Modeling implicit learning in a cross-modal audio-visual serial reaction time task

This study examined implicit learning in a cross-modal condition, where visual and auditory stimuli were presented in an alternating fashion. Each cross-modal transition occurred with a probability of 0.85, enabling participants to gain a reaction time benefit by learning the cross-modal predictive information between colors and tones. Motor responses were randomly remapped to ensure that pure perceptual learning took place. The effect for the implicit learning was extracted from the data by fitting five different models to the data, which was highly variable due to motor variability. To examine individual learning rates for stimulus types of different discriminability and modality, the models were fitted per stimulus type and individually for each participant. The model selection identified the model that included motor variability, surprise effects for deviants and a serial position for effect onset as the most explanatory (Akaike weight 0.87). Further, there was a significant global cross-modal implicit learning effect for predictable versus deviant transitions (40 ms reaction time difference, p