Neural Correlates of Perceptual Grouping Under Conditions of Inattention and Divided Attention
Bibliographic record
Abstract
Grouping local elements of the visual environment together is crucial for meaningful perception. While our attentional system facilitates perception, it is limited in that we are unaware of some aspects of our environment that can still influence how we experience it. In this study, the neural mechanisms underlying the Ponzo illusion were examined under inattention and divided-attention conditions using functional magnetic resonance imaging to investigate the brain regions responsible for accessing visual stimuli. A line discrimination task was performed in which two horizontal lines were superimposed on a background of black and white dots that, on occasion, induced the Ponzo illusion if perceptually grouped together. Our findings revealed activation for perceptual grouping in the frontal, parietal, and occipital regions of the brain and activation in the bilateral frontal, temporal, and cingulate gyrus in response to divided attention compared with inattention trials. A direct comparison between grouping and attention showed involvement of the right supramarginal gyrus in grouping specifically under conditions of inattention, suggesting that even during implicit grouping complex visual processing occurs. Given that much of the visual world is not represented in conscious perception, these findings provide crucial information about how we make sense of visual scenes in the world.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".