The Interplay of Language and Emotion: Using Affective Norms to Explore Word Recognition, Motivation, and Lexicon
Bibliographic record
Abstract
A lack of norms limited previous work on the interplay of language and emotion. Valence and arousal are regularly dichotomized affecting generalizability and accuracy. Important questions remain unexplored such as the interaction between these dimensions along with individual and group differences. Chapters 2 and 3 report collections of affective and concreteness norms. In Chapter 4, these norms are used to reveal that valence is negatively and arousal is positively correlated with reaction time, both monotonically. Previously, it has been argued that people categorically distinguish between positive and negative or prioritize emotional over neutral stimuli. We demonstrate that this automatic vigilance must be graded. Chapter 5 introduces a method for measuring approach and avoidance in proportion to valence and arousal. A previously demonstrated congruency effect between valence and approach and avoidance movements is categorical. We showed that people choose distances proportionally to word valence and that responses are affected by word frequency, gender, and personality. Finally, Chapter 6 combines the distribution of affect with word frequency information to reveal how language is organized around communicative needs. A compound bias toward high-arousal emotional and low-arousal, mid-valence word types along with more frequent use of positive words suggest that humans need tools to talk about danger and thrills as well as the mundane, while fostering relationships by focusing on the positive. Thus, this dissertation provides important resources – large sets of norms – for the extension of studies on emotion and language. It shows the value of these norms in revisiting past studies of word processing, enabling new methods for testing the motivations behind emotional effects, and considering how the distribution of emotion across language informs our understanding of these motivations. Throughout each chapter, group and individual differences are explored.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".