Trait, but not State Mindfulness Improves Resistance to Cognitive Biases
Bibliographic record
Abstract
The dual-process theory of thinking defines a heuristic-based System I which trades-off precision and accuracy in favour of speed. It is prone to consistent range of error modes formalized as cognitive biases. In contrast, System II is slower, requires deliberate engagement but can work through complex problems with lower error. Mindfulness research points to positive improvements in higher-cognition processes with specific emphasis on attention and an attitude of non-judgement; both are thought to underlie successful recruitment of System II. We hypothesized that individuals with higher mindfulness would be less susceptible to common cognitive biases. We evaluate two constructs of mindfulness. Trait mindfulness: a long-term dispositional mindfulness with can enhanced via deliberate training but otherwise remains consistent across an individual’s lifetime. State mindfulness: refers to a short-term experience of mindfulness which is subject to experimental manipulation. The present study consists of two-arms. The first arm (N = 391) was administered completely online and evaluates trait mindfulness. The second arm (N =191) was conducted in-lab and randomized participants into one of two conditions: mindfulness induction or a sham control condition. Participants from both arms underwent performance assessment on a battery of common cognitive bias tasks. We found that trait mindfulness was associated with reduced susceptibility to specific biases: anchoring, resistance to sunk costs, availability, and logical fallacies. Contrary to expectations, experimental manipulation of state mindfulness did not influence susceptibility to cognitive biases when compared to the sham control. These findings suggest trait, but not state mindfulness may improve resistance to cognitive biases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".