Role of LIP persistent activity in visual working memory
Bibliographic record
Abstract
Parietal cortical areas have been implicated as a critical neural substrate for visual working memory. Human fMRI and ERP studies have revealed persistent parietal activation during delay periods of visual working memory tasks and shown that such activation scales with the capacity limit of visual working memory. A second line of evidence has been provided by neural recordings in primates performing memory-guided saccades. Neurons in parietal cortical areas, such as the lateral intraparietal area (LIP), have been shown to exhibit persistent activity during the memory delay of this task, but the contribution of LIP persistent activity to mnemonic processes remains poorly understood. Specifically, it is unclear whether persistent activity carries a retrospective visual or prospective saccade-related representation, or how it could be related to the capacity limit of visual working memory. To address this, we recorded the activity of single LIP neurons in three monkeys while they performed memory-guided saccades and carried out two sets of analyses. We first compared the visual and motor responses of each neuron with persistent delay period activity. LIP neurons exhibited a pattern of activity consistent with a noisy retrospective code: most neurons had greater visual than saccade-related activity, and persistent activity was most strongly linked with the preferred direction of each neuron, but highly variable. We then investigated how persistent activity could relate to visual working memory capacity. We derived estimates of baseline and persistent activity from our sample of LIP neurons, and used these values to compute the theoretical number of discriminable representations that could be carried over a range of realistic simulated activity distributions using an ROC analysis. This number ranged from one to approximately four. These data show that LIP persistent activity is visually biased and suggest a neural basis for the capacity limit of visual working 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".