Neuroanatomical correlates of categorizing emotional valence
Bibliographic record
Abstract
Categorization is fundamental to cognition, and evidence suggests that categorizing emotional stimuli holds a privileged position in human information processing. According to theories on embodied emotion, the subjective emotional feeling elicited by a stimulus plays a causal role in its categorization. Using functional MRI, we tested the hypothesis that categorizing emotional stimuli in terms of valence would activate structures involved in valence-specific experience of emotion. On each trial, two pictures from the International Affective Picture System were presented successively. Upon viewing the second picture, participants categorized it as belonging to the same valence category as or a different valence category from the first picture. Categorization activated an exclusively left-lateralized set of regions implicated in taxonomic categorization (i.e. judging whether two items are of the same kind) including the middle temporal gyrus and precuneus, as well as the posterior cingulate cortex. Critically, for negative pictures categorization activated structures that underlie the experience of negative emotions (anterior insula, left orbitofrontal cortex), whereas for positive pictures categorization activated structures that underlie the experience of positive emotions (dorsomedial and ventromedial prefrontal cortex). Consistent with predictions derived from theories on embodied emotion, these results suggest that experience of emotion contributes to categorizing emotional valence.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".