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

摘要

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 0.004). The learning rates over time differed for both modality and the stimuli within modalities, although there was no correlation to global error rates or reaction time differences between the stimulus types. These results demonstrate a modeling method that is well suited to extract detailed information about the success of implicit learning from high variability data. It further shows a cross-modal implicit learning effect, which extends the understanding of the implicit learning system and highlights the possibility for information to be processed in a cross-modal representation without conscious processing. (C) 2018 Elsevier B.V. All rights reserved.