Does within group diversity always lead to better group recognition?
Bibliographic record
Abstract
The wisdom-of-crowds effect is the tendency of a group to perform better than most individuals and sometimes better than the best individual within the group. This phenomenon was first demonstrated for tasks that required estimations of weights or sizes (Bruce, 1935; Galton, 1907; Gordon, 1924). However, a ubiquitous observation is that certain groups perform better than others, and one factor that appears to play a role in obtaining a good group performance is diversity. For example, simulations have shown that groups of diverse algorithms can outperform groups made of the best algorithms (e.g. Hong & Page, 2004). Here, we evaluated whether group diversity in the individual members’ use of information for face identification—measured using the Bubbles method—is also associated with group performances in face recognition—measured using the Cambridge Face Memory Test (CFMT). We randomly generated groups of sizes 2 to 11 from a sample of 102 participants. Group performance was obtained by averaging the result of the application of the majority rule on all CFMT trials. Group diversity was indexed by the inverse of the average Pearson correlation between the group members’ standardized classification images. Our main result is that, contrary to what we expected from the literature, diversity in use of information is negatively correlated with group performance (across group sizes: r = -.23; p < .05, two-tailed, Bonferroni-corrected). This seems to stem from inefficient human strategies for face identification being more diverse than efficient ones and, therefore, from diverse groups containing more unskilled than skilled participants. In any case, our results show that factors other than diversity can be important for predicting group performances.
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".