On the psychology of self-prediction: Consideration of situational barriers to intended actions
Bibliographic record
Abstract
Abstract When people predict their future behavior, they tend to place too much weight on their current intentions, which produces an optimistic bias for behaviors associated with currently strong intentions. More realistic self-predictions require greater sensitivity to situational barriers, such as obstacles or competing demands, that may interfere with the translation of current intentions into future behavior. We consider three reasons why people may not adjust sufficiently for such barriers. First, self-predictions may focus exclusively on current intentions, ignoring potential barriers altogether. We test this possibility, in three studies, with manipulations that draw greater attention to barriers. Second, barriers may be discounted in the self-prediction process. We test this possibility by comparing prospective and retrospective ratings of the impact of barriers on the target behavior. Neither possibility was supported in these tests, or in a further test examining whether an optimally weighted statistical model could improve on the accuracy of self-predictions by placing greater weight on anticipated situational barriers. Instead, the evidence supports a third possibility: Even when they acknowledge that situational factors can affect the likelihood of carrying out an intended behavior, people do not adequately moderate the weight placed on their current intentions when predicting their future behavior.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".