Jumping to fixations: jumping to conclusions is associated with less hypothesis generation and more fixation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
People who score high in the jumping to conclusions bias (JTC) require relatively little evidence to reach highly confident conclusions. However, they often feel as though they have done ample research in informing their decisions. What factors could account for this discrepancy? The current research examines one potential factor: how individuals (with varying degrees of the JTC bias) generate hypotheses to explain uncertain events prior to searching for evidence. Study 1 demonstrated that high JTC participants generated fewer hypotheses but were more confident that one was right (compared to low JTC participants). Study 2 showed that, when given the choice between generating alternative hypotheses and supporting initial hypotheses, individuals high in JTC chose to support their initial hypotheses more often. Thus, while the JTC bias is associated with limited hypothesising for unexplained events, it also corresponds with “doubling down” and investing research efforts in confirming initial hunches.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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 it