Drift diffusion model of reward and punishment learning in schizophrenia: Modeling and experimental data

Cognitive Mechanisms of Aberrant Self-Referential Social Perception in Psychosis and Bipolar Disorder: Insights from Computational Modeling

Background and Hypothesis

Individuals with schizophrenia (SZ) and bipolar disorder (BD) show disruptions in self-referential gaze perception-a social perceptual process related to symptoms and functioning. However, our current mechanistic understanding of these dysfunctions and relationships is imprecise.

Study Design

The present study used mathematical modeling to uncover cognitive processes driving gaze perception abnormalities in SZ and BD, and how they relate to cognition, symptoms, and social functioning. We modeled the behavior of 28 SZ, 38 BD, and 34 controls (HC) in a self-referential gaze perception task using drift-diffusion models (DDM) parameterized to index key cognitive components: drift rate (evidence accumulation efficiency), drift bias (perceptual bias), start point (expectation bias), threshold separation (response caution), and non- decision time (encoding/motor processes).

Study Results

Results revealed that aberrant gaze perception in SZ and BD was driven by less efficient evidence accumulation, perceptual biases predisposing self-referential responses, and greater caution (SZ only). Across SZ and HC, poorer social functioning was related to greater expectation biases. Within SZ, perceptual and expectancy biases were associated with hallucination and delusion severity, respectively.

Conclusions

These findings indicate that diminished evidence accumulation and perceptual biases may underlie altered gaze perception in patients and that SZ may engage in compensatory cautiousness, sacrificing response speed to preserve accuracy. Moreover, biases at the belief and perceptual levels may relate to symptoms and functioning. Computational modeling can, therefore, be used to achieve a more nuanced, cognitive process-level understanding of the mechanisms of social cognitive difficulties, including gaze perception, in individuals with SZ and BD.

From brain to body: exploring the connection between altered reward processing and physical fitness in schizophrenia

Understanding the underlying mechanisms that link psychopathology and physical comorbidities in schizophrenia is crucial since decreased physical fitness and overweight pose major risk factors for cardio-vascular diseases and decrease the patients' life expectancies. We hypothesize that altered reward anticipation plays an important role in this. We implemented the Monetary Incentive Delay task in a MR scanner and a fitness test battery to compare schizophrenia patients (SZ, n = 43) with sex- and age-matched healthy controls (HC, n = 36) as to reward processing and their physical fitness. We found differences in reward anticipation between SZs and HCs, whereby increased activity in HCs positively correlated with overall physical condition and negatively correlated with psychopathology. On the other handy, SZs revealed stronger activity in the posterior cingulate cortex and in cerebellar regions during reward anticipation, which could be linked to decreased overall physical fitness. These findings demonstrate that a dysregulated reward system is not only responsible for the symptomatology of schizophrenia, but might also be involved in physical comorbidities which could pave the way for future lifestyle therapy interventions.

Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling and Attractor Dynamics

BACKGROUND AND HYPOTHESIS

Cognitive control deficits are prominent in individuals with psychotic psychopathology. Studies providing evidence for deficits in proactive control generally examine average performance and not variation across trials for individuals-potentially obscuring detection of essential contributors to cognitive control. Here, we leverage intertrial variability through drift-diffusion models (DDMs) aiming to identify key contributors to cognitive control deficits in psychosis.

STUDY DESIGN

People with psychosis (PwP; N = 122), their first-degree biological relatives (N = 78), and controls (N = 50) each completed 120 trials of the dot pattern expectancy (DPX) cognitive control task. We fit full hierarchical DDMs to response and reaction time (RT) data for individual trials and then used classification models to compare the DDM parameters with conventional measures of proactive and reactive control.

STUDY RESULTS

PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information. Both PwP and relatives showed protracted nondecision times to infrequent trial sequences suggesting slowed perceptual processing. Classification analyses indicated that DDM parameters differentiated between the groups better than conventional measures and identified drift rates during proactive control, nondecision time during reactive control, and cue bias as most important. DDM parameters were associated with real-world functioning and schizotypal traits.

CONCLUSIONS

Modeling of trial-level data revealed that slow evidence accumulation and longer preparatory periods are the strongest contributors to cognitive control deficits in psychotic psychopathology. This pattern of atypical responding during the DPX is consistent with shallow basins in attractor dynamic models that reflect difficulties in maintaining state representations, possibly mediated by excess neural excitation or poor connectivity.

Dissecting the chain of information processing and its interplay with neurochemicals and fluid intelligence across development

Previous research has highlighted the role of glutamate and gamma-aminobutyric acid (GABA) in perceptual, cognitive, and motor tasks. However, the exact involvement of these neurochemical mechanisms in the chain of information processing, and across human development, is unclear. In a cross-sectional longitudinal design, we used a computational approach to dissociate cognitive, decision, and visuomotor processing in 293 individuals spanning early childhood to adulthood. We found that glutamate and GABA within the intraparietal sulcus (IPS) explained unique variance in visuomotor processing, with higher glutamate predicting poorer visuomotor processing in younger participants but better visuomotor processing in mature participants, while GABA showed the opposite pattern. These findings, which were neurochemically, neuroanatomically and functionally specific, were replicated ~21 mo later and were generalized in two further different behavioral tasks. Using resting functional MRI, we revealed that the relationship between IPS neurochemicals and visuomotor processing is mediated by functional connectivity in the visuomotor network. We then extended our findings to high-level cognitive behavior by predicting fluid intelligence performance. We present evidence that fluid intelligence performance is explained by IPS GABA and glutamate and is mediated by visuomotor processing. However, this evidence was obtained using an uncorrected alpha and needs to be replicated in future studies. These results provide an integrative biological and psychological mechanistic explanation that links cognitive processes and neurotransmitters across human development and establishes their potential involvement in intelligent behavior.

Enhanced decision-making in nicotine dependent individuals who abstain: A computational analysis using Hierarchical Drift Diffusion Modeling

BACKGROUND

Variability in decision-making capacity and reward responsiveness may underlie differences in the ability to abstain from smoking. Computational modeling of choice behavior, as with the Hierarchical Drift Diffusion Model (HDDM), can help dissociate reward responsiveness from underlying components of decision-making. Here we used the HDDM to identify which decision-making or reward-related parameters, extracted from data acquired in a reward processing task, contributed to the ability of people who smoke that are not seeking treatment to abstain from cigarettes during a laboratory task.

METHODS

80 adults who smoke cigarettes completed the Probabilistic Reward Task (PRT) - a signal detection task with a differential reinforcement schedule - following smoking as usual, and the Relapse Analogue Task (RAT) - a task in which participants could earn money for delaying smoking up to 50min - after a period of overnight abstinence. Two cohorts were defined by the RAT; those who waited either 0-min (n=36) or the full 50-min (n=44) before smoking.

RESULTS

PRT signal detection metrics indicated all subjects learned the task contingencies, with no differences in response bias or discriminability between the two groups. However, HDDM analyses indicated faster drift rates in 50-min vs. 0-min waiters.

CONCLUSIONS

Relative to those who did not abstain, computational modeling indicated that people who abstained from smoking for 50min showed faster evidence accumulation during reward-based decision-making. These results highlight the importance of decision-making mechanisms to smoking abstinence, and suggest that focusing on the evidence accumulation process may yield new targets for treatment.

Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling

Cognitive control deficits are consistently identified in individuals with schizophrenia and other psychotic psychopathologies. In this analysis, we delineated proactive and reactive control deficits in psychotic psychopathology via hierarchical Drift Diffusion Modeling (hDDM). People with psychosis (PwP; N=123), their first-degree relatives (N=79), and controls (N=51) completed the Dot Pattern Expectancy task, which allows differentiation between proactive and reactive control. PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information for proactive control. They also showed longer non-decision times than controls on infrequent stimuli sequences suggesting slower perceptual processing. An explainable machine learning analysis indicated that the hDDM parameters were able to differentiate between the groups better than conventional measures. Through DDM, we found that cognitive control deficits in psychosis are characterized by slower motor/perceptual time and slower evidence-integration primarily in proactive control.

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers' ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data - without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work.

Drift-Diffusion Model Reveals Impaired Reward-Based Perceptual Decision-Making Processes Associated with Depression in Late Childhood and Early Adolescent Girls

Adolescent girls are a high-risk stratum for the emergence of depression. Previous research has established that depression is associated with blunted responses to rewards. Research using Drift Diffusion Model (DDM) has found that deficits in accumulating reward-based evidence characterize adult depression. However, little is known about how reduced reward sensitivity is reflected in the computational processes involved in reward-based decision-making in late childhood and early adolescent depression.One hundred and sixty-six 8- to 14-year-old girls completed a probabilistic reward-based decision-making task. Participants were instructed to identify which one of two similar visual stimuli were presented, and correct responses were rewarded with unequal probabilities. Analysis using hierarchical DDM quantified rate of evidence accumulation (i.e., drift rate) and starting point. Depression severity was measured using the Children's Depression Inventory.Across all participants, there was a higher drift rate, indicating faster evidence accumulation, for the more frequently rewarded than the less frequently rewarded decision. In addition, the starting point of the evidence accumulation was closer to the more frequently rewarded decision, indicating a starting point bias. Higher depression severity was associated with a slower drift rate for both types of decisions. Higher depression severity was associated with a smaller starting point bias towards the more frequently rewarded decision.The current study uses computational modeling to reveal that late childhood and early adolescent girls with greater depression demonstrate impairments in the reward-related evidence accumulation process.

On the Need to Improve the Way Individual Differences in Cognitive Function Are Measured With Reaction Time Tasks

The measurement of individual differences in specific cognitive functions has been an important area of study for decades. Often the goal of such studies is to determine whether there are cognitive deficits or enhancements associated with, for example, a specific population, psychological disorder, health status, or age group. The inherent difficulty, however, is that most cognitive functions are not directly observable, so researchers rely on indirect measures to infer an individual’s functioning. One of the most common approaches is to use a task that is designed to tap into a specific function and to use behavioral measures, such as reaction times (RTs), to assess performance on that task. Although this approach is widespread, it unfortunately is subject to a problem of reverse inference: Differences in a given cognitive function can be manifest as differences in RTs, but that does not guarantee that differences in RTs imply differences in that cognitive function. We illustrate this inference problem with data from a study on aging and lexical processing, highlighting how RTs can lead to erroneous conclusions about processing. Then we discuss how employing choice-RT models to analyze data can improve inference and highlight practical approaches to improving the models and incorporating them into one’s analysis pipeline.

Computational modeling reveals strategic and developmental differences in the behavioral impact of reward across adolescence

Studies of reward effects on behavior in adolescence typically rely on performance metrics that confound myriad cognitive and non-cognitive processes, making it challenging to determine which process is impacted by reward. The present longitudinal study applied the diffusion decision model to a reward task to isolate the influence of reward on response caution from influences of processing and motor speed. Participants completed three annual assessments from early to middle adolescence (N = 387, 55% female, M_age  = 12.1 at Wave 1; M_age  = 13.1 at Wave 2, M_age  = 14.1 at Wave 3) and three annual assessments in late adolescence (M_ages  = 17.8, 18.9, 19.9). At each assessment, participants completed a two-choice reaction time task under conditions of no-reward and a block in which points were awarded for speeded accuracy. Reward reduced response caution at all waves, as expected, but had a greater impact as teens moved from early to middle adolescence. Simulations to identify optimal response caution showed that teens were overly cautious in early adolescence but became too focused on speed over accuracy by middle adolescence. By late adolescence, participants adopted response styles that maximized reward. Further, response style was associated with both internalizing and externalizing symptoms in early-to-middle adolescence, providing evidence for the construct validity of a diffusion model approach in this developmental period.

Neural Substrates of the Drift-Diffusion Model in Brain Disorders

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia. This review thus uses DDM to provide a theoretical understanding of different brain disorders.

The Role of Dopaminergic Genes in Probabilistic Reinforcement Learning in Schizophrenia Spectrum Disorders

Schizophrenia spectrum disorders (SZ) are characterized by impairments in probabilistic reinforcement learning (RL), which is associated with dopaminergic circuitry encompassing the prefrontal cortex and basal ganglia. However, there are no studies examining dopaminergic genes with respect to probabilistic RL in SZ. Thus, the aim of our study was to examine the impact of dopaminergic genes on performance assessed by the Probabilistic Selection Task (PST) in patients with SZ in comparison to healthy control (HC) subjects. In our study, we included 138 SZ patients and 188 HC participants. Genetic analysis was performed with respect to the following genetic polymorphisms: rs4680 in COMT, rs907094 in DARP-32, rs2734839, rs936461, rs1800497, and rs6277 in DRD2, rs747302 and rs1800955 in DRD4 and rs28363170 and rs2975226 in DAT1 genes. The probabilistic RL task was completed by 59 SZ patients and 95 HC subjects. SZ patients performed significantly worse in acquiring reinforcement contingencies during the task in comparison to HCs. We found no significant association between genetic polymorphisms and RL among SZ patients; however, among HC participants with respect to the DAT1 rs28363170 polymorphism, individuals with 10-allele repeat genotypes performed better in comparison to 9-allele repeat carriers. The present study indicates the relevance of the DAT1 rs28363170 polymorphism in RL in HC participants.

The Role of User Behaviour in Improving Cyber Security Management

Information security has for long time been a field of study in computer science, software engineering, and information communications technology. The term 'information security' has recently been replaced with the more generic term cybersecurity. The goal of this paper is to show that, in addition to computer science studies, behavioural sciences focused on user behaviour can provide key techniques to help increase cyber security and mitigate the impact of attackers' social engineering and cognitive hacking methods (i.e., spreading false information). Accordingly, in this paper, we identify current research on psychological traits and individual differences among computer system users that explain vulnerabilities to cyber security attacks and crimes. Our review shows that computer system users possess different cognitive capabilities which determine their ability to counter information security threats. We identify gaps in the existing research and provide possible psychological methods to help computer system users comply with security policies and thus increase network and information security.

A New Spin on Spatial Cognition in ADHD: A Diffusion Model Decomposition of Mental Rotation

OBJECTIVES

Multiple studies have found evidence of task non-specific slow drift rate in ADHD, and slow drift rate has rapidly become one of the most visible cognitive hallmarks of the disorder. In this study, we use the diffusion model to determine whether atypicalities in visuospatial cognitive processing exist independently of slow drift rate.

METHODS

Eight- to twelve-year-old children with (n = 207) and without ADHD (n = 99) completed a 144-trial mental rotation task.

RESULTS

Performance of children with ADHD was less accurate and more variable than non-ADHD controls, but there were no group differences in mean response time. Drift rate was slower, but nondecision time was faster for children with ADHD. A Rotation × ADHD interaction for boundary separation was also found in which children with ADHD did not strategically adjust their response thresholds to the same degree as non-ADHD controls. However, the Rotation × ADHD interaction was not significant for nondecision time, which would have been the primary indicator of a specific deficit in mental rotation per se.

CONCLUSIONS

Poorer performance on the mental rotation task was due to slow rate of evidence accumulation, as well as relative inflexibility in adjusting boundary separation, but not to impaired visuospatial processing specifically. We discuss the implications of these findings for future cognitive research in ADHD.

Slow drift rate predicts ADHD symptomology over and above executive dysfunction

Slow drift rate has become one of the most salient cognitive deficits among children with ADHD, and has repeatedly been found to explain slow, variable, and error-prone performance on tasks of executive functioning (EF). The present study applies the diffusion model to determine whether slow drift rate better predicts parent and teacher ratings of ADHD than standard EF metrics. 201 children aged 8-12 completed two tests of speeded decision-making analyzed with the diffusion model and two traditionally scored tests of EF. Latent EF and drift rate factors each independently predicted the general ADHD factor in a bifactor model of ADHD, with poor EF and slow drift rate associated with greater ADHD symptomology. When both EF and drift rate were entered into the model, slow drift rate (but not EF) continued to predict elevated symptomology. These findings suggest that using drift rate to index task performance improves upon conventional approaches to measuring and conceptualizing cognitive dysfunction in ADHD. Implications for future cognitive research in ADHD are discussed.

Neuropsychology of Bipolar Disorder

Bipolar disorder is associated with significant dysfunction in a broad range of neuropsychological domains and processes. Deficits have been reported to occur in symptomatic states (depression, [hypo]mania) as well as in remission (euthymia), having consequences for psychological well-being and social and occupational functioning. The profile and magnitude of neuropsychological deficits in bipolar disorder have been explored in a number of systematic reviews and meta-analyses. After discussing these briefly, this chapter will focus on examining the clinical and demographic factors that influence and modify the pattern and magnitude of deficits, as well as reviewing methods of assessment and analysis approaches which may improve our understanding of these problems.

An EZ-Diffusion Model Analysis of Attentional Ability in Patients With Retinal Pigmentosa

Retinitis pigmentosa (RP) is characterized by visual acuity decrease and visual field loss. However, the impact of visual field loss on the cognitive performance of RP patients remains unknown. In the present study, in order to understand whether and how RP affects spatial processing and attentional function, one spatial processing task and three attentional tasks were conducted on RP patients and healthy controls. In addition, an EZ-diffusion model was performed for further data analysis with four parameters, mean decision time, non-decision time, drift rate, and boundary separation. It was found that in the spatial processing task, compared with the control group, the RP group exhibited a slower response speed in large and medium visual eccentricities, and slower drift rate for the large stimulus, which is strongly verified by the significant linear correlation between the visual field eccentricity with both reaction time (p = 0.047) and non-decision time (p = 0.043) in RP patients. In the attentional orienting task and the attentional switching task, RP exerted a reduction of speed and an increase of non-decision time on every condition, with a decrease of drift rate in the orienting task and boundary separation in the switching task. In addition, the switching cost for large stimulus was observed in the control group but not in the RP group. The stop-signal task demonstrated similar inhibition function between the two groups. These findings implied that RP exerted the impairment of spatial cognition correlated with the visual field eccentricity, mainly in the peripheral visual field. Moreover, specific to the peripheral visual field, RP patients had deficits in the attentional orienting and flexibility but not in the attentional inhibition.

Exploring the interaction between approach-avoidance conflict and memory processing

The medial temporal lobe (MTL) has been implicated in approach-avoidance (AA) conflict processing, which arises when a stimulus is imbued with both positive and negative valences. Notably, since the MTL has been traditionally viewed as a mnemonic brain region, a pertinent question is how AA conflict and memory processing interact with each other behaviourally. We conducted two behavioural experiments to examine whether increased AA conflict processing has a significant impact on incidental mnemonic encoding and inferential reasoning. In Experiment 1, participants first completed a reward and punishment AA task and were subsequently administered a surprise recognition memory test for stimuli that were presented during high and no AA conflict trials. In Experiment 2, participants completed a reward and punishment task in which they learned the valences of objects presented in pairs (AB, BC pairs). Next, we assessed their ability to integrate information across these pairs (infer A-C relationships) and examined whether inferential reasoning was more challenging across objects with conflicting compared to non-conflicting incentive values. We observed that increased motivational conflict did not significantly impact encoding or inferential reasoning. Potential explanations for these findings are considered, including the possibility that AA conflict and memory processing are not necessarily intertwined behaviourally.

Reward and punishment learning in schizophrenia and bipolar disorder

Prior studies on reward learning deficits in psychiatric disorders have used probabilistic learning tasks, making it unclear whether impairment is due to the probabilistic nature of the task rather than reward processing. In this study, we tested probabilistic vs. deterministic reward and punishment learning in healthy controls and three patient groups: schizophrenia (SZ), psychotic bipolar disorder (BD), and nonpsychotic BD. Experimental results show that reward learning was impaired in patients with SZ and patients with psychotic BD in the probabilistic learning task compared to patients with nonpsychotic BD and healthy controls. In contrast, punishment learning in the probabilistic task was impaired in patients with nonpsychotic BD compared to the other patient groups and healthy controls. There were no significant differences among all groups in the deterministic learning task scores. We also found that Hamilton Depression Scale scores negatively correlated with probabilistic learning performance. Our data may suggest that reward learning impairment may be due to the nature of the task as well as subtype of BD.

Emotional Recognition in Schizophrenia: An Analysis of Response Components in Middle-Aged Adults

Ageing seems to present a bias towards positive stimuli that might be reflected in response times. However, this process is more complex for middle-aged adults, and even more in schizophrenia. In order to examine this issue, an experimental study was carried out in which 48 participants were divided into two groups: an experimental group of 24 participants diagnosed with schizophrenia and a control group of 24 subjects with no disorders. The main objective of the study was to evaluate response time components according to the emotional valence of the stimulus, to test recognition and discrimination in both groups. A battery of 120 images from the International Affective Picture System (IAPS), representing positive, negative and neutral emotional valences, was employed. Response times were evaluated in terms of analysis of variance, as well as its inherent response times components. The results showed slower responses in the group with schizophrenia than in the control one. Moreover, a poorer performance was depicted in the latency components this group. Finally, a differential deficit pattern for emotion between groups was not found.

From neuroimaging to daily functioning: A multimethod analysis of reward anticipation in people with schizophrenia

Negative symptoms are a core clinical feature of schizophrenia that are only marginally responsive to current treatments. Recent work suggests that deficits in reinforcement learning and anticipatory responses to reward may be two mechanisms that help explain impairments in motivation in those with schizophrenia. The present study utilized a reinforcement-learning paradigm, which allowed us to examine both reward anticipation and reinforcement learning. Twenty-eight people with schizophrenia and 30 healthy controls completed a reinforcement-learning task while undergoing functional MRI. Participants with schizophrenia also completed a weeklong ecological momentary assessment protocol reporting anticipated motivation and pleasure in their daily activities. Unexpectedly, we found no significant group differences in performance or neural response in reinforcement learning. However, we found that poorer reward learning was associated with greater clinician ratings of negative symptoms and daily reports of anticipatory motivation and pleasure negative symptoms. In regards to anticipatory responses, we found that people with schizophrenia showed blunted activation in the anterior cingulate, insula, caudate, and putamen while anticipating reward. Further, blood oxygen level-dependent (BOLD) response in reward related regions during anticipation of reward was significantly related to both clinician-rated motivation and pleasure deficits as well as daily reports of motivation and pleasure. Our results provide further evidence of deficits during reward anticipation in individuals with schizophrenia, particularly for those with severe negative symptoms, and some evidence for worse reward learning among those with greater negative symptoms. Moreover, our findings suggest that these deficits show important relationships with emotional and motivational functioning in everyday life. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Impact of Context Information on Metaphor Elaboration

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

Modelling reaction time distribution of fast decision tasks in schizophrenia: Evidence for novel candidate endophenotypes

Increased reaction time (RT) and variability of RT in fast decision tasks is observed in patients with schizophrenia and their first degree relatives. This study used modelling of the RT distribution with the aim of identifying novel candidate endophenotypes for schizophrenia. 20 patients with schizophrenia, 15 siblings of patients and 25 healthy controls performed an oddball task of varying working memory load. Increases in mean and standard deviation (SD) of RT were observed for both patients and siblings compared to controls and they were again independent of working memory load. Ex-Gaussian modelling of the RT distribution confirmed that parameters μ, σ and τ increased significantly in patients and siblings compared to controls. The Drift Diffusion Model was applied on RT distributions. A decrease in the diffusion drift rate (v) modeling the accumulation of evidence for reaching the decision to choose one stimulus over the other, was observed in patients and siblings compared to controls. The mean time of the non-decisional sensorimotor processes (t0) and it's variance (st0) was also increased in patients and siblings compared to controls. In conclusion modeling of the RT distribution revealed novel potential cognitive endophenotypes in the quest of heritable risk factors for schizophrenia.

Low and variable correlation between reaction time costs and accuracy costs explained by accumulation models: Meta-analysis and simulations

The underpinning assumption of much research on cognitive individual differences (or group differences) is that task performance indexes cognitive ability in that domain. In many tasks performance is measured by differences (costs) between conditions, which are widely assumed to index a psychological process of interest rather than extraneous factors such as speed-accuracy trade-offs (e.g., Stroop, implicit association task, lexical decision, antisaccade, Simon, Navon, flanker, and task switching). Relatedly, reaction time (RT) costs or error costs are interpreted similarly and used interchangeably in the literature. All of this assumes a strong correlation between RT-costs and error-costs from the same psychological effect. We conducted a meta-analysis to test this, with 114 effects across a range of well-known tasks. Counterintuitively, we found a general pattern of weak, and often no, association between RT and error costs (mean r = .17, range -.45 to .78). This general problem is accounted for by the theoretical framework of evidence accumulation models, which capture individual differences in (at least) 2 distinct ways. Differences affecting accumulation rate produce positive correlation. But this is cancelled out if individuals also differ in response threshold, which produces negative correlations. In the models, subtractions between conditions do not isolate processing costs from caution. To demonstrate the explanatory power of synthesizing the traditional subtraction method within a broader decision model framework, we confirm 2 predictions with new data. Thus, using error costs or RT costs is more than a pragmatic choice; the decision carries theoretical consequence that can be understood through the accumulation model framework. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Is Poor Working Memory a Transdiagnostic Risk Factor for Psychopathology?

In contrast to historical conceptualizations that framed psychological disorders as distinct, categorical conditions, it is now widely understood that co- and multi-morbidities between disorders are extensive. As a result, there has been a call to better understand the dimensional liabilities that are common to and influence the development of multiple psychopathologies, as supported and exemplified by the National Institutes of Mental Health (NIMH) Research Domain Criteria (RDoC) framework. We use a latent variable SEM approach to examine the degree to which working memory deficits represent a cognitive liability associated with the development of common and discrete dimensions of psychopathology. In a sample of 415 community recruited children aged 8-12 (n = 170 girls), we fit a bi-factor model to parent reports of behavior from the DISC-4 and BASC-2, and included a latent working memory factor as a predictor of the internalizing, externalizing, and general "p-factor." We found that both the general "p-factor" and externalizing (but not internalizing) latent factor were significantly associated with working memory. When a bi-factor model of externalizing symptomology was fit to further explore this relationship, working memory was only correlated with the general externalizing dimension; correlation with specific inattention, hyperactive/impulsive, and oppositional factors did not survive once the general externalizing dimension was taken into consideration. These findings held regardless of the sex of the child. Our results suggest that working memory deficits represent both a common cognitive liability for mental health disorders, and a specific liability for externalizing disorders.

Characterising variations in perceptual decision making

Current perspectives propose that observer models accounting for both optimal and suboptimal behaviour may yield real progress in understanding perception. We propose that such models could, in addition, be very useful for precisely characterising the variation in perception across healthy participants and those affected by psychiatric disorders, as well as the effects of neuromodulators such as oxytocin.

The Processing-Speed Impairment in Psychosis Is More Than Just Accelerated Aging

Processing speed is impaired in patients with psychosis, and deteriorates as a function of normal aging. These observations, in combination with other lines of research, suggest that psychosis may be a syndrome of accelerated aging. But do patients with psychosis perform poorly on tasks of processing speed for the same reasons as older adults? Fifty-one patients with psychotic illnesses and 90 controls with similar mean IQ (aged 19-69 years, all African American) completed a computerized processing-speed task, reminiscent of the classic digit-symbol coding task. The data were analyzed using the drift-diffusion model (DDM), and Bayesian inference was used to determine whether psychosis and aging had similar or divergent effects on the DDM parameters. Psychosis and aging were both associated with poor performance, but had divergent effects on the DDM parameters. Patients had lower information-processing efficiency ("drift rate") and longer nondecision time than controls, and psychosis per se did not influence response caution. By contrast, the primary effect of aging was to increase response caution, and had inconsistent effects on drift rate and nondecision time across patients and controls. The results reveal that psychosis and aging influenced performance in different ways, suggesting that the processing-speed impairment in psychosis is more than just accelerated aging. This study also demonstrates the potential utility of computational models and Bayesian inference for finely mapping the contributions of cognitive functions on simple neurocognitive tests.

Acute Stimulant Treatment and Reinforcement Increase the Speed of Information Accumulation in Children with ADHD

The current studies utilized drift diffusion modeling (DDM) to examine how reinforcement and stimulant medication affect cognitive task performance in children with ADHD. In Study 1, children with (n = 25; 88 % male) and without ADHD (n = 33; 82 % male) completed a 2-choice discrimination task at baseline (100 trials) and again a week later under alternating reinforcement and no-reinforcement contingencies (400 trials total). In Study 2, participants with ADHD (n = 29; 72 % male) completed a double-blind, placebo-controlled trial of 0.3 and 0.6 mg/kg methylphenidate and completed the same task utilized in Study 1 at baseline (100 trials). Children with ADHD accumulated information at a much slower rate than controls, as evidenced by a lower drift rate. Groups were similar in nondecision time and boundary separation. Both reinforcement and stimulant medication markedly improved drift rate in children with ADHD (ds = 0.70 and 0.95 for reinforcement and methylphenidate, respectively); both treatments also reduced boundary separation (ds = 0.70 and 0.39). Reinforcement, which emphasized speeded accuracy, reduced nondecision time (d = 0.37), whereas stimulant medication increased nondecision time (d = 0.38). These studies provide initial evidence that frontline treatments for ADHD primarily impact cognitive performance in youth with ADHD by improving the speed/efficiency of information accumulation. Treatment effects on other DDM parameters may vary between treatments or interact with task parameters (number of trials, task difficulty). DDM, in conjunction with other approaches, may be helpful in clarifying the specific cognitive processes that are disrupted in ADHD, as well as the basic mechanisms that underlie the efficacy of ADHD treatments.

Diffusion Decision Model: Current Issues and History

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this review, we relate the models to both earlier and more recent research in psychology.

Eye-tracking reveals a slowdown of social context processing during intention attribution in patients with schizophrenia

BACKGROUND

Schizophrenia is associated with poor theory of mind (ToM), particularly in the attribution of intentions to others. It is also associated with abnormal gaze behaviours and contextual processing. This study investigated to what extent impaired ToM in patients with schizophrenia is related to abnormal processing of social context.

METHODS

We evaluated ToM using a nonverbal intention attribution task based on comic strips depicting social/nonsocial and contextual/noncontextual events while eye movements were recorded. Eye-tracking was used to assess processing time dedicated to visual cues contained in regions of interest identified in a pilot study. We measured cognitive contextual control on a separate task.

RESULTS

We tested 29 patients with schizophrenia and 29 controls. Compared with controls, patients were slower in intention attribution but not in physical reasoning. They looked longer than controls at contextual cues displayed in the first 2 context pictures of the comic strips, and this difference was greater for intention attribution than for physical reasoning. We found no group difference in time spent looking at noncontextual cues. Patients' impairment in contextual control did not explain their increased reaction time and gaze duration on contextual cues during intention attribution.

LIMITATIONS

Difficulty may not have been equivalent between intention attribution and physical reasoning conditions.

CONCLUSION

Overall, schizophrenia was characterized by a delay in intention attribution related to a slowdown of social context processing that was not explained by worse executive contextual control.

On the Complexity of Brain Disorders: A Symptom-Based Approach

Mounting evidence shows that brain disorders involve multiple and different neural dysfunctions, including regional brain damage, change to cell structure, chemical imbalance, and/or connectivity loss among different brain regions. Understanding the complexity of brain disorders can help us map these neural dysfunctions to different symptom clusters as well as understand subcategories of different brain disorders. Here, we discuss data on the mapping of symptom clusters to different neural dysfunctions using examples from brain disorders such as major depressive disorder (MDD), Parkinson’s disease (PD), schizophrenia, posttraumatic stress disorder (PTSD) and Alzheimer’s disease (AD). In addition, we discuss data on the similarities of symptoms in different disorders. Importantly, computational modeling work may be able to shed light on plausible links between various symptoms and neural damage in brain disorders.

Speed-accuracy strategy regulations in prefrontal tumor patients

The ability to flexibly switch between fast and accurate decisions is crucial in everyday life. Recent neuroimaging evidence suggested that left lateral prefrontal cortex plays a role in switching from a quick response strategy to an accurate one. However, the causal role of the left prefrontal cortex in this particular, non-verbal, strategy switch has never been demonstrated. To fill this gap, we administered a perceptual decision-making task to neuro-oncological prefrontal patients, in which the requirement to be quick or accurate changed randomly on a trial-by-trial basis. To directly assess hemispheric asymmetries in speed-accuracy regulation, patients were tested a few days before and a few days after surgical excision of a brain tumor involving either the left (N=13) or the right (N=12) lateral frontal brain region. A group of age- and education-matched healthy controls was also recruited. To gain more insight on the component processes implied in the task, performance data (accuracy and speed) were not only analyzed separately but also submitted to a diffusion model analysis. The main findings indicated that the left prefrontal patients were impaired in appropriately adopting stricter response criteria in speed-to-accuracy switching trials with respect to healthy controls and right prefrontal patients, who were not impaired in this condition. This study demonstrates that the prefrontal cortex in the left hemisphere is necessary for flexible behavioral regulations, in particular when setting stricter response criteria is required in order to successfully switch from a speedy strategy to an accurate one.