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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".