Revealing the face behind the mask: Emergent unconscious perception in object substitution masking
Bibliographic record
Abstract
Human visual awareness is inherently limited. We are conscious of only a small fraction of the available information at a given point in time. Given this limitation, vision scientists have long been fascinated with the depth of processing that occurs outside of awareness, and have thus developed a number of tools for rendering stimuli unconscious, including object substitution masking (OSM). In OSM, perception of a briefly-presented target image is impaired by a sparse common-onsetting, temporally-trailing mask. To what level are successfully masked targets processed? Existing evidence suggests that OSM disrupts face perception. That is, the N170, an ERP waveform that reflects face processing, was obliterated by the delayed offset of the mask (Reiss & Hoffman, 2007). Here, however, we found the first evidence for implicit face processing in OSM. Participants were presented with an OSM array that had either a face or a house target image, followed by a target string of letters that required a speeded lexical decision. Participants then identified the target image from the OSM array. On trials in which the target image was masked and not perceived, we found priming, such that responses to the target word were facilitated when the meaning of the word was compatible with the preceding image, compared with when it was incompatible. That is, the category to which the target object belonged (face, house) systematically influenced participants’ performance of another task. This reveals that there is indeed implicit face perception in OSM. Interestingly, this priming occurred only when participants were unaware of the target. The fact that priming was specific to trials where the target was not perceived demonstrates a qualitative distinction between conscious and unconscious perception. This implies that unconscious perception is more sophisticated than a merely impoverished version of conscious recognition. Meeting abstract presented at VSS 2012
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".