[PS-1.31] Statistical learning of abstract sequences and starting small in dyslexia

Dobó, D. 1, 2 , Lukics, K. 1, 2 , Németh, K. 1 , Szollosi, &. 1, 3 & Lukács, &. 1, 2

1 Department of Cognitive Science, Budapest University of Technology and Economics
2 MTA-BME Lendület Language Acquisition Research Group, Budapest, Hungary
3 Institute of Cognitive Neuroscience and Psychology, Hungarian Academy of Sciences

SL has been shown to be vulnerable in dyslexia in word segmentation and visual artificial grammar learning as well as in visuomotor sequence learning paradigms. Relying on an acoustic artificial grammar learning task based on a linear finite state grammar (Saffran, 2001), we aimed to test whether young adults with dyslexia (n=29, MA = 17.3 years, IQ = 108.9; matched on age and IQ at the group level to TD participants) have difficulties extracting abstract patterns from auditory sequences of nonsense syllables. We also tested whether incremental presentation of stimuli of different length (Starting Small, n=15) has a facilitating effect on learning complex structures in dyslexia as opposed to presenting strings in random order (n=14). After training, participants were required to decide which member of a sequence pair was more similar to the material heard during training. Overall performance was significantly lower in the dyslexic than in the control group. Starting small did not improve performance in either group. This suggests that abstract sequence learning in the acoustic domain is deficient in dyslexia, and calls for further studies of training effects potentially enhancing statistical learning in impaired populations.