Effects of emotional experience in abstract and concrete word processing
Bibliographic record
Abstract
Theories of grounded cognition (Basalou, 2005 Vigliocco, Meteyard, Andrews, & Kousta, 2009) suggest that emotion is a dimension of knowledge important for processing abstract concepts, and to a lesser degree, concrete concepts. Emotional experience (EE) is a variable that has been shown to facilitate the processing of abstract words and inhibit the processing of concrete words in semantic categorization (SCT Newcombe, Campbell, Siakaluk, & Pexman, 2012). The present work extends these findings by examining the effects of EE on abstract and concrete words in lexical decision (LDT), SCT, and semantic lexical decision (SLDT). In LDT, EE exerted facilitatory effects on response latencies for both types of words. In SCT and SLDT, EE exerted facilitatory effects on response latencies and errors for abstract words, but exerted inhibitory effects for concrete words. The results suggest that effects of EE (i.e., emotion knowledge) are dependent on both the nature of the stimuli and task demands. --Leaf ii.
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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 | 0.007 |
| 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) | 0.005 | 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".