bioRxivNeurosciencePreprint
Neural Dynamics of Self-Control: Pre-Choice EEG Signals Predict Yielding to Temptation
Abstract
Self-control is the capacity to resist self-defeating behavior in favor of long-term goals. Such behavioral control is fundamental to adaptive human behavior, yet its neural mechanism remains unclear. A key limitation of prior research is that tasks often conflate self-control with cost-benefit evaluation, making it difficult to isolate self-control encoding. We introduced a novel dynamometer-based task where participants chose between high-effort, larger-later (L) and low-effort, smaller-sooner (S) rewards. In some trials, referred to as temptation trials, participants could maintain their initial commitment (LL/SS) or switch (LS/SL). Twenty-six participants performed the task during a 32-channel EEG recording. Behavioral results showed a significant shift from L to S options in temptation trials, suggesting frequent yielding to temptation. EEG activity covaried with switching behavior. The late positive potential (LPP) differentiated stay (LL) from switch (LS) trials before initial choice, localizing to the posterior cingulate visual area and left somatosensory regions. Enhanced LPP in LS trials is thought to reflect heightened motivational conflict and attentional engagement with alternatives. Time-resolved multivariate decoding reliably discriminated LL from LS outcomes, peaking 304-348ms post-option presentation. This was driven by distributed frontal and centroparietal networks, demonstrating that a neural bias toward yielding is established well before the initial choice at the network level. The EEG activity preceding switching predicted the final choice, reflecting reward anticipation rather than self-control signals. Together, these findings suggest that proactive neural states during deliberation, established before the choice, shape self-control success and failure.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 0
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