Individual Differences in Visual versus Semantic Neural Reactivation: Evidence from Severely Deficient Autobiographical Memory
Bibliographic record
Abstract
Visual memory is intrinsically linked to the reinstatement of low-level visual features, such as edges and luminosity, within early visual cortex. However, individuals with severely deficient autobiographical memory (SDAM) cannot vividly recollect autobiographical experiences yet display normal everyday functioning. We hypothesized that such individuals would depend on semantic features instead of low-level visual features during a challenging visual recognition task due to impaired communication of low-level visual information between the posterior hippocampus and early visual cortex. Two methods were used to measure the content of memory derived from fMRI data collected at encoding and retrieval: one directly measuring feature-specific neural reactivation within the hippocampus and other cortical regions, and another modeling top-down inference to assess the influence of semantic-based recall on reactivation within early visual cortex. In accord with prior findings, recognition accuracy in non-SDAM individuals was linked to low-level visual reactivation within early visual cortex and posterior hippocampus. As predicted, this association was diminished in SDAM individuals, whose recognition accuracy was instead linked to semantic-based reactivation. In addition, non-SDAM individuals exhibited communication of low-level visual information between early visual cortex and hippocampus, whereas SDAM individuals showed communication of semantic information. Given that SDAM participants' performance on the visual memory task was equivalent to non-SDAM subjects, our findings suggest that SDAM individuals successfully compensate for impaired low-level visual memory through semantic recall and highlight the essential role of feature-specific reactivation measures in identifying distinct neural pathways to memory performance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".