Attachment Style Influences Saccades
Bibliographic record
Abstract
The present research examined how attachment style (whether one seeks or avoids closeness in relationships) affects saccadic latency away from faces displaying emotion. Research on attachment shows that high anxiety individuals are particularly attuned to negative emotional stimuli, whereas those high in avoidance often disengage from emotional stimuli (e.g. Mikulincer et al., 2000). However, there has been mixed evidence as to whether individuals high in avoidance automatically attend less to emotional stimuli, and whether they are particularly less sensitive to positive emotions (e.g. Fraley et al., 2006; Dewitte et al., 2007; Vrticˇka et al., 2008), as positive emotions may convey intimacy cues that individuals high in avoidance seek to avoid. Further, it remains unclear whether such differences affect other systems that are sensitive to emotion stimuli, such as the oculomotor system. Subjects were presented with a face displaying either fear, happiness or a neutral expression, or a non-face (oval object) control, and made speeded saccades toward a peripheral target. Results indicate that, as predicted, individuals high in attachment anxiety made quicker saccades following all emotion faces, relative to those lower in attachment anxiety, and relative to control trials. Conversely, individuals with low and high avoidance differed only saccadic latency from happy faces, such that those high in avoidance were slower to respond. Within the non-face control condition, we did not observe differences based on attachment style, suggesting that the attachment differences in saccadic reaction times did not generalize to non-face stimuli. Taken together, these results suggest that attachment style produces differential saccadic latencies to various emotions, consistent with attachment theory predictions. This suggests that even at such an automatic level of responding, those high in attachment anxiety are hypersensitive to emotional stimuli, whereas those high in avoidance are hyposensitive to positive emotions. Meeting abstract presented at VSS 2014
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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 | 0.004 |
| 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) | 0.003 | 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".