Feature-specific Neural Reactivation within the Neocortex and Hippocampus during Episodic Memory
Bibliographic record
Abstract
The ability to mentally reexperience vivid imagery from a past event is a fundamental aspect of cognition and a defining feature of episodic memory. A large body of evidence indicates that vivid episodic recollection is implemented by the reactivation of neural activity that occurred during the recalled episode. However, there is currently little information about the specific representations that are reactivated (e.g., visual vs. semantic), or how reactivation of different representations impact memory performance.In this dissertation, I use multivoxel pattern analysis (MVPA) and fMRI to investigate feature-specific reactivation during episodic recall. In Chapter 2, I develop, validate, and implement feature-specific informational connectivity (FSIC) to map out the distribution of feature-specific reactivation throughout the neocortex. The chapter also investigates the relationship between feature-specific reactivation and subjective (vividness ratings) and objective (recognition accuracy) measures of behavioral memory performance. Chapter 3 extends this investigation to the hippocampus. Together, the results indicate widespread reactivation of low-level visual features, high-level visual features, and semantic features throughout the neocortex and hippocampus. Moreover, reactivation of visual features, particularly low-level features, was positively associated with subsequent vividness ratings and recognition accuracy. However, the association between reactivation and recognition accuracy only held when reactivation of the same features co-occurred within the hippocampus and neocortex on a given trial, providing evidence that the episodic engram is distributed between the two regions. The positive association between recognition accuracy and visual reactivation detailed above only held for participants with above-average recognition lure accuracy. In contrast, participants with below-average lure accuracy had a negative association, suggesting that the recalled features were inaccurate to the point of being misleading. Chapter 4 expands upon these findings by showing that individuals with severely deficient autobiographical memory (SDAM) show similar signs of inaccurate low-level visual reactivation. Moreover, FSIC revealed that SDAM participants had reduced communication of low-level visual information between the hippocampus and neocortex. Taken together, our findings indicate that individual differences in feature-specific reactivation, particularly reactivation of basic visual features, contribute to individual differences in detailed and accurate episodic memory, both within healthy populations and between healthy populations and those with severely deficient autobiographical 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".