Dissociable contributions of thalamic nuclei to recognition memory: novel evidence from a case of medial dorsal thalamic damage
Bibliographic record
Abstract
The thalamic nuclei are thought to play a critical role in recognition memory. Specifically, the anterior thalamic nuclei and medial dorsal nuclei may serve as critical output structures in distinct hippocampal and perirhinal cortex systems, respectively. Existing evidence indicates that damage to the anterior thalamic nuclei leads to impairments in hippocampal-dependent tasks. However, evidence for the opposite pattern following medial dorsal nuclei damage has not yet been identified. In the present study, we investigated recognition memory in NC, a patient with relatively selective medial dorsal nuclei damage, using two object recognition tests with similar foils: a yes/no (YN) test that requires the hippocampus, and a forced choice corresponding test (FCC) that is supported by perirhinal cortex. NC performed normally in the YN test, but was impaired in the FCC test. Critically, FCC performance was impaired only when the study-test delay period was filled with interference. We interpret these results in the context of the representational-hierarchical model, which predicts that memory deficits following damage to the perirhinal system arise due to increased vulnerability to interference. These data provide the first evidence for selective deficits in a task that relies on perirhinal output following damage to the medial dorsal nuclei, providing critical evidence for dissociable thalamic contributions to recognition 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".