WITHDRAWN
Bibliographic record
Abstract
Decision conflicts may arise when the costs and benefits of choices are evaluated as a function of outcomes predicted along a temporal dimension. When economic binary alternatives are offered in the present or in the future (e.g. Do you prefer 5€ now or 15€ in 120 days) people may show different decision strategies depending on how sensitive they are to the discounting effect of time. Electrophysiology studies suggest that during decision conflicts it is possible to record over the medial frontal cortex (MFC) a typical oscillatory activity in theta rhythm named midfrontal theta (MFϴ). Such activity may be an index of the processes underpinning top-down cognitive control. Tellingly, MFϴ appears associated with the temporal dynamics of different brain areas, thus operating as a synchronizer during the request of control. Although the correlational link between activity in MFC and MFϴ has been demonstrated, their causal relation with conflict processing has yet to be deeply explored. A methodological approach that may fill this lack of knowledge is represented by the application of alternating current over the brain areas under investigation. The transcranial Alternating Current Stimulation (tACS) is an emerging, innovative technique that changes endogenous patterns of oscillatory activity by entraining neural networks acting on the behavioural performance in a frequency-dependent manner. In accordance with the Registered Report format, we propose a within-subject, sham controlled, cross-over study, in which we will explore the tendency to choose between economic offers during theta, gamma, and sham tACS, with the goal of modulating reaction times (RTs) and choice preferences when different levels of conflict, induced by combining specific delays and payoffs, occur. Hypothesis testing, sample size estimate and analysis of pilot results have been conducted using Bayesian statistics. We expect that our approach will advance the study of cognitive control and conflict processing during decision making.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.654 | 0.482 |
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".