Jointly modeling behavioral and EEG measures of proactive control in task switching.

In this study, we implement joint modeling of behavioral and single-trial electroencephalography (EEG) data derived from a cued-trials task-switching paradigm to test the hypothesis that trial-by-trial adjustment of response criterion can be linked to changes in the event-related potentials (ERPs) elicited during the cue-target interval (CTI). Specifically, we assess whether ERP components associated with preparation to switch task and preparation of the relevant task are linked to a response criterion parameter derived from a simple diffusion decision model (DDM). Joint modeling frameworks characterize the brain-behavior link by simultaneously modeling behavioral and neural data and implementing a linking function to bind these two submodels. We examined three joint models: The first characterized the core link between EEG and criterion, the second added a switch preparation input parameter and the third also added a task preparation input parameter. The criterion-EEG link was strongest just before target onset. Inclusion of switch and task preparation parameters did not improve the performance of the criterion-EEG link but was necessary to accurately model the ERP waveform morphology. While we successfully jointly modeled latent model parameters and EEG data from a task-switching paradigm, these findings show that customized cognitive models are needed that are tailored to the multiple cognitive control processes underlying task-switching performance. This is the first paper to implement joint modeling of behavioral measures and single-trial electroencephalography (EEG) data derived from the cue-target interval in a cued-trials task-switching paradigm. Model hyperparameters showed a strong link between response criterion and the pre-target negativity amplitude. Additional parameters (switch preparation, task preparation) were necessary to model the cue-locked ERP waveform morphology. This is consistent with multiple cognitive control processes underlying proactive control and points to the need for more nuanced models of task-switching performance.

Speed-accuracy tradeoffs in decision making: Perception shifts and goal activation bias decision thresholds.

A fundamental aspect of decision making is the speed-accuracy tradeoff (SAT): slower decisions tend to be more accurate, but because time is a scarce resource people prefer to conclude decisions more quickly. The current research adds to the SAT literature by documenting two previously unrecognized influences on the SAT: perception shifts and goal activation. Decision makers' perceptions of what constitutes a fast or a slow decision, and what constitutes an accurate or inaccurate decision, are based on prior experience, and these perceptions influence decision speed. Similarly, previous experience in a decision context associates the context with a particular decision goal. Thus, in later decisions the decision context will activate this goal, and influence decision speed. Both of these mechanisms contribute to a specific decision bias: decision speeds are biased toward original decision speeds in a decision context. Four experiments provide evidence for the bias and the two contributing mechanisms. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Self-reported mind wandering reflects executive control and selective attention.

Mind wandering is ubiquitous in everyday life and has a pervasive and profound impact on task-related performance. A range of psychological processes have been proposed to underlie these performance-related decrements, including failures of executive control, volatile information processing, and shortcomings in selective attention to critical task-relevant stimuli. Despite progress in the development of such theories, existing descriptive analyses have limited capacity to discriminate between the theories. We propose a cognitive-model based analysis that simultaneously explains self-reported mind wandering and task performance. We quantitatively compare six explanations of poor performance in the presence of mind wandering. The competing theories are distinguished by whether there is an impact on executive control and, if so, how executive control acts on information processing, and whether there is an impact on volatility of information processing. Across two experiments using the sustained attention to response task, we find quantitative evidence that mind wandering is associated with two latent factors. Our strongest conclusion is that executive control is impaired: increased mind wandering is associated with reduced ability to inhibit habitual response tendencies. Our nuanced conclusion is that executive control deficits manifest in reduced ability to selectively attend to the information value of rare but task-critical events.

Time-evolving psychological processes over repeated decisions.

Many psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiments, time-on-task can have sizable effects on performance, changing the psychological processes under investigation. Most research has either ignored these changes, treating the underlying process as static, or sacrificed some psychological content of the models for statistical simplicity. We use particle Markov chain Monte-Carlo methods to study psychologically plausible time-varying changes in model parameters. Using data from three highly cited experiments, we find strong evidence in favor of a hidden Markov switching process as an explanation of time-varying effects. This embodies the psychological assumption of "regime switching," with subjects alternating between different cognitive states representing different modes of decision-making. The switching model explains key long- and short-term dynamic effects in the data. The central idea of our approach can be applied quite generally to quantitative psychological theories, beyond the models and datasets that we investigate. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes.

Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in-sample descriptive adequacy against model flexibility, with modern approaches advocating the use of marginal likelihood for hierarchical cognitive models. Cross-validation is another popular approach but its implementation remains out of reach for cognitive models evaluated in a Bayesian hierarchical framework, with the major hurdle being its prohibitive computational cost. To address this issue, we develop novel algorithms that make variational Bayes (VB) inference for hierarchical models feasible and computationally efficient for complex cognitive models of substantive theoretical interest. It is well known that VB produces good estimates of the first moments of the parameters, which gives good predictive densities estimates. We thus develop a novel VB algorithm with Bayesian prediction as a tool to perform model comparison by cross-validation, which we refer to as CVVB. In particular, CVVB can be used as a model screening device that quickly identifies bad models. We demonstrate the utility of CVVB by revisiting a classic question in decision making research: what latent components of processing drive the ubiquitous speed-accuracy tradeoff? We demonstrate that CVVB strongly agrees with model comparison via marginal likelihood, yet achieves the outcome in much less time. Our approach brings cross-validation within reach of theoretically important psychological models, making it feasible to compare much larger families of hierarchically specified cognitive models than has previously been possible. To enhance the applicability of the algorithm, we provide Matlab code together with a user manual so users can easily implement VB and/or CVVB for the models considered in this article and their variants. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Emotion experience and expression goals shape emotion regulation strategy choice.

Research has begun to investigate how goals for emotion experience-how people want to feel-influence the selection of emotion regulation strategies to achieve these goals. We make the case that it is not only how people want to feel that affects strategy selection, but also how they want to be seen to feel. Incorporating this expressive dimension distinguishes four unique emotion goals: (1) to experience and express emotion; (2) to experience but not express emotion; (3) to express but not experience emotion; and (4) to neither experience nor express emotion. In six experiments, we investigated whether these goals influenced choices between six common emotion regulation strategies. Rumination and amplification were selected most often to meet Goal 1-to experience and express emotion. Expressive suppression was chosen most often to meet Goal 2-to experience but not express emotion. Amplification was chosen most often to meet Goal 3-to express but not experience emotion. Finally, distraction was chosen most often to meet Goal 4-to neither experience nor express emotion. Despite not being chosen most for any specific goal, reappraisal was the most commonly selected strategy overall. Our findings introduce a new concept to the emotion goals literature and reveal new insights into the process of emotion regulation strategy selection. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Racing against the clock: Evidence-based versus time-based decisions.

Classical dynamic theories of decision making assume that responses are triggered by accumulating a threshold amount of information. Recently, there has been a growing appreciation that the passage of time also plays a role in triggering responses. We propose that decision processes are composed of 2 diffusive accumulation mechanisms-1 evidence-based and 1 time-based-that compete in an independent race architecture. We show that this timed racing diffusion model (TRDM) provides a unified, comprehensive, and quantitatively accurate explanation of key decision phenomena-including the effects of implicit and explicit deadlines and the relative speed of correct and error responses under speed-accuracy trade-offs-without requiring additional mechanisms that have been criticized as being ad hoc in theoretical motivation and difficult to estimate, such as trial-to-trial variability parameters, collapsing thresholds, or urgency signals. In contrast, our addition is grounded in a widely validated account of time-estimation performance, enabling the same mechanism to simultaneously account for interval estimation and decision making with an explicit deadline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Identifying relationships between cognitive processes across tasks, contexts, and time.

It is commonly assumed that a specific testing occasion (task, design, procedure, etc.) provides insights that generalize beyond that occasion. This assumption is infrequently carefully tested in data. We develop a statistically principled method to directly estimate the correlation between latent components of cognitive processing across tasks, contexts, and time. This method simultaneously estimates individual-participant parameters of a cognitive model at each testing occasion, group-level parameters representing across-participant parameter averages and variances, and across-task correlations. The approach provides a natural way to "borrow" strength across testing occasions, which can increase the precision of parameter estimates across all testing occasions. Two example applications demonstrate that the method is practical in standard designs. The examples, and a simulation study, also provide evidence about the reliability and validity of parameter estimates from the linear ballistic accumulator model. We conclude by highlighting the potential of the parameter-correlation method to provide an "assumption-light" tool for estimating the relatedness of cognitive processes across tasks, contexts, and time.

The role of passing time in decision-making.

Theories of perceptual decision making have been dominated by the idea that evidence accumulates in favor of different alternatives until some fixed threshold amount is reached, which triggers a decision. Recent theories have suggested that these thresholds may not be fixed during each decision but change as time passes. These collapsing thresholds can improve performance in particular decision environments, but reviews of data from typical decision-making paradigms have failed to support collapsing thresholds. We designed three experiments to test collapsing threshold assumptions in decision environments specifically tailored to make them optimal. An emphasis on decision speed encouraged the adoption of collapsing thresholds-most strongly through the use of response deadlines but also through instruction to a lesser extent-but setting an explicit goal of reward rate optimality through both instructions and task design did not. Our results suggest that collapsing thresholds models of decision-making are inconsistent with human behaviour even in some situations where there are normative motivations for these models. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Increasing propensity to mind-wander by transcranial direct current stimulation? A registered report.

Transcranial direct current stimulation (tDCS) has been proposed to be able to modulate different cognitive functions. However, recent meta-analyses conclude that its efficacy is still in question. Recently, an increase in subjects' propensity to mind-wander has been reported as a consequence of anodal stimulation of the left dorsolateral prefrontal cortex (Axelrod et al., Proceedings of the National Academy of Sciences of the United States of America, 112, 2015). In addition, an independent group found a decrease in mind wandering after cathodal stimulation of the same region. These findings seem to indicate that high-level cognitive processes such as mind wandering can reliably be influenced by non-invasive brain stimulation. However, these previous studies used low sample sizes and are as such subject to concerns regarding the replicability of their findings. In this registered report, we implement a high-powered replication of Axelrod et al. (2015) finding that mind-wandering propensity can be increased by anodal tDCS. We used Bayesian statistics and a preregistered sequential-sampling design resulting in a total sample size of N = 192 participants collected across three different laboratories. Our findings show support against a stimulation effect on self-reported mind-wandering scores. The effect was small, in the opposite direction as predicted and not reliably different from zero. Using a Bayes Factor specifically designed to test for replication success, we found strong evidence against a successful replication of the original study. Finally, even when combining data from both the original and replication studies, we could not find evidence for an effect of anodal stimulation. Our results underline the importance of designing studies with sufficient power to detect evidence for or against behavioural effects of non-invasive brain stimulation techniques, preferentially using robust Bayesian statistics in preregistered reports.

ChaRTr: An R toolbox for modeling choices and response times in decision-making tasks.

BACKGROUND: Decision-making is the process of choosing and performing actions in response to sensory cues to achieve behavioral goals. Many mathematical models have been developed to describe the choice behavior and response time (RT) distributions of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques. NEW METHOD: We present a toolbox - Choices and Response Times in R, or ChaRTr - that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries. RESULTS: In three different case studies, we demonstrate how ChaRTr can be used to effortlessly discriminate between multiple models of decision-making behavior. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models. COMPARISON WITH EXISTING METHOD(S): Existing software packages surmounted some of the numerical issues but have often focused on the classical decision-making model, the diffusion decision model. Recent models that posit roles for urgency, time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluation among these competing classes of decision-making models. CONCLUSION: ChaRTr can be used to make insightful statements about the cognitive processes underlying observed decision-making behavior and ultimately for deeper insights into decision mechanisms.

A large-scale analysis of task switching practice effects across the lifespan.

An important feature of human cognition is the ability to flexibly and efficiently adapt behavior in response to continuously changing contextual demands. We leverage a large-scale dataset from Lumosity, an online cognitive-training platform, to investigate how cognitive processes involved in cued switching between tasks are affected by level of task practice across the adult lifespan. We develop a computational account of task switching that specifies the temporal dynamics of activating task-relevant representations and inhibiting task-irrelevant representations and how they vary with extended task practice across a number of age groups. Practice modulates the level of activation of the task-relevant representation and improves the rate at which this information becomes available, but has little effect on the task-irrelevant representation. While long-term practice improves performance across all age groups, it has a greater effect on older adults. Indeed, extensive task practice can make older individuals functionally similar to less-practiced younger individuals, especially for cognitive measures that focus on the rate at which task-relevant information becomes available.

Modeling distracted performance.

The sustained attention to response task (SART) has been the primary method of studying the phenomenon of mind wandering. We develop and experimentally test the first integrated cognitive process model that quantitatively explains all stationary features of behavioral performance in the SART. The model assumes that performance is generated by a competitive race between a stimulus-related decision process and a stimulus-unrelated rhythmic response process. We propose that the stimulus-unrelated process entrains to timing regularities in the task environment, and is unconditionally triggered as a habit or 'insurance policy' to protect against the deleterious effects of mind wandering on ongoing task performance. For two SART experiments the model provided a quantitatively precise account of a range of previously reported trends in choice, response time and self-reported mind wandering data. It also accounted for three previously unidentified features of response time distributions that place critical constraints on cognitive models of performance in situations when people might engage in task-unrelated thoughts. Furthermore, the parameters of the rhythmic race model were meaningfully associated with participants' self-reported distraction, even though the model was never informed by these data. In a validation test, we disrupted the latent rhythmic component with a manipulation of inter-trial-interval variability, and showed that the architecture of the model provided insight into its counter-intuitive effect. We conclude that performance in the presence of mind wandering can be conceived as a competitive latent decision vs. rhythmic response process. We discuss how the rhythmic race model is not restricted to the study of distraction or mind wandering; it is applicable to any domain requiring repetitive responding where evidence accumulation is assumed to be an underlying principle of behavior.

Blinding is compromised for transcranial direct current stimulation at 1 mA for 20 min in young healthy adults.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method that is frequently used to study cortical excitability changes and their impact on cognitive functions in humans. While most stimulators are capable of operating in double-blind mode, the amount of discomfort experienced during tDCS may break blinding. Therefore, specifically designed sham stimulation protocols are being used. The "fade-in, short-stimulation, fade-out" (FSF) protocol has been used in hundreds of studies and is commonly believed to be indistinguishable from real stimulation applied at 1 mA for 20 min. We analysed subjective reports of 192 volunteers, who either received real tDCS (n = 96) or FSF tDCS (n = 96). Participants reported more discomfort for real tDCS and correctly guessed the condition above chance-level. These findings indicate that FSF does not ensure complete blinding and that better active sham protocols are needed.

When humans behave like monkeys: Feedback delays and extensive practice increase the efficiency of speeded decisions.

The study of non-human primates has been foundational in understanding the neural origins of human decision processes, yet the approach rests on the assumption that one can validly extrapolate from the animal to the human. In the context of decision making, this requires constancy across species in physiological and cognitive processes. The former cannot be experimentally validated and therefore remains assumed, and recent findings have called into question the latter: non-human primates become increasingly urgent as the time spent making a decision increases, but humans do not; from a normative perspective, monkeys are making closer-to-optimal decisions than humans. Rather than presuming species differences, here we test an alternative hypothesis: previously overlooked differences in methodological procedures from the two research traditions implicitly reinforced fundamentally different decision strategies across the two species. We show that when humans experience decision contexts matched to those experienced by non-human primates - extensive task practice, or time-based penalties - they display increasing levels of urgency as decision time grows longer, in precisely the same manner as non-human primates. Our findings indicate that previously observed differences in decision strategy between humans and non-human primates are eliminated when the decision environment is more closely matched across species, placing a constraint on the interpretation and mapping of neurophysiological results in non-human primates to humans when there are fundamental differences in the task design.

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models.

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

Causal explanation improves judgment under uncertainty, but rarely in a Bayesian way.

Three studies reexamined the claim that clarifying the causal origin of key statistics can increase normative performance on Bayesian problems involving judgment under uncertainty. Experiments 1 and 2 found that causal explanation did not increase the rate of normative solutions. However, certain types of causal explanation did lead to a reduction in the magnitude of errors in probability estimation. This effect was most pronounced when problem statistics were expressed in percentage formats. Experiment 3 used process-tracing methods to examine the impact of causal explanation of false positives on solution strategies. Changes in probability estimation following causal explanation were the result of a mixture of individual reasoning strategies, including non-Bayesian mechanisms, such as increased attention to explained statistics and approximations of subcomponents of Bayes' rule. The results show that although causal explanation of statistics can affect the way that a problem is mentally represented, this does not necessarily lead to an increased rate of normative responding.

Temporally specific miRNA expression patterns in the dorsal and ventral striatum of addiction-prone rats.

MicroRNAs (miRNAs) within the ventral and dorsal striatum have been shown to regulate addiction-relevant behaviours. However, it is unclear how cocaine experience alone can alter the expression of addiction-relevant miRNAs within striatal subregions. Further, it is not known whether differential expression of miRNAs in the striatum contributes to individual differences in addiction vulnerability. We first examined the effect of cocaine self-administration on the expression of miR-101b, miR-137, miR-212 and miR-132 in nucleus accumbens core and nucleus accumbens shell (NAcSh), as well as dorsomedial striatum and dorsolateral striatum (DLS). We then examined the expression of these same miRNAs in striatal subregions of animals identified as being 'addiction-prone', either immediately following self-administration training or following extinction and relapse testing. Cocaine self-administration was associated with changes in miRNA expression in a regionally discrete manner within the striatum, with the most marked changes occurring in the nucleus accumbens core. When we examined the miRNA profile of addiction-prone rats following self-administration, we observed increased levels of miR-212 in the dorsomedial striatum. After extinction and relapse testing, addiction-prone rats showed significant increases in the expression of miR-101b, miR-137, miR-212 and miR-132 in NAcSh, and miR-137 in the DLS. This study identifies temporally specific changes in miRNA expression consistent with the engagement of distinct striatal subregions across the course of the addiction cycle. Increased dysregulation of miRNA expression in NAcSh and DLS at late stages of the addiction cycle may underlie habitual drug seeking, and may therefore aid in the identification of targets designed to treat addiction.

The computations that support simple decision-making: A comparison between the diffusion and urgency-gating models.

We investigate a question relevant to the psychology and neuroscience of perceptual decision-making: whether decisions are based on steadily accumulating evidence, or only on the most recent evidence. We report an empirical comparison between two of the most prominent examples of these theoretical positions, the diffusion model and the urgency-gating model, via model-based qualitative and quantitative comparisons. Our findings support the predictions of the diffusion model over the urgency-gating model, and therefore, the notion that evidence accumulates without much decay. Gross qualitative patterns and fine structural details of the data are inconsistent with the notion that decisions are based only on the most recent evidence. More generally, we discuss some strengths and weaknesses of scientific methods that investigate quantitative models by distilling the formal models to qualitative predictions.