Noninvasive Brain Stimulation over the Frontopolar Cortex Promotes Willingness to Exert Cognitive Effort in a Foraging-Like Sequential Choice Task
Bibliographic record
Abstract
Individuals avoid spending cognitive effort unless expected rewards offset the perceived costs. Recent work employing tasks that provide explicit information about demands and incentives suggests causal involvement of the frontopolar cortex (FPC) in effort-based decision-making. Using transcranial direct current stimulation (tDCS), we examined whether the FPC's role in motivating effort generalizes to sequential choice problems in which task demand and reward rates vary indirectly and as a function of experience. In a double-blind, within-subject design, 46 participants (36 female, 8 male, 1 "neither/other") received anodal (i.e., excitatory) or sham stimulation over the right FPC during an Effort Foraging Task, which required choosing between harvesting patches for successively decreasing resources or traveling to replenished patches by performing a cognitive task with environment-specific difficulty. As expected, participants exited patches later (i.e., exhibited lower exit thresholds) when traveling required greater (versus less) effort, indicating increased travel costs in high-effort environments. Under anodal tDCS, the difference in exit thresholds between environments was significantly smaller relative to sham. Finally, individual differences analyses hint that participants with lower self-reported motivation to exert effort exhibited greater travel cost reductions following tDCS. Together, these findings support the theorized causal role of the FPC in motivating cognitively effortful behavior, expand its role to more ecologically valid serial decisions, and highlight the potential for tDCS as a tool to increase motivation with potential clinical applications.
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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".