Keeping others in our mind or in our heart? Distribution games under cognitive load
Bibliographic record
Abstract
Abstract It has recently been argued that giving is spontaneous while greed is calculated (Rand et al., in Nature 489:427–430, 2012). If greed is calculated we would expect that cognitive load, which is assumed to reduce the influence of cognitive processes, should affect greed. In this paper we study both charitable giving and the behavior of dictators under high and low cognitive load to test if greed is affected by the load. This is tested in three different dictator game experiments. In the dictator games we use both a give frame, where the dictators are given an amount that they may share with a partner, and a take frame, where dictators may take from an amount initially allocated to the partner. The results from all three experiments show that the behavioral effect in terms of allocated money of the induced load is small if at all existent. At the same time, follow-up questions indicate that the subjects’ decisions are more impulsive and less driven by their thoughts under cognitive load.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".