Variation of empathy in viewers impacts facial features encoded in their mental representation of pain expression.
Bibliographic record
Abstract
The experience of pain includes sensory, affective, cognitive and behavioral components (Boccard, 2006) and leads to the contraction of specific facial muscles (Kunz et al., 2012) that are, to some extent, encoded in the mental representation of onlookers (Blais et al., in revision). Exposition to facial expressions of pain has been demonstrated to entail a neural emphatic experience in the viewer (Botvinick et al. 2005, Lamm et al. 2007), which varies as a function of subjects’ empathy level (Saarela et al. 2007). This experiment aims to verify the impact of empathy variations on the facial features stored by individuals in their mental representation of pain facial expressions. 54 participants (18 males) were tested with the Reverse correlation method (Mangini & Biederman, 2004). In 500 trials, participants chose from two stimuli the face that looked the most in pain. For each trial, both stimuli consisted of the same base face with random noise superimposed, one with noise pattern added, and the other subtracted. Empathy level was measured using the Emotional Quotient test (Baron-Cohen & Wheelwright, 2004) and used as weight to generate two Classification images (CI) for high-empathy and low-empathy levels. Those CIs were then presented to an independent sample (N=24) who identified High-empathy CI as significantly more intense in regions usually associated with pain expression (i.e. brow lowering [x2=24, p< 0.005], nose wrinkling/upper lip raising [x2=10.67, p< 0.05]) and eyes narrowing [x2=6, p< 0.05]). A CI of difference was then generated (i.e. High-empathy CI - Low-empathy CI), and submitted to a Stat4CI cluster test (Chauvin et al., 2005) resulting in a significant difference in the mouth area (ZCrit=2.7, K=80, p< 0.025). Taken together these results suggest that mental representation of pain expression varies with individual differences in empathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".