The Neural Corelates of Constructing Conceptual and Perceptual Representations of Autobiographical Memories
Bibliographic record
Abstract
Contemporary neurocognitive frameworks propose that conceptual and perceptual content of autobiographical memories-personal past experiences-are processed by dissociable neural systems. Other work has proposed a central role of the anterior hippocampus in initially constructing autobiographical memories, regardless of the content. Here, we report on an fMRI study that utilized a repeated retrieval paradigm to test these ideas. In an MRI scanner, participants retrieved autobiographical memories at three timepoints. During the third retrieval, participants either shifted their focus to the conceptual content of the memory, the perceptual content of the memory, or retrieved the memory as they had done so on previous trials. We observed stronger anterior hippocampal activity for the first retrieval compared with later retrievals, regardless of whether there was a shift in content in those later trials. We also found evidence for separate cortical systems when constructing autobiographical memories with a focus on conceptual or perceptual content. Finally, we found that there was common engagement between later retrievals that required a shift toward conceptual content and the initial retrieval of a memory. This final finding was explored further with a behavioral experiment that provided evidence that focusing on conceptual content of a memory guides memory construction, whereas perceptual content adds precision to a memory. Together, these findings suggest there are distinct content-oriented cortical systems that work with the anterior hippocampus to construct representations of autobiographical memories.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".