Psychological interpretation of the ex-Gaussian and shifted Wald parameters: A diffusion model analysis

Sex differences in the prospective association of excessively long reaction times and hazardous cannabis use at six months

Objective

The neurocognitive risk mechanisms predicting divergent outcomes likely differ between men and women who use cannabis recreationally. Increasingly, the use of descriptive distributions including the ex-Gaussian has been applied to draw stronger inferences about neurocognitive health in clinical populations. The current project examines whether the long tail of reaction times (RTs) in a distribution, as characterized by the ex-Gaussian parameter tau which may represent difficulty with the regulation of arousal, predicts problematic cannabis use 6 months later in those who use cannabis recreationally, and whether sex moderates these prospective associations.

Method

Young adults (ages 18-30, mean age 20.5 years, N =159, 57.2% women, 69.2% Caucasian) who recreationally used cannabis either occasionally (at least once per month) or frequently (three times or more per week) completed the Stroop Color-Word Task at baseline. Ex-Gaussian parameter tau was estimated for each participant. Self-report of hazardous cannabis use (CUDIT-R) and dysregulation of negative (DERS) and positive emotions (DERS-Positive) were obtained at baseline and 6-month follow-up.

Results

For those with larger tau at baseline, being a man (but not a woman) was associated with increased difficulty regulating positive emotions concurrently (b = -0.01, F (1,159) = 5.48, p = 0.02), and with hazardous cannabis use six months later (b = -0.007, F (1,159) = 4.42, p = 0.037) after controlling for baseline hazardous cannabis use.

Conclusions

Excessively long RTs during cognitive performance may help characterize men at risk for increased hazardous use, which contributes to understanding between-sex heterogeneity in pathways towards cannabis use disorders.

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.

Clinically established early Parkinson's disease patients do not show impaired use of priors in conditions of perceptual uncertainty

The ability to use past learned experiences to guide decisions is an important component of adaptive behavior, especially when decision-making is performed under time pressure or when perceptual information is unreliable. Previous studies using visual discrimination tasks have shown that this prior-informed decision-making ability is impaired in Parkinson's disease (PD), but the mechanisms underlying this deficit and the precise impact of dopaminergic denervation within cortico-basal circuits remain unclear. To shed light on this problem, we evaluated prior-informed decision-making under various conditions of perceptual uncertainty in a sample of 13 clinically established early PD patients, and compared behavioral performance with healthy control (HC) subjects matched in age, sex and education. PD patients and HC subjects performed a random dot motion task in which they had to decide the net direction (leftward vs. rightward) of a field of moving dots and communicate their choices through manual button presses. We manipulated prior knowledge by modulating the probability of occurrence of leftward vs. rightward motion stimuli between blocks of trials, and by explicitly giving these probabilities to subjects at the beginning of each block. We further manipulated stimulus discriminability by varying the proportion of dots moving coherently in the signal direction and speed-accuracy instructions. PD patients used choice probabilities to guide perceptual decisions in both speed and accuracy conditions, and their performance did not significantly differ from that of HC subjects. An additional analysis of the data with the diffusion decision model confirmed this conclusion. These results suggest that the impaired use of priors during visual discrimination observed at more advanced stages of PD is independent of dopaminergic denervation, though additional studies with larger sample sizes are needed to more firmly establish this conclusion.

Sequential Effects on Reaction Time Distributions: Commonalities and Differences Across Paradigms

A common finding across numerous response time (RT) paradigms is that the mean RT in one trial depends strongly on the characteristics of the immediately preceding trial. Although such sequential effects have usually only been considered within each single paradigm in isolation from the others, there are important similarities across paradigms between the theoretical accounts of these effects. However, so far there has been no systematic comparison of sequential effects across paradigms. To investigate the possible relationships between sequential effects in different paradigms, we conducted an experiment examining sequential effects in visual search, two-choice RT, interference, and task-switching paradigms, using methods designed to maximize the similarity of stimuli and responses across paradigms. Detailed analyses of the observed RT distributions were carried out using both descriptive (e.g., ex-Gaussian) and process-oriented (e.g., diffusion models) methods. The results reveal significant empirical similarities and differences between the sequential effects observed across different paradigms, and in some cases even across different conditions within a single paradigm. Furthermore, the sequential effects are more similar to one another for some pairs of paradigms than for others. These results imply that some cognitive processes eliciting sequential effects are shared across paradigms while others seem to be paradigm-specific.

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

A hybrid approach to dynamic cognitive psychometrics : Dynamic cognitive psychometrics

Dynamic cognitive psychometrics measures mental capacities based on the way behavior unfolds over time. It does so using models of psychological processes whose validity is grounded in research from experimental psychology and the neurosciences. However, these models can sometimes have undesirable measurement properties. We propose a "hybrid" modeling approach that achieves good measurement by blending process-based and descriptive components. We demonstrate the utility of this approach in the stop-signal paradigm, in which participants make a series of speeded choices, but occasionally are required to withhold their response when a "stop signal" occurs. The stop-signal paradigm is widely used to measure response inhibition based on a modeling framework that assumes a race between processes triggered by the choice and the stop stimuli. However, the key index of inhibition, the latency of the stop process (i.e., stop-signal reaction time), is not directly observable, and is poorly estimated when the choice and the stop runners are both modeled by psychologically realistic evidence-accumulation processes. We show that using a descriptive account of the stop process, while retaining a realistic account of the choice process, simultaneously enables good measurement of both stop-signal reaction time and the psychological factors that determine choice behavior. We show that this approach, when combined with hierarchical Bayesian estimation, is effective even in a complex choice task that requires participants to perform only a relatively modest number of test trials.

Diminished differentiation of rewards in individuals at clinical high-risk for psychosis

Reward processing is impaired in people with schizophrenia, which may begin in the clinical high-risk (CHR) for psychosis period. The Monetary Incentive Delay (MID) task has been important in understanding the neural correlates of reward processing deficits in various psychiatric disorders. Previous research has found that CHR individuals have an imprecise mental representation of rewards, which leads to a diminished differentiation between rewards, though this has not been observed behaviorally. A total of 19 CHR individuals and 20 controls were given a novel variant of the MID task, designed to examine how modulating reward context may impact responses to reward cues, a process often referred to as "adaptive coding." Both groups appeared to update their behavior in response to the rewards available in this adaptive task. However, when compared to controls who showed a more graded decrease in response time to increasing reward contexts, CHR individuals appeared to have a sharp decrease in response time in the low reward context that is nearly stable across higher reward contexts. This is largely driven by the exponential component of the response time distribution, which is often interpreted to be more cognitively or effortfully influenced. Response times are related to negative symptoms, but not positive symptoms, disorganized symptoms, or estimated intelligence. Although an adaptive coding effect was not observed, these results provide novel insight into the reward processing mechanisms and volitional processes in the CHR population, as this was the first study to observe the diminished differentiation of rewards behaviorally.

Differential Functional Changes in Visual Performance during Acute Exposure to Microgravity Analogue and Their Potential Links with Spaceflight-Associated Neuro-Ocular Syndrome

BACKGROUND

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) is a complex pathology threatening the health of astronauts, with incompletely understood causes and no current specific functional diagnostic or screening test. We investigated the use of the differential performance of the visual system (central vs. perimacular visual function) as a candidate marker of SANS-related pathology in a ground-based microgravity analogue.

METHODS

We used a simple reaction time (SRT) task to visual stimuli, presented in the central and perimacular field of view, as a measure of the overall performance of the visual function, during acute settings (first 10 min) of vertical, bed rest (BR), -6°, and -15° head-down tilt (HDT) presentations in healthy participants (n = 8). We built dose-response models linking the gravitational component to SRT distribution parameters in the central vs. perimacular areas.

RESULTS

Acute exposure to microgravity induces detectable changes between SRT distributions in the perimacular vs. central retina (increased mean, standard deviation, and tau component of the ex-Gaussian function) in HDT compared with vertical presentation.

CONCLUSIONS

Functional testing of the perimacular retina might be beneficial for the earlier detection of SANS-related ailments in addition to regular testing of the central vision. Future diagnostic tests should consider the investigation of the extra-macular areas, particularly towards the optic disc.

Introducing the Intra-Individual Variability Hypothesis in Explaining Individual Differences in Language Development

PURPOSE

Response times (RTs) are commonly used in studying language acquisition. However, previous research utilizing RT in the context of language has largely overlooked the intra-individual variability (IIV) of RTs, which could hold significant information about the processes underlying language acquisition.

METHOD

We explored the association between language abilities and RT variability in visuomotor tasks using two data sets from previously published studies. The participants were 7- to 10-year-old children (n = 77).

RESULTS

Our results suggest that increased variability in RTs is associated with weaker language abilities. Specifically, this within-participant variability in visuomotor RTs, especially the proportion of unusually slow responses, predicted language abilities better than mean RTs, a factor often linked to language skills in past research.

CONCLUSIONS

Based on our findings, we introduce the IIV hypothesis in explaining individual differences in language development. According to our hypothesis, inconsistency in the timing of cognitive processes, reflected by increased IIV in RTs, degrades learning different aspects of language, and results in individual differences in language abilities. Future studies should further examine the relationship between IIV and language abilities, and test the extent to which the possible relationship is causal.

Empathy incites a stable prosocial decision bias

Empathy toward suffering individuals serves as potent driver for prosocial behavior. However, it remains unclear whether prosociality induced by empathy for another person's pain persists once that person's suffering diminishes. To test this, participants underwent functional magnetic resonance imaging while performing a binary social decision task that involved allocation of points to themselves and another person. In block one, participants completed the task after witnessing frequent painful stimulation of the other person, and in block two, after observing low frequency of painful stimulation. Drift-diffusion modeling revealed an increased initial bias toward making prosocial decisions in the first block compared with baseline that persisted in the second block. These results were replicated in an independent behavioral study. An additional control study showed that this effect may be specific to empathy as stability was not evident when prosocial decisions were driven by a social norm such as reciprocity. Increased neural activation in dorsomedial prefrontal cortex was linked to empathic concern after witnessing frequent pain and to a general prosocial decision bias after witnessing rare pain. Altogether, our findings show that empathy for pain elicits a stable inclination toward making prosocial decisions even as their suffering diminishes.

An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition : Trial-varying Wald model

Despite the ubiquitous nature of evidence accumulation models in cognitive and experimental psychology, there has been a comparatively limited uptake of such techniques in the applied literature. While quantifying latent cognitive processing properties has significant potential for applied domains such as adaptive work systems, accumulator models often fall short in practical applications. Two primary reasons for these shortcomings are the complexities and time needed for the application of cognitive models, and the failure of current models to capture systematic trial-to-trial variability in parameters. In this manuscript, we develop a novel, trial-varying extension of the shifted Wald model to address these concerns. By leveraging conjugate properties of the Wald distribution, we derive computationally efficient solutions for threshold and drift parameters which can be updated instantaneously with new data. The resulting model allows the quantification of systematic variation in latent cognitive parameters across trials and we demonstrate the utility of such analyses through simulations and an exemplar application to an existing data set. The analytic nature of our solutions opens the door for real-world applications, significantly extending the reach of computational models of behavioral responses.

Optimal filters for ERP research I: A general approach for selecting filter settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

The neural implausibility of the diffusion decision model doesn't matter for cognitive psychometrics, but the Ornstein-Uhlenbeck model is better

In cognitive psychometrics, the parameters of cognitive models are used as measurements of the processes underlying observed behavior. In decision making, the diffusion decision model (DDM) is by far the most commonly used cognitive psychometric tool. One concern when using this model is that more recent theoretical accounts of decision-making place more emphasis on neural plausibility, and thus incorporate many assumptions not found in the DDM. One such model is the Ising Decision Maker (IDM), which builds from the assumption that two pools of neurons with self-excitation and mutual inhibition receive perceptual input from external excitatory fields. In this study, we investigate whether the lack of such mechanisms in the DDM compromises its ability to measure the processes it does purport to measure. We cross-fit the DDM and IDM, and find that the conclusions of DDM would be mostly consistent with those from an analysis using a more neurally plausible model. We also show that the Ornstein-Uhlenbeck Model (OUM) model, a variant of the DDM that includes the potential for leakage (or self-excitation), reaches similar conclusions to the DDM regarding the assumptions they share, while also sharing an interpretation with the IDM in terms of self-excitation (but not leakage). Since the OUM is relatively easy to fit to data, while being able to capture more neurally plausible mechanisms, we propose that it be considered an alternative cognitive psychometric tool to the DDM.

Comparing silent reading performance for single sentences and paragraphs: an eye movement-based analysis

CLINICAL RELEVANCE

Reading performance in clinical practice is commonly evaluated by reading 'aloud' and 'as fast as possible' single sentences. Assessing comprehensive silent reading performance using passages, composed of multiple sentences, is the preferred reading mode in real-life reading conditions.

BACKGROUND

The purpose of this study was to compare eye movement-based silent reading performance for standardised short sentences and paragraphs.

METHODS

A group of 15 young volunteers (age range: 22-36 years) read silently and comprehensively in two sessions: (a) a paragraph with continuous text and (b) standardised short sentences. Text print size was 0.4 logMAR (1.0 M at 40 cm distance). Eye movements during reading were recorded using video oculography (EyeLink II, SR Research Ltd). Data analysis included computation of reading speed, fixation duration, the number of fixations, saccadic amplitude and percentage of regressions. Moreover, frequency distributions of fixation durations were analysed with ex-Gaussian fittings.

RESULTS

Repeatability coefficient in silent reading speed was found better for the paragraph (66 wpm) than for short sentences (88 wpm). The superiority in repeatability coefficient for the corresponding eye movement parameters, i.e. fixation duration (35 vs 73 ms), regressions (10.1 vs. 22.3%) and fixations per word (0.21 vs. 0.37 fpw), was even more pronounced. In addition, a statistically significant improvement with the paragraph was found in average fixation duration (19 ± 26 ms, p = 0.02), regressions (4.2 ± 7.0%, p = 0.04) and ex-Gaussian fixation parameter, τ (82 vs. 111 ms). No statistically significant difference was found between average reading speed with the paragraph (220 ± 59 wpm) and the short sentences (206 ± 57 wpm) (p = 0.11).

DISCUSSION

Due to their superior repeatability, paragraphs are preferable to short sentences when evaluating silent comprehensive reading. The concurrent recording of eye movement parameters in silent reading further improves variability and could offer an efficient measure of reading performance and a reliable biomarker of visuo-motor function.

The Seven-parameter Diffusion Model: an Implementation in Stan for Bayesian Analyses

Diffusion models have been widely used to obtain information about cognitive processes from the analysis of responses and response-time data in two-alternative forced-choice tasks. We present an implementation of the seven-parameter diffusion model, incorporating inter-trial variabilities in drift rate, non-decision time, and relative starting point, in the probabilistic programming language Stan. Stan is a free, open-source software that gives the user much flexibility in defining model properties such as the choice of priors and the model structure in a Bayesian framework. We explain the implementation of the new function and how it is used in Stan. We then evaluate its performance in a simulation study that addresses both parameter recovery and simulation-based calibration. The recovery study shows generally good recovery of the model parameters in line with previous findings. The simulation-based calibration study validates the Bayesian algorithm as implemented in Stan.

The role of rat prelimbic cortex in decision making

The frontal cortex plays a critical role in decision-making. One specific frontal area, the anterior cingulate cortex, has been identified as crucial for setting a threshold for how much evidence is needed before a choice is made (Domenech & Dreher, 2010). Threshold is a key concept in drift diffusion models, a popular framework used to understand decision-making processes. Here, we investigated the role of the prelimbic cortex, part of the rodent cingulate cortex, in decision making. Male and female rats learned to choose between stimuli associated with high and low value rewards. Females learned faster, were more selective in their responses, and integrated information about the stimuli more quickly. By contrast, males learned more slowly and showed a decrease in their decision thresholds during choice learning. Inactivating the prelimbic cortex in female and male rats sped up decision making without affecting choice accuracy. Drift diffusion modeling found selective effects of prelimbic cortex inactivation on the decision threshold, which was reduced with increasing doses of the GABA-A agonist muscimol. Stimulating the prelimbic cortex through mu opioid receptors slowed the animals' choice latencies and increased the decision threshold. These findings provide the first causal evidence that the prelimbic cortex directly influences decision processes. Additionally, they suggest possible sex-based differences in early choice learning.

Word Frequency and Predictability Dissociate in Naturalistic Reading

Many studies of human language processing have shown that readers slow down at less frequent or less predictable words, but there is debate about whether frequency and predictability effects reflect separable cognitive phenomena: are cognitive operations that retrieve words from the mental lexicon based on sensory cues distinct from those that predict upcoming words based on context? Previous evidence for a frequency-predictability dissociation is mostly based on small samples (both for estimating predictability and frequency and for testing their effects on human behavior), artificial materials (e.g., isolated constructed sentences), and implausible modeling assumptions (discrete-time dynamics, linearity, additivity, constant variance, and invariance over time), which raises the question: do frequency and predictability dissociate in ordinary language comprehension, such as story reading? This study leverages recent progress in open data and computational modeling to address this question at scale. A large collection of naturalistic reading data (six datasets, >2.2 M datapoints) is analyzed using nonlinear continuous-time regression, and frequency and predictability are estimated using statistical language models trained on more data than is currently typical in psycholinguistics. Despite the use of naturalistic data, strong predictability estimates, and flexible regression models, results converge with earlier experimental studies in supporting dissociable and additive frequency and predictability effects.

Differences in Ex-Gaussian Parameters from Response Time Distributions Between Individuals with and Without Attention Deficit/Hyperactivity Disorder: A Meta-analysis

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in childhood and adolescence. Differences in reaction times (RT) in cognitive tasks have been consistently observed between ADHD and typical participants. Instead of estimating means and standard deviations, fitting non-symmetrical distributions like the ex-Gaussian, characterized by three parameters (µ, σ, and τ), account for the whole RT distributions. A meta-analysis is performed with all the available literature using ex-Gaussian distributions for comparisons between individuals with ADHD and controls. Results show that τ and σ are generally greater for ADHD samples, while µ tends to be larger for typical groups but only for younger ages. Differences in τ are also moderated by ADHD subtypes. τ and σ show, respectively, quadratic and linear relationships with inter-stimulus intervals from Continuous Performance Test and Go/No Go tasks. Furthermore, tasks and cognitive domains influence the three parameters. Interpretations of ex-Gaussian parameters and clinical implications of these findings are also discussed. Fitting ex-Gaussian distributions to RT data is a useful way to explore differences between individuals with ADHD and healthy controls.

Bayesian multi-level modelling for predicting single and double feature visual search

Performance in visual search tasks is frequently summarised by "search slopes" - the additional cost in reaction time for each additional distractor. While search tasks with a shallow search slopes are termed efficient (pop-out, parallel, feature), there is no clear dichotomy between efficient and inefficient (serial, conjunction) search. Indeed, a range of search slopes are observed in empirical data. The Target Contrast Signal (TCS) Theory is a rare example of quantitative model that attempts to predict search slopes for efficient visual search. One study using the TCS framework has shown that the search slope in a double-feature search (where the target differs in both colour and shape from the distractors) can be estimated from the slopes of the associated single-feature searches. This estimation is done using a contrast combination model, and a collinear contrast integration model was shown to outperform other options. In our work, we extend TCS to a Bayesian multi-level framework. We investigate modelling using normal and shifted-lognormal distributions, and show that the latter allows for a better fit to previously published data. We run a new fully within-subjects experiment to attempt to replicate the key original findings, and show that overall, TCS does a good job of predicting the data. However, we do not replicate the finding that the collinear combination model outperforms the other contrast combination models, instead finding that it may be difficult to conclusively distinguish between them.

A Nomothetic Span Approach to the Construct Validation of Sustained Attention Consistency: Re-Analyzing Two Latent-Variable Studies of Performance Variability and Mind-Wandering Self-Reports

The ability to sustain attention consistency is frequently assessed using either objective behavioral measures, such as reaction time (RT) variability, or subjective self-report measures, such as rates of task-unrelated thought (TUT). The current studies examined whether the individual-difference covariation in these measures provides a more construct valid assessment of attention consistency than does either alone. We argue that performance and self-report measures mutually validate each other; each measurement approach has its own sources of error, so their shared variance should best reflect the attention consistency construct. We reanalyzed two latent-variable studies where RT variability and TUTs were measured in multiple tasks (Kane et al. in J Exp Psychol Gen 145:1017-1048, 2016; Unsworth et al. in J Exp Psychol Gen 150:1303-1331, 2021), along with several nomological network constructs to test the convergent and discriminant validity of a general attention consistency factor. Confirmatory factor analyses assessing bifactor (preregistered) and hierarchical (non-preregistered) models suggested that attention consistency can be modeled as the shared variance among objective and subjective measures. This attention consistency factor was related to working memory capacity, attention (interference) control, processing speed, state motivation and alertness, and self-reported cognitive failures and positive schizotypy. Although bifactor models of general attention consistency provide the most compelling construct validity evidence for a specific ability to sustain attention, multiverse analyses of outlier decisions suggested they are less robust than hierarchical models. The results provide evidence for the general ability to sustain attention consistency and suggestions for improving its measurement.

Exploring the steps of learning: computational modeling of initiatory-actions among individuals with attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is characterized by difficulty in acting in a goal-directed manner. While most environments require a sequence of actions for goal attainment, ADHD was never studied in the context of value-based sequence learning. Here, we made use of current advancements in hierarchical reinforcement-learning algorithms to track the internal value and choice policy of individuals with ADHD performing a three-stage sequence learning task. Specifically, 54 participants (28 ADHD, 26 controls) completed a value-based reinforcement-learning task that allowed us to estimate internal action values for each trial and stage using computational modeling. We found attenuated sensitivity to action values in ADHD compared to controls, both in choice and reaction-time variability estimates. Remarkably, this was found only for first-stage actions (i.e., initiatory actions), while for actions performed just before outcome delivery the two groups were strikingly indistinguishable. These results suggest a difficulty in following value estimation for initiatory actions in ADHD.

Optimal Filters for ERP Research I: A General Approach for Selecting Filter Settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

Older Adults Show Intraindividual Variation in the Use of Predictive Processing

BACKGROUND

The use of prediction can aid language comprehension through preactivation of relevant word features. However, predictions can be wrong, and it has been proposed that resolving the mismatch between the predicted and presented item requires cognitive resources. Older adults tend not to predict and instead rely more on passive comprehension. Here, we tested, using an intraindividual approach, whether older adults consistently use this less demanding processing strategy while reading or whether they attempt to predict on some trials.

METHODS

We used a cross-task conflict paradigm. Younger and older participants self-paced to read sentences that ended with either an expected or unexpected word. Each sentence was then followed by a flanker stimulus that could be congruent or incongruent. We examined responses within and across the two tasks.

RESULTS

Unexpected words were in general read as quickly as expected words, indicating that typical processing of these words was similar. However, for both younger and older adults, there was a greater proportion of very slow trials for unexpected words, revealing different processing on a subset of trials. Critically, in older adults, these slowly read unexpected words engaged control, as seen in speeded responses to incongruent flanker stimuli.

CONCLUSION

Using a cross-task conflict paradigm, we showed that older adults are able to predict and engage cognitive resources to cope with prediction violations, but do not opt to use these processes consistently.

Preferential Choice to Exert Cognitive Effort in Children with ADHD: a Diffusion Modelling Account

Greater sensitivity to the cost of effortful engagement has long been implicated in the development of Attention Deficit Hyperactivity Disorder (ADHD). The current study evaluated preferential choice to engage in demanding tasks, and did so in combination with computational methods to interrogate the process of choice. Children aged 8-12 with (n = 49) and without (n = 36) ADHD were administered the cognitive effort discounting paradigm (COG-ED, adapted from Westbrook et al., 2013). Diffusion modelling was subsequently applied to the choice data to allow for a better description of the process of affective decision making. All children showed evidence of effort discounting, but, contrary to theoretical expectations, there was no evidence that children with ADHD judged effortful tasks to be lower in subjective value, or that they maintained a bias towards less effortful tasks. However, children with ADHD developed a much less differentiated mental representation of demand than their non-ADHD counterparts even though familiarity with and exposure to the experience of effort was similar between groups. Thus, despite theoretical arguments to the contrary, and colloquial use of motivational constructs to explain ADHD-related behavior, our findings strongly argue against the presence of greater sensitivity to costs of effort or reduced sensitivity to rewards as an explanatory mechanism. Instead, there appears to be a more global weakness in the metacognitive monitoring of demand, which is a critical precursor for cost-benefit analyses that underlie decisions to engage cognitive control.

Intra-individual variability adaptively increases following inhibition training during middle childhood

There is ongoing debate on the relationship between intra-individual variability (IIV) of cognitive processes and task performance. While psychological research has traditionally assumed that lower intra-individual variability (IIV) aids consistent task performance, some studies suggest that greater IIV can also be adaptive, especially when flexible responding is required. Here we selectively manipulate inhibitory control (Stopping) and response speed (Going) by means of a training paradigm to 1) assess how this manipulation impacts Stopping IIV and its relationship to task performance, and 2) replicate previous findings showing that reductions in Going IIV are adaptive. A group of 208 6-13-year-old children were randomly allocated to an 8-week training targeting Stopping (experimental group) or Going (control group). The stop signal task was administered before and after training. Training Stopping led to adaptive increases in Stopping IIV, where greater flexibility in cognitive processing may be required to meet higher task demands. In line with previous studies, training Going led to adaptive reductions in Going IIV, which allows more consistent and efficient Going performance. These findings provide systematic and causal evidence of the process-dependent relationship of IIV and task performance in the context of Stopping and Going, suggesting a more nuanced perspective on IIV with implications for developmental, ageing and intervention studies.

Quantifying Entropy in Response Times (RT) Distributions Using the Cumulative Residual Entropy (CRE) Function

Response times (RT) distributions are routinely used by psychologists and neuroscientists in the assessment and modeling of human behavior and cognition. The statistical properties of RT distributions are valuable in uncovering unobservable psychological mechanisms. A potentially important statistical aspect of RT distributions is their entropy. However, to date, no valid measure of entropy on RT distributions has been developed, mainly because available extensions of discrete entropy measures to continuous distributions were fraught with problems and inconsistencies. The present work takes advantage of the cumulative residual entropy (CRE) function-a well-known differential entropy measure that can circumvent those problems. Applications of the CRE to RT distributions are presented along with concrete examples and simulations. In addition, a novel measure of instantaneous CRE is developed that captures the rate of entropy reduction (or information gain) from a stimulus as a function of processing time. Taken together, the new measures of entropy in RT distributions proposed here allow for stronger statistical inferences, as well as motivated theoretical interpretations of psychological constructs such as mental effort and processing efficiency.

"Reliable organisms from unreliable components" revisited: the linear drift, linear infinitesimal variance model of decision making

Diffusion models of decision making, in which successive samples of noisy evidence are accumulated to decision criteria, provide a theoretical solution to von Neumann's (1956) problem of how to increase the reliability of neural computation in the presence of noise. I introduce and evaluate a new neurally-inspired dual diffusion model, the linear drift, linear infinitesimal variance (LDLIV) model, which embodies three features often thought to characterize neural mechanisms of decision making. The accumulating evidence is intrinsically positively-valued, saturates at high intensities, and is accumulated for each alternative separately. I present explicit integral-equation predictions for the response time distribution and choice probabilities for the LDLIV model and compare its performance on two benchmark sets of data to three other models: the standard diffusion model and two dual diffusion model composed of racing Wiener processes, one between absorbing and reflecting boundaries and one with absorbing boundaries only. The LDLIV model and the standard diffusion model performed similarly to one another, although the standard diffusion model is more parsimonious, and both performed appreciably better than the other two dual diffusion models. I argue that accumulation of noisy evidence by a diffusion process and drift rate variability are both expressions of how the cognitive system solves von Neumann's problem, by aggregating noisy representations over time and over elements of a neural population. I also argue that models that do not solve von Neumann's problem do not address the main theoretical question that historically motivated research in this area.

Elucidating medial temporal and frontal lobe contributions to approach-avoidance conflict decision-making using functional MRI and the hierarchical drift diffusion model

The prefrontal cortex (PFC) has long been associated with arbitrating between approach and avoidance in the face of conflicting and uncertain motivational information, but recent work has also highlighted medial temporal lobe (MTL) involvement. It remains unclear, however, how the contributions of these regions differ in their resolution of conflict information and uncertainty. We designed an fMRI paradigm in which participants approached or avoided object pairs that differed by motivational conflict and outcome uncertainty (complete certainty vs. complete uncertainty). Behavioral data and decision-making parameters estimated using the hierarchical drift diffusion model revealed that participants' responding was driven by conflict rather than uncertainty. Our neural data suggest that PFC areas contribute to cognitive control during approach-avoidance conflict by potentially adjusting response caution and the strength of evidence generated towards either choice, with differential involvement of anterior cingulate cortex and dorsolateral prefrontal cortex. The MTL, on the other hand, appears to contribute to evidence generation, with the hippocampus linked to evidence accumulation for stimuli. Although findings within perirhinal cortex were comparatively equivocal, some evidence suggests contributions to perceptual representations, particularly under conditions of threat. Our findings provide evidence that MTL and PFC regions may contribute uniquely to arbitrating approach-avoidance conflict.

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.

Cardiac timing and threatening stimuli influence response inhibition and ex-Gaussian parameters of reaction time in a Go/No-go task

Sensorimotor responses vary as a function of the cardiac cycle phase. These effects, known as cardiac cycle time effects, have been explained by the inhibition of cardiac afferent signals on information processing. However, the validity of cardiac cycle time effects is challenged by mixed findings. Factors such as current information processing and affective context may modulate cardiac cycle time effects and account for inconsistencies in the literature. The current study examines the influence of cardiac cycle time and threatening stimuli on two aspects of sensorimotor processing, response speed and inhibition. Thirty-four participants (Mage  = 19.35 years; 29 female) completed an auditory Go/No-go task in no face, neutral face, and fearful face conditions. Faces were presented at either cardiac diastole or systole. Participants' reaction times (RTs) during Go trials and failures in response inhibition during No-go trials were recorded. The ex-Gaussian model was fit to RT data in each condition deriving the parameters, mu (μ) and tau (τ), that indicate response speed and attentional lapses, respectively. Repeated measures ANOVA were used to analyze behavioral data. Results showed that cardiac systole prolonged μ but decreased τ, and that cardiac diastole reduced inhibition errors in the fearful face condition but not in other conditions. These findings indicate that cardiac timing differentially modulates sensory-perceptual and top-down attentional processes and cardiac timing interacts with threatening contexts to influence response inhibition. These results highlight the specificity of cardiac cycle time effects on sensorimotor processing.

A novel visual illusion paradigm provides evidence for a general factor of illusion sensitivity and personality correlates

Visual illusions are a gateway to understand how we construct our experience of reality. Unfortunately, important questions remain open, such as the hypothesis of a common factor underlying the sensitivity to different types of illusions, as well as of personality correlates of illusion sensitivity. In this study, we used a novel parametric framework for visual illusions to generate 10 different classic illusions (Delboeuf, Ebbinghaus, Rod and Frame, Vertical-Horizontal, Zöllner, White, Müller-Lyer, Ponzo, Poggendorff, Contrast) varying in strength, embedded in a perceptual discrimination task. We tested the objective effect of the illusions on errors and response times, and extracted participant-level performance scores (n=250) for each illusion. Our results provide evidence in favour of a general factor underlying the sensitivity to different illusions (labelled Factor i). Moreover, we report a positive link between illusion sensitivity and personality traits such as Agreeableness, Honesty-Humility, and negative relationships with Psychoticism, Antagonism, Disinhibition, and Negative Affect.

Daily arousal variation has little effect on sustained attention performance

Sustaining attention is an important cognitive process for everyday functioning and arousal is thought to underpin its performance. Primate studies depict an inverted-u relation between sustained attention and arousal, in which sustained attention performance is most affected at the extreme levels of arousal and peak performance aligns with moderate arousal. Human research findings are, however, inconsistent. This study aimed to investigate the effects of arousal on sustained attention performance in humans using two approaches-a small-N study with an inbuilt replication to test within-participant variation, and a larger sample assessing between-participant variation. The Sustained Attention to Response Task (SART) was used to measure sustained attention performance and the Karolinska Sleepiness Scale (KSS) was used to measure arousal. In the small-N study five participants completed the SART and KSS once an hour between 7 a.m. and 7 p.m., repeated two weeks later. Significant, curvilinear variation in KSS across time-of-day was found. A linear association between SART response time variability (sigma) and KSS was noted, however no other consistent associations between the SART and KSS were found. In the large-N study, 161 participants completed the SART and KSS once, at a time of day of their choosing. There were no significant relations between SART measures and the KSS, indicating that subjective sleepiness was not related to sustained attention performance. Overall, the hypothesized inverted-u relation between arousal and sustained attention performance was not found. The results suggested that diurnal arousal variation does not modify sustained attention performance in adults.

Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI

Response inhibition and interference resolution are often considered subcomponents of an overarching inhibition system that utilizes the so-called cortico-basal-ganglia loop. Up until now, most previous functional magnetic resonance imaging (fMRI) literature has compared the two using between-subject designs, pooling data in the form of a meta-analysis or comparing different groups. Here, we investigate the overlap of activation patterns underlying response inhibition and interference resolution on a within-subject level, using ultra-high field MRI. In this model-based study, we furthered the functional analysis with cognitive modelling techniques to provide a more in-depth understanding of behaviour. We applied the stop-signal task and multi-source interference task to measure response inhibition and interference resolution, respectively. Our results lead us to conclude that these constructs are rooted in anatomically distinct brain areas and provide little evidence for spatial overlap. Across the two tasks, common BOLD responses were observed in the inferior frontal gyrus and anterior insula. Interference resolution relied more heavily on subcortical components, specifically nodes of the commonly referred to indirect and hyperdirect pathways, as well as the anterior cingulate cortex, and pre-supplementary motor area. Our data indicated that orbitofrontal cortex activation is specific to response inhibition. Our model-based approach provided evidence for the dissimilarity in behavioural dynamics between the two tasks. The current work exemplifies the importance of reducing inter-individual variance when comparing network patterns and the value of UHF-MRI for high resolution functional mapping.

Gauging response time distributions to examine the effect of facial expression inversion

Introduction

We used images of facial expressions (FEs) of emotion in a speeded Same/Different task to examine (i) distributional characteristics of response times (RTs) in relation to inter-stimulus similarity and (ii) the impact of inversion on FE processing.

Methods

Stimuli were seven emotion prototypes, posed by one male and one female, and eight intermediate morphs. Image pairs (N = 225) were presented for 500 ms, upright or inverted, in a block design, each 100 times.

Results

For both upright and inverted FEs, RTs were a non-monotonic function: median values were longest for stimulus pairs of intermediate similarity, decreasing for both more-dissimilar and more-similar pairs. RTs of "Same" and "Different" judgments followed ex-Gaussian distributions. The non-monotonicity is interpreted within a dual-process decision model framework as reflecting the infrequency of identical pairs, shifting the balance between the Same and Different processes. The effect of stimulus inversion was gauged by comparing RT-based multidimensional scaling solutions for the two presentation modes. Solutions for upright and inverted FEs showed little difference, with both displaying some evidence of categorical perception. The same features appeared in hierarchical clustering solutions.

Discussion

This outcome replicates and reinforces the solutions derived from accuracy of "Different" responses reported in our earlier companion paper. We attribute this lack of inversion effect to the brief exposure time, allowing low-level visual processing to dominate Same/Different decisions while elevating early featural analysis, which is insensitive to face orientation but enables initial positive/negative valence categorization of FEs.

A cognitive process modeling framework for the ABCD study stop-signal task

The Adolescent Brain Cognitive Development (ABCD) Study is a longitudinal neuroimaging study of unprecedented scale that is in the process of following over 11,000 youth from middle childhood though age 20. However, a design feature of the study's stop-signal task violates "context independence", an assumption critical to current non-parametric methods for estimating stop-signal reaction time (SSRT), a key measure of inhibitory ability in the study. This has led some experts to call for the task to be changed and for previously collected data to be used with caution. We present a cognitive process modeling framework, the RDEX-ABCD model, that provides a parsimonious explanation for the impact of this design feature on "go" stimulus processing and successfully accounts for key behavioral trends in the ABCD data. Simulation studies using this model suggest that failing to account for the context independence violations in the ABCD design can lead to erroneous inferences in several realistic scenarios. However, we demonstrate that RDEX-ABCD effectively addresses these violations and can be used to accurately measure SSRT along with an array of additional mechanistic parameters of interest (e.g., attention to the stop signal, cognitive efficiency), advancing investigators' ability to draw valid and nuanced inferences from ABCD data. AVAILABILITY OF DATA AND MATERIALS: Data from the ABCD Study are available through the NIH Data Archive (NDA): nda.nih.gov/abcd. Code for all analyses featured in this study is openly available on the Open Science Framework (OSF): osf.io/2h8a7/.

Bilingualism can cause enhanced monitoring and occasional delayed responses in a flanker task

Complex cognitive tasks require different stages of processing (i.e. conflict monitoring, attentional resource allocation and stimulus categorisation). Performance differences between bilinguals and monolinguals on conflict tasks can be affected by the balance of these sub-processes. The current study investigated the effect of bilingualism on these sub-processes during a conflict task with medium monitoring demand. Behavioural responses and evoked potentials from bilinguals and monolinguals were examined during a flanker task with 25% incongruent trials. Behavioural differences were analysed by means of averaged response times and exponentially modified Gaussian analyses of response time distributions. For evoked potentials, the study focussed on N2 (reflecting conflict monitoring) and P3 responses (reflecting allocation of attentional resources for cognitive control). Bilinguals had significantly longer response distribution tails compared to monolinguals. Bilinguals were shown to have a more pronounced N2 and smaller P3 compared to monolinguals, independent of condition, suggesting a different balance of sub-processes for the two groups. This suggests that bilinguals were engaged more strongly in monitoring processes, leading to the allocation of fewer attentional resources during stimulus categorisation. Additionally, the P3 amplitudes were negatively related with the length of response distribution tails for bilinguals. These results are consistent with enhanced conflict monitoring in bilinguals that led to reduced engagement of attentional resources for stimulus categorisation. This enhanced conflict monitoring could lead to occasional extremely slow responses. Thus, the bilingual experience appears to impact the balance of cognitive control processes during conflict tasks, which might only be reflected in a minority of responses.

Intra-Subject Variability, Intelligence, and ADHD Traits in a Community-Based Sample

OBJECTIVE

The present study investigates the predictive validity of intra-subject variability (ISV) for ADHD traits in a community-based sample and the stability of the relationship between ISV and fluid intelligence (gf) across the continuum of ADHD traits.

METHOD

Age-residualized data from 426 participants (8-18 years, 6% ADHD) was used to investigate whether ex-Gaussian and DDM parameters derived from simple choice-reaction-time tasks can predict continuously assessed ADHD traits. Multiple-Group-Analyses and Latent-Moderated-Structural-Equations were used to test whether ADHD traits moderate the relationship between ISV and gf.

RESULTS

σ and μ of the ex-Gaussian model as well as DDM parameters drift rate (v) and boundary separation (a) significantly predicted general ADHD traits, while τ predicted attention difficulties specifically. Across the ADHD continuum, σ and v were significant predictors of gf.

CONCLUSION

The results confirm the link between ISV and ADHD. The relationship between ISV and gf appears stable across the ADHD continuum.

Longitudinal developmental trajectories of inhibition and white-matter maturation of the fronto-basal-ganglia circuits

Response inhibition refers to the cancelling of planned (or restraining of ongoing) actions and is required in much of our everyday life. Response inhibition appears to improve dramatically in early development and plateau in adolescence. The fronto-basal-ganglia network has long been shown to predict individual differences in the ability to enact response inhibition. In the current study, we examined whether developmental trajectories of fiber-specific white matter properties of the fronto-basal-ganglia network was predictive of parallel developmental trajectories of response inhibition. 138 children aged 9-14 completed the stop-signal task (SST). A subsample of 73 children underwent high-angular resolution diffusion MRI data for up to three time points. Performance on the SST was assessed using a parametric race modelling approach. White matter organization of the fronto-basal-ganglia circuit was estimated using fixel-based analysis. Contrary to predictions, we did not find any significant associations between maturational trajectories of fronto-basal-ganglia white matter and developmental improvements in SST performance. Findings suggest that the development of white matter organization of the fronto-basal-ganglia and development of stopping performance follow distinct maturational trajectories.

No need to collect more data: ex-Gaussian modelling of existing data (Craig & Lipp, 2018) reveals an interactive effect of face race and face sex on speeded expression recognition

The results of a previous study (Craig & Lipp, 2018) into the effects of multiple social category cues (face race and face sex) on facial emotion recognition indicate that face sex dominates face race, and moreover, participant sex differences contribute little to the observed effects. Here, I modelled the same dataset (https://osf.io/rsmxb/) using the ex-Gaussian, a distribution that is 1) well suited to RT data and 2) separates slow from relatively fast influences. Corroborating recent results (Tipples, 2022) current results show larger effects of face sex (for the faces of White individuals) for female participants. Further novel interaction effects were revealed. For example, results support a different time course for the influence of face sex on expression for the faces of Black compared to White individuals.

Evidence towards a continuum of impairment across neurodevelopmental disorders from basic ocular-motor tasks

Findings of genetic overlap between Schizophrenia, Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) contributed to a renewed conceptualization of these disorders as laying on a continuum based on aetiological, pathophysiological and neurodevelopmental features. Given that cognitive impairments are core to their pathophysiology, we compared patients with schizophrenia, ADHD, ASD, and controls on ocular-motor and manual-motor tasks, challenging crucial cognitive processes. Group comparisons revealed inhibition deficits common to all disorders, increased intra-subject variability in schizophrenia and, to a lesser extent, ADHD as well as slowed processing in schizophrenia. Patterns of deviancies from controls exhibited strong correlations, along with differences that posited schizophrenia as the most impaired group, followed by ASD and ADHD. While vector correlations point towards a common neurodevelopmental continuum of impairment, vector levels suggest differences in the severity of such impairment. These findings argue towards a dimensional approach to Neurodevelopmental Disorders' pathophysiological mechanisms.

Expert agreement in prior elicitation and its effects on Bayesian inference

Bayesian inference requires the specification of prior distributions that quantify the pre-data uncertainty about parameter values. One way to specify prior distributions is through prior elicitation, an interview method guiding field experts through the process of expressing their knowledge in the form of a probability distribution. However, prior distributions elicited from experts can be subject to idiosyncrasies of experts and elicitation procedures, raising the spectre of subjectivity and prejudice. Here, we investigate the effect of interpersonal variation in elicited prior distributions on the Bayes factor hypothesis test. We elicited prior distributions from six academic experts with a background in different fields of psychology and applied the elicited prior distributions as well as commonly used default priors in a re-analysis of 1710 studies in psychology. The degree to which the Bayes factors vary as a function of the different prior distributions is quantified by three measures of concordance of evidence: We assess whether the prior distributions change the Bayes factor direction, whether they cause a switch in the category of evidence strength, and how much influence they have on the value of the Bayes factor. Our results show that although the Bayes factor is sensitive to changes in the prior distribution, these changes do not necessarily affect the qualitative conclusions of a hypothesis test. We hope that these results help researchers gauge the influence of interpersonal variation in elicited prior distributions in future psychological studies. Additionally, our sensitivity analyses can be used as a template for Bayesian robustness analyses that involve prior elicitation from multiple experts.

Ahead of the (ROC) Curve: A Statistical Approach to Utilizing Ex-Gaussian Parameters of Reaction Time in Diagnosing ADHD Across Three Developmental Periods

INTRODUCTION

Performance on executive function (EF) tasks is only modestly predictive of a diagnosis of Attention Deficit Hyperactivity Disorder (ADHD), despite the common assumption that EF deficits are ubiquitous to the disorder. The current study sought to determine whether ex-Gaussian parameters of simple reaction time are better able to discriminate between children and adults with and without ADHD, compared with traditional measures of inhibitory control.

METHODS

Receiver Operating Characteristic (ROC) analyses and the area under the curve (AUC) were used to examine the ability of performance on two commonly used tasks of inhibitory control (i.e. stop signal reaction time (SSRT) and go-no-go tasks) to predict ADHD status in preschool (N = 108), middle childhood (N = 309), and young adulthood (N = 133).

RESULTS

Across all samples, SSRT, go-no-go percentage of failed inhibits, and standard deviation of reaction (SDRT) time to "go" trials, all successfully discriminated between individuals with and without ADHD. Ex-Gaussian decomposition of the RT distribution indicated that both larger tau and larger sigma drove findings for SDRT. Contrary to predictions, traditional measures of inhibitory control were equal if not better predictors of ADHD status than ex-Gaussian parameters.

CONCLUSIONS

Findings support ongoing work to quantify the separate contributions of cognitive subprocesses that drive task performance, which in turn is critical to developing and improving process-based approaches in clinical assessment.

Unraveling the cognitive correlates of heart rate variability with the drift diffusion model

The Neurovisceral Integration Model posits a link between resting vagally mediated heart rate variability (vmHRV) and cognitive control. Empirical support for this link is mixed, potentially due to coarse performance metrics such as mean response time (RT). To clarify this issue, we tested the relationships between resting vmHRV and refined estimates of cognitive control- as revealed by the ex-Gaussian model of RT and, to a greater extent, the drift diffusion model (DDM, a computational model of two-choice performance). Participants (N = 174) completed a five-minute resting baseline while ECG was collected followed by a Simon spatial conflict task. The root mean square of successive differences in interbeat intervals was calculated to index resting vmHRV. Resting vmHRV was unrelated to Simon's mean RT and accuracy rates, but was inversely related to the ex-Gaussian parameter reflecting slow RTs (tau); however, this finding was attenuated after adjustment for covariates. High resting vmHRV was related to faster drift rates and slower non-decision times, DDM parameters reflecting goal-directed cognition and sensorimotor processes, respectively. The DDM effects survived covariate adjustment and were specific to incongruent trials (i.e., when cognitive control demands were high). Findings suggest a link between vmHRV and cognitive control vis-a-vis drift rate, and potentially, a link between vmHRV and motoric inhibition vis-a-vis non-decision time. These cognitive correlates would have been missed with reliance on traditional performance. Findings are discussed with respect to the inhibitory processes that promote effective performance in high vmHRV individuals.

Variability of Reaction Time as a Marker of Executive Function Impairments in Fibromyalgia

In addition to chronic widespread pain and depression and anxiety symptoms, patients with fibromyalgia frequently experience cognitive problems. This study investigated executive functions in fibromyalgia via a Go/No-Go task. To obtain comprehensive information about performance, traditional and ex-Gaussian parameters of reaction time (RT) variability were used, in addition to speed and accuracy indices. Ex-Gaussian parameters show an excellent fit to empirical RT distributions. Fifty-two female fibromyalgia patients and twenty-eight healthy controls participated. The task included 60 visual stimuli, which participants had to respond to (Go stimuli) or withhold the response to (No-Go stimuli). After 30 trials, the task rule changed, such that previous No-Go stimuli had to be responded to. Performance was indexed by the hit rate, false alarm rate, and mean (M) and intraindividual standard deviation (SD) of RT and the ex-Gaussian parameters mu, sigma, and tau. Mu and sigma indicate the M and SD of the Gaussian distribution; tau reflects the M and SD of the exponential function. Patients exhibited a lower hit rate, higher M RT, and higher tau than controls. Moreover, patients showed greater decrease of the hit rate after the change of task rule. In the entire sample, SD, sigma, and tau were inversely associated with the hit rate and positively associated with the false alarm rate. While the greater decline in hit rate after the change in task rule indicates deficient cognitive flexibility, the lack of any difference in false alarm rate suggests intact response inhibition. Higher M RT reflects reduced cognitive or motor speed. Increased tau in fibromyalgia indicates greater fluctuations in executive control and more frequent temporary lapses of attention. For the first time, this study demonstrated that indices of RT variability, in particular those derived from the ex-Gaussian function, may complement speed and accuracy parameters in the assessment of executive function impairments in fibromyalgia. Optimized assessment may facilitate the personalization of therapies aimed at improving the cognitive function of those with the disorder.

The pre-supplementary motor area achieves inhibitory control by modulating response thresholds

The pre-supplementary motor area (pre-SMA) is central for the initiation and inhibition of voluntary action. For the execution of action, the pre-SMA optimises the decision of which action to choose by adjusting the thresholds for the required evidence for each choice. However, it remains unclear how the pre-SMA contributes to action inhibition. Here, we use computational modelling of a stop/no-go task, performed by an adult with a focal lesion in the pre-SMA, and 52 age-matched controls. We show that the patient required more time to successfully inhibit an action (longer stop-signal reaction time) but was faster in terms of go reaction times. Computational modelling revealed that the patient's failure to stop was explained by a significantly lower response threshold for initiating an action, as compared to controls, suggesting that the patient needed less evidence before committing to an action. A similarly specific impairment was also observed for the decision of which action to choose. Together, our results suggest that dynamic threshold modulation may be a general mechanism by which the pre-SMA exerts its control over voluntary action.

Investigating variability in morphological processing with Bayesian distributional models

We investigated the processing of morphologically complex words adopting an approach that goes beyond estimating average effects and allows testing predictions about variability in performance. We tested masked morphological priming effects with English derived ('printer') and inflected ('printed') forms priming their stems ('print') in non-native speakers, a population that is characterized by large variability. We modeled reaction times with a shifted-lognormal distribution using Bayesian distributional models, which allow assessing effects of experimental manipulations on both the mean of the response distribution ('mu') and its standard deviation ('sigma'). Our results show similar effects on mean response times for inflected and derived primes, but a difference between the two on the sigma of the distribution, with inflectional priming increasing response time variability to a significantly larger extent than derivational priming. This is in line with previous research on non-native processing, which shows more variable results across studies for the processing of inflected forms than for derived forms. More generally, our study shows that treating variability in performance as a direct object of investigation can crucially inform models of language processing, by disentangling effects which would otherwise be indistinguishable. We therefore emphasize the importance of looking beyond average performance and testing predictions on other parameters of the distribution rather than just its central tendency.

Evidence for cortical adjustments to perceptual decision criteria during word recognition in noise

Extensive increases in cingulo-opercular frontal activity are typically observed during speech recognition in noise tasks. This elevated activity has been linked to a word recognition benefit on the next trial, termed "adaptive control," but how this effect might be implemented has been unclear. The established link between perceptual decision making and cingulo-opercular function may provide an explanation for how those regions benefit subsequent word recognition. In this case, processes that support recognition such as raising or lowering the decision criteria for more accurate or faster recognition may be adjusted to optimize performance on the next trial. The current neuroimaging study tested the hypothesis that pre-stimulus cingulo-opercular activity reflects criterion adjustments that determine how much information to collect for word recognition on subsequent trials. Participants included middle-age and older adults (N = 30; age = 58.3 ± 8.8 years; m ± sd) with normal hearing or mild sensorineural hearing loss. During a sparse fMRI experiment, words were presented in multitalker babble at +3 dB or +10 dB signal-to-noise ratio (SNR), which participants were instructed to repeat aloud. Word recognition was significantly poorer with increasing participant age and lower SNR compared to higher SNR conditions. A perceptual decision-making model was used to characterize processing differences based on task response latency distributions. The model showed that significantly less sensory evidence was collected (i.e., lower criteria) for lower compared to higher SNR trials. Replicating earlier observations, pre-stimulus cingulo-opercular activity was significantly predictive of correct recognition on a subsequent trial. Individual differences showed that participants with higher criteria also benefitted the most from pre-stimulus activity. Moreover, trial-level criteria changes were significantly linked to higher versus lower pre-stimulus activity. These results suggest cingulo-opercular cortex contributes to criteria adjustments to optimize speech recognition task performance.