Spatial Probability Improves Detection, Orientation Probability Improves Precision: Modelling as Neural Gain versus Tuning
Bibliographic record
Abstract
Frequent targets are detected faster, probable locations searched earlier, and likely orientations estimated more precisely. As attentional manipulations often convey probabilistic cues about where or what stimuli are likely to appear, is it the case that probability effects and attentional effects are largely one and the same? If true, probability effects for space and features should be distinct as they are for attentional cues, e.g. spatial attention has been linked to changes in neuronal gain, while feature-based attention is thought to affect neuronal tuning. To examine dissociations in spatial versus featural probability, we had participants report both location and orientation of gratings, while location or tilt probabilities were independently manipulated. While orientation probability affected the precision of orientation reports, spatial probability only modulated the likelihood of stimulus detection. Our results demonstrate that even when no physical attentional cues are present, acquired probabilistic information on space versus orientation leads to separable 'attention-like' effects on behavior. These behavioral results are consistent with current theories of attentional effects at the neuronal level. We used population vector coding to implement spatial probability as an orientation agnostic increase in the gain of orientation responsive neurons and orientation probability as a change in orientation selective tuning. The result is that total neural signal is boosted for probable locations, but that the perceptual systems' sensitivity to orientations is not affected, consistent with the behavioral finding that spatial probability only affects detection and not precision. By contrast, having orientation probability affected tuning results in orientation sensitivity and interacted with innate perceptual biases that can also be modeled as tuning differences. Together these results support the claim that many attentional effects can be more directly explained as probability effects, and that the mechanism of probability effects are implemented by adjustments in the gain and tuning of selectively responsive neurons. 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".