Conscious versus unconscious thinking in the medical domain: the deliberation-without-attention effect examined
Bibliographic record
Abstract
Previous studies have shown that with important decisions, unconscious thought has surprisingly led to better choices than conscious thought. The present study challenges this so-called 'deliberation-without-attention effect' in the medical domain. In a computerized study, physicians and medical students were asked, after either conscious or unconscious thought, to estimate the 5-year survival probabilities of four fictitious patients with varying medical characteristics. We assumed that experienced physicians would outperform students as a result of their superior knowledge. The central question was whether unconscious thought in this task would lead to better performance in experts or novices, in line with the deliberation-without-attention effect. We created four fictitious male 60-year-old patients, each of whom with signs and symptoms related to likely prognosis, from 12 (Complex) or 4 (Simple) categories. This manipulation resulted in objectively different life expectancies for these patients. Participants (86 experienced physicians and 57 medical students) were randomly allocated to the Simple or Complex condition. Statements were randomly presented for 8 s. Next, each participant assessed the life expectancies after either conscious or unconscious thought. As expected, experienced physicians were better in assessing life expectancies than medical students. No significant differences were found in performance between conscious and unconscious thought, nor did we detect a significant interaction between expertise level and mode of thought. In a medical decision task, unconscious thought did not lead to better performance of experienced physicians or medical students than conscious thought. Our findings do not support the deliberation-without-attention effect.
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How this classification was reachedexpand
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 | 0.006 | 0.223 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".