool for assessing meta-cognitive efficiency in the value-based learning/decision-making.
