Individual differences in the evaluation of ambiguous visual and auditory threat‐related expressions
Bibliographic record
Abstract
This study investigated the neural correlates of the judgement of auditory and visual ambiguous threat-related information, and the influence of state anxiety on this process. Healthy subjects were scanned using a fast, high-resolution functional magnetic resonance imaging (fMRI) multiband sequence while they performed a two-alternative forced-choice emotion judgement task on faces and vocal utterances conveying explicit anger or fear, as well as ambiguous ones. Critically, the latter was specific to each subject, obtained through a morphing procedure and selected prior to scanning following a perceptual decision-making task. Behavioural results confirmed a greater task-difficulty for subject-specific ambiguous stimuli and also revealed a judgement bias for visual fear, and, to a lesser extent, for auditory anger. Imaging results showed increased activity in regions of the salience and frontoparietal control networks (FPCNs) and deactivation in areas of the default mode network for ambiguous, relative to explicit, expressions. In contrast, the right amygdala (AMG) responded more strongly to explicit stimuli. Interestingly, its response to the same ambiguous stimulus depended on the subjective judgement of the expression. Finally, we found that behavioural and neural differences between ambiguous and explicit expressions decreased as a function of state anxiety scores. Taken together, our results show that behavioural and brain responses to emotional expressions are determined not only by emotional clarity but also modality and the subjects' subjective perception of the emotion expressed, and that some of these responses are modulated by state anxiety levels.
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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 | 0.000 | 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".