Abstract
While temporal expectations (TE) generally improve reactions to temporally predictable events, it remains unknown how the learning of temporal regularities (one time point more likely than another time point) and explicit knowledge about temporal regularities contribute to performance improvements; and whether any contributions generalise across modalities. Here, participants discriminated the frequency of diverging auditory, visual or audio-visual targets embedded in auditory, visual or audio-visual distractor sequences. Temporal regularities were manipulated run-wise (early vs. late target within sequence). Behavioural performance (accuracy, RT) plus measures from a computational learning model all suggest that learning of temporal regularities occurred but did not generalise across modalities, and that dynamics of learning (size of TE effect across runs) and explicit knowledge have little to no effect on the strength of TE. Remarkably, explicit knowledge affects performance—if at all—in a context-dependent manner: Only under complex task regimes (here, unknown target modality) might it partially help to resolve response conflict while it is lowering performance in less complex environments.
Highlights
Gathering temporal information is an essential aspect of our life
We show—using a computational learning model—that temporal regularities were learned separately for each modality and that explicit knowledge did not ease the transfer of information across modalities
It is often suggested that explicit knowledge changes the recruitment of cognitive resources
Summary
Gathering temporal information is an essential aspect of our life. We use temporal information to determine when it is most likely we will catch the bus, or, in sports, we estimate when and where a ball has to be kicked, hit, or caught. Temporal regularities can be extracted from our surrounding, for example, by means of statistical learning (Hannula & Greene, 2012; Henke, 2010; Turk-Browne et al, 2009; Turk-Browne et al, 2010) and perceptual learning (Seitz, 2017; Seitz & Watanabe, 2009). The learning of temporal regularities typically results in temporal expectations (TE), expectations for specific moments in time
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