ArticleiScience2023
Brain signatures indexing variation in internal processing during perceptual decision-making.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Frontal EEG Asymmetry and Attachment Style During Sequential Decision-Making in the Secretary Problem.Behavioral sciences (Basel, Switzerland) · 2026Article
- A Practical Guide to Identifying Robust Clusters in Neuroimaging Data.Human brain mapping · 2025Review
- Multiple brain activation patterns for the same perceptual decision-making task.Nature communications · 2025Article
- Brain-based predictive modeling of mind-wandering and involuntary thought: the idiographic approach.Technology, mind, and behavior · 2025Article
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3 authors.
Funding
Abstract
Brain activity is highly variable during a task. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimental manipulations. However, changes in internal processing could arise from many factors independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials. Subjects (N = 25) performed a motion discrimination task with six interleaved levels of coherence. Clustering identified two discrete subtypes of trials with different patterns of activity. Surprisingly, Subtype 1 occurred more frequently in trials with lower motion coherence but was associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight across-trial variability in decision processes traditionally hidden to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.
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