Temporal Preparation Drives Statistical Learning Effects

Abstract

AbstractThe ubiquitous finding in statistical learning research is a performance benefit for regular versus irregular properties of the environment. This regularity benefit is commonly interpreted to emerge from a mechanism that detects and subsequently exploits the regularity as it would be impossible for irregular events. Here, in the temporal domain, we show that this interpretation overlooks the influence of learning various time-related factors that contribute to the regularity benefit. Instead of only learning the temporal regularity, participants exploited the temporal properties of targets that we initially considered as irregular and uninformative. Participants temporally prepared, in parallel, for different actions to perform and locations to attend, irrespective of target’s regularity. In fact, this temporal preparation explained regularity benefits without assuming statistical learning of the regular target. Using a computational model,fMTP, we illustrate that such adaptation can arise from associative memory processes underlying temporal preparation.