Abstract
Self-prioritization is a very influential modulator of human information processing. Still, little is known about the time-frequency dynamics of the self-prioritization network. In this EEG study, we used the familiarity-confound free matching task to investigate the spectral dynamics of self-prioritization and their underlying cognitive functions in a drift-diffusion model. Participants (N = 40) repeatedly associated arbitrary geometric shapes with either “the self” or “a stranger.” Behavioral results demonstrated prominent self-prioritization effects (SPEs) in reaction time and accuracy. Remarkably, EEG cluster analysis also revealed two significant SPEs, one in delta/theta power (2–7 Hz) and one in beta power (19–29 Hz). Drift-diffusion modeling indicated that beta activity was associated with evidence accumulation, whereas delta/theta activity was associated with response selection. The decreased beta suppression of the SPE might indicate more efficient sensorimotor processing of self-associated stimulus–response features, whereas the increased delta/theta SPE might refer to the facilitated retrieval of self-relevant features across a widely distributed associative self-network. These novel oscillatory biomarkers of self-prioritization indicate their function as an associative glue for the self-concept.
Original language | English |
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Article number | e14396 |
Number of pages | 16 |
Journal | Psychophysiology |
Volume | 60 |
Issue number | 12 |
Early online date | 27 Jul 2023 |
DOIs | |
Publication status | Published - Dec 2023 |
Bibliographical note
Funding Information:The research reported in this article was supported by a Grant from the Deutsche Forschungsgemeinschaft (SCHA 2253/1–1).
Open Access funding enabled and organized by Projekt DEAL.
Data Availability Statement
The data sets generated or analyzed during the current study are available in the Open Science Framework (OSF) repository: https://osf.io/2juxk/?view_only=b4e01124337a4801b4beb99dbe803b8d. Code for the hierarchical drift-diffusion modeling (HDDM) can be downloaded as an open-source Python toolbox from https://hddm.readthedocs.io/en/latest/.Keywords
- Drift-diffusion model
- matching task
- self-prioritization
- time-frequency analysis