Bibliographic record
Abstract
A distributed network of brain regions have previously been implicated in the neural processing of facial emotions (see Fusar-Polli et al., 2009). Such studies primarily used univariate methods of analysis; however, multivariate approaches allow new questions to be asked regarding the neural codes underlying categorization of facial emotions (e.g. Raizada & Kriegeskorte, 2009). Here we employed multivariate pattern analysis to investigate two questions concerning the neural coding of facial expressions: 1) do occipito-temporal brain regions contain emotion specific activity patterns? 2) If so, how do such activity patterns relate to perceptual categorization? We presented participants with each of the six basic facial expressions (plus neutral) in a block design while concurrently recording the fMRI BOLD signal. We trained a linear pattern classifier to discriminate the brain activity patterns generated by each expression. Voxels input to the classifier were selected from occipito-temporal regions that had high sensitivity to visual stimulation in an independent set of data. Significant decoding of facial emotions was found in each participant tested. Moreover, the errors in the neural classification of emotions significantly correlated with the errors made in a completely independent behavioral categorization experiment (that of Smith & Schyns, 2009). Thus information pertinent to perceptual categorization of facial emotions is present throughout occipito-temporal cortex.
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 | 0.000 | 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".