The effects dopamine related genes on decision dynamics and psychiatric dimensions

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Simay Üner, Fuat Balcı This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6524080/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dopaminergic function is implicated in different aspects of cognition and affect. However, the role of dopamine in decision process is not fully understood. We investigated the association between the dopaminergic (DA) gene polymorphisms (i.e., COMT , DRD2 , DARPP-32) with the latent variables of the decision-process as well as neuropsychiatric states estimated from factor analysis. 82 students were tested in a perceptual decision-making task with different difficulties and filled out five neuropsychiatric scales. The accuracy and response times gathered from the decision-making task were modeled with a hierarchical Bayesian drift-diffusion model to estimate how the core latent parameters of the decision process changed between different gene polymorphisms. Met/Met allele of COMT gene, which is associated with increased DA in prefrontal cortex had less cautious decision making and more efficient evidence integration when compared to Val carriers. Individuals with this polymorphism also had higher obsessive-compulsive and anxious-depressive scores. There were no significant associations with polymorphisms of DRD2 and DARPP-32 genes. Our results suggest a modulatory (through decision threshold) and enhancing (through the rate of sensory evidence integration) role of prefrontal dopamine related gene COMT polymorphism in perceptual decision making. Our results associate Met/Met allele with an increase in obsessive-compulsive and anxious-depressive scores. Dopamine DRD2 DARPP-32 COMT Drift-Diffusion Model Obsessive Compulsive Disorder Figures Figure 1 Introduction Dopamine (DA) is implicated in the control of various functions in the brain (Missale et al., 1998). For example, DA is involved in motor function (Barbeau, 1974), endocrine regulation (Pivonello et al., 2007), reinforcement (Reynolds et al., 2001), emotion (Nieollon & Coquerel, 2003), cognition (Nieoullon, 2002), motivation (Salamone & Correa, 2012) and reward systems (Wise & Rompre, 1989). The presence of DA in the mammalian forebrain, specifically basal ganglia, is linked with limbic and motor processes (Nieollon & Coquerel, 2003). Crucially, DA is more readily available in the frontal lobes than posterior cortical regions, which correlates with cognitive abilities of the primates, and is vital for higher-order cognitive processes (e.g., executive functions) of the prefrontal cortex (PFC) (Nieoullon, 2002). The relation between DA and decision-making processes is not fully understood. Perceptual decision making provides an ideal domain to use for the study of the role of DAergic function in choice behavior due to the mechanistic granularity by which the underlying decision process can be characterized. For instance, the choice accuracy and response times gathered from such tasks can be accounted for in a unified fashion by evidence integration to threshold models (Brown & Heathcote, 2008; Ratcliff & McKoon, 2008; Usher & McClelland, 2001). A prominent model that is widely applied to choice behavior is the drift-diffusion model (Ratcliff, 1978; Ratcliff & McKoon, 2008). According to this model, sensory evidence is noisy (drift rate), evidence supporting two different alternatives is integrated over time and when this integrated evidence hits one of the two thresholds, the corresponding decision is made. The latency to threshold first crossing times indexes the decision time. A number of psychopharmacological studies that used this decision theoretic approach have so far revealed equivocal results regarding the effect of DAergic manipulations on decision making. For instance, Beste et al. (2018) reported increase in the efficiency of evidence integration (i.e., drift rate) and no difference in decision cautiousness (i.e., threshold setting) as a result of methylphenidate administration. On the other hand, Rawji et al. (2020) reported decrease in both drift rate and threshold as a result of the administration of another DA agonist (i.e., ropinirole). Wagner et al. (Wagner et al., 2020) reported that the administration of DA antagonist haloperidol did not affect either the drift rate or decision threshold. On the clinical front, patients with Parkinson’s disorder had higher drift rate and same threshold setting during ON compared to OFF medication (Huang et al., 2015). These studies suggest that although DA affects the latent processes of decision making, the exact mechanistic nature of this effect needs to be clarified. These different observations might be due to vast variation in neurotransmitter systems. For instance, there are at least five different subtypes of DAreceptors which are divided into two categories: D 1 -like and D 2 -like G-protein coupled DAreceptors (Seeman, 2010). D 1 -like DA receptors (D 1 and D 5 ) are associated with mostly excitatory but also inhibitory processes (1). The action of D 2 -like DA receptors (D 2 , D 3 , and D 4 ) is mostly inhibitory (1). D 1 receptors are known to be the most abundant-whereas the density of D 3 , D 4 , D 5 receptors is relatively lower in the brain (Romanelli et al., 2010). Relatedly, many studies that have demonstrated the effect of DA signaling on cognitive function emphasized the neurobiological aspects and the availability of DA receptors (see Iversen & Iversen, 2007 for a review). For instance, studies emphasizing how genetic components of DAreceptors affect and alter the function of dopamine in the striatum and frontal cortex have focused on specific DAergic gene polymorphisms (e.g., Balcı et al., 2013; Frank et al., 2009) relating their findings with behavioral outcomes by alluding to DA-dependent cellular and neuronal mechanisms. The D2 receptor is encoded by the DRD2 gene of 11th chromosome in the human genome (Noble, 1998). Researchers have identified a multitude of polymorphisms associated with structural and functional changes in the D2 receptor (e.g., Itowaka et al., 1993; Savitz et al., 2013; Zhang et al., 2007). One of the single nucleotide polymorphisms (SNPs) in the DRD2 gene is rs6277, also known as DRD2 C957T. This polymorphism results in one nucleotide change from cytosine (C) to thymine (T), which has been argued to have significant neurobiological effects on the striatum: TT homozygotes have greater D2 receptor density, but lower affinity for DA (Hirvonen et al., 2004). From a cognitive perspective, CC homozygotes were associated with poorer performance in spatial working memory tasks, indicating their importance for executive function (Klaus et al., 2017), and poorer cognitive abilities in general (Chien et al., 2013). Furthermore, Frank and his colleagues (2007) showed that TT carriers had better and faster performance in avoidance learning. In addition to the roles of receptor-encoding genes, it is feasible to study the DA receptor signaling through other molecular targets. One of the most studied molecular targets of DA signaling is DARPP-32, an essential protein in striatal medium spiny neurons (Ouimet & Greengard, 1990). Signaling pathways of both D 1 -like and D 2 -like DA receptors involve DARPP-32 which is phosphorylated by D 1 -like receptor signaling (Hemmings et al., 1984). On the other hand, DARPP-32 is dephosphorylated in D 2 -like receptor signaling (Nishi et al., 1997). A polymorphism of the gene PPP1R1B encoding DARPP-32 is a single nucleotide change to adenine (A) from guanine (G) (rs907094). Individuals with AA genotype have relatively greater amounts of DARPP-32 protein in the striatum (Meyer-Lindenberg et al., 2007). This genotype was formerly associated with better learning from positive outcomes by Frank et al. (2007), greater connectivity between frontal and striatal regions, along with improved cognitive performance (Meyer-Lindenberg et al., 2007). The role of DAergic activity in the PFC has been most directly implicated in cognitive control (e.g., Cools, 2008; Cools & D'Esposito, 2011). Catecholamine neurotransmitters (i.e. dopamine, epinephrine, norepinephrine) are catabolized by the Catecholamine- O -methyltransferase ( COMT ) enzyme in the brain, and its activity becomes significant in PFC where DA receptors are present in comparatively low amounts (Seamans & Yang, 2004). A well-studied polymorphism of the COMT gene is Val158Met (rs4680), resulting in a single G to A nucleotide substitution. Met allele is associated with A nucleotide, and COMT enzymatic activity of Met carriers is reduced by approximately 40% compared to individuals with Val/Val genotype (Chen et al., 2004). Consequently, DA availability of Met carriers is higher in PFC than individuals with Val/Val genotype. Such an effect was associated with an increase in physiological PFC response and responsiveness to reward (Egan et al., 2001; Lancaster et al., 2012) and better adaptation to task when encountered a negative outcome in decision-making (Frank et al., 2007). The current study investigated the role of aforementioned polymorphisms (rs6277, rs907094, rs4680) in a two-alternative forced-choice (2AFC) decision-making task by comparing genotype groups in terms of RT, accuracy, and the parameters of the diffusion model of decision-making (Ratcliff, 1978). These analyses were coupled by the comparison of genotype groups in terms of the neuropsychiatric factors. Methods and Materials Participants A total of 82 participants were recruited either from the subject pool or by announcements around the Koç University campus. All participants provided signed consent to participate in the study and to the collection of their saliva samples for genetic analysis, and their participation was compensated by a monetary reward based on their task performance. We removed a total of 10 participants from the analysis (prior to the analysis) since (a) 5 participants completed the behavioral procedure but their saliva samples were not collected, and (b) behavioral data of 5 participants indicated arbitrary response patterns. Therefore, the analyzed sample consisted of 72 participants (40 females) ranging in age from 18 to 24 ( M = 19.9, SD = 1.4). Participants were asked to complete a medical screening questionnaire prior to the experiment for medical, psychiatric and neurological information of the participant and their families, as well as drug use and demographic information. All procedures were in accordance with the Declaration of Helsinki and they were approved by the Institutional Review Board of Koç University. Stimuli and apparatus Each participant was presented with white dots (3 × 3 pixels) moving randomly with a fixed speed. Stimuli were shown in a circular region (3-inch diameter) at the center of the display with a black background. Only a particular portion (0, 8, or 16%) of the dots had a fixed direction either leftward or rightward on each trial, whereas the remaining portion of the white dots were moving randomly. The stimulus was presented to the participants through the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) on a 21.5-inch MAC monitor and generated in MATLAB (The MathWorks Natick, MA). The participants responded to the stimulus via computer keyboard, and their distance from the monitor was approximately 60 cm. Procedure Turkish versions of the following self-report measures were administered to all participants prior to behavioral task: STAI-A-Trait Scale (Öner & Le Compte, 1985; Spielberger et al., 1971), Beck’s Depression Inventory (Beck et al., 1961; Hisli, 1989), Barratt Impulsivity Scale (BIS-11) (Güleç et al., 2008; Patton et al., 1995), Behavioral Inhibition-Behavioral Activation Scales (BIS-BAS) (Carver & White, 1994; Şişman, 2012), Padua Inventory (Beşiroğlu et al., 2005; Sanavio, 1988). The Turkish versions of the questionnaires listed above had sufficient psychometric properties. The experiment consisted of three sessions. Each session started with a 4-min practice block with 16% motion coherence. Then, participants completed a total of 9 free-response (FR) blocks, each lasted for 4-min. There were 3 blocks for each coherence level (0%, 8%, 16%), and the order of blocks were randomized. In each FR block, participants were expected to indicate the motion direction as accurately and quickly as possible via pressing “M” key for rightward motion with right index finger, and “Z” key for leftward motion with left index finger. In each FR trial, stimulus was presented until a response, and a new trial began after a randomly determined response-to-stimulus time (distributed exponentially in each block, mean = 2 s). Audio feedback was provided for correct responses and participants earned 0.015 TRY for each correct response, while there was no feedback or a penalty for negative responses. Cumulative scores were shown to participants in each 10-trials. Responses given in the first 100 msec after stimulus onset were emitted, as they were premature responses, and signaled by a buzzing sound, followed by 4-sec timeout. Genotyping Until the extraction of DNA, the saliva samples collected from participants were stored at -20°C. DNA was extracted from the saliva samples using Macherey-Nagel NucloSpin Tissue Kit and stored at -20°C. COMT: Tetra-primer PCR procedure was used for the identification of COMT genotypes. Following primers were used: COMT-common-F ‘CCAACCCTGCACAGGCAAGAT’, COMT-common-R ‘CAAGGGTGACCTGGAACAGCG’, COMT-P3-F 'CGGATGGTGGA- TTTCGCTGACG’ and COMT-P4-R ‘TCAGGCATGCACACCTTGTCCTTTAT’ (52). 20 ng DNA was added into each PCR reaction, along with 0.12 mM of COMT-P4-R, 0.20 mM of other three primers, 12.5 ul of Thermo Scientific DreamTaq Green PCR Master Mix (2X) and nuclease-free water up to 25 ul of total volume. Initial denaturation lasted for 4 min at 94°C, then there were 30 cycles of the following steps: denaturation at 94°C for 30 s, annealing at 66°C for 30 s, extension at 72°C for 20 s, followed by 5 min of final extension. PCR products were loaded on 1.5% agarose gel for electrophoresis and then visualized in Biorad Gel Doc XR Imaging System. One of the participant’s DNA was degraded prior to the COMT genotyping therefore it was excluded from the analyses including COMT genotype. Of the remaining 71 samples, 20 of them had no polymorphism (Val/Val), 33 of them had only one polymorphic allele (Val/Met) and 18 of them were homozygous polymorphic (Met/Met). For the analyses, Val/Val and Val/Met genotypes were grouped together to compare with Met/Met genotype (Frank et al., 2009). DRD2: For the determination of DRD2 genotypes, firstly, PCR product was obtained by using following primer pair: DRD2-F ‘ACCAYGGTCTCCACAGCACTCT’ and DRD2-R ‘ATGGCGAGCATCTGAGTGGCT’ (Mohammadi et al., 2018). For each 20 ul PCR reaction, 2 ul DNA was taken from the original stock (variable concentrations, 35 ng/ul on average); 0.2 mM of each primer, 10 ul of Thermo Scientific DreamTaq Green PCR Master Mix (2X) and 7.2 ul of nuclease-free water were added. PCR protocol was started with initial denaturation at 95°C for 5 in, followed by 35 cycles of denaturation at 94°C for 30 s, annealing at 56.9°C for 1 min, extension at 72°C for 1 min, and completed by the final extension step at 72°C for 10 min. Macherey-Nagel NucleoSpin Gel and PCR Clean-up kit was used to obtain PCR products. Afterwards, DRD2 digestion reaction was prepared with the following ingredients: 200 ng PCR product, 0.5 ul Thermo Scientific TaqI (10U/ul) enzyme, 2 ul Thermo Scientific Buffer TaqI (10X), and nuclease-free water up to 20 ul of total reaction. Digestion procedure took a total of 2.5 hours in the thermocycler at 65°C, no heat activation step was required. 4 ul of Thermo Scientific DNA Loading Dye (6X) was added into each reaction prior to agarose gel (3%) electrophoresis. Digestion products were visualized in Biorad Gel Doc XR Imaging System. Among the 72 samples genotyped, there were 13 CC genotypes (no polymorphism), 35 CT genotypes (heterozygous) and 24 TT genotypes (homozygous polymorphic). For the analyses, CT and TT genotypes were grouped together to compare with CC genotypes. DARPP-32: The same PCR reactions were prepared as DRD2 by using these particular primers to obtain DARPP-32 PCR products: DARPP-32-F ‘GCATTGCTGAGTCTCACCTGCAGTCT’ and DARPP-32--R ‘ATTGGGAGAGGGACTGAGCCAAGGATGG’ (20). The PCR protocol was the same as DRD2 for DARPP-32 except the annealing temperature was 63.3°C for the DARPP-32 primer pair. Similarly, PCR products were obtained by using Macherey-Nagel NucleoSpin Gel and PCR Clean-up kit. Then, DARPP-32 digestion reactions were prepared by adding the following: 200 ng PCR product, 0.30 ul BioLabs MseI enzyme (10U/ul), 2 ul BioLabs rCutSmart Buffer (10X), and nuclease-free water up to a total of 20 ul digestion reaction. Digestion protocol was started with 1.5 h of incubation at 37°C, and followed by a heat inactivation step at 65°C for 20 min. After the addition of 4 ul of Thermo Scientific DNA Loading Dye (6X), samples were loaded on 3% agarose gel for electrophoresis. Gels were visualized in Biorad Gel Doc XR Imaging System. Genotyping results revealed that there were two samples with no polymorphism (GG), 41 samples were heterozygous polymorphic (GA) and the remaining 29 samples had two polymorphic alleles (AA). For the analyses, GG and GA genotypes were grouped together to compare with AA genotypes (Frank et al., 2009). Data Analysis Factor analysis: We employed factor analysis with all the five questionnaires used in the study. This way, we aimed to reduce the collinearity between the overall results of the individual questionnaires, the number of statistical tests conducted and the associated Type 1 errors. We strictly followed the methodological approach utilized in Gillan et al. (2016) and Rouault et al. (2018) for factor analysis using Maximum Likelihood Estimation (MLE). By using R software, factor analysis was performed by the factanal() function in the Psych package, with an oblique rotation (oblimin). The selection of the number of the factors was determined by Cattell’s criterion (Cattell, 1966); wherein a sharp transition from horizontal to vertical (‘elbow’) indicates that there is no significant benefit in keeping additional factors. The objective implementation of this criterion was employed by the Cattell-Nelson-Gorsuch (CNG) test, which calculates the slopes of all possible sets of three neighboring eigenvalues and determines the point at which there is the largest differences in slope (nFactors package in R) (Gorsuch & Nelson, 1981). The CNG test indicated the existence of a 3-factor latent structure that includes factors we labeled as ‘Obsessive – Compulsive’, ‘Anxious – Depressive’ and ‘Impulsivity’ based on the strongest individual item loadings. While labeling the factors, we apply 0.25 average loading threshold to the questionnaires as in Gillan et al. (2016). For the ‘Obsessive – Compulsive’ factor, the only questionnaire that reached the threshold we set is the Padua Inventory ( M = 0.42, SD = 0.16). The highest average loadings for the factor ‘Anxious – Depressive’ came from STAI-A-Trait Scale ( M = 0.36, SD = 0.17), followed by the Beck’s Depression Inventory ( M = 0.29, SD = 0.17). Barratt Impulsivity Scale ( M = 0.35, SD = 0.25) is the only questionnaire that reached the threshold for ‘Impulsivity’ factor. STAI-A-Trait Scale ( M = 0.23, SD = 0.16) came very close to the threshold for the ‘Impulsivity’ factor as in Gillan et al. (2016). Behavioral Data Analysis Response times (RTs) and accuracy were obtained from the test sessions and determined as the behavioral units of analysis. There was no exclusion of data based on RT (except for DDM fits for which RTs < 100 ms were excluded and the outlier probability was defined as 5%). We used the data from the last two sessions (considered as steady state) in the analyses. First, independent samples t-test was used to compare accuracy and RTs of different genotype groups, using Jamovi (Version 1.6.23.0). Then, we fit the data using Hierarchical Bayesian estimation of DDM parameters (Wiecki et al., 2013) in Python to test how the threshold and drift rate differed between each group and difficulty level. Results First, we tested whether accuracy and RTs differed across the three difficulty levels (i.e., easy (coherence 16%), medium (coherence 8%) and difficult (coherence 0%)) and determined groups of genotypes (note that accuracy was not tested for difficult condition since it is not possible to label any trial as correct or wrong with 0% coherence). Moreover, we also compared the genotype groups in terms of factor scores calculated from the responses to the questionnaires. Descriptive statistics can be found in Table 1. [Insert Table 1 here] The comparison of COMT genotype groups illustrated that subjects with Met/Met genotype ( M = 0.71, SD = 0.13) were faster than Val carriers (Val/Val or Val/Met - M = 0.83, SD = 0.21) only in easy condition ( t (69) = 2.20, p = .031, d = 0.60), but not in medium and difficult conditions (min p = .353). There was no significant difference between COMT genotype groups in mean accuracy level in either easy or medium condition (min p = .242). The differences between the genotype groups of DRD2 (CC vs. CT/TT) and DARPP-32 (AA vs. GA/GG) were also investigated in terms of accuracy in easy and medium conditions, RTs for each difficulty conditions, and factor scores. No significant differences were found for any of those comparisons between genotype groups (min p = 0.24). Statistics pertaining to the independent samples t -test for aforementioned comparisons can be found in Table 2. The scores in ‘obsessive – compulsive’ factor were found to be statistically higher in Met/Met genotype ( M = 38.49, SD = 15.3) group than those with Val ( M = 28.70, SD = 13.45) genotype ( t (69) = -2.58, p = .012, d = -0.70). Similarly, the scores in ‘anxious depressive’ factor were found to be statistically higher in Met/Met genotype ( M = 31.78, SD = 8.43) group than Val ( M = 26.18, SD = 8.36) carriers ( t (69) = -2.45, p = .017, d = -0.69). The comparison of COMT genotype groups with regard to the ‘impulsivity’ scores did not yield any significant difference ( p = .40). In terms of DRD2 and DARPP-32 genotype groups, there was no difference in factor scores (min p = .239). [Insert Table 2 here] HDDM results revealed that individuals with Met/Met genotype have lower decision thresholds than Val carriers, both in easy ( P a _Met/Met > a _ Val = 0.02) and moderate conditions ( P a _Met/Met > a _ Val = 0.04). Moreover, the Met/Met genotype had marginally higher drift rates in easy condition ( P v _Met/Met > v _ Val = 0.94), while no difference was observed for the medium difficulty condition ( P v _Met/Met > v _ Val = 0.86). There was also no significant difference between determined groups of DRD2 (CC vs. T carriers) and DARPP-32 (AA vs. G carriers) gene polymorphisms in terms of the effect of difficulty on drift rate and threshold parameters of DDM (0.49 a _ T < 0.70 and 0.05 v _ T < 0.74) for DRD2 comparisons; 0.64 a _ G < 0.85 and 0.21 v _ G < 0.67 for DARPP-32 groups). Figure 1 illustrates the distribution of posterior probabilities of threshold and drift rate parameters for COMT , DRD2 and DARPP-32 groups. [Figure 1 about here] Discussion In this study, we investigated the effect of three different polymorphisms associated with the dopaminergic function in the central nervous system on both decision-making dynamics and the scores in psychiatric symptom dimensions. Our results suggest that the Met/Met genotype in COMT gene is associated with the decrease in response time and decision threshold, and a tendency for higher drift rate. We could not detect any relation between other investigated polymorphisms and decision-making parameters. Similarly, we only detect association between Met/Met genotype in the COMT gene with higher scores associated with obsessive-compulsive and anxious-depressive traits. The relations between other SNPs in DARPP-32 and DRD2 and the scores in psychiatric symptom dimensions were not statistically significant. We did not detect any difference between the investigated SNPs in terms of accuracy. Note that the literature also reports equivocal findings on this front. For instance, Malloy-Diniz et al. (Malloy-Diniz et al., 2013) reported that individuals having Val/Val genotype showed better performance in a decision-making task. Relatedly, van den Bos et al. (van den Bos et al., 2009) found that participants having Met/Met genotype had the poorer performance in the Iowa Gambling Task, compared to other genotype groups. On the other hand, several studies reported that Met/Met carriers were better in terms of their cognitive performance (Wishart et al., 2011). In terms of DRD2 and DARPP-32, Frank et al. (2007) did not find any significant differences in accuracy levels in the Go/No Go task. The only difference in terms of response time observed in our study was associated with Val158Met groups. Our results suggest that the Met/Met group were faster in perceptual decision-making task than Val carriers. This finding is in line with the study of Malloy-Diniz et al. (2013) which associates Met/Met group with more impulsive behavior. In another study participants with Met/Met genotype were faster in a cognitive control task (Shehzad et al., 2012). However, because behavioral measures of aforementioned studies did not resemble our experimental paradigm, it is important to further investigate how decision-making behavior differs among Val158Met groups in a two-alternative-forced-choice task. Additionally, future studies should also address the mechanisms that underlie the interaction between task difficulty and genotype (specific effect in easy conditions). In terms of latent parameters of decision making, we observed that Met/Met group present a tendency to have a higher drift rate. Because individuals with Met/Met genotype have higher levels of DA availability in PFC, which was previously associated with evidence accumulation of the decision-maker (Mulder et al., 2014), prefrontal DA levels might indeed have a role in the efficiency by which sensory evidence is integrated in the decision-making context. Moreover, individuals with Met/Met genotype were less cautious in the decision-making process, as they had lower thresholds than Val carriers. Since frontal regions are thought to be significant also for the threshold adjustment (Mulder et al., 2014), increased levels of DA in PFC may be effective on the decision threshold settings based on fronto-basal ganglia network. Note that this result is also consistent with higher impulsivity in the Met/Met group (Malloy-Diniz et al., 2013). In addition to the analysis of behavioral performance, we also investigated the association between psychiatric symptom dimensions and aforementioned SNPs. Consistent with earlier studies (Kumar & Rai, 2020; Olson et al., 2005), we detected associations between Met/Met allele with both obsessive-compulsive and anxious-depressive dimension scores that were in the same direction. Met/Met allele was associated with more anxious-depressive symptoms. Another study showed that patients with OCD diagnosis and Met/Met allele have poorer performance in executive function tasks (Tükel et al., 2013), which is not fully consistent with the behavioral performance of the Met/Met group in the current study. Another meta-analysis reported that Met/Met carriers are more efficient than Val/Val carriers in cognitive processing, but less efficient in emotional processing (Mier et al., 2010). Thus, it is possible that participants with Met/Met allele are associated with better evidence processing and decision-making abilities when the task does not require any emotional processing, as in our study. One of the main limitations of our study is the small sample size for genetic analysis. Thus, the results, especially including psychiatric symptom scores, should be interpreted cautiously. The reliability of factor analysis we conducted increases when the sample size is larger. This study must be replicated by a larger group of participants. It is also important to note that our sample only included college students without any diagnosed psychiatric disorders. The generalizability of the result can be improved by admitting participants with a wide variety of age and level of education. To conclude, this study investigated the single nucleotide polymorphisms associated with dopaminergic function in relation to perceptual decision making and psychiatric symptom dimensions. We found individuals with the Met/Met allele, which is associated with increased DA in PFC had less cautious decision making and a tendency for more efficient evidence integration when compared to Val carriers. Thus, our result suggests an enhancing role of dopamine in perceptual decision-making tasks. Consistent with earlier studies, we also found an association between Met/Met allele and obsessive-compulsive and anxious-depressive scores. Declarations Acknowledgments and Disclosures: The authors gratefully acknowledge use of the services and facilities of the Koç University Research Center for Translational Medicine (KUTTAM), funded by the Presidency of Turkey, Presidency of Strategy and Budget. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Presidency of Strategy and Budget. Declaration of generative AI in scientific writing: Generative AI was not used in the preparation of this manuscript. Funding Declaration: A.Ş.K. was supported by TÜBİTAK (BİDEB-2211). This research was supported by TUBA-GEBIP-2015 award to FB. We would like to thank Bilgehan Cavdaroglu for his help during data collection and Ahmed Moustafa and Tuba Mutluer for their comments on the earlier versions of this manuscript. This work is part of A.Ş.K.’s doctoral thesis. CRediT authorship contribution statement: F.B. conceptualization, investigation, coding, formal analysis, methodology, writing; A.Ş.K. conceptualization, coding, formal analysis, writing; T.T. conceptualization, investigation, coding, formal analysis, methodology; B.S.Ü. Wet lab, writing, formal analysis Clinical trial number: Not applicable Human Ethics and Consent to Participate: All participants provided signed informed consent for their participation in the behavioral experiments, collection and processing of saliva samples, and the publication of the results in an anonymized fashion. 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Proceedings of the National Academy of Sciences , 104 (51), 20552-20557. https://doi.org/10.1073/pnas.0707106104 Tables Table 1 Descriptive Statistics COMT DRD2 DARPP-32 Met/Met Val/Met & Val/Val CC CT & TT AA GA & GG M SD M SD M SD M SD M SD M SD Mean RT (Easy) 0.714 0.1344 0.831 0.2106 0.821 0.1768 0.801 0.2066 0.835 0.2095 0.735 0.1941 Mean RT (Medium) 0.966 0.2806 1.042 0.3010 1.049 0.2437 1.021 0.3075 1.073 0.2909 0.995 0.2981 Mean RT (Difficult) 1.420 0.8644 1.424 0.7616 1.439 0.4728 1.430 0.8361 1.509 0.6708 1.379 0.8498 Accuracy (Easy) 0.951 0.0711 0.930 0.0739 0.930 0.0634 0.935 0.0755 0.936 0.0716 0.934 0.0749 Accuracy (Medium) 0.855 0.0836 0.820 0.1159 0.822 0.1064 0.830 0.1097 0.829 0.0947 0.829 0.1179 Obsessive - Compulsive 38.49 15.30 28.70 13.45 32.54 14.53 30.89 14.45 28.96 11.55 32.69 15.96 Anxious -Depressive 31.78 8.43 26.18 8.36 30.21 9.08 27.08 8.49 27.48 7.99 27.75 9.10 Impulsivity 55.97 10.95 49.92 13.81 54.18 10.60 49.78 13.52 49.10 10.04 51.60 14.81 Note. RTs are given in sec, and difficulty conditions are given in parentheses. COMT groups were Met/Met vs. Val/Met & Val/Val. DRD2 groups were CC vs. CT & TT, DARPP-32 groups were AA vs. GA & GG Table 2 Independent Samples t-test (groups of COMT, DRD2 and DARPP-32 genes) Variable (Condition) COMT DRD2 DARPP-32 t df p Cohen’s d t df p Cohen’s d t df p Cohen’s d Mean RT (Easy) 2.2033 69.0 0.031 0.60109 -0.3265 70.0 0.745 -0.1000 1.0383 70.0 0.303 0.24949 Mean RT (Medium) 0.9358 69.0 0.353 0.25529 -0.3091 70.0 0.758 -0.0947 1.0938 70.0 0.278 0.26283 Mean RT (Difficult) 0.0163 69.0 0.987 0.00445 -0.0379 70.0 0.970 -0.0116 0.6900 70.0 0.492 0.16580 Accuracy (Easy) -1.0572 69.0 0.294 -0.28840 0.2288 70.0 0.820 0.0701 0.1169 70.0 0.907 0.02808 Accuracy (Medium) -1.1793 69.0 0.242 -0.32173 0.2588 70.0 0.797 0.0793 0.0118 70.0 0.991 0.00285 Obsessive - Compulsive -2.576 69.0 0.012 - 0.703 -0.372 70.0 0.711 -0.1140 -1.081 70.0 0.284 -0.2597 Anxious -Depressive -2.447 69.0 0.017 -0.668 -1.187 70.0 0.239 -0.3636 -0.129 70.0 0.897 -0.0311 Impulsivity 0.846 69.0 0.400 -0.231 -1.099 70.0 0.275 -0.3369 -0.806 70.0 0.423 -0.1937 Note. RTs are given in sec, and difficulty conditions are given in parentheses. COMT groups were Met/Met vs. Val/Met & Val/Val. DRD2 groups were CC vs. CT & TT, DARPP-32 groups were AA vs. GA & GG Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6524080","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450530836,"identity":"baa732c1-49cc-4fcb-a488-7f64216110bf","order_by":0,"name":"Anıl Şafak Kaçar","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Anıl","middleName":"Şafak","lastName":"Kaçar","suffix":""},{"id":450530838,"identity":"5e65eec3-b1da-4e86-b0f2-d7339de3dc67","order_by":1,"name":"Tuğçe Tosun","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Tuğçe","middleName":"","lastName":"Tosun","suffix":""},{"id":450530842,"identity":"f5e5eb7b-d0a9-4de5-8e3d-c5c01433e3e6","order_by":2,"name":"B. Simay Üner","email":"","orcid":"","institution":"Bilkent University","correspondingAuthor":false,"prefix":"","firstName":"B.","middleName":"Simay","lastName":"Üner","suffix":""},{"id":450530847,"identity":"a898b2f7-4d79-4adb-868f-45b63d7d15e9","order_by":3,"name":"Fuat Balcı","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIie3PMYvCMBjG8YRAu7ziGq7iZ6gIIgj2q/Qo6BIHZwvNpJPeeh/D6ejYI1CX4izcDbro4lAXcRLfxrlVN4f8CR0e+PFSQkymN8yW+KGSNAhjSTGAflVBggAJEGb5eoDnCQH3vjwkPDhsadyH+gzOH5vwv+HZ89+chP0KMmhJmgXAVe3HEekeANYBJ2lQSjzuI5kycFlBLAXAhcupZBVXhickERLYO+KqSftCZVRBRHFFFcRyRlNNOnhFlRM4jr8/sxX+i9XujRZIsvWg66ercmIPl/kpnjTrX2r3J87Ks2dztcnDSSnR+U8sJpPJZHqlG4GFSl2HueEGAAAAAElFTkSuQmCC","orcid":"","institution":"University of Manitoba","correspondingAuthor":true,"prefix":"","firstName":"Fuat","middleName":"","lastName":"Balcı","suffix":""}],"badges":[],"createdAt":"2025-04-25 00:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6524080/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6524080/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81959639,"identity":"87db9125-4d5d-41a3-a183-9c6ecfefbbe7","added_by":"auto","created_at":"2025-05-05 10:32:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":190621,"visible":true,"origin":"","legend":"\u003cp\u003ePosterior probability distributions for threshold (a) COMT, (b), DRD2, (c) DARPP-32; and for drift rate (d) COMT, (e) DRD2, (f) DARPP-32. For COMT, ‘a’ represents the Met/Met genotype, and ‘g’ represents the Val carriers. For DRD2, ‘c’ indicates the CC genotype, while T carriers are represented by ‘t’. Lastly, for DARPP-32, AA genotype is labeled as ‘aa’, while ‘ga’ includes both GG and GA genotypes.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6524080/v1/24f39b865afea7a1a7dbddd1.png"},{"id":83399653,"identity":"dec5d572-0cfe-45e1-9cd8-6979b551bd59","added_by":"auto","created_at":"2025-05-25 06:02:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":999063,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6524080/v1/7e2dd6cf-1695-4ef8-b89b-e8cafef520c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effects dopamine related genes on decision dynamics and psychiatric dimensions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDopamine (DA) is implicated in the control of various functions in the brain (Missale et al., 1998). For example, DA is involved in motor function (Barbeau, 1974), endocrine regulation (Pivonello et al., 2007), reinforcement (Reynolds et al., 2001), emotion (Nieollon \u0026amp; Coquerel, 2003), cognition (Nieoullon, 2002), motivation (Salamone \u0026amp; Correa, 2012) and reward systems (Wise \u0026amp; Rompre, 1989). The presence of DA in the mammalian forebrain, specifically basal ganglia, is linked with limbic and motor processes (Nieollon \u0026amp; Coquerel, 2003). Crucially, DA is more readily available in the frontal lobes than posterior cortical regions, which correlates with cognitive abilities of the primates, and is vital for higher-order cognitive processes (e.g., executive functions) of the prefrontal cortex (PFC) (Nieoullon, 2002).\u003c/p\u003e \u003cp\u003eThe relation between DA and decision-making processes is not fully understood. Perceptual decision making provides an ideal domain to use for the study of the role of DAergic function in choice behavior due to the mechanistic granularity by which the underlying decision process can be characterized. For instance, the choice accuracy and response times gathered from such tasks can be accounted for in a unified fashion by evidence integration to threshold models (Brown \u0026amp; Heathcote, 2008; Ratcliff \u0026amp; McKoon, 2008; Usher \u0026amp; McClelland, 2001). A prominent model that is widely applied to choice behavior is the drift-diffusion model (Ratcliff, 1978; Ratcliff \u0026amp; McKoon, 2008). According to this model, sensory evidence is noisy (drift rate), evidence supporting two different alternatives is integrated over time and when this integrated evidence hits one of the two thresholds, the corresponding decision is made. The latency to threshold first crossing times indexes the decision time.\u003c/p\u003e \u003cp\u003eA number of psychopharmacological studies that used this decision theoretic approach have so far revealed equivocal results regarding the effect of DAergic manipulations on decision making. For instance, Beste et al. (2018) reported increase in the efficiency of evidence integration (i.e., drift rate) and no difference in decision cautiousness (i.e., threshold setting) as a result of methylphenidate administration. On the other hand, Rawji et al. (2020) reported decrease in both drift rate and threshold as a result of the administration of another DA agonist (i.e., ropinirole). Wagner et al. (Wagner et al., 2020) reported that the administration of DA antagonist haloperidol did not affect either the drift rate or decision threshold. On the clinical front, patients with Parkinson\u0026rsquo;s disorder had higher drift rate and same threshold setting during ON compared to OFF medication (Huang et al., 2015). These studies suggest that although DA affects the latent processes of decision making, the exact mechanistic nature of this effect needs to be clarified.\u003c/p\u003e \u003cp\u003eThese different observations might be due to vast variation in neurotransmitter systems. For instance, there are at least five different subtypes of DAreceptors which are divided into two categories: D\u003csub\u003e1\u003c/sub\u003e-like and D\u003csub\u003e2\u003c/sub\u003e-like G-protein coupled DAreceptors (Seeman, 2010). D\u003csub\u003e1\u003c/sub\u003e-like DA receptors (D\u003csub\u003e1\u003c/sub\u003e and D\u003csub\u003e5\u003c/sub\u003e) are associated with mostly excitatory but also inhibitory processes (1). The action of D\u003csub\u003e2\u003c/sub\u003e-like DA receptors (D\u003csub\u003e2\u003c/sub\u003e, D\u003csub\u003e3\u003c/sub\u003e, and D\u003csub\u003e4\u003c/sub\u003e) is mostly inhibitory (1). D\u003csub\u003e1\u003c/sub\u003e receptors are known to be the most abundant-whereas the density of D\u003csub\u003e3\u003c/sub\u003e, D\u003csub\u003e4\u003c/sub\u003e, D\u003csub\u003e5\u003c/sub\u003e receptors is relatively lower in the brain (Romanelli et al., 2010).\u003c/p\u003e \u003cp\u003eRelatedly, many studies that have demonstrated the effect of DA signaling on cognitive function emphasized the neurobiological aspects and the availability of DA receptors (see Iversen \u0026amp; Iversen, 2007 for a review). For instance, studies emphasizing how genetic components of DAreceptors affect and alter the function of dopamine in the striatum and frontal cortex have focused on specific DAergic gene polymorphisms (e.g., Balcı et al., 2013; Frank et al., 2009) relating their findings with behavioral outcomes by alluding to DA-dependent cellular and neuronal mechanisms.\u003c/p\u003e \u003cp\u003eThe D2 receptor is encoded by the \u003cem\u003eDRD2\u003c/em\u003e gene of 11th chromosome in the human genome (Noble, 1998). Researchers have identified a multitude of polymorphisms associated with structural and functional changes in the D2 receptor (e.g., Itowaka et al., 1993; Savitz et al., 2013; Zhang et al., 2007). One of the single nucleotide polymorphisms (SNPs) in the \u003cem\u003eDRD2\u003c/em\u003e gene is rs6277, also known as \u003cem\u003eDRD2\u003c/em\u003e C957T. This polymorphism results in one nucleotide change from cytosine (C) to thymine (T), which has been argued to have significant neurobiological effects on the striatum: TT homozygotes have greater D2 receptor density, but lower affinity for DA (Hirvonen et al., 2004). From a cognitive perspective, CC homozygotes were associated with poorer performance in spatial working memory tasks, indicating their importance for executive function (Klaus et al., 2017), and poorer cognitive abilities in general (Chien et al., 2013). Furthermore, Frank and his colleagues (2007) showed that TT carriers had better and faster performance in avoidance learning.\u003c/p\u003e \u003cp\u003eIn addition to the roles of receptor-encoding genes, it is feasible to study the DA receptor signaling through other molecular targets. One of the most studied molecular targets of DA signaling is DARPP-32, an essential protein in striatal medium spiny neurons (Ouimet \u0026amp; Greengard, 1990). Signaling pathways of both D\u003csub\u003e1\u003c/sub\u003e-like and D\u003csub\u003e2\u003c/sub\u003e-like DA receptors involve DARPP-32 which is phosphorylated by D\u003csub\u003e1\u003c/sub\u003e-like receptor signaling (Hemmings et al., 1984). On the other hand, DARPP-32 is dephosphorylated in D\u003csub\u003e2\u003c/sub\u003e-like receptor signaling (Nishi et al., 1997). A polymorphism of the gene PPP1R1B encoding DARPP-32 is a single nucleotide change to adenine (A) from guanine (G) (rs907094). Individuals with AA genotype have relatively greater amounts of DARPP-32 protein in the striatum (Meyer-Lindenberg et al., 2007). This genotype was formerly associated with better learning from positive outcomes by Frank et al. (2007), greater connectivity between frontal and striatal regions, along with improved cognitive performance (Meyer-Lindenberg et al., 2007).\u003c/p\u003e \u003cp\u003eThe role of DAergic activity in the PFC has been most directly implicated in cognitive control (e.g., Cools, 2008; Cools \u0026amp; D'Esposito, 2011). Catecholamine neurotransmitters (i.e. dopamine, epinephrine, norepinephrine) are catabolized by the Catecholamine-\u003cem\u003eO\u003c/em\u003e-methyltransferase (\u003cem\u003eCOMT\u003c/em\u003e) enzyme in the brain, and its activity becomes significant in PFC where DA receptors are present in comparatively low amounts (Seamans \u0026amp; Yang, 2004). A well-studied polymorphism of the \u003cem\u003eCOMT\u003c/em\u003e gene is Val158Met (rs4680), resulting in a single G to A nucleotide substitution. Met allele is associated with A nucleotide, and \u003cem\u003eCOMT\u003c/em\u003e enzymatic activity of Met carriers is reduced by approximately 40% compared to individuals with Val/Val genotype (Chen et al., 2004). Consequently, DA availability of Met carriers is higher in PFC than individuals with Val/Val genotype. Such an effect was associated with an increase in physiological PFC response and responsiveness to reward (Egan et al., 2001; Lancaster et al., 2012) and better adaptation to task when encountered a negative outcome in decision-making (Frank et al., 2007).\u003c/p\u003e \u003cp\u003eThe current study investigated the role of aforementioned polymorphisms (rs6277, rs907094, rs4680) in a two-alternative forced-choice (2AFC) decision-making task by comparing genotype groups in terms of RT, accuracy, and the parameters of the diffusion model of decision-making (Ratcliff, 1978). These analyses were coupled by the comparison of genotype groups in terms of the neuropsychiatric factors.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 82 participants were recruited either from the subject pool or by announcements around the Ko\u0026ccedil; University campus. All participants provided signed consent to participate in the study and to the collection of their saliva samples for genetic analysis, and their participation was compensated by a monetary reward based on their task performance. We removed a total of 10 participants from the analysis (prior to the analysis) since (a) 5 participants completed the behavioral procedure but their saliva samples were not collected, and (b) behavioral data of 5 participants indicated arbitrary response patterns. Therefore, the analyzed sample consisted of 72 participants (40 females) ranging in age from 18 to 24 (\u003cem\u003eM\u003c/em\u003e = 19.9, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= 1.4). Participants were asked to complete a medical screening questionnaire prior to the experiment for medical, psychiatric and neurological information of the participant and their families, as well as drug use and demographic information. All procedures were in accordance with the Declaration of Helsinki and they were approved by the Institutional Review Board of Ko\u0026ccedil; University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStimuli and apparatus\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach participant was presented with white dots (3 \u0026times; 3 pixels) moving randomly with a fixed speed. Stimuli were shown in a circular region (3-inch diameter) at the center of the display with a black background. Only a particular portion (0, 8, or 16%) of the dots had a fixed direction either leftward or rightward on each trial, whereas the remaining portion of the white dots were moving randomly. The stimulus was presented to the participants through the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997) on a 21.5-inch MAC monitor and generated in MATLAB (The MathWorks Natick, MA). The participants responded to the stimulus via computer keyboard, and their distance from the monitor was approximately 60 cm. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTurkish versions of the following self-report measures were administered to all participants prior to behavioral task: STAI-A-Trait Scale (\u0026Ouml;ner \u0026amp; Le Compte, 1985; Spielberger et al., 1971), Beck\u0026rsquo;s Depression Inventory (Beck et al., 1961; Hisli, 1989), Barratt Impulsivity Scale (BIS-11) (G\u0026uuml;le\u0026ccedil; et al., 2008; Patton et al., 1995), Behavioral Inhibition-Behavioral Activation Scales (BIS-BAS) (Carver \u0026amp; White, 1994; Şişman, 2012), Padua Inventory (Beşiroğlu et al., 2005; Sanavio, 1988). The Turkish versions of the questionnaires listed above had sufficient psychometric properties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experiment consisted of three sessions. Each session started with a 4-min practice block with 16% motion coherence. Then, participants completed a total of 9 free-response (FR) blocks, each lasted for 4-min. There were 3 blocks for each coherence level (0%, 8%, 16%), and the order of blocks were randomized. In each FR block, participants were expected to indicate the motion direction as accurately and quickly as possible via pressing \u0026ldquo;M\u0026rdquo; key for rightward motion with right index finger, and \u0026ldquo;Z\u0026rdquo; key for leftward motion with left index finger. In each FR trial, stimulus was presented until a response, and a new trial began after a randomly determined response-to-stimulus time (distributed exponentially in each block, mean = 2 s). Audio feedback was provided for correct responses and participants earned 0.015 TRY for each correct response, while there was no feedback or a penalty for negative responses. Cumulative scores were shown to participants in each 10-trials. Responses given in the first 100 msec after stimulus onset were emitted, as they were premature responses, and signaled by a buzzing sound, followed by 4-sec timeout.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotyping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUntil the extraction of DNA, the saliva samples collected from participants were stored at -20\u0026deg;C. DNA was extracted from the saliva samples using Macherey-Nagel NucloSpin Tissue Kit and stored at -20\u0026deg;C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOMT:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTetra-primer PCR procedure was used for the identification of \u003cem\u003eCOMT\u003c/em\u003e genotypes. Following primers were used: COMT-common-F \u0026lsquo;CCAACCCTGCACAGGCAAGAT\u0026rsquo;, COMT-common-R \u0026lsquo;CAAGGGTGACCTGGAACAGCG\u0026rsquo;, COMT-P3-F \u0026apos;CGGATGGTGGA- TTTCGCTGACG\u0026rsquo; and COMT-P4-R \u0026lsquo;TCAGGCATGCACACCTTGTCCTTTAT\u0026rsquo; (52). 20 ng DNA was added into each PCR reaction, along with 0.12 mM of COMT-P4-R, 0.20 mM of other three primers, 12.5 ul of Thermo Scientific DreamTaq Green PCR Master Mix (2X) and nuclease-free water up to 25 ul of total volume. Initial denaturation lasted for 4 min at 94\u0026deg;C, then there were 30 cycles of the following steps: denaturation at 94\u0026deg;C for 30 s, annealing at 66\u0026deg;C for 30 s, extension at 72\u0026deg;C for 20 s, followed by 5 min of final extension. PCR products were loaded on 1.5% agarose gel for electrophoresis and then visualized in Biorad Gel Doc XR Imaging System. One of the participant\u0026rsquo;s DNA was degraded prior to the \u003cem\u003eCOMT\u003c/em\u003e genotyping therefore it was excluded from the analyses including \u003cem\u003eCOMT\u003c/em\u003e genotype. Of the remaining 71 samples, 20 of them had no polymorphism (Val/Val), 33 of them had only one polymorphic allele (Val/Met) and 18 of them were homozygous polymorphic (Met/Met). For the analyses, Val/Val and Val/Met genotypes were grouped together to compare with Met/Met genotype (Frank et al., 2009).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDRD2:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the determination of \u003cem\u003eDRD2\u003c/em\u003e genotypes, firstly, PCR product was obtained by using following primer pair: DRD2-F \u0026lsquo;ACCAYGGTCTCCACAGCACTCT\u0026rsquo; and DRD2-R \u0026lsquo;ATGGCGAGCATCTGAGTGGCT\u0026rsquo; (Mohammadi et al., 2018). For each 20 ul PCR reaction, 2 ul DNA was taken from the original stock (variable concentrations, 35 ng/ul on average); 0.2 mM of each primer, 10 ul of Thermo Scientific DreamTaq Green PCR Master Mix (2X) and 7.2 ul of nuclease-free water were added. PCR protocol was started with initial denaturation at 95\u0026deg;C for 5 in, followed by 35 cycles of denaturation at 94\u0026deg;C for 30 s, annealing at 56.9\u0026deg;C for 1 min, extension at 72\u0026deg;C for 1 min, and completed by the final extension step at 72\u0026deg;C for 10 min. Macherey-Nagel NucleoSpin Gel and PCR Clean-up kit was used to obtain PCR products. Afterwards, \u003cem\u003eDRD2\u003c/em\u003e digestion reaction was prepared with the following ingredients: 200 ng PCR product, 0.5 ul Thermo Scientific TaqI (10U/ul) enzyme, 2 ul Thermo Scientific Buffer TaqI (10X), and nuclease-free water up to 20 ul of total reaction. Digestion procedure took a total of 2.5 hours in the thermocycler at 65\u0026deg;C, no heat activation step was required. 4 ul of Thermo Scientific DNA Loading Dye (6X) was added into each reaction prior to agarose gel (3%) electrophoresis. Digestion products were visualized in Biorad Gel Doc XR Imaging System. Among the 72 samples genotyped, there were 13 CC genotypes (no polymorphism), 35 CT genotypes (heterozygous) and 24 TT genotypes (homozygous polymorphic). For the analyses, CT and TT genotypes were grouped together to compare with CC genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDARPP-32:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe same PCR reactions were prepared as \u003cem\u003eDRD2\u003c/em\u003e by using these particular primers to obtain DARPP-32 PCR products: DARPP-32-F \u0026lsquo;GCATTGCTGAGTCTCACCTGCAGTCT\u0026rsquo; and DARPP-32--R \u0026lsquo;ATTGGGAGAGGGACTGAGCCAAGGATGG\u0026rsquo; (20). The PCR protocol was the same as \u003cem\u003eDRD2\u003c/em\u003e for DARPP-32 except the annealing temperature was 63.3\u0026deg;C\u0026nbsp;for the DARPP-32 primer pair. Similarly, PCR products were obtained by using\u0026nbsp;Macherey-Nagel NucleoSpin Gel and PCR Clean-up kit. Then, DARPP-32 digestion reactions were prepared by adding the following: 200 ng PCR product, 0.30 ul BioLabs MseI enzyme (10U/ul), 2 ul BioLabs rCutSmart Buffer (10X), and nuclease-free water up to a total of 20 ul digestion reaction. Digestion protocol was started with 1.5 h of incubation at 37\u0026deg;C, and followed by a heat inactivation step at 65\u0026deg;C for 20 min. After the addition of 4 ul of Thermo Scientific DNA Loading Dye (6X), samples were loaded on 3% agarose gel for electrophoresis. Gels were visualized in Biorad Gel Doc XR Imaging System. Genotyping results revealed that there were two samples with no polymorphism (GG), 41 samples were heterozygous polymorphic (GA) and the remaining 29 samples had two polymorphic alleles (AA). For the analyses, GG and GA genotypes were grouped together to compare with AA genotypes (Frank et al., 2009).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactor analysis:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed factor analysis with all the five questionnaires used in the study. This way, we aimed to reduce the collinearity between the overall results of the individual questionnaires, the number of statistical tests conducted and the associated Type 1 errors. We strictly followed the methodological approach utilized in Gillan et al. (2016) and Rouault et al. (2018) for factor analysis using Maximum Likelihood Estimation (MLE). By using R software, factor analysis was performed by the \u003cem\u003efactanal()\u003c/em\u003e function in the Psych package, with an oblique rotation (oblimin). The selection of the number of the factors was determined by Cattell\u0026rsquo;s criterion (Cattell, 1966); wherein a sharp transition from horizontal to vertical (\u0026lsquo;elbow\u0026rsquo;) indicates that there is no significant benefit in keeping additional factors. The objective implementation of this criterion was employed by the Cattell-Nelson-Gorsuch (CNG) test, which calculates the slopes of all possible sets of three neighboring eigenvalues and determines the point at which there is the largest differences in slope (nFactors package in R) (Gorsuch \u0026amp; Nelson, 1981). The CNG test indicated the existence of a 3-factor latent structure that includes factors we labeled as \u0026lsquo;Obsessive \u0026ndash; Compulsive\u0026rsquo;, \u0026lsquo;Anxious \u0026ndash; Depressive\u0026rsquo; and \u0026lsquo;Impulsivity\u0026rsquo; based on the strongest individual item loadings.\u003c/p\u003e\n\u003cp\u003eWhile labeling the factors, we apply 0.25 average loading threshold to the questionnaires as in Gillan et al. (2016). For the \u0026lsquo;Obsessive \u0026ndash; Compulsive\u0026rsquo; factor, the only questionnaire that reached the threshold we set is the Padua Inventory (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 0.42, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= 0.16). The highest average loadings for the factor \u0026lsquo;Anxious \u0026ndash; Depressive\u0026rsquo; came from STAI-A-Trait Scale (\u003cem\u003eM\u003c/em\u003e = 0.36, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= 0.17), followed by the Beck\u0026rsquo;s Depression Inventory (\u003cem\u003eM\u003c/em\u003e = 0.29, \u003cem\u003eSD\u003c/em\u003e = 0.17). Barratt Impulsivity Scale (\u003cem\u003eM\u003c/em\u003e = 0.35, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= 0.25) is the only questionnaire that reached the threshold for \u0026lsquo;Impulsivity\u0026rsquo; factor. STAI-A-Trait Scale (\u003cem\u003eM\u003c/em\u003e = 0.23, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= 0.16) came very close to the threshold for the \u0026lsquo;Impulsivity\u0026rsquo; factor as in Gillan et al. (2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBehavioral Data Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResponse times (RTs) and accuracy were obtained from the test sessions and determined as the behavioral units of analysis. There was no exclusion of data based on RT (except for DDM fits for which RTs \u0026lt; 100 ms were excluded and the outlier probability was defined as 5%). We used the data from the last two sessions (considered as steady state) in the analyses. First, independent samples t-test was used to compare accuracy and RTs of different genotype groups, using Jamovi (Version 1.6.23.0). Then, we fit the data using Hierarchical Bayesian estimation of DDM parameters (Wiecki et al., 2013) in Python to test how the threshold and drift rate differed between each group and difficulty level.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFirst, we tested whether accuracy and RTs differed across the three difficulty levels (i.e., easy (coherence 16%), medium (coherence 8%) and difficult (coherence 0%)) and determined groups of genotypes (note that accuracy was not tested for difficult condition since it is not possible to label any trial as correct or wrong with 0% coherence). Moreover, we also compared the genotype groups in terms of factor scores calculated from the responses to the questionnaires. Descriptive statistics can be found in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Insert Table 1 here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe comparison of \u003cem\u003eCOMT\u003c/em\u003e genotype groups illustrated that subjects with Met/Met genotype (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 0.71, \u003cem\u003eSD\u003c/em\u003e = 0.13) were faster than Val carriers (Val/Val or Val/Met - \u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 0.83, \u003cem\u003eSD\u003c/em\u003e = 0.21) only in easy condition (\u003cem\u003et\u003c/em\u003e(69) = 2.20, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .031, \u003cem\u003ed\u003c/em\u003e = 0.60), but not in medium and difficult conditions (min \u003cem\u003ep\u003c/em\u003e = .353). There was no significant difference between \u003cem\u003eCOMT\u003c/em\u003e genotype groups in mean accuracy level in either easy or medium condition (min \u003cem\u003ep\u003c/em\u003e = .242).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe differences between the genotype groups of \u003cem\u003eDRD2\u003c/em\u003e (CC vs. CT/TT) and DARPP-32 (AA vs. GA/GG) were also investigated in terms of accuracy in easy and medium conditions, RTs for each difficulty conditions, and factor scores. No significant differences were found for any of those comparisons between genotype groups (min \u003cem\u003ep\u003c/em\u003e = 0.24). Statistics pertaining to the independent samples \u003cem\u003et\u003c/em\u003e-test for aforementioned comparisons can be found in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe scores in \u0026lsquo;obsessive \u0026ndash; compulsive\u0026rsquo; factor were found to be statistically higher in Met/Met genotype (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 38.49, \u003cem\u003eSD\u003c/em\u003e = 15.3) group than those with Val (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 28.70, \u003cem\u003eSD\u003c/em\u003e = 13.45) genotype (\u003cem\u003et\u003c/em\u003e(69) = -2.58, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .012, \u003cem\u003ed\u003c/em\u003e = -0.70). Similarly, the scores in \u0026lsquo;anxious depressive\u0026rsquo; factor were found to be statistically higher in Met/Met genotype (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 31.78, \u003cem\u003eSD\u003c/em\u003e = 8.43) group than Val (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 26.18, \u003cem\u003eSD\u003c/em\u003e = 8.36) carriers (\u003cem\u003et\u003c/em\u003e(69) = -2.45, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .017, \u003cem\u003ed\u003c/em\u003e = -0.69). The comparison of \u003cem\u003eCOMT\u003c/em\u003e genotype groups with regard to the \u0026lsquo;impulsivity\u0026rsquo; scores did not yield any significant difference (\u003cem\u003ep\u003c/em\u003e = .40). In terms of \u003cem\u003eDRD2\u003c/em\u003e and DARPP-32 genotype groups, there was no difference in factor scores (min \u003cem\u003ep\u003c/em\u003e = .239).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Insert Table 2 here]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHDDM results revealed that individuals with Met/Met genotype have lower decision thresholds than Val carriers, both in easy (\u003cem\u003eP\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e_Met/Met \u0026gt; \u003cem\u003ea\u003c/em\u003e_\u003cem\u003eVal\u0026nbsp;\u003c/em\u003e\u003c/sub\u003e = 0.02) and moderate conditions (\u003cem\u003eP\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e_Met/Met \u0026gt; \u003cem\u003ea\u003c/em\u003e_\u003cem\u003eVal\u0026nbsp;\u003c/em\u003e\u003c/sub\u003e = 0.04). Moreover, the Met/Met genotype had marginally higher drift rates in easy condition (\u003cem\u003eP\u003csub\u003ev\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e_Met/Met \u0026gt; \u003cem\u003ev\u003c/em\u003e_\u003cem\u003eVal\u0026nbsp;\u003c/em\u003e\u003c/sub\u003e = 0.94), while no difference was observed for the medium difficulty condition (\u003cem\u003eP\u003csub\u003ev\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e_Met/Met \u0026gt; \u003cem\u003ev\u003c/em\u003e_\u003cem\u003eVal\u0026nbsp;\u003c/em\u003e\u003c/sub\u003e = 0.86).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was also no significant difference between determined groups of \u003cem\u003eDRD2\u003c/em\u003e (CC vs. T carriers) and DARPP-32 (AA vs. G carriers) gene polymorphisms in terms of the effect of difficulty on drift rate and threshold parameters of DDM (0.49 \u0026lt; \u003cem\u003eP\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e_CC \u0026gt; \u003cem\u003ea\u003c/em\u003e_\u003cem\u003eT\u0026nbsp;\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.70 and 0.05 \u0026lt; \u003cem\u003eP\u003csub\u003ev\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e_CC \u0026gt; \u003cem\u003ev\u003c/em\u003e_\u003cem\u003eT\u0026nbsp;\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.74) for \u003cem\u003eDRD2\u003c/em\u003e comparisons; 0.64 \u0026lt; \u003cem\u003eP\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e_AA \u0026gt; \u003cem\u003ea\u003c/em\u003e_\u003cem\u003eG\u0026nbsp;\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.85 and 0.21 \u0026lt; \u003cem\u003eP\u003csub\u003ev\u003c/sub\u003e\u003c/em\u003e\u003csub\u003e_AA \u0026gt; \u003cem\u003ev\u003c/em\u003e_\u003cem\u003eG\u0026nbsp;\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.67 for DARPP-32 groups). Figure 1 illustrates the distribution of posterior probabilities of threshold and drift rate parameters for \u003cem\u003eCOMT\u003c/em\u003e, \u003cem\u003eDRD2\u003c/em\u003e and DARPP-32 groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Figure 1 about here]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the effect of three different polymorphisms associated with the dopaminergic function in the central nervous system on both decision-making dynamics and the scores in psychiatric symptom dimensions. Our results suggest that the Met/Met genotype in \u003cem\u003eCOMT\u003c/em\u003e gene is associated with the decrease in response time and decision threshold, and a tendency for higher drift rate. We could not detect any relation between other investigated polymorphisms and decision-making parameters. Similarly, we only detect association between Met/Met genotype in the \u003cem\u003eCOMT\u003c/em\u003e gene with higher scores associated with obsessive-compulsive and anxious-depressive traits. The relations between other SNPs in DARPP-32 and \u003cem\u003eDRD2\u003c/em\u003e and the scores in psychiatric symptom dimensions were not statistically significant.\u003c/p\u003e \u003cp\u003eWe did not detect any difference between the investigated SNPs in terms of accuracy. Note that the literature also reports equivocal findings on this front. For instance, Malloy-Diniz et al. (Malloy-Diniz et al., 2013) reported that individuals having Val/Val genotype showed better performance in a decision-making task. Relatedly, van den Bos et al. (van den Bos et al., 2009) found that participants having Met/Met genotype had the poorer performance in the Iowa Gambling Task, compared to other genotype groups. On the other hand, several studies reported that Met/Met carriers were better in terms of their cognitive performance (Wishart et al., 2011). In terms of \u003cem\u003eDRD2\u003c/em\u003e and DARPP-32, Frank et al. (2007) did not find any significant differences in accuracy levels in the Go/No Go task.\u003c/p\u003e \u003cp\u003eThe only difference in terms of response time observed in our study was associated with Val158Met groups. Our results suggest that the Met/Met group were faster in perceptual decision-making task than Val carriers. This finding is in line with the study of Malloy-Diniz et al. (2013) which associates Met/Met group with more impulsive behavior. In another study participants with Met/Met genotype were faster in a cognitive control task (Shehzad et al., 2012). However, because behavioral measures of aforementioned studies did not resemble our experimental paradigm, it is important to further investigate how decision-making behavior differs among Val158Met groups in a two-alternative-forced-choice task. Additionally, future studies should also address the mechanisms that underlie the interaction between task difficulty and genotype (specific effect in easy conditions).\u003c/p\u003e \u003cp\u003eIn terms of latent parameters of decision making, we observed that Met/Met group present a tendency to have a higher drift rate. Because individuals with Met/Met genotype have higher levels of DA availability in PFC, which was previously associated with evidence accumulation of the decision-maker (Mulder et al., 2014), prefrontal DA levels might indeed have a role in the efficiency by which sensory evidence is integrated in the decision-making context. Moreover, individuals with Met/Met genotype were less cautious in the decision-making process, as they had lower thresholds than Val carriers. Since frontal regions are thought to be significant also for the threshold adjustment (Mulder et al., 2014), increased levels of DA in PFC may be effective on the decision threshold settings based on fronto-basal ganglia network. Note that this result is also consistent with higher impulsivity in the Met/Met group (Malloy-Diniz et al., 2013).\u003c/p\u003e \u003cp\u003eIn addition to the analysis of behavioral performance, we also investigated the association between psychiatric symptom dimensions and aforementioned SNPs. Consistent with earlier studies (Kumar \u0026amp; Rai, 2020; Olson et al., 2005), we detected associations between Met/Met allele with both obsessive-compulsive and anxious-depressive dimension scores that were in the same direction. Met/Met allele was associated with more anxious-depressive symptoms. Another study showed that patients with OCD diagnosis and Met/Met allele have poorer performance in executive function tasks (T\u0026uuml;kel et al., 2013), which is not fully consistent with the behavioral performance of the Met/Met group in the current study. Another meta-analysis reported that Met/Met carriers are more efficient than Val/Val carriers in cognitive processing, but less efficient in emotional processing (Mier et al., 2010). Thus, it is possible that participants with Met/Met allele are associated with better evidence processing and decision-making abilities when the task does not require any emotional processing, as in our study.\u003c/p\u003e \u003cp\u003eOne of the main limitations of our study is the small sample size for genetic analysis. Thus, the results, especially including psychiatric symptom scores, should be interpreted cautiously. The reliability of factor analysis we conducted increases when the sample size is larger. This study must be replicated by a larger group of participants. It is also important to note that our sample only included college students without any diagnosed psychiatric disorders. The generalizability of the result can be improved by admitting participants with a wide variety of age and level of education.\u003c/p\u003e \u003cp\u003eTo conclude, this study investigated the single nucleotide polymorphisms associated with dopaminergic function in relation to perceptual decision making and psychiatric symptom dimensions. We found individuals with the Met/Met allele, which is associated with increased DA in PFC had less cautious decision making and a tendency for more efficient evidence integration when compared to Val carriers. Thus, our result suggests an enhancing role of dopamine in perceptual decision-making tasks. Consistent with earlier studies, we also found an association between Met/Met allele and obsessive-compulsive and anxious-depressive scores.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments and Disclosures:\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge use of the services and facilities of the Ko\u0026ccedil; University Research Center for Translational Medicine (KUTTAM), funded by the Presidency of Turkey, Presidency of Strategy and Budget. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Presidency of Strategy and Budget. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI in scientific writing:\u0026nbsp;\u003c/strong\u003eGenerative AI was not used in the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eA.Ş.K. was supported by T\u0026Uuml;BİTAK (BİDEB-2211). This research was supported by TUBA-GEBIP-2015 award to FB. We would like to thank Bilgehan Cavdaroglu for his help during data collection and Ahmed Moustafa and Tuba Mutluer for their comments on the earlier versions of this manuscript. This work is part of A.Ş.K.\u0026rsquo;s doctoral thesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement:\u0026nbsp;\u003c/strong\u003eF.B. conceptualization, investigation, coding, formal analysis, methodology, writing; A.Ş.K. conceptualization, coding, formal analysis, writing; T.T. conceptualization, investigation, coding, formal analysis, methodology; B.S.\u0026Uuml;. Wet lab, writing, formal analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate:\u0026nbsp;\u003c/strong\u003eAll participants provided signed informed consent for their participation in the behavioral experiments, collection and processing of saliva samples, and the publication of the results in an anonymized fashion.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBalcı, F., Wiener, M., \u0026Ccedil;avdaroğlu, B., \u0026amp; Coslett, H. B. (2013). Epistasis effects of dopamine genes on interval timing and reward magnitude in humans. \u003cem\u003eNeuropsychologia\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(2), 293-308. https://doi.org/10.1016/j.neuropsychologia.2012.08.002\u003c/li\u003e\n\u003cli\u003eBarbeau, A. (1974). 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(2011). COMT Val158Met genotype and individual differences in executive function in healthy adults. \u003cem\u003eJournal of the International Neuropsychological Society: JINS\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 174. https://doi.org/10.1017/S1355617710001402\u003c/li\u003e\n\u003cli\u003eZhang, Y., Bertolino, A., Fazio, L., Blasi, G., Rampino, A., Romano, R., ... \u0026amp; Sad\u0026eacute;e, W. (2007). Polymorphisms in human dopamine D2 receptor gene affect gene expression, splicing, and neuronal activity during working memory. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e104\u003c/em\u003e(51), 20552-20557. https://doi.org/10.1073/pnas.0707106104\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDescriptive Statistics\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"864\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 243px;\"\u003e\n \u003cp\u003eCOMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDRD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDARPP-32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 107px;\"\u003e\n \u003cp\u003eMet/Met\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eVal/Met \u0026amp; Val/Val\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 107px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 107px;\"\u003e\n \u003cp\u003eCT \u0026amp; TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 107px;\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 107px;\"\u003e\n \u003cp\u003eGA \u0026amp; GG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMean RT\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Easy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMean RT (Medium)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMean RT (Difficult)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.8644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.7616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.8361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.6708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.8498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAccuracy (Easy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAccuracy (Medium)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.0947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eObsessive - Compulsive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e38.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e15.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e28.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e13.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e32.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e14.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e30.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e14.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e28.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e32.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e15.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAnxious -Depressive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e31.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e26.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e30.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e27.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e27.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e7.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e27.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eImpulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e55.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e49.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e54.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e49.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e13.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e49.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e10.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e51.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e14.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eRTs are given in sec, and difficulty conditions are given in parentheses.\u0026nbsp;COMT groups were Met/Met vs. Val/Met \u0026amp; Val/Val. DRD2 groups were CC vs. CT \u0026amp; TT, DARPP-32 groups were AA vs. GA \u0026amp; GG\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eIndependent Samples t-test (groups of COMT, DRD2 and DARPP-32 genes)\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"864\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVariable (Condition)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eCOMT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eDRD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003eDARPP-32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eMean RT (Easy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.2033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.60109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.3265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.1000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.0383\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.24949\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eMean RT (Medium)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.9358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.25529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.3091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.0938\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.26283\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eMean RT (Difficult)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.00445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0116\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.6900\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.16580\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAccuracy (Easy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.0572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.28840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.2288\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0701\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.1169\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.02808\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAccuracy (Medium)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.1793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.32173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.2588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.00285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eObsessive - Compulsive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-2.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e- 0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.1140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.2597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAnxious -Depressive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-2.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.3636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eImpulsivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.3369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.1937\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eRTs are given in sec, and difficulty conditions are given in parentheses. COMT groups were Met/Met vs. Val/Met \u0026amp; Val/Val. DRD2 groups were CC vs. CT \u0026amp; TT, DARPP-32 groups were AA vs. GA \u0026amp; GG\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dopamine, DRD2, DARPP-32, COMT, Drift-Diffusion Model, Obsessive Compulsive Disorder","lastPublishedDoi":"10.21203/rs.3.rs-6524080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6524080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDopaminergic function is implicated in different aspects of cognition and affect. However, the role of dopamine in decision process is not fully understood. We investigated the association between the dopaminergic (DA) gene polymorphisms (i.e., \u003cem\u003eCOMT\u003c/em\u003e, \u003cem\u003eDRD2\u003c/em\u003e, DARPP-32) with the latent variables of the decision-process as well as neuropsychiatric states estimated from factor analysis. 82 students were tested in a perceptual decision-making task with different difficulties and filled out five neuropsychiatric scales. The accuracy and response times gathered from the decision-making task were modeled with a hierarchical Bayesian drift-diffusion model to estimate how the core latent parameters of the decision process changed between different gene polymorphisms. Met/Met allele of \u003cem\u003eCOMT\u003c/em\u003e gene, which is associated with increased DA in prefrontal cortex had less cautious decision making and more efficient evidence integration when compared to Val carriers. Individuals with this polymorphism also had higher obsessive-compulsive and anxious-depressive scores. There were no significant associations with polymorphisms of \u003cem\u003eDRD2\u003c/em\u003e and \u003cem\u003eDARPP-32\u003c/em\u003e genes. Our results suggest a modulatory (through decision threshold) and enhancing (through the rate of sensory evidence integration) role of prefrontal dopamine related gene \u003cem\u003eCOMT\u003c/em\u003e polymorphism in perceptual decision making. Our results associate Met/Met allele with an increase in obsessive-compulsive and anxious-depressive scores.\u003c/p\u003e","manuscriptTitle":"The effects dopamine related genes on decision dynamics and psychiatric dimensions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 10:32:15","doi":"10.21203/rs.3.rs-6524080/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"55c73d9e-0d5d-4c8f-befd-3a35fb731bf5","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-25T05:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 10:32:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6524080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6524080","identity":"rs-6524080","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}