Social aloofness is associated with non-social explore-exploit decisions
Bibliographic record
Abstract
How humans resolve the explore-exploit dilemma in decision making is central to how we flexibly interact with both social and non-social aspects of dynamic environments. However, how individual differences in the cognitive computations underlying exploration relate to social and non-social psychological flexibility traits remains unclear. To test this, we probed decision-making strategies in a cognitive flexibility task, a restless three-armed bandit task, and examined how individual differences in cognitive strategy related to social and non-social traits measured by the Broad Autism Phenotype Questionnaire (BAPQ), a well-validated, clinically-relevant, community instrument, in a large (N = 1001) online sample. In contrast to prior links found between exploratory behavior and cognitive rigidity, we found that differences in choice behavior and exploration were primarily associated with social phenotypes as captured by the BAPQ aloof subscale. Higher scores on the BAPQ aloof subscale, indicative of reduced social interest and engagement, were associated with decreased shift rates, increased win-stay/lose-shift behavior, heightened sensitivity to negative outcomes, and reduced exploration. Reinforcement learning (RL) modeling further revealed that reduced exploration in high aloof individuals was driven by lower decision noise rather than increased cognitive rigidity, suggesting that decreased exploratory behavior may reflect a reduced tendency for stochastic exploration rather than an inflexible learning process. Sparse canonical correlation analysis reveals that the strongest loading for these non-social reward-related measures are in fact socially coded items. These results suggest that differences in motivation to seek information, especially in social contexts, may manifest as decreased exploratory behavior in a non-social decision-making task. Our findings additionally highlight the potential for using computational approaches to reveal general cognitive mechanisms underlying social functioning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".