The neural basis of face ensemble processing: An EEG-based investigation of facial identity summary statistics
Bibliographic record
Abstract
Extensive behavioural work has documented our ability to extract summary statistics from groups of faces such as average emotion, gender, and identity. However, to date little is known about the neural mechanisms subserving the extraction of summary statistics from face ensembles. Here, we used electroencephalography (EEG) to examine and compare the neural processing of facial identity from ensembles and single faces. To this end, we collected EEG data across 14 participants who viewed ensembles composed of 6 faces as well as single faces presented one at a time. Critically, ensembles were designed such that, though they consisted of different individual faces, they could lead, half of the time, to the same summary representation (i.e., to the same average face), and, half of the time, to different summary representations. Pattern analyses were then conducted across spatiotemporal signals recorded from 12 bilateral occipitotemporal electrodes. These analyses found, first, that single faces can be well discriminated from their corresponding EEG signal, consistent with previous work. Second, ensembles with different average identities, but not those with the same average identity, could be discriminated from each other above chance. Third, critically, classifiers trained on ensembles with different average identities were able to successfully discriminate their corresponding average identities presented as single stimuli. Finally, face ensembles and single faces exhibited different time courses of discrimination. Specifically, ensemble discrimination reached significance earlier, but peaked later than single-face discrimination, suggesting an extensive interval of evidence accumulation and information processing (i.e., average identity extraction). Thus, to our knowledge, the present findings provide the first evidence based on EEG data regarding the extraction of summary statistics from face ensembles. Further, they serve to characterize the temporal profile of ensemble processing and its relationship with single face recognition. Meeting abstract presented at VSS 2018
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".