Category-Based Learning About Deviant Outgroup Members Hinders Performance in Trust Decision Making
Bibliographic record
Abstract
The present research examines whether individuation and categorization processes influence trust decisions about strangers at first and across repeated interactions. In a partial replication of the study reported by Cañadas et al. (2015), participants played an adaptation of the multi-round trust game paradigm and had to decide whether or not to cooperate with unknown partners. Gender (Study 1a) and ethnicity (Studies 1b, 2, and 3) served to create distinct social categories among the game partners, whose reciprocation rates were manipulated at group and individual levels. At the group level, two social groups (i.e., ingroup vs. outgroup) were associated with opposite reciprocation rates (i.e., high vs. low reciprocation rate). At the individual level, consistency was manipulated by altering the reciprocation rate of one out of four members of each social group. That is, there was one inconsistent individual in each group showing a pattern of reciprocation opposite to the group reciprocation rate. Our data, contrary to Cañadas et al.'s (2015) findings, suggested that ingroup partners were individuated given that participants made their decisions to cooperate with the trustees according to their individual reciprocation rate and independently of the group reciprocation rate. In contrast, decisions about outgroup partners (i.e., men in Study 1a and Blacks in Studies 1b, 2, and 3) were affected by category-based thinking. At the same time, in comparison with ingroup, greater cooperation was observed with ethnic outgroups but not with gender outgroups. The consistency of our results with the previous literature on social categorization and across the three experiments seems to indicate they are reliable, supporting the hypothesis that categorization and individuation processes guide trust decision-making, promoting individuation mainly for ingroup and categorization among outgroup members.
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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 | 0.001 | 0.009 |
| 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 | 0.000 |
| 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".