Dissociation between eye position and working memory signals during virtual reality tasks in the primate lateral prefrontal cortex
Bibliographic record
Abstract
Neurons in the primate lateral prefrontal cortex (LPFC) maintain working memory (WM) representations of space. However, a proportion of LPFC neurons also encode signals related to eye position. Potential interference between eye related signals and WM representations has prompted strict control of eye position in traditional WM tasks. Therefore, it is unclear how unrestrained eye position may affect performance of a WM task and task related LPFC activity. To explore this, we trained two rhesus monkeys on a spatial WM task set in a naturalistic virtual environment. During task trials, a target was presented at 1 of 9 locations in the environment. The target then disappeared during a two second delay epoch, after which the animals were required to navigate to the cued target location using a joystick. Animals were permitted free visual exploration throughout the task. We recorded neuronal activity using two 96-channel Utah Arrays implanted in LPFC area 8ad/v. Even with unrestrained eye position, animals only spent 3.6% of total fixation time during the delay period looking at the target location. The duration in which animals looked at the target location did not influence trial outcome (Kruskal Wallis, p=0.151). We tested whether neuronal population activity during fixations could predict eye position on targets. Classifiers using neuronal population activity during fixation periods were unable to decode eye position above chance (T-Test, p=0.646). Moreover, we calculated the proportion of neurons tuned for saccade landing position in different reference frames. Only 2% of neurons were tuned for both target location and saccades in the retinocentric frame and 3% were tuned for target location and saccades in the spatiocentric frame. These results indicate that in a virtual environment, unrestricted eye position does not diminish performance on a spatial WM task. Results suggest a dissociation between eye position and WM signals within LPFC.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".