Dissociating representations of affect and motion in visual cortices
Bibliographic record
Abstract
While a delicious dessert being presented to us may elicit strong feelings of happiness and excitement, the same treat falling slowly away can lead to sadness and disappointment. Our emotional response to the item depends on its visual motion direction. Despite this importance, it remains unclear whether (and how) cortical areas devoted to decoding motion direction represents or integrates emotion with perceived motion direction. Motion-selective visual area V5/MT+ sits, both functionally and anatomically, at the nexus of dorsal and ventral visual streams. These pathways, however, differ in how they are modulated by emotional cues. The current study was designed to disentangle how emotion and motion perception interact, as well as use emotion-dependent modulation of visual cortices to understand the relation of V5/MT+ to canonical processing streams. During functional magnetic resonance imaging (fMRI), approaching, receding, or static motion after-effects (MAEs) were induced on stationary positive, negative, and neutral stimuli. An independent localizer scan was conducted to identify the visual-motion area V5/MT+. Through univariate and multivariate analyses, we demonstrated that emotion representations in V5/MT+ share a more similar response profile to that observed in ventral visual than dorsal, visual structures. Specifically, V5/MT+ and ventral structures were sensitive to the emotional content of visual stimuli, whereas dorsal visual structures were not. Overall, this work highlights the critical role of V5/MT+ in the representation and processing of visually acquired emotional content. It further suggests a role for this region in utilizing affectively salient visual information to augment motion perception of biologically relevant stimuli.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".