Semantic and visual relatedness of distractors impairs episodic retrieval of pictures in a divided attention paradigm
Bibliographic record
Abstract
We used a divided attention (DA) paradigm to infer the representational codes needed to support episodic retrieval of pictures, by measuring susceptibility to memory interference from different distracting tasks. Participants made recognition memory decisions to semantically categorized sets of pictures while simultaneously making size judgments to a set of visually-presented distractor pictures. Recognition accuracy was worse and response times were slower under DA conditions relative to full attention (FA), regardless of semantic relatedness of distractors to targets (Experiment 1). Similarly, we found no differential memory interference under DA relative to FA when distractor pictures were either visually (but not semantically), semantically (but not visually), or unrelated to the targets (Experiment 2). In Experiment 3, memory interference was significantly larger under DA at retrieval when distractors were both semantically and visually similar to the targets. Findings suggest episodic memory for pictures requires access to either visually- or semantically-based representations for optimal performance.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".