Mnemonic representations in human lateral geniculate nucleus
Bibliographic record
Abstract
There is a growing appreciation for the role of the thalamus in high-level cognition. Motivated by findings that internal cognitive state drives activity in feedback layers of primary visual cortex (V1) that target the lateral geniculate nucleus (LGN), we investigated the role of LGN in working memory (WM). Specifically, we leveraged model-based neuroimaging approaches to test the hypothesis that human LGN encodes information about spatial locations temporarily encoded in WM. First, we localized and derived a detailed topographic organization in LGN that accords well with previous findings in humans and non-human primates. Next, we used models constructed on the spatial preferences of LGN populations in order to reconstruct spatial locations stored in WM as subjects performed modified memory-guided saccade tasks. We found that population LGN activity faithfully encoded the spatial locations held in memory in all subjects. Importantly, our tasks and models allowed us to dissociate the locations of retinal stimulation and the motor metrics of memory-guided saccades from the maintained spatial locations, thus confirming that human LGN represents true WM information. These findings add LGN to the growing list of subcortical regions involved in WM, and suggest a key pathway by which memories may influence incoming processing at the earliest levels of the visual hierarchy.
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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".