Flicker motion aftereffect produces fMRI activation in MT
Bibliographic record
Abstract
Purpose: Motion area MT+ shows enhanced activity during the perception of the motion aftereffect (MAE) when static test stimuli are used (static MAE; Tootell et al., 1995, Nature). MAEs can also be observed with flickering test stimuli (flicker MAE) and psychophysical evidence suggests potentially different neural substrates than the static MAE. We used functional magnetic resonance imaging (fMRI) to examine neural activity during the flicker MAE. Methods: Using a 4 Tesla fMRI, we first identified MT+ using a motion localizer and then measured MT+ activity during the flicker MAE. Six subjects viewed an adapting grating that either rotated continuously (MAE condition) or reversed direction every 2 sec (Control condition) for 24 sec. Subsequently, a radial counterphase flickering test grating was presented for 21 sec and the duration of the resulting aftereffect was measured. Following continuous motion adaptation, subjects perceived the flickering test pattern as moving in the opposite direction; whereas, following the control condition, no MAE was perceived. Results: When the test grating was presented, activity in MT+ remained elevated for a longer period in the MAE condition than the control condition. Conclusions: During the flicker MAE, like the static MAE, activity in MT+ is correlated with the illusory motion percept. Future research will address the contribution of attention.
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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 | 0.000 |
| 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) | 0.003 | 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".