Noam Siegelman|Louisa Bogaerts|Morten H. Christiansen|Ram Frost
Towards a theory of individual differences in statistical learning
Hebrew University of Jerusalem|Aix-Marseille University|French National Centre for Scientific Research|Cornell University|Haskins Laboratories|Hebrew University of Jerusalem|Haskins Laboratories|Basque Center on Cognition, Brain and Language
recent years statistical learning sl research seen growing interest tracking individual performance sl tasks mainly predictor linguistic abilities review studies line research outline three presuppositions underlying experimental approach employ sl unified theoretical construct ii current sl tasks interchangeable equally valid assessing sl ability iii performance standard forcedchoice test task good proxy sl ability argue three critical presuppositions subject number theoretical empirical issues first sl shows patterns modality informationalspecificity suggesting sl cannot treated unified construct second different sl tasks may tap separate subcomponents sl necessarily interchangeable third commonly used forcedchoice tests sl tasks subject inherent limitations confounds first step offer methodological approach explicitly spells potential set different sl dimensions allowing better transparency choosing specific sl task predictor given linguistic outcome offer possible methodological solutions better tracking measuring sl ability taken together discussions provide novel theoretical methodological approach assessing individual differences sl clear testable predictions article part themed issue new frontiers statistical learning cognitive sciences