Neuronal and Perceptual Effects of Selective Attention in the Primate Visual System
Bibliographic record
Abstract
The work included in this thesis examines cognitive influences on the processing of visual information both on the neural and the behavioral level. A prominent mechanism for the selection and modulation of behaviorally relevant sensory information in the brain is selective attention. Recording single unit activity and local field potentials (LFPs) in middle temporal area (MT) of the macaque monkey, we have gained deeper insights into the neural mechanisms of various aspects of selective attention that, so far, had been unexplored. Specifically, we demonstrated that attention individually enhances representations of multiple moving objects. Furthermore, we showed that attention modulates the input signals of MT neurons, and that these effects are reflected in certain frequency bands of LFP oscillations. Finally, we showed that spike timing is a source of information likely used by the brain for encoding different features of visual stimuli. Complementing the electrophysiological studies, our behavioral experiments investigated consequences of dividing attention between stimuli represented in two different reference frames and the influence of feature-based attention on the processing of information from non-overlapping spatial locations. Collectively, these studies show that cognitive factors strongly modulate the processing of sensory information in primate visual cortex.
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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) | 0.001 | 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".