Dopaminergic modulation of the exploration/exploitation trade-off in human decision-making

1A central aspect of a broad spectrum of decision problems is the weighting of when to exploit, that is to choose a familiar option with a well-known reward value, and when to explore, that is to try an alternative option with an uncertain but potentially higher payoff. This decision dilemma is commonly known as the ‘exploration/exploitation trade-off’. Striking a balance between exploration and exploitation is essential for maximizing rewards and minimizing costs in the long term. Too much exploitation prevents an agent from gathering new information in a volatile environment, and fosters inflexibility and habit formation. Too much exploration, on the other hand, may lead to inefficient and inconsistent decision-making, thereby reducing long-term payoffs. Despite the high relevance of the explore/exploit trade-off for optimal decision-making, research is only beginning to unravel the mechanisms through which animals and humans solve this dilemma.

2Several tasks have been developed to test explore/exploit behavior in both animals and humans. The most widely used paradigm in both human (Daw) and primate work is the multi-armed bandit task. It mirrors a casino’s slot-machine with multiple arms. Several implementations exist that differ according to the number of arms and their underlying reward structure. The restless bandit paradigm uses continuous, slowly drifting rewards for each bandit that encourage participants to strike a balance between exploiting the currently best option and exploring alternative bandits to keep track of their evolving rewards. Other prominent paradigms include the (patch) foraging task (;; Constantino & Daw, 2015) that mirrors exploration and exploitation of food sources in a more naturalistic setting, and the horizon task that examines exploration in series of discrete games. These paradigms offer different approaches to measure explore/exploit behavior and may be used to address different research questions. Computational models have served as an elegant tool for modeling behavior on these tasks, yielding insights into latent cognitive processes, and inter individual differences.

3These computational models have at least two components: a learning rule and a choice rule. The learning rule describes how subjective value estimates of an option’s mean outcome are updated for each choice option based on experience, for example via the classical ‘Delta rule’ from reinforcement learning theory. Work on explore/exploit behavior has also to utilized a ‘Bayesian learner’ model that relies on a Kalman filter model that simultaneously tracks estimates of outcome mean and uncertainty (e.g.; ), and updates values based on an uncertainty-dependent delta rule. The choice rule then accounts for how learned values give rise to choices. Here, exploration can be due to at least two mechanisms. First, exploration could result from a probabilistic selection of sub-optimal options as in ε-greedy or softmax choice rules, henceforth referred to as ‘random exploration’. Recently, reported evidence for random exploration to depend on the summed uncertainty over all choice options, in line with ‘Thompson sampling’. In contrast, exploration could also be based on the degree of uncertainty associated with a single option, such that highly uncertain options have a higher probability to be strategically explored by an agent, henceforth referred to as ‘directed exploration’. However, estimation of exploration/exploitation behavior might be partially confounded by perseveration (i.e. repeating previous choices irrespective of value or uncertainty), a factor that has not been incorporated in previous models.

4Dopamine (DA) neurotransmission is thought to play a central role in the explore/exploit trade-off. Striatal phasic DA release is tightly linked to reward learning based on reward prediction errors (RPEs) that reflect differences between experienced and expected outcomes, and serve as a ‘teaching signal’ that update value predictions. Exploitation has been linked to polymorphisms in genes controlling striatal DA signaling, namely the DRD2 gene predictive of striatal D2 receptor availability, and the DARPP-32 gene involved in striatal D1 receptor-mediated synaptic plasticity and reward learning (e.g.; ). Variation in the slower tonic DA signal might also contribute to an adaptive regulation of exploration/exploitation. found that dopamine-transporter (DAT) knockdown mice that are characterized by increased striatal levels of tonic DA showed higher random exploration compared to wild-type controls. In addition to striatal DA, prefrontal DA might also be involved in explore/exploit behavior. In humans, both directed and random exploration have been associated with variations in the catechol-O-methyltransferase (COMT) gene that modulates prefrontal DAergic tone. Participants with putatively higher prefrontal DA tone had highest levels of exploration.

5These findings regarding the roles of striatal and frontal DA in exploration resonate with cognitive neuroscience studies suggesting that exploration and exploitation rely on distinct neural systems. showed that frontopolar cortex (FPC) is activated during exploratory choices, possibly facilitating behavioral switching between an exploitative and exploratory mode by overriding value-driven choice tendencies. In line with this idea, up- and down-regulation of FPC excitability via transcranial direct current stimulation (TDCS) increases and decreases exploration during reward-based learning. Anterior cingulate cortex (ACC) and anterior insula (AI), have also been implicated in exploration, although their precise computational role remains elusive. Both regions may trigger attentional reallocation to salient choice options in the light of increasing uncertainty. Exploitation, on the other hand, is thought to be predominantly supported by structures within a ‘valuation’ network including ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), ventral striatum and hippocampus.

6Despite these advances, evidence for a causal link between DA transmission, exploration and the underlying neural mechanisms in humans is still lacking. To address this issue, we combined computational modeling and functional magnetic resonance imaging (fMRI) with a pharmacological intervention in a double-blind, counterbalanced, placebo-controlled within-subjects study. Participants performed a restless four-armed bandit task during fMRI under three drug conditions: the DA precursor L-dopa (150 mg), the DRD2 antagonist haloperidol (2 mg), and placebo. While L-dopa is thought to stimulate DA transmission by providing increased substrate for DA synthesis, haloperidol reduces DA transmission by blocking D2 receptors. However, we note that Haloperidol might also increase DA release via action on presynaptic D2 autoreceptors (z.B. ).

7We extended previous modeling approaches of exploration behavior using a hierarchical Bayesian estimation scheme. Specifically, we jointly examined dopaminergic drug-effects on directed and random exploration as well as perseveration, hypothesizing that choice behavior would be best accounted for by a model that accounts for all three processes. We hypothesized both random and directed exploration to increase under L-dopa and decrease under haloperidol compared to placebo. We further hypothesized that this would be accompanied by a corresponding modulation of brain activity in regions implicated in exploration, focusing specifically on FPC, ACC and AI.

8On each testing day, separated by exactly one week, participants performed 300 trials of a four-armed restless bandit task (; Figure 1; for more details, see Materials and methods section) during fMRI, under three pharmacological conditions (Placebo, Haloperidol, L-DOPA). Overall, participants’ choice behavior indicated that they understood the task structure, and tracked the most valuable bandit throughout the task (see Figure 2). On trial 1, participants randomly selected one of the four bandits (probability to choose best bandit: 21.5 ± 7.49%, M ± SE). After five trials, participants already selected the most valuable option with 49.03% (±4.98%; M ± SE), which was significantly above chance level of 25% (t30 = 4.83, p=3.82*10−5, Figure 2), and consistently kept choosing the bandit with the highest payoff with on average 67.89% (±2.78%). Thus, participants continuously adjusted their choices to the fluctuating outcomes of the four bandits.

9We first tested for possible drug effects on model-free measures of task performance. These variables included the overall monetary payout (payout), the percentage of choices of the bandit with the highest actual payoff (%bestbandit, Figure 2), the percentage of choice switches (%switches), and median reaction times (median RT). Yet, rmANOVAs yielded no significant drug effect on any of these four model-free choice variables (payout: F2,60=0.06, p=0.943; %bestbandit: F2,60=0.34, p=0.711; %switches: F2,60=1.02, p=0.366; median RT: F2,60=0.50, p=0.611).

10We then used computational modeling to examine whether choice behavior indeed contained signatures of random exploration, directed exploration and perseveration. To this end, we set up eight separate computational models that differed regarding the implemented learning and choice rules within a hierarchical Bayesian framework using the STAN modeling language (version 2.17.0; ). We compared two learning rules: the classical Delta rule from temporal-difference algorithms (e.g. ), and a Bayesian learner that formalizes the updating process with a Kalman filter. In the former model, values are updated based on prediction errors that are weighted with a constant learning rate. In contrast, the Kalman filter additionally tracks the uncertainty of each bandit’s value, and value updating is proportional to the uncertainty of the chosen bandit (Kalman gain, see Materials and methods section). These learning rules were combined with four different choice rules that were all based on a softmax action selection rule. Choice rule 1 was a standard softmax with a single inverse temperature parameter (β) modeling random exploration. Choice rule 2 included an additional free parameter modeling an exploration bonus that scaled with the estimated uncertainty of the chosen bandit (directed exploration). Choice rule 3 included an additional free parameter (ρ) modeling a perseveration bonus for the bandit chosen on the previous trial. Finally, choice rule 4 included an additional term to capture random exploration scaling with total uncertainty across all bandits. Leave-one-out (LOO) cross-validation estimates were computed over all drug-conditions, and for each condition separately to assess the models’ predictive accuracies. The Bayesian learning model with terms for directed exploration and perseveration (Bayes-SMEP) showed highest predictive accuracy in each drug condition and overall (Figure 3). The most complex model including an additional total-uncertainty dependent term provided a slightly inferior account of the data compared to the model without this term (loo log-likelihood: Bayes-SME(R)P: -0.5983 (-0.59989)).

11In the best-fitting Bayesian model (Bayes-SMEP), participants’ choices are stochastically dependent on three factors: the prior belief of the mean reward value of each bandit ( Figure 4a), the exploration bonus, that is the prior belief of each bandit’s payout variance (‘uncertainty’) scaled with the exploration bonus parameter (; Figure 4b), and the perseveration bonus (Iρ; where denotes an indicator function with respect to the bandit chosen on the previous trial; Figure 4c). Based on these quantities, which are computed for each bandit, the model computes the choice probabilities for all four bandits on each trial (; Figure 4d, See Equation 7 in the Materials and methods section). Between trials, participants’ prior belief of the chosen bandit’s mean reward value is updated according to the reward prediction error (, Figure 4e), as the difference between their prior belief and the actual reward outcome of the chosen bandit. Based on the model, participants’ choices can be classified as exploitation (i.e. when the bandit with highest expected value was selected), or exploration (i.e. when any other bandit was selected). We extended this binary classification of by further dividing exploration trials into directed exploration (i.e. trials where the bandit with the highest exploration bonus was chosen), and random exploration trials (i.e. trials where one of the remaining bandits was chosen). The trinary classification scheme corresponded well with the respective model parameters (β-random exploration, -directed exploration, ρ-perseveration; Appendix 1—figure 1). Over trials, the summed uncertainty over all bandits ( Figure 4f) fluctuates in relation to the fraction of exploration.

12Next, we tested for possible drug effects on the percentage of exploitation and exploration trials (overall, random and directed) per subject. Three separate rmANOVAs with within factors drug and trial (6 blocks of 50 trials each) were computed for each of the following four dependent variables: the percentage of (a) exploitation trials, (b) random exploration trials, and (c) directed exploration trials. We found a significant drug effect only for the percentage of directed explorations (F1.66,49.91=7.18, p=.003; Figure 5c). The percentage of random explorations (F2,60=0.55, p=.58, Figure 5b) or exploitations (F2,60=1.57, p=.22; Figure 5a) were not significantly modulated by drug. All drug × trial interactions were not significant (p>=0.19). Post-hoc, paired t-tests showed a significant reduction in the percentage of directed explorations under L-dopa compared to placebo (mean difference P-D=2.82, t30=4.69, p<.001) and haloperidol (mean difference H-D = 2.42, t30=2.76, p=.010), but not between placebo and haloperidol (mean difference P-H=0.39, t30=0.43, p=.667). Notably, an exploratory t-test revealed that the percentage of exploitations was marginally increased under L-dopa compared to placebo (mean difference P-D=-2.61, t30=-1.92, p=.065).

13Importantly, the observed attenuation of directed exploration trials under L-dopa was mirrored in our analysis of drug effects on the model parameters’ posterior distributions. Dopaminergic drug effects were first examined for the group-level (mean posteriors for β (random exploration), (directed exploration) and ρ (perseveration)) of the best-fitting Bayesian model, which were estimated separately for each drug. In accordance with the drug effects on fraction of directed explorations, under L-dopa, the posterior group-level mean of was substantially reduced compared to both placebo and haloperidol (Figure 6), such that the 90% highest density intervals of the difference of the posterior distributions of did not overlap with zero (Appendix 1—figure 1b). In contrast, we did not observe effects of L-dopa on random exploration (β, Figure 6) or perseveration (ρ, Figure 6). Somewhat surprisingly, haloperidol showed no effects on the posterior group-level means of any parameter.

14In summary, we found that boosting central dopamine with L-dopa specifically attenuated exploratory choice patterns that aimed at reducing uncertainty of highly uncertain choice options. At the same time, exploitative choices were marginally increased. Although we observed no significant drug × trial interactions, the L-dopa-induced effects appeared to be more pronounced in the first third of the task (Figure 5a, c).

15Analysis of the imaging data proceeded in two steps. First, we examined our data for overall effects of exploration/exploitation based on a binary trial classification, as well as on the model-based parametric effects of expected value and uncertainty. In a second step, we examined the neural basis of the drug-induced change in exploration. All reported fMRI results are based on statistical parametric maps (SPMs) thresholded at p<0.05, FWE-corrected for whole brain volume (unless stated otherwise, for example for visualization purposes). In addition to whole-brain analyses, drug-induced changes were additionally assessed in a regions of interest approach based on small volume FWE correction (p<0.05) for seven regions that have previously been associated with exploration: the left/right FPC and left/right IPS, as well as the dACC and left/right AI. Regions used for small volume correction were defined by 10 mm radius spheres centered at peak voxels reported in these previous studies (Appendix 1—table 5).

16In a first general linear model (GLM), differences in brain activity between exploratory and exploitative choices were modeled (at trial onset) across all participants and drug conditions, using the binary classification previously described by. In accordance with previous work, the pattern of brain activity differed markedly between both types of choices (Figure 7, Appendix 1—figure 5). Highly similar activation patterns were found with a second GLM that was based on the parametric regressors expected value and uncertainty of the chosen bandit, both modeled at trial onset. While expected value related neural activity largely overlapped with the one for exploitative choices (Figure 7a), uncertainty associated patterns of neural activation overlapped with the ones for exploratory choices (Figure 7b).

17Replicating earlier findings (e.g.; ), exploration trials were associated with greater activation in bilateral frontopolar cortex (FPC; left: −42, 27, 27 mm; z = 6.07; right: 39, 34, 28 mm; z = 7.56), in a large cluster along the bilateral intraparietal sulcus (IPS; cluster peak at −48,–33, 52; z = 10.45), and in bilateral anterior insula (AI; left: −36, 15, 3 mm; z = 6.69; right: 36, 20, 3 mm; z = 6.87) as well as dorsal anterior cingulate cortex (dACC; cluster peak at 8, 12, 45 mm; z = 8.47). Clusters within thalamus, cerebellum, and supplementary motor area also showed increased bilateral activation during exploration compared to exploitation.

18In contrast, exploitative choices were associated with greater activation in the ventromedial prefrontal cortex (vmPFC; −2, 40,–10 mm; z = 5.67) and in bilateral lateral orbitofrontal cortex (lOFC; left: −38, 34,–14 mm; z = 5.81; right: 38, 36,–12 mm; z = 5.02). Furthermore, greater activation during exploitative trials was also observed in a cluster spanning the left posterior cingulate cortex (PCC) and left precuneus (cluster peak at −6,–52, 15 mm; z = 7.40), as well as in the angular gyrus (left: −42,–74, 34 mm; z = 8.04; right: 52,–68, 28 mm; z = 7.02), hippocampus (left: −24,–16, −15 mm; z = 4.16; only at p<0.001, uncorrected; right: 32,–16, −15 mm; z = 5.09), and several clusters along the superior and middle temporal gyrus. A complete list of activations associated with explorative and exploitative choices, as well as activation related to model-based PEs can be found in Appendix 1. We observed no differential activation patterns for directed and random exploration types when expanding the original trial classification of in a third GLM (Appendix 1—figure 6).

19To test for a main effect of drug on differential explore/exploit-related brain activation, we conducted rmANOVAs on the second level contrasts explore vs. exploit of the first GLM as well as on the contrasts related to the parametric regressors of expected value and uncertainty of the second GLM. Surprisingly, we found no suprathreshold activations on the whole-brain level, nor in any of seven regions of interest (ROIs) with small volume correction applied (i.e. left/right FPC, left/right IPS, left/right AI, and dACC).

20The same analyses were run for the contrasts from the third GLM, that is directed exploration vs.exploit, randomexploration vs.exploit, and randomvs.directed exploration. Again, we found no significant drug-related modulation of brain activation for these contrasts in any of our a priori ROIs. Likewise, further analyses revealed no drug effects on neural activation (trial onset, reward onset, PE, and outcome value; see Appendix 1 for details) and no evidence for an association of drug effects with putative proxy measures of baseline DA availability (spontaneous blink rate and working memory span, see Appendix 1 for details).

21Based on the null findings in the planned analyses, we reasoned that L-dopa might attenuate directed exploration not by modulating brain activation for exploratory/exploitative choices in a direct manner, but rather by modulating neural computations that are involved in switching from exploitation to directed exploration. Thus, L-dopa might delay the time point at which directed exploration is triggered in response to accumulating ‘total’ (summed) uncertainty. As we did not find evidence (according to model comparison, see Figure 3) for total (summed) uncertainty related random exploration, this was not accounted for in our final Bayesian model. Still, total uncertainty is linked (via the exploration bonus parameter ) to the probability to explore a previously unchosen bandit. gradually increases during a series of exploitations but reduces abruptly when a bandit with high uncertainty is explored (see Figure 2f). We therefore included model-based overall uncertainty as a parametric regressor modeled at trial onset in a new GLM to reveal brain activation that tracks accumulating uncertainty during learning. The contrast images for this regressor were then used in a second-level random effects analysis with the factors drug condition and subject. In the placebo condition alone, no voxels survived whole-brain FWE correction (p<.05), but a more lenient threshold (p<.001, uncorrected) revealed activity in the bilateral dACC (cluster peak at -3, 21, 39mm; z=3.96), right anterior insula (42, 15, -6mm; z=3.46), and left posterior insula (PI) (-34, -20, 8mm; z=4.63) that was positively correlated with the overall uncertainty (Figure 8a, Table 2). To test our exploratory hypothesis, we computed directed t-contrasts for L-dopa vs. placebo (placebo>L-dopa and L-dopa>placebo). While the contrast L-dopa>placebo yielded no suprathreshold activations, the reverse contrast (placebo>L-dopa) revealed a significant activation in the left PI (-34, -20, 8mm; z=5.05). At a reduced threshold (p<.001, uncorrected), left AI (-38, 6, 14mm; z=4.88) and bilateral dACC (left: -2, 36, 33mm; z=3.32; right: 4, 14, 28mm; z=3.41) showed a stronger correlation with the overall uncertainty under placebo compared to L-dopa (Figure 8b, Table 2).

22Here, we directly tested the causal role of DA in human explore/exploit behavior in a pharmacological, computational fMRI approach, using L-dopa (DA precursor) and haloperidol (DA antagonist) in a double-blind, placebo-controlled, counterbalanced, within-subjects design. Model comparison revealed that choice behavior was best accounted for by a novel extension of a Bayesian learning model, that included separate terms for directed exploration and choice perseveration. Modeling revealed that directed exploration was reduced under L-dopa compared to placebo and haloperidol. In contrast, no drug effects were observed on parameters capturing random exploration (β) or perseveration (ρ). On the neural level, exploration was associated with higher activity in the FPC, IPS, dACC, and insula, whereas exploitation showed higher activity in the vmPFC, OFC, PCC, precuneus, angular gyrus, and hippocampus, replicating previous studies. Surprisingly, no drug effects were found for these effects, nor on striatal reward prediction error signaling (see Appendix 1 for details). However, an exploratory model-based analysis revealed that L-dopa reduced insular and dACC activity associated with total (summed) uncertainty.

23We examined two learning rules (Delta rule vs. Bayesian learner) and four choice rules resulting in a total of eight computational models. Model comparison revealed that the Bayesian learning model (Kalman Filter) outperformed the Delta rule for each of the choice rules. Although both learning rules are based on the same error-driven learning principle, the Bayesian learner assumes that subjects additionally track the variance (uncertainty) of reward expectation and adjust the learning rate from trial to trial according to the current level of uncertainty - learning is high when reward predictions are uncertain (i.e. during exploration), but decreases when predictions become more accurate (i.e. during exploitation).

24For both learning rules the model including separate parameters for random exploration, directed exploration and perseveration accounted for the data best, in line with recent work from our group. Note that we also replicated this effect in the original data from the paper. Random exploration was implemented via adding stochasticity to the action selection with a softmax formulation. Directed exploration was implemented via an exploration bonus parameter (e.g.; ). In this scheme, ‘uncertainty’ or ‘information’ biases choices toward more uncertain/informative options by increasing their value. In contrast to, we found no evidence in our data that including an additional term for total-uncertainty based exploration further improved model fit.

25Overall, our finding that pharmacological manipulation of the DA system impacts the exploration/exploitation trade-off is in line with previous animal and human studies (e.g.;; ). However, the observed pattern of drug effects did not match our initial hypothesis, according to which both random and directed exploration were expected to increase under L-dopa vs. placebo and decrease under haloperidol vs. placebo.

26L-dopa administration reduced directed exploration compared to placebo, while random exploration (β) was unaffected. In the model, this could reflect (1) a reduced tendency for directed exploration and/or (2) an increased tendency for value-driven exploitation. Accordingly, when classifying all choices per subject into exploitations, directed exploration and random exploration, L-dopa was found to reduce the percentage of directed but not random exploration compared to placebo across subjects, and marginally increase the percentage of exploitations.

27Previous studies that found that DA promotes exploration primarily focused on prefrontal DA availability or examined effects of tonic DA modulation. L-Dopa, however, likely increases DA transmission most prominently within striatum, and to a much lesser degree in PFC. PET studies in humans also indicate that L-dopa primarily increases phasic rather than tonic striatal DA activity. Increased phasic DA release was associated with improved learning under L-dopa compared to placebo, presumably by enhancing the reinforcing effect of positive feedback during learning. Thus, L-dopa might strengthen-positive reinforcing effects of immediate rewards via increased phasic striatal DA release, fostering both impulsive and exploitative choice behavior.

28In addition, the striatum is densely interconnected with frontal cortices. Thus, L-dopa might have also attenuated exploration via indirectly modulating frontal activity through bottom-up interference.

29Contrary to our hypothesis, we did not observe a significant modulation of exploration/exploitation under haloperidol. Computational modeling revealed no changes in the group-level mean parameters for directed exploration, random exploration (β) or perseveration.

30Haloperidol is a potent D2 receptor antagonist. Thus, the absence of a clear effect on exploration and/or exploitation is at first glance somewhat puzzling. We predicted haloperidol to attenuate tonic DA signaling and reduce directed and random exploration. However, alternatively one could have predicted haloperidol to reduce phasic signaling within striatum and attenuate reward exploitation and thus increase exploration.

31In addition, numerous studies found opposite effects of single doses of haloperidol, and the directionality of effects might depend on the dosage. Low doses of D2 antagonists can stimulate DA signaling, possibly via acting on presynaptic auto-receptors, which contrasts with the antidopaminergic effects observed under chronic and high-dose treatment.

32Finally, a potential baseline dependency may have given rise to a complex non-linear pattern of drug effects. However, we also examined two proxy measures for striatal DA transmission (spontaneous eye blink rate and working memory capacity), both of which showed no modulation of drug-effects (see Appendix 1).

33Consistent with previous research in primates and humans, the pattern of brain activity differed markedly between exploration and exploitation, both using a binary classification of trials and via parametric model-based analyses: Exploratory choices were associated with higher activity in the FPC, IPS, dACC, and AI, replicating previous human fMRI studies. It has been suggested that the FPC may track information relevant for exploratory decisions, such as the expected reward and uncertainty of unchosen choice options, and trigger a behavioral switches from an exploitative to an exploratory mode whenever the accumulated evidence supports such a switch. The IPS, in contrast, has been suggested to serve as an interface between frontal areas and motor output areas, initiating behavioral responses to implement exploratory actions. The dACC and AI, on the other hand, are thought to form a salience network involved in detecting and orienting toward salient stimuli, which may also subserve attentional and behavioral switching from exploitation to exploration.

34Exploitative choices were associated with greater activation in vmPFC and OFC, again replicating previous work. Both regions are implicated in coding the subjective value of attainable goods. Thus, vmPFC and OFC might foster exploitation based on computing subjective values of decision options.

35In addition, greater activation during exploitation was also observed in the PCC, angular gyrus, precuneus, and hippocampus, partly replicating the results of earlier studies. Together with the medial PFC, these regions are hypothesized to form a large-scale brain system referred to as the ‘default mode network’ (DMN;; ). Thus, activity within these regions during exploitation may also relate to a reduced cognitive and attentional demand during exploitation compared to exploration. The angular gyrus is also heavily implicated in number monitoring and thus may monitor reward values during exploitation (see ). The PCC is considered to be part of the brain’s valuation system and may encode reward-related information during exploitation, although in primates PCC neurons were shown to signal exploratory decisions. A further characterization of the hypothesized functions of specific subregions of the exploitation- and exploration networks naturally requires direct experimental tests in the future.

36We found no direct drug effects on exploration- or exploitation-related brain activity, nor on the neural correlates of reward PE signals (see Appendix 1). We hypothesized DA drug effects on exploratory behavior to be associated with changes in the activity of brain regions implicated in exploratory choices. This was not supported by the fMRI data. Alternatively (or additionally), the observed L-dopa effect on explore/exploit behavior could also be due to an enhanced phasic DA release and PE signaling in the striatum (see above). In such a model, L-dopa would be expected to increase the magnitude of the striatal reward PE signal, as previously shown by. However, a recent study did not observe such a modulatory effect of L-dopa on PE coding in a sequential reinforcement learning task in a large community sample (N = 65).

37Several factors might have contributed to the absence of significant L-dopa effects on the neural correlates of explore/exploit decisions or the reward PE. First, this failure may simply be due to a lack of statistical power provided by the modest sample size of 31 subjects. Notably, however, previous studies (e.g. used a substantially smaller sample size, and in our design power was increased due to the within-subjects design. Second, L-dopa has a plasma half-life of only 60 to 90 min and reaches peak plasma concentrations (tmax) about 30 to 60 min after oral ingestion. However, the time schedule of the current experiment was adjusted to this tmax. In addition, such considerations fall short in explaining the clear behavioral effect of L-Dopa that was observed in the present study.

38Obviously, the BOLD signal does not directly reflect DA release, and the precise physiological relationship between DA release and BOLD signal is still to be revealed. A recent optogenetic study in rats suggests that canonical BOLD responses in the reward system may mainly represent the activity of non-dopaminergic neurons, such as glutamatergic projecting neurons. Thus, it is also conceivable that L-dopa might have enhanced striatal DA release to some degree without triggering a (detectable) BOLD signal change.

39For the haloperidol condition, the null findings on the neural level are less surprising, given the lack of a consistent behavioral effect across subjects. As discussed above, it can be assumed that the low dose (2 mg) of haloperidol used in this study exerted a mixture of presynaptic (DA-stimulating) and postsynaptic (DA-antagonizing) effects across subjects, potentially explaining why no overall haloperidol effects were found on the behavioral and neural level. Similarly, also did not observe significant effects of haloperidol (1.5 mg) on reward-related striatal activity or choice behavior. Future studies should consider using higher doses of haloperidol to achieve more consistent antidopaminergic effects from postsynaptic D2 receptor blockade across subjects, or other DA antagonists with a lower side effect profile.

40We reasoned that L-dopa may have reduced exploration not by directly affecting the neural signatures of explore/exploit decisions, but instead by modulating the neural correlates involved in behavioral switching from exploitation to exploration in response to accumulating uncertainty. Thus, L-dopa might delay the time point at which directed exploration is triggered, resulting in less directed exploration trials over time.

41We examined this alternative hypothesis with an additional model-based fMRI analysis, in which a trial-by-trial estimate for overall uncertainty (summed standard deviation over all bandits), was used as a parametric regressor in the GLM. Activity in the bilateral insula and dACC positively correlated with the overall uncertainty in the placebo condition, suggesting that these regions may either track the overall uncertainty directly or encode an affective or motivational state that increases with accumulating uncertainty. Insula and dACC thus may trigger exploration under conditions of high overall uncertainty, for example by facilitating switching between the currently exploited option and salient, more uncertain choice alternatives (, p.97; ). Indeed, numerous studies have found greater activation in these regions during decision-making under uncertainty, and have implicated both regions in encoding outcome uncertainty or risk. The insula is also considered to play a key role for integrating interoceptive signals about bodily states into conscious feelings, such as urgency, that can influence decision-making under risk and uncertainty. The ACC is known to monitor response conflict, which should increase with the overall uncertainty, and to trigger attentional and behavioral changes for reducing future conflict.

42Importantly, we found that L-dopa reduced uncertainty-related activity in the insula and dACC compared to placebo. Expression of D1 and D2 receptors is much higher in striatum than in insula and ACC. Hence, L-dopa may have affected uncertainty-related activity in the insula and ACC indirectly by modulating DA transmission on the striatal level. More specifically, L-dopa might have modulated striatal processing of reward uncertainty that subsequently is transmitted to cortical structures for integration with other decision parameters to guide explore/exploit behavior. To further test this hypothesis, future studies should more closely examine the role of the insula and ACC in triggering exploration in response to accumulating uncertainty and further investigate how frontal and/or striatal DA transmission might modulate this process.

43In addition to the limitations discussed above, the moderate sample size of 31 subjects in this within-subjects manipulation study may have contributed to the absence of haloperidol effects and the absence of drug-associated differences in categorical contrasts of explore/exploit trials in the fMRI analysis. A second limitation relates to the fact that while the applied pharmacological fMRI approach can examine DA drug effects on the BOLD signal, it remains unclear which effects directly reflect local changes in DA signaling, and which reflect downstream effects that may also involve other neurotransmitter systems. Needless to say, the BOLD signal provides an indirect index of blood oxygenation rather than a direct measure of DA activity. Hence, an observed BOLD signal change must not necessarily rely on a change in DA transmission, and a change in DA transmission must not necessarily produce a (detectable) BOLD signal change. Future research should therefore complement pharmacological fMRI studies with other in vivo techniques that specifically monitor local changes in DA activity, such as molecular imaging with PET and SPECT (single photon emission computed tomography) in humans.

44The present study examined the causal role of DA in human explore/exploit behavior in a pharmacological model-based fMRI approach, using the dopamine precursor L-dopa and the D2 antagonist haloperidol in a placebo-controlled, within-subjects design. First, our cognitive modeling results confirm that humans use both random and directed exploration to solve the explore/exploit tradeoff. Notably, we extend previous findings by showing that accounting for choice perseveration improves model fit and interpretability of the parameter capturing directed exploration. Our results support the notion that DA is causally involved in the explore/exploit trade-off in humans by regulating the extent to which subjects engage in directed exploration. Interestingly, our neuroimaging data do not support the hypothesis that DA controls this trade-off by modulating the neural signatures of exploratory and exploitative decisions per se. In contrast, we provide first evidence that DA modulates tracking of overall uncertainty in a cortical control network comprising the insula and dACC, which might then drive exploration in the face of accumulating uncertainty. Future research should more closely examine the potential role of these regions in driving exploration based on emotional responses to increasing uncertainty, and further investigate how prefrontal and/or striatal DA may be involved in this process.

45In total, 34 healthy male subjects participated in the study (aged 19 to 35 years, M = 26.85, SD = 4.01). Three subjects dropped out of the study due to illness or personal reasons, two after the initial baseline session and one after the first fMRI session. Only males were included, as female sex hormones fluctuate during menstrual cycle which may affect DA signaling. Sample-size (n = 31, within-subject design) was based on previous work regarding exploration-related brain activation (; n = 14), and dopaminergic manipulation of reinforcement learning (; n = 13, between-subject design). We aimed at a sample-size of least twice the size of the above-mentioned studies for replication purposes. Participants were recruited online and included mainly university students. Inclusion criteria were the following: male, age 18–35 years, normal weight (BMI 18.5–25.0), right-handed, fluent German in speaking and writing, normal or corrected to normal vision, no hearing impairments, no major past or present psychological, neurological, or physical disorders, non-smoker, no excessive consumption of alcohol (<10 glasses per week), no consumption of illegal drugs or prescription drugs within two months prior to the study, no irreversibly attached metal in or on the body, no claustrophobia (the latter two due to the fMRI measurement). Before participating in the study, all subjects provided informed written consent and had to pass a medical check by a physician including an electrocardiogram (ECG) and an interview about their medical history and present health status. Participants received a fixed amount (270€) plus a variable bonus depending on task performance (30–50€). The study procedures were approved by the local ethics committee (Hamburg Medical Council).

46We employed a double-blind, placebo-controlled, counterbalanced, within-subjects design. Each subject (n = 31) was tested in four separate sessions: one baseline session and three fMRI sessions. The baseline screening was scheduled five to six days prior to the first fMRI session.

47The baseline screening started with spontaneous eye blink rate assessment, followed by a computerized testing of working memory capacity comprising four working memory tasks (as proxy measures for participants’ DA baseline; see Appendix 1), two tasks testing delay and probability discounting behavior (not reported here), and it ended with a psychological questionnaire battery. Participants were encouraged to take small breaks in between the tasks to aid concentration. Blink rate was measured via electromyography for 5 min under resting conditions via three Ag/AgCl electrodes and an MP100 hardware system running under AcqKnowledge (version 3.9.1; Biopac Systems, Goleta, CA). Working memory capacity was based on the following tasks: Rotation Span, Operation Span, Listening Span (; based on the English version by ), and Digit Span (Wechsler Adult Intelligence Scale: WAIS-IV; ). All tasks were implemented using the software MATLAB (R2014b; MathWorks, Natick, MA) with the Psychophysics Toolbox extensions (version 3.0.12;; ).

48At the end of the baseline screening, subjects completed a computer-based questionnaire battery assessing demographics, personality traits, addictive behavior, and various symptoms of psychopathology. Most of the questionnaires were assessed for a separate study and are of no further importance here. Only the Symptom Checklist-90-Revised (SCL-90-R;; German version by ) was used to exclude subjects with psychiatric symptoms. A cut-off was calculated for each subject and transformed into T values based on a German norm sample of male students (see SCL-90-R manual by Franke, 2000, p.310–329). As instructed in this manual, the screening cut-off was set to TGSI ≥ 63 or T ≥ 63 for at least two of the nine subscales, which was reached by none of the participants. Further, the Edinburgh Handedness Inventory was used to ensure that all participants were right-handed.

49In the three fMRI sessions, each participant performed two tasks inside the MRI scanner under the three different drug conditions. The procedure for each scanning session was as follows: Upon arrival (2.5 hr before testing in the MRI scanner), participants received a first pill containing either 2 mg haloperidol or placebo (maize starch). Two hours later, subjects received a second pill containing either Madopar (150 mg L-dopa + 37.5 mg benserazide) or placebo. Over the course of the study, each subject received one dose of Madopar in one session, one dose of haloperidol in another session, and two placebo pills in the remaining session (counterbalanced). Half an hours later, subjects first performed the restless four-armed bandit task, followed by an additional short reinforcement learning task (not reported here) both inside the MRI scanner. Both tasks were trained on a practice version outside the scanner a priori. On the first fMRI session, a structural MR image (T1) was additionally obtained. Each fMRI session ended with a post-fMRI testing outside the scanner, to assess several control variables (see Appendix 1). Throughout each fMRI session, further control variables were assessed at different time points, including physical wellbeing parameters and mood (see Appendix 1). Subjects were not allowed to eat or drink anything but water throughout the fMRI session but were offered a small snack (cereal bar) after testing in the fMRI scanner to aid concentration for the post-fMRI testing.

50The restless four-armed bandit task was adapted from. The task included 300 trials, which were separated by short breaks into four blocks of 75 trials. Each trial started with the presentation of four different colored squares (‘bandits’) representing four choice options (Figure 1). The squares were displayed on a screen that was reflected in a head coil mirror inside the fMRI scanner. Participants selected one option using a button box held in their right hand. Subjects had a maximum of 1.5 s to indicate their choice. If no button was pressed during that time, a large red X was displayed 4.2 s in the center of the screen indicating a missed trial with no points earned. If subjects pressed a button before the response deadline the selected bandit was highlighted with a black frame. After a waiting period of 3 s during which three black dots were shown within the chosen bandit, the number of points earned in this trial was displayed within the chosen bandit for 1 s. Subsequently, the bandits disappeared, and a fixation cross remained on screen until the trial ended 6 s after trial onset. This was followed by a jittered inter-trial interval (poisson distribution, mean: 2 s (0–5 s)). At the end of the task, the sum of points earned as well as the monetary payout resulting from these points were displayed on screen. Participants were told in advance that 5% of all points earned would be paid out after the experiment (5 cents per 100 points). The mean payoffs of the four bandits drifted randomly across trials according to a decaying Gaussian random walk. We used the three instantiations from for the three fMRI sessions of the current study. One of these instantiations is shown in Figure 1. The order of these three instantiations across fMRI sessions was the same for all subjects, thereby unconfounded with the drug order, which was counterbalanced across subjects. The task was implemented using the software MATLAB (R2014b; MathWorks, Natick, MA) with the Psychophysics Toolbox extensions (version 3.0.12;; ).

51Choice behavior in the four-armed bandit task was modeled using eight different computational models of explore/exploit choice behavior (Table 1). The best fitting model (in terms of predictive accuracy) was selected for subsequent analyses of behavioral and fMRI data, and pharmacological intervention effects. Each computational model was composed of two components: First, a learning rule (Delta rule, Bayesian learner) described how participants generate and update subjective reward value estimates for each choice option (bandit) based on previous choices and obtained rewards. Second, a choice rule (softmax, softmax + exploration bonus, softmax + exploration bonus + perseveration bonus, softmax + exploration bonus + (total uncertainty dependent) random exploration bonus + perseveration bonus) modeled how these learned values influence choices. By combining two different learning rules with four different choice rules, a total of eight models entered for model comparison.

52For the sake of brevity, here we only outline the architecture of the Bayesian learner models (see Appendix 1 for the models implementing the Delta rule), which consistently outperformed the Delta rule models. This model implements the Kalman filter as the Bayesian mean-tracking rule for the reward-generating diffusion process in the bandit task. The model assumes that subjects form an internal representation of the true underlying reward structure of the task. The payoff in trial of bandit followed a decaying Gaussian random walk with mean payoff and variance = 42 (observation variance). From one trial to the next, the mean payoffs changed according to: with parameters = 0.9836 (decay parameter), = 50 (decay center), and diffusion noise drawn independently in each trial from a Gaussian distribution with zero mean and = 2.82(diffusion variance). In the model, subjects’ estimates of these parameters are denoted accordingly as and According to the model, participants update their reward expectations of the chosen bandit according to Bayes’ theorem. They start each trial with a prior belief about each bandit’s mean payoff, that is normally distributed with mean and variance for bandit on trial. For the chosen bandit, this prior distribution is updated by the reward observation, resulting in a posterior distribution with mean and variance according to:

53Here, denotes the Kalman gain that is computed for each trial as:

54The Kalman gain determines the fraction of the prediction error that is used for updating. In contrast to the learning rate (Delta rule), the Kalman gain varies from trial to trial depending on the current variance of the expected reward’s prior distribution and the estimated observation variance. The observation variance indicates how much the actual rewards vary around the (to be estimated) mean reward of a bandit and therefore reflects how reliable each trial’s reward observation (each new data point) is for estimating the true underlying mean. If the prior variance is large compared to the estimated observation variance, that is if a subject’s reward prediction is very uncertain while the reward observation is very reliable, the Kalman gain approaches one and a large fraction of the prediction error is used for updating. If, in contrast, the prior variance is very small compared to the estimated observation variance, that is if a subject’s reward estimation is very reliable while reward observations are very noisy, then the Kalman gain approaches 0 and only a small fraction of the prediction error is used for updating. Similar to the Delta rule, the expected rewards (prior mean and variance) of all unchosen bandits are not updated within a trial, that is their posteriors equal the prior for that trial. However, prior distributions of all four bandits are updated between trials based on the subject’s belief about the underlying Gaussian random walk by:

55The trial-by-trial updating process was initialized for all bandits with the same prior distribution, with and as additional free parameters of the model.

56The three choice rules were based on the softmax function. The first implementation utilized the softmax (SM) with the inverse temperature parameter β modeling inherent choice randomness (random exploration). The second choice rule (SM+E) modeled directed exploration in addition, the third choice rule further modeled choice perseveration (SM+E+P), and the fourth model additionally accounted for total uncertainty-based random exploration (SM+E+R+P), all three via a bonus that was added to the expected value. The resulting probabilities to choose bandit on trial were then:

57Choice rule 2 is the “softmax (random exploration) with exploration bonus (directed exploration)”, as used in. Here, denotes the exploration bonus parameter, which reflects the degree to which choices are influenced by the uncertainty associated with each bandit.

58Choice rule 3 is a novel extension of this model called “softmax with exploration and perseveration bonus”. It includes an extra perseveration bonus, which is a constant value (free parameter) only added to the expected value of the bandit chosen in the previous trial. Here, ρ denotes the perseveration bonus parameter and an indicator function that equals 1 for the bandit that was chosen in the previous trial (indexed by ) and 0 for all other bandits.

59Choice rule 4 further extends choice rule 3 by adding a second random exploration term that is discounted by the estimated total (i.e. summed) uncertainty of all bandits. Here, denotes the uncertainty-based random exploration parameter which captures the recent observation that choice randomness may increase with increasing overall uncertainty.

60As mentioned above, all four choice rules were also implemented within a simple Delta rule learning scheme (see Appendix 1). Taken together, by combing each learning rule with each choice rule, eight cognitive models entered model comparison. The parameters for each model are summarized in Table 1.

61Posterior parameter distributions were estimated for each subject and drug condition using hierarchical Bayesian modeling within Stan (version 2.17.0; ), operating under the general statistical package R (version 3.4.3; ). Stan is based on Hamiltonian Monte Carlo sampling for approximation. Sampling was performed with four chains, each chain running for 1000 iterations without thinning after a warmup period of 1000 iterations. The prior for each group-level mean was uniformly distributed within the limits as given in Figure 9. For each group-level standard deviation, a half-Cauchy distribution with location parameter 0 and scale parameter 1 was used as a weakly informative prior. Priors for all subject-level parameters were normally distributed with a parameter-specific mean and standard deviation (denoted by and for any parameter ). Group-level posterior distributions of the three parameters (; mean and standard deviation) were estimated separately for each drug condition, which allowed the comparison of subject-level as well as group-level parameters between drugs (for details on the fixed parameters see Appendix 1).

62Following parameter estimation, the eight cognitive models were compared in terms of predictive accuracy using a Bayesian leave-one-out (LOO) cross-validation approach. LOO cross-validation computes pointwise out-of-sample predictive accuracy by repeatedly taking one data set ('testing set') out of the sample, refitting the model to the reduced data ('training set'), and then measuring how accurately the refitted model predicts the data of the testing set. A testing set was defined as the data of one subject under one drug condition, compounded over all trials. Model comparison was performed using the data sets from all 31 participants once combined over all drug conditions (yielding 93 data sets) and once separately for each drug condition (each with 31 data sets). To reduce computational burden, the R package loo was used, which applies Pareto-smoothed importance sampling to calculate LOO estimates as a close approximation. LOO estimates were calculated for each model fit based on its Stan output, using the log likelihood function evaluated at the sampled posterior parameter values. The log likelihood for each subject was calculated as the logarithmized product of choice probabilities of the chosen bandits (indexed by ) compounded over trials:. Please note that since cross-validation measures like LOO are not biased in favor of more complex models (like ordinary goodness-of-fit measures), no penalty term is needed here to compensate for model complexity in order to prevent over-fitting. Based on the results of the model comparison (Figure 3), the cognitive model with the highest predictive accuracy (Bayes-learner with exploration and perseveration bonus (choice rule 3)) was then selected for further data analysis.

63Functional imaging data were acquired on a Siemens Trio 3T scanner (Erlangen, Germany) equipped with a 32 channel head-coil. For each subject and drug condition, four blocks à 75 trials were recorded for the bandit task. The first five scans of each block served as dummy scans to allow for magnetic field saturation and were discarded. Functional volumes were recorded using a T2*-weighted EPI sequence. Each volume consisted of 40 slices with 2 mm isotropic voxels and 1 mm gap, acquired with a repetition time of 2470 ms, an echo time of 26 ms, and a flip angle of 80°. In addition, a high-resolution structural image was acquired for each subject at the end of the first fMRI session, using a T1-weighted magnetization prepared rapid gradient echo (MPRAGE) sequence with 1 mm isotropic voxels and 240 slices. The experimental task was projected onto a mirror attached to the head coil and participants responded by using a button box with four buttons held in the right hand.

64Preprocessing and statistical analysis of fMRI data was performed using SPM12 (Wellcome Department of Imaging Neuroscience, London, UK). The preprocessing included four steps: (1) realignment and unwarping to the first image of the placebo session; (2) slice time correction to the onset of the middle slice; (3) spatial normalization to Montreal Neurological Institute (MNI) space utilizing the DARTEL approach (Ashburner, 2007) with a resampling of functional images to 1.5 mm isotropic resolution; (4) spatial smoothing using a Gaussian kernel of 6 mm full-width at half-maximum (FWHM).

65For the first-level analysis of fMRI data, a general linear model (GLM) was created for each subject and drug condition, concatenated over all four blocks of the bandit task. For each trial in which a bandit was chosen, two different time points were included in the model: trial onset and outcome presentation. GLM regressors for these time points were created by convolving these event onsets (stick function of zero duration) with the canonical hemodynamic response function (HRF). Parametric modulators for both onset regressors were included in the model: (1) the type of each choice (1 = explore, 0 = exploit, see ) as a parametric modulator for the trial onset regressor; (2) the reward prediction error and the outcome value as separate parametric modulators for the outcome regressor. For trials in which no bandit was chosen, the model contained an additional error regressor. Four sessions constants (not convolved with the HRF) were included in the model. Low-frequency noise was removed by employing a temporal high-pass filter with a cut-off frequency of 1/128 Hz, and a first order autoregressive model AR(1) was used to remove serial correlations. Regressor-specific contrast images were created for each subject and drug condition for the five regressors of interest: trial onset, outcome onset, choice type, prediction error, and outcome value.

66In addition to the main GLM, two alternative GLMs were created. Both alternative GLMs only differed from the main GLM with respect to the regressors modeled at trial onset, while the remaining regressors were the same. The second GLM included one trial onset regressor with two parametric modulators: the expected value and uncertainty of the chosen option (in that order), both derived from the cognitive model as described in the results section. The third GLM included three trial onset regressors: one for directed explorations (directed), one for random explorations (random), and one for exploitations (exploit). These three choice types were defined according to the trinary classification scheme as described in the results section.

67Utilizing a second-level random effects analysis approach, the subject- and drug-specific contrast images for each first-level regressor were submitted to a flexible factorial model in SPM12, including the factors drug (three levels, within-subject), subject (31 levels), and a constant. For each contrast-specific second-level analysis, a t-contrast image was created that tested for the main effect of that specific contrast over all subjects and drug conditions, calculated by weighting each drug level by one and each subject level by 3/31. For the choice type regressor (explore = 1, exploit = 0), t-contrast were computed twice, once with positive and once with negative weights to create t-contrast images for both comparisons explore > exploit and exploit > explore. For the second GLM, t-contrasts for both parameteric modulators, that is expected value and uncertainty, were included in the second-level random effects analysis. For the third GLM, the second-level random effects analysis included the t-contrasts directed > exploit, random > exploit, directed > random, and random > directed.

68To test for DA drug effects across subjects, an F-contrast image was created for each contrast-specific second-level analysis with the weights [1 -1 0; 0 1 -1] over the three drug levels [P D H] and zero weights for all 31 subject levels.

69In addition, a second-level regression analysis was conducted for each drug pair to test whether DA drug effects on exploration-specific brain activity were linearly predicted by DA drug effects on exploratory behavior. For this, the subject- and drug-specific contrast images for explore vs. exploit were used to calculate the difference image of this contrast for a given drug pair (P-D, P-H, or D-H) for each subject. These difference images entered a second-level regression analysis, including the subject-specific drug differences of the exploration bonus parameter posterior medians for the same drug pair as explanatory variable. The same kind of regression analysis was also performed for the contrasts of expected value and uncertainty of the second GLM, and directed vs. exploit and random vs. exploit of the third GLM.

70Post-hoc, a fourth first-level GLM was created for an additional exploratory analysis. This fourth GLM differed from the main GLM only with respect to the parametric modulator of the trial onset regressor, replacing the binary variable choice type (explore/exploit) by a continuous model-based variable termed overall uncertainty ), which is the summed uncertainty over all four bandits. The contrast images for the overall uncertainty regressor were then used in a second-level random effects analysis to test for drug differences in the parametric effects of this regressor across subjects. Since this post-hoc analysis specifically focused on a comparison of the placebo and L-dopa condition (based on the behavioral findings), the second-level model only included these two drug conditions. Based on this model, different t-contrast images were created to test for the parametric effects of this regressor in the placebo condition alone, and for its differential parametric effects between both drug conditions (placebo >L dopa, L-dopa >placebo).

71For completeness, also a second-level analysis with all three drug conditions was performed to test for the remaining pairwise drug effects accordingly (placebo > haloperidol, haloperidol > placebo, L-dopa > haloperidol, haloperidol > placebo). Finally, also a second-level regression analysis was performed for this regressor.

72All fMRI results are reported at a threshold of p<0.05, FWE-corrected for the whole brain volume, unless stated otherwise. In addition, results of the drug-effect related second-level ANOVAs and associated regression analyses were also analyzed using small volume FWE correction (p<0.05) for seven regions that have previously been associated with exploratory choices: the left/right FPC and left/right IPS, as well as the dACC and left/right AI. Regions used for small volume correction were defined by a 10 mm radius sphere around the respective peak voxel reported by the previous studies (Appendix 1—table 5). For display purposes, an uncorrected threshold of p<0.001 was used (unless stated otherwise), and activation maps were overlaid on the mean structural scan of all participants.