Testing the validity of conflict drift-diffusion models for use in estimating cognitive processes: A parameter-recovery study

Statistical learning shapes pain perception and prediction independently of external cues

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Putting the Spotlight Back Onto the Flanker Task in Autism: Autistic Adults Show Increased Interference from Foils Compared with Non-autistic Adults

Autistic people may have a less focused spotlight of spatial selective attention than non-autistic people, meaning that distracting stimuli are less effectively suppressed. Previous studies using the flanker task have supported this suggestion with observations of increased congruency effects in autistic participants. However, findings across studies have been mixed, mainly based on research in children and on response time measures, which may be influenced by differences in response strategy between autistic and non-autistic people rather than differences in selective attention. In this pre-registered study, 153 autistic and 147 non-autistic adults completed an online flanker task. The aims of this study were to test whether increased congruency effects replicate in autistic adults and to extend previous work by fitting a computational model of spatial selective attention on the flanker task to the data. Congruency effects were increased in the autistic group. The modelling revealed that the interference time from the foils was increased in the autistic group. This suggests that the activation of the foils was increased, meaning suppression was less effective for autistic participants. There were also differences in non-interference parameters between the groups. The estimate of response caution was increased in the autistic group and the estimate of perceptual efficiency was decreased. Together these findings suggest inefficient suppression, response strategy and perceptual processing all contribute to differences in performance on the flanker task between autistic and non-autistic people.

The neutral condition in conflict tasks: On the violation of the midpoint assumption in reaction time trends

Although the relation between congruent and incongruent conditions in conflict tasks has been the primary focus of cognitive control studies, the neutral condition is often set as a baseline directly between the two conditions. However, empirical evidence suggests that the average neutral reaction time (RT) is not placed evenly between the two opposing conditions. This article set out to establish two things: First, to reinforce the informative nature of the neutral condition and second, to highlight how it can be useful for modelling. We explored how RT in the neutral condition of conflict tasks (Stroop, Flanker, and Simon Tasks) deviated from the predictions of current diffusion models. Current diffusion models of conflict tasks predict a neutral RT that is the average of the congruent and incongruent RT, called the midpoint assumption. To investigate this, we first conducted a cursory limited search that recorded the average RT's of conflict tasks with neutral conditions. Upon finding evidence of a midpoint assumption violation which showed a larger disparity between average neutral and incongruent RT, we tested the previously mentioned conflict tasks with two different sets of stimuli to establish the robustness of the effect. The midpoint assumption violation is sometimes inconsistent with the prediction of diffusion models of conflict processing (e.g., the Diffusion Model of Conflict), suggesting possible elaborations of such models.

Humans reconfigure target and distractor processing to address distinct task demands

When faced with distraction, we can focus more on goal-relevant information (targets) or focus less on goal-conflicting information (distractors). How people use cognitive control to distribute attention across targets and distractors remains unclear. We address this question by developing a novel Parametric Attentional Control Task that can "tag" participants' sensitivity to target and distractor information. We use these precise measures of attention to develop a novel process model that can explain how participants control attention toward targets and distractors. Across three experiments, we find that participants met the demands of this task by independently controlling their processing of target and distractor information, exhibiting distinct adaptations to manipulations of incentives and conflict. Whereas incentives preferentially led to target enhancement, conflict in the previous trial preferentially led to distractor suppression. These distinct drivers of control altered sensitivity to targets and distractors early in the trial, promptly followed by reactive reconfiguration toward task-appropriate feature sensitivity. To provide a process-level account of these empirical findings, we develop a novel neural network model of evidence accumulation with attractor dynamics over feature weights that reconfigure target and distractor processing. These results provide a computational account of control reconfiguration that provides new insights into how multivariate attentional signals are optimized to achieve task goals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A revised diffusion model for conflict tasks

The recently developed diffusion model for conflict tasks (DMC) Ulrich et al. (Cognitive Psychology, 78, 148-174, 2015) provides a good account of data from all standard conflict tasks (e.g., Stroop, Simon, and flanker tasks) within a common evidence accumulation framework. A central feature of DMC's processing dynamics is that there is an initial phase of rapid accumulation of distractor evidence that is then selectively withdrawn from the decision mechanism as processing continues. We argue that this assumption is potentially troubling because it could be viewed as implying qualitative changes in the representation of distractor information over the time course of processing. These changes suggest more than simple inhibition or suppression of distractor information, as they involve evidence produced by distractor processing "changing sign" over time. In this article, we (a) develop a revised DMC (RDMC) whose dynamics operate strictly within the limits of inhibition/suppression (i.e., evidence strength can change monotonically, but cannot change sign); (b) demonstrate that RDMC can predict the full range of delta plots observed in the literature (i.e., both positive-going and negative-going); and (c) show that the model provides excellent fits to Simon and flanker data used to benchmark the original DMC at both the individual and group level. Our model provides a novel account of processing differences across Simon and flanker tasks. Specifically, that they differ in how distractor information is processed on congruent trials, rather than incongruent trials: congruent trials in the Simon task show relatively slow attention shifting away from distractor information (i.e., location) while complete and rapid attention shifting occurs in the flanker task. Our new model highlights the importance of considering dynamic interactions between top-down goals and bottom-up stimulus effects in conflict processing.

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.

Applying the Accumulator model to predict driver's reaction time based on looming in approaching and braking conditions

INTRODUCTION

The prediction of when the driver will react to a change in the lead vehicle motion is critical for assessing rear-end crash risk using car-following models. Past studies have assumed constant reaction time and driver's continuous reaction. However, these assumptions are not valid as the driver's reaction time can vary in different car-following situations and the driver does not continuously react to the lead vehicle motion. Thus, this study predicted the driver's reaction time using the Wiedemann car-following model and the Accumulator model. The Accumulator model assumes the driver's start of reaction based on the accumulation of looming and thereby reflects the driver's intermittent reaction.

METHOD

Fifty drivers' behavior was observed using a driving simulator in two scenarios: (1) approach and follow a moving lead vehicle and (2) approach a stopped lead vehicle. The Accumulator model predicted the reaction times based on different looming variables (angular velocity and tau-inverse), lead vehicle type (car and truck), and lead vehicle brake lights (on or off).

RESULTS

The Accumulator model showed lower prediction errors of the reaction time than the Wiedemann model, which assumes reaction based on the fixed looming threshold. The Accumulator model predicted the reaction times more accurately when it was calibrated with the angular velocity due to width and height of lead vehicles. Moreover, the Accumulator model with tau-inverse produced the smallest prediction error of reaction times among different Accumulator models and the Wiedemann model when lead vehicle brake lights were on.

CONCLUSIONS

This study demonstrates that the Accumulator model is a promising method of predicting the driver's reaction time in car-following situations, which affects rear-end crash risk.

PRACTICAL APPLICATIONS

The Accumulator model can be incorporated into a car-following model for the prediction of reaction times and can estimate the rear-end collision risk of vehicles more accurately.

The Bayesian Mutation Sampler Explains Distributions of Causal Judgments

One consistent finding in the causal reasoning literature is that causal judgments are rather variable. In particular, distributions of probabilistic causal judgments tend not to be normal and are often not centered on the normative response. As an explanation for these response distributions, we propose that people engage in 'mutation sampling' when confronted with a causal query and integrate this information with prior information about that query. The Mutation Sampler model (Davis & Rehder, 2020) posits that we approximate probabilities using a sampling process, explaining the average responses of participants on a wide variety of tasks. Careful analysis, however, shows that its predicted response distributions do not match empirical distributions. We develop the Bayesian Mutation Sampler (BMS) which extends the original model by incorporating the use of generic prior distributions. We fit the BMS to experimental data and find that, in addition to average responses, the BMS explains multiple distributional phenomena including the moderate conservatism of the bulk of responses, the lack of extreme responses, and spikes of responses at 50%.

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.

A Diffusion Model Analysis of Object-Based Selective Attention in the Eriksen Flanker Task

Selective attention might be space-, feature-, and/or object-based. Clear support for the involvement of an object-based mechanism is rather scarce, possibly because the predictions of models from these different classes often overlap. Yet, only object-based models can account for a larger congruency effect (CE) in the Eriksen flanker task when flankers are more (vs. less) strongly grouped to the target, but spacing and other response-irrelevant features of target and flankers are held constant. Exactly this was observed by Kramer and Jacobson (1991). So far, this theoretically relevant finding has not been replicated closely. We replicated the finding in two web-based experiments. Specifically, CEs were larger when flanker lines were connected to the central target line (vs. to outer neutral lines). We also successfully fitted the Diffusion Model for Conflict tasks (DMC) to the experimental data. Critically, diffusion modeling (DMC) and distributional analyses (delta functions) revealed that object membership primarily affected target processing strength rather than strength or timing of flanker processing. This challenges the prominent attentional spreading (sensory enhancement) account of object-based selective attention and motivates an alternative target attenuation account.

Episodic Memory Retrieval Affects the Onset and Dynamics of Evidence Accumulation during Value-based Decisions

In neuroeconomics, there is much interest in understanding simple value-based choices where agents choose between visually presented goods, comparable to a shopping scenario in a supermarket. However, many everyday decisions are made in the physical absence of the considered goods, requiring agents to recall information about the goods from memory. Here, we asked whether and how this reliance on an internal memory representation affects the temporal dynamics of decision making on a cognitive and neural level. Participants performed a remember-and-decide task in which they made simple purchasing decisions between money offers and snack items while undergoing EEG. Snack identity was presented either visually (value trials) or had to be recalled from memory (memory trials). Behavioral data indicated comparable choice consistency across both trial types, but considerably longer RTs in memory trials. Drift-diffusion modeling suggested that this RT difference was because of longer nondecision time of decision processes as well as altered evidence accumulation dynamics (lower accumulation rate and higher decision threshold). The nondecision time effect was supported by a delayed onset of the lateralized readiness potential. These results show that both decision and nondecision processes are prolonged when participants need to resort to internal memory representations during value-based decisions.

Performance-contingent reward increases the use of congruent distracting information

In conflict tasks like the Simon task, participants are instructed to respond to a task-relevant target dimension while ignoring additional distracting information. In the Simon task the distracting spatial information can be congruent or incongruent with the task-relevant target information, causing a congruency effect. As seen in the proportion congruency effect and the congruency sequence effect, this congruency effect is larger in mostly congruent blocks and following congruent trials, respectively. Common theories suggest that when the proportion of incongruent trials is high or after an incongruent trial, focus on the task-relevant target information is increased and distracting information is inhibited. In two experiments, we investigated how reward modulates these phenomena. Specifically, performance-contingent reward - but not non-contingent reward - increased the usage of the distracting information in mostly congruent blocks or following congruent trials, while the adaptation to incongruency (i.e., mostly incongruent blocks or preceding incongruent trials) was the same in all conditions. Additional diffusion model analyses found that this effect of performance-contingent reward was captured by the drift rate parameter. These results suggest an increased focus on the target information by incongruent trials independent from reward, while the adaptation to (mostly) congruent trials characterized by increased usage of distracting information can be motivationally boosted. That is, performance-contingent reward increases the use of congruent distracting information beyond a mere relaxation of the increased target-focus following (mostly) congruent trials.

The role of task-relevant and task-irrelevant information in congruency sequence effects: Applying the diffusion model for conflict tasks

In conflict tasks, such as the Simon, Eriksen flanker, or Stroop task, the congruency effect is often reduced after an incongruent compared to a congruent trial: the congruency sequence effect (CSE). It was suggested that the CSE may reflect increased processing of task-relevant information and/or suppression of task-irrelevant information after experiencing an incongruent relative to a congruent trial. In the present study, we contribute to this discussion by applying the Diffusion Model for Conflict tasks (DMC) framework in the context of CSEs to flanker and Simon tasks. We argue that DMC independently models the task-relevant and task-irrelevant information and thus is a first good candidate for disentangling their unique contributions. As a first approach, we fitted DMC conjointly or separately to previously congruent or incongruent trials, using four empirical flanker and two Simon data sets. For the flanker task, we fitted the classical DMC version. For the Simon task, we fitted a generalized DMC version which allows the task-irrelevant information to undershoot when swinging back to zero. After considering the model fits, we present a second approach, where we implemented a cognitive control mechanism to simulate the influence of increased processing of task-relevant information or increased suppression of task-irrelevant information. Both approaches demonstrate that the suppression of task-irrelevant information is essential to create the typical CSE pattern. Increased processing of task-relevant information, however, could rarely describe the CSE accurately.

Reliability as Lindley Information

This paper introduces a definition of reliability based on Lindley information, which is the mutual information between an observed measure and latent attribute. This definition reduces to the traditional definition of reliability in the case of normal variables, but can be applied to any joint distribution of observed and latent variables. Importantly, unlike traditional definitions of reliability, this formulation of reliability applies to vector- or matrix-valued estimates and summaries of responses, and therefore generalizes reliability to sets of scores and estimates in addition to individual scores and estimates. This formulation also leads to new bounds for reliability, as well as newly reported relationships between reliability and the traditional Fisher information function familiar in item response theory literature. This form of reliability can be estimated using formulae, or methods used in Bayesian inference such as Markov Chain Monte Carlo (MCMC) depending on the case. Examples based on well-studied datasets are provided, as well as applications to drift-diffusion modeling and randomly-varying intraindividual covariance structures.

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.

A diffusion model for the congruency sequence effect

Two-choice reaction tasks for which stimuli differ on irrelevant and relevant dimensions (e.g., Simon, flanker, and Stroop tasks) show congruency effects. The diffusion model for conflict tasks (DMC) has provided a quantitative account of the mechanisms underlying decisions in such conflict tasks, but it has not been applied to the congruency sequence effect (CSE) for which the congruency on the prior trial influences performance on the current trial. The present study expands analysis of the reaction time (RT) distributions reflected by delta plots to the CSE, and then extends the DMC to simulate the results. With increasing RT: (1) the spatial Simon effect was almost unchanged following congruent trials but initially became smaller and finally reversed following incongruent trials; (2) the arrow-based Simon effects increased following both congruent and incongruent trials, but more so for the former than the latter; (3) the flanker congruency effect varied quadratically following congruent trials but increased linearly following incongruent trials. These results were modeled by the CSE-DMC, extended from the DMC with two additional assumptions: (1) feature integration influences only the controlled processes; (2) following incongruent trials, the automatic process is weakened. The results fit better with the CSE-DMC than with two variants that separately had only one of the two additional assumptions. These findings indicate that the CSEs for different conflict tasks have disparate RT distributions and that these disparities are likely due to the controlled and automatic processes being influenced differently for each trial sequence.

Strategy and processing speed eclipse individual differences in control ability in conflict tasks

Response control or inhibition is one of the cornerstones of modern cognitive psychology, featuring prominently in theories of executive functioning and impulsive behavior. However, repeated failures to observe correlations between commonly applied tasks have led some theorists to question whether common response conflict processes even exist. A challenge to answering this question is that behavior is multifaceted, with both conflict and nonconflict processes (e.g., strategy, processing speed) contributing to individual differences. Here, we use a cognitive model to dissociate these processes; the diffusion model for conflict tasks (Ulrich et al., 2015). In a meta-analysis of fits to seven empirical datasets containing combinations of the flanker, Simon, color-word Stroop, and spatial Stroop tasks, we observed weak (r < .05) zero-order correlations between tasks in parameters reflecting conflict processing, seemingly challenging a general control construct. However, our meta-analysis showed consistent positive correlations in parameters representing processing speed and strategy. We then use model simulations to evaluate whether correlations in behavioral costs are diagnostic of the presence or absence of common mechanisms of conflict processing. We use the model to impose known correlations for conflict mechanisms across tasks, and we compare the simulated behavior to simulations when there is no conflict correlation across tasks. We find that correlations in strategy and processing speed can produce behavioral correlations equal to, or larger than, those produced by correlated conflict mechanisms. We conclude that correlations between conflict tasks are only weakly informative about common conflict mechanisms if researchers do not control for strategy and processing speed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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

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

Conflict and competition between model-based and model-free control

A large literature has accumulated suggesting that human and animal decision making is driven by at least two systems, and that important functions of these systems can be captured by reinforcement learning algorithms. The "model-free" system caches and uses stimulus-value or stimulus-response associations, and the "model-based" system implements more flexible planning using a model of the world. However, it is not clear how the two systems interact during deliberation and how a single decision emerges from this process, especially when they disagree. Most previous work has assumed that while the systems operate in parallel, they do so independently, and they combine linearly to influence decisions. Using an integrated reinforcement learning/drift-diffusion model, we tested the hypothesis that the two systems interact in a non-linear fashion similar to other situations with cognitive conflict. We differentiated two forms of conflict: action conflict, a binary state representing whether the systems disagreed on the best action, and value conflict, a continuous measure of the extent to which the two systems disagreed on the difference in value between the available options. We found that decisions with greater value conflict were characterized by reduced model-based control and increased caution both with and without action conflict. Action conflict itself (the binary state) acted in the opposite direction, although its effects were less prominent. We also found that between-system conflict was highly correlated with within-system conflict, and although it is less clear a priori why the latter might influence the strength of each system above its standard linear contribution, we could not rule it out. Our work highlights the importance of non-linear conflict effects, and provides new constraints for more detailed process models of decision making. It also presents new avenues to explore with relation to disorders of compulsivity, where an imbalance between systems has been implicated.

Evidence integration and decision confidence are modulated by stimulus consistency

Evidence integration is a normative algorithm for choosing between alternatives with noisy evidence, which has been successful in accounting for vast amounts of behavioural and neural data. However, this mechanism has been challenged by non-integration heuristics, and tracking decision boundaries has proven elusive. Here we first show that the decision boundaries can be extracted using a model-free behavioural method termed decision classification boundary, which optimizes choice classification based on the accumulated evidence. Using this method, we provide direct support for evidence integration over non-integration heuristics, show that the decision boundaries collapse across time and identify an integration bias whereby incoming evidence is modulated based on its consistency with preceding information. This consistency bias, which is a form of pre-decision confirmation bias, was supported in four cross-domain experiments, showing that choice accuracy and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, the consistency bias fosters performance by enhancing robustness to integration noise.

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

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

Different Forms of Variability Could Explain a Difference Between Human and Rat Decision Making

When observers make rapid, difficult perceptual decisions, their response time is highly variable from trial to trial. In a visual motion discrimination task, it has been reported that human accuracy declines with increasing response time, whereas rat accuracy increases with response time. This is of interest because different mathematical theories of decision-making differ in their predictions regarding the correlation of accuracy with response time. On the premise that perceptual decision-making mechanisms are likely to be conserved among mammals, we seek to unify the rodent and primate results in a common theoretical framework. We show that a bounded drift diffusion model (DDM) can explain both effects with variable parameters: trial-to-trial variability in the starting point of the diffusion process produces the pattern typically observed in rats, whereas variability in the drift rate produces the pattern typically observed in humans. We further show that the same effects can be produced by deterministic biases, even in the absence of parameter stochasticity or parameter change within a trial.

mixtur: An R package for designing, analysing, and modelling continuous report visual short-term memory studies

Visual short-term memory (vSTM) is often measured via continuous-report tasks whereby participants are presented with stimuli that vary along a continuous dimension (e.g., colour) with the goal of memorising the stimulus features. At test, participants are probed to recall the feature value of one of the memoranda in a continuous manner (e.g., by clicking on a colour wheel). The angular deviation between the participant response and the true feature value provides an estimate of recall precision. Two prominent models of performance on such tasks are the two- and three-component mixture models (Bays et al., Journal of Vision, 9(10), Article 7, 2009; Zhang and Luck, Nature, 453(7192), 233–235, 2008). Both models decompose participant responses into probabilistic mixtures of: (1) responses to the true target value based on a noisy memory representation; (2) random guessing when memory fails. In addition, the three-component model proposes (3) responses to a non-target feature value (i.e., binding errors). Here we report the development of mixtur, an open-source package written for the statistical programming language R that facilitates the fitting of the two- and three-component mixture models to continuous report data. We also conduct simulations to develop recommendations for researchers on trial numbers, set sizes, and memoranda similarity, as well as parameter recovery and model recovery. In the Discussion, we discuss how mixtur can be used to fit the slots and the slots-plus-averaging models, as well as how mixtur can be extended to fit explanatory models of visual short-term memory. It is our hope that mixtur will lower the barrier of entry for utilising mixture modelling.

A Bayesian inference model for metamemory

The dual-basis theory of metamemory suggests that people evaluate their memory performance based on both processing experience during the memory process and their prior beliefs about overall memory ability. However, few studies have proposed a formal computational model to quantitatively characterize how processing experience and prior beliefs are integrated during metamemory monitoring. Here, we introduce a Bayesian inference model for metamemory (BIM) which provides a theoretical and computational framework for the metamemory monitoring process. BIM assumes that when people evaluate their memory performance, they integrate processing experience and prior beliefs via Bayesian inference. We show that BIM can be fitted to recall or recognition tasks with confidence ratings on either a continuous or discrete scale. Results from data simulation indicate that BIM can successfully recover a majority of generative parameter values, and demonstrate a systematic relationship between parameters in BIM and previous computational models of metacognition such as the stochastic detection and retrieval model (SDRM) and the meta-d' model. We also show examples of fitting BIM to empirical data sets from several experiments, which suggest that the predictions of BIM are consistent with previous studies on metamemory. In addition, when compared with SDRM, BIM could more parsimoniously account for the data of judgments of learning (JOLs) and memory performance from recall tasks. Finally, we discuss an extension of BIM which accounts for belief updating, and conclude with a discussion of how BIM may benefit metamemory research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Mapping effects in choice-response and go/no-go variants of the lexical decision task: A case for polarity correspondence

Previous research has shown that responses to words are faster and more accurate in the go/no-go version of the lexical decision task (LDT) than in the choice-response version. This finding has been attributed to reduced response-selection demands in the go/no-go task. Here, we test an alternative account assuming similar response-selection demands in the two tasks, but an additional impact of polarity correspondence between stimuli and responses in the go/no-go task. Positive and negative polarities have been described as a frequent characteristic of binary stimulus and response dimensions. Only for the go/no-go version of the LDT, an apparent polarity difference between go and no-go responses exists, with go responses having the same polarity as words and no-go responses the same polarity as nonwords. Thus, compared with the choice-response LDT, in the go/no-go LDT, go responses to words should be facilitated by polarity correspondence, and go responses to nonwords should be inhibited by polarity noncorrespondence. In this study, each participant performed a go/no-go LDT and a choice-response LDT. Responses to words were faster and more accurate than responses to nonwords, and-consistent with the alternative account-this difference was larger in the go/no-go LDT than in the choice-response LDT. An analysis of performance by means of sequential-sampling models that take into account a decaying influence of irrelevant stimulus features supported the effect of polarity correspondence in the go/no-go LDT. This analysis suggested an effect in the choice-response LDT as well, though of a smaller size and a faster decay.

Delta plots for conflict tasks: An activation-suppression race model

We describe a mathematically simple yet precise model of activation suppression that can explain the negative-going delta plots often observed in standard Simon tasks. The model postulates a race between the identification of the relevant stimulus attribute and the suppression of irrelevant location-based activation, with the irrelevant activation only having an effect if the irrelevant activation is still present at the moment when central processing of the relevant attribute starts. The model can be fitted by maximum likelihood to observed distributions of RTs in congruent and incongruent trials, and it provides good fits to two previously-reported data sets with plausible parameter values. R and MATLAB software for use with the model is provided.

Tracking dynamic adjustments to decision making and performance monitoring processes in conflict tasks

How we exert control over our decision-making has been investigated using conflict tasks, which involve stimuli containing elements that are either congruent or incongruent. In these tasks, participants adapt their decision-making strategies following exposure to incongruent stimuli. According to conflict monitoring accounts, conflicting stimulus features are detected in medial frontal cortex, and the extent of experienced conflict scales with response time (RT) and frontal theta-band activity in the Electroencephalogram (EEG). However, the consequent adjustments to decision processes following response conflict are not well-specified. To characterise these adjustments and their neural implementation we recorded EEG during a modified Flanker task. We traced the time-courses of performance monitoring processes (frontal theta) and multiple processes related to perceptual decision-making. In each trial participants judged which of two overlaid gratings forming a plaid stimulus (termed the S1 target) was of higher contrast. The stimulus was divided into two sections, which each contained higher contrast gratings in either congruent or incongruent directions. Shortly after responding to the S1 target, an additional S2 target was presented, which was always congruent. Our EEG results suggest enhanced sensory evidence representations in visual cortex and reduced evidence accumulation rates for S2 targets following incongruent S1 stimuli. Results of a follow-up behavioural experiment indicated that the accumulation of sensory evidence from the incongruent (i.e. distracting) stimulus element was adjusted following response conflict. Frontal theta amplitudes positively correlated with RT following S1 targets (in line with conflict monitoring accounts). Following S2 targets there was no such correlation, and theta amplitude profiles instead resembled decision evidence accumulation trajectories. Our findings provide novel insights into how cognitive control is implemented following exposure to conflicting information, which is critical for extending conflict monitoring accounts.

A diffusion model analysis of belief bias: Different cognitive mechanisms explain how cognitive abilities and thinking styles contribute to conflict resolution in reasoning

Recent results have challenged the widespread assumption of dual process models of belief bias that sound reasoning relies on slow, careful reflection, whereas biased reasoning is based on fast intuition. Instead, parallel process models of reasoning suggest that rule- and belief-based problem features are processed in parallel and that reasoning problems that elicit a conflict between rule- and belief-based problem features may also elicit more than one Type 1 response. This has important implications for individual-differences research on reasoning, because rule-based responses by certain individuals may reflect that these individuals were either more likely to give a rule-based default response or that they successfully inhibited and overrode a belief-based default response. In two studies, we used the diffusion model to describe decision making in a transitive reasoning task. In Study 1, 41 participants were asked to evaluate conclusions based on their validity. In Study 2, 133 participants evaluated conclusions based on their validity or believability. We tested which diffusion model parameters reflected conflict resolution and related those model parameters to individual differences in cognitive abilities and thinking styles. Individual differences in need for cognition predicted successful conflict resolution under logic instruction, which suggests that a disposition to engage in reflective thinking facilitates the inhibition and override of Type 1 responses. Intelligence, however, was negatively related to successful conflict resolution under belief instruction, which suggests that individuals with high cognitive abilities quickly generated a higher-level logical response that interfered with their ability to evaluate lower-level intrinsic problem features. Taken together, this double dissociation indicates that cognitive abilities and thinking styles affect the processing of conflict information through different mechanisms and at different stages: Greater cognitive abilities facilitate the efficient creation of decoupled problem representations, whereas a greater disposition to engage in critical thinking facilitates the detection and override of Type 1 responses.

Effortful control is associated with executive attention: A computational study

INTRODUCTION

Effortful control (EC) is the self-regulatory aspect of temperament that is thought to reflect the efficiency of executive attention (EA). Findings on relationship between EC and performance on EA tasks among adults are still contradictory. This study used a computational approach to clarify whether greater self-reported EC reflects better EA.

METHODS

Four hundred twenty-seven healthy subjects completed the Adult Temperament Questionnaires and the Attention Network Task-revised, a conflict resolution task that gauges EA as the flanker effect (FE), that is, the difference in performances between incongruent and congruent trials. Here we also employed a drift-diffusion model in which parameters reflecting the actual decisional process (drift rate) and the extra-decisional time are extracted for congruent and incongruent trials.

RESULTS

EC was not correlated with the FE computed with the classic approach, but correlated positively with drift rate for the incongruent trials, even when controlling for the drift rate in the congruent condition and the extra-decisional time in the incongruent condition.

CONCLUSION

This study demonstrates an association between self-reported EC and EA among adults. Specifically, EC is not associated with overall response facilitation but specifically with a greater ability to make goal-oriented decisions when facing conflicting information.

Self-reported impulsivity does not predict response caution

The broad construct of impulsivity is one that spans both personality and cognitive ability. Despite a common overarching construct, previous research has found no relationship between self-report measures of impulsivity and people's ability to inhibit pre-potent responses. Here, we use evidence accumulation models of choice reaction time tasks to extract a measure of "response caution" (boundary separation) and examine whether this correlates with self-reported impulsivity as measured by the UPPS-P questionnaire. Response caution reflects whether an individual makes decisions based on more (favouring accuracy) or less (favouring speed) evidence. We reasoned that this strategic dimension of behaviour is conceptually closer to the tendencies that self-report impulsivity measures probe than what is traditional measured by inhibition tasks. In a meta-analysis of five datasets (total N = 296), encompassing 19 correlations per subscale, we observe no evidence that response caution correlates with self-reported impulsivity. Average correlations between response caution and UPPS-P subscales ranged from rho = -0.02 to -0.04. While the construct of response caution has demonstrated value in understanding individual differences in cognition, brain functioning and aging; the factors underlying what has been called "impulsive information processing" appear to be distinct from the concept of impulsivity derived from self-report.

Quantifying the benefits of using decision models with response time and accuracy data

Response time and accuracy are fundamental measures of behavioral science, but discerning participants' underlying abilities can be masked by speed-accuracy trade-offs (SATOs). SATOs are often inadequately addressed in experiment analyses which focus on a single variable or which involve a suboptimal analytic correction. Models of decision-making, such as the drift diffusion model (DDM), provide a principled account of the decision-making process, allowing the recovery of SATO-unconfounded decision parameters from observed behavioral variables. For plausible parameters of a typical between-groups experiment, we simulate experimental data, for both real and null group differences in participants' ability to discriminate stimuli (represented by differences in the drift rate parameter of the DDM used to generate the simulated data), for both systematic and null SATOs. We then use the DDM to fit the generated data. This allows the direct comparison of the specificity and sensitivity for testing of group differences of different measures (accuracy, reaction time, and the drift rate from the model fitting). Our purpose here is not to make a theoretical innovation in decision modeling, but to use established decision models to demonstrate and quantify the benefits of decision modeling for experimentalists. We show, in terms of reduction of required sample size, how decision modeling can allow dramatically more efficient data collection for set statistical power; we confirm and depict the non-linear speed-accuracy relation; and we show how accuracy can be a more sensitive measure than response time given decision parameters which reasonably reflect a typical experiment.

Shyness and perceived monitoring by peers affect children's performance in a divided attention task

To engage effectively in a dynamic social world, children must be prepared to process incoming information quickly and efficiently. For some, the perception that one may be evaluated by peers may directly affect how they attend to and engage with the world around them. The current study examined how children's performance on a hierarchical figures task varies under perceived social and nonsocial conditions as a function of temperamental shyness. A total of 78 8-year-olds completed a self-report measure of shyness and two blocks of a divided attention task in which they identified targets appearing at the global or local level of a hierarchical figure. Children completed one block under standard laboratory conditions (Baseline condition) and completed the other block under the impression that their performance was being recorded and would be shown to other children of the same age (Social Monitoring condition). Results showed that children were faster and more accurate when targets appeared at the global level and when targets remained at the same level across trials. Furthermore, as shyness increased, children responded slower in the Social Monitoring condition relative to the Baseline condition. Notably, these changes in response time were not reflected by commensurate increases in accuracy. Potential processes and motivations underlying these differences in performance, as well as implications for children in real-world situations, are discussed.

Serial Recall Predicts Vocoded Sentence Recognition Across Spectral Resolutions

Purpose The goal of this study was to determine how various aspects of cognition predict speech recognition ability across different levels of speech vocoding within a single group of listeners. Method We tested the ability of young adults (N = 32) with normal hearing to recognize Perceptually Robust English Sentence Test Open-set (PRESTO) sentences that were degraded with a vocoder to produce different levels of spectral resolution (16, eight, and four carrier channels). Participants also completed tests of cognition (fluid intelligence, short-term memory, and attention), which were used as predictors of sentence recognition. Sentence recognition was compared across vocoder conditions, predictors were correlated with individual differences in sentence recognition, and the relationships between predictors were characterized. Results PRESTO sentence recognition performance declined with a decreasing number of vocoder channels, with no evident floor or ceiling performance in any condition. Individual ability to recognize PRESTO sentences was consistent relative to the group across vocoder conditions. Short-term memory, as measured with serial recall, was a moderate predictor of sentence recognition (ρ = 0.65). Serial recall performance was constant across vocoder conditions when measured with a digit span task. Fluid intelligence was marginally correlated with serial recall, but not sentence recognition. Attentional measures had no discernible relationship to sentence recognition and a marginal relationship with serial recall. Conclusions Verbal serial recall is a substantial predictor of vocoded sentence recognition, and this predictive relationship is independent of spectral resolution. In populations that show variable speech recognition outcomes, such as listeners with cochlear implants, it should be possible to account for the independent effects of spectral resolution and verbal serial recall in their speech recognition ability. Supplemental Material https://doi.org/10.23641/asha.12021051.

Double responding: A new constraint for models of speeded decision making

Evidence accumulation models (EAMs) have become the dominant models of speeded decision making, which are able to decompose choices and response times into cognitive parameters that drive the decision process. Several models within the EAM framework contain fundamentally different ideas of how the decision making process operates, though previous assessments have found that these models display a high level of mimicry, which has hindered the ability of researchers to contrast these different theoretical viewpoints. Our study introduces a neglected phenomenon that we term "double responding", which can help to further constrain these models. We show that double responding produces several interesting benchmarks, and that the predictions of different EAMs can be distinguished in standard experiment paradigms when they are constrained to account for the choice response time distributions and double responding behaviour in unison. Our findings suggest that lateral inhibition (e.g., the leaky-competing accumulator) provides models with a universal ability to make accurate predictions for these data. Furthermore, only models containing feed-forward inhibition (e.g., the diffusion model) performed poorly under both of our proposed extensions of the standard EAM framework to double responding, suggesting a general inability of feed-forward inhibition to accurately predict these data. We believe that our study provides an important step forward in further constraining models of speeded decision making, though additional research on double responding is required before broad conclusions are made about which models provide the best explanation of the underlying decision-making process.

Decomposing Simon task BOLD activation using a drift-diffusion model framework

The Simon effect is observed in spatial conflict tasks where the response time of subjects is increased if stimuli are presented in a lateralized manner so that they are incongruous with the response information that they represent symbolically. Previous studies have used fMRI to investigate this phenomenon, and while some have been driven by considerations of an underlying model, none have attempted to directly tie model and BOLD response together. It is likely that this is due to Simon models having been predominantly descriptive of the phenomenon rather than capturing the full spectrum of behavior at the level of individual subjects. Sequential sampling models (SSM) which capture full response distributions for correct and incorrect responses have recently been extended to capture conflict tasks. In this study we use our freely available framework for fitting and comparing non-standard SSMs to fit the Simon effect SSM (SE-SSM) to behavioral data. This model extension includes specific estimates of automatic response bias and a conflict counteraction parameter to individual subject behavioral data. We apply this approach in order to investigate whether our task specific model parameters have a correlate in BOLD response. Under the assumption that the SE-SSM reflects aspects of neural processing in this task, we go on to examine the BOLD correlates with the within trial expected decision-variable. We find that the SE-SSM captures the behavioral data and that our two conflict specific model parameters have clear across subject BOLD correlates, while other model parameters, as well as more standard behavioral measures do not. We also find that examining BOLD in terms of the expected decision-variable leads to a specific pattern of activation that would not be otherwise possible to extract.

Neurocognitive Psychometrics of Intelligence: How Measurement Advancements Unveiled the Role of Mental Speed in Intelligence Differences

More intelligent individuals typically show faster reaction times. However, individual differences in reaction times do not represent individual differences in a single cognitive process but in multiple cognitive processes. Thus, it is unclear whether the association between mental speed and intelligence reflects advantages in a specific cognitive process or in general processing speed. In this article, we present a neurocognitive-psychometrics account of mental speed that decomposes the relationship between mental speed and intelligence. We summarize research employing mathematical models of cognition and chronometric analyses of neural processing to identify distinct stages of information processing strongly related to intelligence differences. Evidence from both approaches suggests that the speed of higher-order processing is greater in smarter individuals, which may reflect advantages in the structural and functional organization of brain networks. Adopting a similar neurocognitive-psychometrics approach for other cognitive processes associated with intelligence (e.g., working memory or executive control) may refine our understanding of the basic cognitive processes of intelligence.

Improving parameter recovery for conflict drift-diffusion models

Several drift-diffusion models have been developed to account for the performance in conflict tasks. Although a common characteristic of these models is that the drift rate changes within a trial, their architecture is rather different. Comparative studies usually examine which model fits the data best. However, a good fit does not guarantee good parameter recovery, which is a necessary condition for a valid interpretation of any fit. A recent simulation study revealed that recovery performance varies largely between models and individual parameters. Moreover, recovery was generally not very impressive. Therefore, the aim of the present study was to introduce and test an improved fit procedure. It is based on a grid search for determining the initial parameter values and on a specific criterion for assessing the goodness of fit. Simulations show that not only the fit performance but also parameter recovery improved substantially by applying this procedure, compared to the standard one. The improvement was largest for the most complex model.

Test-retest reliability of the simon task: a short version proposal

Background: The standard version of the Simon task (an instrument for assessing attentional demands, inhibitory control and the perception-action cycle) comprises a total of 168 trials. Different versions of this task are used in the literature, but it is not clear whether the Simon effect can be found using brief versions of the test.Purpose: This study aims to investigate the presence of the Simon effect and its test-retest reliability in a brief version of 28 trials of the Simon task.Material and Methods: Ninety-two (92) subjects between the ages of 18-30 participated in this study. Participants performed two sessions (test and retest) in which the brief battery of the Simon task was used. Latency (reaction time - RT) and the accuracy (number of errors) were measured for the two typical conditions of the test (corresponding and non-corresponding).Results: A significant interaction (two-way ANOVA) between condition and moment (test vs. retest) was found for RT. Main effects were observed for both conditions (corresponding vs. non- corresponding) and moment (test vs. retest). A good measurement of reliability (α Cronbach = 0.883) was also observed.Conclusions: The 28-trial battery of the Simon Task seems to be efficient for eliciting the Simon Effect and it can therefore be considered reliable.

Mild acute stress improves response speed without impairing accuracy or interference control in two selective attention tasks: Implications for theories of stress and cognition

Acute stress is generally thought to impair performance on tasks thought to rely on selective attention. This effect has been well established for moderate to severe stressors, but no study has examined how a mild stressor-the most common type of stressor-influences selective attention. In addition, no study to date has examined how stress influences the component processes involved in overall selective attention task performance, such as controlled attention, automatic attentional activation, decision-making, and motor abilities. To address these issues, we randomly assigned 107 participants to a mild acute stress or control condition. As expected, the mild acute stress condition showed a small but significant increase in cortisol relative to the control condition. Following the stressor, we assessed attention with two separate flanker tasks. One of these tasks was optimized to investigate component attentional processes using computational cognitive modeling, whereas the other task employed mouse-tracking to illustrate how response conflict unfolded over time. The results for both tasks showed that mild acute stress decreased response time (i.e., increased response speed) without influencing accuracy or interference control. Further, computational modeling and mouse-tracking analyses indicated that these effects were due to faster motor action execution time for chosen actions. Intriguingly, however, cortisol responses were unrelated to any of the observed effects of mild stress. These results have implications for theories of stress and cognition, and highlight the importance of considering motor processes in understanding the effects of stress on cognitive task performance.

Slow and steady? Strategic adjustments in response caution are moderately reliable and correlate across tasks

Speed-accuracy trade-offs are often considered a confound in speeded choice tasks, but individual differences in strategy have been linked to personality and brain structure. We ask whether strategic adjustments in response caution are reliable, and whether they correlate across tasks and with impulsivity traits. In Study 1, participants performed Eriksen flanker and Stroop tasks in two sessions four weeks apart. We manipulated response caution by emphasising speed or accuracy. We fit the diffusion model for conflict tasks and correlated the change in boundary (accuracy - speed) across session and task. We observed moderate test-retest reliability, and medium to large correlations across tasks. We replicated this between-task correlation in Study 2 using flanker and perceptual decision tasks. We found no consistent correlations with impulsivity. Though moderate reliability poses a challenge for researchers interested in stable traits, consistent correlation between tasks indicates there are meaningful individual differences in the speed-accuracy trade-off.

Modeling the effects of methylphenidate on interference and evidence accumulation processes using the conflict linear ballistic accumulator

RATIONALE

Although methylphenidate and other stimulants have been demonstrated to improve task performance across a variety of domains, a computationally rigorous account of how these drugs alter cognitive processing remains elusive. Recent applications of mathematical models of cognitive processing and electrophysiological methods to this question have suggested that stimulants improve the integrity of evidence accumulation processes for relevant choices, potentially through catecholaminergic modulation of neural signal-to-noise ratios. However, this nascent line of work has thus far been limited to simple perceptual tasks and has largely omitted more complex conflict paradigms that contain experimental manipulations of specific top-down interference resolution processes.

OBJECTIVES AND METHODS

To address this gap, this study applied the conflict linear ballistic accumulator (LBA), a newly proposed model designed for conflict tasks, to data from healthy adults who performed the Multi-Source Interference Task (MSIT) after acute methylphenidate or placebo challenge.

RESULTS

Model-based analyses revealed that methylphenidate improved performance by reducing individuals' response thresholds and by enhancing evidence accumulation processes across all task conditions, either by improving the quality of evidence or by reducing variability in accumulation processes. In contrast, the drug did not reduce bottom-up interference or selectively facilitate top-down interference resolution processes probed by the experimental conflict manipulation.

CONCLUSIONS

Enhancement of evidence accumulation is a biologically plausible and task-general mechanism of stimulant effects on cognition. Moreover, the assumption that methylphenidate's effects on behavior are only visible with complex executive tasks may be misguided.

Modulation of Conflict Processing by Theta-Range tACS over the Dorsolateral Prefrontal Cortex

Behavioral response conflict arises in the color-word Stroop task and triggers the cognitive control network. Midfrontal theta-band oscillations correlate with adaptive control mechanisms during and after conflict resolution. In order to prove causality, in two experiments, we applied transcranial alternating current stimulation (tACS) at 6 Hz to the dorsolateral prefrontal cortex (DLPFC) during Stroop task performance. Sham stimulation served as a control in both experiments; 9.7 Hz tACS served as a nonharmonic alpha band control in the second experiment. We employed generalized linear mixed models for analysis of behavioral data. Accuracy remained unchanged by any type of active stimulation. Over both experiments, the Stroop effect (response time difference between congruent and incongruent trials) was reduced by 6 Hz stimulation as compared to sham, mainly in trials without prior conflict adaptation. Alpha tACS did not modify the Stroop effect. Theta tACS can both reduce the Stroop effect and modulate adaptive mechanisms of the cognitive control network, suggesting midfrontal theta oscillations as causally involved in cognitive control.

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

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

Dynamics of attentional focusing in the Eriksen flanker task

Eriksen and Eriksen (Perception & Psychophysics, 16, 143-149, 1974) explained the flanker compatibility effect in terms of response competition. A simplified version of the original flanker task, featuring a 1-to-1 mapping of stimuli onto responses, has become prominent in the literature. Compatible flanker trials present identical items (HHHHH), whereas incompatible trials present different items (HHSHH). The 1-to-1 mapping is potentially problematic because it invites a strategy that people could use to perform the task. Subjects could first determine whether all the items are the same and focus attention on the central target only if they are not. Response times (RTs) would be longer for incompatible trials partly because they require the extra step of focusing attention. We tested this conditional focusing hypothesis by combining a 1-to-1 flanker task with a digit probe detection procedure. In half of the trials, the digit '7' appeared immediately after the response to the flanker display, at the target or a flanker location. Three experiments showed a V-shaped function of RTs to digits across locations that was not modulated by flanker compatibility. These results demonstrate that subjects focused attention on the central target regardless of the same/different configuration of the display, refuting the conditional focusing hypothesis. Our findings support Eriksen and Eriksen's original interpretation of the flanker task.

Reaching trajectories unravel modality-dependent temporal dynamics of the automatic process in the Simon task: a model-based approach

The Simon effect represents a phenomenon in which the location of the stimuli affects the speed and accuracy of the response, despite being irrelevant for the task demands. This is believed to be due to an automatic activation of a response corresponding to the location of the stimuli, which conflicts with the controlled decision process based on relevant stimuli features. Previously, differences in the nature of the Simon effect (i.e., the pattern of change of the effect across the distribution of response times) between visual and somatosensory stimuli were reported. We hypothesize that the temporal dynamics of visual and somatosensory automatic and controlled processes vary, thus driving the reported behavioral differences. While most studies have used response times to study the underlying mechanisms involved, in this study we had participants reach out to touch the targets and recorded their arm movements using a motion capture system. Importantly, the participants started their movements before a final decision was made. In this way, we could analyze the movements to gain insights into the competition between the automatic and controlled processes. We used this technique to describe the results in terms of a model assuming automatic activation due to location-based evidence, followed by inhibition. We found that for the somatosensory Simon effect, the decay of the automatic process is significantly slower than for the visual Simon effect, suggesting quantitative differences in this automatic process between the visual and somatosensory modalities.