Daniel J. Schad | Universitaet Potsdam (original) (raw)
Papers by Daniel J. Schad
Applied Psychology: Health and Well-being, Nov 9, 2022
Modern societies provide an abundance of opportunities, which could lead to acceleration and time... more Modern societies provide an abundance of opportunities, which could lead to acceleration and time poverty, thereby paradoxically limiting well‐being. This study examines this issue using social distancing measures introduced during the COVID‐19 pandemic. We analyzed a data set of over four million responses, collected by the German online newspaper “ZEIT ONLINE,” where people responded to the question “How are you today?” with “good” or “bad,” assessing subjective well‐being, and an optional self‐descriptive adjective of mood. The results showed that subjective well‐being significantly increased with the onset of social distancing regulations. This increase was closely accompanied by a rise in adjectives associated with deceleration, the daily usage of which best predicted daily well‐being during COVID‐19. Factor analysis showed that Factor 1 best predicted daily well‐being and was effectively described by adjectives associated with deceleration. An analysis of potential mechanisms of deceleration during the pandemic revealed lower stress levels during workdays and weekends, as well as better sleep. These findings provide large‐scale support to theories suggesting that acceleration and time poverty in modern societies may impair well‐being.
PLOS ONE, Jan 26, 2023
Background If our attention wanders to other thoughts while making a decision, then the decision ... more Background If our attention wanders to other thoughts while making a decision, then the decision might not be directed towards future goals, reflecting a lack of model-based decision making, but may instead be driven by habits, reflecting model-free decision making. Here we aimed to investigate if and how model-based versus model-free decision making is reduced by trait spontaneous mind wandering. Methods and findings We used a sequential two-step Markov decision task and a self-report questionnaire assessing trait spontaneous and deliberate mind wandering propensity, to investigate how trait mind wandering relates to model-free as well as model-based decisions. We estimated parameters of a computational neurocognitive dual-control model of decision making. Analyzing estimated model parameters, we found that trait spontaneous mind wandering was related to impaired model-based decisions, while model-free choice stayed unaffected. Conclusions Our findings suggest trait spontaneous mind wandering is associated with impaired modelbased decision making, and it may reflect model-based offline replay for other tasks (e.g., real-life goals) outside the current lab situation.
Alcoholism: Clinical and Experimental Research, Mar 28, 2022
BackgroundImpaired decision making, a key characteristic of alcohol dependence (AD), manifests in... more BackgroundImpaired decision making, a key characteristic of alcohol dependence (AD), manifests in continuous alcohol consumption despite severe negative consequences. The neural basis of this impairment in individuals with AD and differences with known neural decision mechanisms among healthy subjects are not fully understood. In particular, it is unclear whether the choice behavior among individuals with AD is based on a general impairment of decision mechanisms or is mainly explained by altered value attribution, with an overly high subjective value attributed to alcohol‐related stimuli.MethodsHere, we use a functional magnetic resonance imaging (fMRI) monetary reward task to compare the neural processes of model‐based decision making and value computation between AD individuals (n = 32) and healthy controls (n = 32). During fMRI, participants evaluated monetary offers with respect to dynamically changing constraints and different levels of uncertainty.ResultsIndividuals with AD showed lower activation associated with model‐based decision processes in the caudate nucleus than controls, but there were no group differences in value‐related neural activity or task performance.ConclusionsOur findings highlight the role of the caudate nucleus in impaired model‐based decisions of alcohol‐dependent individuals.
arXiv (Cornell University), Apr 29, 2019
The principled Bayesian workflow, with a different (non-cognitive) running example analysis, was ... more The principled Bayesian workflow, with a different (non-cognitive) running example analysis, was previously developed and documented by Betancourt (2018) on http://bit.ly/396lGSX. Moreover, the content of this manuscript, including the Bayesian workflow and the cognitive example data analysis, was presented by DJS as a talk at the 52nd Annual Meeting of the Society for Mathematical Psychology 2019 in Montreal. The example experimental data analyzed in this manuscript was previously published by Gibson & Wu (2013).
Frontiers in Psychiatry
BackgroundAlcohol use disorder is characterized by perseverative alcohol use despite negative con... more BackgroundAlcohol use disorder is characterized by perseverative alcohol use despite negative consequences. This hallmark feature of addiction potentially relates to impairments in behavioral flexibility, which can be measured by probabilistic reversal learning (PRL) paradigms. We here aimed to examine the cognitive mechanisms underlying impaired PRL task performance in patients with alcohol use disorder (AUDP) using computational models of reinforcement learning.MethodsTwenty-eight early abstinent AUDP and 27 healthy controls (HC) performed an extensive PRL paradigm. We compared conventional behavioral variables of choices (perseveration; correct responses) between groups. Moreover, we fitted Bayesian computational models to the task data to compare differences in latent cognitive variables including reward and punishment learning and choice consistency between groups.ResultsAUDP and HC did not significantly differ with regard to direct perseveration rates after reversals. However,...
van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comp... more van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison.
arXiv (Cornell University), Jul 27, 2018
Factorial experiments in research on memory, language, and in other areas are often analyzed usin... more Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator degrees of freedom, e.g., for experimental factors with more than two levels, the ANOVA omnibus F-test is not informative about the source of a main effect or interaction. Because researchers typically have specific hypotheses about which condition means differ from each other, a priori contrasts (i.e., comparisons planned before the sample means are known) between specific conditions or combinations of conditions are the appropriate way to represent such hypotheses in the statistical model. Many researchers have pointed out that contrasts should be "tested instead of, rather than as a supplement to, the ordinary 'omnibus' F test" (Hays, 1973, p. 601). In this tutorial, we explain the mathematics underlying different kinds of contrasts (i.e., treatment, sum, repeated, polynomial, custom, nested, interaction contrasts), discuss their properties, and demonstrate how they are applied in the R System for Statistical Computing (R Core Team, 2018). In this context, we explain the generalized inverse which is needed to compute the coefficients for contrasts that test hypotheses that are not covered by the default set of contrasts. A detailed understanding of contrast coding is crucial for successful and correct specification in linear models (including linear mixed models). Contrasts defined a priori yield far more useful confirmatory tests of experimental hypotheses than standard omnibus F-test. Reproducible code is available from https://osf.io/7ukf6/.
Computational Brain & Behavior, Mar 4, 2022
We discuss an important issue that is not directly related to the main theses of the van Doorn et... more We discuss an important issue that is not directly related to the main theses of the van Doorn et al. (Computational Brain and Behavior, 2021) paper, but which frequently comes up when using Bayesian linear mixed models: how to determine sample size in advance of running a study when planning a Bayes factor analysis. We adapt a simulation-based method proposed by Wang and Gelfand (Statistical Science 193-208, 2002) for a Bayes factor-based design analysis, and demonstrate how relatively complex hierarchical models can be used to determine approximate sample sizes for planning experiments.
Psychopharmacology, 2019
Background Aversive stimuli in the environment influence human actions. This includes valence-dep... more Background Aversive stimuli in the environment influence human actions. This includes valence-dependent influences on action selection, e.g., increased avoidance but decreased approach behavior. However, it is yet unclear how aversive stimuli interact with complex learning and decision-making in the reward and avoidance domain. Moreover, the underlying computational mechanisms of these decision-making biases are unknown. Methods To elucidate these mechanisms, 54 healthy young male subjects performed a two-step sequential decision-making task, which allows to computationally model different aspects of learning, e.g., model-free, habitual, and model-based, goal-directed learning. We used a within-subject design, crossing task valence (reward vs. punishment learning) with emotional context (aversive vs. neutral background stimuli). We analyzed choice data, applied a computational model, and performed simulations. Results Whereas model-based learning was not affected, aversive stimuli interacted with model-free learning in a way that depended on task valence. Thus, aversive stimuli increased model-free avoidance learning but decreased model-free reward learning. The computational model confirmed this effect: the parameter lambda that indicates the influence of reward prediction errors on decision values was increased in the punishment condition but decreased in the reward condition when aversive stimuli were present. Further, by using the inferred computational parameters to simulate choice data, our effects were captured. Exploratory analyses revealed that the observed biases were associated with subclinical depressive symptoms. Conclusion Our data show that aversive environmental stimuli affect complex learning and decision-making, which depends on task valence. Further, we provide a model of the underlying computations of this affective modulation. Finally, our finding of increased decision-making biases in subjects reporting subclinical depressive symptoms matches recent reports of amplified Pavlovian influences on action selection in depression and suggests a potential vulnerability factor for mood disorders. We discuss our findings in the light of the involvement of the neuromodulators serotonin and dopamine.
European Archives of Psychiatry and Clinical Neuroscience
Everyone experiences the natural ebb and flow of task-unrelated thoughts. Given how common the fl... more Everyone experiences the natural ebb and flow of task-unrelated thoughts. Given how common the fluctuations in these thoughts are, surprisingly, we know very little about how they shape individuals' responses to alcohol use. Here, we investigated if mind wandering is associated with a risk of developing problematic alcohol use. We undertook an online survey among the general population in China (N = 1123) and Germany (N = 1018) from December 2021 to February 2022 and examined the subjective experience of mind wandering and problematic alcohol use through the Mind Wandering Questionnaire (MWQ) and the Alcohol Use Disorders Identification Test (AUDIT). We compared mind wandering and problematic alcohol use between two countries and investigated the association between MWQ scores with AUDIT scores. We found higher scores on the MWQ and a high percentage of problematic alcohol use (i.e., AUDIT score ≥ 8) in Germany (22.5%) as compared to in China (14.5%). Higher self-reported mind wandering was associated with higher AUDIT scores. AUDIT scores were increased mostly in male, elder, and high-mind wandering people. Our findings highlight that mind wandering and problematic alcohol use enhanced in Germany as compared to in China. Our study sheds light on the relationship between mind wandering and problematic alcohol use that may help to further investigate causal effects of interventions.
Translational Psychiatry, 2017
Alcohol-related cues acquire incentive salience through Pavlovian conditioning and then can marke... more Alcohol-related cues acquire incentive salience through Pavlovian conditioning and then can markedly affect instrumental behavior of alcohol-dependent patients to promote relapse. However, it is unclear whether similar effects occur with alcohol-unrelated cues. We tested 116 early-abstinent alcohol-dependent patients and 91 healthy controls who completed a delay discounting task to assess choice impulsivity, and a Pavlovian-to-instrumental transfer (PIT) paradigm employing both alcohol-unrelated and alcoholrelated stimuli. To modify instrumental choice behavior, we tiled the background of the computer screen either with conditioned stimuli (CS) previously generated by pairing abstract pictures with pictures indicating monetary gains or losses, or with pictures displaying alcohol or water beverages. CS paired to money gains and losses affected instrumental choices differently. This PIT effect was significantly more pronounced in patients compared to controls, and the group difference was mainly driven by highly impulsive patients. The PIT effect was particularly strong in trials in which the instrumental stimulus required inhibition of instrumental response behavior and the background CS was associated to monetary gains. Under that condition, patients performed inappropriate approach behavior, contrary to their previously formed behavioral intention. Surprisingly, the effect of alcohol and water pictures as background stimuli resembled that of aversive and appetitive CS, respectively. These findings suggest that positively valenced background CS can provoke dysfunctional instrumental approach behavior in impulsive alcohol-dependent patients. Consequently, in real life they might be easily seduced by environmental cues to engage in actions thwarting their longterm goals. Such behaviors may include, but are not limited to, approaching alcohol.
European Psychiatry, 2017
The mesolimbic dopaminergic system has been implicated in two kinds of reward processing, one in ... more The mesolimbic dopaminergic system has been implicated in two kinds of reward processing, one in reinforcement learning (e.g prediction error) and another in incentive salience attribution (e.g. cue-reactivity). Both functions have been implicated in alcohol dependence with the former contributing to the persistence of chronic alcohol intake despite severe negative consequences and the latter playing a crucial role in cue-induced craving and relapse. The bicentric study “Learning in alcohol dependence (LeAD)” aims to bridge a gap between these processes by investigating reinforcement learning mechanisms and the influence that Pavlovian cues exert over behavior. We here demonstrate that alcohol dependent subjects show alterations in goal-directed, model-based reinforcement learning (Sebold et al., 2014) and demonstrate that prospective relapsing patients show reductions in the medial prefrontal cortex activation during goal-directed control. Moreover we show that in alcohol dependent...
Bayesian linear mixed-effects models are increasingly being used in the cognitive sciences to per... more Bayesian linear mixed-effects models are increasingly being used in the cognitive sciences to perform null hypothesis tests, where a null hypothesis that an effect is zero is compared with an alternative hypothesis that the effect exists and is different from zero. While software tools for Bayes factor null hypothesis tests are easily accessible, how to specify the data and the model correctly is often not clear. In Bayesian approaches, many authors recommend data aggregation at the by-subject level and running Bayes factors on aggregated data. Here, we use simulation-based calibration for model inference to demonstrate that null hypothesis tests can yield biased Bayes factors, when computed from aggregated data. Specifically, when random slope variances differ (i.e., violated sphericity assumption), Bayes factors are too conservative for contrasts where the variance is small and they are too liberal for contrasts where the variance is large. Moreover, Bayes factors for by-subject aggregated data are biased (too liberal) when random item variance is present but ignored in the analysis. We also perform corresponding frequentist analyses (type I and II error probabilities) to illustrate that the same problems exist and are well known from frequentist tools. These problems can be circumvented by running Bayesian linear mixed-effects models on non-aggregated data such as on individual trials and by explicitly modeling the full random effects structure. Reproducible code is available from https://osf.io/mjf47/.
doi: 10.3389/fpsyg.2014.01450 Processing speed enhances model-based over model-free reinforcement... more doi: 10.3389/fpsyg.2014.01450 Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning
Applied Psychology: Health and Well-being, Nov 9, 2022
Modern societies provide an abundance of opportunities, which could lead to acceleration and time... more Modern societies provide an abundance of opportunities, which could lead to acceleration and time poverty, thereby paradoxically limiting well‐being. This study examines this issue using social distancing measures introduced during the COVID‐19 pandemic. We analyzed a data set of over four million responses, collected by the German online newspaper “ZEIT ONLINE,” where people responded to the question “How are you today?” with “good” or “bad,” assessing subjective well‐being, and an optional self‐descriptive adjective of mood. The results showed that subjective well‐being significantly increased with the onset of social distancing regulations. This increase was closely accompanied by a rise in adjectives associated with deceleration, the daily usage of which best predicted daily well‐being during COVID‐19. Factor analysis showed that Factor 1 best predicted daily well‐being and was effectively described by adjectives associated with deceleration. An analysis of potential mechanisms of deceleration during the pandemic revealed lower stress levels during workdays and weekends, as well as better sleep. These findings provide large‐scale support to theories suggesting that acceleration and time poverty in modern societies may impair well‐being.
PLOS ONE, Jan 26, 2023
Background If our attention wanders to other thoughts while making a decision, then the decision ... more Background If our attention wanders to other thoughts while making a decision, then the decision might not be directed towards future goals, reflecting a lack of model-based decision making, but may instead be driven by habits, reflecting model-free decision making. Here we aimed to investigate if and how model-based versus model-free decision making is reduced by trait spontaneous mind wandering. Methods and findings We used a sequential two-step Markov decision task and a self-report questionnaire assessing trait spontaneous and deliberate mind wandering propensity, to investigate how trait mind wandering relates to model-free as well as model-based decisions. We estimated parameters of a computational neurocognitive dual-control model of decision making. Analyzing estimated model parameters, we found that trait spontaneous mind wandering was related to impaired model-based decisions, while model-free choice stayed unaffected. Conclusions Our findings suggest trait spontaneous mind wandering is associated with impaired modelbased decision making, and it may reflect model-based offline replay for other tasks (e.g., real-life goals) outside the current lab situation.
Alcoholism: Clinical and Experimental Research, Mar 28, 2022
BackgroundImpaired decision making, a key characteristic of alcohol dependence (AD), manifests in... more BackgroundImpaired decision making, a key characteristic of alcohol dependence (AD), manifests in continuous alcohol consumption despite severe negative consequences. The neural basis of this impairment in individuals with AD and differences with known neural decision mechanisms among healthy subjects are not fully understood. In particular, it is unclear whether the choice behavior among individuals with AD is based on a general impairment of decision mechanisms or is mainly explained by altered value attribution, with an overly high subjective value attributed to alcohol‐related stimuli.MethodsHere, we use a functional magnetic resonance imaging (fMRI) monetary reward task to compare the neural processes of model‐based decision making and value computation between AD individuals (n = 32) and healthy controls (n = 32). During fMRI, participants evaluated monetary offers with respect to dynamically changing constraints and different levels of uncertainty.ResultsIndividuals with AD showed lower activation associated with model‐based decision processes in the caudate nucleus than controls, but there were no group differences in value‐related neural activity or task performance.ConclusionsOur findings highlight the role of the caudate nucleus in impaired model‐based decisions of alcohol‐dependent individuals.
arXiv (Cornell University), Apr 29, 2019
The principled Bayesian workflow, with a different (non-cognitive) running example analysis, was ... more The principled Bayesian workflow, with a different (non-cognitive) running example analysis, was previously developed and documented by Betancourt (2018) on http://bit.ly/396lGSX. Moreover, the content of this manuscript, including the Bayesian workflow and the cognitive example data analysis, was presented by DJS as a talk at the 52nd Annual Meeting of the Society for Mathematical Psychology 2019 in Montreal. The example experimental data analyzed in this manuscript was previously published by Gibson & Wu (2013).
Frontiers in Psychiatry
BackgroundAlcohol use disorder is characterized by perseverative alcohol use despite negative con... more BackgroundAlcohol use disorder is characterized by perseverative alcohol use despite negative consequences. This hallmark feature of addiction potentially relates to impairments in behavioral flexibility, which can be measured by probabilistic reversal learning (PRL) paradigms. We here aimed to examine the cognitive mechanisms underlying impaired PRL task performance in patients with alcohol use disorder (AUDP) using computational models of reinforcement learning.MethodsTwenty-eight early abstinent AUDP and 27 healthy controls (HC) performed an extensive PRL paradigm. We compared conventional behavioral variables of choices (perseveration; correct responses) between groups. Moreover, we fitted Bayesian computational models to the task data to compare differences in latent cognitive variables including reward and punishment learning and choice consistency between groups.ResultsAUDP and HC did not significantly differ with regard to direct perseveration rates after reversals. However,...
van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comp... more van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison.
arXiv (Cornell University), Jul 27, 2018
Factorial experiments in research on memory, language, and in other areas are often analyzed usin... more Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator degrees of freedom, e.g., for experimental factors with more than two levels, the ANOVA omnibus F-test is not informative about the source of a main effect or interaction. Because researchers typically have specific hypotheses about which condition means differ from each other, a priori contrasts (i.e., comparisons planned before the sample means are known) between specific conditions or combinations of conditions are the appropriate way to represent such hypotheses in the statistical model. Many researchers have pointed out that contrasts should be "tested instead of, rather than as a supplement to, the ordinary 'omnibus' F test" (Hays, 1973, p. 601). In this tutorial, we explain the mathematics underlying different kinds of contrasts (i.e., treatment, sum, repeated, polynomial, custom, nested, interaction contrasts), discuss their properties, and demonstrate how they are applied in the R System for Statistical Computing (R Core Team, 2018). In this context, we explain the generalized inverse which is needed to compute the coefficients for contrasts that test hypotheses that are not covered by the default set of contrasts. A detailed understanding of contrast coding is crucial for successful and correct specification in linear models (including linear mixed models). Contrasts defined a priori yield far more useful confirmatory tests of experimental hypotheses than standard omnibus F-test. Reproducible code is available from https://osf.io/7ukf6/.
Computational Brain & Behavior, Mar 4, 2022
We discuss an important issue that is not directly related to the main theses of the van Doorn et... more We discuss an important issue that is not directly related to the main theses of the van Doorn et al. (Computational Brain and Behavior, 2021) paper, but which frequently comes up when using Bayesian linear mixed models: how to determine sample size in advance of running a study when planning a Bayes factor analysis. We adapt a simulation-based method proposed by Wang and Gelfand (Statistical Science 193-208, 2002) for a Bayes factor-based design analysis, and demonstrate how relatively complex hierarchical models can be used to determine approximate sample sizes for planning experiments.
Psychopharmacology, 2019
Background Aversive stimuli in the environment influence human actions. This includes valence-dep... more Background Aversive stimuli in the environment influence human actions. This includes valence-dependent influences on action selection, e.g., increased avoidance but decreased approach behavior. However, it is yet unclear how aversive stimuli interact with complex learning and decision-making in the reward and avoidance domain. Moreover, the underlying computational mechanisms of these decision-making biases are unknown. Methods To elucidate these mechanisms, 54 healthy young male subjects performed a two-step sequential decision-making task, which allows to computationally model different aspects of learning, e.g., model-free, habitual, and model-based, goal-directed learning. We used a within-subject design, crossing task valence (reward vs. punishment learning) with emotional context (aversive vs. neutral background stimuli). We analyzed choice data, applied a computational model, and performed simulations. Results Whereas model-based learning was not affected, aversive stimuli interacted with model-free learning in a way that depended on task valence. Thus, aversive stimuli increased model-free avoidance learning but decreased model-free reward learning. The computational model confirmed this effect: the parameter lambda that indicates the influence of reward prediction errors on decision values was increased in the punishment condition but decreased in the reward condition when aversive stimuli were present. Further, by using the inferred computational parameters to simulate choice data, our effects were captured. Exploratory analyses revealed that the observed biases were associated with subclinical depressive symptoms. Conclusion Our data show that aversive environmental stimuli affect complex learning and decision-making, which depends on task valence. Further, we provide a model of the underlying computations of this affective modulation. Finally, our finding of increased decision-making biases in subjects reporting subclinical depressive symptoms matches recent reports of amplified Pavlovian influences on action selection in depression and suggests a potential vulnerability factor for mood disorders. We discuss our findings in the light of the involvement of the neuromodulators serotonin and dopamine.
European Archives of Psychiatry and Clinical Neuroscience
Everyone experiences the natural ebb and flow of task-unrelated thoughts. Given how common the fl... more Everyone experiences the natural ebb and flow of task-unrelated thoughts. Given how common the fluctuations in these thoughts are, surprisingly, we know very little about how they shape individuals' responses to alcohol use. Here, we investigated if mind wandering is associated with a risk of developing problematic alcohol use. We undertook an online survey among the general population in China (N = 1123) and Germany (N = 1018) from December 2021 to February 2022 and examined the subjective experience of mind wandering and problematic alcohol use through the Mind Wandering Questionnaire (MWQ) and the Alcohol Use Disorders Identification Test (AUDIT). We compared mind wandering and problematic alcohol use between two countries and investigated the association between MWQ scores with AUDIT scores. We found higher scores on the MWQ and a high percentage of problematic alcohol use (i.e., AUDIT score ≥ 8) in Germany (22.5%) as compared to in China (14.5%). Higher self-reported mind wandering was associated with higher AUDIT scores. AUDIT scores were increased mostly in male, elder, and high-mind wandering people. Our findings highlight that mind wandering and problematic alcohol use enhanced in Germany as compared to in China. Our study sheds light on the relationship between mind wandering and problematic alcohol use that may help to further investigate causal effects of interventions.
Translational Psychiatry, 2017
Alcohol-related cues acquire incentive salience through Pavlovian conditioning and then can marke... more Alcohol-related cues acquire incentive salience through Pavlovian conditioning and then can markedly affect instrumental behavior of alcohol-dependent patients to promote relapse. However, it is unclear whether similar effects occur with alcohol-unrelated cues. We tested 116 early-abstinent alcohol-dependent patients and 91 healthy controls who completed a delay discounting task to assess choice impulsivity, and a Pavlovian-to-instrumental transfer (PIT) paradigm employing both alcohol-unrelated and alcoholrelated stimuli. To modify instrumental choice behavior, we tiled the background of the computer screen either with conditioned stimuli (CS) previously generated by pairing abstract pictures with pictures indicating monetary gains or losses, or with pictures displaying alcohol or water beverages. CS paired to money gains and losses affected instrumental choices differently. This PIT effect was significantly more pronounced in patients compared to controls, and the group difference was mainly driven by highly impulsive patients. The PIT effect was particularly strong in trials in which the instrumental stimulus required inhibition of instrumental response behavior and the background CS was associated to monetary gains. Under that condition, patients performed inappropriate approach behavior, contrary to their previously formed behavioral intention. Surprisingly, the effect of alcohol and water pictures as background stimuli resembled that of aversive and appetitive CS, respectively. These findings suggest that positively valenced background CS can provoke dysfunctional instrumental approach behavior in impulsive alcohol-dependent patients. Consequently, in real life they might be easily seduced by environmental cues to engage in actions thwarting their longterm goals. Such behaviors may include, but are not limited to, approaching alcohol.
European Psychiatry, 2017
The mesolimbic dopaminergic system has been implicated in two kinds of reward processing, one in ... more The mesolimbic dopaminergic system has been implicated in two kinds of reward processing, one in reinforcement learning (e.g prediction error) and another in incentive salience attribution (e.g. cue-reactivity). Both functions have been implicated in alcohol dependence with the former contributing to the persistence of chronic alcohol intake despite severe negative consequences and the latter playing a crucial role in cue-induced craving and relapse. The bicentric study “Learning in alcohol dependence (LeAD)” aims to bridge a gap between these processes by investigating reinforcement learning mechanisms and the influence that Pavlovian cues exert over behavior. We here demonstrate that alcohol dependent subjects show alterations in goal-directed, model-based reinforcement learning (Sebold et al., 2014) and demonstrate that prospective relapsing patients show reductions in the medial prefrontal cortex activation during goal-directed control. Moreover we show that in alcohol dependent...
Bayesian linear mixed-effects models are increasingly being used in the cognitive sciences to per... more Bayesian linear mixed-effects models are increasingly being used in the cognitive sciences to perform null hypothesis tests, where a null hypothesis that an effect is zero is compared with an alternative hypothesis that the effect exists and is different from zero. While software tools for Bayes factor null hypothesis tests are easily accessible, how to specify the data and the model correctly is often not clear. In Bayesian approaches, many authors recommend data aggregation at the by-subject level and running Bayes factors on aggregated data. Here, we use simulation-based calibration for model inference to demonstrate that null hypothesis tests can yield biased Bayes factors, when computed from aggregated data. Specifically, when random slope variances differ (i.e., violated sphericity assumption), Bayes factors are too conservative for contrasts where the variance is small and they are too liberal for contrasts where the variance is large. Moreover, Bayes factors for by-subject aggregated data are biased (too liberal) when random item variance is present but ignored in the analysis. We also perform corresponding frequentist analyses (type I and II error probabilities) to illustrate that the same problems exist and are well known from frequentist tools. These problems can be circumvented by running Bayesian linear mixed-effects models on non-aggregated data such as on individual trials and by explicitly modeling the full random effects structure. Reproducible code is available from https://osf.io/mjf47/.
doi: 10.3389/fpsyg.2014.01450 Processing speed enhances model-based over model-free reinforcement... more doi: 10.3389/fpsyg.2014.01450 Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning