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Abstract:
Humans possess a rich repertoire of abstract concepts about which they can often judge their confidence. These judgements help guide behaviour, but the mechanisms underlying them are still poorly understood. Here, we examine the evolution of people's sense of confidence as they engage in probabilistic concept learning. Participants learned a continuous function of four continuous features, reporting their predictions and confidence about these predictions. Participants indeed had insight into their uncertainties: confidence was correlated with the accuracy of predictions, increasing as learning progressed. There were substantial individual differences. In contrast to many classical models that try to explain only the predictions, we formalized human function learning in Bayesian terms as Gaussian process inference. This model generates posterior distributions, allowing us to link predictions and confidence judgements. Gaussian process inference well matched participants' predictions, and also the confidence judgements of metacognitively competent participants. Our results show that human confidence judgements during learning are tied to uncertainty, suggesting that concept learning is fundamentally probabilistic.
