To update or to create? The influence of novelty and prior knowledge on memory networks
Bibliographic record
Abstract
Schemas are foundational mental structures shaped by experience. They influence behaviour, guide the encoding of new memories and are shaped by associated information. The adaptability of memory schemas facilitates the integration of new information that aligns with existing knowledge structures. First, we discuss how novel information consistent with an existing schema can be swiftly assimilated when presented. This cognitive updating is facilitated by the interaction between the hippocampus and the prefrontal cortex. Second, when novel information is inconsistent with the schema, it likely engages the hippocampus to encode the information as part of an episodic memory trace. Third, novelty may enhance hippocampal dopamine through either the locus coeruleus or ventral tegmental area pathways, with the pathway involved potentially depending on the type of novelty encountered. We propose a gradient theory of schema and novelty to elucidate the neural processes by which schema updating or novel memory traces are formed. It is likely that experiences vary along a familiarity-novelty continuum, and the degree to which new experiences are increasingly novel will guide whether memory for a new experience either integrates into an existing schema or prompts the creation of a new cognitive framework. This article is part of the theme issue 'Long-term potentiation: 50 years on'.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".