Temporal dynamics of competition between statistical learning and episodic memory in intracranial recordings of human visual cortex

Abstract

The function of long-term memory is not just to reminisce about the past, but also to make predictions that help us behave appropriately and efficiently in the future. This predictive function of memory provides a new perspective on the classic question from memory research of why we remember some things but not others. If prediction is a key outcome of memory, then the extent to which an item generates a prediction signifies that this information already exists in memory and need not be encoded. We tested this principle using human intracranial EEG as a time-resolved method to quantify prediction in visual cortex during a statistical learning task and link the strength of these predictions to subsequent episodic memory behavior. Epilepsy patients viewed rapid streams of scenes, some of which contained regularities that allowed the category of the next scene to be predicted. We verified that statistical learning occurred using neural frequency tagging and measured category prediction with multivariate pattern analysis. Although neural prediction was robust overall, this was driven entirely by items that were subsequently forgotten. Such interference provides a mechanism by which prediction can regulate memory formation to prioritize encoding of information that could help learn new predictive relationships.

Multiple mechanisms of visual prediction as revealed by the timecourse of scene–object facilitation

“…In lieu of a unitized representational account, we propose that predictive processing unfolds gradually over time in the form of ramping/maintenance activity that encodes features of the predicted object. This would seem consistent with a similar conclusion reached by Sherman et al (2022) for scene-scene pairs. Sherman et al recorded local field potentials from visually responsive cortex in participants as they passively viewed statistically associated scenes.…”