Dynamic models of choice

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

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

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.

Clarifying the longitudinal factor structure, temporal stability, and construct validity of Go/No-Go task-related neural activation across adolescence and young adulthood

This study aimed to clarify the psychometric properties and development of Go/No-Go (GNG) task-related neural activation across critical periods of neurobiological maturation by examining its longitudinal stability, factor structure, developmental change, and associations with a computational index of task-general cognitive control. A longitudinal sample (N=289) of adolescents from the Michigan Longitudinal Study was assessed at four time-points (mean number of timepoints per participant=2.05; standard deviation=0.89) spanning early adolescence (ages 10-13) to young adulthood (22-25). Results suggested that regional neural activations from the "successful inhibition" (SI>GO) and "failed inhibition" (FI>GO; error-monitoring) contrasts are each described well by a single general factor. Neural activity across both contrasts showed developmental increases throughout adolescence that plateau in young adulthood. Neural activity metrics evidenced low temporal stability across this period of marked developmental change, and the SI>GO factor showed no relations with a behavioral index of cognitive control. The FI>GO factor displayed stronger criterion validity in the form of significant, positive associations with behaviorally measured cognitive control. Findings emphasize the utility of well-validated psychometric methods and longitudinal data for clarifying the measurement properties of functional neuroimaging metrics and improving measurement practices in developmental cognitive neuroscience.

Human decision making balances reward maximization and policy compression

Policy compression is a computational framework that describes how capacity-limited agents trade reward for simpler action policies to reduce cognitive cost. In this study, we present behavioral evidence that humans prefer simpler policies, as predicted by a capacity-limited reinforcement learning model. Across a set of tasks, we find that people exploit structure in the relationships between states, actions, and rewards to "compress" their policies. In particular, compressed policies are systematically biased towards actions with high marginal probability, thereby discarding some state information. This bias is greater when there is redundancy in the reward-maximizing action policy across states, and increases with memory load. These results could not be explained qualitatively or quantitatively by models that did not make use of policy compression under a capacity limit. We also confirmed the prediction that time pressure should further reduce policy complexity and increase action bias, based on the hypothesis that actions are selected via time-dependent decoding of a compressed code. These findings contribute to a deeper understanding of how humans adapt their decision-making strategies under cognitive resource constraints.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSION

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

Corticospinal excitability reductions during action preparation and action stopping in humans: Different sides of the same inhibitory coin?

Motor functions and cognitive processes are closely associated with each other. In humans, this linkage is reflected in motor system state changes both when an action must be prepared and stopped. Single-pulse transcranial magnetic stimulation showed that both action preparation and action stopping are accompanied by a reduction of corticospinal excitability, referred to as preparatory and response inhibition, respectively. While previous efforts have been made to describe both phenomena extensively, an updated and comprehensive comparison of the two phenomena is lacking. To ameliorate such deficit, this review focuses on the role and interpretation of single-coil (single-pulse and paired-pulse) and dual-coil TMS outcome measures during action preparation and action stopping in humans. To that effect, it aims to identify commonalities and differences, detailing how TMS-based outcome measures are affected by states, traits, and psychopathologies in both processes. Eventually, findings will be compared, and open questions will be addressed to aid future research.

PyBEAM: A Bayesian approach to parameter inference for a wide class of binary evidence accumulation models

Many decision-making theories are encoded in a class of processes known as evidence accumulation models (EAM). These assume that noisy evidence stochastically accumulates until a set threshold is reached, triggering a decision. One of the most successful and widely used of this class is the Diffusion Decision Model (DDM). The DDM however is limited in scope and does not account for processes such as evidence leakage, changes of evidence, or time varying caution. More complex EAMs can encode a wider array of hypotheses, but are currently limited by computational challenges. In this work, we develop the Python package PyBEAM (Bayesian Evidence Accumulation Models) to fill this gap. Toward this end, we develop a general probabilistic framework for predicting the choice and response time distributions for a general class of binary decision models. In addition, we have heavily computationally optimized this modeling process and integrated it with PyMC, a widely used Python package for Bayesian parameter estimation. This 1) substantially expands the class of EAM models to which Bayesian methods can be applied, 2) reduces the computational time to do so, and 3) lowers the entry fee for working with these models. Here we demonstrate the concepts behind this methodology, its application to parameter recovery for a variety of models, and apply it to a recently published data set to demonstrate its practical use.

What can evidence accumulation modelling tell us about human social cognition?

Evidence accumulation models are a series of computational models that provide an account for speeded decision-making. These models have been used extensively within the cognitive psychology literature to great success, allowing inferences to be drawn about the psychological processes that underlie cognition that are sometimes not available in a traditional analysis of accuracy or reaction time (RT). Despite this, there have been only a few applications of these models within the domain of social cognition. In this article, we explore several ways in which the study of human social information processing would benefit from application of evidence accumulation modelling. We begin first with a brief overview of the evidence accumulation modelling framework and their past success within the domain of cognitive psychology. We then highlight five ways in which social cognitive research would benefit from an evidence accumulation approach. This includes (1) greater specification of assumptions, (2) unambiguous comparisons across blocked task conditions, (3) quantifying and comparing the magnitude of effects in standardised measures, (4) a novel approach for studying individual differences, and (5) improved reproducibility and accessibility. These points are illustrated using examples from the domain of social attention. Finally, we outline several methodological and practical considerations, which should help researchers use evidence accumulation models productively. Ultimately, it will be seen that evidence accumulation modelling offers a well-developed, accessible, and commonly understood framework that can reveal inferences about cognition that may otherwise be out of reach in a traditional analysis of accuracy and RT. This approach, therefore, has the potential to substantially revise our understanding of social cognition.

Similarities of SNARC, cognitive Simon, and visuomotor Simon effects in terms of response time distributions, hand-stimulus proximity, and temporal dynamics

The spatial-numerical association of response codes (SNARC) and Simon effects are attributed to the same type of conflict according to dimensional overlap (DO) theory: the congruency of task-irrelevant spatial information and the selected response (e.g., left or right). However, previous studies have yielded inconsistent results regarding the relationship between the two effects, with some studies reporting an interaction while others did not. This discrepancy may be attributed to the use of different types of Simon effects (visuomotor and cognitive Simon effects) in these studies, as the spatial codes associated with these two types of Simon effects are distinct (exogenous and endogenous, respectively). The aim of this study was to address these inconsistencies and gain a better understanding of the similarities and differences in spatial representations generated by spatial location, semantic information, and numerical information. We attempted to classify the relationships among the SNARC and Simon effects. Specifically, the visuomotor Simon, cognitive Simon, and SNARC effects were compared from three perspectives: the response time (RT) distribution, hand-stimulus proximity, and temporal dynamics (with the drift diffusion model; DDM). Regarding RTs, the results showed that the visuomotor Simon effect decreased with increased values of RT bins, while the cognitive Simon and SNARC effects increased. Additionally, the visuomotor Simon effect was the only effect influenced by hand-stimulus proximity, with a stronger effect observed in the hand-proximal condition than in the hand-distal condition. Regarding the DDM results, only the visuomotor Simon effect exhibited a higher drift rate and longer non-decision time in the incompatible condition than in the compatible condition. Conversely, both the SNARC and cognitive Simon effects exhibited an inverse pattern regarding the drift rate and no significant difference in non-decision time between the two conditions. These findings suggest that the SNARC effect is more similar to the cognitive Simon effect than the visuomotor Simon effect, indicating that the endogenous spatial-numerical representation of the SNARC effect might share an underlying processing mechanism with the endogenous spatial-semantic representation of the cognitive Simon effect but not with the exogenous location representation of the visuomotor Simon effect. Our results further demonstrate that the origin of spatial information could impact the classification of conflicts and supplement DO theory.

A unified account of simple and response-selective inhibition

Response inhibition is a key attribute of human executive control. Standard stop-signal tasks require countermanding a single response; the speed at which that response can be inhibited indexes the efficacy of the inhibitory control networks. However, more complex stopping tasks, where one or more components of a multi-component action are cancelled (i.e., response-selective stopping) cannot be explained by the independent-race model appropriate for the simple task (Logan and Cowan 1984). Healthy human participants (n=28; 10 male; 19-40 years) completed a response-selective stopping task where a 'go' stimulus required simultaneous (bimanual) button presses in response to left and right pointing green arrows. On a subset of trials (30%) one, or both, arrows turned red (constituting the stop signal) requiring that only the button-press(es) associated with red arrows be cancelled. Electromyographic recordings from both index fingers (first dorsal interosseous) permitted the assessment of both voluntary motor responses that resulted in overt button presses, and activity that was cancelled prior to an overt response (i.e., partial, or covert, responses). We propose a simultaneously inhibit and start (SIS) model that extends the independent race model and provides a highly accurate account of response-selective stopping data. Together with fine-grained EMG analysis, our model-based analysis offers converging evidence that the selective-stop signal simultaneously triggers a process that stops the bimanual response and triggers a new unimanual response corresponding to the green arrow. Our results require a reconceptualisation of response-selective stopping and offer a tractable framework for assessing such tasks in healthy and patient populations. Significance Statement Response inhibition is a key attribute of human executive control, frequently investigated using the stop-signal task. After initiating a motor response to a go signal, a stop signal occasionally appears at a delay, requiring cancellation of the response. This has been conceptualised as a 'race' between the go and stop processes, with the successful (or failed) cancellation determined by which process wins the race. Here we provide a novel computational model for a complex variation of the stop-signal task, where only one component of a multicomponent action needs to be cancelled. We provide compelling muscle activation data that support our model, providing a robust and plausible framework for studying these complex inhibition tasks in both healthy and pathological cohorts.

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.

Failing to attend versus failing to stop: Single-trial decomposition of action-stopping in the stop signal task

The capacity to stop impending or ongoing actions contributes to executive control over behavior. Action-stopping, however, is difficult to directly quantify. It is therefore assayed via computational modeling of behavior in the stop signal task to estimate the latency of stopping (stop signal reaction time, SSRT) and, more recently, the reliability of stopping in terms of the distribution of SSRTs (standard deviation, SD-SSRT) and the frequency with which one outright fails to react to a stop signal (trigger failures, TF). Critically, the validity of computational estimates remains unknown because we currently have no direct readouts of behavior against which to compare them. Here, we developed a method for providing single-trial behavioral readouts of SSRT and trigger failures. The method relies on an adaptation of the stop signal task in which participants respond by moving a computer mouse. In two online experiments, we used movement kinematics to quantify stopping performance (SSRT, SD-SSRT, and TF), and then applied the standard Race Model and recent BEESTS model in order to examine the convergent validity of the methods. Overall, we demonstrate good correspondence between kinematics- and model-based estimates of stopping performance at the group and individual level. We conclude that the new method provides valid estimates of stopping performance that, unlike model-based estimates, can be read out at the level of single trials. Our approach might therefore be useful for interrogating single-trial neurophysiological correlates of stopping and for large-scale, online studies of behavioral stopping.

Proactive cues facilitate faster action reprogramming, but not stopping, in a response-selective stop signal task

The ability to stop simple ongoing actions has been extensively studied using the stop signal task, but less is known about inhibition in more complex scenarios. Here we used a task requiring bimanual responses to go stimuli, but selective inhibition of only one of those responses following a stop signal. We assessed how proactive cues affect the nature of both the responding and stopping processes, and the well-documented stopping delay (interference effect) in the continuing action following successful stopping. In this task, estimates of the speed of inhibition based on a simple-stopping model are inappropriate, and have produced inconsistent findings about the effects of proactive control on motor inhibition. We instead used a multi-modal approach, based on improved methods of detecting and interpreting partial electromyographical responses and the recently proposed SIS (simultaneously inhibit and start) model of selective stopping behaviour. Our results provide clear and converging evidence that proactive cues reduce the stopping delay effect by slowing bimanual responses and speeding unimanual responses, with a negligible effect on the speed of the stopping process.

Locus Coeruleus Integrity Is Linked to Response Inhibition Deficits in Parkinson's Disease and Progressive Supranuclear Palsy

Parkinson's disease (PD) and progressive supranuclear palsy (PSP) both impair response inhibition, exacerbating impulsivity. Inhibitory control deficits vary across individuals and are linked with worse prognosis, and lack improvement on dopaminergic therapy. Motor and cognitive control are associated with noradrenergic innervation of the cortex, arising from the locus coeruleus (LC) noradrenergic system. Here we test the hypothesis that structural variation of the LC explains response inhibition deficits in PSP and PD. Twenty-four people with idiopathic PD, 14 with PSP-Richardson's syndrome, and 24 age- and sex-matched controls undertook a stop-signal task and ultrahigh field 7T magnetization-transfer-weighted imaging of the LC. Parameters of "race models" of go- versus stop-decisions were estimated using hierarchical Bayesian methods to quantify the cognitive processes of response inhibition. We tested the multivariate relationship between LC integrity and model parameters using partial least squares. Both disorders impaired response inhibition at the group level. PSP caused a distinct pattern of abnormalities in inhibitory control with a paradoxically reduced threshold for go responses, but longer nondecision times, and more lapses of attention. The variation in response inhibition correlated with the variability of LC integrity across participants in both clinical groups. Structural imaging of the LC, coupled with behavioral modeling in parkinsonian disorders, confirms that LC integrity is associated with response inhibition and LC degeneration contributes to neurobehavioral changes. The noradrenergic system is therefore a promising target to treat impulsivity in these conditions. The optimization of noradrenergic treatment is likely to benefit from stratification according to LC integrity.SIGNIFICANCE STATEMENT Response inhibition deficits contribute to clinical symptoms and poor outcomes in people with Parkinson's disease and progressive supranuclear palsy. We used cognitive modeling of performance of a response inhibition task to identify disease-specific mechanisms of abnormal inhibitory control. Response inhibition in both patient groups was associated with the integrity of the noradrenergic locus coeruleus, which we measured in vivo using ultra-high field MRI. We propose that the imaging biomarker of locus coeruleus integrity provides a trans-diagnostic tool to explain individual differences in response inhibition ability beyond the classic nosological borders and diagnostic criteria. Our data suggest a potential new stratified treatment approach for Parkinson's disease and progressive supranuclear palsy.

A spurious correlation between difference scores in evidence-accumulation model parameters

Evidence-accumulation models are a useful tool for investigating the cognitive processes that give rise to behavioural data patterns in reaction times (RTs) and error rates. In their simplest form, evidence-accumulation models include three parameters: The average rate of evidence accumulation over time (drift rate) and the amount of evidence that needs to be accumulated before a response becomes selected (boundary) both characterise the response-selection process; a third parameter summarises all processes before and after the response-selection process (non-decision time). Researchers often compute experimental effects as simple difference scores between two within-subject conditions and such difference scores can also be computed on model parameters. In the present paper, we report spurious correlations between such model parameter difference scores, both in empirical data and in computer simulations. The most pronounced spurious effect is a negative correlation between boundary difference and non-decision difference, which amounts to r = - .70 or larger. In the simulations, we only observed this spurious negative correlation when either (a) there was no true difference in model parameters between simulated experimental conditions, or (b) only drift rate was manipulated between simulated experimental conditions; when a true difference existed in boundary separation, non-decision time, or all three main parameters, the correlation disappeared. We suggest that care should be taken when using evidence-accumulation model difference scores for correlational approaches because the parameter difference scores can correlate in the absence of any true inter-individual differences at the population level.

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.

No effects of 1 Hz offline TMS on performance in the stop-signal game

Stopping an already initiated action is crucial for human everyday behavior and empirical evidence points toward the prefrontal cortex playing a key role in response inhibition. Two regions that have been consistently implicated in response inhibition are the right inferior frontal gyrus (IFG) and the more superior region of the dorsolateral prefrontal cortex (DLPFC). The present study investigated the effect of offline 1 Hz transcranial magnetic stimulation (TMS) over the right IFG and DLPFC on performance in a gamified stop-signal task (SSG). We hypothesized that perturbing each area would decrease performance in the SSG, albeit with a quantitative difference in the performance decrease after stimulation. After offline TMS, functional short-term reorganization is possible, and the domain-general area (i.e., the right DLPFC) might be able to compensate for the perturbation of the domain-specific area (i.e., the right IFG). Results showed that 1 Hz offline TMS over the right DLPFC and the right IFG at 110% intensity of the resting motor threshold had no effect on performance in the SSG. In fact, evidence in favor of the null hypothesis was found. One intriguing interpretation of this result is that within-network compensation was triggered, canceling out the potential TMS effects as has been suggested in recent theorizing on TMS effects, although the presented results do not unambiguously identify such compensatory mechanisms. Future studies may result in further support for this hypothesis, which is especially important when studying reactive response in complex environments.

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.

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.

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/.

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 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.

Capturing Dynamic Performance in a Cognitive Model: Estimating ACT-R Memory Parameters With the Linear Ballistic Accumulator

The parameters governing our behavior are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modelers. Here, we demonstrate a mapping of ACT-R's declarative memory onto the linear ballistic accumulator (LBA), a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT-R parameters without requiring the modeler to build and fit an entire ACT-R model. Existing parameter estimation methods for the LBA can be used, instead of the computationally expensive parameter sweeps that are traditionally done. We conduct a parameter recovery study to confirm that the LBA can recover ACT-R parameters from simulated data. Then, as a proof of concept, we use the LBA to estimate ACT-R parameters from an empirical dataset. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. In addition, we find that the mapping between ACT-R and LBA lends a more concrete interpretation to ACT-R's latency factor parameter, namely as a measure of response caution. This work contributes to a growing movement towards integrating formal modeling approaches in cognitive science.

Winner Takes All! What Are Race Models, and Why and How Should Psychologists Use Them?

Interest in the processes that mediate between stimuli and responses is at the heart of most modern psychology and neuroscience. These processes cannot be directly measured but instead must be inferred from observed responses. Race models, through their ability to account for both response choices and response times, have been a key enabler of such inferences. Examples of such models appeared contemporaneously with the cognitive revolution, and since then have become increasingly prominent and elaborated, so that psychologists now have a powerful array of race models at their disposal. We showcase the state of the art for race models and describe why and how they are used.

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

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

Beyond Drift Diffusion Models: Fitting a Broad Class of Decision and Reinforcement Learning Models with HDDM

Computational modeling has become a central aspect of research in the cognitive neurosciences. As the field matures, it is increasingly important to move beyond standard models to quantitatively assess models with richer dynamics that may better reflect underlying cognitive and neural processes. For example, sequential sampling models (SSMs) are a general class of models of decision-making intended to capture processes jointly giving rise to RT distributions and choice data in n-alternative choice paradigms. A number of model variations are of theoretical interest, but empirical data analysis has historically been tied to a small subset for which likelihood functions are analytically tractable. Advances in methods designed for likelihood-free inference have recently made it computationally feasible to consider a much larger spectrum of SSMs. In addition, recent work has motivated the combination of SSMs with reinforcement learning models, which had historically been considered in separate literatures. Here, we provide a significant addition to the widely used HDDM Python toolbox and include a tutorial for how users can easily fit and assess a (user-extensible) wide variety of SSMs and how they can be combined with reinforcement learning models. The extension comes batteries included, including model visualization tools, posterior predictive checks, and ability to link trial-wise neural signals with model parameters via hierarchical Bayesian regression.

Flexible and efficient simulation-based inference for models of decision-making

Inferring parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model—however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced likelihood approximation networks (LANs, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation efficient. Our approach, mixed neural likelihood estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations and is significantly more accurate than LANs when both are trained with the same budget. Our approach enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.

Mind Wandering Impedes Response Inhibition by Affecting the Triggering of the Inhibitory Process

Mind wandering is a state in which our mental focus shifts toward task-unrelated thoughts. Although it is known that mind wandering has a detrimental effect on concurrent task performance (e.g., decreased accuracy), its effect on executive functions is poorly studied. Yet the latter question is relevant to many real-world situations, such as rapid stopping during driving. Here, we studied how mind wandering would affect the requirement to subsequently stop an incipient motor response. In healthy adults, we tested whether mind wandering affected stopping and, if so, which component of stopping was affected: the triggering of the inhibitory brake or the implementation of the brake following triggering. We observed that during mind wandering, stopping latency increased, as did the percentage of trials with failed triggering. Indeed, 67% of the variance of the increase in stopping latency was explained by increased trigger failures. Thus, mind wandering primarily affects stopping by affecting the triggering of the brake.

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.

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

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

OSARI, an Open-Source Anticipated Response Inhibition Task

The stop-signal paradigm has become ubiquitous in investigations of inhibitory control. Tasks inspired by the paradigm, referred to as stop-signal tasks, require participants to make responses on go trials and to inhibit those responses when presented with a stop-signal on stop trials. Currently, the most popular version of the stop-signal task is the 'choice-reaction' variant, where participants make choice responses, but must inhibit those responses when presented with a stop-signal. An alternative to the choice-reaction variant of the stop-signal task is the 'anticipated response inhibition' task. In anticipated response inhibition tasks, participants are required to make a planned response that coincides with a predictably timed event (such as lifting a finger from a computer key to stop a filling bar at a predefined target). Anticipated response inhibition tasks have some advantages over the more traditional choice-reaction stop-signal tasks and are becoming increasingly popular. However, currently, there are no openly available versions of the anticipated response inhibition task, limiting potential uptake. Here, we present an open-source, free, and ready-to-use version of the anticipated response inhibition task, which we refer to as the OSARI (the Open-Source Anticipated Response Inhibition) task.

Partial response electromyography as a marker of action stopping

Response inhibition is among the core constructs of cognitive control. It is notoriously difficult to quantify from overt behavior, since the outcome of successful inhibition is the lack of a behavioral response. Currently, the most common measure of action stopping, and by proxy response inhibition, is the model-based stop signal reaction time (SSRT) derived from the stop signal task. Recently, partial response electromyography (prEMG) has been introduced as a complementary physiological measure to capture individual stopping latencies. PrEMG refers to muscle activity initiated by the go signal that plummets after the stop signal before its accumulation to a full response. Whereas neither the SSRT nor the prEMG is an unambiguous marker for neural processes underlying response inhibition, our analysis indicates that the prEMG peak latency is better suited to investigate brain mechanisms of action stopping. This study is a methodological resource with a comprehensive overview of the psychometric properties of the prEMG in a stop signal task, and further provides practical tips for data collection and analysis.

Deficient prefrontal-amygdalar connectivity underlies inefficient face processing in adolescent major depressive disorder

Adolescence represents a critical developmental period where the prevalence of major depressive disorder (MDD) increases. Aberrant emotion processing is a core feature of adolescent MDD that has been associated with functional alterations within the prefrontal-amygdala circuitry. In this study, we tested cognitive and neural mechanisms of emotional face processing in adolescents with MDD utilizing a combination of computational modeling and neuroimaging. Thirty adolescents with MDD (age: M = 16.1 SD = 1.4, 20 females) and 33 healthy controls (age: M = 16.2 SD = 1.9, 20 females) performed a dynamic face- and shape-matching task. A linear ballistic accumulator model was fit to the behavioral data to study differences in evidence accumulation. We used dynamic causal modeling (DCM) to study effective connectivity in the prefrontal-amygdala network to reveal the neural underpinnings of cognitive impairments while performing the task. Face processing efficiency was reduced in the MDD group and most pronounced for ambiguous faces with neutral emotional expressions. Critically, this reduction was related to increased deactivation of the subgenual anterior cingulate (sgACC). Connectivity analysis showed that MDD exhibited altered functional coupling in a distributed network spanning the fusiform face area-lateral prefrontal cortex-sgACC and the sgACC-amygdala pathway. Our results suggest that MDD is related to impairments of processing nuanced facial expressions. Distributed dysfunctional coupling in the face processing network might result in inefficient evidence sampling and inappropriate emotional responses contributing to depressive symptomatology. Our study provides novel insights in the characterization of brain function in adolescents with MDD that strongly emphasize the critical role of aberrant prefrontal-amygdala interactions during emotional face processing.

Environment and body-brain interplay affect inhibition and decision-making

The fine-tuned interplay of brain and body underlies human ability to cope with changes in the internal and external milieus. Previous research showed that cardiac interoceptive changes (e.g., cardiac phase) affect cognitive functions, notably inhibition that is a key element for adaptive behaviour. Here we investigated the influence on cognition of vestibular signal, which provides the brain with sensory information about body position and movement. We used a centrifuge-based design to disrupt vestibular signal in healthy human volunteers while their inhibition and decision-making functions were assessed with the stop-signal paradigm. Participants performed the standard and a novel, sensorial version of the stop-signal task to determine whether disrupted vestibular signal influences cognition as a function of its relevance to the context. First, we showed that disrupted vestibular signal was associated with a larger variability of longest inhibition latencies, meaning that participants were even slower to inhibit in the trials where they had the most difficulty inhibiting. Second, we revealed that processing of bodily information, as required in the sensorial stop-signal task, also led to a larger variability of longest inhibition latencies, which was all the more important when vestibular signal was disrupted. Lastly, we found that such a degraded response inhibition performance was due in part to the acceleration of decision-making process, meaning that participants made a decision more quickly even when strength of sensory evidence was reduced. Taken together, these novel findings provide direct evidence that vestibular signal affects the cognitive functions of inhibition and decision-making.

Right inferior frontal gyrus damage is associated with impaired initiation of inhibitory control, but not its implementation

Inhibitory control is one of the most important control functions in the human brain. Much of our understanding of its neural basis comes from seminal work showing that lesions to the right inferior frontal gyrus (rIFG) increase stop-signal reaction time (SSRT), a latent variable that expresses the speed of inhibitory control. However, recent work has identified substantial limitations of the SSRT method. Notably, SSRT is confounded by trigger failures: stop-signal trials in which inhibitory control was never initiated. Such trials inflate SSRT, but are typically indicative of attentional, rather than inhibitory deficits. Here, we used hierarchical Bayesian modeling to identify stop-signal trigger failures in human rIFG lesion patients, non-rIFG lesion patients, and healthy comparisons. Furthermore, we measured scalp-EEG to detect β-bursts, a neurophysiological index of inhibitory control. rIFG lesion patients showed a more than fivefold increase in trigger failure trials and did not exhibit the typical increase of stop-related frontal β-bursts. However, on trials in which such β-bursts did occur, rIFG patients showed the typical subsequent upregulation of β over sensorimotor areas, indicating that their ability to implement inhibitory control, once triggered, remains intact. These findings suggest that the role of rIFG in inhibitory control has to be fundamentally reinterpreted.

A cognitive model of response omissions in distraction paradigms

The effects of distraction on responses manifest in three ways: prolonged reaction times, and increased error and response omission rates. However, the latter effect is often ignored or assumed to be due to a separate cognitive process. We investigated omissions occurring in two paradigms that manipulated distraction. One required simple stimulus detection of younger participants, the second required choice responses and was completed by both younger and older participants. We fit data from these paradigms with a model that identifies three causes of omissions: two are related to the process of accumulating the evidence on which a response is based: intrinsic omissions (due to between-trial variation in accumulation rates making it impossible to ever reach the evidence threshold) and design omissions (due to response windows that cause slow responses not to be recorded; a third, contaminant omissions, allows for a cause unrelated to the response process. In both data sets systematic differences in omission rates across conditions were accounted for by task-related omissions. Intrinsic omissions played a lesser role than design omissions, even though the presence of design omissions was not evident in descriptive analyses of the data. The model provided an accurate account of all aspects of the detection data and the choice-response data, but slightly underestimated overall omissions in the choice paradigm, particularly in older participants, suggesting that further investigation of contaminant omission effects is needed.

Inhibitory Cognitive Control Allows Automated Advice to Improve Accuracy While Minimizing Misuse

Humans increasingly use automated decision aids. However, environmental uncertainty means that automated advice can be incorrect, creating the potential for humans to act on incorrect advice or to disregard correct advice. We present a quantitative model of the cognitive process by which humans use automation when deciding whether aircraft would violate requirements for minimum separation. The model closely fitted the performance of 24 participants, who each made 2,400 conflict-detection decisions (conflict vs. nonconflict), either manually (with no assistance) or with the assistance of 90% reliable automation. When the decision aid was correct, conflict-detection accuracy improved, but when the decision aid was incorrect, accuracy and response time were impaired. The model indicated that participants integrated advice into their decision process by inhibiting evidence accumulation toward the task response that was incongruent with that advice, thereby ensuring that decisions could not be made solely on automated advice without first sampling information from the task environment.

Top-down Inhibitory Motor Control Is Preserved in Adults with Developmental Coordination Disorder

Two paradigms were employed to disentangle information processing from executive motor inhibition in adults with Developmental Coordination Disorder (DCD). Choice Reaction and Stop Signal Tasks were compared between 13 adults fulfilling DSM-5 DCD criteria and 42 typically developing adults. Additional analyses included 16 probable DCD (pDCD) participants, who had motor difficulties but did not fulfil DSM-5 criteria. Analyses employed frequentist and Bayesian modeling. While DCD+pDCD showed slower reaction times and difficulty initiating Go responses, no impairments in Stop actions were found. These findings indicated no executive deficit in DCD, suggesting that previous results may be explained by inefficient information processing.

Locus coeruleus integrity and the effect of atomoxetine on response inhibition in Parkinson's disease

Cognitive decline is a common feature of Parkinson's disease, and many of these cognitive deficits fail to respond to dopaminergic therapy. Therefore, targeting other neuromodulatory systems represents an important therapeutic strategy. Among these, the locus coeruleus-noradrenaline system has been extensively implicated in response inhibition deficits. Restoring noradrenaline levels using the noradrenergic reuptake inhibitor atomoxetine can improve response inhibition in some patients with Parkinson's disease, but there is considerable heterogeneity in treatment response. Accurately predicting the patients who would benefit from therapies targeting this neurotransmitter system remains a critical goal, in order to design the necessary clinical trials with stratified patient selection to establish the therapeutic potential of atomoxetine. Here, we test the hypothesis that integrity of the noradrenergic locus coeruleus explains the variation in improvement of response inhibition following atomoxetine. In a double-blind placebo-controlled randomized crossover design, 19 patients with Parkinson's disease completed an acute psychopharmacological challenge with 40 mg of oral atomoxetine or placebo. A stop-signal task was used to measure response inhibition, with stop-signal reaction times obtained through hierarchical Bayesian estimation of an ex-Gaussian race model. Twenty-six control subjects completed the same task without undergoing the drug manipulation. In a separate session, patients and controls underwent ultra-high field 7 T imaging of the locus coeruleus using a neuromelanin-sensitive magnetization transfer sequence. The principal result was that atomoxetine improved stop-signal reaction times in those patients with lower locus coeruleus integrity. This was in the context of a general impairment in response inhibition, as patients on placebo had longer stop-signal reaction times compared to controls. We also found that the caudal portion of the locus coeruleus showed the largest neuromelanin signal decrease in the patients compared to controls. Our results highlight a link between the integrity of the noradrenergic locus coeruleus and response inhibition in patients with Parkinson's disease. Furthermore, they demonstrate the importance of baseline noradrenergic state in determining the response to atomoxetine. We suggest that locus coeruleus neuromelanin imaging offers a marker of noradrenergic capacity that could be used to stratify patients in trials of noradrenergic therapy and to ultimately inform personalized treatment approaches.

Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions

This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decision-making and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism. To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanisms-pertinent to a broad range of value-based decisions-and social emotions and cognitions commonly recruited when our decisions involve other people. Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research. This article is categorized under: Economics > Interactive Decision-Making Psychology > Emotion and Motivation Neuroscience > Cognition Economics > Individual Decision-Making.

Modeling the influence of working memory, reinforcement, and action uncertainty on reaction time and choice during instrumental learning

What determines the speed of our decisions? Various models of decision-making have focused on perceptual evidence, past experience, and task complexity as important factors determining the degree of deliberation needed for a decision. Here, we build on a sequential sampling decision-making framework to develop a new model that captures a range of reaction time (RT) effects by accounting for both working memory and instrumental learning processes. The model captures choices and RTs at various stages of learning, and in learning environments with varying complexity. Moreover, the model generalizes from tasks with deterministic reward contingencies to probabilistic ones. The model succeeds in part by incorporating prior uncertainty over actions when modeling RT. This straightforward process model provides a parsimonious account of decision dynamics during instrumental learning and makes unique predictions about internal representations of action values.

Cognitive Control of Working Memory: A Model-Based Approach

Working memory (WM)-based decision making depends on a number of cognitive control processes that control the flow of information into and out of WM and ensure that only relevant information is held active in WM's limited-capacity store. Although necessary for successful decision making, recent work has shown that these control processes impose performance costs on both the speed and accuracy of WM-based decisions. Using the reference-back task as a benchmark measure of WM control, we conducted evidence accumulation modeling to test several competing explanations for six benchmark empirical performance costs. Costs were driven by a combination of processes, running outside of the decision stage (longer non-decision time) and showing the inhibition of the prepotent response (lower drift rates) in trials requiring WM control. Individuals also set more cautious response thresholds when expecting to update WM with new information versus maintain existing information. We discuss the promise of this approach for understanding cognitive control in WM-based decision making.

Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience

In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models are evaluated for convenience, even when other models might be superior. Likelihood-free methods exist but are limited by their computational cost or their restriction to particular inference scenarios. Here, we propose neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations without further training.

Face the (trigger) failure: Trigger failures strongly drive the effect of reward on response inhibition

Response inhibition is typically understood as the ability to stop inappropriate actions and is often investigated using the stop-signal task, in which a go response, triggered by a go signal, has to be inhibited upon the onset of a stop signal. In this task, response inhibition has been formalized as a race between a go and a stop process, which allows the latency of the stop process (stop-signal reaction time; SSRT) to be estimated. Yet, non-parametric SSRT estimations assume that the stop process is initiated without fail, which appears problematic as it is known that participants fail to do so on a subset of trials ("trigger failures"). Importantly, non-parametric methods systematically overestimate SSRT when trigger failures are present, and a growing literature is demonstrating that reported SSRT differences between groups and individuals are also (or rather) driven by differential trigger-failure rates. In the present study, we extend this line of research to a within-individual manipulation, namely the influence of reward on stop performance. We first reanalyzed four data sets of studies that had reported a facilitating effect of stimulus-based reward on SSRTs. Reanalyzing this data, we found that reward decreased the rates of trigger failures. When accounting for these differential trigger-failure rates, the effect of reward on SSRTs (i.e., stop latency) appeared to be virtually abolished. We then conducted a preregistered online follow-up study, implementing a typical block-based reward manipulation. The results of this study indicated simultaneous reward effects on trigger-failure rates and on SSRT. In sum, the present results indicate that trigger failures are an important source of variance in response inhibition, dovetailing with an evolving multicomponential view of response inhibition.

Spatial Attention and Saccade Preparation Both Independently Contribute to the Discrimination of Oblique Orientations

The extent to which the preparation of an eye movement and spatial attention both independently influence performance within the same task has long been debated. In a recent study that combined computational modelling with a dual-task, both saccade preparation and spatial cueing were revealed to separately contribute to the discrimination of targets oriented along the cardinal axis (horizontal and vertical). However, it remains to be seen whether and to what degree the same holds true when different perceptual stimuli are used. In the present study, we combined evidence accumulation modelling with a dual-task paradigm to assess the extent to which both saccade preparation and spatial attention contribute to the discrimination of full contrast targets oriented along the oblique axis (diagonal). The results revealed a separate and quantifiable contribution of both types of orienting to discrimination performance. Comparison of the magnitude of these effects to those obtained for cardinal orientation discrimination revealed the influence of saccade preparation and spatial attention to be six times smaller for oblique orientations. Importantly, the results revealed a separate and quantifiable contribution of both saccade preparation and spatial attention regardless of perceptual stimuli or stimulus contrast.

A new model of decision processing in instrumental learning tasks

Learning and decision-making are interactive processes, yet cognitive modeling of error-driven learning and decision-making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision-making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient design of experiments. Importantly, parameter reviews provide crucial-if not indispensable-information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters (Lee, 2018). In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model (DDM; Ratcliff and McKoon, 2008), the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process.

EEG-based Identification of Evidence Accumulation Stages in Decision-Making

Dating back to the 19th century, the discovery of processing stages has been of great interest to researchers in cognitive science. The goal of this paper is to demonstrate the validity of a recently developed method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA), for discovering stages directly from EEG data, in contrast to classical reaction-time-based methods. To test the validity of stages discovered with the HsMM-MVPA method, we applied it to two relatively simple tasks where the interpretation of processing stages is straightforward. In these visual discrimination EEG data experiments, perceptual processing and decision difficulty were manipulated. The HsMM-MVPA revealed that participants progressed through five cognitive processing stages while performing these tasks. The brain activation of one of those stages was dependent on perceptual processing, whereas the brain activation and the duration of two other stages were dependent on decision difficulty. In addition, evidence accumulation models (EAMs) were used to assess to what extent the results of HsMM-MVPA are comparable to standard reaction-time-based methods. Consistent with the HsMM-MVPA results, EAMs showed that nondecision time varied with perceptual difficulty and drift rate varied with decision difficulty. Moreover, nondecision and decision time of the EAMs correlated highly with the first two and last three stages of the HsMM-MVPA, respectively, indicating that the HsMM-MVPA gives a more detailed description of stages discovered with this more classical method. The results demonstrate that cognitive stages can be robustly inferred with the HsMM-MVPA.