Revealing robust neural correlates of conscious and unconscious visual processing: an activation likelihood estimation meta-analysis.
Bibliographic record
Abstract
Our ability to consciously perceive information from the visual scene relies on a myriad of intrinsic neural mechanisms. Functional neuroimaging studies have sought to identify the neural correlates of conscious visual processing and to further dissociate from those pertaining to preconscious and unconscious visual processing. However, delineating what core brain regions are involved in eliciting a conscious or unconscious percept remains a challenge, particularly with regards to the role of the frontal-parietal network and posterior regions. We performed a systematic search of the literature that yielded a total of 55 functional neuroimaging studies. We conducted two quantitative meta-analyses using activation likelihood estimation (ALE) to identify reliable patterns of activation engaged by contrasts related to i. conscious (n = 704 participants) and ii. unconscious (n = 262 participants) visual processing during various task performances. Results of the first meta-analysis (included sub-contrasts: conscious> unconscious, contrasting conscious percepts, conscious> preconscious) quantitatively revealed reliable activations in the frontal-parietal network, particularly in the superior parietal lobule, anterior cingulate gyrus, middle and inferior frontal gyri, angular gyrus, inferior temporal gyrus and insula. Reverse inference, computed in Neurosynth, for cognition associated with this reliable activity revealed conscious visual processing to be linked with cognitive terms related to attention, working memory and task difficulty. Results of the second meta-analysis on unconscious visual processing revealed consistent activations in the lateral occipital complex, temporo-parietal cortex and precuneus. These regions were recruited during tasks related to various subliminal cognitive functions such as implicit semantic and visual processing, implicit working memory and learning. Our findings highlight the notion that conscious and unconscious visual processing are interlinked with different cognitive functions, where conscious visual processing seems to readily engage higher order regions including frontal areas, and where unconscious visual processing reliably recruits posterior regions lying at the intersection of the temporo-parieto-occipital region.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".