Distinct Neural Suppression and Encoding Effects for Conceptual Novelty and Familiarity
Bibliographic record
Abstract
Like yin and yang, novelty and familiarity are typically described as separate-yet-complementary aspects of an experience, two ends of a single continuum. However, novelty and familiarity are also multifaceted. For instance, novelty can sometimes result in enhanced mnemonic performance, whereas at other times familiarity is better remembered. As previous investigations focused primarily on the experimental aspect of novelty, the mechanisms supporting conceptual novelty (the novel combination of two previously unrelated existing concepts) remain unclear. Importantly, conceptual novelty can be recognized as such only when compared with preexperimental familiar knowledge, regardless of experimental status. Here we applied a combined repetition suppression/subsequent memory fMRI paradigm, focusing on the conceptual aspect of novelty and familiarity as the subject matter. Conceptual novelty was characterized by sustained neural activity; familiarity, on the other hand, exhibited repetition effects in multiple cortical regions, a subset of which was modulated by successful encoding. Subsequent memory of novelty was associated only with activation differences in a distinct set of regions, including the hippocampus and medial cortical regions. These results suggest that conceptual novelty (a) does not (easily) trigger the repetition suppression phenomenon but requires sustained neural recruitment and (b) activates dedicated encoding mechanisms. Conceptual familiarity, in contrast, allows rapid neural processing that depends upon existing neural representations. Overall, these findings challenge the definition of novelty as a unitary concept. Furthermore, they bear important implications for research into the neural bases of knowledge representation and recognition memory.
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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 | 0.001 |
| 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".