Encoding-phase orientation toward thematic content over perceptual style benefits picture memory
Bibliographic record
Abstract
Orienting toward the meaning versus perceptual features of an experience benefits subsequent memory. Yet given that past work encouraged these orientations with different tasks, it is not clear if this memory benefit is solely due to internal processing factors versus external task-related ones. Moreover, it remains unclear how this benefit generalises from verbal to detailed picture memory. Here, we developed a novel paradigm that cued participants' attention to thematic (story) or stylistic (artist style) dimensions of storybook-style illustrations during a repeat-detection task. Afterwards, participants completed a recognition memory test with studied illustrations and lures along thematic and stylistic dimensions. In contrast to past work, both orienting tasks were identical except for the dimension participants were cued to attend to. Furthermore, our thematic and stylistic dimensions enabled us to separately examine memory quality along each dimension. We found that thematic attention yielded superior memory for studied illustrations over stylistic orientations. False alarms to lures varied by dimension and attention: errors were greater to thematic than stylistic lures overall and stylistic attention elevated false alarms to stylistic lures. Our results show that semantic encoding orientations enhance detailed picture memory, without a cost to memory quality along semantic or perceptual dimensions of experience.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".