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).

Figure 1 with 1 supplement see all

Download asset Open asset

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 ).

Figure 2 with 2 supplements see all

Download asset Open asset

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.

Data is available via Dryad (https://dx.doi.org/10.5061/dryad.g5f7v1j)

1. 1. Ripollés P
   2. Ferreri L
   3. Mas-Herrero E
   4. Alicart H
   5. Gómez-Andrés A
   6. Marco-Pallares J
   7. Antonijoan R
   8. Noesselt T
   9. Valle M
   10. Riba J
   11. Rodriguez-Fornells A

   (2018) Data from: Intrinsically regulated learning is modulated by synaptic dopamine signaling

   Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

   https://dx.doi.org/10.5061/dryad.g5f7v1j

1. 1. Adcock RA
   2. Thangavel A
   3. Whitfield-Gabrieli S
   4. Knutson B
   5. Gabrieli JD

   (2006) Reward-motivated learning: mesolimbic activation precedes memory formation

   Neuron 50:507–517.

   https://doi.org/10.1016/j.neuron.2006.03.036

   • PubMed
   • Google Scholar
2. 1. Alexander GE
   2. DeLong MR
   3. Strick PL

   (1986) Parallel organization of functionally segregated circuits linking basal ganglia and cortex

   Annual Review of Neuroscience 9:357–381.

   https://doi.org/10.1146/annurev.ne.09.030186.002041

   • PubMed
   • Google Scholar
3. 1. Angwin AJ
   2. Arnott WL
   3. Copland DA
   4. Haire MP
   5. Murdoch BE
   6. Silburn PA
   7. Chenery HJ

   (2009) Semantic activation in Parkinson's disease patients on and off levodopa

   Cortex 45:950–959.

   https://doi.org/10.1016/j.cortex.2009.02.012

   • PubMed
   • Google Scholar
4. 1. Angwin AJ
   2. Chenery HJ
   3. Copland DA
   4. Arnott WL
   5. Murdoch BE
   6. Silburn PA

   (2004) Dopamine and semantic activation: an investigation of masked direct and indirect priming

   Journal of the International Neuropsychological Society 10:15–25.

   https://doi.org/10.1017/S1355617704101033

   • PubMed
   • Google Scholar
5. 1. Apitz T
   2. Bunzeck N

   (2013) Dopamine controls the neural dynamics of memory signals and retrieval accuracy

   Neuropsychopharmacology 38:2409–2417.

   https://doi.org/10.1038/npp.2013.141

   • PubMed
   • Google Scholar
6. 1. Barto A

   (2013)

   Intrinsically Motivated Learning in Natural and Artificial Systems. Springer

   17–47, Intrinsic motivation and reinforcement learning, Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, 10.1007/978-3-642-32375-1_2.

   • Google Scholar
7. Book

   1. Berlyne D

   (1960) Conflict, Arousal and Curiosity

   McGraw-Hill.

   https://doi.org/10.1037/11164-000

   • Google Scholar
8. 1. Berridge KC
   2. Kringelbach ML

   (2008) Affective neuroscience of pleasure: reward in humans and animals

   Psychopharmacology 199:457–480.

   https://doi.org/10.1007/s00213-008-1099-6

   • PubMed
   • Google Scholar
9. 1. Berthier ML
   2. Pulvermüller F
   3. Dávila G
   4. Casares NG
   5. Gutiérrez A

   (2011) Drug therapy of post-stroke aphasia: a review of current evidence

   Neuropsychology Review 21:302–317.

   https://doi.org/10.1007/s11065-011-9177-7

   • PubMed
   • Google Scholar
10. 1. Bethus I
    2. Tse D
    3. Morris RG

    (2010) Dopamine and memory: modulation of the persistence of memory for novel hippocampal NMDA receptor-dependent paired associates

    Journal of Neuroscience 30:1610–1618.

    https://doi.org/10.1523/JNEUROSCI.2721-09.2010

    • PubMed
    • Google Scholar
11. 1. Bhogal SK
    2. Teasell R
    3. Speechley M

    (2003) Intensity of aphasia therapy, impact on recovery

    Stroke 34:987–993.

    https://doi.org/10.1161/01.STR.0000062343.64383.D0

    • PubMed
    • Google Scholar
12. 1. Boehler CN
    2. Hopf JM
    3. Krebs RM
    4. Stoppel CM
    5. Schoenfeld MA
    6. Heinze HJ
    7. Noesselt T

    (2011) Task-load-dependent activation of dopaminergic midbrain Areas in the absence of reward

    Journal of Neuroscience 31:4955–4961.

    https://doi.org/10.1523/JNEUROSCI.4845-10.2011

    • PubMed
    • Google Scholar
13. 1. Brady MC
    2. Kelly H
    3. Godwin J
    4. Enderby P

    (2012) Speech and language therapy for aphasia following stroke

    The Cochrane Database of Systematic Reviews 5:CD000425.

    https://doi.org/10.1002/14651858.CD000425.pub3

    • Google Scholar
14. 1. Brainard DH

    (1997) The psychophysics toolbox

    Spatial Vision 10:433–436.

    https://doi.org/10.1163/156856897X00357

    • PubMed
    • Google Scholar
15. 1. Breitenstein C
    2. Korsukewitz C
    3. Baumgärtner A
    4. Flöel A
    5. Zwitserlood P
    6. Dobel C
    7. Knecht S

    (2015) L-dopa does not add to the success of high-intensity language training in aphasia

    Restorative Neurology and Neuroscience 33:115–120.

    https://doi.org/10.3233/RNN-140435

    • PubMed
    • Google Scholar
16. 1. Breitenstein C
    2. Wailke S
    3. Bushuven S
    4. Kamping S
    5. Zwitserlood P
    6. Ringelstein EB
    7. Knecht S

    (2004) D-amphetamine boosts language learning independent of its cardiovascular and motor arousing effects

    Neuropsychopharmacology 29:1704–1714.

    https://doi.org/10.1038/sj.npp.1300464

    • PubMed
    • Google Scholar
17. 1. Brozoski TJ
    2. Brown RM
    3. Rosvold HE
    4. Goldman PS

    (1979) Cognitive deficit caused by regional depletion of dopamine in prefrontal cortex of rhesus monkey

    Science 205:929–932.

    https://doi.org/10.1126/science.112679

    • PubMed
    • Google Scholar
18. 1. Bunzeck N
    2. Düzel E

    (2006) Absolute coding of stimulus novelty in the human substantia nigra/VTA

    Neuron 51:369–379.

    https://doi.org/10.1016/j.neuron.2006.06.021

    • PubMed
    • Google Scholar
19. 1. Bunzeck N
    2. Guitart-Masip M
    3. Dolan RJ
    4. Duzel E

    (2014) Pharmacological dissociation of novelty responses in the human brain

    Cerebral Cortex 24:1351–1360.

    https://doi.org/10.1093/cercor/bhs420

    • PubMed
    • Google Scholar
20. 1. Burstein ES
    2. Ma J
    3. Wong S
    4. Gao Y
    5. Pham E
    6. Knapp AE
    7. Nash NR
    8. Olsson R
    9. Davis RE
    10. Hacksell U
    11. Weiner DM
    12. Brann MR

    (2005) Intrinsic efficacy of antipsychotics at human D2, D3, and D4 dopamine receptors: identification of the clozapine metabolite N-desmethylclozapine as a D2/D3 partial agonist

    Journal of Pharmacology and Experimental Therapeutics 315:1278–1287.

    https://doi.org/10.1124/jpet.105.092155

    • PubMed
    • Google Scholar
21. 1. Callan DE
    2. Schweighofer N

    (2008) Positive and negative modulation of word learning by reward anticipation

    Human Brain Mapping 29:237–249.

    https://doi.org/10.1002/hbm.20383

    • PubMed
    • Google Scholar
22. 1. Camara E
    2. Krämer UM
    3. Cunillera T
    4. Marco-Pallarés J
    5. Cucurell D
    6. Nager W
    7. Mestres-Missé A
    8. Bauer P
    9. Schüle R
    10. Schöls L
    11. Tempelmann C
    12. Rodriguez-Fornells A
    13. Münte TF

    (2010) The effects of COMT (Val108/158Met) and DRD4 (SNP -521) dopamine genotypes on brain activations related to Valence and magnitude of rewards

    Cerebral Cortex 20:1985–1996.

    https://doi.org/10.1093/cercor/bhp263

    • PubMed
    • Google Scholar
23. 1. Caseras X
    2. Àvila C
    3. Torrubia R

    (2003) The measurement of individual differences in behavioural inhibition and behavioural activation systems: a comparison of personality scales

    Personality and Individual Differences 34:999–1013.

    https://doi.org/10.1016/S0191-8869(02)00084-3

    • Google Scholar
24. 1. Chapman LJ
    2. Chapman JP
    3. Raulin ML

    (1976) Scales for physical and social anhedonia

    Journal of Abnormal Psychology 85:374–382.

    https://doi.org/10.1037/0021-843X.85.4.374

    • PubMed
    • Google Scholar
25. 1. Chowdhury R
    2. Guitart-Masip M
    3. Bunzeck N
    4. Dolan RJ
    5. Düzel E

    (2012) Dopamine modulates episodic memory persistence in old age

    Journal of Neuroscience 32:14193–14204.

    https://doi.org/10.1523/JNEUROSCI.1278-12.2012

    • PubMed
    • Google Scholar
26. Book

    1. Cohen J

    (1988)

    Statistical Power Analysis for the Behavioral Sciences Hillsdale

    New Jersey: Lawrence Erlbaum Associates.

    • Google Scholar
27. 1. Copland DA
    2. McMahon KL
    3. Silburn PA
    4. de Zubicaray GI

    (2009) Dopaminergic neuromodulation of semantic processing: a 4-T FMRI study with levodopa

    Cerebral Cortex 19:2651–2658.

    https://doi.org/10.1093/cercor/bhp017

    • PubMed
    • Google Scholar
28. 1. Cummings JL

    (1993) Frontal-subcortical circuits and human behavior

    Archives of Neurology 50:873–880.

    https://doi.org/10.1001/archneur.1993.00540080076020

    • PubMed
    • Google Scholar
29. 1. Daniel R
    2. Pollmann S

    (2012) Striatal activations signal prediction errors on confidence in the absence of external feedback

    NeuroImage 59:3457–3467.

    https://doi.org/10.1016/j.neuroimage.2011.11.058

    • PubMed
    • Google Scholar
30. 1. Daniel R
    2. Pollmann S

    (2014) A universal role of the ventral striatum in reward-based learning: evidence from human studies

    Neurobiology of Learning and Memory 114:90–100.

    https://doi.org/10.1016/j.nlm.2014.05.002

    • PubMed
    • Google Scholar
31. 1. de Vries MH
    2. Ulte C
    3. Zwitserlood P
    4. Szymanski B
    5. Knecht S

    (2010) Increasing dopamine levels in the brain improves feedback-based procedural learning in healthy participants: an artificial-grammar-learning experiment

    Neuropsychologia 48:3193–3197.

    https://doi.org/10.1016/j.neuropsychologia.2010.06.024

    • PubMed
    • Google Scholar
32. 1. Der-Avakian A
    2. Markou A

    (2012) The neurobiology of anhedonia and other reward-related deficits

    Trends in Neurosciences 35:68–77.

    https://doi.org/10.1016/j.tins.2011.11.005

    • PubMed
    • Google Scholar
33. 1. Diehl DJ
    2. Gershon S

    (1992) The role of dopamine in mood disorders

    Comprehensive Psychiatry 33:115–120.

    https://doi.org/10.1016/0010-440X(92)90007-D

    • PubMed
    • Google Scholar
34. 1. Drijgers RL
    2. Verhey FR
    3. Tissingh G
    4. van Domburg PH
    5. Aalten P
    6. Leentjens AF

    (2012) The role of the dopaminergic system in mood, motivation and cognition in Parkinson's disease: a double blind randomized placebo-controlled experimental challenge with pramipexole and methylphenidate

    Journal of the Neurological Sciences 320:121–126.

    https://doi.org/10.1016/j.jns.2012.07.015

    • PubMed
    • Google Scholar
35. 1. Désaméricq G
    2. Schurhoff F
    3. Meary A
    4. Szöke A
    5. Macquin-Mavier I
    6. Bachoud-Lévi AC
    7. Maison P

    (2014) Long-term neurocognitive effects of antipsychotics in schizophrenia: a network meta-analysis

    European Journal of Clinical Pharmacology 70:127–134.

    https://doi.org/10.1007/s00228-013-1600-y

    • PubMed
    • Google Scholar
36. 1. Düzel E
    2. Bunzeck N
    3. Guitart-Masip M
    4. Wittmann B
    5. Schott BH
    6. Tobler PN

    (2009) Functional imaging of the human dopaminergic midbrain

    Trends in Neurosciences 32:321–328.

    https://doi.org/10.1016/j.tins.2009.02.005

    • PubMed
    • Google Scholar
37. 1. Favrod J
    2. Ernst F
    3. Giuliani F
    4. Bonsack C

    (2009) [Validation of the temporal experience of pleasure scale (TEPS) in a French-speaking environment]

    L'Encéphale 35:241–248.

    https://doi.org/10.1016/j.encep.2008.02.013

    • Google Scholar
38. 1. Ferenczi EA
    2. Zalocusky KA
    3. Liston C
    4. Grosenick L
    5. Warden MR
    6. Amatya D
    7. Katovich K
    8. Mehta H
    9. Patenaude B
    10. Ramakrishnan C
    11. Kalanithi P
    12. Etkin A
    13. Knutson B
    14. Glover GH
    15. Deisseroth K

    (2016) Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior

    Science 351:aac9698.

    https://doi.org/10.1126/science.aac9698

    • PubMed
    • Google Scholar
39. 1. Ferreri L
    2. Rodriguez-Fornells A

    (2017) Music-related reward responses predict episodic memory performance

    Experimental Brain Research 235:3721–3731.

    https://doi.org/10.1007/s00221-017-5095-0

    • PubMed
    • Google Scholar
40. 1. Fonseca-Pedrero E
    2. Paino M
    3. Lemos-Giráldez S
    4. García-Cueto E
    5. Villazón-García U
    6. Bobes J
    7. Muñiz J

    (2009) Psychometric properties of the revised physical and social anhedonia scales in non-clinical young adults

    The Spanish Journal of Psychology 12:815–822.

    https://doi.org/10.1017/S1138741600002183

    • PubMed
    • Google Scholar
41. 1. Frey U
    2. Schroeder H
    3. Matthies H

    (1990) Dopaminergic antagonists prevent long-term maintenance of posttetanic LTP in the CA1 region of rat hippocampal slices

    Brain Research 522:69–75.

    https://doi.org/10.1016/0006-8993(90)91578-5

    • PubMed
    • Google Scholar
42. 1. Goto Y
    2. Grace AA

    (2005) Dopaminergic modulation of limbic and cortical drive of nucleus accumbens in goal-directed behavior

    Nature Neuroscience 8:805–812.

    https://doi.org/10.1038/nn1471

    • PubMed
    • Google Scholar
43. 1. Gottlieb J
    2. Oudeyer PY
    3. Lopes M
    4. Baranes A

    (2013) Information-seeking, curiosity, and attention: computational and neural mechanisms

    Trends in Cognitive Sciences 17:585–593.

    https://doi.org/10.1016/j.tics.2013.09.001

    • PubMed
    • Google Scholar
44. 1. Gruber MJ
    2. Gelman BD
    3. Ranganath C

    (2014) States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit

    Neuron 84:486–496.

    https://doi.org/10.1016/j.neuron.2014.08.060

    • PubMed
    • Google Scholar
45. 1. Guggenmos M
    2. Wilbertz G
    3. Hebart MN
    4. Sterzer P

    (2016) Mesolimbic confidence signals guide perceptual learning in the absence of external feedback

    eLife 5:e13388.

    https://doi.org/10.7554/eLife.13388

    • PubMed
    • Google Scholar
46. 1. Guitart-Masip M
    2. Bunzeck N
    3. Stephan KE
    4. Dolan RJ
    5. Düzel E

    (2010) Contextual novelty changes reward representations in the striatum

    Journal of Neuroscience 30:1721–1726.

    https://doi.org/10.1523/JNEUROSCI.5331-09.2010

    • PubMed
    • Google Scholar
47. 1. Haber SN
    2. Knutson B

    (2010) The reward circuit: linking primate anatomy and human imaging

    Neuropsychopharmacology 35:4–26.

    https://doi.org/10.1038/npp.2009.129

    • PubMed
    • Google Scholar
48. 1. Hansen N
    2. Manahan-Vaughan D

    (2014) Dopamine D1/D5 receptors mediate informational saliency that promotes persistent hippocampal long-term plasticity

    Cerebral Cortex 24:845–858.

    https://doi.org/10.1093/cercor/bhs362

    • PubMed
    • Google Scholar
49. 1. Hori H
    2. Noguchi H
    3. Hashimoto R
    4. Nakabayashi T
    5. Omori M
    6. Takahashi S
    7. Tsukue R
    8. Anami K
    9. Hirabayashi N
    10. Harada S
    11. Saitoh O
    12. Iwase M
    13. Kajimoto O
    14. Takeda M
    15. Okabe S
    16. Kunugi H

    (2006) Antipsychotic medication and cognitive function in schizophrenia

    Schizophrenia Research 86:138–146.

    https://doi.org/10.1016/j.schres.2006.05.004

    • PubMed
    • Google Scholar
50. 1. Huang YY
    2. Kandel ER

    (1995) D1/D5 receptor agonists induce a protein synthesis-dependent late potentiation in the CA1 region of the Hippocampus

    PNAS 92:2446–2450.

    https://doi.org/10.1073/pnas.92.7.2446

    • PubMed
    • Google Scholar
51. Software

    1. JASP Team

    (2018) JASP, version (Version 0.8.6)

    [Computer software].

    https://jasp-stats.org/
52. 1. Kamiński J
    2. Mamelak AN
    3. Birch K
    4. Mosher CP
    5. Tagliati M
    6. Rutishauser U

    (2018) Novelty-Sensitive dopaminergic neurons in the human substantia nigra predict success of declarative memory formation

    Current Biology 28:1333–1343.

    https://doi.org/10.1016/j.cub.2018.03.024

    • PubMed
    • Google Scholar
53. 1. Keefe RS
    2. Silva SG
    3. Perkins DO
    4. Lieberman JA

    (1999) The effects of atypical antipsychotic drugs on neurocognitive impairment in schizophrenia: a review and meta-analysis

    Schizophrenia Bulletin 25:201–222.

    https://doi.org/10.1093/oxfordjournals.schbul.a033374

    • PubMed
    • Google Scholar
54. 1. Keller J
    2. Young CB
    3. Kelley E
    4. Prater K
    5. Levitin DJ
    6. Menon V

    (2013) Trait anhedonia is associated with reduced reactivity and connectivity of mesolimbic and paralimbic reward pathways

    Journal of Psychiatric Research 47:1319–1328.

    https://doi.org/10.1016/j.jpsychires.2013.05.015

    • PubMed
    • Google Scholar
55. 1. Kizilirmak JM
    2. Galvao Gomes da Silva J
    3. Imamoglu F
    4. Richardson-Klavehn A

    (2016) Generation and the subjective feeling of "aha!" are independently related to learning from insight

    Psychological Research 80:1059–1074.

    https://doi.org/10.1007/s00426-015-0697-2

    • PubMed
    • Google Scholar
56. 1. Knecht S
    2. Breitenstein C
    3. Bushuven S
    4. Wailke S
    5. Kamping S
    6. Flöel A
    7. Zwitserlood P
    8. Ringelstein EB

    (2004) Levodopa: faster and better word learning in normal humans

    Annals of Neurology 56:20–26.

    https://doi.org/10.1002/ana.20125

    • PubMed
    • Google Scholar
57. 1. Knutson B
    2. Gibbs SE

    (2007) Linking nucleus accumbens dopamine and blood oxygenation

    Psychopharmacology 191:813–822.

    https://doi.org/10.1007/s00213-006-0686-7

    • PubMed
    • Google Scholar
58. 1. Leemann B
    2. Laganaro M
    3. Chetelat-Mabillard D
    4. Schnider A

    (2011) Crossover trial of subacute computerized aphasia therapy for Anomia with the addition of either levodopa or placebo

    Neurorehabilitation and Neural Repair 25:43–47.

    https://doi.org/10.1177/1545968310376938

    • PubMed
    • Google Scholar
59. 1. Lehéricy S
    2. Ducros M
    3. Van de Moortele PF
    4. Francois C
    5. Thivard L
    6. Poupon C
    7. Swindale N
    8. Ugurbil K
    9. Kim DS

    (2004) Diffusion tensor fiber tracking shows distinct corticostriatal circuits in humans

    Annals of Neurology 55:522–529.

    https://doi.org/10.1002/ana.20030

    • PubMed
    • Google Scholar
60. 1. Linssen AM
    2. Sambeth A
    3. Vuurman EF
    4. Riedel WJ

    (2014) Cognitive effects of methylphenidate in healthy volunteers: a review of single dose studies

    The International Journal of Neuropsychopharmacology 17:961–977.

    https://doi.org/10.1017/S1461145713001594

    • PubMed
    • Google Scholar
61. 1. Lisman J
    2. Grace AA
    3. Duzel E

    (2011) A neoHebbian framework for episodic memory; role of dopamine-dependent late LTP

    Trends in Neurosciences 34:536–547.

    https://doi.org/10.1016/j.tins.2011.07.006

    • Google Scholar
62. 1. Lisman JE
    2. Grace AA

    (2005) The hippocampal-VTA loop: controlling the entry of information into long-term memory

    Neuron 46:703–713.

    https://doi.org/10.1016/j.neuron.2005.05.002

    • PubMed
    • Google Scholar
63. 1. Mandelblat-Cerf Y
    2. Las L
    3. Denisenko N
    4. Fee MS

    (2014) A role for descending auditory cortical projections in songbird vocal learning

    eLife 3:e02152.

    https://doi.org/10.7554/eLife.02152

    • Google Scholar
64. 1. Marco-Pallarés J
    2. Cucurell D
    3. Cunillera T
    4. Krämer UM
    5. Càmara E
    6. Nager W
    7. Bauer P
    8. Schüle R
    9. Schöls L
    10. Münte TF
    11. Rodriguez-Fornells A

    (2009) Genetic variability in the dopamine system (dopamine receptor D4, catechol-O-methyltransferase) modulates neurophysiological responses to gains and losses

    Biological Psychiatry 66:154–161.

    https://doi.org/10.1016/j.biopsych.2009.01.006

    • PubMed
    • Google Scholar
65. 1. Martínez-Molina N
    2. Mas-Herrero E
    3. Rodríguez-Fornells A
    4. Zatorre RJ
    5. Marco-Pallarés J

    (2016) Neural correlates of specific musical anhedonia

    PNAS 113:E7337–E7345.

    https://doi.org/10.1073/pnas.1611211113

    • Google Scholar
66. 1. Mas-Herrero E
    2. Karhulahti M
    3. Marco-Pallares J
    4. Zatorre RJ
    5. Rodriguez-Fornells A

    (2018) The impact of visual art and emotional sounds in specific musical anhedonia

    Progress in Brain Research 237:399–413.

    https://doi.org/10.1016/bs.pbr.2018.03.017

    • PubMed
    • Google Scholar
67. 1. Mas-Herrero E
    2. Zatorre RJ
    3. Rodriguez-Fornells A
    4. Marco-Pallarés J

    (2014) Dissociation between musical and monetary reward responses in specific musical anhedonia

    Current Biology 24:699–704.

    https://doi.org/10.1016/j.cub.2014.01.068

    • PubMed
    • Google Scholar
68. 1. McNamara CG
    2. Tejero-Cantero Á
    3. Trouche S
    4. Campo-Urriza N
    5. Dupret D

    (2014) Dopaminergic neurons promote hippocampal reactivation and spatial memory persistence

    Nature Neuroscience 17:1658–1660.

    https://doi.org/10.1038/nn.3843

    • PubMed
    • Google Scholar
69. 1. Mehta MA
    2. Riedel WJ

    (2006) Dopaminergic enhancement of cognitive function

    Current Pharmaceutical Design 12:2487–2500.

    https://doi.org/10.2174/138161206777698891

    • PubMed
    • Google Scholar
70. 1. Mestres-Missé A
    2. Rodriguez-Fornells A
    3. Münte TF

    (2007) Watching the brain during meaning acquisition

    Cerebral Cortex 17:1858–1866.

    https://doi.org/10.1093/cercor/bhl094

    • PubMed
    • Google Scholar
71. 1. Mestres-Missé A
    2. Münte TF
    3. Rodriguez-Fornells A

    (2009) Functional neuroanatomy of contextual acquisition of concrete and abstract words

    Journal of Cognitive Neuroscience 21:2154–2171.

    https://doi.org/10.1162/jocn.2008.21171

    • PubMed
    • Google Scholar
72. 1. Mestres-Missé A
    2. Münte TF
    3. Rodriguez-Fornells A

    (2014) Mapping concrete and abstract meanings to new words using verbal contexts

    Second Language Research 30:191–223.

    https://doi.org/10.1177/0267658313512668

    • Google Scholar
73. 1. Mestres-Missé A
    2. Rodriguez-Fornells A
    3. Münte TF

    (2010) Neural differences in the mapping of verb and noun concepts onto novel words

    NeuroImage 49:2826–2835.

    https://doi.org/10.1016/j.neuroimage.2009.10.018

    • Google Scholar
74. Software

    1. Morey RD
    2. Rouder JN
    3. Jamil T

    (2015)  Package ‘BayesFactor’

     Package ‘BayesFactor’.

    http://cran.r-project.org/web/packages/BayesFactor/BayesFactor.pdf
75. 1. Murayama K
    2. Kitagami S

    (2014) Consolidation power of extrinsic rewards: reward cues enhance long-term memory for irrelevant past events

    Journal of Experimental Psychology: General 143:15–20.

    https://doi.org/10.1037/a0031992

    • Google Scholar
76. 1. Murty VP
    2. Adcock RA

    (2014) Enriched encoding: reward motivation organizes cortical networks for hippocampal detection of unexpected events

    Cerebral Cortex 24:2160–2168.

    https://doi.org/10.1093/cercor/bht063

    • PubMed
    • Google Scholar
77. 1. Nieoullon A
    2. Coquerel A

    (2003) Dopamine: a key regulator to adapt action, emotion, motivation and cognition

    Current Opinion in Neurology 16 Suppl 2:S3–S9.

    https://doi.org/10.1097/00019052-200312002-00002

    • PubMed
    • Google Scholar
78. 1. Oyarzún JP
    2. Packard PA
    3. de Diego-Balaguer R
    4. Fuentemilla L

    (2016) Motivated encoding selectively promotes memory for future inconsequential semantically-related events

    Neurobiology of Learning and Memory 133:1–6.

    https://doi.org/10.1016/j.nlm.2016.05.005

    • PubMed
    • Google Scholar
79. 1. Padrão G
    2. Mallorquí A
    3. Cucurell D
    4. Marco-Pallares J
    5. Rodriguez-Fornells A

    (2013) Neurophysiological differences in reward processing in anhedonics

    Cognitive, Affective, & Behavioral Neuroscience 13:102–115.

    https://doi.org/10.3758/s13415-012-0119-5

    • Google Scholar
80. 1. Patil A
    2. Murty VP
    3. Dunsmoor JE
    4. Phelps EA
    5. Davachi L

    (2017) Reward retroactively enhances memory consolidation for related items

    Learning & Memory 24:65–69.

    https://doi.org/10.1101/lm.042978.116

    • PubMed
    • Google Scholar
81. 1. Rabella M
    2. Grasa E
    3. Corripio I
    4. Romero S
    5. Mañanas MÀ
    6. Antonijoan RM
    7. Münte TF
    8. Pérez V
    9. Riba J

    (2016) Neurophysiological evidence of impaired self-monitoring in schizotypal personality disorder and its reversal by dopaminergic antagonism

    NeuroImage: Clinical 11:770–779.

    https://doi.org/10.1016/j.nicl.2016.05.019

    • PubMed
    • Google Scholar
82. 1. Ripollés P
    2. Biel D
    3. Peñaloza C
    4. Kaufmann J
    5. Marco-Pallarés J
    6. Noesselt T
    7. Rodríguez-Fornells A

    (2017) Strength of temporal white matter pathways predicts semantic learning

    The Journal of Neuroscience 37:11101–11113.

    https://doi.org/10.1523/JNEUROSCI.1720-17.2017

    • PubMed
    • Google Scholar
83. 1. Ripollés P
    2. Marco-Pallarés J
    3. Alicart H
    4. Tempelmann C
    5. Rodríguez-Fornells A
    6. Noesselt T

    (2016) Intrinsic monitoring of learning success facilitates memory encoding via the activation of the SN/VTA-Hippocampal loop

    eLife 5:e17441.

    https://doi.org/10.7554/eLife.17441

    • PubMed
    • Google Scholar
84. 1. Ripollés P
    2. Marco-Pallarés J
    3. Hielscher U
    4. Mestres-Missé A
    5. Tempelmann C
    6. Heinze HJ
    7. Rodríguez-Fornells A
    8. Noesselt T

    (2014) The role of reward in word learning and its implications for language acquisition

    Current Biology 24:2606–2611.

    https://doi.org/10.1016/j.cub.2014.09.044

    • PubMed
    • Google Scholar
85. 1. Rossato JI
    2. Bevilaqua LR
    3. Izquierdo I
    4. Medina JH
    5. Cammarota M

    (2009) Dopamine controls persistence of long-term memory storage

    Science 325:1017–1020.

    https://doi.org/10.1126/science.1172545

    • PubMed
    • Google Scholar
86. 1. Rouder JN
    2. Morey RD

    (2012) Default bayes factors for model selection in regression

    Multivariate Behavioral Research 47:877–903.

    https://doi.org/10.1080/00273171.2012.734737

    • PubMed
    • Google Scholar
87. 1. Sakurai H
    2. Bies RR
    3. Stroup ST
    4. Keefe RS
    5. Rajji TK
    6. Suzuki T
    7. Mamo DC
    8. Pollock BG
    9. Watanabe K
    10. Mimura M
    11. Uchida H

    (2013) Dopamine D2 receptor occupancy and cognition in schizophrenia: analysis of the CATIE data

    Schizophrenia Bulletin 39:564–574.

    https://doi.org/10.1093/schbul/sbr189

    • PubMed
    • Google Scholar
88. 1. Salimpoor VN
    2. Benovoy M
    3. Larcher K
    4. Dagher A
    5. Zatorre RJ

    (2011) Anatomically distinct dopamine release during anticipation and experience of peak emotion to music

    Nature Neuroscience 14:257–262.

    https://doi.org/10.1038/nn.2726

    • PubMed
    • Google Scholar
89. 1. Schott BH
    2. Minuzzi L
    3. Krebs RM
    4. Elmenhorst D
    5. Lang M
    6. Winz OH
    7. Seidenbecher CI
    8. Coenen HH
    9. Heinze HJ
    10. Zilles K
    11. Düzel E
    12. Bauer A

    (2008) Mesolimbic functional magnetic resonance imaging activations during reward anticipation correlate with reward-related ventral striatal dopamine release

    Journal of Neuroscience 28:14311–14319.

    https://doi.org/10.1523/JNEUROSCI.2058-08.2008

    • PubMed
    • Google Scholar
90. 1. Seniów J
    2. Litwin M
    3. Litwin T
    4. Leśniak M
    5. Członkowska A

    (2009) New approach to the rehabilitation of post-stroke focal cognitive syndrome: effect of levodopa combined with speech and language therapy on functional recovery from aphasia

    Journal of the Neurological Sciences 283:214–218.

    https://doi.org/10.1016/j.jns.2009.02.336

    • PubMed
    • Google Scholar
91. 1. Shellshear L
    2. MacDonald AD
    3. Mahoney J
    4. Finch E
    5. McMahon K
    6. Silburn P
    7. Nathan PJ
    8. Copland DA

    (2015) Levodopa enhances explicit new-word learning in healthy adults: a preliminary study

    Human Psychopharmacology: Clinical and Experimental 30:341–349.

    https://doi.org/10.1002/hup.2480

    • PubMed
    • Google Scholar
92. 1. Shohamy D
    2. Adcock RA

    (2010) Dopamine and adaptive memory

    Trends in Cognitive Sciences 14:464–472.

    https://doi.org/10.1016/j.tics.2010.08.002

    • PubMed
    • Google Scholar
93. 1. Surmeier DJ

    (2007) Dopamine and working memory mechanisms in prefrontal cortex

    The Journal of Physiology 581:885.

    https://doi.org/10.1113/jphysiol.2007.134502

    • PubMed
    • Google Scholar
94. 1. Torrubia R
    2. Ávila C
    3. Moltó J
    4. Caseras X

    (2001) The sensitivity to punishment and sensitivity to reward questionnaire (SPSRQ) as a measure of gray's anxiety and impulsivity dimensions

    Personality and Individual Differences 31:837–862.

    https://doi.org/10.1016/S0191-8869(00)00183-5

    • Google Scholar
95. 1. Uchida H
    2. Rajji TK
    3. Mulsant BH
    4. Kapur S
    5. Pollock BG
    6. Graff-Guerrero A
    7. Menon M
    8. Mamo DC

    (2009) D2 receptor blockade by risperidone correlates with attention deficits in late-life schizophrenia

    Journal of Clinical Psychopharmacology 29:571–575.

    https://doi.org/10.1097/JCP.0b013e3181bf4ea3

    • PubMed
    • Google Scholar
96. 1. Wagenmakers EJ
    2. Love J
    3. Marsman M
    4. Jamil T
    5. Ly A
    6. Verhagen J
    7. Selker R
    8. Gronau QF
    9. Dropmann D
    10. Boutin B
    11. Meerhoff F
    12. Knight P
    13. Raj A
    14. van Kesteren EJ
    15. van Doorn J
    16. Šmíra M
    17. Epskamp S
    18. Etz A
    19. Matzke D
    20. de Jong T
    21. van den Bergh D
    22. Sarafoglou A
    23. Steingroever H
    24. Derks K
    25. Rouder JN
    26. Morey RD

    (2018a) Bayesian inference for psychology. part II: example applications with JASP

    Psychonomic Bulletin & Review 25:58–76.

    https://doi.org/10.3758/s13423-017-1323-7

    • PubMed
    • Google Scholar
97. 1. Wagenmakers EJ
    2. Marsman M
    3. Jamil T
    4. Ly A
    5. Verhagen J
    6. Love J
    7. Selker R
    8. Gronau QF
    9. Šmíra M
    10. Epskamp S
    11. Matzke D
    12. Rouder JN
    13. Morey RD

    (2018b) Bayesian inference for psychology. part I: theoretical advantages and practical ramifications

    Psychonomic Bulletin & Review 25:35–57.

    https://doi.org/10.3758/s13423-017-1343-3

    • PubMed
    • Google Scholar
98. 1. Whiting E
    2. Chenery H
    3. Chalk J
    4. Darnell R
    5. Copland D

    (2007) Dexamphetamine enhances explicit new word learning for novel objects

    The International Journal of Neuropsychopharmacology 10:805–816.

    https://doi.org/10.1017/S1461145706007516

    • PubMed
    • Google Scholar
99. 1. Whiting E
    2. Chenery HJ
    3. Chalk J
    4. Darnell R
    5. Copland DA

    (2008) The explicit learning of new names for known objects is improved by dexamphetamine

    Brain and Language 104:254–261.

    https://doi.org/10.1016/j.bandl.2007.03.003

    • PubMed
    • Google Scholar
100. 1. Wittmann BC
     2. D'Esposito M

     (2015) Levodopa administration modulates striatal processing of punishment-associated items in healthy participants

     Psychopharmacology 232:135–144.

     https://doi.org/10.1007/s00213-014-3646-7

     • PubMed
     • Google Scholar
101. 1. Wittmann BC
     2. Schott BH
     3. Guderian S
     4. Frey JU
     5. Heinze HJ
     6. Düzel E

     (2005) Reward-related FMRI activation of dopaminergic midbrain is associated with enhanced hippocampus-dependent long-term memory formation

     Neuron 45:459–467.

     https://doi.org/10.1016/j.neuron.2005.01.010

     • PubMed
     • Google Scholar
102. 1. Wolosin SM
     2. Zeithamova D
     3. Preston AR

     (2012) Reward modulation of hippocampal subfield activation during successful associative encoding and retrieval

     Journal of Cognitive Neuroscience 24:1532–1547.

     https://doi.org/10.1162/jocn_a_00237

     • PubMed
     • Google Scholar

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

• 2,467

  views

• 359

  downloads

• 41

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.