Dissociation of sensory facilitation and decision bias in statistical learning

ABSTRACT

The detection of regularities in the sensory environment, known as statistical learning, is an important brain function that has been observed in many experimental contexts. In these experiments, statistical learning of patterned sensory stimulation leads to improvements in the speed and/or accuracy with which subsequent stimuli are recognized. That is, statistical learning facilitates the transformation of sensory stimuli into motor responses, but the mechanism by which this occurs is unclear. Statistical learning could improve the efficiency of sensory processing, or it could bias responses toward particular outcomes. The distinction is important, as these different hypotheses imply different functions and different neural substrates for statistical learning. Here we address this problem by studying statistical learning as a decision-making process, which allows us to leverage the extensive computational literature on this topic. Specifically we describe a method for applying the Diffusion Decision Model (DDM) to isolate different sensory and cognitive processes associated with decision-making. The results indicate that statistical learning improves performance on a visual learning task in two distinct ways: by altering the efficiency of sensory processing and by introducing biases in the decision-making process. By fitting the parameters of the DDM to data from individual subjects, we find that the prominence of these two factors differed substantially across the population, and that these differences were predictive of individual performance on the psychophysical task. Overall, these results indicate that different cognitive processes can be recruited by statistical learning, and that the DDM is a powerful framework for detecting these influences.