Opposing Influences of Affective State Valence on Visual Cortical Encoding
Why this work is in the frame
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Bibliographic record
Abstract
Positive and negative emotional states are thought to have originated from fundamentally opposing approach and avoidance behaviors. Furthermore, affective valence has been hypothesized to exert opposing biases in cognitive control. Here we examined with functional magnetic resonance imaging whether the opposing influences of positive and negative states extend to perceptual encoding in the visual cortices. Based on prior behavioral research, we hypothesized that positive states would broaden and negative states would narrow visual field of view (FOV). Positive, neutral, and negative states were induced on alternating blocks. To index FOV, observers then viewed brief presentations (300 ms) of face/place concentric center/surround stimuli on interleaved blocks. Central faces were attended, rendering the place surrounds unattended. As face and place information was presented at different visual eccentricities, our physiological metric of FOV was a valence-dependent modulation of place processing in the parahippocampal place area (PPA). Consistent with our hypotheses, positive affective states increased and negative states decreased PPA response to novel places as well as adaptation to repeated places. Individual differences in self-reported positive and negative affect correlated inversely with PPA encoding of peripheral places, as well as with activation in the mesocortical prefrontal cortex and amygdala. Psychophysiological interaction analyses further demonstrated that valence-dependent responses in the PPA arose from opponent coupling with extrafoveal regions of the primary visual cortex during positive and negative states. These findings collectively suggest that affective valence differentially biases gating of early visual inputs, fundamentally altering the scope of perceptual encoding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it