The nature and time‐course of medial temporal lobe contributions to semantic retrieval: An fMRI study on verbal fluency
Bibliographic record
Abstract
Recent investigations have shown that the medial temporal lobe (MTL), a region thought to be exclusive to episodic memory, can also influence performance on tests of semantic memory. The present study examined further the nature of MTL contributions to semantic memory tasks by tracking MTL activation as participants performed category fluency, a traditional test of semantic retrieval. For categories that were inherently autobiographical (e.g. names of friends), the MTLs were activated throughout the time period in which items were generated, consistent with the MTLs role in retrieving autobiographical memories. For categories that could not benefit from autobiographical or spatial/context information (e.g. governmental offices), the MTL was not implicated at any time point. For categories for which both prototypical and episodically-related information exists (e.g. kitchen utensils), there was more robust MTL activity for the open-ended, late generation periods compared with the more well-defined, early item generation time periods. We interpret these results as suggesting that early in the generation phase, responses are based on well-rehearsed prototypical knowledge whereas later performance relies more on open-ended strategies, such as deriving exemplars from personally relevant contextual information (e.g. imagining one's own kitchen). These findings and interpretation were consistent with the results of an initial, separate behavioral study (Expt 1), that used the distinctiveness of responses as a measure of open-endedness across the generation phase: Response distinctiveness corresponded to the predicted open-endedness of the various tasks at early and late phases. Overall, this is consistent with the view that as generation of semantic information becomes open-ended, it recruits processes from other domains, such as episodic memory, to support 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 | 0.001 |
| 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 | 0.000 |
| 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".