Online confidence control of decision-making in dynamic sensory environments

Perceptual decisions come with a feeling of confidence that reflects decision accuracy. Rather than a post-decision reflection, we show separable EEG representations of confidence online, during decision-making. This online confidence could be used to control decision efficiency by guiding how we commit to decisions. In dynamic sensory environments, where the quality of the evidence changes over time, observers could use their confidence to down-weight low-quality sensory evidence or capitalise on high-quality evidence. We formulated an extension of sequential sampling models of decision-making in which confidence is used online to actively moderate the quality and quantity of evidence accumulated for 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. Our model with confidence control offers a far superior description of human behaviour in this environment. 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. These results support a neurobiologically plausible framework featuring confidence as an active control mechanism for driving efficient behaviour.

Tarryn Balsdon est invité par Michaël Pereira.