Siegelman, N. 1 , Bogaerts , L. 2 & Frost, R. 1, 3, 4
1 The Hebrew University of Jerusalem
2 Ghent University
3 Haskins Laboratories
4 The Basque Center for Brain and Language
Most research in Statistical Learning (SL) has focused on mean success rate of participants in detecting statistical contingencies at a group level, overlooking the variance in performance between individuals. In recent years, however, researchers show increased interest in individual abilities in SL, aiming to predict a range of cognitive capacities. Most, if not all of this research enterprise employs SL tasks that were originally designed for group-level studies. We argue that from an individual difference perspective, such tasks are psychometrically weak and sometimes even flawed. In particular, existing SL tasks have two major shortcomings: (1) A large proportion of the sample performs at chance level so that most of the data points reflect noise, and (2) test items following familiarization are all of the same type and identical level of difficulty. These two factors lead to high measurement error, inevitably resulting in low reliability and thereby doubtful validity. Here we present a novel method specifically designed for the measurement of individual differences in visual and auditory SL. The novel tasks we offer display substantially superior psychometric properties. We report data regarding the reliability of these tasks, and discuss future directions for researching individual differences in SL.