Dopamine Modulates the Efficiency of Sensory Evidence Accumulation During Perceptual Decision Making

Dopamine Modulates the Efficiency of Sensory Evidence Accumulation During Perceptual Decision Making Background: Perceptual decision making is the process through which available sensory information is gathered and processed to guide our choices. However, the neuropsychopharmacological basis of this important cognitive function is largely elusive. Yet, theoretical considerations suggest that the dopaminergic system may play an important role. Methods: In a double-blind, randomized, placebo-controlled study design, we examined the effect of methylphenidate in 2 dosages (0.25 mg/kg and 0.5 mg/kg body weight) in separate groups of healthy young adults. We used a moving dots task in which the coherency of the direction of moving dots stimuli was manipulated in 3 levels (5%, 15%, and 35%). Drift diffusion modelling was applied to behavioral data to capture subprocesses of perceptual decision making. Results: The findings show that only the drift rate (v), reflecting the efficiency of sensory evidence accumulation, but not the decision criterion threshold (a) or the duration of nondecisional processes (),Te is affected b r y methylphenidate vs placebo administration. Compared with placebo, administering 0.25 mg/kg methylphenidate increased v, but only in the 35% coherence condition. Administering 0.5 mg/kg methylphenidate did not induce modulations. Conclusions: The data suggest that dopamine selectively modulates the efficacy of evidence accumulation during perceptual decision making. This modulation depends on 2 factors: (1) the degree to which the dopaminergic system is modulated using methylphenidate (i.e., methylphenidate dosage) and (2) the signal-to-noise ratio of the visual information. Dopamine affects sensory evidence accumulation only when dopamine concentration is not shifted beyond an optimal level and the incoming information is less noisy. Keywords: dopamine, drift diffusion model, perceptual decision making, pharmacology Received: December 14, 2017; Revised: February 20, 2018; Accepted: March 28, 2018 © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, 1 provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 2 | International Journal of Neuropsychopharmacology, 2018 Significance Statement Perceptual decision making is the process through which available sensory information is gathered and processed to guide our choices. Perceptual decision making can be further dissected into several subprocesses. Currently, it is unclear how neuro- transmitter systems may affect these subprocesses. Here, we focus on the dopamine system in light of prior research on its role in regulating the fidelity of neural information processing. We show that using methylphenidate (MPH) as a pharmacological modulation of the dopamine system selectively modulates how efficient sensory evidence is accumulated to drive our decisions. However, this depends on the level of MPH and the quality of incoming sensory information. These results provide insights into the neuropharmacological basis that drive important aspects of human perception and decision making. Introduction The external environment is full of different sensory signals that parameter, which strongly depends on the SNR of incoming could influence our behavior. Perceptual decision making is the sensory information (Ratcliff et  al., 2009Ratcliff, ; 2014). Given process through which available sensory information is gath- that MPH affects the dopaminergic system, which is known to ered and processed to guide our choices. Often sensory informa- be important to regulate the SNR, we hypothesize that modu- tion is noisy; thus, prominent theories of perceptual decision lation of the DA system by MPH affects efficiency of sensory making posit that sensory evidence needs to be accumulated evidence accumulation during perceptual decision making and (integrated) across multiple samples to arrive at a clearer per - may interact with environmental task factors affecting stimu- ceptual representation, based on which choice or action can lus noise. Furthermore, during perceptual decision making it be taken (Ratcliff et  al., 2009). Thus, when the brain processes is also important to consider how much information is needed sensory information, it needs to account for stimulus noise as until one is certain to make a specific decision. Such decision well as inherent processing noise. Theoretical (Servan-Schreiber criterion or threshold (Ratcliff and McKoon, 2008 Hoffmann ; et  al., 1990; Li et  al., 2001; Ziegler et  al., 2016) and empirical and Beste, 2015) is captured by the boundary separation param- (Yousif et  al., 2016; Ziegler et  al., 2016) research has proposed eter (a). It has been hypothesized that the dopaminergic system dopaminergic modulation as a mechanism for regulating the may modulate the decision threshold (Winkel et al., 2012), since signal-to-noise ratio (SNR) of neural information processing. response selection processes are known to be modulated by the Empirically, evidence from animal research shows that dopa- dopaminergic system (Willemssen et al., 2011 Sc ; hulz et al., 2012; mine modulates persistent synaptic activity and enhances the Yildiz et al., 2013; Stock et al., 2014). Moreover, the threshold for SNR in the prefrontal cortex (Kroener et al., 2009). Furthermore, action execution is likely to be modulated by the strength of the prefrontal dopamine signals have also been shown to regulate cortico-striatal synapse (Lo and Wang, 2006 Gold ; and Shadlen, visual cortical signals (Noudoost and Moore, 2011). In humans, 2007; Bogacz et al., 2010), which is known to be modulated by DA the availability of dopamine D1 receptors was found to be neg- effects (Surmeier et al., 2007). Yet, a pharmacological manipula- atively correlated with intra-individual reaction time variabil-tion using bromocriptine did not reveal modulatory effects, and ity (MacDonald et  al., 2012). Recently, it has also been shown it was argued that this could be due to the receptor specificity that the dopamine receptor agonist pergolide improved visual of bromocriptine (Winkel et al., 2012). In fronto-striatal circuits, cortical SNR and counteracted the impairing effect of inhibi- the general dopamine level is strongly regulated by dopamine’s tory transcranial magnetic stimulation on visual perceptual presynaptic autoreceptor DAT, which removes dopamine from learning (Yousif et  al., 2016). To investigate dopamine’s role in the synaptic cleft and is highly expressed in nigro-striatal and regulating sensory evidence integration during visual percep- meso-corticolimbic pathways (Ciliax et  al., 1999). MPH acts as tion in humans, we combined pharmacological intervention a mixed dopamine/norepinephrine transporter blocker, thus of methylphenidate (MPH), a mixed dopamine/norepinephrine increasing dopamine (norepinephrine) levels in fronto-striatal transporter blocker, with a perceptual decision task of visual structures (Volkow et al., 1999 Skirr ; ow et al., 2015). Since MPH motion in which we could systematically manipulate exter - is not specific for dopamine’s postsynaptic receptor subsystems nal sensory stimulus noise by the extent of motion coherence. and generally plays an important role in striatal DA level regu- Furthermore, since both theoretical (Li and Sikström, 2002) and lation, it is possible that it might also modulate the boundary empirical (Vijayraghavan et al., 2007 Cools and D’Esposito ; , 2011; separation threshold (a). Chowdhury et al., 2012) studies showed that dopamine’s signal We examine these hypotheses in a double-blind, rand- turning effect on cognition follows an inverted-U shaped func- omized, placebo-controlled study in healthy adults in which we tion, with too little or excessive dopamine being suboptimal for use a moving dots task to examine perceptual decision making. performance, here we investigated effects of dopamine signal- We examine the effects of MPH by administering 2 MPH dos- ing at different dosages. ages: 0.25 and 0.5 mg/kg body weight. As reviewed above, vary- To explore effects of dopamine on subprocesses of percep- ing the dosage of MPH makes it possible to investigate how the tion decision making, we fitted drift diffusion models (DDM) above effects are scaled by variations in dopamine level. Since to the behavioral data to derive estimates of parameters that we examined healthy young adults with supposedly no dopa- reflect different aspects of the perceptual decision-making mine system deficiencies, it is possible that the higher dosage process (Wagenmakers et  al., 2007, 2008; Ratcliff, 2014). The shifts the dopamine system beyond the optimal level, which DDM assumes that a perceptual decision is a stochastic pro- may then result in null treatment benefit or decreases in the cess that sequentially samples and accumulates sensory evi- efficiency of sensory evidence accumulation during percep- dence for arriving at a perceptual decision (Winkel et al., 2012; tual decision making. Doing so, it will be possible to estimate Ratcliff, 2014; Stock et  al., 2017). In DDM, the efficiency of sen- boundary conditions of the dopamine system for subprocesses sory evidence accumulation is modeled by the drift rate (v) involved in perceptual decision making. Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Beste et al. | 3 experimental blocks with 48 trials each. The experiment setup Materials and Methods is shown in Figure 1. Between the blocks, participants could decide via button Participants press when to continue. Each trial consisted of a central fixation Fifty healthy young participants took part in this study. They cross presented for 500 ms. After that, the “cloud” of 30 rectangu- were randomly assigned to 2 equally sized groups (n = 25 eac h) lar moving dots was presented for 1000 ms spanning a viewing for the higher dosage level (mean age 25.5 y, 12 females) and angle of 4.72°. The 30 random dots changed positions at a speed the lower dosage level (mean age 22.9 y, 13 females). The dos- of 9 pixels per frame, which created the illusion of motion. The ages of MPH were 0.5 and 0.25 mg/kg for the higher and lower coherence of motion with dots moving either towards the left levels, respectively. Screenings before the first appointment or right direction was manipulated by varying the percentage ensured that participants were right-handed, had no regular of dots moving in the same direction. Specifically, we included drug and/or medication intake, and did not consume caffeine on 3 levels of coherence, with 5%, 15%, or 35% of all presented dots the same days as the appointments. They were informed about moved in the same direction. This coherence manipulation the goals and procedure of this study and gave written consent. varied the SNR of the incoming visual information, which is All participants received monetary compensation after the sec- known to affect the efficiency of sensory evidence accumula- ond appointment. The study was approved by the local Ethics tion (Ratcliff et al., 2009). The moving directions of the remaining Committee of TU Dresden, and the experiment was conducted dots were random. Each coherence condition occurred equally according to the Helsinki Declaration of 1975. All subjects pro- frequent in each of the 9 experimental blocks (i.e., 16 times in vided written informed consent. each experimental block). The response interval was 1000  ms. Participants were instructed to report a “left” motion with the Y MPH Administration key and a coherent “right” motion with the M key on a standard German PC keyboard. The study consisted of 2 appointments in one of which par - ticipants received a single dose of MPH. At the other appoint- Estimating Parameters of Drift Diffusion Model ment, participants were administered a placebo. The order of drug administration (MPH or placebo first) was counterbalanced The drift diffusion model captures perceptual decision making across participants and gender, and the experimenter was as a process of continuous sampling of noisy sensory evidence blind to this order. The individual MPH dosage was calculated until a decision boundary in favor of one of the choice options based on the participant’s body weight at the beginning of the is reached (rightward or leftward motion in our case). According first appointment. The moving dots task started approximately to the model, the distributions of choice accuracy and reaction 2 h after drug administration, which falls inside the time span times (RTs) across trials depend on a number of parameters, 3 when MPH is at maximum plasma concentration (Challman of which are central in most perceptual decision processes. As and Lipsky, 2000; Rösler et al., 2009). Prior to working on the task mentioned in the introduction, the drift rate ( ) models the effi v - described in this publication, the participants spent 60 min per - ciency with which decision evidence could be integrated across forming 2 other tasks, the results of which have not been pub- trials to approach the decision boundaries: high drift rates reflect lished so far. more efficient evidence integration. The boundary separation parameter (a) indicates the amount of evidence needed until a decision threshold is reached: wider decision boundaries would Moving Dots Task reflect more cautious but slower decisions. The non-decision time parameter (Te) r captures the time taken by sensory encod- The experimental task was programmed using Java and pre- sented on a 23.8-inch screen with resolution 1920 × 1080 pix- ing and motor processes. To estimate the values of these 3 param- eters for each participant, we applied the EZ-diffusion model els and a refresh rate of 144 Hz. The experiment consisted of 9 Figure 1. Illustration of the experimental setup and the moving dot stimuli. The fixation cross was presented for 500 ms, the moving dots for 1000 ms. In the “cloud” of moving dots, the extent of motion coherence was varied in 3 steps by manipulating the percentages (i.e., 5%, 15%, or 35%) of dots moving in the same direction, that is, either towards left or right. The rest of the dots move randomly. Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 4 | International Journal of Neuropsychopharmacology, 2018 (Wagenmakers et  al., 2007) to our data to further characterize η = .93) showing that the drift rate was largest in the 35% potential effects of dopamine pharmacology on different aspects (0.24 ± 0.008) compared to 15% (0.14± 0.005) and 5% motion con- of the perceptual decision process. The EZ model was used, dition (0.04± 0.005). Importantly, there was an interaction dose x because the parameter fitting procedure is more straight-forward motion coherence x drug/placebo (F(2,96) = 3.49; P = .034; η = .07), than in the classical Ratcliff diffusion model. Moreover, the EZ which is shown in Figure 2. All other main or interaction effects model is optimized for experiments having a more limited num- were not significant (all F< 0.44; P > .643). ber of trials. The Ratcliff model requires the entire RT distribution Further analyses of the interaction dose x motion coherence (i.e., including error trials). Error trials will therefore have to occurx drug/place bo showed that there was an interaction motion in a reasonable frequency (Wagenmakers et al., 2007). We applied coherence x drug/placebo in the group receiving 0.25  mg/kg the EZ model to individual participant’s data from each of the 3 MPH (F(2,48) = 5.40; P = .008; η = .18), but not in the group receiv- coherence conditions separately to estimate the 3 drift diffusion ing 0.50 mg/kg MPH (F(2,48)= 0.53; P = .589; η = .02). In the group parameters described above. The parameters are estimated based receiving 0.25  mg/kg MPH, posthoc tests show that there was on the individual’s choice accuracy as well as the mean and vari- no difference between MPH and placebo in 5% and the 15% ance of RTs of the correct responses. In the context of the moving motion conditions (all t < -1.61; P > .120). However, the drift rate dot task, these 3 parameters presumably reflect the efficiency of was larger under MPH administration than placebo in the 35% integrating sensory information for perceived motion ( ), strin v - motion condition (t(24) = -2.24; P = .017), suggesting that the effi- gency of the decision criterion (), a and sensorimotor processing ciency of sensory evidence accumulation became higher. time (Ter). Before applying the model to the data, RT distribu- Concerning the boundary separation parameter (a), the tions associated with choices made in each of the 3 states were mixed effects ANOVA revealed only a main effect of “motion inspected separately for the coherence condition. All distribu- coherence” (F(2,48) = 3.58; P = .032; η = .07), and it is shown that tions can be characterized as ex-Gaussian, which is expected for parameter a was larger in the condition with 15% coherency RT distributions. We fit the data from all 3 coherence conditions compared with the other coherency condition (5% = 0.095 ± 0.002; separately. The starting point Z was not modelled. This is because 35% = 0.095 ± 0.003). All other main or interaction effects were not there is reason to assume that there is a bias with the subjects to prefer 1 of the 2 possible response options. Also, the experimental procedure did not induce such a bias. Statistical Analysis The data were analyzed using mixed effects ANOVAs. The factor “motion coherence” (i.e., 5%, 15%, and 35%) was included as a 3-level, within-subject factor, and the factor “placebo/drug” was included as a 2-level within subject factor. The factor “dosage” (25 or 50 mg/kg bodyweight) was included at a 2-level between- subject factor. Greenhouse-Geisser correction was applied for all analyses and posthoc tests were Bonferroni-corrected. Separate ANOVAs were calculated for the different DDM parameters (i.e., v, a, and Ter) as well as for the basic RT and accuracy data. For descriptive statistics, the mean and SEM are given. For nonsig- nificant results including the factors “dosage” and “placebo/ drug,” we also ran Bayesian analyses to examine the probabil- ity of the null hypothesis being true, given the obtained data (P(H |D) (Wagenmakers, 2007; Masson, 2011); that is, we evalu- ated the relative strength of evidence for the null hypothesis. The Bayesian analysis was performed base on the sum of squares of the error term and the effect term provided by the ANOVAs (Wagenmakers, 2007; Masson, 2011). For the descriptive statistics, the mean and SEM are given. Results For the mean RTs, the mixed effects ANOVA revealed a main effect of “motion coherence” (F(2,96)= 231.56; P < .001; η = .83). As expected, RTs became shorter with increasing coher - ency levels (5% = 738 ms ± 0.019; 15% = 665 ms ± 0.015; 35% = 567 ms ± 0.012). All other main or interaction effects were not signifi- cant (all F < 2.35; P > .101). Concerning the accuracy, similar to the results of RTs there was only a main effect “motion coherence” (F(2,96) = 911.22; P < .001; η = .95). It is shown that the accuracy increased with increasing coherency levels (5% = 59.0% ± 0.7; Figure  2. The interaction MPH dosage x motion coherence x drug/placebo is 15% = 77.6% ± 1.0; 35% = 88.6% ± 0.8). No other main or interaction shown for the drift rate parameter (v) of the DDM. The top panel shows results of effect was significant (all F< 1.32; P > .255). MPH dosage level at 0.25 mg/kg, whereas the bottom panel shows results at the For the drift rate (v), the mixed effects ANOVA revealed a dosage level of 0.5 mg/kg group are shown. Dosage was manipulated between groups (the mean and SEM are given). main effect of “motion coherence” (F(2,96)= 677.75; P < .001; Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Beste et al. | 5 significant (all F < 0.26; P > .771). The Bayesian analysis of the data above a sufficient level of signal strength. This is evidenced by revealed that P(H|D) = 0.97. Thus, the Bayesian analysis provides the lack of modulatory effects in the 2 experimental conditions very strong evidence for the null hypothesis, that is, that there with lower coherence of the moving dots stimuli. This finding is no differential effect of MPH/placebo, motion coherency, and cannot be attributed to the degree to which the dopaminergic drug dosage on the boundary separation parameter (a). system was modulated, as when the MPH concentration was Concerning the duration of nondecisional processes (Te ) r doubled (i.e., 0.5 mg/kg MPH was administered), no modulations there was, again, only a main effect of “motion coherence” of the drift rate compared with placebo were observed in the (F(2,48) = 136.42; P < .001; η = .74). Ter was largest in the 5% motion conditions with lower sensory SNR either. Importantly, this find- condition (0.51± 0.01) and decreased in the 15% (0.45± 0.1) and ing also suggests that there is an optimal level of dopaminergic 35% motion condition (0.4± 0.007). All conditions differed from activity in which the efficiency of sensory evidence accumula- each other (P< .001). No other main or interaction effects were tion is maximally amplified. This likely reflects an effect of the significant (all F < 0.41; P > .665). In the Bayesian analysis it is generally inverted-U function relating DA signaling and cogni- shown that P(H|D) = 0.95. Thus, the Bayesian analysis provides tion (Cools and D’Esposito, 2011). It is possible that the MPH dos- very strong evidence for the null hypothesis, that is, that there age of 0.5 mg/kg has shifted the dopamine system beyond the is no differential effect of MPH/placebo, motion coherency, and optimal level and thus not yielding treatment benefit on per - drug dosage on the duration of non-decisional processes (Te ).r formance, while the 0.25  mg/kg dosage shifted the dopamine Even though the sessions in which MPH or placebo was system to the optimal level and that is why the efficiency of administered were counterbalanced across subjects, we also information accumulation was increased. examined whether test order affected the results. Additional From the current data, we can only speculate which func- control analyses showed that including this variable in the tional neuroanatomical structures are associated with the above analyses did not change the pattern of results, that is, observed effects. In principle, functions of the prefrontal cortex there was no main or interaction effect including the factor test as well as striatal areas may be associated with these effects. order (all F < 0.31; P > .711). Yet, MPH acts as a mixed dopamine/norepinephrine transporter blocker (Volkow et al., 1999Skirr ; ow et al., 2015) and DAT regu- lates dopamine turnover at the striatal level (Ciliax et al., 1999), Discussion but not at a neocortical level where enzymes regulate dopamin- In the current study, we examined the effects of dopamine ergic turnover (Goldberg and Weinberger, 2004). It is therefore possible that the effects observed are related to striatal pro- modulation of subprocesses of visual perceptual decision mak- ing by modeling the behavioral data using DDM. This was done cesses. In line with that interpretation, DDM-like processes have been shown to be associated with the basal ganglia (Forstmann in a double-blind, randomized, placebo-controlled study in healthy young adults. The results show that only the drift rate et  al., 2008, 2010). Moreover, it has been shown that the basal ganglia receive signals from primary sensory cortices (Hikosaka (v), but not the boundary/threshold separation (a) or the dur - ation of non-decisional processes (Te) is modulated. r The lack of et al., 1989; Redgrave and Gurney, 2006; Znamenskiy and Zador, 2013; Reig and Silberberg, 2014) and that there is evidence from modulatory effects for the parameters a and Te wrere supported by a Bayesian analysis of the data, which provided very strong animal research that striatal dopamine modulates interactions between perceptual processes (Ward and Brown, 1996 Bao et  ; al., evidence for the null hypotheses. This underlines that the mod- ulatory effects of MPH on the dopaminergic system are targeting 2001; Brown et  al., 2010). All these aspects make it likely that striatal processes play an important role in the observed mod- specific subprocesses during perceptual decision making. The current findings show that the modulation of the effi- ulatory effects. However, this needs to be further validated in future studies. ciency of sensory evidence accumulation (indexed by the drift rate v) by MPH depends on 2 factors: the degree to which the Whereas the role of dopamine modulation of the efficacy of sensory evidence accumulation (the v parameter) during visual dopaminergic system is modulated using MPH (i.e., MPH dosage) and the SNR of the incoming visual information. Significantly perceptual decision making is clearly based on the data reported here and previous evidence, the role of dopamine in affecting higher drift rates in MPH compared with placebo administra- tion were only observed using a dosage of 0.25  mg/kg body- decision boundary is still equivocal. Whereas our observa- tion of the boundary separation parameter being not affected weight, and this effect was restricted to the condition where the coherence of stimulus motion was sufficiently high (i.e., with at by MPH administration corroborates other findings also show- ing no modulations of the boundary separation parameter by a least 35% of dots coherently moving in the same direction). No drug/placebo modulations in any of the coherence levels were pharmacological modulation of the dopamine system (Winkel et  al., 2012), it has recently also been shown that the effects observed using an MPH dosage of 0.5  mg/kg bodyweight. MPH acts as a mixed dopamine/norepinephrine transporter blocker, of dopamine agonist, pergolide, counteracted the impairing effect of inhibitory transcranial magnetic stimulation on mul- thus increasing dopamine (norepinephrine) levels in fronto- striatal structures (Volkow et al., 1999 Skirr ; ow et al., 2015). The tiple aspects of perceptual decision making, including drift rate, boundary separation, and sensory noise (Yousif et al., 2016). The results therefore show that increased dopaminergic concentra- tions in these circuits foster the efficiency of sensory evidence empirical inconsistencies regarding dopamine’s role in affect- ing decision threshold may in part arise from the specifics of accumulation. This is in line with theoretical conceptions sug- gesting that the dopaminergic system regulates the SNR of dopamine pharmacology applied and experimental conditions. Nonetheless, our finding that the boundary separation param- neural information processing (Servan-Schreiber et al., 1990 Li ; et  al., 2001; Yousif et  al., 2016; Ziegler et  al., 2016). Enhancing eter remained stable using a drug that modulates striatal dopa- mine concentrations by affecting DAT suggests that striatal SNR of sensory inputs by dopamine modulation may enhance the distinctiveness of sensory-perceptual representations, dopaminergic levels are not important to be considered as a fac- tor modulating the amount of information needed for a decision. leading to more efficient accumulation of information (Li and Rieckmann, 2014; Yousif et  al., 2016). Notably, this seems to be Specifically, this does not in general undermine theoretical con- siderations suggesting that the strength of the cortico-striatal the case only once the SNR of incoming sensory information is Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 6 | International Journal of Neuropsychopharmacology, 2018 synapse modulates the decision threshold (Lo and Wang, 2006), Chowdhury R, Guitart-Masip M, Bunzeck N, Dolan RJ, Düzel E which is assumed to be generally important for perceptual deci- (2012) Dopamine modulates episodic memory persistence in sion-making processes (Beste et  al., 2008T ; omkins et  al., 2013; old age. J Neurosci 32:14193–14204. Beste et  al., 2014, 2017); instead, it suggests that the criterion Ciliax BJ, Drash GW, Staley JK, Haber S, Mobley CJ, Miller GW, setting process may rather be modulated by top-down cortical Mufson EJ, Mash DC, Levey AI (1999) Immunocytochemical dopamine modulation. However, this conjecture awaits further localization of the dopamine transporter in human brain. empirical validations. At this point, it should be noted that drugs J Comp Neurol 409:38–56. modulating DAT in monkeys have been to modulate novelty- Cools R, D’Esposito M (2011) Inverted-U-shaped dopamine seeking behavior but not the rate at which monkeys learned actions on human working memory and cognitive control. what cues are predictive for rewards (Costa et al., 2014). Future Biol Psychiatry 69:e113–e125. studies may therefore be conducted to examined with compu- Costa VD, Tran VL, Turchi J, Averbeck BB (2014) Dopamine mod- tational, model-driven aspects showing differential modulatory ulates novelty seeking behavior during decision making. profiles of striatal dopamine-related functions. These may also Behav Neurosci 128:556–566. more precisely examine the role of norepinephrine in percep- Forstmann BU, Dutilh G, Brown S, Neumann J, von Cramon DY, tual decision making. Ridderinkhof KR, Wagenmakers EJ (2008) Striatum and pre- In summary, the study suggests that dopamine modulates SMA facilitate decision-making under time pressure. Proc specific perceptual decision-making subprocesses, that is, Natl Acad Sci U S A 105:17538–17542. the efficacy of evidence accumulation during perceptual deci- Forstmann BU, Anwander A, Schäfer A, Neumann J, Brown S, sion making. This modulation depends on 2 factors: the level Wagenmakers EJ, Bogacz R, Turner R (2010) Cortico-striatal of pharmacological upregulation of dopamine neurotransmis- connections predict control over speed and accuracy in sion and the SNR of the incoming visual information. Dopamine perceptual decision making. Proc Natl Acad Sci U S A 107: affects perceptual decision-making subprocesses only when 15916–15920. dopamine concentration is not shifted beyond an optimal level Gold JI, Shadlen MN (2007) The neural basis of decision making. and the incoming information is not too noisy. Annu Rev Neurosci 30:535–574. Goldberg TE, Weinberger DR (2004) Genes and the parsing of cog- nitive processes. Trends Cogn Sci 8:325–335. Acknowledgments Hikosaka O, Sakamoto M, Usui S (1989) Functional properties of monkey caudate neurons. III. Activities related to expectation This work was supported by a grant from the Deutsche Forschungsgemeinschaft to Christian Beste (BE4045/26-1), Shu- of target and reward. J Neurophysiol 61:814–832. Hoffmann S, Beste C (2015) A perspective on neural and cogni- Chen Li (LI879/18-1), Veit Roessner (RO3876/8-1), and Susanne Passow (PA2972/1-1). tive mechanisms of error commission. Front Behav Neurosci 9:50. Kroener S, Chandler LJ, Phillips PE, Seamans JK (2009) Dopamine Statement of Interest modulates persistent synaptic activity and enhances the sig- nal-to-noise ratio in the prefrontal cortex. Plos One 4:e6507. None. Li SC, Lindenberger U, Sikström S (2001) Aging cognition: from neuromodulation to representation. Trends Cogn Sci References 5:479–486. Li SC, Rieckmann A (2014) Neuromodulation and aging: implica- Bao S, Chan VT, Merzenich MM (2001) Cortical remodelling tions of aging neuronal gain control and cognition. Curr Opin induced by activity of ventral tegmental dopamine neurons. Neurobiol 29:148–158. Nature 412:79–83. Li SC, Sikström S (2002) Integrative neurocomputational per - Beste C, Humphries M, Saft C (2014) Striatal disorders dissociate spectives on cognitive aging, neuromodulation and represen- mechanisms of enhanced and impaired response selection— tation. Neurosci Biobehav Rev 26:795–808. evidence from cognitive neurophysiology and computational Lo CC, Wang XJ (2006) Cortico-basal ganglia circuit mechanism modelling. Neuroimage Clin 4:623–634. for a decision threshold in reaction time tasks. Nat Neurosci Beste C, Mückschel M, Rosales R, Domingo A, Lee L, Ng A, Klein 9:956–963. C, Münchau A (2017) Dysfunctions in striatal microstructure MacDonald SW, Karlsson S, Rieckmann A, Nyberg L, Bäckman can enhance perceptual decision making through deficits in L (2012) Aging-related increases in behavioral variability: predictive coding. Brain Struct Funct 222:3807–3817. relations to losses of dopamine D1 receptors. J Neurosci Beste C, Saft C, Güntürkün O, Falkenstein M (2008) Increased 32:8186–8191. cognitive functioning in symptomatic Huntington’s disease Masson ME (2011) A tutorial on a practical Bayesian alternative as revealed by behavioral and event-related potential indi- to null-hypothesis significance testing. Behav Res Methods ces of auditory sensory memory and attention. J Neurosci 43:679–690. 28:11695–11702. Noudoost B, Moore T (2011) Control of visual cortical signals by Bogacz R, Wagenmakers EJ, Forstmann BU, Nieuwenhuis S (2010) prefrontal dopamine. Nature 474:372–375. The neural basis of the speed-accuracy tradeoff. Trends Ratcliff R (2014) Measuring psychometric functions with the dif- Neurosci 33:10–16. fusion model. J Exp Psychol Hum Percept Perform 40:870–888. Brown JA, Emnett RJ, White CR, Yuede CM, Conyers SB, Ratcliff R, McKoon G (2008) The diffusion decision model: the- O’Malley KL, Wozniak DF, Gutmann DH (2010) Reduced ory and data for 2-choice decision tasks. Neural Comput striatal dopamine underlies the attention system dysfunc- 20:873–922. tion in neurofibromatosis-1 mutant mice. Hum Mol Genet Ratcliff R, Philiastides MG, Sajda P (2009) Quality of evidence for 19:4515–4528. perceptual decision making is indexed by trial-to-trial vari- Challman TD, Lipsky JJ (2000) Methylphenidate: its pharmacol- ability of the EEG. Proc Natl Acad Sci U S A 106:6539–6544. ogy and uses. Mayo Clin Proc 75:711–721. Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Beste et al. | 7 Redgrave P, Gurney K (2006) The short-latency dopamine sig- Volkow ND, Wang GJ, Fowler JS, Gatley SJ, Logan J, Ding YS, Dewey nal: a role in discovering novel actions? Nat Rev Neurosci SL, Hitzemann R, Gifford AN, Pappas NR (1999) Blockade of 7:967–975. striatal dopamine transporters by intravenous methyl- Reig R, Silberberg G (2014) Multisensory integration in the mouse phenidate is not sufficient to induce self-reports of “high.” striatum. Neuron 83:1200–1212. J Pharmacol Exp Ther 288:14–20. Rösler M, Fischer R, Ammer R, Ose C, Retz W (2009) A randomised, Wagenmakers EJ (2007) A practical solution to the pervasive placebo-controlled, 24-week, study of low-dose extended- problems of P values. Psychon Bull Rev 14:779–804. release methylphenidate in adults with attention-deficit/ Wagenmakers EJ, van der Maas HL, Dolan CV, Grasman RP (2008) hyperactivity disorder. Eur Arch Psychiatry Clin Neurosci EZ does it! Extensions of the EZ-diffusion model. Psychon 259:120–129. Bull Rev 15:1229–1235. Schulz S, Arning L, Pinnow M, Wascher E, Epplen JT, Beste C Wagenmakers EJ, van der Maas HL, Grasman RP (2007) An (2012) When control fails: influence of the prefrontal but not EZ-diffusion model for response time and accuracy. Psychon striatal dopaminergic system on behavioural flexibility in a Bull Rev 14:3–22. change detection task. Neuropharmacology 62:1028–1033. Ward NM, Brown VJ (1996) Covert orienting of attention in the Servan-Schreiber D, Printz H, Cohen JD (1990) A network model rat and the role of striatal dopamine. J Neurosci 16:3082–3088. of catecholamine effects: gain, signal-to-noise ratio, and Willemssen R, Falkenstein M, Schwarz M, Müller T, Beste C (2011) behavior. Science 249:892–895. Effects of aging, Parkinson’s disease, and dopaminergic medi- Skirrow C, McLoughlin G, Banaschewski T, Brandeis D, Kuntsi J, cation on response selection and control. Neurobiol Aging Asherson P (2015) Normalisation of frontal theta activity fol- 32:327–335. lowing methylphenidate treatment in adult attention-deficit/ Winkel J, van Maanen L, Ratcliff R, van der Schaaf ME, hyperactivity disorder. Eur Neuropsychopharmacol 25:85–94. van Schouwenburg MR, Cools R, Forstmann BU (2012) Stock AK, Arning L, Epplen JT, Beste C (2014) DRD1 and DRD2 Bromocriptine does not alter speed-accuracy tradeoff. Front genotypes modulate processing modes of goal activation Neurosci 6:126. processes during action cascading. J Neurosci 34:5335–5341. Yildiz A, Chmielewski W, Beste C (2013) Dual-task performance Stock AK, Hoffmann S, Beste C (2017) Effects of binge drinking and is differentially modulated by rewards and punishments. hangover on response selection sub-processes-a study using Behav Brain Res 250:304–307. EEG and drift diffusion modeling. Addict Biol 22:1355–1365. Yousif N, Fu RZ, Abou-El-Ela Bourquin B, Bhrugubanda V, Schultz Surmeier DJ, Ding J, Day M, Wang Z, Shen W (2007) D1 and D2 SR, Seemungal BM (2016) Dopamine activation preserves vis- dopamine-receptor modulation of striatal glutamatergic ual motion perception despite noise interference of human signaling in striatal medium spiny neurons. Trends Neurosci V5/MT. J Neurosci 36:9303–9312. 30:228–235. Ziegler S, Pedersen ML, Mowinckel AM, Biele G (2016) Modelling Tomkins A, Vasilaki E, Beste C, Gurney K, Humphries MD (2013) ADHD: a review of ADHD theories through their predictions Transient and steady-state selection in the striatal micr -ocir for computational models of decision-making and reinforce- cuit. Front Comput Neurosci 7:192. ment learning. Neurosci Biobehav Rev 71:633–656. Vijayraghavan S, Wang M, Birnbaum SG, Williams GV, Arnsten AF Znamenskiy P, Zador AM (2013) Corticostriatal neurons in audi- (2007) Inverted-U dopamine D1 receptor actions on prefrontal tory cortex drive decisions during auditory discrimination. neurons engaged in working memory. Nat Neurosci 10:376–384. Nature 497:482–485. Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Neuropsychopharmacology Oxford University Press

Dopamine Modulates the Efficiency of Sensory Evidence Accumulation During Perceptual Decision Making

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Abstract

Background: Perceptual decision making is the process through which available sensory information is gathered and processed to guide our choices. However, the neuropsychopharmacological basis of this important cognitive function is largely elusive. Yet, theoretical considerations suggest that the dopaminergic system may play an important role. Methods: In a double-blind, randomized, placebo-controlled study design, we examined the effect of methylphenidate in 2 dosages (0.25 mg/kg and 0.5 mg/kg body weight) in separate groups of healthy young adults. We used a moving dots task in which the coherency of the direction of moving dots stimuli was manipulated in 3 levels (5%, 15%, and 35%). Drift diffusion modelling was applied to behavioral data to capture subprocesses of perceptual decision making. Results: The findings show that only the drift rate (v), reflecting the efficiency of sensory evidence accumulation, but not the decision criterion threshold (a) or the duration of nondecisional processes (),Te is affected b r y methylphenidate vs placebo administration. Compared with placebo, administering 0.25 mg/kg methylphenidate increased v, but only in the 35% coherence condition. Administering 0.5 mg/kg methylphenidate did not induce modulations. Conclusions: The data suggest that dopamine selectively modulates the efficacy of evidence accumulation during perceptual decision making. This modulation depends on 2 factors: (1) the degree to which the dopaminergic system is modulated using methylphenidate (i.e., methylphenidate dosage) and (2) the signal-to-noise ratio of the visual information. Dopamine affects sensory evidence accumulation only when dopamine concentration is not shifted beyond an optimal level and the incoming information is less noisy. Keywords: dopamine, drift diffusion model, perceptual decision making, pharmacology Received: December 14, 2017; Revised: February 20, 2018; Accepted: March 28, 2018 © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, 1 provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 2 | International Journal of Neuropsychopharmacology, 2018 Significance Statement Perceptual decision making is the process through which available sensory information is gathered and processed to guide our choices. Perceptual decision making can be further dissected into several subprocesses. Currently, it is unclear how neuro- transmitter systems may affect these subprocesses. Here, we focus on the dopamine system in light of prior research on its role in regulating the fidelity of neural information processing. We show that using methylphenidate (MPH) as a pharmacological modulation of the dopamine system selectively modulates how efficient sensory evidence is accumulated to drive our decisions. However, this depends on the level of MPH and the quality of incoming sensory information. These results provide insights into the neuropharmacological basis that drive important aspects of human perception and decision making. Introduction The external environment is full of different sensory signals that parameter, which strongly depends on the SNR of incoming could influence our behavior. Perceptual decision making is the sensory information (Ratcliff et  al., 2009Ratcliff, ; 2014). Given process through which available sensory information is gath- that MPH affects the dopaminergic system, which is known to ered and processed to guide our choices. Often sensory informa- be important to regulate the SNR, we hypothesize that modu- tion is noisy; thus, prominent theories of perceptual decision lation of the DA system by MPH affects efficiency of sensory making posit that sensory evidence needs to be accumulated evidence accumulation during perceptual decision making and (integrated) across multiple samples to arrive at a clearer per - may interact with environmental task factors affecting stimu- ceptual representation, based on which choice or action can lus noise. Furthermore, during perceptual decision making it be taken (Ratcliff et  al., 2009). Thus, when the brain processes is also important to consider how much information is needed sensory information, it needs to account for stimulus noise as until one is certain to make a specific decision. Such decision well as inherent processing noise. Theoretical (Servan-Schreiber criterion or threshold (Ratcliff and McKoon, 2008 Hoffmann ; et  al., 1990; Li et  al., 2001; Ziegler et  al., 2016) and empirical and Beste, 2015) is captured by the boundary separation param- (Yousif et  al., 2016; Ziegler et  al., 2016) research has proposed eter (a). It has been hypothesized that the dopaminergic system dopaminergic modulation as a mechanism for regulating the may modulate the decision threshold (Winkel et al., 2012), since signal-to-noise ratio (SNR) of neural information processing. response selection processes are known to be modulated by the Empirically, evidence from animal research shows that dopa- dopaminergic system (Willemssen et al., 2011 Sc ; hulz et al., 2012; mine modulates persistent synaptic activity and enhances the Yildiz et al., 2013; Stock et al., 2014). Moreover, the threshold for SNR in the prefrontal cortex (Kroener et al., 2009). Furthermore, action execution is likely to be modulated by the strength of the prefrontal dopamine signals have also been shown to regulate cortico-striatal synapse (Lo and Wang, 2006 Gold ; and Shadlen, visual cortical signals (Noudoost and Moore, 2011). In humans, 2007; Bogacz et al., 2010), which is known to be modulated by DA the availability of dopamine D1 receptors was found to be neg- effects (Surmeier et al., 2007). Yet, a pharmacological manipula- atively correlated with intra-individual reaction time variabil-tion using bromocriptine did not reveal modulatory effects, and ity (MacDonald et  al., 2012). Recently, it has also been shown it was argued that this could be due to the receptor specificity that the dopamine receptor agonist pergolide improved visual of bromocriptine (Winkel et al., 2012). In fronto-striatal circuits, cortical SNR and counteracted the impairing effect of inhibi- the general dopamine level is strongly regulated by dopamine’s tory transcranial magnetic stimulation on visual perceptual presynaptic autoreceptor DAT, which removes dopamine from learning (Yousif et  al., 2016). To investigate dopamine’s role in the synaptic cleft and is highly expressed in nigro-striatal and regulating sensory evidence integration during visual percep- meso-corticolimbic pathways (Ciliax et  al., 1999). MPH acts as tion in humans, we combined pharmacological intervention a mixed dopamine/norepinephrine transporter blocker, thus of methylphenidate (MPH), a mixed dopamine/norepinephrine increasing dopamine (norepinephrine) levels in fronto-striatal transporter blocker, with a perceptual decision task of visual structures (Volkow et al., 1999 Skirr ; ow et al., 2015). Since MPH motion in which we could systematically manipulate exter - is not specific for dopamine’s postsynaptic receptor subsystems nal sensory stimulus noise by the extent of motion coherence. and generally plays an important role in striatal DA level regu- Furthermore, since both theoretical (Li and Sikström, 2002) and lation, it is possible that it might also modulate the boundary empirical (Vijayraghavan et al., 2007 Cools and D’Esposito ; , 2011; separation threshold (a). Chowdhury et al., 2012) studies showed that dopamine’s signal We examine these hypotheses in a double-blind, rand- turning effect on cognition follows an inverted-U shaped func- omized, placebo-controlled study in healthy adults in which we tion, with too little or excessive dopamine being suboptimal for use a moving dots task to examine perceptual decision making. performance, here we investigated effects of dopamine signal- We examine the effects of MPH by administering 2 MPH dos- ing at different dosages. ages: 0.25 and 0.5 mg/kg body weight. As reviewed above, vary- To explore effects of dopamine on subprocesses of percep- ing the dosage of MPH makes it possible to investigate how the tion decision making, we fitted drift diffusion models (DDM) above effects are scaled by variations in dopamine level. Since to the behavioral data to derive estimates of parameters that we examined healthy young adults with supposedly no dopa- reflect different aspects of the perceptual decision-making mine system deficiencies, it is possible that the higher dosage process (Wagenmakers et  al., 2007, 2008; Ratcliff, 2014). The shifts the dopamine system beyond the optimal level, which DDM assumes that a perceptual decision is a stochastic pro- may then result in null treatment benefit or decreases in the cess that sequentially samples and accumulates sensory evi- efficiency of sensory evidence accumulation during percep- dence for arriving at a perceptual decision (Winkel et al., 2012; tual decision making. Doing so, it will be possible to estimate Ratcliff, 2014; Stock et  al., 2017). In DDM, the efficiency of sen- boundary conditions of the dopamine system for subprocesses sory evidence accumulation is modeled by the drift rate (v) involved in perceptual decision making. Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Beste et al. | 3 experimental blocks with 48 trials each. The experiment setup Materials and Methods is shown in Figure 1. Between the blocks, participants could decide via button Participants press when to continue. Each trial consisted of a central fixation Fifty healthy young participants took part in this study. They cross presented for 500 ms. After that, the “cloud” of 30 rectangu- were randomly assigned to 2 equally sized groups (n = 25 eac h) lar moving dots was presented for 1000 ms spanning a viewing for the higher dosage level (mean age 25.5 y, 12 females) and angle of 4.72°. The 30 random dots changed positions at a speed the lower dosage level (mean age 22.9 y, 13 females). The dos- of 9 pixels per frame, which created the illusion of motion. The ages of MPH were 0.5 and 0.25 mg/kg for the higher and lower coherence of motion with dots moving either towards the left levels, respectively. Screenings before the first appointment or right direction was manipulated by varying the percentage ensured that participants were right-handed, had no regular of dots moving in the same direction. Specifically, we included drug and/or medication intake, and did not consume caffeine on 3 levels of coherence, with 5%, 15%, or 35% of all presented dots the same days as the appointments. They were informed about moved in the same direction. This coherence manipulation the goals and procedure of this study and gave written consent. varied the SNR of the incoming visual information, which is All participants received monetary compensation after the sec- known to affect the efficiency of sensory evidence accumula- ond appointment. The study was approved by the local Ethics tion (Ratcliff et al., 2009). The moving directions of the remaining Committee of TU Dresden, and the experiment was conducted dots were random. Each coherence condition occurred equally according to the Helsinki Declaration of 1975. All subjects pro- frequent in each of the 9 experimental blocks (i.e., 16 times in vided written informed consent. each experimental block). The response interval was 1000  ms. Participants were instructed to report a “left” motion with the Y MPH Administration key and a coherent “right” motion with the M key on a standard German PC keyboard. The study consisted of 2 appointments in one of which par - ticipants received a single dose of MPH. At the other appoint- Estimating Parameters of Drift Diffusion Model ment, participants were administered a placebo. The order of drug administration (MPH or placebo first) was counterbalanced The drift diffusion model captures perceptual decision making across participants and gender, and the experimenter was as a process of continuous sampling of noisy sensory evidence blind to this order. The individual MPH dosage was calculated until a decision boundary in favor of one of the choice options based on the participant’s body weight at the beginning of the is reached (rightward or leftward motion in our case). According first appointment. The moving dots task started approximately to the model, the distributions of choice accuracy and reaction 2 h after drug administration, which falls inside the time span times (RTs) across trials depend on a number of parameters, 3 when MPH is at maximum plasma concentration (Challman of which are central in most perceptual decision processes. As and Lipsky, 2000; Rösler et al., 2009). Prior to working on the task mentioned in the introduction, the drift rate ( ) models the effi v - described in this publication, the participants spent 60 min per - ciency with which decision evidence could be integrated across forming 2 other tasks, the results of which have not been pub- trials to approach the decision boundaries: high drift rates reflect lished so far. more efficient evidence integration. The boundary separation parameter (a) indicates the amount of evidence needed until a decision threshold is reached: wider decision boundaries would Moving Dots Task reflect more cautious but slower decisions. The non-decision time parameter (Te) r captures the time taken by sensory encod- The experimental task was programmed using Java and pre- sented on a 23.8-inch screen with resolution 1920 × 1080 pix- ing and motor processes. To estimate the values of these 3 param- eters for each participant, we applied the EZ-diffusion model els and a refresh rate of 144 Hz. The experiment consisted of 9 Figure 1. Illustration of the experimental setup and the moving dot stimuli. The fixation cross was presented for 500 ms, the moving dots for 1000 ms. In the “cloud” of moving dots, the extent of motion coherence was varied in 3 steps by manipulating the percentages (i.e., 5%, 15%, or 35%) of dots moving in the same direction, that is, either towards left or right. The rest of the dots move randomly. Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 4 | International Journal of Neuropsychopharmacology, 2018 (Wagenmakers et  al., 2007) to our data to further characterize η = .93) showing that the drift rate was largest in the 35% potential effects of dopamine pharmacology on different aspects (0.24 ± 0.008) compared to 15% (0.14± 0.005) and 5% motion con- of the perceptual decision process. The EZ model was used, dition (0.04± 0.005). Importantly, there was an interaction dose x because the parameter fitting procedure is more straight-forward motion coherence x drug/placebo (F(2,96) = 3.49; P = .034; η = .07), than in the classical Ratcliff diffusion model. Moreover, the EZ which is shown in Figure 2. All other main or interaction effects model is optimized for experiments having a more limited num- were not significant (all F< 0.44; P > .643). ber of trials. The Ratcliff model requires the entire RT distribution Further analyses of the interaction dose x motion coherence (i.e., including error trials). Error trials will therefore have to occurx drug/place bo showed that there was an interaction motion in a reasonable frequency (Wagenmakers et al., 2007). We applied coherence x drug/placebo in the group receiving 0.25  mg/kg the EZ model to individual participant’s data from each of the 3 MPH (F(2,48) = 5.40; P = .008; η = .18), but not in the group receiv- coherence conditions separately to estimate the 3 drift diffusion ing 0.50 mg/kg MPH (F(2,48)= 0.53; P = .589; η = .02). In the group parameters described above. The parameters are estimated based receiving 0.25  mg/kg MPH, posthoc tests show that there was on the individual’s choice accuracy as well as the mean and vari- no difference between MPH and placebo in 5% and the 15% ance of RTs of the correct responses. In the context of the moving motion conditions (all t < -1.61; P > .120). However, the drift rate dot task, these 3 parameters presumably reflect the efficiency of was larger under MPH administration than placebo in the 35% integrating sensory information for perceived motion ( ), strin v - motion condition (t(24) = -2.24; P = .017), suggesting that the effi- gency of the decision criterion (), a and sensorimotor processing ciency of sensory evidence accumulation became higher. time (Ter). Before applying the model to the data, RT distribu- Concerning the boundary separation parameter (a), the tions associated with choices made in each of the 3 states were mixed effects ANOVA revealed only a main effect of “motion inspected separately for the coherence condition. All distribu- coherence” (F(2,48) = 3.58; P = .032; η = .07), and it is shown that tions can be characterized as ex-Gaussian, which is expected for parameter a was larger in the condition with 15% coherency RT distributions. We fit the data from all 3 coherence conditions compared with the other coherency condition (5% = 0.095 ± 0.002; separately. The starting point Z was not modelled. This is because 35% = 0.095 ± 0.003). All other main or interaction effects were not there is reason to assume that there is a bias with the subjects to prefer 1 of the 2 possible response options. Also, the experimental procedure did not induce such a bias. Statistical Analysis The data were analyzed using mixed effects ANOVAs. The factor “motion coherence” (i.e., 5%, 15%, and 35%) was included as a 3-level, within-subject factor, and the factor “placebo/drug” was included as a 2-level within subject factor. The factor “dosage” (25 or 50 mg/kg bodyweight) was included at a 2-level between- subject factor. Greenhouse-Geisser correction was applied for all analyses and posthoc tests were Bonferroni-corrected. Separate ANOVAs were calculated for the different DDM parameters (i.e., v, a, and Ter) as well as for the basic RT and accuracy data. For descriptive statistics, the mean and SEM are given. For nonsig- nificant results including the factors “dosage” and “placebo/ drug,” we also ran Bayesian analyses to examine the probabil- ity of the null hypothesis being true, given the obtained data (P(H |D) (Wagenmakers, 2007; Masson, 2011); that is, we evalu- ated the relative strength of evidence for the null hypothesis. The Bayesian analysis was performed base on the sum of squares of the error term and the effect term provided by the ANOVAs (Wagenmakers, 2007; Masson, 2011). For the descriptive statistics, the mean and SEM are given. Results For the mean RTs, the mixed effects ANOVA revealed a main effect of “motion coherence” (F(2,96)= 231.56; P < .001; η = .83). As expected, RTs became shorter with increasing coher - ency levels (5% = 738 ms ± 0.019; 15% = 665 ms ± 0.015; 35% = 567 ms ± 0.012). All other main or interaction effects were not signifi- cant (all F < 2.35; P > .101). Concerning the accuracy, similar to the results of RTs there was only a main effect “motion coherence” (F(2,96) = 911.22; P < .001; η = .95). It is shown that the accuracy increased with increasing coherency levels (5% = 59.0% ± 0.7; Figure  2. The interaction MPH dosage x motion coherence x drug/placebo is 15% = 77.6% ± 1.0; 35% = 88.6% ± 0.8). No other main or interaction shown for the drift rate parameter (v) of the DDM. The top panel shows results of effect was significant (all F< 1.32; P > .255). MPH dosage level at 0.25 mg/kg, whereas the bottom panel shows results at the For the drift rate (v), the mixed effects ANOVA revealed a dosage level of 0.5 mg/kg group are shown. Dosage was manipulated between groups (the mean and SEM are given). main effect of “motion coherence” (F(2,96)= 677.75; P < .001; Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Beste et al. | 5 significant (all F < 0.26; P > .771). The Bayesian analysis of the data above a sufficient level of signal strength. This is evidenced by revealed that P(H|D) = 0.97. Thus, the Bayesian analysis provides the lack of modulatory effects in the 2 experimental conditions very strong evidence for the null hypothesis, that is, that there with lower coherence of the moving dots stimuli. This finding is no differential effect of MPH/placebo, motion coherency, and cannot be attributed to the degree to which the dopaminergic drug dosage on the boundary separation parameter (a). system was modulated, as when the MPH concentration was Concerning the duration of nondecisional processes (Te ) r doubled (i.e., 0.5 mg/kg MPH was administered), no modulations there was, again, only a main effect of “motion coherence” of the drift rate compared with placebo were observed in the (F(2,48) = 136.42; P < .001; η = .74). Ter was largest in the 5% motion conditions with lower sensory SNR either. Importantly, this find- condition (0.51± 0.01) and decreased in the 15% (0.45± 0.1) and ing also suggests that there is an optimal level of dopaminergic 35% motion condition (0.4± 0.007). All conditions differed from activity in which the efficiency of sensory evidence accumula- each other (P< .001). No other main or interaction effects were tion is maximally amplified. This likely reflects an effect of the significant (all F < 0.41; P > .665). In the Bayesian analysis it is generally inverted-U function relating DA signaling and cogni- shown that P(H|D) = 0.95. Thus, the Bayesian analysis provides tion (Cools and D’Esposito, 2011). It is possible that the MPH dos- very strong evidence for the null hypothesis, that is, that there age of 0.5 mg/kg has shifted the dopamine system beyond the is no differential effect of MPH/placebo, motion coherency, and optimal level and thus not yielding treatment benefit on per - drug dosage on the duration of non-decisional processes (Te ).r formance, while the 0.25  mg/kg dosage shifted the dopamine Even though the sessions in which MPH or placebo was system to the optimal level and that is why the efficiency of administered were counterbalanced across subjects, we also information accumulation was increased. examined whether test order affected the results. Additional From the current data, we can only speculate which func- control analyses showed that including this variable in the tional neuroanatomical structures are associated with the above analyses did not change the pattern of results, that is, observed effects. In principle, functions of the prefrontal cortex there was no main or interaction effect including the factor test as well as striatal areas may be associated with these effects. order (all F < 0.31; P > .711). Yet, MPH acts as a mixed dopamine/norepinephrine transporter blocker (Volkow et al., 1999Skirr ; ow et al., 2015) and DAT regu- lates dopamine turnover at the striatal level (Ciliax et al., 1999), Discussion but not at a neocortical level where enzymes regulate dopamin- In the current study, we examined the effects of dopamine ergic turnover (Goldberg and Weinberger, 2004). It is therefore possible that the effects observed are related to striatal pro- modulation of subprocesses of visual perceptual decision mak- ing by modeling the behavioral data using DDM. This was done cesses. In line with that interpretation, DDM-like processes have been shown to be associated with the basal ganglia (Forstmann in a double-blind, randomized, placebo-controlled study in healthy young adults. The results show that only the drift rate et  al., 2008, 2010). Moreover, it has been shown that the basal ganglia receive signals from primary sensory cortices (Hikosaka (v), but not the boundary/threshold separation (a) or the dur - ation of non-decisional processes (Te) is modulated. r The lack of et al., 1989; Redgrave and Gurney, 2006; Znamenskiy and Zador, 2013; Reig and Silberberg, 2014) and that there is evidence from modulatory effects for the parameters a and Te wrere supported by a Bayesian analysis of the data, which provided very strong animal research that striatal dopamine modulates interactions between perceptual processes (Ward and Brown, 1996 Bao et  ; al., evidence for the null hypotheses. This underlines that the mod- ulatory effects of MPH on the dopaminergic system are targeting 2001; Brown et  al., 2010). All these aspects make it likely that striatal processes play an important role in the observed mod- specific subprocesses during perceptual decision making. The current findings show that the modulation of the effi- ulatory effects. However, this needs to be further validated in future studies. ciency of sensory evidence accumulation (indexed by the drift rate v) by MPH depends on 2 factors: the degree to which the Whereas the role of dopamine modulation of the efficacy of sensory evidence accumulation (the v parameter) during visual dopaminergic system is modulated using MPH (i.e., MPH dosage) and the SNR of the incoming visual information. Significantly perceptual decision making is clearly based on the data reported here and previous evidence, the role of dopamine in affecting higher drift rates in MPH compared with placebo administra- tion were only observed using a dosage of 0.25  mg/kg body- decision boundary is still equivocal. Whereas our observa- tion of the boundary separation parameter being not affected weight, and this effect was restricted to the condition where the coherence of stimulus motion was sufficiently high (i.e., with at by MPH administration corroborates other findings also show- ing no modulations of the boundary separation parameter by a least 35% of dots coherently moving in the same direction). No drug/placebo modulations in any of the coherence levels were pharmacological modulation of the dopamine system (Winkel et  al., 2012), it has recently also been shown that the effects observed using an MPH dosage of 0.5  mg/kg bodyweight. MPH acts as a mixed dopamine/norepinephrine transporter blocker, of dopamine agonist, pergolide, counteracted the impairing effect of inhibitory transcranial magnetic stimulation on mul- thus increasing dopamine (norepinephrine) levels in fronto- striatal structures (Volkow et al., 1999 Skirr ; ow et al., 2015). The tiple aspects of perceptual decision making, including drift rate, boundary separation, and sensory noise (Yousif et al., 2016). The results therefore show that increased dopaminergic concentra- tions in these circuits foster the efficiency of sensory evidence empirical inconsistencies regarding dopamine’s role in affect- ing decision threshold may in part arise from the specifics of accumulation. This is in line with theoretical conceptions sug- gesting that the dopaminergic system regulates the SNR of dopamine pharmacology applied and experimental conditions. Nonetheless, our finding that the boundary separation param- neural information processing (Servan-Schreiber et al., 1990 Li ; et  al., 2001; Yousif et  al., 2016; Ziegler et  al., 2016). Enhancing eter remained stable using a drug that modulates striatal dopa- mine concentrations by affecting DAT suggests that striatal SNR of sensory inputs by dopamine modulation may enhance the distinctiveness of sensory-perceptual representations, dopaminergic levels are not important to be considered as a fac- tor modulating the amount of information needed for a decision. leading to more efficient accumulation of information (Li and Rieckmann, 2014; Yousif et  al., 2016). Notably, this seems to be Specifically, this does not in general undermine theoretical con- siderations suggesting that the strength of the cortico-striatal the case only once the SNR of incoming sensory information is Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 6 | International Journal of Neuropsychopharmacology, 2018 synapse modulates the decision threshold (Lo and Wang, 2006), Chowdhury R, Guitart-Masip M, Bunzeck N, Dolan RJ, Düzel E which is assumed to be generally important for perceptual deci- (2012) Dopamine modulates episodic memory persistence in sion-making processes (Beste et  al., 2008T ; omkins et  al., 2013; old age. J Neurosci 32:14193–14204. Beste et  al., 2014, 2017); instead, it suggests that the criterion Ciliax BJ, Drash GW, Staley JK, Haber S, Mobley CJ, Miller GW, setting process may rather be modulated by top-down cortical Mufson EJ, Mash DC, Levey AI (1999) Immunocytochemical dopamine modulation. However, this conjecture awaits further localization of the dopamine transporter in human brain. empirical validations. At this point, it should be noted that drugs J Comp Neurol 409:38–56. modulating DAT in monkeys have been to modulate novelty- Cools R, D’Esposito M (2011) Inverted-U-shaped dopamine seeking behavior but not the rate at which monkeys learned actions on human working memory and cognitive control. what cues are predictive for rewards (Costa et al., 2014). Future Biol Psychiatry 69:e113–e125. studies may therefore be conducted to examined with compu- Costa VD, Tran VL, Turchi J, Averbeck BB (2014) Dopamine mod- tational, model-driven aspects showing differential modulatory ulates novelty seeking behavior during decision making. profiles of striatal dopamine-related functions. These may also Behav Neurosci 128:556–566. more precisely examine the role of norepinephrine in percep- Forstmann BU, Dutilh G, Brown S, Neumann J, von Cramon DY, tual decision making. Ridderinkhof KR, Wagenmakers EJ (2008) Striatum and pre- In summary, the study suggests that dopamine modulates SMA facilitate decision-making under time pressure. Proc specific perceptual decision-making subprocesses, that is, Natl Acad Sci U S A 105:17538–17542. the efficacy of evidence accumulation during perceptual deci- Forstmann BU, Anwander A, Schäfer A, Neumann J, Brown S, sion making. This modulation depends on 2 factors: the level Wagenmakers EJ, Bogacz R, Turner R (2010) Cortico-striatal of pharmacological upregulation of dopamine neurotransmis- connections predict control over speed and accuracy in sion and the SNR of the incoming visual information. Dopamine perceptual decision making. Proc Natl Acad Sci U S A 107: affects perceptual decision-making subprocesses only when 15916–15920. dopamine concentration is not shifted beyond an optimal level Gold JI, Shadlen MN (2007) The neural basis of decision making. and the incoming information is not too noisy. Annu Rev Neurosci 30:535–574. Goldberg TE, Weinberger DR (2004) Genes and the parsing of cog- nitive processes. Trends Cogn Sci 8:325–335. Acknowledgments Hikosaka O, Sakamoto M, Usui S (1989) Functional properties of monkey caudate neurons. III. Activities related to expectation This work was supported by a grant from the Deutsche Forschungsgemeinschaft to Christian Beste (BE4045/26-1), Shu- of target and reward. J Neurophysiol 61:814–832. Hoffmann S, Beste C (2015) A perspective on neural and cogni- Chen Li (LI879/18-1), Veit Roessner (RO3876/8-1), and Susanne Passow (PA2972/1-1). tive mechanisms of error commission. Front Behav Neurosci 9:50. Kroener S, Chandler LJ, Phillips PE, Seamans JK (2009) Dopamine Statement of Interest modulates persistent synaptic activity and enhances the sig- nal-to-noise ratio in the prefrontal cortex. Plos One 4:e6507. None. Li SC, Lindenberger U, Sikström S (2001) Aging cognition: from neuromodulation to representation. Trends Cogn Sci References 5:479–486. Li SC, Rieckmann A (2014) Neuromodulation and aging: implica- Bao S, Chan VT, Merzenich MM (2001) Cortical remodelling tions of aging neuronal gain control and cognition. Curr Opin induced by activity of ventral tegmental dopamine neurons. Neurobiol 29:148–158. Nature 412:79–83. Li SC, Sikström S (2002) Integrative neurocomputational per - Beste C, Humphries M, Saft C (2014) Striatal disorders dissociate spectives on cognitive aging, neuromodulation and represen- mechanisms of enhanced and impaired response selection— tation. Neurosci Biobehav Rev 26:795–808. evidence from cognitive neurophysiology and computational Lo CC, Wang XJ (2006) Cortico-basal ganglia circuit mechanism modelling. Neuroimage Clin 4:623–634. for a decision threshold in reaction time tasks. Nat Neurosci Beste C, Mückschel M, Rosales R, Domingo A, Lee L, Ng A, Klein 9:956–963. C, Münchau A (2017) Dysfunctions in striatal microstructure MacDonald SW, Karlsson S, Rieckmann A, Nyberg L, Bäckman can enhance perceptual decision making through deficits in L (2012) Aging-related increases in behavioral variability: predictive coding. Brain Struct Funct 222:3807–3817. relations to losses of dopamine D1 receptors. J Neurosci Beste C, Saft C, Güntürkün O, Falkenstein M (2008) Increased 32:8186–8191. cognitive functioning in symptomatic Huntington’s disease Masson ME (2011) A tutorial on a practical Bayesian alternative as revealed by behavioral and event-related potential indi- to null-hypothesis significance testing. Behav Res Methods ces of auditory sensory memory and attention. J Neurosci 43:679–690. 28:11695–11702. Noudoost B, Moore T (2011) Control of visual cortical signals by Bogacz R, Wagenmakers EJ, Forstmann BU, Nieuwenhuis S (2010) prefrontal dopamine. Nature 474:372–375. The neural basis of the speed-accuracy tradeoff. Trends Ratcliff R (2014) Measuring psychometric functions with the dif- Neurosci 33:10–16. fusion model. J Exp Psychol Hum Percept Perform 40:870–888. Brown JA, Emnett RJ, White CR, Yuede CM, Conyers SB, Ratcliff R, McKoon G (2008) The diffusion decision model: the- O’Malley KL, Wozniak DF, Gutmann DH (2010) Reduced ory and data for 2-choice decision tasks. Neural Comput striatal dopamine underlies the attention system dysfunc- 20:873–922. tion in neurofibromatosis-1 mutant mice. Hum Mol Genet Ratcliff R, Philiastides MG, Sajda P (2009) Quality of evidence for 19:4515–4528. perceptual decision making is indexed by trial-to-trial vari- Challman TD, Lipsky JJ (2000) Methylphenidate: its pharmacol- ability of the EEG. Proc Natl Acad Sci U S A 106:6539–6544. ogy and uses. Mayo Clin Proc 75:711–721. Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018 Beste et al. | 7 Redgrave P, Gurney K (2006) The short-latency dopamine sig- Volkow ND, Wang GJ, Fowler JS, Gatley SJ, Logan J, Ding YS, Dewey nal: a role in discovering novel actions? Nat Rev Neurosci SL, Hitzemann R, Gifford AN, Pappas NR (1999) Blockade of 7:967–975. striatal dopamine transporters by intravenous methyl- Reig R, Silberberg G (2014) Multisensory integration in the mouse phenidate is not sufficient to induce self-reports of “high.” striatum. Neuron 83:1200–1212. J Pharmacol Exp Ther 288:14–20. Rösler M, Fischer R, Ammer R, Ose C, Retz W (2009) A randomised, Wagenmakers EJ (2007) A practical solution to the pervasive placebo-controlled, 24-week, study of low-dose extended- problems of P values. Psychon Bull Rev 14:779–804. release methylphenidate in adults with attention-deficit/ Wagenmakers EJ, van der Maas HL, Dolan CV, Grasman RP (2008) hyperactivity disorder. Eur Arch Psychiatry Clin Neurosci EZ does it! Extensions of the EZ-diffusion model. Psychon 259:120–129. Bull Rev 15:1229–1235. Schulz S, Arning L, Pinnow M, Wascher E, Epplen JT, Beste C Wagenmakers EJ, van der Maas HL, Grasman RP (2007) An (2012) When control fails: influence of the prefrontal but not EZ-diffusion model for response time and accuracy. Psychon striatal dopaminergic system on behavioural flexibility in a Bull Rev 14:3–22. change detection task. Neuropharmacology 62:1028–1033. Ward NM, Brown VJ (1996) Covert orienting of attention in the Servan-Schreiber D, Printz H, Cohen JD (1990) A network model rat and the role of striatal dopamine. J Neurosci 16:3082–3088. of catecholamine effects: gain, signal-to-noise ratio, and Willemssen R, Falkenstein M, Schwarz M, Müller T, Beste C (2011) behavior. Science 249:892–895. Effects of aging, Parkinson’s disease, and dopaminergic medi- Skirrow C, McLoughlin G, Banaschewski T, Brandeis D, Kuntsi J, cation on response selection and control. Neurobiol Aging Asherson P (2015) Normalisation of frontal theta activity fol- 32:327–335. lowing methylphenidate treatment in adult attention-deficit/ Winkel J, van Maanen L, Ratcliff R, van der Schaaf ME, hyperactivity disorder. Eur Neuropsychopharmacol 25:85–94. van Schouwenburg MR, Cools R, Forstmann BU (2012) Stock AK, Arning L, Epplen JT, Beste C (2014) DRD1 and DRD2 Bromocriptine does not alter speed-accuracy tradeoff. Front genotypes modulate processing modes of goal activation Neurosci 6:126. processes during action cascading. J Neurosci 34:5335–5341. Yildiz A, Chmielewski W, Beste C (2013) Dual-task performance Stock AK, Hoffmann S, Beste C (2017) Effects of binge drinking and is differentially modulated by rewards and punishments. hangover on response selection sub-processes-a study using Behav Brain Res 250:304–307. EEG and drift diffusion modeling. Addict Biol 22:1355–1365. Yousif N, Fu RZ, Abou-El-Ela Bourquin B, Bhrugubanda V, Schultz Surmeier DJ, Ding J, Day M, Wang Z, Shen W (2007) D1 and D2 SR, Seemungal BM (2016) Dopamine activation preserves vis- dopamine-receptor modulation of striatal glutamatergic ual motion perception despite noise interference of human signaling in striatal medium spiny neurons. Trends Neurosci V5/MT. J Neurosci 36:9303–9312. 30:228–235. Ziegler S, Pedersen ML, Mowinckel AM, Biele G (2016) Modelling Tomkins A, Vasilaki E, Beste C, Gurney K, Humphries MD (2013) ADHD: a review of ADHD theories through their predictions Transient and steady-state selection in the striatal micr -ocir for computational models of decision-making and reinforce- cuit. Front Comput Neurosci 7:192. ment learning. Neurosci Biobehav Rev 71:633–656. Vijayraghavan S, Wang M, Birnbaum SG, Williams GV, Arnsten AF Znamenskiy P, Zador AM (2013) Corticostriatal neurons in audi- (2007) Inverted-U dopamine D1 receptor actions on prefrontal tory cortex drive decisions during auditory discrimination. neurons engaged in working memory. Nat Neurosci 10:376–384. Nature 497:482–485. Downloaded from https://academic.oup.com/ijnp/advance-article-abstract/doi/10.1093/ijnp/pyy019/4958213 by Ed 'DeepDyve' Gillespie user on 08 June 2018

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International Journal of NeuropsychopharmacologyOxford University Press

Published: Apr 2, 2018

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