fMRI adaptation reveals interactions between responses to achromatic and S-cone isolating stimuli across visual cortex
Bibliographic record
Abstract
Introduction: We used fMRI adaptation to investigate cortical selectivity to S-cone isolating (BY) and achromatic (Ach) stimuli. Previous work (Mullen et al, EJN, 2015, doi: 10.1111/ejn.13090) shows that for red-green (RG) and Ach contrast, there is increasing RG color selectivity in the higher ventral areas, especially VO. Here we used a similar paradigm to test the selective of responses to BY/Ach stimuli. Methods: We measured BOLD adaptation (3T scanner, TR=3s, 1.5 or 3mm isovoxels) to BY/Ach stimulus pairs (n=12), using similar methods to Mullen et al 2015. Both adapting and test stimuli were sinewave counter-phasing rings (0.5cpd, 2Hz), presented in a counterbalanced block design of adapt/no adapt, test and fixation blocks, with 96 repeats/subject. We used standard retinotopic mapping and localisers to define 9 ROIs (V1, V2, V3, V3a/b, LO, hMT, hV4, VO1 and VO2) and analysed data using AFNI/SUMA. Results: Across visual cortex, we found robust adaptation for all adaptor/test stimulus combinations (BY/BY, Ach/Ach, BY/Ach and Ach/BY). Across the 9 ROIs there was no significant main effect of either test or adaptor stimulus, but there was a significant interaction between these effects (F(1,385) = 9.23, p< 0.01). Ach test stimuli have a greater signal loss following BY than Ach adaptation, while BY tests had similar signal loss for both adaptors. Interestingly, the interaction was in the opposite in direction to that expected, with greater cross-stimulus adaptation than within-stimulus adaptation. Conclusion: Our data suggest that the S-cone pathway has qualitatively different interactions with cortical responses to Ach contrast than to RG. Responses to BY and Ach contrast are unselective across the cortical areas tested, unlike previous results for RG/Ach stimuli. The cross-adaptation of S-cone isolating stimuli on achromatic responses reveals an unexpected non-linear effect that is not accounted for by conventional stimulus adaptation models. Meeting abstract presented at VSS 2018
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".