Memory representations in a cross-modal matching task: evidence for a verbal component
Bibliographic record
Abstract
In everyday tasks, one often uses touch to find what has been seen. Recent research has identified that when individuals view or touch an object, they may create a verbal memory representation; however, this research involved object naming, which may have prompted the use of verbal strategies. Research has also identified variability in memory representations for objects, which may indicate individual differences. To investigate memory representations and their associations with individual differences in cognitive styles, we measured the cognitive styles of 127 participants and had them complete a non-verbal matching task without distractors, or with verbal or visual distractors. In the task, they viewed an object and then touched an object - or vice versa - and indicated whether the objects were the same or different. On trials where different objects were presented, participants responded consistently more slowly and made more matching errors for similar objects compared to distinct objects. Importantly, higher scores on the verbalizer cognitive style predicted faster reaction times on the matching task across all trial types and distraction conditions. Overall, this indicates that cross-modal object processing in short-term memory may be facilitated by a verbal code.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".