Ventromedial prefrontal theta activity during rapid eye movement sleep is associated with improved decision-making on the Iowa Gambling Task.
Bibliographic record
Abstract
Recent research is beginning to reveal an intricate relationship between sleep and decision-making. The Iowa Gambling Task (IGT) is a unique decision-making task that relies on the ventromedial prefrontal cortex (vmPFC), an area that integrates and weighs previous experiences with reward and loss to select choices with the highest overall value. Recently, it has been demonstrated that a period of sleep can enhance decision-making on this task. Our study investigated the sleep mechanisms (sleep stages and cortical activity) that underlie this improvement. We recorded electrophysiology for 3 consecutive nights: a habituation, baseline, and acquisition night. On acquisition night participants were administered either a 200-trial IGT (IGT group; n = 13) or a 200-trial control (IGT-control group; n = 8) version of the task prior to sleep. Compared with baseline, the IGT group had a significant increase in theta frequency (4 Hz-8 Hz) on cites located above vmPFC and left prefrontal cortex during REM sleep. This increase correlated with subsequent performance improvement from deck B, a high reward deck with negative long-term outcomes. Furthermore, presleep emotional arousal (measured via skin conductance response) toward deck B correlated to increased theta activity above the right vmPFC during REM sleep. Overall, these results suggests that insight into deck B may be enhanced via vmPFC theta activity during REM sleep and REM sleep may have distinct mechanisms for processing decision-making information. (PsycINFO Database Record
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".