Are Emotions Natural Kinds After All? Rethinking the Issue of Response Coherence
Bibliographic record
Abstract
The synchronized co-activation of multiple responses—motivational, behavioral, and physiological—has been taken as a defining feature of emotion. Such response coherence has been observed inconsistently however, and this has led some to view emotion programs as lacking biological reality. Yet, response coherence is not always expected or desirable if an emotion program is to carry out its adaptive function. Rather, the hallmark of emotion is the capacity to orchestrate multiple mechanisms adaptively—responses will co-activate in stereotypical fashion or not depending on how the emotion orchestrator interacts with the situation. Nevertheless, might responses cohere in the general case where input variables are specified minimally? Here we focus on shame as a case study. We measure participants’ responses regarding each of 27 socially devalued actions and personal characteristics. We observe internal and external coherence: The intensities of felt shame and of various motivations of shame (hiding, lying, destroying evidence, and threatening witnesses) vary in proportion ( i) to one another, and ( ii) to the degree to which audiences devalue the disgraced individual—the threat shame defends against. These responses cohere both within and between the United States and India. Further, alternative explanations involving the low-level variable of arousal do not seem to account for these results, suggesting that coherence is imparted by a shame system. These findings indicate that coherence can be observed at multiple levels and raise the possibility that emotion programs orchestrate responses, even in those situations where coherence is low.
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 | 0.037 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.010 | 0.031 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".