Multimodal investigations of human face perception in neurotypical and autistic adults
Bibliographic record
Abstract
Faces are among the most important visual stimuli that we perceive in everyday life. Although there is a plethora of literature studying many aspects of face perception, the vast majority of them focuses on a single aspect of face perception using unimodal approaches. In this review, we advocate for studying face perception using multimodal cognitive neuroscience approaches. We highlight two case studies: the first study investigates ambiguity in facial expressions of emotion, and the second study investigates social trait judgment. In the first set of studies, we revealed an event-related potential that signals emotion ambiguity and we found convergent response to emotion ambiguity using functional neuroimaging and single-neuron recordings. In the second set of studies, we discussed recent findings about neural substrates underlying comprehensive social evaluation, and the relationship between personality factors and social trait judgements. Notably, in both sets of studies, we provided an in-depth discussion of altered face perception in people with autism spectrum disorder (ASD) and offered a computational account for the behavioral and neural markers of atypical facial processing in ASD. Finally, we suggest new perspectives for studying face perception. All data discussed in the case studies of this review are publicly available.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".