Semantic and affective representations of valence: Prediction of autonomic and facial responses from feelings-focused and knowledge-focused self-reports.
Bibliographic record
Abstract
can refer to either the affective response (e.g., "I feel bad") or the semantic knowledge about a stimulus (e.g., "car accidents are bad"). Accordingly, the content of self-reports can be more "experience-near" and proxy to the mental state of affective feelings, or, alternatively, involve nonexperiential semantic knowledge. In this work we compared three experimental protocol instructions: feelings-focused self-reports that encourage participants to report their feelings (but not knowledge); knowledge-focused self-reports that encourage participants to report about semantic knowledge (and not feelings); and "feelings-naïve", in which participants were asked to report their feelings but are not explicitly presented with the distinction between feelings and knowledge. We compared the ability of the three types of self-report data to predict facial electromyography, heart rate, and electrodermal changes in response to affective stimuli. The relationship between self-reports and both physiological signal intensity and signal discriminability were examined. The results showed a consistent advantage for feelings-focused over knowledge-focused instructions in prediction of physiological response with feelings-naïve instructions falling in between. The results support the theoretical distinction between affective and semantic representations of valence and the validity of feelings-focused and knowledge-focused self-report instructions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.002 | 0.028 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".