Neural correlates of unconscious processing in functional magnetic resonance imaging: does brain activity contain more information than can be consciously reported?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A central question of consciousness research is which cognitive processes can occur unconsciously. To investigate this, researchers typically compare participants' ability to consciously discriminate a stimulus to their unconscious processing of the same stimulus (e.g. measured via reaction time or brain activity). If participants are not significantly different from chance in the awareness (or "direct") measure while nevertheless there is a significant effect in the processing (or "indirect") measure, researchers argue that there is no conscious processing of the stimulus, while the stimulus is nevertheless somehow processed, as indicated by the processing measure. In consequence researchers conclude that the stimulus has been processed unconsciously. Using neuroimaging techniques such as functional magnetic resonance imaging (fMRI), researchers then infer which brain regions are involved in unconscious versus conscious processing. However, this methodology is based on a fundamental statistical fallacy that has likely led to an overestimation of the scope of unconscious processing, regarding both its capacity and the brain areas involved. The key problem is that sensitivities in the two measures are never directly compared. Therefore, it is not appropriate to conclude that the processing measure had higher sensitivity than the awareness measure. We reanalyzed the results from 16 fMRI studies directly comparing the sensitivities of both measures in 80 experimental conditions. Our results show that, using this sensitivity comparison method, only eight experimental conditions provide evidence for unconscious processing. These results question the validity of the interpretations commonly drawn in the field.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it