Dopamine effects on evidence gathering and integration
Bibliographic record
Abstract
BACKGROUND: Disturbances in evidence gathering and disconfirmatory evidence integration have been associated with the presence of or propensity for delusions. Previous evidence suggests that these 2 types of reasoning bias might be differentially affected by antipsychotic medication. We aimed to investigate the effects of a dopaminergic agonist (L-dopa) and a dopaminergic antagonist (haloperidol) on evidence gathering and disconfirmatory evidence integration after single-dose administration in healthy individuals. METHODS: The study used a randomized, double-blind, placebo-controlled, 3-way crossover design. Participants were healthy individuals aged 18-40 years. We administered a new data-gathering task designed to increase sensitivity to change compared with traditional tasks. The Bias Against Disconfirmatory Evidence (BADE) task was used as a measure of disconfirmatory evidence integration. RESULTS: We included 30 individuals in our study. In the data-gathering task, dopaminergic modulation had no significant effect on the amount of evidence gathered before reaching a decision. In contrast, the ability of participants to integrate disconfirmatory evidence showed a significant linear dopaminergic modulation pattern (highest with haloperidol, intermediate with placebo, lowest with L-dopa), with the difference between haloperidol and L-dopa marginally reaching significance. LIMITATIONS: Although the doses used for haloperidol and L-dopa were similar to those used in previous studies, drug plasma level measurements would have added to the validity of findings. CONCLUSION: Evidence gathering and disconfirmatory evidence integration might be differentially influenced by dopaminergic agents. Our findings are in support of a dual-disturbance account of delusions and provide a plausible neurobiological basis for the use of interventions targeted at improving reasoning biases as an adjunctive treatment in patients with psychotic disorders.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".