The direction-selective contrast response of area 18 neurons is different for first- and second-order motion
Bibliographic record
Abstract
Cortical neurons selective for the direction of motion often exhibit some limited response to motion in their nonpreferred directions. Here we examine the dependence of neuronal direction selectivity on stimulus contrast, both for first-order (luminance-modulated, sine-wave grating) and second-order (contrast-modulated envelope) stimuli. We measured responses from single neurons in area 18 of cat visual cortex to both kinds of moving stimuli over a wide range of contrasts (1.25-80%). Direction-selective contrast response functions (CRFs) were calculated as the preferred-minus-null difference in average firing frequency as a function of contrast. We also applied receiver operating characteristic analysis to our CRF data to obtain neurometric functions characterizing the potential ability of each neuron to discriminate motion direction at each contrast level tested. CRFs for sine-wave gratings were usually monotonic; however, a substantial minority of neurons (35%) exhibited nonmonotonic CRFs (such that the degree of direction selectivity decreased with increasing contrast). The underlying preferred and nonpreferred direction CRFs were diverse, often having different shapes in a given neuron. Neurometric functions for direction discrimination showed a similar degree of heterogeneity, including instances of nonmonotonicity. For contrast-modulated stimuli, however, CRFs for either carrier or envelope contrast were always monotonic. In a given neuron, neurometric thresholds were typically much higher for second- than for first-order stimuli. These results demonstrate that the degree of a cell's direction selectivity depends on the contrast at which it is measured, and therefore is not a characteristic parameter of a neuron. In general, contrast response functions for first-order stimuli were very heterogeneous in shape and sensitivity, while those for second-order stimuli showed less sensitivity and were quite stereotyped in shape.
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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".