Bibliographic record
Abstract
Within extrastriate visual cortex of humans, monkeys and cats, individual cortical areas are specialized for spatial or motion processing. In cat auditory cortex, four regions have been identified to be critical for accurately determining the spatial location of an acoustic stimulus. The purpose of the present investigation was to determine if there is an area in auditory cortex specialized for acoustic motion processing or if areas involved in spatial localization are also critical for acoustic motion processing. Or, is there an acoustic MT? To accomplish this, cats were trained to perform two tasks: a spatial localization task using a static stimulus and a task that required the animals to discriminate leftward from rightward apparent acoustic motion. Focal reversible cooling was used to bilaterally deactivate each of the thirteen areas of cat auditory cortex. Overall, the results show that areas involved in acoustic motion processing are also involved in static spatial localization. An area that is uniquely involved in acoustic motion processing was not identified. These results suggest that spatial localization functions may be a prerequisite for acoustic motion processing in auditory cortex. Supported by the Canadian Institutes of Health Research and the Natural Sciences and Engineering Research Council of Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".