Effects of attention and distractor contrast on the responses of middle temporal area neurons to transient motion direction changes
Bibliographic record
Abstract
The ability of primates to detect transient changes in a visual scene can be influenced by the allocation of attention, as well as by the presence of distractors. We investigated the neural substrates of these effects by recording the responses of neurons in the middle temporal area (MT) of two monkeys while they detected a transient motion direction change in a moving target. We found that positioning a distractor near the target impaired the change-detection performance of the animals. This impairment monotonically decreased as the distractor's contrast decreased. A neural correlate of this effect was a decrease in the ability of MT neurons to signal the direction change (detection sensitivity or DS) when a distractor was near the target, both located inside the neuron's receptive field. Moreover, decreasing distractor contrast increased neuronal DS. On the other hand, directing attention away from the target decreased neuronal DS. At the level of individual neurons, we found a negative correlation between the degree of response normalization and the DS. Finally, the intensity of a neuron's response to the change was predictive of the animal's reaction time, suggesting that the activity of our recorded neurons was linked to the animal's detection performance. Our results suggest that the ability of an MT neuron to signal a transient direction change is regulated by the degree of inhibitory drive into the cell. The presence of distractors, their contrast and the allocation of attention influence such inhibitory drive, therefore modulating the ability of the neurons to signal transient changes in stimulus features and consequently behavioral performance.
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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 | 0.002 |
| 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 | 0.001 |
| 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".