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

The forced-response method: A new chronometric approach to measure conflict processing

Despite long-standing concerns about the use of free reaction times (RTs) in cognitive psychology, they remain a prevalent measure of conflict resolution. This report presents the forced-response method as a fresh approach to examine speed-accuracy trade-off functions (SATs) in conflict tasks. The method involves fixing the overall response time, varying the onset of stimuli, and observing response expression. We applied this method to an arrow flanker task. By systematically varying the time between stimulus onset and response, we reveal a comprehensive time course of the flanker interference effect that is rarely observed in previous literature. We further show that influential manipulations observed in free-RT paradigms similarly affect accuracy within the forced-response technique, suggesting that the forced-response method retains the core cognitive processing characteristics of traditional free-RT conflict tasks. As a behavioral method that examines the time course of cognitive processing, the forced-response method provides a novel and more nuanced look into the dynamics of conflict resolution.

Leveraging bifactor modeling to test prospective direct and indirect effects of adolescent alcohol use and externalizing symptoms on the development of task-general executive functioning

Adolescence is a period of substantial maturation in brain regions underlying Executive Functioning (EF). Adolescence is also associated with initiation and escalation of Alcohol Use (AU), and adolescent AU has been proposed to produce physiological and neurobiological events that derail healthy EF development. However, support has been mixed, which may be due to (1) failure to consider co-occurring externalizing symptoms (including other drug use) and poor social adaptation, and (2) heterogeneity and psychometric limitations in EF measures. We aimed to clarify the AU-EF association by: (1) distinguishing general externalizing symptoms from specific symptoms (AU, aggression, drug use) using bifactor modeling, (2) testing prospective associations between general externalizing symptoms and specific symptoms, and task-general EF, as indexed by a well-validated computational modeling framework (diffusion decision model), and (3) examining indirect pathways from externalizing symptoms to deficits in task-general EF through poor social adaptation. A high-risk longitudinal sample (N = 919) from the Michigan Longitudinal Study was assessed at four time-points spanning early adolescence (10-13 years) to young adulthood (22-25). Results suggested a critical role of social adaptation within peer and school contexts in promoting healthy EF. There was no evidence that specific, neurotoxic effects of alcohol or drug use derailed task-general EF development.

When 2 become 1: Autistic simultaneity judgements about asynchronous audiovisual speech

It has been proposed that autistic people experience a temporal distortion whereby the temporal binding window of multisensory integration is extended. Research to date has focused on autistic children so whether these differences persist into adulthood remains unknown. In addition, the possibility that the previous observations have arisen from between-group differences in response bias, rather than perceptual differences, has not been addressed. Participants completed simultaneity judgements of audiovisual speech stimuli across a range of stimulus-onset asynchronies. Response times and accuracy data were fitted to a drift-diffusion model so that the drift rate (a measure of processing efficiency) and starting point (response bias) could be estimated. In Experiment 1, we tested a sample of non-autistic adults who completed the Autism Quotient questionnaire. Autism Quotient score was not correlated with either drift rate or response bias, nor were there between-group differences when splitting based on the first and third quantiles of scores. In Experiment 2, we compared the performance of autistic with a group of non-autistic adults. There were no between-group differences in either drift rate or starting point. The results of this study do not support the previous suggestion that autistic people have an extended temporal binding window for audiovisual speech. In addition, exploratory analysis revealed that operationalising the temporal binding window in different ways influenced whether a group difference was observed, which is an important consideration for future work.

Value-based decision-making in regular alcohol consumers following experimental manipulation of alcohol value

BACKGROUND

Devaluation of alcohol leads to reductions in alcohol choice and consumption; however, the cognitive mechanisms that underpin this relationship are not well-understood. In this study we applied a computational model of value-based decision-making (VBDM) to decisions made about alcohol and alcohol-unrelated cues following experimental manipulation of alcohol value.

METHOD

Using a pre-registered within-subject design, thirty-six regular alcohol consumers (≥14 UK units per week) completed a two-alternative forced choice task where they chose between two alcohol images (in one block) or two soft drink images (in a different block) after watching videos that emphasised the positive (alcohol value), and separately, the negative (alcohol devalue) consequences of alcohol. On each block, participants pressed a key to select the image depicting the drink they would rather consume. A drift-diffusion model (DDM) was fitted to reaction time and choice data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks in each experimental condition.

FINDINGS

In the alcohol devalue condition, soft drink EA rates were significantly higher compared to alcohol EA rates (p = 0.04, d = 0.31), and compared to soft drink EA rates in the alcohol value condition (p = 0.01, d = 0.38). However, EA rates for alcoholic drinks and response thresholds (for either drink type) were unaffected by the experimental manipulation.

CONCLUSIONS

In line with behavioural economic models of addiction that emphasise the important role of alternative reinforcement, experimentally manipulating alcohol value is associated with changes in the internal cognitive processes that precede soft drink choice.

Empathy incites a stable prosocial decision bias

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

Confidence ratings increase response thresholds in decision making

Many mental processes are reactive - they are altered as a result of introspection and monitoring. It has been documented that soliciting trial-by-trial confidence ratings (CRs) reactively improves decision accuracy and lengthens response times (RTs), but the cognitive mechanisms underlying CR reactivity in decision-making remain unknown. The current study conducted two experiments and employed the drift-diffusion model (DDM) to explore why reporting confidence reactively alters the decision-making process. The results showed that CRs led to enhanced decision accuracy, longer RTs, and higher response thresholds. The findings are consistent with an increased conservatism hypothesis which asserts that soliciting CRs provokes feelings of uncertainty and makes individuals more cautious in their decision making.

Information processing style and institutional trust as factors of COVID vaccine hesitancy

This study investigates the factors contributing to COVID vaccine hesitancy. Vaccine hesitancy has commonly been attributed to susceptibility to misinformation and linked to particular socio-demographic factors and personality traits. We present a new perspective, emphasizing the interplay between individual cognitive styles and perceptions of public health institutions. In January 2020, before the COVID-19 pandemic, 318 participants underwent a comprehensive assessment, including self-report measures of personality and clinical characteristics, as well as a behavioral task that assessed information processing styles. During 2021, attitudes towards vaccines, scientists, and the CDC were measured at three time points (February-October). Panel data analysis and structural equation modeling revealed nuanced relationships between these measures and information processing styles over time. Trust in public health institutions, authoritarian submission, and lower information processing capabilities together contribute to vaccine acceptance. Information processing capacities influenced vaccination decisions independently from the trust level, but their impact was partially mediated by authoritarian tendencies. These findings underscore the multifactorial nature of vaccine hesitancy, which emerges as a product of interactions between individual cognitive styles and perceptions of public health institutions. This novel perspective provides valuable insights into the underlying mechanisms that drive this complex phenomenon.

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

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

To see or not to see: the parallel processing of self-relevance and facial expressions

The self, like the concept of central "gravity", facilitates the processing of information that is directly relevant to the self. This phenomenon is known as the self-prioritization effect. However, it remains unclear whether the self-prioritization effect extends to the processing of emotional facial expressions. To fill this gap, we used a self-association paradigm to investigate the impact of self-relevance on the recognition of emotional facial expressions while controlling for confounding factors such as familiarity and overlearning. Using a large and diverse sample, we replicated the effect of self-relevance on face processing but found no evidence for a modulation of self-relevance on facial emotion recognition. We propose two potential theoretical explanations to account for these findings and emphasize that further research with different experimental designs and a multitasks measurement approach is needed to understand this mechanism fully. Overall, our study contributes to the literature on the parallel cognitive processing of self-relevance and facial emotion recognition, with implications for both social and cognitive psychology.

Dopamine Alters the Effect of Brain Stimulation on Decision-Making

Noninvasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), show promise in treating a range of psychiatric and neurologic conditions. However, optimization of such applications requires a better understanding of how tDCS alters cognition and behavior. Existing evidence implicates dopamine in tDCS alterations of brain activity and plasticity; however, there is as yet no causal evidence for a role of dopamine in tDCS effects on cognition and behavior. Here, in a preregistered, double-blinded study, we examined how pharmacologically manipulating dopamine altered the effect of tDCS on the speed-accuracy trade-off, which taps ubiquitous strategic operations. Cathodal tDCS was delivered over the left prefrontal cortex and the superior medial frontal cortex before participants (N = 62, 24 males, 38 females) completed a dot-motion task, making judgments on the direction of a field of moving dots under instructions to emphasize speed, accuracy, or both. We leveraged computational modeling to uncover how our interventions altered latent decisional processes driving the speed-accuracy trade-off. We show that dopamine in combination with tDCS (but not tDCS alone nor dopamine alone) not only impaired decision accuracy but also impaired discriminability, which suggests that these manipulations altered the encoding or representation of discriminative evidence. This is, to the best of our knowledge, the first direct evidence implicating dopamine in the way tDCS affects cognition and behavior.SIGNIFICANCE STATEMENT tDCS can improve cognitive and behavioral impairments in clinical conditions; however, a better understanding of its mechanisms is required to optimize future clinical applications. Here, using a pharmacological approach to manipulate brain dopamine levels in healthy adults, we demonstrate a role for dopamine in the effects of tDCS in the speed-accuracy trade-off, a strategic cognitive process ubiquitous in many contexts. In doing so, we provide direct evidence implicating dopamine in the way tDCS affects cognition and behavior.

Dissociated amplitude and phase effects of alpha oscillation in a nested structure of rhythm- and sequence-based temporal expectation

The human brain can utilize various information to form temporal expectations and optimize perceptual performance. Here we show dissociated amplitude and phase effects of prestimulus alpha oscillation in a nested structure of rhythm- and sequence-based expectation. A visual stream of rhythmic stimuli was presented in a fixed sequence such that their temporal positions could be predicted by either the low-frequency rhythm, the sequence, or the combination. The behavioral modeling indicated that rhythmic and sequence information additively led to increased accumulation speed of sensory evidence and alleviated threshold for the perceptual discrimination of the expected stimulus. The electroencephalographical results showed that the alpha amplitude was modulated mainly by rhythmic information, with the amplitude fluctuating with the phase of the low-frequency rhythm (i.e. phase-amplitude coupling). The alpha phase, however, was affected by both rhythmic and sequence information. Importantly, rhythm-based expectation improved the perceptual performance by decreasing the alpha amplitude, whereas sequence-based expectation did not further decrease the amplitude on top of rhythm-based expectation. Moreover, rhythm-based and sequence-based expectations collaboratively improved the perceptual performance by biasing the alpha oscillation toward the optimal phase. Our findings suggested flexible coordination of multiscale brain oscillations in dealing with a complex environment.

Mechanisms of Training-Related Change in Processing Speed: A Drift-Diffusion Model Approach

Processing speed is a crucial ability that changes over the course of the lifespan. Training interventions on processing speed have shown promising effects and have been associated with improved cognitive functioning. While training-related changes in processing speed are often studied using reaction times (RTs) and error rates, these measures provide limited insight into the mechanisms underlying changes during training. The drift-diffusion model provides estimates of the cognitive processes underlying speeded decision tasks, such as the rate of evidence accumulation (drift rate), response strategies (boundary separation), as well as time for other processes such as stimulus encoding and motor response (non-decision time). In the current study, we analyzed existing data of an extensive multi-session training intervention (von Bastian & Oberauer, 2013) to disentangle changes in drift rate, boundary separation, and non-decision time during training of different speeded choice-RT tasks. During this training intervention, 30 participants performed 20 training sessions over the course of four weeks, completing three tasks each session: a face-matching, a pattern-matching, and a digit-matching task. Our results show that processing speed training increased drift rates throughout training. Boundary separation and non-decision time decreased mostly during the initial parts of training. This pattern of prolonged training-related changes in rate of evidence accumulation as well as early changes in response strategy and non-decision processes was observed across all three tasks. Future research should investigate how these training-related changes relate to improvements in cognitive functioning more broadly.

Listening efficiency in adult cochlear-implant users compared with normally-hearing controls at ecologically relevant signal-to-noise ratios

Introduction

Due to having to work with an impoverished auditory signal, cochlear-implant (CI) users may experience reduced speech intelligibility and/or increased listening effort in real-world listening situations, compared to their normally-hearing (NH) peers. These two challenges to perception may be usefully integrated in a measure of listening efficiency: conceptually, the amount of accuracy achieved for a certain amount of effort expended.

Methods

We describe a novel approach to quantifying listening efficiency based on the rate of evidence accumulation toward a correct response in a linear ballistic accumulator (LBA) model of choice decision-making. Estimation of this objective measure within a hierarchical Bayesian framework confers further benefits, including full quantification of uncertainty in parameter estimates. We applied this approach to examine the speech-in-noise performance of a group of 24 CI users (M age: 60.3, range: 20-84 years) and a group of 25 approximately age-matched NH controls (M age: 55.8, range: 20-79 years). In a laboratory experiment, participants listened to reverberant target sentences in cafeteria noise at ecologically relevant signal-to-noise ratios (SNRs) of +20, +10, and +4 dB SNR. Individual differences in cognition and self-reported listening experiences were also characterised by means of cognitive tests and hearing questionnaires.

Results

At the group level, the CI group showed much lower listening efficiency than the NH group, even in favourable acoustic conditions. At the individual level, within the CI group (but not the NH group), higher listening efficiency was associated with better cognition (i.e., working-memory and linguistic-closure) and with more positive self-reported listening experiences, both in the laboratory and in daily life.

Discussion

We argue that listening efficiency, measured using the approach described here, is: (i) conceptually well-motivated, in that it is theoretically impervious to differences in how individuals approach the speed-accuracy trade-off that is inherent to all perceptual decision making; and (ii) of practical utility, in that it is sensitive to differences in task demand, and to differences between groups, even when speech intelligibility remains at or near ceiling level. Further research is needed to explore the sensitivity and practical utility of this metric across diverse listening situations.

Problems when fixing the response bias parameter z in drift diffusion analysis : A Commentary on Stafford et al. (2020)

In a simulation study, Stafford et al. (Behavior Research Methods, 52, 2142-2155, 2020) explored the effect of sample size on detecting group differences in ability in the presence of speed-accuracy trade-offs using the Drift Diffusion Model (DDM) and introduced an online tool to perform a power analysis. They found that the DDM approach was superior to analyzing the observed response times and response accuracies alone. In their simulation, they applied the EZ method to estimate the model parameters. In this article, we demonstrate that the EZ method, which cannot estimate the response bias parameter of the DDM, causes severe estimation bias for all parameters if the true response bias is not 0.5. Moreover, the bias patterns differ between EZ and the equivalent maximum likelihood estimation with z fixed at 0.5. This should be taken into consideration when using the otherwise excellent power analysis tool for experimental designs, in which z≠ 0.5 cannot be ruled out or even stipulate it.

Valence framing induces cognitive bias

Valence framing effects refer to inconsistent choice preferences in response to positive versus negative formulation of mathematically equivalent outcomes. Here, we manipulate valence framing in a two-alternative forced choice dictator game using gains and losses as frames to investigate the cognitive mechanisms underlying valence framing. We applied a Drift-Diffusion Model (DDM) to examine whether gain (i.e., “take” money) and loss (i.e., “give” money) frames evoke a cognitive bias as previous research did not consistently reveal framing effects using reaction times and response frequency as dependent variables. DDMs allow decomposing the decision process into separate cognitive mechanisms, whereby a cognitive bias was repeatedly associated with a shift in the starting point of the model. Conducting both a laboratory (N = 62) and an online study (N = 109), female participants allocated money between themselves and another person in a prosocial or selfish way. In each study, one group was instructed to give money (give frame), the other to take money (take frame). Consistent with previous studies, no differences were found in response times and response frequencies. However, in both studies, substantial bias towards the selfish option was found in the take frame groups, captured by the starting point of the DDM. Thus, our results suggest that valence framing induces a cognitive bias in decision processing in women, even when no behavioral differences are present.

Integration of visual motion and orientation signals in dyslexic children: an equivalent noise approach

Dyslexic individuals have been reported to have reduced global motion sensitivity, which could be attributed to various causes including atypical magnocellular or dorsal stream function, impaired spatial integration, increased internal noise and/or reduced external noise exclusion. Here, we applied an equivalent noise experimental paradigm alongside a traditional motion-coherence task to determine what limits global motion processing in dyslexia. We also presented static analogues of the motion tasks (orientation tasks) to investigate whether perceptual differences in dyslexia were restricted to motion processing. We compared the performance of 48 dyslexic and 48 typically developing children aged 8 to 14 years in these tasks and used equivalent noise modelling to estimate levels of internal noise (the precision associated with estimating each element's direction/orientation) and sampling (the effective number of samples integrated to judge the overall direction/orientation). While group differences were subtle, dyslexic children had significantly higher internal noise estimates for motion discrimination, and higher orientation-coherence thresholds, than typical children. Thus, while perceptual differences in dyslexia do not appear to be restricted to motion tasks, motion and orientation processing seem to be affected differently. The pattern of results also differs from that previously reported in autistic children, suggesting perceptual processing differences are condition-specific.

Behavioural and neural indices of perceptual decision-making in autistic children during visual motion tasks

Many studies report atypical responses to sensory information in autistic individuals, yet it is not clear which stages of processing are affected, with little consideration given to decision-making processes. We combined diffusion modelling with high-density EEG to identify which processing stages differ between 50 autistic and 50 typically developing children aged 6-14 years during two visual motion tasks. Our pre-registered hypotheses were that autistic children would show task-dependent differences in sensory evidence accumulation, alongside a more cautious decision-making style and longer non-decision time across tasks. We tested these hypotheses using hierarchical Bayesian diffusion models with a rigorous blind modelling approach, finding no conclusive evidence for our hypotheses. Using a data-driven method, we identified a response-locked centro-parietal component previously linked to the decision-making process. The build-up in this component did not consistently relate to evidence accumulation in autistic children. This suggests that the relationship between the EEG measure and diffusion-modelling is not straightforward in autistic children. Compared to a related study of children with dyslexia, motion processing differences appear less pronounced in autistic children. Exploratory analyses also suggest weak evidence that ADHD symptoms moderate perceptual decision-making in autistic children.

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.

Making heads or tails of body inversion effects: Do heads matter?

Observers are better at discriminating upright bodies than inverted bodies, and this body inversion effect (BIE) is reliable with whole figures (bodies with heads), but not with bodies presented without heads or the heads occluded suggesting that heads may be key to BIEs. Some studies present whole figures and bodies without heads between groups, and BIEs are not found for bodies without heads [1]. Other studies present whole figures and bodies without heads in the same blocks and BIEs are found with bodies without heads [2]. Does seeing the heads of whole figures induce BIEs in bodies without heads? Here, participants discriminated bodies with either whole figures and bodies without heads presented within blocks, or in separate blocks with bodies without heads presented first. We tested body identity and posture discrimination and measured participants’ gaze. BIEs were found with whole figures and bodies without heads in both identity and posture discrimination, and in both study designs. However, efficiency scores were better for the whole figures than the bodies without heads, but only when whole figures appeared in separate blocks. The magnitude of the BIE was overall stronger for whole figures compared to bodies without heads, but only in identity discrimination. BIE magnitudes were similar in the identity and posture tasks. Participants were better at identity discrimination, yet, there was greater looking at heads and less at bodies. During posture discrimination, greater looking at bodies and less at heads was associated with better performance. Faces might influence BIEs but are not essential. Configural representations of bodies without heads are sufficient for BIEs in posture and identity discrimination.

Reciprocal associations between implicit attitudes and drinking in emerging adulthood

BACKGROUND

Implicit alcohol attitudes are considered important in the etiology of drinking, and theory posits reciprocal associations between them. Research testing reciprocal associations between implicit attitudes (using the Implicit Association Task, IAT) and drinking is limited by a failure to consider multiple processes influencing performance on the IAT and to disaggregate within- and between-person effects. The current study addressed these limitations by using a diffusion model to analyze IAT data and Latent Curve Models with Structured Residuals to test reciprocal associations.

METHODS

The sample included 314 emerging adults from the community (52% female; predominantly non-Hispanic Caucasian (76%) or African American (15%)) assessed annually for three years. Differences between IAT conditions in the drift rate parameter of the EZ-diffusion model (vΔ) were used as an alternative to traditional response-time-based indices from the IAT (d-scores). Differences in drift rate have been found to index implicit attitudes effectively.

RESULTS

Within-person reciprocal associations were supported, but between-person associations were not. Positive implicit alcohol attitudes (vΔ) were prospectively associated with heavy drinking, which was positively associated with subsequent positive implicit alcohol attitudes.

CONCLUSIONS

We found that positive implicit alcohol attitudes and heavy drinking reinforce each other in a negative cascade within individuals. The results highlight the importance of disaggregating within- and between-person prospective effects when testing dual process models and suggest that the diffusion model may be a fruitful approach to enhance the construct validity of IAT assessed implicit attitudes.

Neural Substrates of the Drift-Diffusion Model in Brain Disorders

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia. This review thus uses DDM to provide a theoretical understanding of different brain disorders.

Visual Motion and Decision-Making in Dyslexia: Reduced Accumulation of Sensory Evidence and Related Neural Dynamics

Children with and without dyslexia differ in their behavioral responses to visual information, particularly when required to pool dynamic signals over space and time. Importantly, multiple processes contribute to behavioral responses. Here we investigated which processing stages are affected in children with dyslexia when performing visual motion processing tasks, by combining two methods that are sensitive to the dynamic processes leading to responses. We used a diffusion model which decomposes response time and accuracy into distinct cognitive constructs, and high-density EEG. Fifty children with dyslexia (24 male) and 50 typically developing children (28 male) 6-14 years of age judged the direction of motion as quickly and accurately as possible in two global motion tasks (motion coherence and direction integration), which varied in their requirements for noise exclusion. Following our preregistered analyses, we fitted hierarchical Bayesian diffusion models to the data, blinded to group membership. Unblinding revealed reduced evidence accumulation in children with dyslexia compared with typical children for both tasks. Additionally, we identified a response-locked EEG component which was maximal over centro-parietal electrodes which indicated a neural correlate of reduced drift rate in dyslexia in the motion coherence task, thereby linking brain and behavior. We suggest that children with dyslexia tend to be slower to extract sensory evidence from global motion displays, regardless of whether noise exclusion is required, thus furthering our understanding of atypical perceptual decision-making processes in dyslexia.SIGNIFICANCE STATEMENT Reduced sensitivity to visual information has been reported in dyslexia, with a lively debate about whether these differences causally contribute to reading difficulties. In this large preregistered study with a blind modeling approach, we combine state-of-the art methods in both computational modeling and EEG analysis to pinpoint the stages of processing that are atypical in children with dyslexia in two visual motion tasks that vary in their requirement for noise exclusion. We find reduced evidence accumulation in children with dyslexia across both tasks, and identify a neural marker, allowing us to link brain and behavior. We show that children with dyslexia exhibit general difficulties with extracting sensory evidence from global motion displays, not just in tasks that require noise exclusion.

Magnitude-sensitivity: rethinking decision-making

Magnitude-sensitivity refers to the result that performance in decision-making, across domains and organisms, is affected by the total value of the possible alternatives. This simple result offers a window into fundamental issues in decision-making and has led to a reconsideration of ecological decision-making, prominent computational models of decision-making, and optimal decision-making. Moreover, magnitude-sensitivity has inspired the design of new robotic systems that exploit natural solutions and apply optimal decision-making policies. In this article, we review the key theoretical and empirical results about magnitude-sensitivity and highlight the importance that this phenomenon has for the understanding of decision-making. Furthermore, we discuss open questions and ideas for future research.

Raising the bar: improving methodological rigour in cognitive alcohol research

BACKGROUND AND AIMS

A range of experimental paradigms claim to measure the cognitive processes underpinning alcohol use, suggesting that heightened attentional bias, greater approach tendencies and reduced cue-specific inhibitory control are important drivers of consumption. This paper identifies methodological shortcomings within this broad domain of research and exemplifies them in studies focused specifically on alcohol-related attentional bias.

ARGUMENT AND ANALYSIS

We highlight five main methodological issues: (i) the use of inappropriately matched control stimuli; (ii) opacity of stimulus selection and validation procedures; (iii) a credence in noisy measures; (iv) a reliance on unreliable tasks; and (v) variability in design and analysis. This is evidenced through a review of alcohol-related attentional bias (64 empirical articles, 68 tasks), which reveals the following: only 53% of tasks use appropriately matched control stimuli; as few as 38% report their stimulus selection and 19% their validation procedures; less than 28% used indices capable of disambiguating attentional processes; 22% assess reliability; and under 2% of studies were pre-registered.

CONCLUSIONS

Well-matched and validated experimental stimuli, the development of reliable cognitive tasks and explicit assessment of their psychometric properties, and careful consideration of behavioural indices and their analysis will improve the methodological rigour of cognitive alcohol research. Open science principles can facilitate replication and reproducibility in alcohol research.

Cognitive efficiency beats top-down control as a reliable individual difference dimension relevant to self-control

Top-down control of responses is a key construct in cognitive science that is thought to be critical for self-control. It is typically measured by subtracting performance in experimental conditions in which top-down control is theoretically present against performance in matched conditions in which it is assumed to be absent. Recently, however, subtraction-based metrics of top-down control have been criticized for having low test-retest reliability, weak intercorrelations, and little relation to self-report measures of self-control. Concurrently, there is growing evidence that task-general cognitive efficiency, indexed by the drift rate parameter of the diffusion model (Ratcliff, 1978), constitutes a cohesive, reliable individual difference dimension relevant to self-control. However, no previous studies have directly compared latent factors for top-down control (derived from subtraction metrics) with factors for task-general efficiency "head-to-head" in the same sample in terms of their cohesiveness, temporal stability, and relation to self-control. In this re-analysis of a large open data set (Eisenberg et al., 2019; N = 522), we find that top-down control metrics fail to form cohesive latent factors, that the resulting factors have poor temporal stability, and that they exhibit tenuous connections to questionnaire measures of self-control. In contrast, cognitive efficiency measures-drawn from conditions of the same tasks that both are, and are not, assumed to demand top-down control-form a robust, temporally stable factor that correlates with questionnaire measures of self-control. These findings suggest that task-general efficiency is a central individual difference dimension relevant to self-control. Moreover, they go beyond recent measurement-based critiques of top-down control metrics, and instead suggest problems with key theoretical assumptions that have long guided this research paradigm.

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

RATIONALE

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

OBJECTIVES

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

METHODS AND RESULTS

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

CONCLUSIONS

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

Volition in prospective Memory: Evidence against differences between free and fixed target events

Volition is the extent to which actions are generated as a result of internal states in contrast to responses to external conditions or dictated by external events. Delayed intentions about future action are stored in prospective memory until the intended action has to be formed at a later point in time. It is unknown how voluntary choice affects prospective memory. We compared the difference between freely chosen and fixed targets on the reaction times and task performance in the ongoing task and for the target stimuli in a prospective memory task. The task performance and the reaction time was modelled using a Bayesian hierarchical drift-diffusion model. The analysis showed no differences between self-chosen and fixed prospective memory cues on task responses. The result suggests that volition in choosing the cue to act upon or given a fixed cue does not make a difference for prospective memory task performance.

OCD Influences Evidence Accumulation During Decision Making in Males but Not Females During Perceptual and Value-Driven Choice

Individuals with obsessive-compulsive disorder (OCD) often have difficulty making decisions. Valuation and value-based judgements are particularly difficult. The mechanisms underlying these impairments are still poorly understood. Previous work has suggested that individuals with OCD require more information prior to making a choice during perceptual discrimination tasks. Little previous work has examined value-guided choice in OCD. Here we examined perceptual and value-based decision making in adults with OCD, using a novel task in which the two types of decision are tested in parallel using the same individually calibrated sets of visual stimuli (Perceptual and Value-based decision-making task, PVDM). Twenty-seven unmedicated participants with OCD (16 female) and thirty-one healthy controls (15 female) were tested. Data were analyzed using hierarchical drift-diffusion modeling (HDDM). Decision formation was altered in OCD, but differentially between genders: males with OCD, but not females, accumulated more information (i.e., were more cautious) and were less effective in evidence accumulation than age- and IQ-matched healthy males. Furthermore, males with OCD, but not females, were less likely than controls to adjust the process of evidence accumulation across decision contexts. These unexpectedly gender-dimorphic effects suggest that more attention should be paid to gender differences in studies of OCD, and of pathophysiology more broadly.

Shifting attention between modalities: Revisiting the modality-shift effect in autism

Selective attention to a sensory modality has been observed experimentally in studies of the modality-shift effect – a relative performance benefit for targets preceded by a target in the same modality, compared to a different modality. Differences in selective attention are commonly observed in autism and we investigated whether exogenous (automatic) shift costs between modalities are increased. Autistic adults and neurotypical controls made speeded discrimination responses to simple visual, tactile and auditory targets. Shift costs were observed for each target modality in participant response times and were largest for auditory targets, reflective of fast responses on auditory repeat trials. Critically, shift costs were similar between the groups. However, integrating speed and accuracy data using drift-diffusion modelling revealed that shift costs in drift rates (reflecting the quality of information extracted from the stimulus) were reduced for autistic participants compared with neurotypicals. It may be that, unlike neurotypicals, there is little difference between attention within and between sensory modalities for autistic people. This finding also highlights the benefit of combining reaction time and accuracy data using decision models to better characterise selective attention in autism.

On the Utility of Integrated Speed-Accuracy Measures when Speed-Accuracy Trade-off is Present

In an attempt to simplify data analysis and to avoid confounds due to speed-accuracy trade-off, sometimes integrated measures of speed and accuracy are used. Although it has been claimed that some of these combined measures are insensitive to speed-accuracy trade-off (SAT), a systematic and broad examination of such claims has not been performed thus far. The present article reports the results of four simulation studies in which five established integrated measures were studied in different speed-accuracy trade-off contexts. All four studies used repeated measures designs crossing an experimental factor (variable of interest) with a factor representing SAT settings, with all conditions occurring randomly over the sequence of trials to avoid condition-wise SATs (mixed conditions repeated measures design). The first study used speed modulations that were balanced by accuracy changes in the opposite direction. The other studies were all based on SAT as modeled either by the drift-diffusion model, with pro-active trade-off settings (Study 2) or with reactive trade-off modulations (Study 3) or by a discontinuous two-phase model (Study 4). Only the studies based on balanced trade-offs showed that two of the measures were insensitive to SAT settings, while in all other contexts, all measures were sensitive to SAT. Nevertheless, as the mixed conditions design distributes the SAT effects over the conditions of the variable of interest, all integrated measures reliably detected the effect of this variable in all SAT conditions. Although integrated measures are sensitive to SAT, these effects can be neutralised by using a mixed conditions repeated measures design.

Why We Learn Less from Observing Outgroups

Humans are less likely to learn from individuals belonging to a different group (outgroup) than from individuals of their own group (ingroup), yet the source of this societally relevant deficit has remained unclear. Here we used neuroimaging and computational modeling to investigate how people learn from observing the actions and outcomes of ingroup and outgroup demonstrators. Politically left-wing male and female participants performed worse when observing computer-simulated actions they believed were from a right-wing outgroup member compared with those from a left-wing ingroup member. A control experiment in which participants observed choices from a nonhuman agent confirmed that this performance difference reflected an outgroup deficit, rather than an ingroup gain. Accounting for the outgroup deficit, a computational model showed that participants relied less on information from outgroup actions compared with ingroup actions, while learning from outgroup outcomes was not impaired. At the neural level, the differences in observational ingroup versus outgroup learning were reflected in lateral prefrontal activity. The stronger the activity in this region, the more strongly participants weighed ingroup compared with outgroup learning signals (action prediction errors), which formally captured deficits in outgroup learning. Together, our work provides a computational and neural account of why people learn less from observing outgroups.SIGNIFICANCE STATEMENT Learning from observing others is an efficient way to acquire knowledge. In our globalized world, "the others" often are people from a different social group (outgroup). There is evidence that people learn less from observing outgroup individuals compared with individuals from their own group (ingroup). However, the source of this outgroup deficit in observational learning remained unknown, which limits our chances to improve intergroup learning. Our results showed that participants rely less on observed outgroup actions compared with ingroup actions, while learning from outgroup outcomes is not impaired. On the neural level, this outgroup deficit was reflected in the activation of the inferior frontal gyrus. These findings imply that intergroup learning should rely on observing outcomes, rather than actions.

Comparing Eight Parameter Estimation Methods for the Ratcliff Diffusion Model Using Free Software

The Ratcliff Diffusion Model has become an important and widely used tool for the evaluation of psychological experiments. Concurrently, numerous programs and routines have appeared to estimate the model's parameters. The present study aims at comparing some of the most widely used tools with special focus on freely available routines (i.e., open source). Our simulations show that (1) starting point and non-decision time were recovered better than drift rate, (2) the Bayesian approach outperformed all other approaches when the number of trials was low, (3) the Kolmogorov-Smirnov and χ² approaches revealed more bias than Bayesian or Maximum Likelihood based routines, and (4) EZ produced substantially biased estimates of threshold separation, non-decision time and drift rate when starting point z ≠ a/2. We discuss the implications for the choice of parameter estimation approaches for real data and suggest that if biased starting point cannot be excluded, EZ will produce deviant estimates and should be used with great care.