ArticleScientific reports2019
EEG Decoding Reveals the Strength and Temporal Dynamics of Goal-Relevant Representations.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
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- The underlying mechanisms of tDCS-Based cognitive enhancement in unpredictable task switching: A machine learning-based approach for EEG signal analysis.Neuroimage. Reports · 2026Article
- Effects of "temporal preparation" on neural processing of physical and affective material attributes.Journal of vision · 2026Article
- Steering through task uncertainty: Evidence for activation modulation over dynamic binding.Psychonomic bulletin & review · 2026Article
- Neural traces of composite tasks in complex task representation in the human brain reflects learning performance.PLoS biology · 2026Article
- Neuroscience meets food choice: Implicit and explicit consumer responses to plant-based vs animal-based foods.Current research in food science · 2026Article
- Goal-directed modulation of neural activity during working memory maintenance.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Contextual control demands determine whether stability and flexibility trade off against each other.Attention, perception & psychophysics · 2024Article
- Using multivariate pattern analysis to increase effect sizes for event-related potential analyses.Psychophysiology · 2024Article
- Bridging the Gap between Psychophysiological and Audiological Factors in the Assessment of Tinnitus: An EEG Investigation in the Beta Band.Brain sciences · 2024Article
- Using Multivariate Pattern Analysis to Increase Effect Sizes for Event-Related Potential Analyses.bioRxiv : the preprint server for biology · 2024Article
- Proactive and reactive language control in bilingual language production revealed by decoding sustained potentials and electroencephalography oscillations.Human brain mapping · 2023Article
- A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees.Sensors (Basel, Switzerland) · 2023Article
- Time Courses of Attended and Ignored Object Representations.Journal of cognitive neuroscience · 2023Article
- No semantic information is necessary to evoke general neural signatures of face familiarity: evidence from cross-experiment classification.Brain structure & function · 2023Article
- Neuropsychological Alterations of Prolactinomas' Cognitive Flexibility in Task Switching.Brain sciences · 2023Article
- Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior.PLoS biology · 2022Article
- The Role of Conjunctive Representations in Stopping Actions.Psychological science · 2022Article
- Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces.Frontiers in human neuroscience · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
Models of action control assume that attentional control settings regulate the processing of lower-level stimulus/response representations. Yet, little is known about how exactly control and sensory/response representations relate to each other to produce goal-directed behavior. Addressing this question requires time-resolved information about the strength of the different, potentially overlapping representations, on a trial-by-trial basis. Using a cued task-switching paradigm, we show that information about relevant representations can be extracted through decoding analyses from the scalp electrophysiological signal (EEG) with high temporal resolution. Peaks in representational strength-indexed through decoding accuracy-proceeded from superficial task cues, to stimulus locations, to features/responses. In addition, attentional-set representations were prominent throughout almost the entire processing cascade. Trial-by-trial analyses provided detailed information about when and to what degree different representations predict performance, with attentional settings emerging as a strong and consistent predictor of within-individual and across-individual variability in performance. Also, the strength of attentional sets was related to target representations early in the post-stimulus period and to feature/response representations at a later period, suggesting control of successive, lower-level representations in a concurrent manner. These results demonstrate a powerful approach towards uncovering different stages of information processing and their relative importance for performance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.