Variability in competitive decision-making speed and quality against exploiting and exploitative opponents
Bibliographic record
Abstract
A presumption in previous work has been that sub-optimality in competitive performance following loss is the result of a reduction in decision-making time (i.e., post-error speeding). The main goal of this paper is to test the relationship between decision-making speed and quality, with the hypothesis that slowing down decision-making should increase the likelihood of successful performance in cases where a model of opponent domination can be implemented. Across Experiments 1-3, the speed and quality of competitive decision-making was examined in a zero-sum game as a function of the nature of the opponent (unexploitable, exploiting, exploitable). Performance was also examined against the nature of a credit (or token) system used as a within-experimental manipulation (no credit, fixed credit, variable credit). To compliment reaction time variation as a function of outcome, both the fixed credit and variable credit conditions were designed to slow down decision-making, relative to a no credit condition where the game could be played in quick succession and without interruption. The data confirmed that (a) self-imposed reductions in processing time following losses (post-error speeding) were causal factors in determining poorer-quality behaviour, (b) the expression of lose-shift was less flexible than the expression of win-stay, and, (c) the use of a variable credit system may enhance the perceived control participants have against exploitable opponents. Future work should seek to disentangle temporal delay and response interruption as determinants of decision-making quality against numerous styles of opponency.
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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 | 0.005 | 0.031 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".