Drift-diffusion Model Research Articles

Clarifying Cognitive Control Deficits in Psychosis via Drift Diffusion Modeling and Attractor Dynamics.

Cognitive control deficits are prominent in individuals with psychotic psychopathology. Studies providing evidence for deficits in proactive control generally examine average performance and not variation across trials for individuals-potentially obscuring detection of essential contributors to cognitive control. Here, we leverage intertrial variability through drift-diffusion models (DDMs) aiming to identify key contributors to cognitive control deficits in psychosis. People with psychosis (PwP; N = 122), their first-degree biological relatives (N = 78), and controls (N = 50) each completed 120 trials of the dot pattern expectancy (DPX) cognitive control task. We fit full hierarchical DDMs to response and reaction time (RT) data for individual trials and then used classification models to compare the DDM parameters with conventional measures of proactive and reactive control. PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information. Both PwP and relatives showed protracted nondecision times to infrequent trial sequences suggesting slowed perceptual processing. Classification analyses indicated that DDM parameters differentiated between the groups better than conventional measures and identified drift rates during proactive control, nondecision time during reactive control, and cue bias as most important. DDM parameters were associated with real-world functioning and schizotypal traits. Modeling of trial-level data revealed that slow evidence accumulation and longer preparatory periods are the strongest contributors to cognitive control deficits in psychotic psychopathology. This pattern of atypical responding during the DPX is consistent with shallow basins in attractor dynamic models that reflect difficulties in maintaining state representations, possibly mediated by excess neural excitation or poor connectivity.

Stochastic and self-consistent 3D modeling of streamer discharge trees with Kinetic Monte Carlo

This paper contains the foundation for a new Particle-In-Cell model for gas discharges, based on Îto diffusion and Kinetic Monte Carlo (KMC). In the new model the electrons are described with a microscopic drift-diffusion model rather than a macroscopic one. We discuss the connection of the Îto-KMC model to the equations of fluctuating hydrodynamics and the advection-diffusion-reaction equation which is conventionally used for simulating streamer discharges. The new model is coupled to a particle description of photoionization, providing a non-kinetic all-particle method with several attractive properties, such as: 1) Taking the same input as a fluid model, e.g. mobility coefficients, diffusion coefficients, and reaction rates. 2) Guaranteed non-negative densities. 3) Intrinsic support for reactive and diffusive fluctuations. 4) Exceptional stability properties. The model is implemented as a particle-mesh model on cut-cell grids with Cartesian adaptive mesh refinement. Positive streamer discharges in atmospheric air are considered as the primary application example, and we demonstrate that we can self-consistently simulate large discharge trees.

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.

Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning.

Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such contexts for their ability to mimic the underlying neural mechanisms. One such model for gradual longitudinal learning was recently developed in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). In practice, category response accuracies are often the only reliable measure recorded by behavioral scientists to describe human learning. Category response accuracies are, however, often the only reliable measure recorded by behavioral scientists to describe human learning. To our knowledge, however, drift-diffusion models for such scenarios have never been considered in the literature before. To address this gap, in this article, we build carefully on Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021), but now with latent response times integrated out, to derive a novel biologically interpretable class of 'inverse-probit' categorical probability models for observed categories alone. However, this new marginal model presents significant identifiability and inferential challenges not encountered originally for the joint model in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). We address these new challenges using a novel projection-based approach with a symmetry-preserving identifiability constraint that allows us to work with conjugate priors in an unconstrained space. We adapt the model for group and individual-level inference in longitudinal settings. Building again on the model's latent variable representation, we design an efficient Markov chain Monte Carlo algorithm for posterior computation. We evaluate the empirical performance of the method through simulation experiments. The practical efficacy of the method is illustrated in applications to longitudinal tone learning studies.

Cortical β Power Reflects a Neural Implementation of Decision Boundary Collapse in Speeded Decisions.

A prominent account of decision-making assumes that information is accumulated until a fixed response threshold is crossed. However, many decisions require weighting of information appropriately against time. Collapsing response thresholds are a mathematically optimal solution to this decision problem. However, our understanding of the neurocomputational mechanisms underlying dynamic response thresholds remains significantly incomplete. To investigate this issue, we used a multistage drift-diffusion model (DDM) and also analyzed EEG β power lateralization (BPL). The latter served as a neural proxy for decision signals. We analyzed a large dataset (n = 863; 434 females and 429 males) from a speeded flanker task and data from an independent confirmation sample (n = 119; 70 females and 49 males). We showed that a DDM with collapsing decision thresholds, a process wherein the decision boundary reduces over time, captured participants' time-dependent decision policy more accurately than a model with fixed thresholds. Previous research suggests that BPL over motor cortices reflects features of a decision signal and that its peak, coinciding with the motor response, may serve as a neural proxy for the decision threshold. We show that BPL around the response decreased with increasing RTs. Together, our findings offer compelling evidence for the existence of collapsing decision thresholds in decision-making processes.

Brief mindfulness-based meditation enhances the speed of learning following positive prediction errors.

Recent research has demonstrated that mindfulness-based meditation facilitates basic aspects of cognition, including memory and attention. Further developing this line of inquiry, here we considered the possibility that similar effects may extend to another core psychological process-instrumental learning. To explore this matter, in combination with a probabilistic selection task, computational modelling (i.e., reinforcement drift diffusion model analysis) was adopted to establish whether and how brief mindfulness-based meditation influences learning under conditions of uncertainty (i.e., choices based on the perceived likelihood of positive and negative outcomes). Three effects were observed. Compared with performance in the control condition (i.e., no meditation), mindfulness-based meditation (1) accelerated the rate of learning following positive prediction errors; (2) elicited a preference for the exploration (vs. exploitation) of choice selections; and (3) increased response caution. Collectively, these findings elucidate the pathways through which brief meditative experiences impact learning and decision-making, with implications for interventions designed to debias aspects of social-cognitive functioning using mindfulness-based meditation.

Cognitive deficits and enhancements in youth from adverse conditions: An integrative assessment using Drift Diffusion Modeling in the ABCD study.

Childhood adversity can lead to cognitive deficits or enhancements, depending on many factors. Though progress has been made, two challenges prevent us from integrating and better understanding these patterns. First, studies commonly use and interpret raw performance differences, such as response times, which conflate different stages of cognitive processing. Second, most studies either isolate or aggregate abilities, obscuring the degree to which individual differences reflect task-general (shared) or task-specific (unique) processes. We addressed these challenges using Drift Diffusion Modeling (DDM) and structural equation modeling (SEM). Leveraging a large, representative sample of 9-10 year-olds from the Adolescent Brain Cognitive Development (ABCD) study, we examined how two forms of adversity-material deprivation and household threat-were associated with performance on tasks measuring processing speed, inhibition, attention shifting, and mental rotation. Using DDM, we decomposed performance on each task into three distinct stages of processing: speed of information uptake, response caution, and stimulus encoding/response execution. Using SEM, we isolated task-general and task-specific variances in each processing stage and estimated their associations with the two forms of adversity. Youth with more exposure to household threat (but not material deprivation) showed slower task-general processing speed, but showed intact task-specific abilities. In addition, youth with more exposure to household threat tended to respond more cautiously in general. These findings suggest that traditional assessments might overestimate the extent to which childhood adversity reduces specific abilities. By combining DDM and SEM approaches, we can develop a more nuanced understanding of how adversity affects different aspects of youth's cognitive performance. RESEARCH HIGHLIGHT: To understand how childhood adversity shapes cognitive abilities, the field needs analytical approaches that can jointly document and explain patterns of lowered and enhanced performance. Using Drift Diffusion Modeling and Structural Equation Modeling, we analyzed associations between adversity and processing speed, inhibition, attention shifting, and mental rotation. Household threat, but not material deprivation, was mostly associated with slower task-general processing speed and more response caution. In contrast, task-specific abilities were largely intact. Researchers might overestimate the impact of childhood adversity on specific abilities and underestimate the impact on general processing speed and response caution using traditional measures.

Acoustic spin rotation in heavy-metal-ferromagnet bilayers

Through pumping a spin current from ferromagnet into heavy metal (HM) via magnetization precession, parts of the injected spins are in-plane rotated by the lattice vibration, namely acoustic spin rotation (ASR), which manifests itself as an inverse spin Hall voltage in HM with an additional 90° difference in angular dependency. When reversing the stacking order of bilayer with a counter-propagating spin current or using HMs with an opposite spin Hall angle, such ASR voltage shows the same sign, strongly suggesting that ASR changes the rotation direction due to interface spin-orbit interaction. With the drift-diffusion model of spin transport, we quantify the efficiency of ASR up to 30%. The finding of ASR endows the acoustic device with an ability to manipulate spin, and further reveals a new spin-orbit coupling between spin current and lattice vibration.

Using a Drift Diffusion Model to Validate the Quantification of Style Prototypicality as Assessed by the Viewers of Paintings

Abstract When appreciating a painting, people often classify it into a style. The extent to which the painting is regarded as a typical example of the style is called its style prototypicality. The authors propose a method of quantifying style prototypicalities and conduct an experiment to validate this method using the drift rate parameter of the drift diffusion model. This parameter is calculated using participants’ decision-making response times. The authors find a positive correlation (r = .88, p < .001) between the drift rates and the quantified prototypicalities for the paintings used in this study, confirming the psychological appropriateness of the proposed quantification method.

Teleological reasoning bias is predicted by pupil dynamics: Evidence for the extensive integration account of bias in reasoning.

Teleological reasoning is the tendency for humans to see purpose and intentionality in natural phenomena when there is none. In this study, we assess three competing theories on how bias in reasoning arises by examining performance on a teleological reasoning task while measuring pupil size and response times. We replicate that humans (N = 45) are prone to accept false teleological explanations. Further, we show that errors on the teleological reasoning task are associated with slower response times, smaller baseline pupil size, and larger pupil dilations. The results are in line with the single-process extensive integration account and directly oppose predictions from dual-processing accounts. Lastly, by modeling responses with a drift-diffusion model, we find that larger baseline pupil size is associated with lower decision threshold and higher drift rate, whereas larger pupil dilations are associated with higher decision threshold and lower drift rate. The results highlight the role of neural gain and the Locus Coeruleus-Norepinephrine system in modulating evidence integration and bias in reasoning. Thus, teleological reasoning and susceptibility to bias likely arise due to extensive processing rather than through fast and effortless processing.

Analysis of Nanowire pn-Junction with Combined Current-Voltage, Electron-Beam-Induced Current, Cathodoluminescence, and Electron Holography Characterization.

We present the characterization of a pn-junction GaAs nanowire. For the characterization, current-voltage, electron-beam-induced current, cathodoluminescence, and electron holography measurements are used. We show that by combining information from these four methods, in combination with drift-diffusion modelling, we obtain a detailed picture of how the nanowire pn-junction is configured and how the recombination lifetime varies axially in the nanowire. We find (i) a constant doping concentration and 600 ps recombination lifetime in the n segment at the top part of the nanowire; (ii) a 200-300 nm long gradient in the p doping next to the pn-junction; and (iii) a strong gradient in the recombination lifetime on the p side, with 600 ps lifetime at the pn-junction, which drops to 10 ps at the bottom of the p segment closest to the substrate. We recommend such complementary characterization with multiple methods for nanowire-based optoelectronic devices.

Nudging human drivers via implicit communication by automated vehicles: Empirical evidence and computational cognitive modeling

Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage interactions even if they have the right of way over humans, e.g., a human driver taking a left turn in front of the approaching AV. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. To address this issue, here we investigated behavior of human drivers (N = 19) when interacting with an oncoming AV during unprotected left turns in a driving simulator experiment. We measured the outcomes (Go or Stay) and timing of participants’ decisions when interacting with an AV which performed subtle longitudinal nudging maneuvers, e.g. briefly decelerating and then accelerating back to its original speed. We found that participants’ behavior was sensitive to deceleration nudges but not acceleration nudges. We compared the obtained data to predictions of several variants of a drift-diffusion model of human decision making. The most parsimonious model that captured the data hypothesized noisy integration of dynamic information on time-to-arrival and distance to a fixed decision boundary, with an initial accumulation bias towards the Go decision. Our model not only accounts for the observed behavior but can also flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers, and can be used for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks for AV interaction planning.

A Godunov-type stabilization scheme for the Boltzmann transport equation of III-V devices with a 3D k-space

This paper presents a deterministic approach for solving the Boltzmann transport equation (BTE) together with the Poisson equation (PE) for III-V semiconductor devices with a three-dimensional k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extbf {k}}$$\\end{document}-space. The BTE is stabilized using Godunov’s scheme, whose linearity in the distribution function simplifies the application of the Newton–Raphson method to the coupled discrete BTE and PE. The formulation of the discrete equations ensures the nonnegativity of the distribution function regardless of the scattering rate, which can include the Pauli exclusion principle, and exhibits excellent numerical stability under steady state as well as transient conditions. In the latter case, both implicit and explicit time integration methods can be used and even slow processes (e.g., recombination) can be handled using this approach. In addition, the direct solution of the BTE can be easily extended to the small-signal case for arbitrary frequencies. Exemplary BTE results are shown for a GaAs N+NN+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extrm{N}}^{+}{\ extrm{NN}}^{+}$$\\end{document}-structure, revealing, inter alia, that the approximations of the drift-diffusion model can fail for large built-in fields in III-V devices.

Reinforcement learning of adaptive control strategies

Humans can up- or downregulate the degree to which they rely on task information for goal-directed behaviour, a process often referred to as cognitive control. Adjustments in cognitive control are traditionally studied in response to experienced or expected task-rule conflict. However, recent theories suggest that people can also learn to adapt control settings through reinforcement. Across three preregistered task switching experiments (n = 415), we selectively rewarded correct performance on trials with either more (incongruent) or less (congruent) task-rule conflict. Results confirmed the hypothesis that people rewarded more on incongruent trials showed smaller task-rule congruency effects, thus optimally adapting their control settings to the reward scheme. Using drift diffusion modelling, we further show that this reinforcement of cognitive control may occur through conflict-dependent within-trial adjustments of response thresholds after conflict detection. Together, our findings suggest that, while people remain more efficient at learning stimulus-response associations through reinforcement, they can similarly learn cognitive control strategies through reinforcement.

Impairment in recognition memory may be associated with near-term risk for suicide attempt in a high-risk sample

IntroductionPrior work has implicated several neurocognitive domains, including memory, in patients with a history of prior suicide attempt. The current study evaluated whether a delayed recognition test could enhance prospective prediction of near-term suicide outcomes in a sample of patients at high-risk for suicide. Methods132 Veterans at high-risk for suicide completed a computer-based recognition memory test including semantically-related and -unrelated words. Outcomes were coded as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as aborted/interrupted attempt or preparatory behavior, or neither (noSE), within 90 days after testing. ResultsReduced performance was a significant predictor of upcoming ASA, but not OtherSE, after controlling for standard clinical variables such as current suicidal ideation and history of prior suicide attempt. However, compared to the noSE reference group, the OtherSE group showed a reduction in the expected benefit of semantic relatedness in recognizing familiar words. A computational model, the drift diffusion model (DDM), to explore latent cognitive processes, revealed the OtherSE group had decreased decisional efficiency for semantically-related compared to semantically-unrelated familiar words. LimitationsThis study was a secondary analysis of an existing dataset, involving participants in a treatment trial, and requires replication; ~10 % of the sample was excluded from analysis due to failure to master the practice tasks and/or apparent noncompliance. ConclusionImpairments in recognition memory may be associated with near-term risk for suicide attempt, and may provide a tool to improve prediction of when at-risk individuals may be transitioning into a period of heightened risk for suicide attempt.

Of Rodents and Primates: Time-Variant Gain in Drift-Diffusion Decision Models.

The online version contains supplementary material available at 10.1007/s42113-023-00194-1.

Regulation of craving training to support healthy food choices under stress: A randomized control trial employing the hierarchical drift-diffusion model.

Stress increases the likelihood of consuming unhealthy food in some individuals. Previous research has demonstrated that the Regulation of Craving - Training (ROC-T) intervention can reduce unhealthy food intake. However, its effectiveness under stress and the underlying mechanism remained uncertain. This study aimed to assess the efficacy of the ROC-T intervention in improving healthy food choices and to explore the intervention mechanism through computational modeling employing the hierarchical drift-diffusion model (HDDM). This study adopted a 2 (ROC-T intervention vs. control)*2 (stress vs. no-stress) between-subject experimental design. A total of 118 employees (72 women, Mage=28.74) participated in the online experiment. Results show that the ROC-T intervention increases healthy food choices under stress and no-stress conditions. The HDDM results reveal a significant two-way interaction for non-decision time (Bayes factor, BF = 32.722) and initial bias (BF = 27.350). Specifically, in the no-stress condition, the ROC-T intervention resulted in lower non-decision time and higher initial bias compared with the control group. The findings validated the negative impact of stress on healthy food choices, and that the ROC-T intervention promotes healthy food choices both under stress and no-stress conditions.

The effect of total sleep deprivation on working memory: evidence from diffusion model.

Working memory is crucial in human daily life and is vulnerable to sleep loss. The current study investigated the impact of sleep deprivation on working memory from the information processing perspective, to explore whether sleep deprivation affects the working memory via impairing information manipulation. Thirty-seven healthy adults attended two counterbalanced protocols: a normal sleep night and a total sleep deprivation (TSD). The N-back and the psychomotor vigilance task (PVT) assessed working memory and sustained attention. Response time distribution and drift-diffusion model analyses were applied to explore cognitive process alterations. TSD increased the loading effect of accuracy, but not the loading effect of response time in the N-back task. TSD reduced the speed of information accumulation, increased the variability of the speed of accumulation, and elevated the decision threshold only in 1-back task. Moreover, the slow responses of PVT and N-back were severely impaired after TSD, mainly due to increased information accumulation variability. The present study provides a new perspective to investigate behavioral performance by using response time distribution and drift-diffusion models, revealing that sleep deprivation affected multicognitive processes underlying working memory, especially information accumulation processes.

Mapping expectancy-based appetitive placebo effects onto the brain in women

Suggestions about hunger can generate placebo effects on hunger experiences. But, the underlying neurocognitive mechanisms are unknown. Here, we show in 255 women that hunger expectancies, induced by suggestion-based placebo interventions, determine hunger sensations and economic food choices. Functional magnetic resonance imaging in a subgroup (n = 57/255) provides evidence that the strength of expecting the placebo to decrease hunger moderates medial prefrontal cortex activation at the time of food choice and attenuates ventromedial prefrontal cortex (vmPFC) responses to food value. Dorsolateral prefrontal cortex activation linked to interference resolution formally mediates the suggestion-based placebo effects on hunger. A drift-diffusion model characterizes this effect by showing that the hunger suggestions bias participants’ food choices and how much they weigh tastiness against the healthiness of food, which further moderates vmPFC–dlPFC psychophysiological interactions when participants expect decreased hunger. Thus, suggestion-induced beliefs about hunger shape hunger addressing economic choices through cognitive regulation of value computation within the prefrontal cortex.

Insight into differing decision-making strategies that underlie cognitively effort-based decision making using computational modeling in rats.

The rat cognitive effort task (rCET), a rodent model of cognitive rather than physical effort, requires animals to choose between an easy or hard visuospatial discrimination, with a correct hard choice more highly rewarded. Like in humans, there is stable individual variation in choice behavior. In previous reports, animals were divided into two groups-workers and slackers-based on their mean preference for the harder option. Although these groups differed in their response to pharmacological challenges, the rationale for using this criterion for grouping was not robust. We collated experimental data from multiple cohorts of male and female rats performing the rCET and used a model-based framework combining drift diffusion modeling with cluster analysis to identify the decision-making processes underlying variation in choice behavior. We verified that workers and slackers are statistically different groups but also found distinct intra-group profiles. These subgroups exhibited dissociable performance during the attentional phase, linked to distinct decision-making profiles during choice. Reanalysis of previous pharmacology data using this model-based framework showed that serotonergic drug effects were explained by changes in decision boundaries and non-decision times, while scopolamine's effects were driven by changes in decision starting points and rates of evidence accumulation. Modeling revealed the decision-making processes that are associated with cognitive effort costs, and how these differ across individuals. Reanalysis of drug data provided insight into the mechanisms through which different neurotransmitter systems impact cognitively effortful attention and decision-making processes, with relevance to multiple psychiatric disorders.