Social Psychological Models of Choice Behavior and Drivers' Left Turns
Bibliographic record
Abstract
ABSTRACT. The authors reanalyzed data from a simulated left-turn experiment (P. A. Hancock, J. K. Caird, S. Shekhar, & M. Vercruyssen, 1991) to test the adequacy of the nonlinear Gray-Tallman satisfaction balance model of choice behavior (L. N. Gray & I. Tallman, 1984) in predicting left turns. Participants (Hancock et al., 1991) were 40 experienced U.S. drivers who were exposed to simulated oncoming traffic; the size of the vehicle (motorcycle, compact vehicle, full-sized vehicle, delivery truck), its speed (10-70 mph, or 16-112 kmph), and the intervehicle time gap (3-9 s) varied. Hancock et al. (1991) measured (a) the likelihood of a left turn and (b) the occurrence of a collision. The probability of a left turn was greater for larger intervehicle time gaps and for oncoming smaller vehicles traveling at higher speeds. The Gray-Tallman (1984) model explained 69% of the variation in turning versus 57% for a linear regression model. In making decisions people tend to treat the values and costs affecting choices in a multiplicative, rather than linear, fashion. The Gray-Tallman model also has the potential for incorporating, both theoretically and mathematically, an unlimited range of potential values and costs that may influence left turn decisions.
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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".