Semantic richness, concreteness, and object domain: An electrophysiological study.
Bibliographic record
Abstract
Results from previous event-related potential (ERP) studies of semantic richness and concreteness effects have been mixed. Feature production norms have been used to derive one measure of semantic richness, the number of listed semantic features (NOF) for a given concept. Whereas some ERP studies have found evidence for a semantic richness continuum from abstract concepts, to concrete concepts with few features, to concrete concepts with several features, other studies have not. The present study assessed the effects of NOF (within concrete concepts) and concreteness (concrete vs. abstract concepts), on ERP amplitudes and behavioural decision latencies during a concrete/abstract decision task. It is important we also manipulated object domain, which has been found to influence ERP amplitude and topography. High and low NOF concepts were selected from animal and nonliving thing categories and all four conditions were matched on several potential confounds. We show that although decision latencies support a semantic richness continuum, electrophysiological activity does not. Whereas concrete concepts produce a larger negativity than abstract concepts, low NOF concepts are associated with larger negativities than high NOF concepts. We also replicate an increased posterior positivity for processing animal concepts, and report an interaction between object domain and semantic richness such that the NOF effect is larger within animal 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".