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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".