Modeling Individual Differences in the Go/No-go Task with a Diffusion Model

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.

The Tweedledum and Tweedledee of dynamic decisions: Discriminating between diffusion decision and accumulator models

Theories of dynamic decision-making are typically built on evidence accumulation, which is modeled using racing accumulators or diffusion models that track a shifting balance of support over time. However, these two types of models are only two special cases of a more general evidence accumulation process where options correspond to directions in an accumulation space. Using this generalized evidence accumulation approach as a starting point, I identify four ways to discriminate between absolute-evidence and relative-evidence models. First, an experimenter can look at the information that decision-makers considered to identify whether there is a filtering of near-zero evidence samples, which is characteristic of a relative-evidence decision rule (e.g., diffusion decision model). Second, an experimenter can disentangle different components of drift rates by manipulating the discriminability of the two response options relative to the stimulus to delineate the balance of evidence from the total amount of evidence. Third, a modeler can use machine learning to classify a set of data according to its generative model. Finally, machine learning can also be used to directly estimate the geometric relationships between choice options. I illustrate these different approaches by applying them to data from an orientation-discrimination task, showing converging conclusions across all four methods in favor of accumulator-based representations of evidence during choice. These tools can clearly delineate absolute-evidence and relative-evidence models, and should be useful for comparing many other types of decision theories.

A cognitive model of perceptual anomalies: The role of source monitoring, top-down influence and inhibitory processes for hallucinations in schizophrenia spectrum disorders and hallucinatory-like experiences in the general population

BACKGROUND

Cognitive models emphasise that source monitoring, top-down processes, and inhibitory control are mechanisms of perceptual anomalies, particularly auditory hallucinations (AHs) and hallucinatory-like experiences (HLEs). Nonetheless, limited research integrates clinical and non-clinical perceptual anomalies to examine these cognitive mechanisms and the connections between them. The present study aimed to investigate the role of three cognitive processes within the perceptual anomalies continuum. Moreover, the study examines the relationship between perceptual anomalies, cognitive processes, self-disturbances, and general functioning.

METHODS

Eighty-nine patients with schizophrenia spectrum disorders (SSD) were divided into two groups based on AHs presence - 46 with AHs and 43 - non-hallucinating, 43 matched healthy controls (HC), and a sample selected from the general population of 40 participants with high HLEs and 43 with low HLEs performed three experimental tasks assessing top-down processes (False Perception Task - FPT), source monitoring (Action Memory Task - AMT), and inhibitory control (Go/No-Go Task).

RESULTS

Both patient groups committed significantly more source monitoring errors and more false perceptions (after accounting for response bias) than HC, with no differences between SSD with AH vs SSD without current AH and high HLEs vs low HLEs. No significant group differences were found for false alarms in the Go/No-Go Task. However, there was a significant relationship between perceptual anomalies and all the cognitive processes as well as self-disturbances and functioning in the entire sample.

CONCLUSIONS

This study sheds further light on the mechanisms and correlates of perceptual anomalies in clinical and non-clinical populations.

Lateral prefrontal theta oscillations causally drive a computational mechanism underlying conflict expectation and adaptation

Adapting our behavior to environmental demands relies on our capacity to perceive and manage potential conflicts within our surroundings. While evidence implicates the involvement of the lateral prefrontal cortex and theta oscillations in detecting conflict stimuli, their causal role in conflict expectation remains elusive. Consequently, the exact computations and neural mechanisms underlying these cognitive processes still need to be determined. We employed an integrative approach involving cognitive computational modeling, fMRI, TMS, and EEG to establish a causal link between oscillatory brain function, its neurocomputational role, and the resulting conflict processing and adaptation behavior. Our results reveal a computational process underlying conflict expectation, which correlates with BOLD-fMRI and theta activity in the superior frontal gyrus (SFG). Modulation of theta activity via rhythmic TMS applied over the SFG induces endogenous theta activity, which in turn enhances computations associated with conflict expectation. These findings provide evidence for the causal involvement of SFG theta activity in learning and allocating cognitive resources to address forthcoming conflict stimuli.

Mapping computational cognitive profiles of aging to dissociable brain and sociodemographic factors

Aging is associated with declines in cognition and brain structural integrity. However, there is equivocality over (1) the specificity of affected domains in different people, (2) the location of associated patterns of brain structural deterioration, and (3) the sociodemographic factors contributing to 'unhealthy' cognition. We aimed to identify cognitive profiles displayed by older adults and determine brain and sociodemographic features potentially shaping these profiles. A sample of Southeast-Asian older adults (N = 386) participated in a multi-session study comprising cognitive testing, neuroimaging, and a structured interview. We used computational models to extract latent mechanisms underlying cognitive flexibility and response inhibition. Data-driven methods were used to construct cognitive profiles based on standard performance measures and model parameters. We also investigated grey matter volume and machine-learning derived 'brain-ages'. A profile associated with poor set-shifting and rigid focusing was associated with widespread grey matter reduction in cognitive control regions. A slow responding profile was associated with advanced brain-age. Both profiles were correlated with poor socioeconomic standing and cognitive reserve. We found that the impact of sociodemographic factors on cognitive profiles was partially mediated by total grey and white matter, and dorsolateral prefrontal and cerebellar volumes. This study furthers understanding of how distinct aging profiles of cognitive impairment uniquely correspond to specific vs. global brain deterioration and the significance of socioeconomic factors in informing cognitive performance in older age.

EZ-CDM: Fast, simple, robust, and accurate estimation of circular diffusion model parameters

The investigation of cognitive processes that form the basis of decision-making in paradigms involving continuous outcomes has gained the interest of modeling researchers who aim to develop a dynamic decision theory that accounts for both speed and accuracy. One of the most important of these continuous models is the circular diffusion model (CDM, Smith. Psychological Review, 123(4), 425. 2016), which posits a noisy accumulation process mathematically described as a stochastic two-dimensional Wiener process inside a disk. Despite the considerable benefits of this model, its mathematical intricacy has limited its utilization among scholars. Here, we propose a straightforward and user-friendly method for estimating the CDM parameters and fitting the model to continuous-scale data using simple formulas that can be readily computed and do not require theoretical knowledge of model fitting or extensive programming. Notwithstanding its simplicity, we demonstrate that the aforementioned method performs with a level of accuracy that is comparable to that of the maximum likelihood estimation method. Furthermore, a robust version of the method is presented, which maintains its simplicity while exhibiting a high degree of resistance to contaminant responses. Additionally, we show that the approach is capable of reliably measuring the key parameters of the CDM, even when these values are subject to across-trial variability. Finally, we demonstrate the practical application of the method on experimental data. Specifically, an illustrative example is presented wherein the method is employed along with estimating the probability of guessing. It is hoped that the straightforward methodology presented here will, on the one hand, help narrow the divide between theoretical constructs and empirical observations on continuous response tasks and, on the other hand, inspire cognitive psychology researchers to shift their laboratory investigations towards continuous response paradigms.

Preadolescent externalizing and internalizing symptoms are differentially related to drift-diffusion model parameters and neural activation during a go/no-go task

Background:

Mental health disorders are among the leading causes of disability in childhood and adolescence. Notably, mental health disorders commonly co-occur, indicating that critical mechanisms may relate to multiple forms of psychopathology. One potential transdiagnostic mechanism that has been examined in youth is inhibitory control. However, previous neuroimaging research on inhibitory control in youth has typically not examined transdiagnostic symptom dimensions or used computational modeling to quantify subcomponent processes that affect inhibition.

Methods:

In the present study, a diverse sample of preadolescents who were oversampled for psychopathology risk (N = 86, ages 8–12) completed a Go/No-Go task during an fMRI scan. Parents completed a series of questionnaires that were used to capture externalizing and internalizing symptom dimensions. Drift-diffusion modeling (DDM) was applied to preadolescents’ task behavior, and model parameters were linked with neural activation using two contrasts: successful inhibition and failed inhibition.

Results:

During successful inhibition, bilateral inferior temporal gyrus (ITG) activation was related to non-decision time, and ITG activation was associated with externalizing and internalizing symptoms. During failed inhibition, insula and putamen activation were associated with drift rates, as well as with internalizing symptoms, independent of externalizing symptoms.

Conclusions:

Results indicate that distinct cognitive processes are related to broader psychopathology symptom dimensions in preadolescents. Future studies should assess relations among computational parameters and clinical risk in youth, which may reveal important prevention and/or intervention targets.

Hierarchical differences in the encoding of amplitude modulation in the subcortical auditory system of awake nonhuman primates

Sinusoidal amplitude modulation (SAM) is a key feature of complex sounds. Although psychophysical studies have characterized SAM perception, and neurophysiological studies in anesthetized animals report a transformation from the cochlear nucleus' (CN; brainstem) temporal code to the inferior colliculus' (IC; midbrain's) rate code, none have used awake animals or nonhuman primates to compare CN and IC's coding strategies to modulation-frequency perception. To address this, we recorded single-unit responses and compared derived neurometric measures in the CN and IC to psychometric measures of modulation frequency (MF) discrimination in macaques. IC and CN neurons often exhibited tuned responses to SAM in rate and spike-timing measures of modulation coding. Neurometric thresholds spanned a large range (2-200 Hz ΔMF). The lowest 40% of IC thresholds were less than or equal to psychometric thresholds, regardless of which code was used, whereas CN thresholds were greater than psychometric thresholds. Discrimination at 10-20 Hz could be explained by indiscriminately pooling 30 units in either structure, whereas discrimination at higher MFs was best explained by more selective pooling. This suggests that pooled CN activity was sufficient for AM discrimination. Psychometric and neurometric thresholds decreased as stimulus duration increased, but IC and CN thresholds were higher and more variable than behavior at short durations. This slower subcortical temporal integration compared with behavior was consistent with a drift diffusion model that reproduced individual differences in performance and can constrain future neurophysiological studies of temporal integration. These measures provide an account of AM perception at the neurophysiological, computational, and behavioral levels.NEW & NOTEWORTHY In everyday environments, the brain is tasked with extracting information from sound envelopes, which involves both sensory encoding and perceptual decision-making. Different neural codes for envelope representation have been characterized in midbrain and cortex, but studies of brainstem nuclei such as the cochlear nucleus (CN) have usually been conducted under anesthesia in nonprimate species. Here, we found that subcortical activity in awake monkeys and a biologically plausible perceptual decision-making model accounted for sound envelope discrimination behavior.

Direct serotonin release in humans shapes aversive learning and inhibition

The role of serotonin in human behaviour is informed by approaches which allow in vivo modification of synaptic serotonin. However, characterising the effects of increased serotonin signalling in human models of behaviour is challenging given the limitations of available experimental probes, notably selective serotonin reuptake inhibitors. Here we use a now-accessible approach to directly increase synaptic serotonin in humans (a selective serotonin releasing agent) and examine its influence on domains of behaviour historically considered core functions of serotonin. Computational techniques, including reinforcement learning and drift diffusion modelling, explain participant behaviour at baseline and after week-long intervention. Reinforcement learning models reveal that increasing synaptic serotonin reduces sensitivity for outcomes in aversive contexts. Furthermore, increasing synaptic serotonin enhances behavioural inhibition, and shifts bias towards impulse control during exposure to aversive emotional probes. These effects are seen in the context of overall improvements in memory for neutral verbal information. Our findings highlight the direct effects of increasing synaptic serotonin on human behaviour, underlining its role in guiding decision-making within aversive and more neutral contexts, and offering implications for longstanding theories of central serotonin function.

High stakes slow responding, but do not help overcome Pavlovian biases in humans

"Pavlovian" or "motivational" biases are the phenomenon that the valence of prospective outcomes modulates action invigoration: the prospect of reward invigorates actions, while the prospect of punishment suppresses actions. Effects of the valence of prospective outcomes are well established, but it remains unclear how the magnitude of outcomes ("stake magnitude") modulates these biases. In this preregistered study (N = 55), we manipulated stake magnitude (high vs. low) in an orthogonalized Motivational Go/NoGo Task. We tested whether higher stakes (a) strengthen biases or (b) elicit cognitive control recruitment, enhancing the suppression of biases in motivationally incongruent conditions. Confirmatory tests showed that high stakes slowed down responding, especially in motivationally incongruent conditions. However, high stakes did not affect whether a response was made or not, and did not change the magnitude of Pavlovian biases. Reinforcement-learning drift-diffusion models (RL-DDMs) fit to the data suggested that response slowing was best captured by stakes prolonging the non-decision time. There was no effect of the stakes on the response threshold (as in typical speed-accuracy trade-offs). In sum, these results suggest that high stakes slow down responses without affecting the expression of Pavlovian biases in behavior. We speculate that this slowing under high stakes might reflect heightened cognitive control, which is however ineffectively used, or reflect positive conditioned suppression, i.e., the interference between goal-directed and consummatory behaviors, a phenomenon previously observed in rodents that might also exist in humans. Pavlovian biases and slowing under high stakes may arise in parallel to each other.

Transcutaneous auricular vagus nerve stimulation enhanced emotional inhibitory control via increasing intrinsic prefrontal couplings

Background

Inhibitory control represents a core executive function that critically facilitates adaptive behavior and survival in an ever-changing environment. Non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has been hypothesized to improve behavioral inhibition performance, however the neurocomputational mechanism of taVNS-induced neuroenhancement remains elusive.

Method

In the current study, we investigated the efficacy of taVNS in a sham-controlled between-subject functional near infrared spectroscopy (fNIRS) experiment with an emotional face Go/No-Go paradigm in ninety healthy young adults.

Results

After a data quality check, eighty-two subjects were included in the final data analysis. Behaviorally, the taVNS improved No-Go response accuracy, together with computational modeling using Hierarchical Bayesian estimation of the Drift Diffusion Model (HDDM) indicating that it specifically reduced the information accumulation rate for Go responses, and this was negatively associated with increased accuracy of No-Go responses. On the neural level, taVNS enhanced engagement of the bilateral inferior frontal gyrus (IFG) during inhibition of angry expression faces and modulated functional couplings (FCs) within the prefrontal inhibitory control network. Mediation models revealed that taVNS-induced facilitation of inhibitory control was critically mediated by a decreased information accumulation for Go responses and concomitantly enhanced neurofunctional coupling between the inferior and orbital frontal cortex.

Discussion

Our findings demonstrate a potential for taVNS to improve emotional inhibitory control via reducing pre-potent responses and enhancing FCs within prefrontal inhibitory control networks, suggesting a promising therapeutic role in treating specific disorders characterized by inhibitory control deficits.

Kurvers RHJ. A Cognitive Computational Approach to Social and Collective Decision-Making

Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.

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).

Are there jumps in evidence accumulation, and what, if anything, do they reflect psychologically? An analysis of Lévy Flights models of decision-making

According to existing theories of simple decision-making, decisions are initiated by continuously sampling and accumulating perceptual evidence until a threshold value has been reached. Many models, such as the diffusion decision model, assume a noisy accumulation process, described mathematically as a stochastic Wiener process with Gaussian distributed noise. Recently, an alternative account of decision-making has been proposed in the Lévy Flights (LF) model, in which accumulation noise is characterized by a heavy-tailed power-law distribution, controlled by a parameter, [Formula: see text]. The LF model produces sudden large "jumps" in evidence accumulation that are not produced by the standard Wiener diffusion model, which some have argued provide better fits to data. It remains unclear, however, whether jumps in evidence accumulation have any real psychological meaning. Here, we investigate the conjecture by Voss et al. (Psychonomic Bulletin & Review, 26(3), 813-832, 2019) that jumps might reflect sudden shifts in the source of evidence people rely on to make decisions. We reason that if jumps are psychologically real, we should observe systematic reductions in jumps as people become more practiced with a task (i.e., as people converge on a stable decision strategy with experience). We fitted five versions of the LF model to behavioral data from a study by Evans and Brown (Psychonomic Bulletin & Review, 24(2), 597-606, 2017), using a five-layer deep inference neural network for parameter estimation. The analysis revealed systematic reductions in jumps as a function of practice, such that the LF model more closely approximated the standard Wiener model over time. This trend could not be attributed to other sources of parameter variability, speaking against the possibility of trade-offs with other model parameters. Our analysis suggests that jumps in the LF model might be capturing strategy instability exhibited by relatively inexperienced observers early on in task performance. We conclude that further investigation of a potential psychological interpretation of jumps in evidence accumulation is warranted.

Evidence Accumulation Rate Moderates the Relationship between Enriched Environment Exposure and Age-Related Response Speed Declines

Older adults exposed to enriched environments (EEs) maintain relatively higher levels of cognitive function, even in the face of compromised markers of brain health. Response speed (RS) is often used as a simple proxy to measure the preservation of global cognitive function in older adults. However, it is unknown which specific selection, decision, and/or motor processes provide the most specific indices of neurocognitive health. Here, using a simple decision task with electroencephalography (EEG), we found that the efficiency with which an individual accumulates sensory evidence was a critical determinant of the extent to which RS was preserved in older adults (63% female, 37% male). Moreover, the mitigating influence of EE on age-related RS declines was most pronounced when evidence accumulation rates were shallowest. These results suggest that the phenomenon of cognitive reserve, whereby high EE individuals can better tolerate suboptimal brain health to facilitate the preservation of cognitive function, is not just applicable to neuroanatomical indicators of brain aging but can be observed in markers of neurophysiology. Our results suggest that EEG metrics of evidence accumulation may index neurocognitive vulnerability of the aging brain.Significance Statement Response speed in older adults is closely linked with trajectories of cognitive aging. Here, by recording brain activity while individuals perform a simple computer task, we identify a neural metric that is a critical determinant of response speed. Older adults exposed to greater cognitive and social stimulation throughout a lifetime could maintain faster responding, even when this neural metric was impaired. This work suggests EEG is a useful technique for interrogating how a lifetime of stimulation benefits brain health in aging.

Hill L, Kline A, Stanley BH, Interian A. Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task

BACKGROUND

Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide.

METHOD

136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences.

RESULTS

On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window. In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA.

CONCLUSIONS

These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.

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.

ImGo: A Novel Tool for Behavioral Impulsivity Assessment Based on Go/NoGo Tasks

This manuscript aims to present a novel behavioral impulsivity test ImGo, which is suitable for impulsivity assessment in the general population. A series of three studies was conducted to validate its psychometric qualities. In Study 1 we describe the principles of ImGo and verify its test-retest and split-half reliability and its convergent validity with an impulsivity self-report scale and Stop Signal test. In Study 2 we re-analyze the convergent validity of ImGo with a Stop Signal test and examine the potential relationship between ImGo and oculomotor inhibition measured by an Anti-Saccades test. In Study 3 we present a robust research with a large sample size and investigate the discriminant validity of ImGo with tests of other related cognitive and executive processes. Backed by our findings from these studies we can safely claim ImGo is a powerful tool with a good level of reliability (both test-retest and split-half) and validity (convergent and discriminant). Its potential lies in its use in diagnostic and research practice of experts from various countries as the test has already been translated to 9 languages so far. The open-source Hypothesis platform, on which the ImGo test is running, provides the option of both individual and group testing in laboratory conditions as well as remotely through an internet browser.

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.

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

Quantifying individual differences in higher-order cognitive functions is a foundational area of cognitive science that also has profound implications for research on psychopathology. For the last two decades, the dominant approach in these fields has been to attempt to fractionate higher-order functions into hypothesized components (e.g., "inhibition", "updating") through a combination of experimental manipulation and factor analysis. However, the putative constructs obtained through this paradigm have recently been met with substantial criticism on both theoretical and empirical grounds. Concurrently, an alternative approach has emerged focusing on parameters of formal computational models of cognition that have been developed in mathematical psychology. These models posit biologically plausible and experimentally validated explanations of the data-generating process for cognitive tasks, allowing them to be used to measure the latent mechanisms that underlie performance. One of the primary insights provided by recent applications of such models is that individual and clinical differences in performance on a wide variety of cognitive tasks, ranging from simple choice tasks to complex executive paradigms, are largely driven by efficiency of evidence accumulation (EEA), a computational mechanism defined by sequential sampling models. This review assembles evidence for the hypothesis that EEA is a central individual difference dimension that explains neurocognitive deficits in multiple clinical disorders and identifies ways in which in this insight can advance clinical neuroscience research. We propose that recognition of EEA as a major driver of neurocognitive differences will allow the field to make clearer inferences about cognitive abnormalities in psychopathology and their links to neurobiology.

Characterizing Underlying Cognitive Components of ADHD Presentations and Co-morbid Diagnoses: A Diffusion Decision Model Analysis

OBJECTIVE

To Explore whether subtypes and comorbidities of attention-deficit hyperactivity disorder (ADHD) induce distinct biases in cognitive components involved in information processing.

METHOD

Performance on the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT) was compared between 150 children (aged 7 to 10) with ADHD, grouped by DSM-5 presentation (ADHD-C, ADHD-I) or co-morbid diagnoses (anxiety, oppositional defiant disorder [ODD], both, neither), and 60 children without ADHD. Diffusion decision modeling decomposed performance into cognitive components.

RESULTS

Children with ADHD had poorer information integration than controls. Children with ADHD-C were more sensitive to changes in presentation modality (auditory/visual) than those with ADHD-I and controls. Above and beyond these results, children with ADHD+anxiety+ODD had larger increases in response biases when targets became frequent than children with ADHD-only or with ADHD and one comorbidity.

CONCLUSION

ADHD presentations and comorbidities have distinct cognitive characteristics quantifiable using DDM and IVA-CPT. We discuss implications for tailored cognitive-behavioral therapy.

Chronic stress and executive functioning: A specification-curve analysis

To investigate the association between chronic stress and executive functioning (EF), we assessed 514 young to middle-aged adults in three EF tasks (i.e., Number-Letter, 2-Back, Go/Nogo) that assessed shifting, updating, and inhibition. Chronic stress was assessed by various self-report measures and hair cortisol concentrations as indicators of subjective and objective chronic stress, respectively. In order to test the association between chronic stress and EF, we fit a structural equation model with a latent common EF factor predicted by subjective and objective chronic stress on Kaplan-Meier estimates of response times. Controlling for participants' sex, age household income and the delay between cognitive testing and hair sample collection, neither subjective nor objective chronic stress showed a meaningful association with common EF. Exploratory analyses suggested a moderation effect of income on the association between subjective chronic stress and common EF, with a smaller association for high-income participants. Additionally, we conducted a specification-curve analysis on the association between chronic stress and EF to assess the influence of different analysis choices on results in our dataset. This analysis confirmed the absence of a coherent association between chronic stress and EF by showing that the majority of analytical choices produced null effects and only a small number of analytical choices produced meaningful associations (negative or positive). Taken together, our findings suggest that common EF likely remains preserved under the influence of chronic stress. Our specification-curve analysis, however, also shows that chronic stress may also have either a positive or a negative effect on EF, depending on the choice of covariates and measures of chronic stress and EF. Consequently, more research on the role of these factors for the association between chronic stress and EF is needed to avoid the interpretation of non-replicable stress-EF associations caused by analytical choices or selection bias.

Transcranial direct current stimulation (tDCS) alters the pattern of information processing in children with ADHD: Evidence from drift diffusion modeling

OBJECTIVE

Performance accuracy and reaction time in cognitive tasks are routinely used to evaluate the efficacy of tDCS to affect cognitive task performance. tDCS alters the excitability of targeted brain areas and thereby alters performance of cognitive tasks. The drift diffusion model (DDM) provides some additional measures to explore information processing style, such as (non)decision time, bias for decision, and speed of information processing. DDM parameters are informative for the study of cognitive impairments in children with ADHD. In the present study, we aimed to evaluate the impact of tDCS on cognitive performance via DDM measures.

METHODS

This study conducted DDM modeling and reanalysis of two exploratory, single-blinded, within-subject design experiments, which were published earlier. In the first experiment, twenty- four children with ADHD performed a Go/ No- Go task during anodal or sham tDCS over the right dlPFC. In the second experiment, twenty- five children with ADHD performed the 1- back working memory task during anodal or sham tDCS over the left dlPFC. We reanalyzed the data after DDM modeling.

RESULTS

The conventional performance measures revealed no significant effect of tDCS on No- Go accuracy in the first experiment and 1-back accuracy in the second experiment. The 1-back reaction time and speed-accuracy tradeoff were however improved under the real stimulation condition. The DDM measures identified increased No-Go- bias and decision time with respect to inhibitory control, and an increased threshold and amount of information required for response in the 1- back test.

CONCLUSION

In children with ADHD, anodal tDCS over the right dlPFC induces more conservative and less impulsive decisions. Furthermore, anodal tDCS over the left dlPFC enhanced efficacy of working memory performance with respect to agility and capacity. The experimental results show that drift diffusion modeling is useful for evaluation of the impact of tDCS on the style of information processing.

Long-Term Stress and Trait Anxiety Affect Brain Network Balance in Dynamic Cognitive Computations

Long-term stress has a profound impact on executive functions. Trait anxiety is recognized as a vulnerable factor accounting for stress-induced adaptive or maladaptive effects. However, the neurocognitive mechanisms underlying long-term stress and trait anxiety interactions remain elusive. Here we investigated how long-term stress and trait anxiety interact to affect dynamic decisions during n-back task performance by altering functional brain network balance. In comparison to controls, participants under long-term stress experienced higher psychological distress and exhibited faster evidence accumulation but had a lower decision-threshold when performing n-back tasks in general. This corresponded with hyper-activation in the anterior insula, less deactivation in the default-mode network, and stronger default-mode network decoupling with the frontoparietal network. Critically, high trait anxiety under long-term stress led to slower evidence accumulation through higher frontoparietal activity during cognitively demanding task, and increased decoupling between the default-mode and frontoparietal networks. Our findings suggest a neurocognitive model of how long-term stress and trait anxiety interplay to affect latent dynamic computations in executive functioning with adaptive and maladaptive changes, and inform personalized assessments and preventions for stress vulnerability.

Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood

RATIONALE

Substance use peaks during the developmental period known as emerging adulthood (ages 18-25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here, we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology.

OBJECTIVES

We test whether lower efficiency of evidence accumulation (EEA), a computationally characterized individual difference variable that drives performance on the go/no-go and other neurocognitive tasks, is a risk factor for substance use in emerging adults.

METHODS AND RESULTS

In an fMRI substudy within a sociobehavioral longitudinal study (n = 106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network structures) measured at ages 18-21 are both prospectively related to greater substance use during ages 22-26, even after adjusting for other well-known risk factors. Results from Bayesian model comparisons corroborated inferences from conventional hypothesis testing and provided evidence that both EEA and its neuroimaging correlates contain unique predictive information about substance use involvement.

CONCLUSIONS

These findings highlight EEA as a computationally characterized neurocognitive risk factor for substance use during a critical developmental period, with clear links to both neuroimaging measures and well-established formal theories of brain function.

Children's inhibitory control abilities in the presence of rewards are related to weight status and eating in the absence of hunger

The Reflective-Impulsive Dual Processes Model suggests that overeating occurs when the temptation to consume food overrides inhibitory control processes. However, how rewards interact with inhibitory control and their relation to children's weight status and food intake is not understood. Here, 7-to-11-year-old children (n = 66; 32 overweight/obese) completed two versions (baseline [i.e., non-reward incentivized/control] and reward incentivized [food, money, no reward]) of a Go/Nogo task. Intake of palatable foods in the absence of hunger (i.e., eating in the absence of hunger-EAH) was measured following a standardized meal. A drift diffusion model was used to characterize children's performance parameters on the Go/Nogo. On the baseline Go/Nogo, children with higher weight status responded more cautiously, but on reward trials for food/money children were more cautions and made more false alarms relative to the no reward condition. Energy intake during EAH positively correlated with FA errors for food and money vs. no reward, but sex moderated this effect such that FA positively associated with EAH in girls but not boys. Independent of sex, FA for money vs. no reward and food vs. money were both positively associated with energy consumed during EAH. These results suggest that the presence of food and money rewards impair inhibitory control processing, especially in children with higher weight status. Further, increased inhibitory control impairment in response to food rewards, specifically, may be a risk factor for disinhibited eating in girls. Though preliminary, results may be useful in the development of targeted treatments to help moderate excess consumption in children.

Recognising the dynamic form of fire

Encoding and recognising complex natural sequences provides a challenge for human vision. We found that observers could recognise a previously presented segment of a video of a hearth fire when embedded in a longer sequence. Recognition performance declined when the test video was spatially inverted, but not when it was hue reversed or temporally reversed. Sampled motion degraded forwards/reversed playback discrimination, indicating observers were sensitive to the asymmetric pattern of motion of flames. For brief targets, performance increased with target length. More generally, performance depended on the relative lengths of the target and embedding sequence. Increased errors with embedded sequence length were driven by positive responses to non-target sequences (false alarms) rather than omissions. Taken together these observations favour interpreting performance in terms of an incremental decision-making model based on a sequential statistical analysis in which evidence accrues for one of two alternatives. We also suggest that prediction could provide a means of providing and evaluating evidence in a sequential analysis model.

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks

Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop methods for modeling behavior across multiple tasks by connecting cognitive model parameters to common latent constructs. This approach can be used to assess whether 2 tasks measure the same dimensions of cognition, or actually improve the estimates of cognitive models when there are overlapping cognitive processes between 2 related tasks. The approach is then applied to connecting decision data on 2 behavioral tasks that evaluate clinically relevant deficits, the delay discounting task and Cambridge gambling task, to determine whether they both measure the same dimension of impulsivity. We find that the discounting rate parameters in the models of each task are not closely related, although substance users exhibit more impulsive behavior on both tasks. Instead, temporal discounting on the delay discounting task as quantified by the model is more closely related to externalizing psychopathology like aggression, while temporal discounting on the Cambridge gambling task is related more to response inhibition failures. The methods we develop thus provide a new way to connect behavior across tasks and grant new insights onto the different dimensions of impulsivity and their relation to substance use. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Interplay between components of pupil-linked phasic arousal and its role in driving behavioral choice in Go/No-Go perceptual decision-making

In decision-making tasks, neural circuits involved in different aspects of information processing may activate the central arousal system, likely through their interconnection with brainstem arousal nuclei, collectively contributing to the observed pupil-linked phasic arousal. However, the individual components of the phasic arousal associated with different elements of information processing and their effects on behavior remain little known. In this study, we used machine learning techniques to decompose pupil-linked phasic arousal evoked by different components of information processing in rats performing a Go/No-Go perceptual decision-making task. We found that phasic arousal evoked by stimulus encoding was larger for the Go stimulus than the No-Go stimulus. For each session, the separation between distributions of phasic arousal evoked by the Go and by the No-Go stimulus was predictive of perceptual performance. The separation between distributions of decision-formation-evoked arousal on correct and incorrect trials was correlated with decision criterion but not perceptual performance. When a Go stimulus was presented, the action of go was primarily determined by the phasic arousal evoked by stimulus encoding. On the contrary, when a No-Go stimulus was presented, the action of go was determined by phasic arousal elicited by both stimulus encoding and decision formation. Drift diffusion modeling revealed that the four model parameters were better accounted for when phasic arousal elicited by both stimulus encoding and decision formation was considered. These results suggest that the interplay between phasic arousal evoked by both stimulus encoding and decision formation has important functional consequences on forming behavioral choice in perceptual decision-making.

Sleep Deprivation and Racial Bias in the Decision to Shoot: A Diffusion Model Analysis

The current study examines the effect of sleep deprivation and caffeine use on racial bias in the decision to shoot. Participants deprived of sleep for 24 hr (vs. rested participants) made more errors in a shooting task and were more likely to shoot unarmed targets. A diffusion decision model analysis revealed sleep deprivation decreased participants’ ability to extract information from the stimuli, whereas caffeine impacted the threshold separation, reflecting decreased caution. Neither sleep deprivation nor caffeine moderated anti-Black racial bias in shooting decisions or at the process level. We discuss how our results clarify discrepancies in past work testing the impact of fatigue on racial bias in shooting decisions.

Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains

Decisions are often made by accumulating ambiguous evidence over time. The brain's arousal systems are activated during such decisions. In previous work in humans, we found that evoked responses of arousal systems during decisions are reported by rapid dilations of the pupil and track a suppression of biases in the accumulation of decision-relevant evidence (de Gee et al., 2017). Here, we show that this arousal-related suppression in decision bias acts on both conservative and liberal biases, and generalizes from humans to mice, and from perceptual to memory-based decisions. In challenging sound-detection tasks, the impact of spontaneous or experimentally induced choice biases was reduced under high phasic arousal. Similar bias suppression occurred when evidence was drawn from memory. All of these behavioral effects were explained by reduced evidence accumulation biases. Our results point to a general principle of interplay between phasic arousal and decision-making.

Cognitive Modeling Informs Interpretation of Go/No-Go Task-Related Neural Activations and Their Links to Externalizing Psychopathology

BACKGROUND

Individuals with attention-deficit/hyperactivity disorder and other externalizing psychopathologies tend to display poor behavioral performance on the go/no-go task, which is thought to reflect deficits in inhibitory control. However, clinical neuroimaging studies using this task have yielded conflicting results, raising basic questions about what the task measures and which aspects of the task relate to clinical outcomes. We used computational modeling to provide a clearer understanding of how neural activations from this task relate to the cognitive mechanisms that underlie performance and to probe the implications of these relationships for clinical research.

METHODS

A total of 143 young adults (8-21 years of age) performed the go/no-go task during functional magnetic resonance imaging scanning. We used the diffusion decision model (DDM), a cognitive modeling approach, to quantify distinct neurocognitive processes that underlie go/no-go performance. We then assessed correlations between DDM parameters and brain activation from standard go/no-go contrasts and assessed relationships of DDM parameters and associated neural measures with clinical ratings.

RESULTS

Right-lateralized prefrontal activations on correct inhibition trials, which are generally assumed to isolate neural processes involved in inhibition, were unrelated to DDM parameters (and other performance indices). However, responses to failed inhibitions in brain regions associated with error monitoring were strongly related to more efficient task performance and correlated with externalizing behavior and attention-deficit/hyperactivity disorder symptoms.

CONCLUSIONS

Our findings cast doubt on conventional interpretations of go/no-go task-related activations as reflecting the neural basis of inhibitory functioning. We instead found evidence that error-related contrasts provide clinically relevant information about neural systems involved in monitoring and optimizing the efficiency of cognitive performance.

A diffusion model analysis of sustained attention in children with attention deficit hyperactivity disorder

OBJECTIVE

Whether children with attention deficit hyperactivity disorder (ADHD) have deficits in sustained attention remains unresolved due to the ongoing use of cognitive paradigms that are not optimized for studying vigilance and the fact that relatively few studies report performance over time.

METHOD

In three independent samples of school-age children with (total N = 128) and without ADHD (total N = 59), we manipulated event rate, difficulty of discrimination, and use signal detection (SDT) and diffusion models (DM) to evaluate the cause of the vigilance decrement during a continuous performance task.

RESULTS

For both groups of children, a bias toward "no-go" over time (as indexed by the SDT parameter B″ and the DM parameter z/a) was responsible for generating the vigilance decrement. However, among children with ADHD, the rate at which information accumulated to make a no-go decision (vNoGo) also increased with time on task, representing a possible secondary mechanism that biases children against engagement. At all time points, children with ADHD demonstrated reduced sensitivity to discriminate targets from nontargets.

CONCLUSION

Children with ADHD are particularly sensitive to the cost of task engagement, but nonspecific slower drift rate may ultimately provide a better conceptualization of the cognitive atypicalities commonly observed in that group. Results are interpreted in the context of updated conceptualizations of sustained attention and vigilance. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The Reaction Switching Produces A Greater Bias to Prepotent Response than Reaction Inhibition

There is a discussion about common or various mechanisms of response inhibition and response switching. To understand these mechanisms, we used a modified Go/NoGo task with three stimulus categories. The subjects were instructed to press a button in response to frequent Go stimuli, press another button in response to rare Go stimuli and hold any motor response following the presentation of NoGo stimuli. The results showed a decrease in reaction time for frequent Go, following both categories of rare stimuli and the decrease was greater following rare Go. Also, the total number of errors did not differ between Go and NoGo, however, a greater bias of error rate towards frequent Go stimuli was found for rare Go compared to NoGo. Finally, positive correlations were found between the increase in reaction time for rare Go compared to frequent Go and the number of errors for both rare Go and rare NoGo. Together, these results indicate that both rare Go and NoGo stimuli required to inhibit the prepotent response, but rare Go in comparison to NoGo stimuli also evoked a conflict between prepotent and alternative responses, which is expressed in greater response bias toward frequent Go.

Using drift diffusion modeling to understand inattentive behavior in preterm and term-born children

Objective: Children born very preterm are at increased risk of inattention, but it remains unclear whether the underlying processes are the same as in their term-born peers. Drift diffusion modeling (DDM) may better characterize the cognitive processes underlying inattention than standard reaction time (RT) measures. This study used DDM to compare the processes related to inattentive behavior in preterm and term-born children. Method: Performance on a cued continuous performance task was compared between 33 children born very preterm (VP; ≤32 weeks’ gestation) and 32 term-born peers (≥37 weeks’ gestation), aged 8–11 years. Both groups included children with a wide spectrum of parent-rated inattention (above average attention to severe inattention). Performance was defined using standard measures (RT, RT variability and accuracy) and modeled using a DDM. A hierarchical regression assessed the extent to which standard or DDM measures explained variance in parent-rated inattention and whether these relationships differed between VP and term-born children. Results: There were no group differences in performance on standard or DDM measures of task performance. Parent-rated inattention correlated significantly with hit rate, RT variability, and drift rate (a DDM estimate of processing efficiency) in one or both groups. Regression analysis revealed that drift rate was the best predictor of parent-rated inattention. This relationship did not differ significantly between groups. Conclusions: Findings suggest that less efficient information processing is a common mechanism underlying inattention in both VP and term-born children. This study demonstrates the benefits of using DDM to better characterize atypical cognitive processing in clinical samples.

Less efficient information processing during a sustained attention task explained individual differences in inattentive behavior. This was true both in 8- to 11-year-olds born very preterm and their term-born peers. Drift diffusion modeling provides a way to help us better characterize the processes that underlie task performance. This is valuable for understanding processing differences that affect clinical groups.

Cognitive Modeling Suggests That Attentional Failures Drive Longer Stop-Signal Reaction Time Estimates in Attention Deficit/Hyperactivity Disorder

Mean stop-signal reaction time (SSRT) is frequently employed as a measure of response inhibition in cognitive neuroscience research on ADHD. However, this measurement model is limited by two factors which may bias SSRT estimation in this population: 1) excessive skew in "go" RT distributions, and 2) trigger failures, or instances in which individuals fail to trigger an inhibition process in response to the "stop" signal. We use a Bayesian parametric approach, which allows unbiased estimation of the shape of entire SSRT distributions and the probability of trigger failures, to clarify mechanisms of stop-signal task deficits in ADHD. Children with ADHD displayed greater positive skew than their peers in both "go" RT and SSRT distributions. However, they also displayed more frequent trigger failures, which appeared to drive ADHD-related stopping difficulties. Results suggest that stop-signal task performance in ADHD reflects impairments in early attentional processes, rather than inefficiency in the stop process.

On the importance of avoiding shortcuts in applying cognitive models to hierarchical data

Psychological experiments often yield data that are hierarchically structured. A number of popular shortcut strategies in cognitive modeling do not properly accommodate this structure and can result in biased conclusions. To gauge the severity of these biases, we conducted a simulation study for a two-group experiment. We first considered a modeling strategy that ignores the hierarchical data structure. In line with theoretical results, our simulations showed that Bayesian and frequentist methods that rely on this strategy are biased towards the null hypothesis. Secondly, we considered a modeling strategy that takes a two-step approach by first obtaining participant-level estimates from a hierarchical cognitive model and subsequently using these estimates in a follow-up statistical test. Methods that rely on this strategy are biased towards the alternative hypothesis. Only hierarchical models of the multilevel data lead to correct conclusions. Our results are particularly relevant for the use of hierarchical Bayesian parameter estimates in cognitive modeling.

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

Decision bias is traditionally conceptualized as an internal reference against which sensory evidence is compared. Instead, we show that individuals implement decision bias by shifting the rate of sensory evidence accumulation toward a decision bound. Participants performed a target detection task while we recorded EEG. We experimentally manipulated participants' decision criterion for reporting targets using different stimulus-response reward contingencies, inducing either a liberal or a conservative bias. Drift diffusion modeling revealed that a liberal strategy biased sensory evidence accumulation toward target-present choices. Moreover, a liberal bias resulted in stronger midfrontal pre-stimulus 2-6 Hz (theta) power and suppression of pre-stimulus 8-12 Hz (alpha) power in posterior cortex. Alpha suppression in turn was linked to the output activity in visual cortex, as expressed through 59-100 Hz (gamma) power. These findings show that observers can intentionally control cortical excitability to strategically bias evidence accumulation toward the decision bound that maximizes reward.

One hundred fifty years after Donders: Insights from unpublished data, a replication, and modeling of his reaction times

Mental processes take a measurable amount of time, which was discovered by Donders one hundred fifty years ago. He reports process durations in his classic study with the a-, b-, and c-methods (i.e., simple, choice, go/no-go) using a speech repetition task. His reaction time pattern was a < c < b. He reasoned that the c - a difference gives the discrimination duration, and the b - c difference the choice duration. A few years later, Wundt criticized the c-method by arguing that it does involve a choice (i.e., whether or not to respond, which is an act of executive control), whereas Donders maintained that it may not involve full discrimination. The substance of this historical controversy relates closely to modern issues in the study of reaction times. Here, I show that an analysis of unpublished data from a handwritten laboratory notebook of Donders reveals no b - c difference for his students, supporting Wundt's concern. Moreover, a replication of Donders' study using his original stimulus lists yielded only a small b - c difference for myself, supporting Wundt. A computer simulation using a modern model of speech repetition indicates that the difference between Donders and his students may plausibly result from choice in the c-method. To conclude, unpublished data, a replication, and modern modeling resolve a 150-year-old issue, stressing the importance of examining individual differences and executive control in performance.

Pavlovian Control of Escape and Avoidance

To survive in complex environments, animals need to have mechanisms to select effective actions quickly, with minimal computational costs. As perhaps the computationally most parsimonious of these systems, Pavlovian control accomplishes this by hardwiring specific stereotyped responses to certain classes of stimuli. It is well documented that appetitive cues initiate a Pavlovian bias toward vigorous approach; however, Pavlovian responses to aversive stimuli are less well understood. Gaining a deeper understanding of aversive Pavlovian responses, such as active avoidance, is important given the critical role these behaviors play in several psychiatric conditions. The goal of the current study was to establish a behavioral and computational framework to examine aversive Pavlovian responses (activation vs. inhibition) depending on the proximity of an aversive state (escape vs. avoidance). We introduce a novel task in which participants are exposed to primary aversive (noise) stimuli and characterized behavior using a novel generative computational model. This model combines reinforcement learning and drift-diffusion models so as to capture effects of invigoration/inhibition in both explicit choice behavior as well as changes in RT. Choice and RT results both suggest that escape is associated with a bias for vigorous action, whereas avoidance is associated with behavioral inhibition. These results lay a foundation for future work seeking insights into typical and atypical aversive Pavlovian responses involved in psychiatric disorders, allowing us to quantify both implicit and explicit indices of vigorous choice behavior in the context of aversion.

Using the Diffusion Model to Explain Cognitive Deficits in Attention Deficit Hyperactivity Disorder

Slow, variable, and error-prone performance on speeded reaction time (RT) tasks has been well documented in childhood ADHD, but equally well documented is the context-dependent nature of those deficits, particularly with respect to event rate. As event rates increase (or, as the interstimulus intervals become shorter), RTs decrease, a pattern of performance that has long been interpreted as evidence that cognitive deficits in ADHD are a downstream consequence of a fundamental difficulty in the regulation of arousal to meet task demands. We test the extent to which this is a misinterpretation of the data that occurs when RT and accuracy are considered separately, as is common in neurocognitive research. In two samples of children aged 8-10 with (N = 97; 33 girls) and without (N = 39; 26 girls) ADHD, we used the diffusion model, an influential computational model of RT, to examine the effect of event rate on inhibitory control in a go-no-go task. Contrary to longstanding belief, we found that fast event rates slowed the rate at which children with ADHD accumulated evidence to make a decision to "no-go", as indexed by drift rate. This in turn resulted in a higher proportion of failed inhibits, and occurred despite increased task engagement, as reflected by changes in the starting point of the decision process. Thus, although faster event rates increased task engagement among children with ADHD, the increased engagement was unable to counteract the concurrent slowing of processing speed to "no-go" decisions. Implications for theoretical models of ADHD and treatments are discussed.