Growing evidence both from animal and human studies support the notion that midbrain dopaminergic neurons of the substantia nigra/ventral tegmental area complex (SN/VTA), along with the ventral striatum (VS) and the hippocampus (HP), form a functional loop (the SN/VTA-HP loop) in the service of learning and memory (Lisman and Grace, 2005; Goto and Grace, 2005; Lisman et al., 2011; Shohamy and Adcock, 2010; Kamiński et al., 2018). In the downward arm of the circuit, signals are sent from the HP to the SN/VTA through the VS, which is thought to integrate affective, motivational, and goal-directed information into the loop (Lisman and Grace, 2005; Goto and Grace, 2005). In the upward arm of the loop, dopamine is released from the SN/VTA back into the HP, which in turn enhances memory formation and learning through long-term potentiation (LTP) processes (Lisman et al., 2011; Lisman and Grace, 2005; Shohamy and Adcock, 2010). Within this loop, dopamine plays a critical role, as its release promotes the creation of stable memories by allowing LTP to persist over time (Bethus et al., 2010; Frey et al., 1990; Hansen and Manahan-Vaughan, 2014; Huang and Kandel, 1995; McNamara et al., 2014; Rossato et al., 2009).

In this vein, fMRI research in humans has consistently shown that both explicit (Adcock et al., 2006; Wittmann et al., 2005; Wolosin et al., 2012; Callan and Schweighofer, 2008) and implicit reward (Ripollés et al., 2016), and even intrinsic motivational states (i.e., curiosity; Gruber et al., 2014), can promote the storage of new information into long-term memory through the activation of the SN/VTA-HP loop (see Figure 8 in, Ripollés et al., 2016). However, although fMRI activity within the SN/VTA is usually associated with the release of dopamine (Düzel et al., 2009 ; Ferenczi et al., 2016; Knutson and Gibbs, 2007; Salimpoor et al., 2011; Schott et al., 2008), neuroimaging studies can only provide indirect evidence of the actual involvement of the dopaminergic mesolimbic system. In order to prove that a dopamine-dependent mechanism plays a critical role in learning and memory processes, one avenue to pursue is to directly manipulate dopaminergic neurotransmission in the human brain through pharmacological interventions. In this vein, several studies have shown that administration of dexamphetamine and methylphenidate (which increase dopamine concentrations in the synapsis by blocking its reuptake; Breitenstein et al., 2004; Whiting et al., 2007; Whiting et al., 2008; Linssen et al., 2014) and specially, levodopa (the immediate precursor of dopamine) can enhance memory and learning in both healthy (Shellshear et al., 2015; Bunzeck et al., 2014; Chowdhury et al., 2012; Knecht et al., 2004) and clinical populations (Berthier et al., 2011).

We recently provided behavioral, functional and physiological evidence by means of fMRI and skin conductance response, showing that an intrinsic reward-related signal—triggered by successful learning in absence of any external feedback or explicit reward—modulated the entrance of new information into long-term memory via the activation of the SN/VTA-HP loop (Ripollés et al., 2016). Here, we used a double-blind, within-subject randomized pharmacological intervention to directly assess the hypothesis that synaptic dopamine availability plays a causal role in this learning process. A group of 29 individuals were asked to perform a language-learning task (that mimics our capacity to learn the meaning of new-words presented in verbal contexts; Ripollés et al., 2014; Ripollés et al., 2016; Ripollés et al., 2017; Mestres-MisseMissé et al., 2007) after the intake of three different pharmacological treatments: a dopaminergic precursor (levodopa, 100 mg +carbidopa, 25 mg), a dopamine antagonist (risperidone, 2 mg), or a placebo (lactose). Levodopa is rapidly taken up by dopaminergic neurons, transformed into dopamine and stored in vesicles from which it will be released into the synaptic cleft each time the neuron fires. Thus, levodopa leads to a general increase in dopamine available for release in brain areas innervated by dopaminergic afferents. On the other hand, risperidone—a dopamine antagonist—interferes with dopaminergic neurotransmission by binding with a group of receptors known as D2 or D2-like (Burstein et al., 2005). Therefore, in the presence of risperidone, the transmission of dopamine-mediated signals to post-synaptic neurons will be reduced due to the blockade of the D2 receptor family.

We aimed at assessing the influence of dopamine signaling on learning and reward using the pharmacological approach described above. Each of the two experimental sessions involving active drugs were intended to shift dopaminergic neurotransmission away from each individual’s physiological status, as measured in a placebo session, and in opposite directions: levodopa to enhance the dopamine availability for release into the synapse, and risperidone to reduce synaptic transmission of the dopamine-associated signal by hindering dopamine-receptor interactions (for the use of levodopa or risperidone during cognitive tasks, see e.g. Rabella et al., 2016; Wittmann and D'Esposito, 2015; de Vries et al., 2010; Knecht et al., 2004) . Accordingly, we predicted that behavioral measures of both learning and reward should increase and decrease under levodopa and risperidone, thus modulating—with opposite effects—the memory benefits for the learned words after a consolidation period (24 hr).

Twenty-nine healthy participants completed a behavioral version of our word-learning task (see Materials and methods), in which the meaning of a new-word could be learned from the context provided by two sentences built with an increasing degree of contextual constraint (Mestres-Missé et al., 2010). Only half of the pairs of sentences disambiguated multiple meanings, allowing the encoding of a congruent meaning of the new-word during its second presentation (M+ condition). For the other pairs, the new-word was not associated with a congruent meaning across the sentences and could not be learned (M- condition). This condition, as in our previous study (Ripollés et al., 2016), was included to control for possible confounds related to novelty, attention and task difficulty (Guitart-Masip et al., 2010; Bunzeck and Düzel, 2006; Boehler et al., 2011). At the end of each learning trial (i.e. after the second sentence for a particular new-word appeared), participants first provided a confidence rating (a subjective evaluation of their performance) and then rated their emotions with respect to arousal and pleasantness. After approximately 24 hr (no drug intake occurred during the second day of testing), participants completed a recognition test to assess their learning (chance level was 25%; see Materials and methods). Three participants were excluded from the analyses (see Materials and methods) and thus the final sample was reduced to 26 individuals (17 women, mean age = 22.27 ± 3.69).

We first assessed whether our participants’ performance under the placebo condition replicated our previous results. Participants ascribed correct meaning to 60 ± 10% of new-words from the M+ condition during the encoding phase. In 61 ± 15% of the M- trials, participants correctly indicated an absence of coherent meaning. After 24 hr, participants still recognized the correct meaning of 65 ± 17% of learned new-words during the encoding phase [significantly above 25% chance level, t(25)=12.28, p<0.001, d = 2.33; Bayes Factor-BF₁₀- equal to 1.9e + 9] and correctly indicated that 41 ± 22% of M- new-words identified during the encoding phase had no meaning ascribed [significantly above 25% chance level, t(25)=3.70, p<0.001, d = 0.70; BF₁₀ = 35.38].

In order to compare this performance with our previous results (24 participants from Exp. three in Ripollés et al., 2016), we submitted the learning scores to a mixed repeated measures ANOVA with Condition (M+,M-) as a within-subjects variable and Group (Pharmacological Group, Exp. 3 in Ripollés et al., 2016) as a between subjects variable. No significant effect of Group [Learning Day 1: F(1,48)=0.246, p=0.622, partial η2 = 0.005, BF_Inclusion = 0.297; Recognition Day 2: F(1,48)=3.56, p=0.065, partial η2 = 0.069, BF_Inclusion = 1.05] or Group × Condition interaction [Learning Day 1: F(1,48)=0.749, p=0.391, partial η2 = 0.015, BF_Inclusion = 0.381; Recognition Day 2: F(1,48)=0.222, p=0.639, partial η2 = 0.005, BF_Inclusion = 0.313] was found for the learning scores of Day 1 or the recognition rate after 24 hr. This shows that the new group of participants, during the placebo session, learned and remembered words from the M+ condition and correctly identified M- words (i.e. no meaning ascribed) at the same rate as in our previous experiment.

We then focused our analyses on learned (on Day 1) and still remembered (on Day 2) M+ new words. In our previous work (Ripollés et al., 2016), this was the condition associated to the largest fMRI activity within the SN/VTA-HP loop, the largest physiological response and the highest subjective pleasantness ratings, even when compared with learned words that were forgotten after 24 hr (as a control, we used M- new-words correctly identified during the encoding phase and after 24 hr). Accordingly, in the present study subjective pleasantness and confidence ratings on Day 1 were higher for remembered than for forgotten M+ new words in the 24 hr recognition test [pleasantness, t(25)=2.75, p=0.011, d = 0.42, BF₁₀ = 4.39; confidence, t(25)=4.56, p<0.001, d = 0.68, BF₁₀ = 232.82], while no difference in arousal ratings was encountered [t(25)=0.20, p=0.835, d = 0.025, BF₁₀ = 0.21]. In replicating our previous results (Ripollés et al., 2016), these findings confirm that intrinsic reward (i.e. derived from an internal monitoring of learning success) had a modulatory effect on long-term memory. Regarding the M- control condition, as expected, there was no difference in subjective pleasantness [t(25)=1.40, p=0.172, d = 0.26, BF₁₀ = 0.49], arousal [t(25)=1.28, p=0.212, d = 0.20, BF₁₀ = 0.43] or confidence ratings [t(25)=1.18, p=0.247, d = 0.18, BF₁₀ = 0.38] for M- new-words which were correctly identified during the encoding phase and still correctly rejected in the 24 hr test and those which were not. When submitting these ratings to a mixed repeated measures ANOVA with Condition (M+,M-) and Group (Pharmacological, Previous Data) as factors (in our previous analyses we excluded four participants from the rating analyses, see Ripollés et al., 2016; thus in this ANOVA we compare 20 participants from the previous dataset against 26 for the placebo session), no significant effect of Group [Pleasantness: F(1,44)=0.143, p=0.707, partial η2 = 0.003, BF_Inclusion = 0.349; Arousal: F(1,44)=1.66, p=0.204, partial η2 = 0.036, BF_Inclusion = 0.80; Confidence: F(1,44)=3.49, p=0.068, partial η2 = 0.073, BF_Inclusion = 1.17] or Group × Condition interaction [Pleasantness: F(1,44)=0.239, p=0.627, partial η2 = 0.005, BF_Inclusion = 0.321; Arousal: F(1,44)=0.216, p=0.645, partial η2 = 0.005, BF_Inclusion = 0.304; Confidence: F(1,44)=0.028, p=0.868, partial η2 = 0.001, BF_Inclusion = 0.280] was found. This shows that participants’ ratings were also in line with those of our previous experiment (Ripollés et al., 2016).

Hence, we calculated the drug effect on the behavioral data. Specifically, for the levodopa and risperidone interventions and for each subject, we calculated the percentages of change in learning scores and behavioral ratings with respect to the placebo session (see Materials and methods). Notably, for the M+ condition, our findings show a pharmacological modulation of learning performance and behavioral reward ratings. The percentage of learned words during the encoding phase was higher under levodopa than under risperidone [as compared to placebo; t(25)=2.72, p=0.012, d = 0.56, BF₊₀=8.26]. Importantly, this effect was still present at 24 hr for the total number of remembered new-words [t(25)=2.10, p=0.046, d = 0.45, BF₊₀=2.62; see Figure 1A]. In addition, while no significant changes were found for the arousal ratings [t(25)=0.31, p=0.757, d = 0.049, BF₊₀=0.26], the drug effect approached significance for confidence ratings [t(25)=2.05, p=0.051, d = 0.51, BF₊₀=2.40] and was significant for pleasantness ratings [t(25)=2.70, p=0.012, d = 0.64, BF₊₀=7.93], where scores for remembered words at 24 hr where higher under levodopa than under risperidone (as compared to placebo; see Figure 1B). There was not, however, a significant effect of drug on the recognition rate [i.e. the percentage of remembered words in the recognition test of Day 2 compared to those that were learned on Day 1; t(25)=-0.013, p=0.989, d = 0.003, BF₊₀=0.20]. This suggests that the pharmacological intervention was able to modulate measures of reward, memory and online learning selectively during the main M+ condition (see the Individual Variability of the Drug Effect section of Appendix I, for a more in depth description of the individual differences found for the drug effect for each measure). Additional analyses using the values for the three sessions separately (instead of the percentage of change from placebo) further confirmed this pattern of results (see the Supplemental Behavioral Analyses section of Appendix 1 and Figure 1—figure supplementary 1A–C).

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As expected, for the control M- condition no significant differences between the risperidone and levodopa interventions as compared to placebo were found for the online learning scores on Day 1 [t(25)=1.53, p=0.137, d = 0.28, BF₊₀=1.08], the total number of correctly rejected M- words at 24 hr [t(25)=0.62, p=0.538, d = 0.15, BF₊₀=0.35], the recognition rate [percentage of words correctly rejected on Day 2, in respect to those correctly rejected on Day1; t(25)=-0.04, p=0.968, d = 0.011, BF₊₀=0.20; see Figure 1A], or the subjective ratings of arousal [t(23)=1.72, p=0.097, d = 0.45, BF₊₀=1.46] and confidence [t(23)=0.36, p=0.720, d = 0.10, BF₊₀=0.28; see Figure 1B; two participants were excluded from the rating analyses after not correctly rejecting any M- word at 24 hr from those correctly rejected during encoding in the levodopa intervention]. For the pleasantness ratings, however, the difference was close to significance [t(23)=2.02, p=0.055, d = 0.40, BF₊₀=2.32]. However, it is important to note that the pleasantness ratings for M- trials remembered at 24 hr were not different from 0 at any session [Risperidone mean rating = −0.23, t(23)=-1.03, p=0.309, d = 0.20, BF₁₀ = 0.34; Placebo mean rating = 0.24, t(23)=1.01, p=0.319, d = 0.20, BF₁₀ = 0.34; Levodopa mean rating = 0.30, t(23)=1.20, p=0.240, d = 0.23, BF₁₀ = 0.40], implying that participants did not find this learning condition particularly rewarding even if the pharmacological intervention slightly modified their subjective ratings.

Given that our learning task modulates activity within the reward network and is associated with increased subjective reports of pleasure (Ripollés et al., 2014; Ripollés et al., 2016), we further tested whether individual differences in sensitivity to reward interacted with the drug intervention to modulate memory benefits (Ferreri and Rodriguez-Fornells, 2017; de Vries et al., 2010; Apitz and Bunzeck, 2013). Twenty-four out of the 26 participants completed the Physical Anhedonia Scale (PAS; Chapman et al., 1976); mean score = 11.62 ± 5.47). The PAS is a well-validated scale that evaluates difficulty in feeling physical and aesthetic pleasure in response to typical pleasurable stimuli (Padrão et al., 2013; Mas-Herrero et al., 2014). We correlated (using Spearman´s rho) participants’ individual scores with the drug effect for each learning condition (the drug effect was calculated as the subtraction of the percentage of change from placebo of the levodopa intervention minus the percentage of change from placebo of the risperidone intervention, see Materials and methods). As a control and in order to take into account previous results (Chowdhury et al., 2012),we also assessed the relationship of the learning scores with the weight-dependent measure of drug dose (calculated in mg of levodopa/risperidone administered per kilogram, mean value = 1.66 ± 0.23). As expected, no significant correlations were found between the M- learning scores and the PAS [Learning Day 1 r_s = −0.19, p=0.372; number of correctly rejected words during Day 2, r_s = −0.34, p=0.097; recognition rate, r_s = −0.19, p=0.372]. In addition, no significant linear correlation or inverted U-shape relationship (Chowdhury et al., 2012) was found for any learning score (M+ or M-) and the weight dependent drug dosage (all ps > 0.13). However, for the drug effect for M+ trials, the number of learned words during encoding (r_s = −0.45, p=0.025), the total number of remembered words during Day 2 (r_s = −0.67, p<0.001) and, strikingly, the recognition rate (r_s = −0.49, p=0.017), showed a significant correlation with the PAS (all correlations were FDR-corrected at a p<0.05 threshold, see Figure 2A). One participant was excluded from the correlations with the learning scores on Day 2 after being identified as a bivariate outlier (note that if included, the correlations become more significant: number of remembered words during Day 2, r_s = −0.71, p<0.001; recognition rate, r_s = −0.55, p=0.005). Importantly, this participant obtained the highest (more anhedonic) score on the PAS (score of 24, more than two standard deviations above the mean score of 11.62 of the group). Additional correlational analyses with the results of each intervention separately (risperidone, placebo, levodopa) instead of the drug effect only, further confirm a relationship between learning and memory scores and the PAS, with Spearman’s rhos for this relationship being consistently lower than placebo for risperidone and higher than placebo for levodopa (see the Supplemental Correlational Analyses section of Appendix 1 and Figure 2—figure supplement 1). Although the correlation between the drug effect (calculated as the subtraction of the percentage of change from placebo of the levodopa intervention minus the percentage of change from placebo of the risperidone intervention) for pleasantness ratings and the PAS was not significant for the main condition of interest (M+, r_s = −0.274, p=0.196), raw pleasantness ratings during placebo and levodopa interventions separately were indeed correlated with participants’ individual differences in sensitivity to reward (see the Supplemental Correlational Analyses section of Appendix 1 and Figure 2—figure supplement 2 ).

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Altogether, this pattern of results suggests that the dopaminergic pharmacological intervention induced greater memory and accuracy benefits/deficits in those participants with high sensitivity to reward. Note that the drug effect for the recognition rate, which showed no differences in memory performance when pooling all participants together, becomes significant if we divide our participants into high and low sensitivity to reward (i.e. hedonic) groups [U = 35, p=0.033, η2 = 0.19, BF₊₀=2.81; groups divided according to the median split of their PAS scores; due to the reduced N of the groups we used a Mann-Whitney non-parametric statistical tests, see Materials and methods, Figure 2B, Appendix 1 and Figure 1-Figure supplementary 1B]. This difference was also present when comparing high/low groups for the number of words learned during encoding [U = 33, p=0.024, η2 = 0.21, BF₊₀=4.34] and the total number of words remembered on Day 2 [U = 12.5, p<0.001, η2 = 0.49, BF₊₀=78.74].

All in all, these results show that the dopaminergic pharmacological intervention did have an effect in terms of both online learning and subjective pleasantness in our learning task, inducing greater memory benefits in those participants more sensitive to reward.

By using a double-blind, within-subject randomized pharmacological intervention during a learning task—guided by an intrinsically regulated reward process—known to activate the SN/VTA-HP loop (Ripollés et al., 2016), we showed that dopamine can modulate the entrance of new information into long-term memory. In particular, the administration of a dopaminergic precursor (levodopa) and a dopaminergic antagonist (risperidone), increased and decreased, respectively, both the learning rate and the level of pleasantness experienced by the participants during encoding, as well as the number of words remembered after a consolidation period (24 hr; see Figure 1B). Strikingly, the memory effects induced by the dopaminergic pharmacological intervention were stronger in participants with a higher sensitivity to reward (i.e. more hedonic; see Figure 2).

In a previous study using the same task (Ripollés et al., 2016), we showed that successful learning was in itself (i.e. in the absence of external feedback) associated with increased reward processing and heightened activity within the SN/VTA and the VS. We suggested that this intrinsic reward-related signal induced a higher release of dopamine in the HP, which ultimately resulted in enhanced memory formation due to the well-known role of dopamine in mediating LTP processes. The memory effects reported here (after a 24 hr consolidation period) are also in agreement with the prediction that the SN/VTA-HP loop should specially enhance delayed memory (Lisman and Grace, 2005; Lisman et al., 2011; Adcock et al., 2006; Wittmann et al., 2005; Wolosin et al., 2012; Murayama and Kitagami, 2014). In addition to the delayed memory effects reported, here we show that dopamine had an additional role during online learning on Day 1: participants not only learned more words (i.e. they performed better) under levodopa than under risperidone, but also found the learning experience more rewarding when potentiating, rather than hindering, dopaminergic synaptic signaling. This result is in accord with previous work demonstrating that dopamine improves feedback-based learning in humans (de Vries et al., 2010) and also with research showing that internally generated signals of self-performance—driven by mesolimbic areas and in absence of external feedback—can guide and improve perceptual learning in humans (Daniel and Pollmann, 2012; Daniel and Pollmann, 2014; Guggenmos et al., 2016) and song learning (i.e. motor performance) in songbirds (Mandelblat-Cerf et al., 2014). Moreover, these results also converge with proposals highlighting the role of internally triggered and regulated reward signals in sustaining intrinsic motivation to perform activities that allow acquiring and storing new information (Gottlieb et al., 2013; Berlyne, 1960). Importantly, the ability to glue information acquisition activities to internal reward mechanisms might have been crucial to the evolutionary success of humans (e.g. increasing environmental control, acquiring language; Barto, 2013; Ripollés et al., 2014).

An interesting interpretation of our results is therefore that the level of dopamine directly affected the reward value or the salience (Knecht et al., 2004) of the learning outcome in our task (i.e. learning was more enjoyable), prompting participants to be more motivated (Murty and Adcock, 2014) and to perform better. The VS, through its connections to the prefrontal cortex (PFC; Lehéricy et al., 2004; Cummings, 1993; Alexander et al., 1986), is located in a perfect anatomical position to add information about the relevance, salience and motivational value (Berridge and Kringelbach, 2008) of the stimuli to be learned into the SN/VTA-HP loop. As both the VS and the PFC are known to contain and receive dopaminergic receptors and projections (Haber and Knutson, 2010), dopamine might be able to alter this input, thus modulating the perceived reward underpinning the learning processes. This, together with the significant correlation between the PAS and the memory and learning scores (see Figure 2), is also in agreement with previous studies showing that anhedonia is associated with both reduced activity and connectivity between regions within the mesolimbic reward pathway (especially between the VS and the SN/VTA; Keller et al., 2013). An alternative explanation, which cannot be fully ruled out, is that the benefit in performance was driven by the suggested role of dopamine in working memory and attention (Surmeier, 2007; Brozoski et al., 1979; Linssen et al., 2014; Drijgers et al., 2012; Mehta and Riedel, 2006). However, the fact that no significant learning or memory benefits were induced in the control M- condition and, especially, the relationship between the learning improvements during the encoding phase and the participants’ sensitivity to reward (for a similar effect, see Ferreri and Rodriguez-Fornells, 2017), suggest that the learning enhancement was partially driven by reward-related and dopamine-dependent processes (Diehl and Gershon, 1992; Nieoullon and Coquerel, 2003; de Vries et al., 2010).

The dopaminergic-dependent memory effects reported in this work are also in line with previous studies using risperidone and levodopa, although note that there is a lack of data on the memory effects of risperidone in healthy humans. The cognitive effects caused by the blockade of D2 receptors have usually been studied in the treatment of schizophrenia (Rabella et al., 2016; Désaméricq et al., 2014), with antipsychotic drugs (including risperidone) sometimes improving cognitive function (Keefe et al., 1999), but also leading to impairments in several cognitive domains (Sakurai et al., 2013), including executive functions and memory (Uchida et al., 2009; Hori et al., 2006). On the other hand, levodopa intake has been related to improvements in feedback-based grammar learning (de Vries et al., 2010), semantic activation and priming (Angwin et al., 2004 and Angwin et al., 2009; Copland et al., 2009) and, most importantly, memory and learning (Shellshear et al., 2015; Knecht et al., 2004; Chowdhury et al., 2012). In this vein, the studies in which levodopa intake was related to long-term memory benefits, used associative learning tasks and suggested that the memory enhancements occurred possibly due to the increase of the levels of dopamine in the HP (Shellshear et al., 2015; Knecht et al., 2004; Chowdhury et al., 2012). However, our findings draw a more complex and perhaps more informative picture: the lack of a clear and significant memory enhancement for the control M- condition and the fact that more hedonic participants benefitted the most from the dopaminergic intervention only in the learning condition related to reward (M+), suggest that when using a reward-based learning task (Apitz and Bunzeck, 2013; Patil et al., 2017; Oyarzún et al., 2016; Kizilirmak et al., 2016; de Vries et al., 2010), the level of memory enhancement depends on dopamine synaptic signaling, but also on individual differences in sensitivity to reward (Ferreri and Rodriguez-Fornells, 2017; Mas-Herrero et al., 2014; Camara et al., 2010; Marco-Pallarés et al., 2009; Padrão et al., 2013). This discovery can be crucial for dopamine-related pharmacological interventions in, for example, clinical populations with language deficits (Berthier et al., 2011). Indeed, studies with levodopa in aphasia recovery, have resulted in both positive (Seniów et al., 2009) and negative (Breitenstein et al., 2015; Leemann et al., 2011) effects. In this type of therapy, in which patients try to learn of re-learn words that are no longer accessible (Brady et al., 2012), the intensity of the language training is usually related with recovery (Bhogal et al., 2003) and it has been suggested that high-training intensity may cause a ceiling effect that prevents levodopa from providing additional memory benefits (Breitenstein et al., 2015; Leemann et al., 2011). A reward-based learning task such as the one used here, along with a better understanding of the interaction between the dopaminergic precursor and the patient’s hedonic state could aid to achieve a more personalized and efficient rehabilitation success, without the need for high intensive training.

In conclusion, here we show that a dopaminergic pharmacological intervention is able to modulate behavioral measures of pleasantness, task-performance and long-term memory according to inter-individual differences in reward sensitivity. These findings further advance the idea that learning—even when achieved using a task guided by intrinsic reward—is a dopamine-dependent process, and shed new light on possible reward-based interventions for learning stimulation and/or rehabilitation.

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Author details

1. Pablo Ripollés

   1. Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute- IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
   2. Department of Cognition, Development and Educational Psychology, Campus Bellvitge, University of Barcelona, L’Hospitalet de Llobregat, Barcelona, Spain
   3. Department of Psychology, New York University, New York, United States

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Laura Ferreri, Marta Valle, Jordi Riba and Antoni Rodriguez-Fornells

   For correspondence

   pabloripollesvidal@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8463-3723

2. Laura Ferreri

   1. Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute- IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
   2. Department of Cognition, Development and Educational Psychology, Campus Bellvitge, University of Barcelona, L’Hospitalet de Llobregat, Barcelona, Spain

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Project administration

   Contributed equally with

   Pablo Ripollés, Marta Valle, Jordi Riba and Antoni Rodriguez-Fornells

   Competing interests

   No competing interests declared

3. Ernest Mas-Herrero

   1. Montreal Neurological Institute, McGill University, Montreal, Canada
   2. International Laboratory for Brain, Music, and Sound Research, MontrealQC, Canada
   3. Centre for Research on Brain, Language and Music, Montreal, Canada

   Contribution

   Data curation, Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Helena Alicart

   Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute- IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain

   Contribution

   Data curation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

5. Alba Gómez-Andrés

   Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute- IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain

   Contribution

   Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

6. Josep Marco-Pallares

   1. Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute- IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
   2. Department of Cognition, Development and Educational Psychology, Campus Bellvitge, University of Barcelona, L’Hospitalet de Llobregat, Barcelona, Spain

   Contribution

   Formal analysis, Writing—review and editing

   Competing interests

   No competing interests declared

7. Rosa Maria Antonijoan

   1. Department of Pharmacology and Therapeutics, Universitat Autònoma de Barcelona, Barcelona, Spain
   2. Centre d’Investigació de Medicaments, Servei de Farmacologia Clínica, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain

   Contribution

   Data curation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

8. Toemme Noesselt

   1. Department of Neurology, Otto-von-Guericke University, Leipziger Straße, Magdeburg, Germany
   2. Department of Biological Psychology, Otto-von-Guericke-University Magdeburg, Postfach, Magdeburg, Germany
   3. Center for Behavioral Brain Sciences, Magdeburg, Germany

   Contribution

   Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9611-9713

9. Marta Valle

   1. Department of Pharmacology and Therapeutics, Universitat Autònoma de Barcelona, Barcelona, Spain
   2. Pharmacokinetic/Pharmacodynamic Modeling and Simulation Group, Sant Pau Institute of Biomedical Research, Barcelona, Spain

   Contribution

   Data curation, Investigation, Methodology, Project administration, Writing—review and editing

   Contributed equally with

   Pablo Ripollés, Laura Ferreri, Jordi Riba and Antoni Rodriguez-Fornells

   Competing interests

   No competing interests declared

10. Jordi Riba

    Human Neuropsychopharmacology Group, Sant Pau Institute of Biomedical Research, Barcelona, Spain

    Present address

    Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, The Netherlands

    Contribution

    Conceptualization, Supervision, Investigation, Methodology, Project administration, Writing—review and editing

    Contributed equally with

    Pablo Ripollés, Laura Ferreri, Marta Valle and Antoni Rodriguez-Fornells

    Competing interests

    No competing interests declared

11. Antoni Rodriguez-Fornells

    1. Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute- IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
    2. Department of Cognition, Development and Educational Psychology, Campus Bellvitge, University of Barcelona, L’Hospitalet de Llobregat, Barcelona, Spain
    3. Catalan Institution for Research and Advanced Studies, Barcelona, Spain

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing—review and editing

    Contributed equally with

    Pablo Ripollés, Laura Ferreri, Marta Valle and Jordi Riba

    For correspondence

    antoni.rodriguez@icrea.cat

    Competing interests

    No competing interests declared

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