Goh, H. & Onnis, L.
Nanyang Technological University
Statistical learning (SL) refers to the process in which we learn to identify regular patterns in sensory input. This process is believed to be largely implicit, occurring without intent or conscious knowledge. However, despite the fact that SL is often couched as being an entirely automatic process, the question of whether or not attention plays a role in driving this process remains debated in current literature. We propose that the source of this conflict could be due to an inadequate control of attentional resources in studies in existing literature. In order to address this issue, this study therefore utilises an innovative paradigm combining SL with perceptual load theory - the theory that attention can only be restricted to certain stimuli if the perceptual complexity of a task is high enough to exhaust all available perceptual resources (Lavie, Beck, & Konstantinou, 2014). By manipulating the perceptual load of visual search arrays with embedded artificial grammars we asked whether statistical learning of to-be-ignored artificial grammars can occur under conditions in which attentional resources are maximally taxed by perceptually complex task demands. The study has been pre-registered. Data collection is ongoing and is expected to be concluded and analysed well before the conference.