Effects of Emotional and Sensorimotor Knowledge in Semantic Processing of Concrete and Abstract Nouns
Bibliographic record
Abstract
There is much empirical evidence that words' relative imageability and body-object interaction (BOI) facilitate lexical processing for concrete nouns (e.g., Bennett et al., 2011). These findings are consistent with a grounded cognition framework (e.g., Barsalou, 2008), in which sensorimotor knowledge is integral to lexical processing. In the present study, we examined whether lexical processing is also sensitive to the dimension of emotional experience (i.e., the ease with which words evoke emotional experience), which is also derived from a grounded cognition framework. We examined the effects of emotional experience, imageability, and BOI in semantic categorization for concrete and abstract nouns. Our results indicate that for concrete nouns, emotional experience was associated with less accurate categorization, whereas imageability and BOI were associated with faster and more accurate categorization. For abstract nouns, emotional experience was associated with faster and more accurate categorization, whereas BOI was associated with slower and less accurate categorization. This pattern of results was observed even with many other lexical and semantic dimensions statistically controlled. These findings are consistent with Vigliocco et al.'s (2009) theory of semantic representation, which states that emotional knowledge underlies meanings for abstract concepts, whereas sensorimotor knowledge underlies meanings for concrete concepts.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".