Does visualisation help or hinder concrete word processing?
Bibliographic record
Abstract
) was found. Malhi and Buchanan proposed that this reverse concreteness, or abstractness, effect was the result of participants taking a visualisation/imagining (time-costly) approach towards the concrete word pairs and an emotional/intuitive (time-efficient) approach towards the abstract word pairs. This study tested this proposal by asking participants questions about strategy use (Experiment 1). In the forced-choice questions, all participants reported using a visualisation/imagining approach towards the concrete word pairs and most participants reported using an emotional/intuitive approach towards the abstract word pairs. In the open-ended responses, visual-spatial reasoning and real-life experience emerged as themes for the concrete word pairs and social norms and values emerged as themes for the abstract word pairs, adding to our understanding of the grounding of abstract words. In Experiment 2, participants were supplied with pictures as an aid to visualisation with the expectation that this would reduce the time required for concrete word processing. Supplying pictures made participants faster and more accurate at completing the task. Experiment 3 manipulated the type of visual aid by also supplying pictures that did not match the orientation of the word pairs. Participants were only more accurate when the pictures were in the correct and iconic spatial arrangement. A flexible abstractness and concreteness effects (FACE) theory is proposed which integrates symbolic and embodied accounts and introduces constructs such as direct and constrained imageability for concrete words and indirect and free imageability for abstract words.
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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 | 0.041 |
| 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 | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".