The automatic visual simulation of words: A memory reactivated mask slows down conceptual access.
Bibliographic record
Abstract
How do we represent the meaning of words? The present study assesses whether access to conceptual knowledge requires the reenactment of the sensory components of a concept. The reenactment-that is, simulation-was tested in a word categorisation task using an innovative masking paradigm. We hypothesised that a meaningless reactivated visual mask should interfere with the simulation of the visual dimension of concrete words. This assumption was tested in a paradigm in which participants were not aware of the link between the visual mask and the words to be processed. In the first phase, participants created a tone-visual mask or tone-control stimulus association. In the test phase, they categorised words that were presented with 1 of the tones. Results showed that words were processed more slowly when they were presented with the reactivated mask. This interference effect was only correlated with and explained by the value of the visual perceptual strength of the words (i.e., our experience with the visual dimensions associated with concepts) and not with other characteristics. We interpret these findings in terms of word access, which may involve the simulation of sensory features associated with the concept, even if participants were not explicitly required to access visual properties. (PsycINFO Database Record
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".