PT - JOURNAL ARTICLE AU - Gacoin, Maƫva AU - Hamed, Suliann Ben TI - Fluoxetine degrades luminance perceptual thresholds while enhancing motivation and reward sensitivity AID - 10.1101/2022.11.11.516168 DP - 2022 Jan 01 TA - bioRxiv PG - 2022.11.11.516168 4099 - http://biorxiv.org/content/early/2022/11/13/2022.11.11.516168.short 4100 - http://biorxiv.org/content/early/2022/11/13/2022.11.11.516168.full AB - Selective serotonin reuptake inhibitors (SSRIs) increase serotonin activity in the brain. While they are mostly known for their antidepressant properties, they have been shown to improve visual functions in amblyopia and impact cognitive functions ranging from attention to motivation and sensitivity to reward. Yet, a clear understanding of the specific action of serotonin to each of bottom-up sensory and top-down cognitive control components and their interaction is still missing. To address this question, we characterize, in two adult macaques, the behavioral effects of fluoxetine, a specific SSRI, on visual perception under varying bottom-up (luminosity, distractors) and top-down (uncertainty, reward biases) constraints while they are performing three different visual tasks. We first manipulate target luminosity in a visual detection task, and we show that fluoxetine degrades luminance perceptual thresholds. We then use a target detection task in the presence of spatial distractors, and we show that under fluoxetine, monkeys display both more liberal responses as well as a degraded perceptual spatial resolution. In a last target selection task, involving free choice in the presence of reward biases, we show that monkeys display an increased sensitivity to reward outcome under fluoxetine. In addition, we report that monkeys produce, under fluoxetine, more trials and less aborts, increased pupil size, shorter blink durations, as well as task-dependent changes in reaction times. Overall, while low level vision appears to be degraded by fluoxetine, performance in the visual tasks are maintained under fluoxetine due to enhanced top-down control based on task outcome and reward maximization.Competing Interest StatementThe authors have declared no competing interest.