Decoding the allocation of visual attention from prefrontal neural assemblies in behaving primates
Bibliographic record
Abstract
The primate prefrontal cortex is thought to play an important role in intelligent goal-directed behaviour. Single neurons in different regions of the PFC are tuned for the allocation of attention as well as for the final position of saccades. Here we show that the activity of a small population of simultaneously recorded neurons from macaque PFC area 8a can be reliably decoded to signal the allocation of attention to one of four Gabor stimuli presented on a computer screen with 71% accuracy, and the goal of a saccade to the same stimulus with 95% accuracy. The presence of a transient change in one of the unattended distracters slightly decreased the coding accuracy by 25%, demonstrating that the encoding was robust to interference by transient distracter changes. Moreover, the population code was equally reliable when we used the pooled multiunit activity of single electrodes rather than the sorted single unit activity. Importantly, the code was constant across a timespan of multiple weeks, suggesting a stable functional network architecture underlying the coding of attention and saccade goal. Our results demonstrate that the activity of a small population of neurons, distributed over an area of ~16 mm2 of PFC, contains sufficient information to decode the allocation of spatial attention as well as the goal of a saccade with high accuracy, robustness, and stability over time. They suggest that PFC area 8a could be a target for brain machine interfaces (BMI) that take into account the relevance of environmental stimuli to produce goal-oriented behaviour. Meeting abstract presented at VSS 2014
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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".