Rationality and Emotionality Interplay and Economic Contributions: A Neuroeconomics Experiment
Bibliographic record
Abstract
In non-neuroscience-based studies, economists demonstrate that humans make suboptimal economic contributions (between zero and the theoretical maximum) in collective-action scenarios. Using a neuroeconomics experiment that integrates economics and neuroscience, this study investigates why humans make such suboptimal economic contributions. In this study, 90 adult participants, divided into 15 smaller groups, collectively participated in a computer-based public goods game while wearing electroencephalography headsets that recorded their neural activities during the game. The results show that when participants modified institutional arrangements (such as face-to-face communication), their economic contributions increased, albeit suboptimally. Furthermore, simultaneously and suboptimally in the frontal and temporal lobes, the participants’ positive rationality and emotionality increased, whereas their negative rationality and emotionality decreased. We suggest that suboptimal rationality and emotionality may underlie suboptimal economic contributions. This study offers broad implications for collectively and sustainably managing local, regional, and global commons, including the atmosphere in which humans cause climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".