Training can enhance unconscious response priming on fast trials even when measuring consciousness on a trial-by-trial basis
Bibliographic record
Abstract
Measuring awareness on a trial-by-trial basis might impose a multi-task cost on the observed effect. Here, we examined this potential cost, asking if it can be mitigated by training. In two experiments, one group of participants reported awareness offline, in a post-test, and another reported it online, in each trial. To test the effect of training, all participants completed two sessions on separate days. When analyzing all trials, we found overall slower reaction times (RTs) in the online group, suggesting a multi-task cost, but no interaction with the priming effect. Notably, this difference was smaller in the second session, implying that the multi-task cost is reduced by training. Critically however, this analysis yielded no convincing evidence for unconscious priming (due to potential threat of regression to the mean). We accordingly analyzed only trials where RTs were fast. Convincing response priming was found, as well as an interaction between priming and session. This suggests that training did increase priming. We also exploratorily tested for individual differences in priming and found between-session consistency mostly for the offline condition. Taken together, our results indicate that although multi-tasking adds noise and prolongs RTs, it does not necessarily diminish unconscious response priming for fast trials, which in turn can be enhanced by training. Costs and benefits of these methodological choices should thus be considered in future studies, as well as targeting only fast responses, where the effects were more compelling. Future work should also test if these patterns apply to other types of priming.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".