Neural signals of statistical learning in the primate brain: network, connectivity and electrophysiology.
Animals are sensitive to temporal regularities in their sensory environment. These regularities correspond to properties of the environment that repeat, e.g. the sequence of buildings encountered when driving home along a familiar road. Behavioral studies have shown that mere exposure to sequences of visual stimuli is sufficient to learn their embedded regularities, which is called visual statistical learning. Electrophysiological studies in macaques showed learning signals for visual sequences in their temporal cortex. However, it is not clear which other areas in- and outside the visual cortex show such signals. To fill this gap in our knowledge of the neural correlates of visual statistical learning, we will map, using fMRI, visual statistical learning and associated prediction related signals in the macaque brain. Then, we will map the connectivity between these areas, followed up by recordings of single unit responses to the familiar sequences. We will study the information flow between different areas of the network with chemogenetic methods. This proposal is not only relevant for understanding the neural correlates of statistical learning, but also will provide data that can constrain computational models of brain function, such as “predictive coding” or other models of cognition that involve predictions. Since statistical learning and prediction-related processing is reported to be dysfunctional in several brain disorders, it has also clinical relevance.