The catecholamine precursor Tyrosine reduces autonomic arousal and decreases decision thresholds in reinforcement learning and temporal discounting

Abstract

Supplementation with the catecholamine precursor L-Tyrosine might enhance cognitive performance, but overall findings are mixed. Here, we investigate the effect of a single dose of tyrosine (2g) vs. placebo on two catecholamine-dependent trans-diagnostic traits: model-based control during reinforcement learning (2-step task) and temporal discounting, using a double-blind, placebo-controlled, within-subject design (n = 28 healthy male participants). We leveraged drift diffusion models in a hierarchical Bayesian framework to jointly model participants’ choices and response times in both tasks. Furthermore, comprehensive autonomic monitoring (heart rate, heart rate variability, pupillometry, spontaneous eye-blink rate) was performed both pre- and post-supplementation, to explore potential physiological effects of supplementation. Across tasks, tyrosine consistently reduced participants’ RTs without deteriorating task-performance. Diffusion modeling linked this effect to attenuated decision-thresholds in both tasks and further revealed increased model-based control (2-step task) and (if anything) attenuated temporal discounting. On the physiological level, participants’ pupil dilation was predictive of the individual degree of temporal discounting. Tyrosine supplementation reduced physiological arousal as revealed by increases in pupil dilation variability and reductions in heart rate. Supplementation-related changes in physiological arousal predicted individual changes in temporal discounting. Our findings provide first evidence that tyrosine supplementation might impact psychophysiological parameters, and suggest that modeling approaches based on sequential sampling models can yield novel insights into latent cognitive processes modulated by amino-acid supplementation.

Decomposition of reinforcement learning deficits in gambling disorder via drift diffusion modeling and functional magnetic resonance imaging

“…Therefore, one could speculate that an increase in overall uncertainty and concomitant dopamine release [67], combined with a potentially general increase in dopamine levels in the gambling group [70] might underlie the observed effects. In line wth this interpretation, decision threholds / boundary separation parameters were reduced by pharmacologically increasing dopamine levels using the same task reported here [41], and the dopamine precursor tyrosine reduced decision thresholds across two different decision-making tasks [71].…”

Dopamine alters the effect of brain stimulation on decision-making

“…The absence of an effect of Levodopa in the sham stimulation condition is consistent with previous findings showing evidence for no effect of dopamine drug manipulations on either response caution or discriminability in simple decisionmaking tasks (e.g., Winkel et al, 2012). Dopamine drug manipulations seem to only alter response caution or discriminability under specific circumstances: for example when decision-making was embedded within paradigms requiring proactive inhibition (Rawji et al, 2020), reinforcement learning (Chakroun et al, 2022;Mathar et al, 2022), or temporal discounting (Wagner et al, 2020), or only in clinical populations with impaired dopamine function and decision-making behaviour (Huang et al, 2015;Wagner et al, 2020). Studies which have found effects of dopamine drug manipulations on the speed-accuracy trade-off ((e.g., Pedersen et al, 2017;Beste et al, 2018;Bensmann et al, 2019) have used methylphenidate, a drug that works by blocking the re-uptake of dopamine and noradrenaline, resulting in increased dopamine levels in both frontal cortex (Claussen & Dafny, 2014) and the basal ganglia (Westbrook et al, 2019).…”

Distinct roles of dopamine and acetylcholine in delay- and effort-based decision making in humans

“…Consistent with previous studies that examined drug effects on changes in discounting (Mathar et al, 2022;Peters & D'Esposito, 2020;Wagner et al, 2020)…”

“…In this model, the SV of the HC option and the SV of the LC option are calculated separately. Once again, R t denotes the corresponding reward magnitude, and E t represents the associated effort in percentage on trial t. Consistent with previous studies that examined drug effects on changes in discounting (Mathar et al, 2022; Peters & D’Esposito, 2020; Wagner et al, 2020), we extended the original single-parameter model by incorporating two additional free parameters (Eq. 4).…”