Learning Concrete and Abstract Novel Words in Emotional Contexts: Evidence from Incidental Vocabulary Learning
Bibliographic record
Abstract
This study investigates the role of emotional linguistic input in learning novel words with abstract and concrete denotations. It is widely accepted that concrete words are processed more easily than abstract ones. Several theories of vocabulary acquisition additionally propose a critical role of sensorimotor and emotional information during novel word learning. In this study, proficient adult speakers of English read novel words denoting concrete and abstract words (e.g. boat vs religion) embedded in informative passages with different emotional valence (positive, neutral, and negative). After five exposures to each novel word in an emotionally consistent context, participants were tested on orthographic and semantic vocabulary learning, and provided valence judgments of these novel words. A concreteness advantage was seen in both tasks measuring semantic learning. Critically, valence of linguistic contexts was more influential for novel words with concrete denotations. In line with previous reports, the transfer of context emotionality to novel words (i.e. semantic prosody) took place in concrete stimuli but it was not found in abstract stimuli, even though both were embedded in emotional contexts. An equal advantage was seen for semantic learning of novel words with both concrete and abstract denotations seen in positive contexts. These findings provide support for weak embodied theories of cognition, which propose experiential and linguistic information as critical for concrete and abstract novel word learning.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".