A rational model of sequential self-assessment

AbstractPeople's assessment of their ability varies in whether it is measured once follow-ing a task or sequentially via confidence judgments recorded throughout. Multiple models have been developed to predict one-off judgments of performance, which have often distinguished between peoples’ biases about their general ability ina domain and their sensitivity to correctness. We propose a rational model of sequential self-assessment which allows us to make predictions about each individual separately—unlike in the one-off case which looks exclusively at the population level—and to identify, in addition to bias and sensitivity, the extent to which individuals’ beliefs are responsive to their most recent evidence over the course of a task. We fit our model to data where participants solve algebraic equations and show that bias, sensitivity, and responsiveness vary meaningfully across participants.

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