Flexible recruitment of semantic richness: context modulates body-object interaction effects in lexical-semantic processing
Bibliographic record
Abstract
Body-object interaction (BOI) is a semantic richness variable that measures the perceived ease with which the human body can physically interact with a word's referent. Lexical and semantic processing is facilitated when words are associated with relatively more bodily experience. To date, BOI effects have only been examined in the context of one semantic categorization task (SCT; is it imageable?). It has been argued that semantic processing is dynamic and can be modulated by context. We examined these influences by testing how task knowledge modulated BOI effects. Participants discriminated between the same sets of entity (high- and low-BOI) and action words in each of four SCTs. Task framing was manipulated: participants were told about one (is it an action? vs. is it an entity?) or both (action or entity? vs. entity or action?) categories of words in the decision task. Facilitatory BOI effects were only observed when participants knew that "entity" was part of the decision category. That BOI information was only useful when participants had expectations that entity words would be presented suggests a strong role for the decision context in lexical-semantic processing, and supports a dynamic view of conceptual knowledge.
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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 | 0.005 |
| 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) | 0.003 | 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".