The Influence of Adult Relationship Attachment Style on the Networks of Attention
Bibliographic record
Abstract
Relationship attachment style has been found to influence performance associated with two of the three networks of attention. Adaptations to information processing for individuals with insecure attachment styles are thought to be a result of increased vigilance to external threats. Attachment avoidance has been shown to influence the executive network of attention, wherein individuals higher in attachment avoidance show reduced interference in the standard Eriksen Flanker task. Attachment anxiety has been shown to influence the executive network as well, in addition to the orienting network of attention, wherein individuals higher in attachment anxiety show larger endogenous cueing effects. The current study was designed to replicate these prior findings, and to examine the heretofore untested relationship between attachment styles and the alerting network of attention, all in a single task. AttentionTrip is an engaging, gamified version of the Attention Network Task, played on an iPad tablet. Observers pilot a ship through a tunnel, using helpful signals (spatial and temporal) to prepare for upcoming targets. The correct weapon must be used to destroy targets, which can be flanked congruently or incongruently by distractors. As in previous work, attachment styles were assessed using the Experience in Close Relationships-Revised Questionnaire. Across individuals, robust network scores were observed for each network of attention. Attachment styles modulated the network effects in the same manner as previously observed, although the sizes of the effects of attachment appear to be smaller in our task. Pertaining to the relationship between attachment and the alerting network, no statistically significant finding was observed. However, individuals with insecure attachment showed slightly faster overall aggregate RTs, lending additional support to the hypothesis that adaptations to attention due to insecure attachment are a consequence of increased vigilance.
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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 | 0.001 | 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) | 0.002 | 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".