A dissociation between the use of implicit and explicit priors in perceptual inference
Caroline Bévalot, Florent Meyniel
Abstract
The brain constantly uses prior knowledge of the statistics of its environment to shape perception. These statistics are often implicit (not directly observable) and learned incrementally from observation, but they can also be explicitly communicated to the observer, especially in humans. Here, we show that priors are used differently in human perceptual inference depending on whether they are explicit or implicit in the environment. Bayesian modeling of learning and perception revealed that the weight of the sensory likelihood in perceptual decisions was highly correlated across participants between tasks with implicit and explicit priors, and slightly stronger in the implicit task. By contrast, the weight of priors was much less correlated across tasks, and it was markedly smaller for explicit priors. The model comparison also showed that different computations underpinned perceptual decisions depending on the origin of the priors. This dissociation may resolve previously conflicting results about the appropriate use of priors in human perception. Whether priors are implicit or explicit affects the computations underlying perceptual decisions. The integration of priors and likelihood combination is closer to Bayesian integration when priors are implicit, but more akin to a simpler heuristic when they are explicit.