Implicit spatial sequential learning facilitates attentional selection in covert visual search. An event-related potentials study

Abstract

IntroductionWhile most studies on implicit sequential learning focus on object learning, the hidden structure of target location and onset time can also be a subject of implicitly gathered knowledge. In our study, we wanted to investigate the effect of implicitly learned spatial and temporal sequential predictability on performance in a localization task in a paradigm in which covert selective attention is engaged. We were also interested in the neural mechanism of the facilitating effect of the predictable spatio-temporal context on visual search processes. Specifically, with the use of an event-related potential technique, we wanted to verify whether perceptual, attentional, and motor processes can be enhanced by the predictive spatio-temporal context of visual stimuli.MethodsWe analyzed data from 15 young, healthy adults who took part in an experimental electroencephalographic (EEG) study and performed a visual search localization task. Predictable sequences of four target locations and/or target onset times were presented in separate blocks of trials that formed the Space, Space- Time, and Time conditions. One block of trials with randomly presented stimuli served as a control condition.ResultsThe behavioral results revealed that participants successfully learned only the spatial dimension of target predictability. Although spatial predictability was a response-relevant dimension, we found that attentional selection–instead of motor preparation–was the facilitation mechanism in this type of visual search task. This was manifested by a shorter latency and more negative amplitude of the N2pc component and the lack of an effect on the sLRP component. We observed no effect of predictability on perceptual processing (P1 component).DiscussionWe discuss these results with reference to the current knowledge on sequential learning. Our findings also contribute to the current debate on the predictive coding theory.