Studying unconscious processing: Contention and consensus
Bibliographic record
Abstract
The scope of unconscious processing has long been, and still remains, a hotly debated issue. This is driven in part by the current diversity of methods to manipulate and measure perceptual consciousness. Here, we provide ten recommendations and nine outstanding issues about designing experimental paradigms, analyzing data, and reporting the results of studies on unconscious processing. These were formed through dialogue among a group of researchers representing a range of theoretical backgrounds. We acknowledge that some of these recommendations naturally do not align with some existing approaches and are likely to change following theoretical and methodological development. Nevertheless, we hold that at this stage of the field they are instrumental in evoking a much-needed discussion about the norms of studying unconscious processes and helping researchers make more informed decisions when designing experiments. In the long run, we aim for this paper and future discussions around the outstanding issues to lead to a more convergent corpus of knowledge about the extent - and limits - of unconscious processing.
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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 | 0.178 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.012 | 0.034 |
| Open science | 0.015 | 0.016 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".