Trans-saccadic integration of spatial frequency information in an fMRIa paradigm.
Bibliographic record
Abstract
To date, there is no direct evidence showing where and how visual features are stored and integrated across saccades in the human brain. Recently, using an fMRI 'adaptation' paradigm we found two cortical regions that showed greater sensitivity to same vs. different stimulus orientations that were more robust with intervening saccades than fixation: one in the right inferior parietal lobule (supramarginal gyrus; SMG) and one in right extrastriate cortex, likely V4 (Dunkley and Crawford, Society for Neuroscience Abstracts 2012). Here, we used a similar paradigm to test if these trans-saccadic interactions are feature-specific. Eleven participants viewed a vertical grating of a given spatial frequency in the center of the screen whilst fixating to the left or right of the stimulus. Subsequently, a second stimulus was presented with the same spatial frequency (Repeated condition) or with a different spatial frequency (Novel condition). In the intervening period, participants were required to either fixate in the same position (Fixation task) or to make a saccade to the opposite fixation point (Saccade task). Participants were required to indicate if the stimulus changed or stayed the same. The Saccade task data produced significant (p <0.05) adaptation (novel > repeated frequency) in both left and right parietal cortex around SMG, consistent with our results from the previous spatial orientation study. However, we found no significant adaptation or summation effect around V4 (consistent with its known greater sensitivity to orientation vs. frequency). Results from the Fixation task showed significant adaptation in left inferior parietal cortex. Taken together with our previous experiment, this demonstrates that inferior parietal cortex is involved in trans-saccadic integration of both spatial orientation and frequency, whereas V4 is involved in field- and feature-specific integration for orientation. This suggests dual feature-specific and feature-independent mechanisms for trans-saccadic integration of objects in human cortex. Meeting abstract presented at VSS 2014
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".