Intolerance of uncertainty and threat generalization: A replication and extension
Bibliographic record
Abstract
Intolerance of uncertainty (IU) is a transdiagnostic risk factor for internalizing disorders. Prior work has found that IU may be associated with either increased reactivity to threat or, alternatively, with decreased differential responding between threat and nonthreat/safety cues (i.e., threat generalization). For example, work by Morriss, Macdonald, & van Reekum (2016) found that higher IU was associated with increased threat generalization during acquisition (using skin conductance response (SCR)), as well as less differentiation between acquisition and extinction (using subjective uneasiness ratings). Here, three labs attempted direct and conceptual replications of Morriss, Macdonald, et al. (2016). Results showed that the direct replication failed, despite being conducted at the same lab site as the original study; moreover, in contrast to Morriss, Macdonald, et al. (2016), the direct replication found that higher IU was associated with greater SCR discrimination between threat and safety cues (across acquisition and extinction), as well as greater differences in uneasiness ratings between acquisition and extinction. Nonetheless, in the conceptual replications, higher IU was associated with greater threat generalization, as well as less discrimination between acquisition and extinction, as measured using SCR. Higher IU was also associated with larger late positive potentials to threat versus safety cues during extinction-results that mirror those observed by Morriss, Macdonald, et al. (2016) using SCR. Results are discussed with regards to the challenge involved in defining a successful replication attempt, the benefits of collaborative replication and the use and reliability of multiple measures.
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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 | 0.037 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".