Examining the development of memory for temporal context and its underlying neural processes using event-related potentials
Bibliographic record
Abstract
Time is a critical feature of episodic memory-memory for events from a specific time and place (Tulving, 1972). Previous research indicates that temporal memory (memory for 'when') is slower to develop than memory for other details (e.g., 'what' and 'where'), with improvements observed across middle and late childhood. The factors that drive these changes are not yet clear. We used an event-related potential (ERP) recognition memory paradigm to investigate the underlying processes of memory for temporal context in middle to late childhood (7-9-year-olds; 10-12-year-olds) and young adulthood. Behaviorally, we observed age-related improvements in the ability to place events in temporal context. ERP analyses showed old/new effects for children and adults. We also found brain-behavior relations for 1) episodic memory (ERP mean amplitude difference between source hits and correctly identified new trials was correlated to behavioral accuracy), and 2) temporal memory (ERP mean amplitude difference between source hits and source error trials was correlated to accuracy of temporal memory judgments). This work furthers our understanding of the cognitive processes and neural signatures supporting temporal memory development in middle to late childhood, and has implications for episodic memory development more broadly.
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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 | 0.001 |
| 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) | 0.001 | 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".