Model Complexity in Diffusion Modeling: Benefits of Making the Model More Parsimonious

Context modulates evidence accumulation in split-second handball penalty decisions

Due to severe time constraints, goalkeepers regularly face the challenging task to make decisions within just a few hundred milliseconds. A key finding of anticipation research is that experts outperform novices by using advanced cues which can be derived from either kinematic or contextual information. Yet, how context modulates decision-making in split-second decisions remains to be determined. Here, we aimed at examining the influence of contextual information on real-time evidence accumulation in split-second decision-making using handball penalty decisions. In Exp. 1 we validated the applicability of hierarchical drift-diffusion modeling (HDDM) for the chosen split-second handball penalty scenario. Following validation, Exp. 2 directly addressed the main aim, namely, to examine how contextual information affects the HDDM parameters drift rate, as the rate of evidence accumulation, and non-decision time, which encompasses perceptual and motor processes. Participants predicted shot direction in temporally occluded videos of handball penalties, with probability (i.e., contextual) information regarding the shot direction being manipulated in half of the trials. Results showed that contextual information systematically affected the drift rate, indicating faster evidence accumulation when context information predicts the subsequent action. Vice versa, incongruent context information resulted in slower evidence accumulation. By contrast, non-decision time was only affected by the mere presence of contextual information (i.e., longer with context information). Our study is the first to show that contextual information modulates evidence accumulation on extremely short timescales in highly time-constrained penalty decisions.

Information accumulation on the item versus source test of source monitoring: Insights from diffusion modeling

Source monitoring involves attributing previous experiences (e.g., studied words as items) to their origins (e.g., screen positions as sources). The present study aimed toward a better understanding of temporal aspects of item and source processing. Participants made source decisions for recognized items either in succession (i.e., the standard format) or in separate test blocks providing independent measures of item and source decision speed. Comparable speeds of item and source decision across the test formats would suggest a full separation between item and source processing, whereas different speeds would imply their (partial) temporal overlap. To test these alternatives, we used the drift rate parameter of the diffusion model (Ratcliff, Psychological Review, 85, 59-108, 1978). We examined whether the drift rates, together with the other parameters, assessed separately for the item and source decision varied as a function of the test format. Threshold separation and nondecision time differed between the test formats, but item and source decision speeds represented by drift rates did not change significantly. Thus, despite facilitation on the source decision when the item decision was immediately followed by a test for source memory than when item and source were tested in separate blocks, findings did not suggest that source information already begins accumulating in the item test in the standard format. We discuss the temporal sequence of item and source processing in light of different assumptions about the contribution of familiarity and recollection.

EZ-CDM: Fast, simple, robust, and accurate estimation of circular diffusion model parameters

The investigation of cognitive processes that form the basis of decision-making in paradigms involving continuous outcomes has gained the interest of modeling researchers who aim to develop a dynamic decision theory that accounts for both speed and accuracy. One of the most important of these continuous models is the circular diffusion model (CDM, Smith. Psychological Review, 123(4), 425. 2016), which posits a noisy accumulation process mathematically described as a stochastic two-dimensional Wiener process inside a disk. Despite the considerable benefits of this model, its mathematical intricacy has limited its utilization among scholars. Here, we propose a straightforward and user-friendly method for estimating the CDM parameters and fitting the model to continuous-scale data using simple formulas that can be readily computed and do not require theoretical knowledge of model fitting or extensive programming. Notwithstanding its simplicity, we demonstrate that the aforementioned method performs with a level of accuracy that is comparable to that of the maximum likelihood estimation method. Furthermore, a robust version of the method is presented, which maintains its simplicity while exhibiting a high degree of resistance to contaminant responses. Additionally, we show that the approach is capable of reliably measuring the key parameters of the CDM, even when these values are subject to across-trial variability. Finally, we demonstrate the practical application of the method on experimental data. Specifically, an illustrative example is presented wherein the method is employed along with estimating the probability of guessing. It is hoped that the straightforward methodology presented here will, on the one hand, help narrow the divide between theoretical constructs and empirical observations on continuous response tasks and, on the other hand, inspire cognitive psychology researchers to shift their laboratory investigations towards continuous response paradigms.

Age-Related Differences in Decision-Making: Evidence Accumulation is More Gradual in Older Age

Older adults tend to exhibit longer response times than younger adults in choice tasks across cognitive domains, such as perception, attention, and memory. The diffusion model has emerged as a standard model for analyzing age differences in choice behavior. Applications of the diffusion model to choice data from younger and older adults indicate that age-related slowing is driven by a more cautious response style and slower non-decisional processes, rather than by age differences in the rate of information accumulation. The Lévy flight model, a new evidence accumulation model that extends the diffusion model, was recently developed to account for differences in response times for correct and error responses. In the Lévy flight model, larger jumps in evidence accumulation can be accommodated compared to the diffusion model. It is currently unknown whether younger and older adults differ with respect to the jumpiness of evidence accumulation. In the current study, younger and older adults (N = 40 per age group) completed a letter-number-discrimination task. Results indicate that older adults show a more gradual (less "jumpy") pattern of evidence accumulation compared to younger adults. Implications for research on cognitive aging are discussed.

A Diffusion Model Account of Cognitive Variability in Healthy Aging and Mild Cognitive Impairment

Within-person variation in cognitive performance is linked to pathological aging. Cognitive fluctuations have not been analyzed using cognitive process models, such as the diffusion model, to characterize which cognitive processes contribute to variability in cognition. We collected 21 daily assessments of attention and personality in younger adults, healthy older adults, and those with mild cognitive impairment. We employed mixed-effects location scale models to analyze group differences on mean diffusion parameters and daily variability. Discussion focuses on how these methods extend our understanding of how cognitive deficits might appear in aging and disease and the moderating influence of daily personality.

Beyond discrete-choice options

While decision theories have evolved over the past five decades, their focus has largely been on choices among a limited number of discrete options, even though many real-world situations have a continuous-option space. Recently, theories have attempted to address decisions with continuous-option spaces, and several computational models have been proposed within the sequential sampling framework to explain how we make a decision in continuous-option space. This article aims to review the main attempts to understand decisions on continuous-option spaces, give an overview of applications of these types of decisions, and present puzzles to be addressed by future developments.

The effect of anxiety and its interplay with social cues when perceiving aggressive behaviours

Contextual cues and emotional states carry expectations and biases that are used to attribute meaning to what we see. In addition, emotional states, such as anxiety, shape our visual systems, increasing overall, and particularly threat-related, sensitivity. It remains unclear, however, how anxiety interacts with additional cues when categorising sensory input. This is especially important in social scenarios where ambiguous gestures are commonplace, thus requiring the integration of cues for a proper interpretation. To this end, we decided to assess how states of anxiety might bias the perception of potentially aggressive social interactions, and how external cues are incorporated in this process. Participants (N = 71) were tasked with signalling the presence of aggression in ambiguous social interactions. Simultaneously, an observer (facial expression) reacted (by showing an emotional expression) to this interaction. Importantly, participants performed this task under safety and threat of shock conditions. Decision measures and eye-tracking data were collected. Our results showed that threat of shock did not affect sensitivity nor criterion when detecting aggressive interactions. The same pattern was observed for response times. Drift diffusion modelling analysis, however, suggested quicker evidence accumulation when under threat. Finally, dwell times over the observer were higher when under threat, indicating a possible association between anxiety states and a bias towards potentially threat-related indicators. Future probing into this topic remains a necessity to better explain the current findings.

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.

Social influence and social identity: A diffusion model analysis

Building on the seminal studies of Solomon Asch and Muzafer Sherif, recent research has advanced our understanding of the mechanisms underlying social influence by applying a diffusion model analysis. Here, we combined the social identity approach to social influence with a diffusion model analysis to unravel the mechanisms underlying social influence. In particular, we aimed to disentangle whether the difference between in-group and out-group influence on perceptual decision-making is driven by a judgmental bias (i.e., changes in decision criteria) or a perceptual bias (i.e., changes in the uptake of sensory information). Preregistered analyses indicated that in-groups exerted stronger social influence than out-groups because in-groups induced a stronger perceptual bias than out-groups. This finding is in line with the single process assumption of the social identity approach because it implicates that the single process driving social influence (i.e., self-categorisation) translates into a change in a single subprocess of decision-making (i.e., biased information uptake). In conclusion, our results highlight that our theoretical understanding of social influence can be expanded by integrating the social identity approach with a diffusion model analysis.

Prioritized neural processing of social threats during perceptual decision-making

Emotional signals, notably those signaling threat, benefit from prioritized processing in the human brain. Yet, it remains unclear whether perceptual decisions about the emotional, threat-related aspects of stimuli involve specific or similar neural computations compared to decisions about their non-threatening/non-emotional components. We developed a novel behavioral paradigm in which participants performed two different detection tasks (emotion vs. color) on the same, two-dimensional visual stimuli. First, electroencephalographic (EEG) activity in a cluster of central electrodes reflected the amount of perceptual evidence around 100 ms following stimulus onset, when the decision concerned emotion, not color. Second, participants' choice could be predicted earlier for emotion (240 ms) than for color (380 ms) by the mu (10 Hz) rhythm, which reflects motor preparation. Taken together, these findings indicate that perceptual decisions about threat-signaling dimensions of facial displays are associated with prioritized neural coding in action-related brain regions, supporting the motivational value of socially relevant signals.

The common factor of executive functions measures nothing but speed of information uptake

There is an ongoing debate about the unity and diversity of executive functions and their relationship with other cognitive abilities such as processing speed, working memory capacity, and intelligence. Specifically, the initially proposed unity and diversity of executive functions is challenged by discussions about (1) the factorial structure of executive functions and (2) unfavorable psychometric properties of measures of executive functions. The present study addressed two methodological limitations of previous work that may explain conflicting results: The inconsistent use of (a) accuracy-based vs. reaction time-based indicators and (b) average performance vs. difference scores. In a sample of 148 participants who completed a battery of executive function tasks, we tried to replicate the three-factor model of the three commonly distinguished executive functions shifting, updating, and inhibition by adopting data-analytical choices of previous work. After addressing the identified methodological limitations using drift-diffusion modeling, we only found one common factor of executive functions that was fully accounted for by individual differences in the speed of information uptake. No variance specific to executive functions remained. Our results suggest that individual differences common to all executive function tasks measure nothing more than individual differences in the speed of information uptake. We therefore suggest refraining from using typical executive function tasks to study substantial research questions, as these tasks are not valid for measuring individual differences in executive functions.

Does allowing for changes of mind influence initial responses?

Evidence accumulation models (EAMs) have become the dominant theoretical framework for rapid decision-making, and while many theoretically distinct variants exist, comparisons have proved challenging due to strong mimicry in their predictions about choice response time data. One solution to reduce mimicry is constraining these models with double responses, which are a second response that is made after the initial response. However, instructing participants that they are allowed to change their mind could influence their strategy for initial responding, meaning that explicit double responding paradigms may not generalise to standard paradigms. Here, we provide a validation of explicit double responding paradigms, by assessing whether participants' initial decisions - as measured by diffusion model parameters - differ based on whether or not they were instructed that they could change their response after their initial response. Across three experiments, our results consistently indicate that allowing for changes of mind does not influence initial responses, with Bayesian analyses providing at least moderate evidence in favour of the null in all cases. Our findings suggest that explicit double responding paradigms should generalise to standard paradigms, validating the use of explicit double responding in future rapid decision-making studies.

Improved Perception of Aggression Under (un)Related Threat of Shock

Anxiety shifts visual attention and perceptual mechanisms, preparing oneself to detect potentially threatening information more rapidly. Despite being demonstrated for threat-related social stimuli, such as fearful expressions, it remains unexplored if these effects encompass other social cues of danger, such as aggressive gestures/actions. To this end, we recruited a total of 65 participants and asked them to identify, as quickly and accurately as possible, potentially aggressive actions depicted by an agent. By introducing and manipulating the occurrence of electric shocks, we induced safe and threatening conditions. In addition, the association between electric shocks and aggression was also manipulated. Our result showed that participants have improved sensitivity, with no changes to criterion, when detecting aggressive gestures during threat compared to safe conditions. Furthermore, drift diffusion model analysis showed that under threat participants exhibited faster evidence accumulation toward the correct perceptual decision. Lastly, the relationship between threat source and aggression appeared to not impact any of the effects described above. Overall, our results indicate that the benefits gained from states of anxiety, such as increased sensitivity toward threat and greater evidence accumulation, are transposable to social stimuli capable of signaling danger other than facial expressions.

Uncovering the cognitive mechanisms underlying the gaze cueing effect

The gaze cueing effect is the tendency for people to respond faster to targets appearing at locations gazed at by others, compared with locations gazed away from by others. The effect is robust, widely studied, and is an influential finding within social cognition. Formal evidence accumulation models provide the dominant theoretical account of the cognitive processes underlying speeded decision-making, but they have rarely been applied to social cognition research. In this study, using a combination of individual-level and hierarchical computational modelling techniques, we applied evidence accumulation models to gaze cueing data (three data sets total, N = 171, 139,001 trials) for the first time to assess the relative capacity that an attentional orienting mechanism and information processing mechanisms have for explaining the gaze cueing effect. We found that most participants were best described by the attentional orienting mechanism, such that response times were slower at gazed away from locations because they had to reorient to the target before they could process the cue. However, we found evidence for individual differences, whereby the models suggested that some gaze cueing effects were driven by a short allocation of information processing resources to the gazed at location, allowing for a brief period where orienting and processing could occur in parallel. There was exceptionally little evidence to suggest any sustained reallocation of information processing resources neither at the group nor individual level. We discuss how this individual variability might represent credible individual differences in the cognitive mechanisms that subserve behaviourally observed gaze cueing effects.

Are there jumps in evidence accumulation, and what, if anything, do they reflect psychologically? An analysis of Lévy Flights models of decision-making

According to existing theories of simple decision-making, decisions are initiated by continuously sampling and accumulating perceptual evidence until a threshold value has been reached. Many models, such as the diffusion decision model, assume a noisy accumulation process, described mathematically as a stochastic Wiener process with Gaussian distributed noise. Recently, an alternative account of decision-making has been proposed in the Lévy Flights (LF) model, in which accumulation noise is characterized by a heavy-tailed power-law distribution, controlled by a parameter, [Formula: see text]. The LF model produces sudden large "jumps" in evidence accumulation that are not produced by the standard Wiener diffusion model, which some have argued provide better fits to data. It remains unclear, however, whether jumps in evidence accumulation have any real psychological meaning. Here, we investigate the conjecture by Voss et al. (Psychonomic Bulletin & Review, 26(3), 813-832, 2019) that jumps might reflect sudden shifts in the source of evidence people rely on to make decisions. We reason that if jumps are psychologically real, we should observe systematic reductions in jumps as people become more practiced with a task (i.e., as people converge on a stable decision strategy with experience). We fitted five versions of the LF model to behavioral data from a study by Evans and Brown (Psychonomic Bulletin & Review, 24(2), 597-606, 2017), using a five-layer deep inference neural network for parameter estimation. The analysis revealed systematic reductions in jumps as a function of practice, such that the LF model more closely approximated the standard Wiener model over time. This trend could not be attributed to other sources of parameter variability, speaking against the possibility of trade-offs with other model parameters. Our analysis suggests that jumps in the LF model might be capturing strategy instability exhibited by relatively inexperienced observers early on in task performance. We conclude that further investigation of a potential psychological interpretation of jumps in evidence accumulation is warranted.

Social norms shape visual appearance: Taking a closer look at the link between social norm learning and perceptual decision-making

One of the most fundamental questions in social psychology is whether norms can change individuals' minds by shaping the visual appearance of stimuli. This question was first raised by Muzafer Sherif (1935). Drawing on the extended social reinforcement account (Germar and Mojzisch, 2019), we aimed to provide a rigorous test of the hypothesis that norm learning leads to a persistent perceptual bias and, hence, to a change in the visual appearance of stimuli. From a methodological perspective, we used both a diffusion model approach and the method of adjustment, a well-established technique from psychophysics and vision research. The results of Experiments 1-3 show that norm effects on perceptual decision-making are robustly replicable, and are due to genuine social influence, that is, they cannot be explained by non-social priming, contingency learning effects (Experiments 1 and 2) or anchoring effects (Experiment 3). Most importantly, by using a psychophysical approach, Experiment 4 shows, for the first time, that social norm learning alters individuals' point of subjective equality and, hence, the visual appearance of stimuli.

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.

Movement characteristics impact decision-making and vice versa

Previous studies suggest that humans are capable of coregulating the speed of decisions and movements if promoted by task incentives. It is unclear however whether such behavior is inherent to the process of translating decisional information into movements, beyond posing a valid strategy in some task contexts. Therefore, in a behavioral online study we imposed time constraints to either decision- or movement phases of a sensorimotor task, ensuring that coregulating decisions and movements was not promoted by task incentives. We found that participants indeed moved faster when fast decisions were promoted and decided faster when subsequent finger tapping movements had to be executed swiftly. These results were further supported by drift diffusion modelling and inspection of psychophysical kernels: Sensorimotor delays related to initiating the finger tapping sequence were shorter in fast-decision as compared to slow-decision blocks. Likewise, the decisional speed-accuracy tradeoff shifted in favor of faster decisions in fast-tapping as compared to slow-tapping blocks. These findings suggest that decisions not only impact movement characteristics, but that properties of movement impact the time taken to decide. We interpret these behavioral results in the context of embodied decision-making, whereby shared neural mechanisms may modulate decisions and movements in a joint fashion.

Duration discrimination: A diffusion decision modeling approach

The human ability to discriminate the duration of two subsequently presented stimuli is often studied with tasks that involve a comparison between a standard stimulus (with fixed duration) and comparison stimuli (with varying durations). The performance in such tasks is influenced by the presentation order of these successively presented stimuli. The so-called Type A effect refers to the impact of presentation order on the point of subjective equality. The Type B effect describes effects of presentation order on the just-noticeable-difference. Cognitive models that account for these context effects assume that participants' duration estimation is influenced by the history of previously encountered stimuli. For example, the internal reference model assumes that the magnitude of a "typical" stimulus is represented by an internal reference. This internal reference evolves throughout an experiment and is updated on every trial. Different recent models have in common that they describe how the internal reference is computed but are agnostic to the decision process itself. In this study, we develop a new model that incorporates the mechanisms of perceptual discrimination models into a diffusion model. The diffusion model focuses on the dynamics of the decision process itself and accounts for choice and response times based on a set of latent cognitive variables. We show that our model accurately predicts the accuracy and response time distribution in a classical duration discrimination task. Further, model parameters were sensitive to the Type A and B effect. The proposed model opens up new opportunities for studying human discrimination performance (e.g., individual differences).

How speed impacts threat assessment in lethal force decisions

Despite the importance of being both fast and accurate in lethal force decisions, there is little empirical evidence to identify how speed impacts threat-related decisions and perception. Two experiments used speeded and unspeeded manipulations to determine how the speed imperative impacted threat assessments. Experiment 1 used drift diffusion modeling to quantify decision parameters, including rate of information processing, decision threshold, bias, and non-decisional processes. Speeded conditions reduced the information threshold needed to make decisions and shortened non-decisional processes, yet this manipulation had no impact on the rate of information processing or starting bias. Experiment 2 explored perceptual differences in threat assessment. Participants confidently made threat assessments despite only 30 ms exposure to stimuli with little impact on their subjective threat ratings based on exposure duration. Taken together, these results document the influence of speed on decision-making parameters of threat assessments while demonstrating little impact on threat perception.

Explicit and implicit attitudes toward smoking: Dissociation of attitudes and different characteristics for an implicit attitude in smokers and nonsmokers

Smoking is a global health risk for premature death and disease. Recently, addictive behaviors, like smoking, were considered to be guided by explicit and implicit processes. The existence of a dissociation between the two attitudes in nonsmokers and the causes of the differences in implicit attitudes toward smoking have not been fully investigated. We investigated the explicit and implicit attitudes toward smoking via a self-reported scale and the single category implicit association test (SC-IAT), respectively, among undergraduate and graduate health sciences students. In addition, we applied the drift-diffusion model (DDM) on the SC-IAT and examined the behavioral characteristics that caused differences in implicit attitude toward smoking between smokers and nonsmokers. The results showed the existence of a dissociation between explicit and implicit attitudes toward smoking among nonsmokers. In addition, nonsmokers had a higher decision threshold than smokers and a higher drift rate in the condition where negative words were associated with smoking. Nonsmokers engaged in SC-IAT with more cautious attitudes and responded more easily in a negative condition since it was consistent with their true attitudes. Conversely, smokers did not show a significant difference in the drift rate between the conditions. These results suggested that the differences in an implicit attitude between smokers and nonsmokers were caused by differences in evidence accumulation speed between the positive and negative conditions. The existence of dissociation between implicit and explicit attitudes toward smoking may indicate the difficulty of measuring true attitude in nonsmokers in a questionnaire survey. Additionally, the DDM results explained the difference of implicit attitude between smokers and nonsmokers; it may provide information on the mechanisms of addictive behaviors and a basis for therapy. However, whether these results are affected by cultural differences requires further investigation.

Social norm learning from non-human agents can induce a persistent perceptual bias: A diffusion model approach

Seminal studies on social influence (Asch, 1956; Sherif, 1935) were based on face-to-face interactions between humans. Nowadays, computer-mediated communication is steadily becoming ubiquitous, and we are increasingly influenced by non-human agents, such as algorithms, robots, and chatbots. The present research aimed to answer two important questions: Can non-human agents exert social influence in a persistent manner and, thus, contribute to the emergence of social norms? And if this is the case, is social influence exerted by non-human agents mediated by the same or by different cognitive mechanisms as social influence exerted by human agents? To answer these questions, we used an online version of an established paradigm in research on social norm learning. To examine the cognitive underpinnings of social influence, we used a diffusion model approach. Our results provide strong evidence for the notion that non-human agents can induce persistent social influence outside an immediate group context and, hence, can contribute to the emergence of social norms. Furthermore, results from our diffusion model analyses support the notion that social influence exerted by non-human agents is mainly mediated by the same cognitive mechanisms as social influence exerted by human agents.

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

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

How are emotional facial expressions detected rapidly and accurately? A diffusion model analysis

Previous psychological studies have shown that people detect emotional facial expressions more rapidly and accurately than neutral facial expressions. However, the cognitive mechanisms underlying the efficient detection of emotional facial expressions remain unclear. To investigate this issue, we used diffusion model analyses to estimate the cognitive parameters of a visual search task in which participants detected faces with normal expressions of anger and happiness and their anti-expressions within a crowd of neutral faces. The anti-expressions were artificially created to control the visual changes of facial features but were usually recognized as emotionally neutral. We tested the hypothesis that the emotional significance of the target's facial expressions modulated the non-decisional time and the drift rate. We also conducted an exploratory investigation of the effect of facial expressions on threshold separation. The results showed that the non-decisional time was shorter, and the drift rate was larger for targets with normal expressions than with anti-expressions. Subjective emotional arousal ratings of facial targets were negatively related to the non-decisional time and positively associated with the drift rate. In addition, the threshold separation was larger for normal expressions than for anti-expressions and positively associated with arousal ratings for facial targets. These results suggest that the efficient detection of emotional facial expressions is accomplished via the faster and more cautious accumulation of emotional information of facial expressions which is initiated more rapidly by enhanced attentional allocation.

The neural computation of human prosocial choices in complex motivational states

Motives motivate human behavior. Most behaviors are driven by more than one motive, yet it is unclear how different motives interact and how such motive combinations affect the neural computation of the behaviors they drive. To answer this question, we induced two prosocial motives simultaneously (multi-motive condition) and separately (single motive conditions). After the different motive inductions, participants performed the same choice task in which they allocated points in favor of the other person (prosocial choice) or in favor of themselves (egoistic choice). We used fMRI to assess prosocial choice-related brain responses and drift diffusion modeling to specify how motive combinations affect individual components of the choice process. Our results showed that the combination of the two motives in the multi-motive condition increased participants' choice biases prior to the behavior itself. On the neural level, these changes in initial prosocial bias were associated with neural responses in the bilateral dorsal striatum. In contrast, the efficiency of the prosocial decision process was comparable between the multi-motive and the single-motive conditions. These findings provide insights into the computation of prosocial choices in complex motivational states, the motivational setting that drives most human behaviors.

Impact of total sleep deprivation and related mood changes on approach-avoidance decisions to threat-related facial displays

STUDY OBJECTIVES

Total sleep deprivation is known to have significant detrimental effects on cognitive and socio-emotional functioning. Nonetheless, the mechanisms by which total sleep loss disturbs decision-making in social contexts are poorly understood. Here, we investigated the impact of total sleep deprivation on approach/avoidance decisions when faced with threatening individuals, as well as the potential moderating role of sleep-related mood changes.

METHODS

Participants (n = 34) made spontaneous approach/avoidance decisions in the presence of task-irrelevant angry or fearful individuals, while rested or totally sleep deprived (27 h of continuous wakefulness). Sleep-related changes in mood and sustained attention were assessed using the Positive and Negative Affective Scale and the psychomotor vigilance task, respectively.

RESULTS

Rested participants avoided both fearful and angry individuals, with stronger avoidance for angry individuals, in line with previous results. On the contrary, totally sleep deprived participants favored neither approach nor avoidance of fearful individuals, while they still comparably avoided angry individuals. Drift-diffusion models showed that this effect was accounted for by the fact that total sleep deprivation reduced value-based evidence accumulation toward avoidance during decision making. Finally, the reduction of positive mood after total sleep deprivation positively correlated with the reduction of fearful display avoidance. Importantly, this correlation was not mediated by a sleep-related reduction in sustained attention.

CONCLUSIONS

All together, these findings support the underestimated role of positive mood-state alterations caused by total sleep loss on approach/avoidance decisions when facing ambiguous socio-emotional displays, such as fear.

Repetition effects in action planning reflect effector- but not hemisphere-specific coding

Action choices are influenced by future and recent past action states. For example, when performing two actions in succession, response times (RTs) to initiate the second action are reduced when the same hand is used. These findings suggest the existence of effector-specific processing for action planning. However, given that each hand is primarily controlled by the contralateral hemisphere, the RT benefit might actually reflect effector-independent, hemisphere-specific rather than effector-specific repetition effects. Here, participants performed two consecutive movements, each with a hand or a foot, in one of two directions. Direction was specified in an egocentric reference frame (inward, outward) or in an allocentric reference frame (left, right). Successive actions were initiated faster when the same limb (e.g., left hand-left hand), but not the other limb of the same body side (e.g., left foot-left hand), executed the second action. The same-limb advantage was evident even when the two movements involved different directions, whether specified egocentrically or allocentrically. Corroborating evidence from computational modeling lends support to the claim that repetition effects in action planning reflect persistent changes in baseline activity within neural populations that encode effector-specific action plans.

NEW & NOTEWORTHY Repeated hand use facilitates the initiation of successive actions (repetition effect). This finding has been interpreted as evidence for effector-specific action plans. However, given that each hand is primarily controlled by the contralateral hemisphere, any differences might reflect effector-independent, hemisphere-specific rather than effector-specific processing. We dissociated these alternatives by asking participants to perform successive actions with hands and feet and provide novel evidence that repetition effects in limb use truly reflect effector-specific coding.

Preregistration in diverse contexts: a preregistration template for the application of cognitive models

In recent years, open science practices have become increasingly popular in psychology and related sciences. These practices aim to increase rigour and transparency in science as a potential response to the challenges posed by the replication crisis. Many of these reforms-including the increasingly used preregistration-have been designed for purely experimental work that tests straightforward hypotheses with standard inferential statistical analyses, such as assessing whether an experimental manipulation has an effect on a variable of interest. But psychology is a diverse field of research. The somewhat narrow focus of the prevalent discussions surrounding and templates for preregistration has led to debates on how appropriate these reforms are for areas of research with more diverse hypotheses and more intricate methods of analysis, such as cognitive modelling research within mathematical psychology. Our article attempts to bridge the gap between open science and mathematical psychology, focusing on the type of cognitive modelling that Crüwell et al. (Crüwell S, Stefan AM, Evans NJ. 2019 Robust standards in cognitive science. Comput. Brain Behav. 2, 255-265) labelled model application, where researchers apply a cognitive model as a measurement tool to test hypotheses about parameters of the cognitive model. Specifically, we (i) discuss several potential researcher degrees of freedom within model application, (ii) provide the first preregistration template for model application and (iii) provide an example of a preregistered model application using our preregistration template. More broadly, we hope that our discussions and concrete proposals constructively advance the mostly abstract current debate surrounding preregistration in cognitive modelling, and provide a guide for how preregistration templates may be developed in other diverse or intricate research contexts.

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.

Healthful choices depend on the latency and rate of information accumulation

The drift diffusion model provides a parsimonious explanation of decisions across neurobiological, psychological and behavioural levels of analysis. Although most drift diffusion model implementations assume that only a single value guides decisions, choices often involve multiple attributes that could make separable contributions to choice. Here we fit incentive-compatible dietary choices to a multi-attribute, time-dependent drift diffusion model, in which taste and health could differentially influence the evidence accumulation process. We find that these attributes shaped both the relative value signal and the latency of evidence accumulation in a manner consistent with participants' idiosyncratic preferences. Moreover, by using a dietary prime, we showed how a healthy choice intervention alters multi-attribute, time-dependent drift diffusion model parameters that in turn predict prime-dependent choices. Our results reveal that different decision attributes make separable contributions to the strength and timing of evidence accumulation, providing new insights into the construction of interventions to alter the processes of choice.

Age differences in diffusion model parameters: a meta-analysis

Older adults typically show slower response times in basic cognitive tasks than younger adults. A diffusion model analysis allows the clarification of why older adults react more slowly by estimating parameters that map distinct cognitive components of decision making. The main components of the diffusion model are the speed of information uptake (drift rate), the degree of conservatism regarding the decision criterion (boundary separation), and the time taken up by non-decisional processes (i.e., encoding and motoric response execution; non-decision time). While the literature shows consistent results regarding higher boundary separation and longer non-decision time for older adults, results are more complex when it comes to age differences in drift rates. We conducted a multi-level meta-analysis to identify possible sources of this variance. As possible moderators, we included task difficulty and task type. We found that age differences in drift rate are moderated both by task type and task difficulty. Older adults were inferior in drift rate in perceptual and memory tasks, but information accumulation was even increased in lexical decision tasks for the older participants. Additionally, in perceptual and lexical decision tasks, older individuals benefitted from high task difficulty. In the memory tasks, task difficulty did not moderate the negative impact of age on drift. The finding of higher boundary separation and longer non-decision time in older than younger adults generalized over task type and task difficulty. The results of our meta-analysis are consistent with recent findings of a more pronounced age-related decline in memory than in vocabulary performance.

The Hunger Games: Homeostatic State-Dependent Fluctuations in Disinhibition Measured with a Novel Gamified Test Battery

Food homeostatic states (hunger and satiety) influence the cognitive systems regulating impulsive responses, but the direction and specific mechanisms involved in this effect remain elusive. We examined how fasting, and satiety, affect cognitive mechanisms underpinning disinhibition using a novel framework and a gamified test-battery. Thirty-four participants completed the test-battery measuring three cognitive facets of disinhibition: attentional control, information gathering and monitoring of feedback, across two experimental sessions: one after overnight fasting and another after a standardised meal. Homeostatic state was assessed using subjective self-reports and biological markers (i.e., blood-derived liver-expressed antimicrobial protein 2 (LEAP-2), insulin and leptin). We found that participants who experienced greater subjective hunger during the satiety session were more impulsive in the information gathering task; results were not confounded by changes in mood or anxiety. Homeostatic state did not significantly influence disinhibition mechanisms linked to attentional control or feedback monitoring. However, we found a significant interaction between homeostatic state and LEAP-2 on attentional control, with higher LEAP-2 associated with faster reaction times in the fasted condition only. Our findings indicate lingering hunger after eating increases impulsive behaviour via reduced information gathering. These findings identify a novel mechanism that may underpin the tendency to overeat and/or engage in broader impulsive behaviours.

Prior information use and response caution in perceptual decision-making: No evidence for a relationship with autistic-like traits

Interpreting the world around us requires integrating incoming sensory signals with prior information. Autistic individuals have been proposed to rely less on prior information and make more cautious responses than non-autistic individuals. Here, we investigated whether these purported features of autistic perception vary as a function of autistic-like traits in the general population. We used a diffusion model framework, whereby decisions are modelled as noisy evidence accumulation processes towards one of two bounds. Within this framework, prior information can bias the starting point of the evidence accumulation process. Our pre-registered hypotheses were that higher autistic-like traits would relate to reduced starting point bias caused by prior information and increased response caution (wider boundary separation). 222 participants discriminated the direction of coherent motion stimuli as quickly and accurately as possible. Stimuli were preceded with a neutral cue (square) or a directional cue (arrow). 80% of the directional cues validly predicted the upcoming motion direction. We modelled accuracy and response time data using a hierarchical Bayesian model in which starting point varied with cue condition. We found no evidence for our hypotheses, with starting point bias and response caution seemingly unrelated to Adult Autism Spectrum Quotient (AQ) scores. Alongside future research applying this paradigm to autistic individuals, our findings will help refine theories regarding the role of prior information and altered decision-making strategies in autistic perception. Our study also has implications for models of bias in perceptual decision-making, as the most plausible model was one that incorporated bias in both decision-making and sensory processing.

Think fast! The implications of emphasizing urgency in decision-making

Evidence accumulation models (EAMs) have become the dominant explanation of how the decision-making process operates, proposing that decisions are the result of a process of evidence accumulation. The primary use of EAMs has been as "measurement tools" of the underlying decision-making process, where researchers apply EAMs to empirical data to estimate participants' task ability (i.e., the "drift rate"), response caution (i.e., the "decision threshold"), and the time taken for other processes (i.e., the "non-decision time"), making EAMs a powerful tool for discriminating between competing psychological theories. Recent studies have brought into question the mapping between the latent parameters of EAMs and the theoretical constructs that they are thought to represent, showing that emphasizing urgent responding - which intuitively should selectively influence decision threshold - may also influence drift rate and/or non-decision time. However, these findings have been mixed, leading to differences in opinion between experts in the field. The current study aims to provide a more conclusive answer to the implications of emphasizing urgent responding, providing a re-analysis of 6 data sets from previous studies using two different EAMs - the diffusion model and the linear ballistic accumulator (LBA) - with state-of-the-art methods for model selection based inference. The findings display clear evidence for a difference in conclusions between the two models, with the diffusion model suggesting that decision threshold and non-decision time decrease when urgency is emphasized, and the LBA suggesting that decision threshold and drift rate decrease when urgency is emphasized. Furthermore, although these models disagree regarding whether non-decision time or drift rate decrease under urgency emphasis, both show clear evidence that emphasizing urgency does not selectively influence decision threshold. These findings suggest that researchers should revise their assumptions about certain experimental manipulations, the specification of certain EAMs, or perhaps both.

Stability and Change in Diffusion Model Parameters over Two Years

In recent years, mathematical models of decision making, such as the diffusion model, have been endorsed in individual differences research. These models can disentangle different components of the decision process, like processing speed, speed–accuracy trade-offs, and duration of non-decisional processes. The diffusion model estimates individual parameters of cognitive process components, thus allowing the study of individual differences. These parameters are often assumed to show trait-like properties, that is, within-person stability across tasks and time. However, the assumption of temporal stability has so far been insufficiently investigated. With this work, we explore stability and change in diffusion model parameters by following over 270 participants across a time period of two years. We analysed four different aspects of stability and change: rank-order stability, mean-level change, individual differences in change, and profile stability. Diffusion model parameters showed strong rank-order stability and mean-level changes in processing speed and speed–accuracy trade-offs that could be attributed to practice effects. At the same time, people differed little in these patterns across time. In addition, profiles of individual diffusion model parameters proved to be stable over time. We discuss implications of these findings for the use of the diffusion model in individual differences research.

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.

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.

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.

Sequential sampling models without random between-trial variability: the racing diffusion model of speeded decision making

Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models.

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.

Do Non-Decision Times Mediate the Association between Age and Intelligence across Different Content and Process Domains?

In comparison to young adults, middle-aged and old people show lower scores in intelligence tests and slower response times in elementary cognitive tasks. Whether these well-documented findings can both be attributed to a general cognitive slow-down across the life-span has become subject to debate in the last years. The drift diffusion model can disentangle three main process components of binary decisions, namely the speed of information processing, the conservatism of the decision criterion and the non-decision time (i.e., time needed for processes such as encoding and motor response execution). All three components provide possible explanations for the association between response times and age. We present data from a broad study using 18 different response time tasks from three different content domains (figural, numeric, verbal). Our sample included people between 18 to 62 years of age, thus allowing us to study age differences across young-adulthood and mid-adulthood. Older adults generally showed longer non-decision times and more conservative decision criteria. For speed of information processing, we found a more complex pattern that differed between tasks. We estimated mediation models to investigate whether age differences in diffusion model parameters account for the negative relation between age and intelligence, across different intelligence process domains (processing capacity, memory, psychometric speed) and different intelligence content domains (figural, numeric, verbal). In most cases, age differences in intelligence were accounted for by age differences in non-decision time. Content domain-general, but not content domain-specific aspects of non-decision time were related to age. We discuss the implications of these findings on how cognitive decline and age differences in mental speed might be related.

A flexible framework for simulating and fitting generalized drift-diffusion models

The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are solved numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We demonstrate fitting of GDDMs within our framework to both animal and human datasets from perceptual decision-making tasks, with better accuracy and fewer parameters than several DDMs implemented using the latest methodology, to test hypothesized decision-making mechanisms. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

Dissecting the cognitive phenotype of post-stroke fatigue using computerized assessment and computational modeling of sustained attention

Post-stroke fatigue (PSF) is prevalent among stroke patients, but its mechanisms are poorly understood. Many patients with PSF experience cognitive difficulties, but studies aiming to identify cognitive correlates of PSF have been largely inconclusive. With the aim of characterizing the relationship between subjective fatigue and attentional function, we collected behavioral data using the attention network test (ANT) and self-reported fatigue scores using the fatigue severity scale (FSS) from 53 stroke patients. In order to evaluate the utility and added value of computational modeling for delineating specific underpinnings of response time (RT) distributions, we fitted a hierarchical drift diffusion model (hDDM) to the ANT data. Results revealed a relationship between fatigue and RT distributions. Specifically, there was a positive interaction between FSS score and elapsed time on RT. Group analyses suggested that patients without PSF increased speed during the course of the session, while patients with PSF did not. In line with the conventional analyses based on observed RT, the best fitting hDD model identified an interaction between elapsed time and fatigue on non-decision time, suggesting an increase in time needed for stimulus encoding and response execution rather than cognitive information processing and evidence accumulation. These novel results demonstrate the significance of considering the sustained nature of effort when defining the cognitive phenotype of PSF, intuitively indicating that the cognitive phenotype of fatigue entails an increased vulnerability to sustained effort, and suggest that the use of computational approaches offers a further characterization of specific processes underlying behavioral differences.

The diffusion model visualizer: an interactive tool to understand the diffusion model parameters

Response time (RT) data play an important role in psychology. The diffusion model (DM) allows to analyze RT-data in a two-alternative-force-choice paradigm using a particle drift diffusion modeling approach. It accounts for right-skewed distributions in a natural way. However, the model incorporates seven parameters, the roles of which are difficult to comprehend from the model equation. Therefore, the present article introduces the diffusion model visualizer (DMV) allowing for interactive manipulation of each parameter and plotting the resulting RT densities. Thus, the DMV serves as a valuable tool for understanding the specific role of each model parameter. It may come in handy for didactical purposes and in research context. It allows for tracking down parameter estimation problems by delivering the model-based ideal densities, which can be juxtaposed to the data-based densities. It will also serve a valuable purpose in detecting outliers. The article describes the basics of the DM along with technical details of the DMV and gives several hints for its usage.

Aggressive Realism: More Efficient Processing of Anger in Physically Aggressive Individuals

Physically aggressive individuals' heightened tendency to decide that ambiguous faces are angry is thought to contribute to their destructive interpersonal behavior. Although this tendency is commonly attributed to bias, other cognitive processes could account for the emotion-identification patterns observed in physical aggression. Diffusion modeling is a valuable tool for parsing the contributions of several cognitive processes known to influence decision-making, including bias, drift rate (efficiency of information accumulation), and threshold separation (extent of information accumulation). In a sample of 90 incarcerated men, we applied diffusion modeling to an emotion-identification task. Physical aggression was positively associated with drift rate (i.e., more efficient information accumulation) for anger, and drift rate mediated the association between physical aggression and heightened anger identification. Physical aggression was not, however, associated with bias or threshold separation. These findings implicate processing efficiency for anger-related information as a potential mechanism driving aberrant emotion identification in physical aggression.