Memory specificity through visual production: Multimodal recognition and source memory misattributions
Bibliographic record
Abstract
Successfully retrieving information from memory is critical to our ability to learn from experience to better inform our decision-making and interactions with the environment. As such, various strategies have been developed to aid memory encoding, from verbal mnemonics to visualization. Among these, drawing, or visual production, has emerged as a powerful tool. Beyond some traditional approaches, visual production engages several forms of cognition (visual, motoric, and elaborative processes) simultaneously. Therefore, drawing provides an excellent test case for studying how these multisensory components interact to improve memory. Here, we tested the relative mnemonic impact of the various cognitive features that are involved in the act of drawing. Participants (N = 60) completed four encoding tasks - Drawing, No Ink drawing, Tracing, and Visualizing - designed to differentially engage the multi-sensory ‘components’ thought to underlie drawing. We found that engaging all three components during Drawing was associated with the best memory, while engaging only one component during Visualizing was the worst. This is consistent with prior work, and we demonstrated it in both classic old/new recognition (Experiment 1), and source memory (Experiment 2). Despite lacking the visual component, recognition memory for No Ink drawing items was as good as Drawing, but interestingly, resulted in the most source memory confusion: No Ink items were frequently mistaken as Drawn items. We replicated this finding in Experiment 3, which only compared drawing with and without ink. Across these three experiments, lack of visual feedback did not influence recognition memory overall, but clearly undermined the precision of the source memory. Ongoing fMRI work is investigating the underlying neural mechanisms supporting memory in each of these tasks to clarify how shared visuomotor processes might lead to confusable representations. Together, our results extend our current understanding of the cognitive processes and visuomotor interactions underlying successful retrieval from memory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".