The representation of stimulus features during stable fixation and active vision
Bibliographic record
Abstract
Abstract Predictive updating of an object’s spatial coordinates from its retinotopic pre-saccadic to post-saccadic position contributes to stable visual perception. However, whether object features are predictively represented at the remapped location remains contested. Many previous studies showing evidence of feature remapping neglect the spatially invariant representation of features in the visual system. For example, feature-based attention boosts attended features across the entire visual field, potentially contributing to the maintenance of stimulus features across saccades. We set out to characterise the spatiotemporal dynamics of feature processing during stable fixation and active vision. To do so, we applied multivariate decoding methods to electroencephalography (EEG) data collected while participants viewed brief visual stimuli. Stimuli appeared at different locations across the visual field at either high or low spatial frequency (SF). During fixation, classifiers were trained to decode SF presented at one parafoveal location and cross-tested on SF from either the same, adjacent or more peripheral locations. When training and testing on the same location, SF was classified shortly after stimulus onset (∼80 ms). Decoding of SF at locations farther from the trained location emerged later (∼150-300 ms), with decoding latency modulated by eccentricity. This analysis provides a detailed time course for the spread of feature information across the visual field. Next, we investigated how active vision impacts the emergence of SF information. In the presence of a saccade, the decoding time of peripheral SF at parafoveal locations was accelerated, indicating predictive anticipation of SF due to the saccade. Crucially however, this predictive effect was not limited to the specific remapped location. Rather, peripheral SF was correctly classified, at an accelerated time course, at all parafoveal positions. This indicates a spatially coarse remapping of stimulus features during active vision, likely enabling a smooth transition on saccade landing. Significance Statement Maintaining a continuous representation of object features across saccades is vital for stable vision. In order to characterise the spatiotemporal dynamics of stimulus feature representation in the brain, we presented stimuli at a high and low spatial frequency at multiple locations across the visual field. Applying EEG-decoding methods we tracked the neural representation of spatial frequency during both stable fixation and active vision. Using this approach, we provide a detailed time course for the spread of feature information across the visual field during fixation. In addition, when a saccade is imminent, we discovered that the spread of feature information is expedited such that access to peripheral spatial frequency is accelerated.
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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 | 0.003 |
| 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".