Effects of Prior-Knowledge on Brain Activation and Connectivity During Associative Memory Encoding
Bibliographic record
Abstract
Forming new associations is a fundamental process of building our knowledge system. At the brain level, how prior-knowledge influences acquisition of novel associations has not been thoroughly investigated. Based on recent cognitive neuroscience literature on multiple-component memory processing, we hypothesize that prior-knowledge triggers additional evaluative, semantic, or episodic-binding processes, mainly supported by the ventromedial prefrontal cortex (vmPFC), anterior temporal pole (aTPL), and hippocampus (HPC), to facilitate new memory encoding. To test this hypothesis, we scanned 20 human participants with functional magnetic resonance imaging (fMRI) while they associated novel houses with famous or nonfamous faces. Behaviorally, we found beneficial effects of prior-knowledge on associative memory. At the brain level, we found that the vmPFC and HPC, as well as the parahippocampal place area (PPA) and fusiform face area, showed stronger activation when famous faces were involved. The vmPFC, aTPL, HPC, and PPA also exhibited stronger activation when famous faces elicited stronger emotions and memories, and when associations were later recollected. Connectivity analyses also suggested that HPC connectivity with the vmPFC plays a more important role in the famous than nonfamous condition. Taken together, our results suggest that prior-knowledge facilitates new associative encoding by recruiting additional perceptual, evaluative, or associative binding 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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".