Statistical Learning Emerges from Temporally Preparing What Action to Perform Where
Timing studies on statistical learning predominantly present temporal regularities in a discrete, trial-by-trial manner. This, however, is a simplified representation of natures complex temporal structure in which regularities are embedded in a continuous stream of interrupting, irregular events. Recent studies using more complex, dynamic experimental tasks have nevertheless replicate the ubiquitous finding of a performance benefit for regular versus irregular timed events. 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 timed events. In contrast, here we show that this interpretation overlooks the influence of learning various time-related factors that contribute to the regularity benefit in such complex temporal environments. 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 targets regularity. In fact, this temporal preparation explained regularity benefits without assuming statistical learning of the regular target. Using a computational model, f MTP, we illustrate that such adaptation can arise from associative memory processes underlying temporal preparation.