On Emotionâs Ability to Modulate Action Output
Bibliographic record
Abstract
It is widely thought that emotional stimuli receive privileged neural status compared to their non-affective counterparts. This prioritization, however, comes at a cost, as the neural capacity of the human brain is finite; the prioritization of any one object comes at the expense of other concurrent objects in the visual array competing for awareness (Desimone & Duncan, 1995). Despite this reality, little work has examined the functional benefit derived from the perceptual prioritization of affective information. Why do we preferentially attend to emotional faces? According to evolutionary accounts, emotions originated as adaptations towards action, helping to prepare the organism for movement (Darwin, 1872; Frijda, 1986). The current dissertation examines this from the perceptive of visual neuroscience and motor cognition. Chapters 1 and 2 examine the mechanisms involved during the perceptual prioritization of emotional content in the context of action system modulation. Chapters 3 and 4 then directly examine emotions effect on oculomotor action output. Results across the studies are discussed in the context of evolutionary theories related to biological origins of emotional expression.
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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 | 0.000 |
| 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) | 0.012 | 0.001 |
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".