Training alters the causal contribution of area MT to visual motion perception
Bibliographic record
Abstract
Visual stimuli elicit neural activation in a large number of distinct cortical areas. In principle, visual perception could arise from a distributed process that combines information across these areas, or it could rely exclusively on the areas that are most specialized for each stimulus. We have examined these possibilities, using a motion discrimination task and reversible inactivation of the middle temporal (MT) area of the visual cortex. Area MT is highly specialized for processing visual motion. Monkeys were trained to report the direction of motion of a moving grating, which has been shown to be represented in many different visual cortical areas. Following this training, reversible inactivation of MT, using muscimol injections, had surprisingly little effect on behavioral performance (22% increase in psychophysical thresholds). This suggests that the brain uses a distributed representation to make perceptual decisions. The same monkeys were then trained to report the motion direction of random dots embedded in noise. This stimulus elicits far stronger direction selectivity in MT than elsewhere, suggesting that it is a specialized probe of MT; as in previous studies, we found that MT inactivation devastated behavioral performance for this stimulus. Surprisingly, following training on the dots task, we found that muscimol injections had a much more powerful effect on the perception of motion for gratings as well (500% increase in psychophysical threshold). This suggests that the readout of sensory information depends strongly on perceptual experience, and that specialized training on one stimulus can have detrimental effects on the perception of other stimuli. Meeting abstract presented at VSS 2016
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".