Exploring the influence of encoding format on subsequent memory
Bibliographic record
Abstract
Distinctive encoding is greatly influenced by gist-based processes and has been shown to suffer when highly similar items are presented in close succession. Thus, elucidating the mechanisms underlying how presentation format affects gist processing is essential in determining the factors that influence these encoding processes. The current study utilised multivariate partial least squares (PLS) analysis to identify encoding networks directly associated with retrieval performance in a blocked and intermixed presentation condition. Subsequent memory analysis for successfully encoded items indicated no significant differences between reaction time and retrieval performance and presentation format. Despite no significant behavioural differences, behaviour PLS revealed differences in brain-behaviour correlations and mean condition activity in brain regions associated with gist-based vs. distinctive encoding. Specifically, the intermixed format encouraged more distinctive encoding, showing increased activation of regions associated with strategy use and visual processing (e.g., frontal and visual cortices, respectively). Alternatively, the blocked format exhibited increased gist-based processes, accompanied by increased activity in the right inferior frontal gyrus. Together, results suggest that the sequence that information is presented during encoding affects the degree to which distinctive encoding is engaged. These findings extend our understanding of the Fuzzy Trace Theory and the role of presentation format on encoding processes.
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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 | 0.008 |
| 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 | 0.001 |
| 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".