Statistical learning of unbalanced exclusive-or temporal sequences in humans

Laura Lazartigues (1) , Fabien Mathy (1) , Frédéric Lavigne (1)

1 BCL, équipe Langage et Cognition

A pervasive issue in statistical learning has been to determine the parameters of regularity extraction. Our hypothesis was that the extraction of transitional probabilities can prevail over frequency if the task involves prediction. Participants were exposed to four repeated sequences of three stimuli (XYZ) with each stimulus corresponding to the position of a red dot on a touch screen that participants were required to touch sequentially. The temporal and spatial structure of the positions corresponded to a serial version of the exclusive-or (XOR) that allowed testing of the respective effect of frequency and first- and second-order transitional probabilities. The XOR allowed the first-order transitional probability to vary while being not completely related to frequency and to vary while the second-order transitional probability was fixed (p(Z|X,Y) = 1). The findings show that first-order transitional probability prevails over frequency to predict the second stimulus from the first and that it also influences the prediction of the third item despite the presence of second-order transitional probability that could have offered a certain prediction of the third item. These results are particularly informative in light of statistical learning models.

Mots clés: Statistical learning; Transitional probabilities; Frequency

Domaines: Psychologie

Laura Lazartigues, Fabien Mathy, Frédéric Lavigne. Statistical learning of unbalanced exclusive-or temporal sequences in humans. PLoS ONE, 2021, 16 (2), pp.e0246826. ⟨10.1371/journal.pone.0246826⟩. ⟨hal-03147542⟩