Perceptual decision making, and the construction of confidence

Perceptual decisions are of fundamental interest, and it is increasingly recognised that a sense of confidence supports decision-making. The drift diffusion model (DDM), provides an excellent account of decisions and response times. It also features the optimal property of tracking the difference in evidence between two options. However, the DDM struggles to account for human confidence reports. Possibly because of this, much confidence research has used non-normative models of the decision mechanism. Motivated by the idea that perceptual decision-making will reflect optimal computation, we consider 10 variants of the DDM. Motivated by the idea that the brain will not duplicate the representation of evidence, in all model variants confidence is read out from the decision mechanism. We compare the models to benchmark findings and make 3 qualitative predictions that we verify in a preregistered study. We develop new computationally cheap predictions for confidence in DDM accounts. Using these predictions we model confidence on a trial-by-trial basis, finding that a subset of model variants provide an excellent account of the precise quantitative effects observed. Specifically, models in which confidence reflects a miscalibrated Bayesian readout perform best. Focusing on the idea of a Bayesian readout, we explore the claim that evidence associated with a selected option is overweighted in confidence. Although this idea appears to challenge Bayesian confidence, we do not find this overweighting in three previously collected datasets, and we suggest an explanation for previous results. Finally, we examine whether the DDM remains normative in settings where rapid learning is more important than the theoretical possibility of achieving maximal reward rate. These results support the claim that confidence is based on the decision mechanism, which is itself optimal. In particular, the DDM with a miscalibrated Bayesian readout is supported on both theoretical and empirical grounds.