Using Magic to Influence Choice in the Absence of Visual Awareness
Bibliographic record
Abstract
A common technique in magic is forcing, the influencing of participants’ decisions in the absence of visual awareness. We investigated the strength of forcing in terms of five perceptual/cognitive characteristics (visibility, memorability, likability, and visual and verbal accessibility; Olson, Amlani, & Rensink, 2012) as well as personality measures such as locus of control (Duttweiler, 1984) and transliminality (the extent to which stimuli enter conscious awareness; Lange, Thalbourne, Houran, & Storm, 2000). Thirty-nine observers were each shown a sequence of 26 cards at a rate of 20–70 ms/card; one target card was shown for longer (150 ms). Observers were asked to freely choose a card and report it at the end of the trial. Each observer saw 28 trials in total. At the end of the experiment, observers were probed on whether they noticed that one card was shown the longest. Overall, personality measures predicted forcing, whereas the characteristics of the cards did not. Many observers (41%) did not notice the manipulation. This group chose the target card in 27% of the trials. Compared to those who did notice that one of the cards was shown the longest, they had a higher internal locus of control (89.1 versus 83.3) and spent less time using computers each day (5.2 versus 6.8 hours, all ps <.001). Degree of forcing was predicted by locus of control (β1 = -0.03, p = .009) and transliminality score (β1 = -0.10, p = .009). These findings confirm magicians’ intuitions that some observers are more affected by unnoticed events, and are therefore more susceptible to magic tricks. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".