Strategic and Automatic Threat Processing in Chronic Musculoskeletal Pain: A Startle Probe Investigation
Why this work is in the frame
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Bibliographic record
Abstract
Attentional bias research with chronic pain samples has yielded conflicting results. In the present investigation the startle paradigm was used to test the postulate that fear-based mechanisms play an important role in attentional biases for pain-related threat in chronic pain. Participants, including 31 individuals with chronic musculoskeletal pain and 20 healthy controls, completed a startle task designed to measure attention to different types of words (neutral vs sensory pain vs affective pain vs health catastrophe) presented at different levels of cognitive processing (strategic vs automatic). Measures of fear-based individual difference variables, including anxiety sensitivity and fear of pain, were also completed. Startle amplitudes and latencies to acoustic startle probes that followed word presentations were recorded. Data were analyzed with repeated measures ANOVAs and correlational analysis. Significant between-group differences were found indicating that, relative to chronic pain participants, healthy controls had higher startle amplitude index scores for health catastrophe words. There was also a trend among patients with chronic pain for greater startle amplitude index scores for strategic presentations of sensory pain words. In the automatic condition, all participants demonstrated a lower startle latency index for sensory words relative to both affect and health catastrophe words, suggesting participants had more difficulty disengaging from affect and health catastrophe words or were more avoidant of sensory words. Correlational analyses indicated that startle response indices for words related to health catastrophe became more pronounced for chronic pain patients as anxiety sensitivity and fear of pain increased. Implications and directions for future research are discussed.
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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 | 0.001 | 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) | 0.000 | 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