Global motion integration in the cat's lateral posterior‐pulvinar complex
Bibliographic record
Abstract
Our laboratory previously showed that thalamic neurons in an extrageniculate nucleus, the lateral posterior-pulvinar complex (LP-pulvinar) could perform higher-order neuronal operations that had until then only been attributed to higher-level cortical areas. To further assess the role of the thalamus in the analysis of complex percepts, we have investigated whether neurons in the LP-pulvinar complex can signal the direction of motion of random-dot kinematograms wherein the individual elements of the pattern do not provide coherent motion cues. Our results indicate that a subset of LP-pulvinar cells can integrate the displacement of individual elements into a global motion percept and that their large receptive fields permit the integration of motion for elements separated by large spatial intervals. We also found that almost all of the global motion-sensitive neurons were not systematically pattern-motion-selective when tested with plaid patterns. The results indicate that LP-pulvinar cells can perform the higher-level spatio-temporal integration required to detect the global displacement of objects in a complex visual scene, further supporting the notion that extrageniculate thalamic cells are involved in higher-order motion processing. Furthermore, these results provide some evidence that there may be specialized mechanisms for processing different types of complex motion within the LP-pulvinar complex.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".