Perceiving pain in others: Validation of a dual processing model
Bibliographic record
Abstract
Accurate perception of another person's painful distress would appear to be accomplished through sensitivity to both automatic (unintentional, reflexive) and controlled (intentional, purposive) behavioural expression. We examined whether observers would construe diverse behavioural cues as falling within these domains, consistent with cognitive neuroscience findings describing activation of both automatic and controlled neuroregulatory processes. Using online survey methodology, 308 research participants rated behavioural cues as "goal directed vs. non-goal directed," "conscious vs. unconscious," "uncontrolled vs. controlled," "fast vs. slow," "intentional (deliberate) vs. unintentional," "stimulus driven (obligatory) vs. self driven," and "requiring contemplation vs. not requiring contemplation." The behavioural cues were the 39 items provided by the PROMIS pain behaviour bank, constructed to be representative of the diverse possibilities for pain expression. Inter-item correlations among rating scales provided evidence of sufficient internal consistency justifying a single score on an automatic/controlled dimension (excluding the inconsistent fast vs. slow scale). An initial exploratory factor analysis on 151 participant data sets yielded factors consistent with "controlled" and "automatic" actions, as well as behaviours characterized as "ambiguous." A confirmatory factor analysis using the remaining 151 data sets replicated EFA findings, supporting theoretical predictions that observers would distinguish immediate, reflexive, and spontaneous reactions (primarily facial expression and paralinguistic features of speech) from purposeful and controlled expression (verbal behaviour, instrumental behaviour requiring ongoing, integrated responses). There are implicit dispositions to organize cues signaling pain in others into the well-defined categories predicted by dual process theory.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".