Psychometric Properties of a Self-Administered Untimed Scrambled Sentences Task Without Cognitive Load
Bibliographic record
Abstract
An abundance of research has shown that interpretation bias (IB), a tendency to interpret ambiguous stimuli in a negative or threatening manner, can be a risk factor for developing a myriad of psychiatric disorders (For a review, see Würtz et al., 2023). One of the most widely used measures for IB is the Scrambled Sentences Task (SST; Wenzlaff & Bates, 1998). In the SST, participants are shown a set of ‘scrambled’ words, each of which can be rearranged into either a negative or positive grammatically correct sentence. SST is an effective method of measuring IB because of its task-based nature, which can offset response biases and demand effects that often come with self-report measures. Traditionally, SST is administered with time limits (i.e., limited time per trial to read and form a sentence) as well as a concurrent cognitive load (e.g., retaining a 6-digit number in mind), to prevent deliberate processing and allow automatic IB to be detected (For a review, see Würtz et al., 2022). Furthermore, researchers often induce a negative mood prior to SST, as it has been argued that negative mood activates otherwise dormant biases (Beck,2008). However, a small body of research suggests that the magnitude of convergent validity is comparable between conditions with and without cognitive load (e.g., Geiger et al., 2014), though the role of time limit is relatively poorly understood. This gap is important as programming and administering IB with time limits, cognitive load, and experimenter supervision can be time and resource intensive. If an un-timed, self-administered SST without cognitive load demonstrates acceptable psychometric properties, it could offer an accessible method for measuring IB for both researchers and participants. Thus, the current study aims to address this gap by reporting the psychometric properties of an untimed, self-administered SST without cognitive load and without a mood induction task by examining its reliability and validity.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".