Metacognitive control drives behavioural efficiency in dynamic sensory environments

Metacognitive evaluations of decision confidence reflect insight into the quality and quantity of evidence supporting our decisions. Signatures of confidence emerge during decision-making, implying confidence may be of functional importance to decision processes themselves. We formulated an extension of sequential sampling models of decision-making in which confidence is used online to actively moderate both the quality and quantity of evidence accumulated for perceptual decisions. The benefit of this model is that it can respond to dynamic changes in sensory evidence quality. We highlighted this feature by designing a dynamic sensory environment where evidence quality can be smoothly adapted within the timeframe of a single decision. Observers made fine-grained motion discrimination decisions about random dot motion displays. Dot directions were sampled from a circular gaussian distribution where the mean and variance of the distribution were adapted frame to frame to create trials with increasing or decreasing decision-evidence quality. Observers made fast, accurate decisions with early high-quality evidence but slowed down when faced with early low-quality evidence (though trials were intermixed). Our model with confidence control offers a far superior description of this pattern of behaviour than can be obtained from traditional models without online control mechanisms. Using multivariate decoding of electroencephalography (EEG), we uncovered EEG correlates of the model’s latent processes, and show stronger EEG-derived confidence control leads to faster, more accurate decisions within participants. These results support a neurobiologically plausible framework featuring confidence as an active control mechanism for driving efficient behaviour, that is, maximising precision given constraints of time and effort.