Investigation of shape processing using psychophysics and fMRI
Bibliographic record
Abstract
In the early stages of visual processing (primary visual cortex) shapes are sampled by discrete, localized, visual filters. The integration of the outputs of these local filters allows us to detect global shape information. Although this integration process is critical for visual processing beyond the primary visual cortex, it remains poorly understood. This thesis investigates what limits the performance of the mechanisms used to detect global structure. In particular, we asked four questions: (1) What information is important for detecting global form? (2) How well can we detect shape defined by changes in contrast? (3) Do the spatial properties of detectors that process global shape change across the visual field? (4) What cortical areas are involved in global shape processing? We used psychophysical methods and functional magnetic resonance imaging (fMRI) to study the integration of local filters for global shape processing in normal adult observers. All our stimuli were spatially bandpass and contained global circular structure. Overall, our findings suggest that the visual system combines the outputs of local detectors both across the visual field and over different stimulus attributes (e.g. contrast, spatial frequency, spatial position, polarity, contrast-defined information). Our excellent sensitivity to these globally structured patterns suggests the involvement of higher-order mechanisms optimized for global processing. However, these higher-order mechanisms are not localized in an individual retinotopic area nor is there a systematic hierarchical increase in activity throughout the ventral processing pathway in response to globally structured stimuli. In conclusion, significant processing of shapes occurs at both the local and the global level.
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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 | 0.000 | 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".