Drift-diffusion Model Research Articles

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.

Reinforcement learning in women remitted from anorexia nervosa: Preliminary examination with a hybrid reinforcement learning/drift diffusion model.

Altered reinforcement learning (RL) and decision-making have been implicated in the pathophysiology of anorexia nervosa. To determine whether deficits observed in symptomatic anorexia nervosa are also present in remission, we investigated RL in women remitted from anorexia nervosa (rAN). Participants performed a probabilistic associative learning task that involved learning from rewarding or punishing outcomes across consecutive sets of stimuli to examine generalization of learning to new stimuli over extended task exposure. We fit a hybrid RL and drift diffusion model of associative learning to model learning and decision-making processes in 24 rAN and 20 female community controls (cCN). rAN showed better learning from negative outcomes than cCN and this was greater over extended task exposure (p < .001, ηp2 = .30). rAN demonstrated a reduction in accuracy of optimal choices (p = .007, ηp2 = .16) and rate of information extraction on reward trials from set 1 to set 2 (p = .012, ηp2 = .14), and a larger reduction of response threshold separation from set 1 to set 2 than cCN (p = .036, ηp2 = .10). rAN extracted less information from rewarding stimuli and their learning became increasingly sensitive to negative outcomes over learning trials. This suggests rAN shifted attention to learning from negative feedback while slowing down extraction of information from rewarding stimuli. Better learning from negative over positive feedback in rAN might reflect a marker of recovery.

High overall values mitigate gaze-related effects in perceptual and preferential choices.

A growing literature has shown that people tend to make faster decisions when choosing between two high-intensity or high-utility options than when choosing between two less-intensity or low-utility options. However, the underlying cognitive mechanisms of this effect of overall value (OV) on response times (RT) remains controversial, partially due to inconsistent findings of OV effects on accuracy but also due to the lack of process-tracing studies testing this effect. Here, we set out to fill this gap by testing and modeling the influence of OV on choices, RT, and eye movements in both perceptual and preferential decisions in a preregistered eye-tracking experiment (N = 61). Across perceptual and preferential tasks, we observed significant and consistently negative correlations between OV and RT, replicating previous work. Accuracy tended to increase with OV, reaching significance in preferential choices only. Eye-tracking analyses revealed a reduction of different gaze-related effects under high OV: a reduced tendency to choose the longer fixated option in perceptual choice and a reduced tendency to choose the last fixated option in preferential choice. Modeling these data with the attentional drift-diffusion model showed that the nonfixated option value was discounted least in the high-OV condition, confirming that higher OV might mitigate the impact of gaze on choices. Our results suggest that OV jointly affects behavior and gaze influences and offer a mechanistic account for the puzzling phenomenon that decisions between options of higher OV tend to be faster, but not less accurate. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Bridging multifluid and drift-diffusion models for bounded plasmas

Fluid models represent a valid alternative to kinetic approaches in simulating low-temperature discharges: a well-designed strategy must be able to combine the ability to predict a smooth transition from the quasineutral bulk to the sheath, where a space charge is built at a reasonable computational cost. These approaches belong to two families: multifluid models, where momenta of each species are modeled separately, and drift-diffusion models, where the dynamics of particles is dependent only on the gradient of particle concentration and on the electric force. It is shown that an equivalence between the two models exists and that it corresponds to a threshold Knudsen number, in the order of the square root of the electron-to-ion mass ratio; for an argon isothermal discharge, this value is given by a neutral background pressure Pn≳1000 Pa. This equivalence allows us to derive two analytical formulas for a priori estimation of the sheath width: the first one does not need any additional hypothesis but relies only on the natural transition from the quasineutral bulk to the sheath; the second approach improves the prediction by imposing a threshold value for the charge separation. The new analytical expressions provide better estimations of the floating sheath dimension in collisions-dominated regimes when tested against two models from the literature.

Dynamic financial risk assessment with TOPSIS and drift diffusion models for partner selection of international engineering projects

Dynamic financial risk assessment with TOPSIS and drift diffusion models for partner selection of international engineering projects

Interplay of aging and practice in conflict processing: A big-data diffusion-model analysis.

We are continually required to exercise cognitive control in order to separate relevant and irrelevant information. Previous studies have produced mixed results as to whether cognitive control declines across adulthood and improves with practice. Moreover, little is known about the influences of aging and practice on the automatic and controlled processes underlying performance in conflict situations. This calls for analyses of extensive data using process models for conflict tasks, akin to earlier drift-diffusion model analyses of performance in cognitive nonconflict tasks. Thus, to understand how aging and practice influence cognitive control at the process level, we analyzed a large-scale data set (1,800 participants aged 21-80 years completing 60 blocklike online games of an arrow-based Eriksen flanker task). At the coarse-grained level of mean response times, the congruency effect increased across adulthood and decreased with practice following an initial increase. The finer-grained distributional response time and error rate data were closely fitted by the diffusion model for conflict tasks, which captures the dynamic interplay of automatic and controlled processing. Best-fitting parameter values revealed multiple, partially counteracting influences of aging and practice: Aging across adulthood slowed down both controlled and automatic processing (besides slowing down nondecisional processes and increasing decision caution). By contrast, practice selectively speeded up controlled processing (besides speeding up nondecisional processes and decreasing decision caution). Taken together, these findings suggest that aging and practice primarily alter the speed of controlled (aging and practice) and automatic processing (aging), rather than causing inhibitory adjustments in the strength of automatic processing. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

The Less Meaningful the Understanding, the Faster the Feeling: Speech Comprehension Changes Perceptual Speech Tempo.

The perception of speech tempo is influenced by both the acoustic properties of speech and the cognitive state of the listener. However, there is a lack of research on how speech comprehension affects the perception of speech tempo. This study aims to disentangle the impact of speech comprehension on the perception of speech tempo by manipulating linguistic structures and measuring perceptual speech tempo at explicit and implicit levels. Three experiments were conducted to explore these relationships. In Experiment 1, two explicit speech tasks revealed that listeners tend to overestimate the speech tempo of sentences with low comprehensibility, although this effect decreased with repeated exposure to the speech. Experiment 2, utilizing an implicit speech tempo task, replicated the main findings of Experiment 1. Furthermore, the results from the drift-diffusion model eliminated the possibility that participants' responses were based on the type of sentence. In Experiment 3, non-native Chinese speakers with varying levels of language proficiency completed the implicit speech rate task. The results showed that non-native Chinese speakers exhibited distinct behavioral patterns compared to native Chinese speakers, as they did not perceive differences in speech tempo between high and low comprehensibility conditions. These findings highlight the intricate relationship between the perception of speech tempo and the comprehensibility of processed speech.

Numerical simulation of surface charge accumulation under negative discharge of different humidities and pressures

The surface discharge phenomenon of polymers severely limits their applications in electrical and electronic devices, especially in complex environments. In this study, a drift-diffusion model based on a hydrodynamic approach was developed to investigate the influence of humidity and gas pressure on the negative surface discharge. The results indicate that the discharge pattern did not change under different humidity conditions. The increased humidity accelerated the formation of discharges and increased the discharge pulse current. In particular, as the humidity increased, tiny pulses occurred at the tail of the first pulse, and the number of tiny pulses increased. The appearance of these tiny pulses changed the surface charge distribution from a “ring-like” distribution to a “spot-like” distribution. Meanwhile, the accumulation of surface charges significantly distorted the spatial electric field distribution and suppressed the electron multiplication stage of the subsequent discharges, thus reducing the current in the Trichel pulse discharge stage. It is precisely because the discharge is stronger under high humidity, resulting in more surface charges accumulating on the surface, which is in keeping with the experimental results. The measured charges at different humidities show a similar distinct spot-like distribution, illustrating a constant pattern of discharge. All these results demonstrated the correctness and applicability of the simulation. The surface discharge under different pressures exhibited some similarities with the case of different humidity levels. As the pressure increased, the number of discharge current pulses and the pulse amplitude decreased, resulting in a decrease in the surface charge density.

Mechanisms of increased pain discrimination by contingent reinforcement: a perceptual decision-making and instrumental learning account.

Recent evidence highlights that monetary rewards can increase the precision at which healthy human volunteers can detect small changes in the intensity of thermal noxious stimuli, contradicting the idea that rewards exert a broad inhibiting influence on pain perception. This effect was stronger with contingent rewards compared with noncontingent rewards, suggesting a successful learning process. In the present study, we implemented a model comparison approach that aimed to improve our understanding of the mechanisms that underlie thermal noxious discrimination in humans. In a between-subject design, 54 healthy human volunteers took part in a pain discrimination task with monetary rewards either contingent or noncontingent on successful discrimination of small changes in the intensity painful heat stimulation. We used models from 2 traditions in decision-making research, perceptual decision-making, and instrumental learning. Replicating the previous findings, only rewards contingent on behavior enhanced pain discrimination. Drift diffusion modelling revealed increased sensory signal strength and decreased response caution and nondecision times as mechanisms underlying this effect of contingent rewards on pain discrimination. In addition, reinforcement learning models indicated a temporal evolution of discriminative abilities reflected by a trial-by-trial increase of perceived signal strength only with contingent rewards but not with noncontingent rewards. Modelling of separate learning rates for positive and negative prediction errors indicated that this temporal evolution of discriminative abilities was driven by positive reward prediction errors. These results might indicate increased sensitivity towards better-than-expected outcomes in the temporal adaptation of pain discrimination abilities to a rewarding context in humans.

Cognitive and neural underpinnings of friend-prioritization in a perceptual matching task.

Previous findings of better behavioral responses to self- over other-related stimuli suggest prioritized cognitive processes of self-related information. However, it is unclear whether the processing of information related to important others (e.g.,friends) may be prioritized over that related to the self in certain subpopulations and, if yes, whether friend-prioritization and self-prioritization engage distinct cognitive and neural mechanisms. We collected behavioral and electroencephalography (EEG) data from a large sample (N=1006) during learning associations between shapes and person labels (self or a friend). Analyses of response times and sensitivities revealed two subpopulations who performed better to friend-shape or self-shape associations, respectively (N=216 for each group). Drift diffusion model (DDM) analyses unraveled faster information acquisition for friend-shape (vs. self-shape) associations in the friend-prioritization group but an opposite pattern in the self-prioritization group . Trial-by-trial regression analyses of EEG data showed that the greater amplitudes of a frontal/central activity at 180-240 ms post-stimulus were correlated with faster information acquisition from friend-shape associations in the friend-prioritization group but from self-shape associations in the self-prioritization group. However, the frontal/central neural oscillations at 8-18 Hz during perceptual learning were specifically associated with speed of information acquisition from friend-shape associations in the friend-prioritization-group. Our findings provide evidence for friend-prioritization in perceptual learning in a subpopulation of adults and clarify the underlying cognitive and neural mechanisms.

Measuring Self-Referent Memory Bias as Marker for Depression: Overview, New Insights, and Recommendations

Negative self-referent memory bias (the preferential memory for negative self-referent information) is a well-known symptom of depression and a risk factor for its development, maintenance, and recurrence. Evidence shows its potential as an add-on tool in clinical practice. However, it is unclear which self-referent memory bias measure(s) could be clinically relevant. Here, as a first step, we investigate which measures best differentiate current depression status and track depressive symptom severity most closely. The total sample (N = 956) from three (naturalistic) psychiatric cohorts with matched controls was divided into a current depression, remitted depression, and non-disordered control group. Self-referent memory bias task measures were calculated and the drift diffusion model (DDM) was applied to assess underlying components of the cognitive self-referent decision making process. Measures were compared between groups and linear regression models were applied to assess their association with depressive symptom severity. The number of negative endorsed words differentiated best between depression status while a combination of the number of positive endorsed words, self-referent negative memory bias, and positive drift rate was most strongly associated with depressive symptom severity. Our results give direction to the clinical implementation of this task. Its value in assessing, monitoring, and predicting depressive state and trait in clinical settings requires further investigation.

Functional brain activations correlated with association strength and prediction error during novel symbol–speech sound learning

Abstract Efficient learning of letters–speech sound associations results in the specialization of visual and audiovisual brain regions, which is crucial for the development of proficient reading skills. However, the brain dynamics underlying this learning process remain poorly understood, and the involvement of learning and performance monitoring networks remains underexplored. Here we applied two mutually dependent feedback learning tasks in which novel symbol–speech sound associations were learned by 39 healthy adults. We employed functional magnetic resonance (fMRI) along with a reinforcement learning drift diffusion model to characterize trial-by-trial learning in behavior and brain. The model-based analysis showed that posterior–occipital activations during stimulus processing were positively modulated by trial-wise learning, as indicated by the increase in association strength between audiovisual pairs. Prediction errors, describing the update mechanism to learn from feedback across trials, modulated activations in several mid-frontal, striatal, and cingulate regions. Both tasks yielded similar patterns of results, despite differences in their relative difficulty. This study elucidates the processes involved in audiovisual learning that contribute to rapid visual specialization within a single experimental session and delineates a set of coactivated regions involved in learning from feedback. Our paradigm provides a framework to advance our understanding of the neurobiology of learning and reading development.

Using hierarchical drift diffusion models to elucidate computational mechanisms of reduced reward sensitivity in adolescent major depressive disorder

BackgroundAnhedonia—a core symptom of major depressive disorder (MDD)—is closely related to diminished reward sensitivity. Nonetheless, the psychopathological and computational mechanism underlying anhedonia in young patients with MDD remains unclear. Therefore, this study aims to investigate reward sensitivity in adolescents and young adults with MDD using computational modelling.MethodsOverall, 70 patients with MDD and 54 age- and sex-matched healthy controls (HC) completed a probabilistic reward task (PRT) to assess their general behavioral inclination towards more frequently reinforced stimuli (i.e., “response bias”). Bayesian hierarchical drift diffusion modeling (HDDM) was employed to determine changes in reward sensitivity and computational process during decision-making.ResultsAdolescents with depression showed a trend toward reduced response bias compared to those in HC. HDDM analysis revealed wider decision thresholds in both adolescents and young adults with MDD group. Adolescents with MDD exhibited significantly lower drift rates and reduced starting point bias compared to those in HC. Higher anhedonia levels were linked to lower drift rates and wider decision thresholds. Additionally, increased discriminability correlated with higher drift rates, while higher response bias was linked to larger starting points.ConclusionsOur findings suggest that reduced reward sensitivity and slower evidence accumulation during reward learning may serve as potential indicators of anhedonia in adolescents with MDD. These findings provided crucial insights into the dysregulated positive affect model, underscoring a dysfunctional reward system as a key factor in anhedonia developmental psychopathology in depression.

Striatal stimulation enhances cognitive control and evidence processing in rodents and humans.

Brain disorders, in particular mental disorders, might be effectively treated by direct electrical brain stimulation, but clinical progress requires understanding of therapeutic mechanisms. Animal models have not helped, because there are no direct animal models of mental illness. Here, we propose a potential path past this roadblock, by leveraging a common ingredient of most mental disorders: impaired cognitive control. We previously showed that deep brain stimulation (DBS) improves cognitive control in humans. We now reverse translate that result using a set-shifting task in rats. DBS-like stimulation of the midstriatum improved reaction times without affecting accuracy, mirroring our human findings. Impulsivity, motivation, locomotor, and learning effects were ruled out through companion tasks and model-based analyses. To identify the specific cognitive processes affected, we applied reinforcement learning drift-diffusion modeling. This approach revealed that DBS-like stimulation enhanced evidence accumulation rates and lowered decision thresholds, improving domain-general cognitive control. Reanalysis of prior human data showed that the same mechanism applies in humans. This reverse/forward translational model could have near-term implications for clinical DBS practice and future trial design.

Disentangling the perceptual underpinnings of autism: Evidence from a face aftereffects experiment.

Existing literature has documented diminished norm-based adaptation (aftereffects) across several perceptual domains in autism. However, the exact underlying mechanisms, such as sensory dominance possibly caused by imprecise priors and/or increased sensory precision, remain elusive. The "Bayesian brain" framework offers refined methods to investigate these mechanisms. This study utilized both model-free (frequentist statistics) and model-based (hierarchical Drift Diffusion Modeling) analytical approaches to compare gender face aftereffects in male adolescents with autism (n = 29) to neurotypical controls (n = 39) using a behavioral choice experiment. Contrary to our initial hypotheses, our analyses did not find support for imprecise priors or increased sensory precision within the autistic group. Instead, we observed generally decreased drift rates towards male but not female stimuli in the autistic group. Thus, our findings suggest a lack of own-gender bias in face processing among the autistic participants. These findings align with more recent behavioral and neurophysiological research observing intact priors in individuals with autism, suggesting that other mechanisms may better explain the perceptual challenges in autism. Our study contributes to the ongoing discourse on perceptual processing in autism, emphasizing the necessity for more nuanced analytical approaches in order to unravel the complexity of this condition.

A spiking neural model of decision making and the speed-accuracy trade-off.

The speed-accuracy trade-off (SAT) is the tendency for fast decisions to come at the expense of accurate performance. Evidence accumulation models such as the drift diffusion model can reproduce a variety of behavioral data related to the SAT, and their parameters have been linked to neural activities in the brain. However, our understanding of how biological neural networks realize the associated cognitive operations remains incomplete, limiting our ability to unify neurological and computational accounts of the SAT. We address this gap by developing and analyzing a biologically plausible spiking neural network that extends the drift diffusion approach. We apply our model to both perceptual and nonperceptual tasks, investigate several contextual manipulations, and validate model performance using neural and behavioral data. Behaviorally, we find that our model (a) reproduces individual response time distributions; (b) generalizes across experimental contexts, including the number of choice alternatives, speed- or accuracy-emphasis, and task difficulty; and (c) predicts accuracy data, despite being fit only to response time data. Neurally, we show that our model (a) recreates observed patterns of spiking neural activity and (b) captures age-related deficits that are consistent with the behavioral data. More broadly, our model exhibits the SAT across a variety of tasks and contexts and explains how individual differences in speed and accuracy arise from synaptic weights within a spiking neural network. Our work showcases a method for translating mathematical models into functional neural networks and demonstrates that simulating such networks permits analyses and predictions that are outside the scope of purely mathematical models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Competing working memory contents: perceptual over semantic prioritization and voluntary retrieval following retro-cueing

ABSTRACT This study investigated how voluntary and involuntary retrospective attention prioritize working memory (WM) representations of low-level visual features (perceptual dimension) and high-level categories (semantic dimension) using real-world objects. Reaction time and accuracy from two retro-cueing experiments were analyzed with a hierarchical drift-diffusion model to assess impacts on representation quality (drift rate) and retrieval time (non-decision time). Voluntariness of attention did not differentially affect perceptual and semantic WM contents. Drift rates showed stronger retro-cueing effects on perceptual compared to semantic contents, while non-decision times revealed retro-cueing effects only for voluntary attention. These findings suggest: (1) voluntariness does not differentiate between perceptual and semantic contents competing in WM; (2) attention prioritizes perceptual over semantic contents; and (3) voluntariness is critical for retrieving WM contents in advance of decision-making. This work highlights how WM content type and attentional voluntariness independently shape the effects of retrospective attention.

Computational modeling of social evaluative decision-making elucidates individual differences in adolescent anxiety.

Adolescents experience significant developmental changes during a time of heightened sensitivity to social cues, particularly rejection by peers, which can be especially overwhelming for those with elevated levels of social anxiety. Social evaluative decision-making tasks have been useful in uncovering the neural correlates of information processing biases; however, linking youths' task-based performance to individual differences in psychopathology (e.g., anxiety symptoms) has proven more elusive. Here, we address this weakness with drift diffusion modeling to decompose youths' performance on the social judgment paradigm (SJP) to determine if this approach is useful in discovering individual differences in anxiety symptoms, as well as puberty, age, and sex. A sample of 103 adolescents (55 males, Mage = 14.49, SD = 1.69) completed the SJP and self-report measures of anxiety, as well as self- and parent-reported measures of puberty. The decision threshold parameter, reflecting the amount of evidence needed to make a social evaluative decision, predicted youth self-reported anxiety, above and beyond typical metrics of SJP performance. Our results highlight the potential advantage of parsing task performance according to the underlying cognitive processes. Future research would likely benefit from applying computational modeling approaches to social judgment tasks when attempting to uncover performance-based individual differences in psychopathology.

Integrated Ising Model with global inhibition for decision making.

Humans and other organisms make decisions choosing between different options, with the aim to maximize the reward and minimize the cost. The main theoretical framework for modeling the decision-making process has been based on the highly successful drift-diffusion model, which is a simple tool for explaining many aspects of this process. However, new observations challenge this model. Recently, it was found that inhibitory tone increases during high cognitive load and situations of uncertainty, but the origin of this phenomenon is not understood. Motivated by this observation, we extend a recently developed model for decision making while animals move towards targets in real space. We introduce an integrated Ising-type model, that includes global inhibition, and use it to explore its role in decision-making. This model can explain how the brain may utilize inhibition to improve its decision-making accuracy. Compared to experimental results, this model suggests that the regime of the brain's decision-making activity is in proximity to a critical transition line between the ordered and disordered. Within the model, the critical region near the transition line has the advantageous property of enabling a significant decrease in error with a small increase in inhibition and also exhibits unique properties with respect to learning and memory decay.

A comparative study of Chinese and foreign research relevant to the diffusion drift model in the last decade

Abstract. This study evaluates the differential research approaches of Chinese and international scholars regarding the drift diffusion model in mental processes through a literature review from the last decade. The application of DDM theory in cognitive research varies between Chinese and foreign study methodologies. The differences in decision-making mechanisms among cultural groups result in this outcome. Inconsistency has been identified in international academic research. They cover reward rate optimization, decision thresholds, decision time, and more. On the contrary, advancements in Chinese studies involve the development of innovative models. They are Bayesian and semi-parametric. These are designed for mixtures combining drift-diffusion processes and the inverse Gaussian distribution. In addition, they encompass elucidating the neural processes and implementing DDM techniques. This use of DDM reveals the rewarding perception and decision-making linked to depression. It also reveals the brain patterns of social anxiety. These comparisons aim to summarize the differences between Chinese and foreign studies. They also analyze them. It will find the causes of these differences. Moreover, it will determine how Chinese clinical care can use them. The DDM studies show its importance in decision-making patterns. Scholars have the opportunity to apply these tools in a variety of scientific fields, including neuroscience, psychology, artificial intelligence, and cognitive research.