Fluoxetine Does Not Enhance Visual Perceptual Learning and Triazolam Specifically Impairs Learning Transfer
Bibliographic record
Abstract
The selective serotonin reuptake inhibitor fluoxetine significantly enhances adult visual cortex plasticity within the rat. This effect is related to decreased gamma-aminobutyric acid (GABA) mediated inhibition and identifies fluoxetine as a potential agent for enhancing plasticity in the adult human brain. We tested the hypothesis that fluoxetine would enhance visual perceptual learning of a motion direction discrimination (MDD) task in humans. We also investigated 1) the effect of fluoxetine on visual and motor cortex excitability and 2) the impact of increased GABA mediated inhibition following a single dose of triazolam on post-training MDD task performance. Within a double blind, placebo controlled design, 20 healthy adult participants completed a 19-day course of fluoxetine (n = 10, 20mg per day) or placebo (n = 10). Participants were trained on the MDD task over the final five days of fluoxetine administration. Accuracy for the trained MDD stimulus and an untrained MDD stimulus configuration was assessed before and after training, after triazolam and one week after triazolam. Motor and visual cortex excitability was measured using transcranial magnetic stimulation. Fluoxetine did not enhance the magnitude or rate of perceptual learning and full transfer of learning to the untrained stimulus was observed for both groups. After training was complete, trazolam had no effect on trained task performance but significantly impaired untrained task performance. No consistent effects of fluoxetine on cortical excitability were observed. The results do not support the hypothesis that fluoxetine can enhance learning in humans. However, the specific effect of triazolam on MDD task performance for the untrained stimulus suggests that learning and learning transfer relay on dissociable neural mechanisms.
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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) | 0.003 | 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".