Task-general efficiency of evidence accumulation as a computationally-defined neurocognitive trait: Implications for clinical neuroscience

Disentangling sources of variability in decision-making

Even the most highly-trained observers presented with identical choice-relevant stimuli will reliably exhibit substantial trial-to-trial variability in the timing and accuracy of their choices. Despite being a pervasive feature of choice behaviour and a prominent phenotype for numerous clinical disorders, the capability to disentangle the sources of such intra-individual variability (IIV) remains limited. In principle, computational models of decision-making offer a means of parsing and estimating these sources, but methodological limitations have prevented this potential from being fully realized in practice. In this Review, we first discuss current limitations of algorithmic models for understanding variability in decision-making behaviour. We then highlight recent advances in behavioural paradigm design, novel analyses of cross-trial behavioural and neural dynamics, and the development of neurally grounded computational models that are now making it possible to link distinct components of IIV to well-defined neural processes. Taken together, we demonstrate how these methods are opening up new avenues for systematically analysing the neural origins of IIV, paving the way for a more refined, holistic understanding of decision-making in health and disease.

Neurophysiology of Perceptual Decision-Making and Its Alterations in Attention-Deficit Hyperactivity Disorder

Despite the prevalence of attention-deficit hyperactivity disorder (ADHD), efforts to develop a detailed understanding of the neuropsychology of this neurodevelopmental condition are complicated by the diversity of interindividual presentations and the inability of current clinical tests to distinguish between its sensory, attentional, arousal, or motoric contributions. Identifying objective methods that can explain the diverse performance profiles across individuals diagnosed with ADHD has been a long-held goal. Achieving this could significantly advance our understanding of etiological processes and potentially inform the development of personalized treatment approaches. Here, we examine key neuropsychological components of ADHD within an electrophysiological (EEG) perceptual decision-making paradigm that is capable of isolating distinct neural signals of several key information processing stages necessary for sensory-guided actions from attentional selection to motor responses. Using a perceptual decision-making task (random dot motion), we evaluated the performance of 79 children (aged 8-17 years) and found slower and less accurate responses, along with a reduced rate of evidence accumulation (drift rate parameter of drift diffusion model), in children with ADHD (n = 37; 13 female) compared with typically developing peers (n = 42; 18 female). This was driven by the atypical dynamics of discrete electrophysiological signatures of attentional selection, the accumulation of sensory evidence, and strategic adjustments reflecting urgency of response. These findings offer an integrated account of decision-making in ADHD and establish discrete neural signals that might be used to understand the wide range of neuropsychological performance variations in individuals with ADHD.

Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention

OBJECTIVE

Develop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU).

METHODS

Analyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI).

RESULTS

A slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment.

CONCLUSIONS

PAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.

Dissociable Contributions of Goal-Relevant Evidence and Goal-Irrelevant Familiarity to Individual and Developmental Differences in Conflict Recognition

Recent studies using the diffusion decision model find that performance across many cognitive control tasks can be largely attributed to a task-general efficiency of evidence accumulation (EEA) factor that reflects individuals' ability to selectively gather evidence relevant to task goals. However, estimates of EEA from an n-back "conflict recognition" paradigm in the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, a large, diverse sample of youth, appear to contradict these findings. EEA estimates from "lure" trials-which present stimuli that are familiar (i.e., presented previously) but do not meet formal criteria for being a target-show inconsistent relations with EEA estimates from other trials and display atypical v-shaped bivariate distributions, suggesting many individuals are responding based largely on stimulus familiarity rather than goal-relevant stimulus features. We present a new formal model of evidence integration in conflict recognition tasks that distinguishes individuals' EEA for goal-relevant evidence from their use of goal-irrelevant familiarity. We then investigate developmental, cognitive, and clinical correlates of these novel parameters. Parameters for EEA and goal-irrelevant familiarity-based processing showed strong correlations across levels of n-back load, suggesting they are task-general dimensions that influence individuals' performance regardless of working memory demands. Only EEA showed large, robust developmental differences in the ABCD sample and an independent age-diverse sample. EEA also exhibited higher test-retest reliability and uniquely meaningful associations with clinically relevant dimensions. These findings establish a principled modeling framework for characterizing conflict recognition mechanisms and have several broader implications for research on individual and developmental differences in cognitive control.

Leveraging bifactor modeling to test prospective direct and indirect effects of adolescent alcohol use and externalizing symptoms on the development of task-general executive functioning

Adolescence is a period of substantial maturation in brain regions underlying Executive Functioning (EF). Adolescence is also associated with initiation and escalation of Alcohol Use (AU), and adolescent AU has been proposed to produce physiological and neurobiological events that derail healthy EF development. However, support has been mixed, which may be due to (1) failure to consider co-occurring externalizing symptoms (including other drug use) and poor social adaptation, and (2) heterogeneity and psychometric limitations in EF measures. We aimed to clarify the AU-EF association by: (1) distinguishing general externalizing symptoms from specific symptoms (AU, aggression, drug use) using bifactor modeling, (2) testing prospective associations between general externalizing symptoms and specific symptoms, and task-general EF, as indexed by a well-validated computational modeling framework (diffusion decision model), and (3) examining indirect pathways from externalizing symptoms to deficits in task-general EF through poor social adaptation. A high-risk longitudinal sample (N = 919) from the Michigan Longitudinal Study was assessed at four time-points spanning early adolescence (10-13 years) to young adulthood (22-25). Results suggested a critical role of social adaptation within peer and school contexts in promoting healthy EF. There was no evidence that specific, neurotoxic effects of alcohol or drug use derailed task-general EF development.

EEG tensor decomposition delineates neurophysiological principles underlying conflict-modulated action restraint and action cancellation

Executive functions are essential for adaptive behavior. One executive function is the so-called 'interference control' or conflict monitoring another one is inhibitory control (i.e., action restraint and action cancelation). Recent evidence suggests an interplay of these processes, which is conceptually relevant given that newer conceptual frameworks imply that nominally different action/response control processes are explainable by a small set of cognitive and neurophysiological processes. The existence of such overarching neural principles has as yet not directly been examined. In the current study, we therefore use EEG tensor decomposition methods, to look into possible common neurophysiological signatures underlying conflict-modulated action restraint and action cancelation as mechanism underlying response inhibition. We show how conflicts differentially modulate action restraint and action cancelation processes and delineate common and distinct neural processes underlying this interplay. Concerning the spatial information modulations are similar in terms of an importance of processes reflected by parieto-occipital electrodes, suggesting that attentional selection processes play a role. Especially theta and alpha activity seem to play important roles. The data also show that tensor decomposition is sensitive to the manner of task implementation, thereby suggesting that switch probability/transitional probabilities should be taken into consideration when choosing tensor decomposition as analysis method. The study provides a blueprint of how to use tensor decomposition methods to delineate common and distinct neural mechanisms underlying action control functions using EEG data.

Flexible adaptation of task-positive brain networks predicts efficiency of evidence accumulation

Efficiency of evidence accumulation (EEA), an individual's ability to selectively gather goal-relevant information to make adaptive choices, is thought to be a key neurocomputational mechanism associated with cognitive functioning and transdiagnostic risk for psychopathology. However, the neural basis of individual differences in EEA is poorly understood, especially regarding the role of largescale brain network dynamics. We leverage data from 5198 participants from the Human Connectome Project and Adolescent Brain Cognitive Development Study to demonstrate a strong association between EEA and flexible adaptation to cognitive demand in the "task-positive" frontoparietal and dorsal attention networks. Notably, individuals with higher EEA displayed divergent task-positive network activation across n-back task conditions: higher activation under high cognitive demand (2-back) and lower activation under low demand (0-back). These findings suggest that brain networks' flexible adaptation to cognitive demands is a key neural underpinning of EEA.

Mild exogenous inflammation blunts neural signatures of bounded evidence accumulation and reward prediction error processing in healthy male participants

BACKGROUND

Altered neural haemodynamic activity during decision making and learning has been linked to the effects of inflammation on mood and motivated behaviours. So far, it has been reported that blunted mesolimbic dopamine reward signals are associated with inflammation-induced anhedonia and apathy. Nonetheless, it is still unclear whether inflammation impacts neural activity underpinning decision dynamics. The process of decision making involves integration of noisy evidence from the environment until a critical threshold of evidence is reached. There is growing empirical evidence that such process, which is usually referred to as bounded accumulation of decision evidence, is affected in the context of mental illness.

METHODS

In a randomised, placebo-controlled, crossover study, 19 healthy male participants were allocated to placebo and typhoid vaccination. Three to four hours post-injection, participants performed a probabilistic reversal-learning task during functional magnetic resonance imaging. To capture the hidden neurocognitive operations underpinning decision-making, we devised a hybrid sequential sampling and reinforcement learning computational model. We conducted whole brain analyses informed by the modelling results to investigate the effects of inflammation on the efficiency of decision dynamics and reward learning.

RESULTS

We found that during the decision phase of the task, typhoid vaccination attenuated neural signatures of bounded evidence accumulation in the dorsomedial prefrontal cortex, only for decisions requiring short integration time. Consistent with prior work, we showed that, in the outcome phase, mild acute inflammation blunted the reward prediction error in the bilateral ventral striatum and amygdala.

CONCLUSIONS

Our study extends current insights into the effects of inflammation on the neural mechanisms of decision making and shows that exogenous inflammation alters neural activity indexing efficiency of evidence integration, as a function of choice discriminability. Moreover, we replicate previous findings that inflammation blunts striatal reward prediction error signals.

Impairment in recognition memory may be associated with near-term risk for suicide attempt in a high-risk sample

INTRODUCTION

Prior work has implicated several neurocognitive domains, including memory, in patients with a history of prior suicide attempt. The current study evaluated whether a delayed recognition test could enhance prospective prediction of near-term suicide outcomes in a sample of patients at high-risk for suicide.

METHODS

132 Veterans at high-risk for suicide completed a computer-based recognition memory test including semantically-related and -unrelated words. Outcomes were coded as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as aborted/interrupted attempt or preparatory behavior, or neither (noSE), within 90 days after testing.

RESULTS

Reduced performance was a significant predictor of upcoming ASA, but not OtherSE, after controlling for standard clinical variables such as current suicidal ideation and history of prior suicide attempt. However, compared to the noSE reference group, the OtherSE group showed a reduction in the expected benefit of semantic relatedness in recognizing familiar words. A computational model, the drift diffusion model (DDM), to explore latent cognitive processes, revealed the OtherSE group had decreased decisional efficiency for semantically-related compared to semantically-unrelated familiar words.

LIMITATIONS

This study was a secondary analysis of an existing dataset, involving participants in a treatment trial, and requires replication; ~10 % of the sample was excluded from analysis due to failure to master the practice tasks and/or apparent noncompliance.

CONCLUSION

Impairments in recognition memory may be associated with near-term risk for suicide attempt, and may provide a tool to improve prediction of when at-risk individuals may be transitioning into a period of heightened risk for suicide attempt.

A diffusion decision model analysis of the cognitive effects of neurofeedback for ADHD

OBJECTIVE

To examine cognitive effects of neurofeedback (NF) for attention-deficit hyperactivity disorder (ADHD) as a secondary outcome of a randomized clinical trial.

METHOD

In a double-blind randomized clinical trial (NCT02251743), 133 7-10-year olds with ADHD received either 38 sessions of NF (n = 78) or control treatment (n = 55) and performed an integrated visual and auditory continuous performance test at baseline, mid- and end-treatment. We used the diffusion decision model to decompose integrated visual and auditory continuous performance test performance at each assessment into cognitive components: efficiency of integrating stimulus information (v), context sensitivity (cv), response cautiousness (a), response bias (z/a), and nondecision time for perceptual encoding and response execution (Ter). Based on prior findings, we tested whether the components known to be deficient improved with NF and explored whether other cognitive components improved using linear mixed modeling.

RESULTS

Before NF, children with ADHD showed main deficits in integrating stimulus information (v), which led to less accurate and slower responses than healthy controls (p = .008). The NF group showed significantly more improvement in integrating auditory stimulus information (v) than control treatment (significant group-by-time-by-modality effect: p = .044).

CONCLUSIONS

NF seems to improve v, deficient in ADHD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains

Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study, we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis, we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two-factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.

Toward objective characterizations of suicide risk: A narrative review of laboratory-based cognitive and behavioral tasks

Although suicide is a leading cause of preventable death worldwide, current prevention efforts have failed to substantively mitigate suicide risk. Suicide research has traditionally relied on subjective reports that may not accurately differentiate those at high versus minimal risk. This narrative review supports the inclusion of objective task-based measures in suicide research to complement existing subjective batteries. The article: 1) outlines risk factors proposed by contemporary theories of suicide and highlights recent empirical findings supporting these theories; 2) discusses ongoing challenges associated with current risk assessment tools and their ability to accurately evaluate risk factors; and 3) analyzes objective laboratory measures that can be implemented alongside traditional measures to enhance the precision of risk assessment. To illustrate the potential of these methods to improve our understanding of suicide risk, the article reviews how acute stress responses in a laboratory setting can be modeled, given that stress is a major precipitant for suicidal behavior. More precise risk assessment strategies can emerge if objective measures are implemented in conjunction with traditional subjective measures.

Processing Speed is Related to the General Psychopathology Factor in Youth

The relationship between the p factor and cognition in youth has largely focused on general cognition (IQ) and executive functions (EF). Another cognitive construct, processing speed (PS), is dissociable from IQ and EF, but has received less research attention despite being related to many different mental health symptoms. The present sample included 795 youth, ages 11-16 from the Colorado Learning Disabilities Research Center (CLDRC) sample. Confirmatory factor analyses tested multiple p factor models, with the primary model being a second-order, multi-reporter p factor. We then tested the correlation between the p factor and a latent PS factor. There was a significant, negative correlation between the p factor and PS (r(87) = -0.42, p < .001), indicating that slower processing speed is associated with higher general mental health symptoms. This association is stronger than previously reported associations with IQ or EF. This finding was robust across models that used different raters (youth and caregiver) and modeling approaches (second-order vs. bifactor). Our findings indicate that PS is related to general psychopathology symptoms. This research points to processing speed as an important transdiagnostic construct that warrants further exploration across development.

Generalizable prediction of childhood ADHD symptoms from neurocognitive testing and youth characteristics

Childhood attention-deficit/hyperactivity disorder (ADHD) symptoms are believed to result from disrupted neurocognitive development. However, evidence for the clinical and predictive value of neurocognitive assessments in this context has been mixed, and there have been no large-scale efforts to quantify their potential for use in generalizable models that predict individuals' ADHD symptoms in new data. Using data drawn from the Adolescent Brain Cognitive Development Study (ABCD), a consortium that recruited a diverse sample of over 10,000 youth (ages 9-10 at baseline) across 21 U.S. sites, we develop and test cross-validated machine learning models for predicting youths' ADHD symptoms using neurocognitive abilities, demographics, and child and family characteristics. Models used baseline demographic and biometric measures, geocoded neighborhood data, youth reports of child and family characteristics, and neurocognitive tests to predict parent- and teacher-reported ADHD symptoms at the 1-year and 2-year follow-up time points. Predictive models explained 15-20% of the variance in 1-year ADHD symptoms for ABCD Study sites that were left out of the model-fitting process and 12-13% of the variance in 2-year ADHD symptoms. Models displayed high generalizability across study sites and trivial loss of predictive power when transferred from training data to left-out data. Features from multiple domains contributed meaningfully to prediction, including neurocognition, sex, self-reported impulsivity, parental monitoring, and screen time. This work quantifies the information value of neurocognitive abilities and other child characteristics for predicting ADHD symptoms and provides a foundational method for predicting individual youths' symptoms in new data across contexts.

Examining reaction time variability on the stop-signal task in the ABCD study

OBJECTIVE

Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs.

METHOD

This study utilized trial-level data from the stop signal task from 8916 children (9-10 years old) to examine Gaussian, ex-Gaussian, and DM indicators of RTV. In addition to describing RTV patterns, we examined interrelations among these indicators, temporal patterns, and sex differences.

RESULTS

There was no one-to-one correspondence between DM and ex-Gaussian parameters. Nonetheless, drift rate was most strongly associated with standard deviation of RT and tau, while nondecisional processes were most strongly associated with RT, mu, and sigma. Performance worsened across time with changes driven primarily by decreasing drift rate. Boys were faster and less variable than girls, likely attributable to girls' wide boundary separation.

CONCLUSIONS

Intercorrelations among model parameters are similar in children as has been observed in adults. Computational approaches play a crucial role in understanding performance changes over time and can also clarify mechanisms of group differences. For example, standard RT models may incorrectly suggest slowed processing speed in girls that is actually attributable to other factors.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Cognitive markers for efficacy of neurofeedback for attention-deficit hyperactivity disorder - personalized medicine using computational psychiatry in a randomized clinical trial

BACKGROUND

Exploring whether cognitive components (identified by baseline cognitive testing and computational modeling) moderate clinical outcome of neurofeedback (NF) for attention-deficit hyperactivity disorder (ADHD).

METHOD

142 children (aged 7-10) with ADHD were randomly assigned to either NF (n = 84) or control treatment (n = 58) in a double-blind clinical trial (NCT02251743). The NF group received live, self-controlled downtraining of electroencephalographic theta/beta ratio power. The control group received identical-appearing reinforcement from prerecorded electroencephalograms from other children. 133 (78 NF, 55 control) children had cognitive processing measured at baseline with the Integrated Visual and Auditory Continuous Performance Test (IVA2-CPT) and were included in this analysis. A diffusion decision model applied to the IVA2-CPT data quantified two latent cognitive components deficient in ADHD: drift rate and drift bias, indexing efficiency and context sensitivity of cognitive processes involving information integration. We explored whether these cognitive components moderated the improvement in parent- and teacher-rated inattention symptoms from baseline to treatment end (primary clinical outcome).

RESULTS

Baseline cognitive components reflecting information integration (drift rate, drift bias) moderated the improvement in inattention due to NF vs. control treatment (p = 0.006). Specifically, those with either the most or least severe deficits in these components showed more improvement in parent- and teacher-rated inattention when assigned to NF (Cohen's d = 0.59) than when assigned to control (Cohen's d = -0.21).

CONCLUSIONS

Pre-treatment cognitive testing with computational modeling identified children who benefitted more from neurofeedback than control treatment for ADHD.

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.

The role of parenting in the intergenerational transmission of executive functioning: A genetically informed approach

Deficits in executive functioning both run in families and serve as a transdiagnostic risk factor for psychopathology. The present study employed twin modeling to examine parenting as an environmental pathway underlying the intergenerational transmission of executive functioning in an at-risk community sample of children and adolescents (N = 354 pairs, 167 monozygotic). Using structural equation modeling of multi-informant reports of parenting and a multi-method measure of child executive functioning, we found that better parent executive functioning related to less harsh, warmer parenting, which in turn related to better child executive functioning. Second, we assessed the etiology of executive functioning via the nuclear twin family model, finding large non-shared environmental effects (E = .69) and low-to-moderate heritability (A = .22). We did not find evidence of shared environmental effects or passive genotype-environment correlation. Third, a bivariate twin model revealed significant shared environmental overlap between both warm and harsh parenting and child executive functioning (which may indicate either passive genotype-environment correlation or environmental mediation), and non-shared environmental overlap between only harsh parenting and child executive functioning (indicating an effect of harsh parenting separable from genetic confounds). In summary, genetics contribute to the intergenerational transmission of executive functioning, with environmental mechanisms, including harsh parenting, also making unique contributions.

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model

A critical issue in addressing medical conditions is measurement. Memory measurement is difficult, especially episodic memory, which is disrupted by many conditions. On-line computer testing can precisely measure and assess several memory functions. This study analyzed memory performances from a large group of anonymous, on-line participants using a continuous recognition task (CRT) implemented at https://memtrax.com. These analyses estimated ranges of acceptable performance and average response time (RT). For 344,165 presumed unique individuals completing the CRT a total of 602,272 times, data were stored on a server, including each correct response (HIT), Correct Rejection, and RT to the thousandth of a second. Responses were analyzed, distributions and relationships of these parameters were ascertained, and mean RTs were determined for each participant across the population. From 322,996 valid first tests, analysis of correctness showed that 63% of these tests achieved at least 45 correct (90%), 92% scored at or above 40 correct (80%), and 3% scored 35 correct (70%) or less. The distribution of RTs was skewed with 1% faster than 0.62 s, a median at 0.890 s, and 1% slower than 1.57 s. The RT distribution was best explained by a novel model, the reverse-exponential (RevEx) function. Increased RT speed was most closely associated with increased HIT accuracy. The MemTrax on-line memory test readily provides valid and reliable metrics for assessing individual episodic memory function that could have practical clinical utility for precise assessment of memory dysfunction in many conditions, including improvement or deterioration over time.

Fronto—Parietal Regions Predict Transient Emotional States in Emotion Modulated Response Inhibition via Low Frequency and Beta Oscillations

The current study evaluated the impact of task-relevant emotion on inhibitory control while focusing on midline cortical regions rather than brain asymmetry. Single-trial time-frequency analysis of electroencephalography recordings linked with response execution and response inhibition was done while thirty-four participants performed the emotion modulated stop-signal task. To evaluate individual differences across decision-making processes involved in inhibitory control, a hierarchical drift-diffusion model was used to fit data from Go-trials for each of the 34 participants. Response threshold in the early processing stage for happy and disgust emotions could be distinguished from the later processing stage at the mid-parietal and mid-frontal regions, respectively, by the single-trial power increments in low frequency (delta and theta) bands. Beta desynchronization in the mid-frontal region was specific for differentiating disgust from neutral emotion in the early as well as later processing stages. The findings are interpreted based on the influence of emotional stimuli on early perceptual processing originating as a bottom-up process in the mid-parietal region and later proceeding to the mid-frontal region responsible for cognitive control processing, which resulted in enhanced inhibitory performance. The results show the importance of mid-frontal and mid-parietal regions in single-trial dynamics of inhibitory control processing.

Impaired control in addiction involves cognitive distortions and unreliable self-control, not compulsive desires and overwhelmed self-control

Impaired control in addiction involves a characteristic but obscure kind of partial control. Certain aspects of control over drug use are clearly reduced, reflected in difficulty cutting back and relapse. However, other aspects of control are clearly preserved, as reflected in substantial sensitivity to situational incentives-for example, the ability to defer use when needed. This juxtaposition is puzzling, and a clear mechanistically precise understanding of impaired control has yet to emerge. In this article, a Distortion model of impaired control is put forward. The key insight of the model is that the puzzling pattern of partial control seen in addiction can be understood in terms of unreliable control. The model posits large populations of distorted automatic thoughts (e.g., about drugs, one's self, one's circumstances, and one's abilities to cope), coupled with unreliable control over these distorted thoughts. These distorted thoughts, typically gradually and cumulatively, lead to illusion-like misvaluation of costs and benefits of drug use, in turn eventually leading to decisions to use. The model captures an elusive middle ground in addiction in which substantially preserved control over drug use for briefer intervals coexists with difficulty maintaining sobriety over the long-term. Moreover, the model explains a range of clinical findings in addiction that are not easily accommodated on leading alternative views.

Cognitive efficiency beats top-down control as a reliable individual difference dimension relevant to self-control

Top-down control of responses is a key construct in cognitive science that is thought to be critical for self-control. It is typically measured by subtracting performance in experimental conditions in which top-down control is theoretically present against performance in matched conditions in which it is assumed to be absent. Recently, however, subtraction-based metrics of top-down control have been criticized for having low test-retest reliability, weak intercorrelations, and little relation to self-report measures of self-control. Concurrently, there is growing evidence that task-general cognitive efficiency, indexed by the drift rate parameter of the diffusion model (Ratcliff, 1978), constitutes a cohesive, reliable individual difference dimension relevant to self-control. However, no previous studies have directly compared latent factors for top-down control (derived from subtraction metrics) with factors for task-general efficiency "head-to-head" in the same sample in terms of their cohesiveness, temporal stability, and relation to self-control. In this re-analysis of a large open data set (Eisenberg et al., 2019; N = 522), we find that top-down control metrics fail to form cohesive latent factors, that the resulting factors have poor temporal stability, and that they exhibit tenuous connections to questionnaire measures of self-control. In contrast, cognitive efficiency measures-drawn from conditions of the same tasks that both are, and are not, assumed to demand top-down control-form a robust, temporally stable factor that correlates with questionnaire measures of self-control. These findings suggest that task-general efficiency is a central individual difference dimension relevant to self-control. Moreover, they go beyond recent measurement-based critiques of top-down control metrics, and instead suggest problems with key theoretical assumptions that have long guided this research paradigm.