Attachment Avoidance and Visual Attention for Emotional Faces over Time
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Bibliographic record
Abstract
Research suggests that attachment avoidance (discomfort with intimacy and closeness) alters the way the visual system processes emotional faces (Zhang, Li, & Zhou, 2008). Because they are uncomfortable with emotion, avoidant individuals are thought to disengage their attention from negative emotional faces (Dewitte, 2011). Conversely, they may be hypervigilant to emotional faces in the early stages of face processing (Dan & Raz, 2012). The current study was designed to test both the hypervigilance and the disengagement hypotheses. Participants were asked to complete a two-alternative forced choice task, with a face fixation and varying stimulus-onset asynchronies (SOA), to determine whether avoidant individuals differently attend to emotional faces over the course of emotional face processing. Participants were presented with happy, fearful, neutral and inverted faces. After a variable SOA (i.e., 0ms, 150ms, 300ms, 450ms, 600ms, 750ms, 1000ms) an L or T appeared on the left or right side of the screen. Participants made speeded manual inputs to indicate which letter appeared. Results suggest that attachment avoidance delayed response times, but only in happy trials. Further, attachment avoidance interacted with SOA, such that individuals high in avoidance made slower responses in happy trials as the SOA increased. Conversely, individuals low in avoidance made faster responses in happy trials as the SOA increased. These effects only emerged at later SOAs (>600ms). The results suggest positive faces actually capture the attention of avoidant individuals, but only in the later stages of emotional face processing. The null results for fearful faces suggest that it may be specific negative faces (e.g, angry) that interact with avoidance. The results do not confirm either the hypervigilance hypothesis or the disengagement hypothesis. Instead, the results suggest the effect of attachment avoidance on emotional face processing is emotion-specific and emerges at specific points in the time course of emotional face processing. Meeting abstract presented at VSS 2016
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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