Re-defining "learning": what does an online measure reveal about statistical learning of visual patterns?

Siegelman, N. 1 , Bogaerts, L. 1 , Kronenfeld, O. 1 & Frost, R. 1, 2, 3

1 The Hebrew University of Jerusalem, Israel
2 Haskins Laboratories, New Haven, CT, USA
3 Basque center of Cognition, Brain and Language (BCBL), San Sebastian, Spain

From a theoretical perspective, most discussions of statistical learning (SL) have naturally focused on the possible ?statistical? properties which are the object of learning. Much less attention has been given to defining what ?learning? is in the context of ?statistical learning?. One major difficulty is that SL research has been monitoring participants? performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of 2-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of experiments we then show clear dissociations between the information provided by offline versus online performance. We demonstrate that critical novel insights for understanding visual SL can be gained once different operational measures of ?learning? are integrated into our theory of assimilating statistical regularities.