Group Differences in the Processing of Emotion-Laden Linguistic Stimuli
Bibliographic record
Abstract
This thesis investigated how different groups of people process the emotionality of linguistic stimuli. Recently, individual and group differences have become topics of interest in psychology, but very few studies have investigated how behavioural responses to emotional stimuli vary as a function of participant demographics. The first study focused on the contrast between how native and non-native speakers of English process the emotional content of English words. The study of emotional processing in non-native speakers is a highly contested issue in the field of bilingualism and experimental psychology. Chapter 2 reports the largest collection of emotion ratings of English words from non-native speakers and provides a theoretical perspective on how bilingual speakers process emotion. In order to facilitate the investigation of individual differences in emotional processing, the field needs a reliable method that allows for the measurement of subtle differences between individuals. In Chapter 3, I demonstrate that a novel ‘slider’ method of measuring valence, proposed by Warriner et al. (2018), is a reliable tool for measuring affective responses to words along a fine-grained sliding scale. In Chapter 4, I demonstrate the use of the sliding scale with the aim of capturing affective differences between those with and without depressive symptoms. This chapter reports that compared to those without depressive symptoms, those with depressive symptoms i) exhibit attenuated responses to emotionally laden stimuli, and ii) are unable to take on the perspective of someone without depression. Overall, this thesis reports on the emotional responses in two hotly debated groups, as well as providing a new method of measuring emotional responses to linguistic stimuli. These findings underscore the importance of studying emotional processing beyond normative populations.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".