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We design a double-or-quits game to compare the speed of learning one'sspecific ability with the speed of rising confidence as the task getsincreasingly difficult. We find that people on average learn to beoverconfident faster than they learn their true ability and we present anintuitive-Bayesian model of confidence which integrates confidence biases andlearning. Uncertainty about one's true ability to perform a task in isolationcan be responsible for large and stable confidence biases, namely limiteddiscrimination, the hard--easy effect, the Dunning--Kruger effect, conservativelearning from experience and the overprecision phenomenon (withoutunderprecision) if subjects act as Bayesian learners who rely only onsequentially perceived performance cues and contrarian illusory signals inducedby doubt. Moreover, these biases are likely to persist since the Bayesianaggregation of past information consolidates the accumulation of errors and theperception of contrarian illusory signals generates conservatism andunder-reaction to events. Taken together, these two features may explain whyintuitive Bayesians make systematically wrong predictions of their ownperformance.

Confidence biases and learning among intuitive Bayesians