Secure Base Priming Diminishes Conflict-Based Anger and Anxiety
Bibliographic record
Abstract
This study examines the impact of a visual representation of a secure base (i.e. a secure base prime) on attenuating experimentally produced anger and anxiety. Specifically, we examined the assuaging of negative emotions through exposure to an image of a mother-infant embrace or a heterosexual couple embracing. Subjects seated at a computer terminal rated their affect (Pre Affect) using the Affect Adjective Checklist (AAC) then listened to two sets of intense two person conflicts. After the first conflict exposure they rated affect again (Post 1 AAC). Following the second exposure they saw a blank screen (control condition), pictures of everyday objects (distraction condition) or a photo of two people embracing (Secure Base Prime condition). They then reported emotions using the Post 2 AAC. Compared to either control or distraction subjects, Secure Base Prime (SBP) subjects reported significantly less anger and anxiety. These results were then replicated using an internet sample with control, SBP and two new controls: Smiling Man (to control for expression of positive affect) and Cold Mother (an unsmiling mother with infant). The SBP amelioration of anger and anxiety was replicated with the internet sample. No control groups produced this effect, which was generated only by a combination of positive affect in a physically embracing dyad. The results are discussed in terms of attachment theory and research on spreading activation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".