Effect of lysergic acid diethylamide (LSD) on reinforcement learning in humans.

BACKGROUND: The non-selective serotonin 2A (5-HT2A) receptor agonist lysergic acid diethylamide (LSD) holds promise as a treatment for some psychiatric disorders. Psychedelic drugs such as LSD have been suggested to have therapeutic actions through their effects on learning. The behavioural effects of LSD in humans, however, remain incompletely understood. Here we examined how LSD affects probabilistic reversal learning (PRL) in healthy humans. METHODS: Healthy volunteers received intravenous LSD (75 μg in 10 mL saline) or placebo (10 mL saline) in a within-subjects design and completed a PRL task. Participants had to learn through trial and error which of three stimuli was rewarded most of the time, and these contingencies switched in a reversal phase. Computational models of reinforcement learning (RL) were fitted to the behavioural data to assess how LSD affected the updating (learning rates) and deployment of value representations (reinforcement sensitivity) during choice, as well as stimulus stickiness (choice repetition irrespective of reinforcement history). RESULTS: Raw data measures assessing sensitivity to immediate feedback (win-stay and lose-shift probabilities) were unaffected, whereas LSD increased the impact of the strength of initial learning on perseveration. Computational modelling revealed that the most pronounced effect of LSD was the enhancement of the reward learning rate. The punishment learning rate was also elevated. Stimulus stickiness was decreased by LSD, reflecting heightened exploration. Reinforcement sensitivity differed by phase. CONCLUSIONS: Increased RL rates suggest LSD induced a state of heightened plasticity. These results indicate a potential mechanism through which revision of maladaptive associations could occur in the clinical application of LSD

Effect of lysergic acid diethylamide (LSD) on reinforcement learning in humans.

BACKGROUND: The non-selective serotonin 2A (5-HT2A) receptor agonist lysergic acid diethylamide (LSD) holds promise as a treatment for some psychiatric disorders. Psychedelic drugs such as LSD have been suggested to have therapeutic actions through their effects on learning. The behavioural effects of LSD in humans, however, remain incompletely understood. Here we examined how LSD affects probabilistic reversal learning (PRL) in healthy humans. METHODS: Healthy volunteers received intravenous LSD (75 μg in 10 mL saline) or placebo (10 mL saline) in a within-subjects design and completed a PRL task. Participants had to learn through trial and error which of three stimuli was rewarded most of the time, and these contingencies switched in a reversal phase. Computational models of reinforcement learning (RL) were fitted to the behavioural data to assess how LSD affected the updating ('learning rates') and deployment of value representations ('reinforcement sensitivity') during choice, as well as 'stimulus stickiness' (choice repetition irrespective of reinforcement history). RESULTS: Raw data measures assessing sensitivity to immediate feedback ('win-stay' and 'lose-shift' probabilities) were unaffected, whereas LSD increased the impact of the strength of initial learning on perseveration. Computational modelling revealed that the most pronounced effect of LSD was the enhancement of the reward learning rate. The punishment learning rate was also elevated. Stimulus stickiness was decreased by LSD, reflecting heightened exploration. Reinforcement sensitivity differed by phase. CONCLUSIONS: Increased RL rates suggest LSD induced a state of heightened plasticity. These results indicate a potential mechanism through which revision of maladaptive associations could occur in the clinical application of LSD.This study was funded by the Walacea.com crowdfunding campaign and the Beckley Foundation, awarded to R.L.C-H. J.W.K. was supported by a Gates Cambridge Scholarship and an Angharad Dodds John Bursary in Mental Health and Neuropsychiatry, T.W.R. by a Wellcome Trust Senior Investigator Grant 104631/Z/14/Z, and H.E.M.d.O. by the Netherlands Organisation for Scientific Research, NWO. R.N.C.’s research is funded by the UK Medical Research Council (MC_PC_17213, MR/W014386/1). Q.L. was partially sup- ported by grants from the National Key Research and Development Program of China (No. 2019YFA0709502), the National Natural Science Foundation of China (No. 81873909), the Science and Technology Commission of Shanghai Municipality (No.s 20ZR1404900 and 20DZ2260300), the Shanghai Municipal Science and Technology Major Project (No.s 2018SHZDZX01 and 2021SHZDZX0103), and the Fundamental Research Funds for the Central Universities. During the prepar- ation of this manuscript, Q.L. was a Visiting Fellow at Clare Hall, University of Cambridge, Cambridge, UK. This research was supported in part by the UK National Health Service (NHS) National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014); the views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care

Cortical dopamine reduces the impact of motivational biases governing automated behaviour

Motivations shape our behaviour: the promise of reward invigorates, while in the face of punishment, we hold back. Abnormalities of motivational processing are implicated in clinical disorders characterised by excessive habits and loss of top-down control, notably substance and behavioural addictions. Striatal and frontal dopamine have been hypothesised to play complementary roles in the respective generation and control of these motivational biases. However, while dopaminergic interventions have indeed been found to modulate motivational biases, these previous pharmacological studies used regionally non-selective pharmacological agents. Here, we tested the hypothesis that frontal dopamine controls the balance between Pavlovian, bias-driven automated responding and instrumentally learned action values. Specifically, we examined whether selective enhancement of cortical dopamine either (i) enables adaptive suppression of Pavlovian control when biases are maladaptive; or (ii) non-specifically modulates the degree of bias-driven automated responding. Healthy individuals (n=35) received the catechol-o-methyltransferase (COMT) inhibitor tolcapone in a randomized, double-blind, placebo-controlled cross-over design, and completed a motivational Go NoGo task known to elicit motivational biases. In support of hypothesis (ii), tolcapone globally decreased motivational bias. Specifically, tolcapone improved performance on trials where the bias was unhelpful, but impaired performance in bias-congruent conditions. These results indicate a non-selective role for cortical dopamine in the regulation of motivational processes underpinning top-down control over automated behaviour. The findings have direct relevance to understanding neurobiological mechanisms underpinning addiction and obsessive-compulsive disorders, as well as highlighting a potential trans-diagnostic novel mechanism to address such symptoms

Effects of methylphenidate on reinforcement learning depend on working memory capacity

RATIONALE: Brain catecholamines have long been implicated in reinforcement learning, exemplified by catecholamine drug and genetic effects on probabilistic reversal learning. However, the mechanisms underlying such effects are unclear. OBJECTIVES AND METHODS: Here we investigated effects of an acute catecholamine challenge with methylphenidate (20 mg, oral) on a novel probabilistic reversal learning paradigm in a within-subject, double-blind randomised design. The paradigm was designed to disentangle effects on punishment avoidance from effects on reward perseveration. Given the known large individual variability in methylphenidate’s effects, we stratified our effects by working memory capacity and trait impulsivity, putatively modulating the effects of methylphenidate, in a large sample (n = 102) of healthy volunteers. RESULTS: Contrary to our prediction, methylphenidate did not alter performance in the reversal phase of the task. Our key finding is that methylphenidate altered learning of choice-outcome contingencies in a manner that depended on individual variability in working memory span. Specifically, methylphenidate improved performance by adaptively reducing the effective learning rate in participants with higher working memory capacity. CONCLUSIONS: This finding emphasises the important role of working memory in reinforcement learning, as reported in influential recent computational modelling and behavioural work, and highlights the dependence of this interplay on catecholaminergic function. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00213-021-05974-w

Challenging the negative learning bias hypothesis of depression: Reversal learning in a naturalistic psychiatric sample

Background Classic theories posit that depression is driven by a negative learning bias. Most studies supporting this proposition used small and selected samples, excluding patients with comorbidities. However, comorbidity between psychiatric disorders occurs in up to 70% of the population. Therefore, the generalizability of the negative bias hypothesis to a naturalistic psychiatric sample as well as the specificity of the bias to depression, remain unclear. In the present study, we tested the negative learning bias hypothesis in a large naturalistic sample of psychiatric patients, including depression, anxiety, addiction, attention-deficit/hyperactivity disorder, and/or autism. First, we assessed whether the negative bias hypothesis of depression generalized to a heterogeneous (and hence more naturalistic) depression sample compared with controls. Second, we assessed whether negative bias extends to other psychiatric disorders. Third, we adopted a dimensional approach, by using symptom severity as a way to assess associations across the sample. Methods We administered a probabilistic reversal learning task to 217 patients and 81 healthy controls. According to the negative bias hypothesis, participants with depression should exhibit enhanced learning and flexibility based on punishment v. reward. We combined analyses of traditional measures with more sensitive computational modeling. Results In contrast to previous findings, this sample of depressed patients with psychiatric comorbidities did not show a negative learning bias. Conclusions These results speak against the generalizability of the negative learning bias hypothesis to depressed patients with comorbidities. This study highlights the importance of investigating unselected samples of psychiatric patients, which represent the vast majority of the psychiatric population