Perceptual decision making processes are affected by intensity of an acoustic stimulus in an inspection time paradigm
Bibliographic record
Abstract
Previously we showed that irrelevant acoustic stimuli could influence the amount of time required for a visual stimulus to be accurately perceived. Participants performed an inspection time (IT) task whereby they identified which leg of a briefly presented (15-135ms) figure was longer, but on some trials it was accompanied with a tone presented to the left or right ear via headphones. The tone was presented on the same side as the longest leg of the figure (congruent), or opposite (incongruent). At short presentation times congruent auditory stimuli led to an increase in the proportion of correct identifications, whereas incongruent stimuli decreased the proportion of correct responses at these times. It was unclear, however, whether subjects were simply choosing the side where the tone was presented or whether the acoustic stimulus was involuntarily influencing their response. The purpose of the current study was to address this by varying the intensity of the tones. It was hypothesized that if participants were simply choosing the side with the tone, then its intensity (either quiet:60 dB, or loud:85 dB) should not influence decision making. Results showed that on incongruent trials participants were significantly more likely to incorrectly report the longest leg of the pi figure when the acoustic stimulus was loud, as opposed to when it was quiet for the shortest visual presentation time. Because the likelihood of responding to the side of the acoustic stimulus scaled with intensity, these results suggest irrelevant accessory stimuli can subliminally/involuntarily affect perceptual decision making processes. Acknowledgments: Supported by NSERC and the Ontario Ministry of Research and Innovation.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".