Dopamine increases risky choice while D2 blockade shortens decision time

Abstract

Dopamine is crucially involved in decision-making and overstimulation within dopaminergic pathways can lead to impulsive behaviour, including a desire to take risks and reduced deliberation before acting. These behavioural changes are side effects of treatment with dopaminergic drugs in Parkinson disease, but their likelihood of occurrence is difficult to predict and may be influenced by the individual’s baseline endogenous dopamine state, and indeed correlate with sensation-seeking personality traits. We here collected data on a standard gambling task in healthy volunteers given either placebo, 2.5 mg of the dopamine antagonist haloperidol or 100/25 mg of the dopamine precursor levodopa in a within-subject design. We found an increase in risky choices on levodopa. Choices were, however, made faster on haloperidol with no effect of levodopa on deliberation time. Shortened deliberation times on haloperidol occurred in low sensation-seekers only, suggesting a correlation between sensation-seeking personality trait and baseline dopamine levels. We hypothesise that levodopa increases risk-taking behaviour via overstimulation at both D1 and D2 receptor level, while a single low dose of haloperidol, as previously reported (Frank and O’Reilly 2006), may block D2 receptors pre- and post-synaptically and may paradoxically lead to higher striatal dopamine acting on remaining striatal D1 receptors, causing speedier decision without influencing risk tolerance. These effects could also fit with a recently proposed computational model of the basal ganglia (Moeller and Bogacz 2019; Moeller et al. 2021). Furthermore, our data suggest that the actual dopaminergic drug effect may be dependent on the individual’s baseline dopamine state, which may influence our therapeutic decision as clinicians in the future.

A critical review of risk‐sensitive foraging

“…This could offer a mechanistic basis for adaptive RSF. By contrast, Moeller et al (2021) have presented a model where risk preferences arise as side-effects of dopaminergic reward prediction errors whose main purpose is to guide learning of reward value, and in the scaled prediction error (SPE) model of Möller, Manohar & Bogacz (2022) the function of the learned risk estimate is to scale the aforementioned reward prediction errors to enhance their effect on learning [see Hirschbichler, Rothwell & Manohar (2022) for some experimental support]. Future research could further explore interconnections between these dopaminergic computational models, their relationships with adaptive and non-adaptive RSF models and the extent to which they improve understanding of the ecological data on RSF.…”