Sound-induced flash illusion in forced-choice and go/no-go tasks
Bibliographic record
Abstract
The sound-induced flash illusion (SIFI), the misperception of flash number when presented with an incongruent number of clicks, has been studied almost exclusively using a forced-choice task. Compared to forced-choice tasks, Go/No-Go tasks remove the need for response selection. Understanding the audiovisual interaction under different cognitive loads will pave the way for studies in different development trajectories (e.g. cochlear implant). The task was to discriminate 1 vs. 2 flashes of Gabor stimuli at threshold contrast while ignoring the sound of clicks (0, 1 or 2). Participants were told to make two-alternative forced-choice 2AFC (i.e. left-for-1 and right-for-2) or Go/No-Go responses (either Go-for-1 or Go-for-2) in three separated blocks with order counterbalanced between participants. In all conditions, participants reported more "two-flash" in 2-click trials and vice versa in 1-click trials, when compared to 0-click trials. Furthermore, the effect in 2-click trials was larger in the Go-for-1 task compared to the Go-for-2 and 2AFC tasks. The effect in 1-click trials did not differ among tasks. Repeated measures model indicates that both click number (F1,19=42.06, p<.001) and task type (F1,19=38.00, p<.001) have significant impacts on accuracy. Despite the SIFI being established for 20 years, there has yet to be a departure from the forced-choice task design measuring the illusion. The differences in SIFI effect based on different task designs show that how the illusion is measured can affect the outcome. The basis for this difference is the subject of future work.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".