Neurophysiological correlates of collective perceptual decision‐making
Bibliographic record
Abstract
Humans frequently perform tasks collaboratively in daily life. Collaborating with others may or may not result in higher task performance than if one were to complete the task alone (i.e., a collective benefit). A recent study on collective benefits in perceptual decision-making showed that dyad members with similar individual performances attain collective benefit. However, little is known about the physiological basis of these results. Here, we replicate this earlier work and also investigate the neurophysiological correlates of decision-making using EEG. In a two-interval forced-choice task, co-actors individually indicated presence of a target stimulus with a higher contrast and then indicated their confidence on a rating scale. Viewing the individual ratings, dyads made a joint decision. Replicating earlier work, we found a positive correlation between the similarity of individual performances and collective benefit. We analyzed event-related potentials (ERPs) in three phases (i.e., stimulus onset, response and feedback) using explorative cluster mass permutation tests. At stimulus onset, ERPs were significantly linearly related to our manipulation of contrast differences, validating our manipulation of task difficulty. For individual and joint responses, we found a significant centro-parietal error-related positivity for correct versus incorrect responses, which suggests that accuracy is already evaluated at the response level. At feedback presentation, we found a significant late positive fronto-central potential elicited by incorrect joint responses. In sum, these results demonstrate that response- and feedback-related components elicited by an error-monitoring system differentially integrate conflicting information exchanged during the joint decision-making process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".