Interactions between rhythmic and feature predictions to create parallel time-content associations

DOI

The brain is inherently proactive, constantly predicting the when (moment) and what (content) of future input in order to optimize information processing. Previous research on such predictions has mainly studied the ‘when’ or ‘what’ domain separately, missing to investigate the potential integration of both types of predictive information. In the absence of such integration, temporal cues are assumed to enhance any upcoming content at the predicted moment in time (general temporal predictor). However, if the when and what prediction domain were integrated, a much more flexible neural mechanism may be proposed in which temporal-feature interactions would allow for the creation of multiple concurrent time-content predictions (parallel time-content predictor). Here, we used a temporal association paradigm in two experiments in which sound identity was systematically paired with a specific time delay after the offset of a rhythmic visual input stream. In Experiment 1, we revealed that participants associated the time delay of presentation with the identity of the sound. In Experiment 2, we unexpectedly found that the strength of this temporal association was negatively related to the EEG steady-state evoked responses (SSVEP) in preceding trials, showing that after high neuronal responses participants responded inconsistent with the time-content associations, similar to adaptation mechanisms. In this experiment, time-content associations were only present for low SSVEP responses in previous trials. These results tentatively show that it is possible to represent multiple time-content paired predictions in parallel, however future 32 research is needed to investigate this interaction further.

Identifier
DOI https://doi.org/10.34894/FQEJVD
Metadata Access https://dataverse.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34894/FQEJVD
Provenance
Creator Oever Ten, Sanne ORCID logo; Sack, Alexander T. ORCID logo
Publisher DataverseNL
Contributor Oever Ten, Sanne; faculty data manager FPN
Publication Year 2019
Rights info:eu-repo/semantics/restrictedAccess
OpenAccess false
Contact Oever Ten, Sanne (Maastricht University); faculty data manager FPN (Maastricht University)
Representation
Resource Type Experimental data; Dataset
Format application/zip
Size 33675671; 11223895; 71718188
Version 1.0
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Life Sciences; Social Sciences; Social and Behavioural Sciences; Soil Sciences