  We design a double-or-quits game to compare the speed of learning one's
specific ability with the speed of rising confidence as the task gets
increasingly difficult. We find that people on average learn to be
overconfident faster than they learn their true ability and we present an
intuitive-Bayesian model of confidence which integrates confidence biases and
learning. Uncertainty about one's true ability to perform a task in isolation
can be responsible for large and stable confidence biases, namely limited
discrimination, the hard--easy effect, the Dunning--Kruger effect, conservative
learning from experience and the overprecision phenomenon (without
underprecision) if subjects act as Bayesian learners who rely only on
sequentially perceived performance cues and contrarian illusory signals induced
by doubt. Moreover, these biases are likely to persist since the Bayesian
aggregation of past information consolidates the accumulation of errors and the
perception of contrarian illusory signals generates conservatism and
under-reaction to events. Taken together, these two features may explain why
intuitive Bayesians make systematically wrong predictions of their own
performance.
