Effects of dopaminergic modulation on automatic semantic priming: a double-blind study
Bibliographic record
Abstract
BACKGROUND: Enhanced automatic spreading of activation in the semantic network has been suggested to underlie formal thought disorder in patients with schizophrenia, but it is not clear how this relates to the dopaminergic dysfunction implicated in the disorder. Previous studies on dopaminergic modulation of priming in healthy volunteers have focused on controlled rather than automatic processes. The present study aimed to examine the effects of both a dopaminergic agonist and a dopaminergic antagonist on semantic priming while minimizing the contribution of controlled processes. METHODS: We investigated the effects of levodopa (L-Dopa; 100 mg), haloperidol (2 mg) and placebo on priming in healthy participants within a randomized, double-blind, crossover design. We used a pronunciation priming task with word triplets; the middle word was an ambiguous word, whereas the first word of the triplet served to provide either a congruent, incongruent or unbiased context for the target word. Two stimulus onset asynchronies (SOA) were used: 150 ms and 750 ms. RESULTS: The study involved 34 participants. At an SOA of 150 ms, L-Dopa accelerated responses to incongruent targets and subordinate targets of ambiguous words, whereas haloperidol was associated with faster responses in congruent contexts and dominant targets. At an SOA of 750 ms, haloperidol accelerated responses to subordinate targets. LIMITATIONS: Modulations in the relative magnitude of priming according to substance and condition rather than absolute priming were assessed. CONCLUSION: Effects of L-Dopa on automatic priming processes appear to be different than those on controlled processes. Our results are consistent with those of studies on semantic priming and the effects on antipsychotics in patients with schizophrenia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".