Pattern‐motion selective responses in MT, MST and the pulvinar of humans
Bibliographic record
Abstract
Plaid stimuli are often used to investigate the mechanisms involved in the integration and segregation of motion information. Considering the perceptual importance of such mechanisms, only a very limited number of visual brain areas have been found to be specifically involved in motion integration. These are the human (h)MT+ complex, area V3 and the pulvinar. The hMT+ complex can be functionally subdivided into two separate areas, middle temporal area (MT) and medial superior temporal area (MST); however, it is currently unclear whether these distinct sub-regions have different responses to plaid stimuli. To address this issue we used functional magnetic resonance imaging to quantify the relative response of MT and MST to component and pattern motion. Participants viewed plaid stimuli that were constrained to result in the perception of either component motion (segregation of motion information) or pattern motion (integration of motion information). MT/MST segregation was achieved using a moving dot stimulus that allowed stimulation of each visual hemifield either in unison or separately. We found pattern motion selective responses in both MT and MST. Consistent with previous reports, activity indicative of pattern motion selectivity was also found in the pulvinar as well as in other extrastriate areas. These results demonstrate that MT, MST and the pulvinar are involved in the complex motion integration mechanisms that are triggered by plaid stimuli. This reinforces the concept that integrative computations take place in a distributed neuronal circuit both in cortical and sub-cortical networks.
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 | 0.001 |
| 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) | 0.003 | 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".