Spatial Frequency Tuning during the Conscious and Non-Conscious Perception of Emotional Facial Expressions – An Intracranial ERP Study
Bibliographic record
Abstract
Previous studies have shown that complex visual stimuli, such as emotional facial expressions, can influence brain activity independently of the observers' awareness. Little is known yet, however, about the "informational correlates" of consciousness - i.e., which low-level information correlates with brain activation during conscious vs. non-conscious perception. Here, we investigated this question in the spatial frequency (SF) domain. We examined which SFs in disgusted and fearful faces modulate activation in the insula and amygdala over time and as a function of awareness, using a combination of intracranial event-related potentials (ERPs), SF Bubbles (Willenbockel et al., 2010a), and Continuous Flash Suppression (CFS; Tsuchiya and Koch, 2005). Patients implanted with electrodes for epilepsy monitoring viewed face photographs (13° × 7°) that were randomly SF filtered on a trial-by-trial basis. In the conscious condition, the faces were visible; in the non-conscious condition, they were rendered invisible using CFS. The data were analyzed by performing multiple linear regressions on the SF filters from each trial and the transformed ERP amplitudes across time. The resulting classification images suggest that many SFs are involved in the conscious and non-conscious perception of emotional expressions, with SFs between 6 and 10 cycles per face width being particularly important early on. The results also revealed qualitative differences between the awareness conditions for both regions. Non-conscious processing relied on low SFs more and was faster than conscious processing. Overall, our findings are consistent with the idea that different pathways are employed for the processing of emotional stimuli under different degrees of awareness. The present study represents a first step to mapping how SF information "flows" through the emotion-processing network with a high temporal resolution and to shedding light on the informational correlates of consciousness in general.
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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".