Perception With and Without Attention: Neural Correlates of Grouping by Similarity in Preattention and Divided-Attention Conditions
Bibliographic record
Abstract
Grouping local elements of the visual environment 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 our world. It is unclear which brain networks underlie this attentional capability, and whether the same brain networks are responsible for accessing visual stimuli regardless of one’s ability to accurately report seeing it. In this study, the neural mechanisms underlying the Ponzo Illusion were investigated under conditions of pre-attention (before awareness of the illusion) and divided-attention (after awareness of the illusion) using fMRI. Participants performed a line discrimination task where two horizontal lines were superimposed on a background of black and white dots. In half of the trials, the dots were organized to induce the Ponzo Illusion if perceptually grouped together. Increased activation was found bilaterally in the early visual cortex (EVC), left lateral occipital complex (LOC), left inferotemporal cortex, right supramarginal gyrus (SMG), and right medial temporal lobe (MTL) to illusory stimuli in the pre-attention condition. Illusory stimuli in the divided-attention condition elicited bilateral activation in the EVC, inferotemporal cortex, superior parietal lobe (SPL), and inferior parietal sulcus (IPS). A direct contrast between pre- and divided-attention conditions revealed increased bilateral activity in IPS, SPL, and EVC for divided-attention, but increased bilateral activation in MTL and frontal cortex for the pre-attention condition. Results show that while there are overlapping regions involved in perceptual grouping regardless of attentional condition, distinct regions of activation arise when grouping is performed under pre-attention versus divided-attention conditions. A different activation of network for pre-attentive grouping suggests that visual information we are unaware of still influences perception of the visual world, and that the neural mechanisms driving perception are modulated by attentional resources.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".