Neural Substrates of the Drift-Diffusion Model in Brain Disorders

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

Начальные стадии формирования импульсного разряда в промежутке с геометрией острие-плоскость в предварительно ионизированном аргоне

The study of the effect of the initial conditions on the features of the formation and development of the anodic ionization wave between two electrodes with a tip – plane gap geometry in argon at atmospheric pressure is performed on the basis of a two-dimensional axisymmetric drift-diffusion model.

Influence of the Metal–Semiconductor Interface Model on Power Conservation Principle in a Simulation of Bipolar Devices

The purpose of the study is to present a proper approach that ensures the energy conservation principle during electrothermal simulations of bipolar devices. The simulations are done using Sentaurus TCAD software from Synopsys. We focus on the drift-diffusion model that is still widely used for power device simulations. We show that without a properly designed contact(metal)–semiconductor interface, the energy conservation is not obeyed when bipolar devices are considered. This should not be accepted for power semiconductor structures, where thermal design issues are the most important. The correct model of the interface is achieved by proper doping and mesh of the contact-semiconductor region or by applying a dedicated model. The discussion is illustrated by simulation results obtained for the GaN p–n structure; additionally, Si and SiC structures are also presented. The results are also supported by a theoretical analysis of interface physics.

High-Efficiency Ultrathin Si-Based Solar Cells by Cascading Dilute-Nitride GaNAsP

Thin-film solar cells are currently an important research subject. In this study, a lattice-matched GaNAsP/Si tandem cell was designed. We adopted the drift-diffusion model to analyze the power conversion efficiency (PCE) of the solar cell. To find the maximum solar cell PCE, the recombination terms and the interlayer between subcells was omitted. For an optimal tandem cell PCE, this study analyzed the mole fraction combinations of GaNAsP and the thickness combinations between the GaNAsP and the Si subcells of the tandem cell. Our results showed the superiority of the tandem cell over the Si cell. The 4.5 μm tandem cell had a 12.7% PCE, the same as that of the 10.7 μm Si cell. The 11.5 μm tandem cell had 20.2% PCE, while the 11.5 μm Si cell processed 12.7% PCE. We also analyzed the Si subcell thickness ratio of sub-12 μm tandem cells for maximum PCE. The tandem cell with a thickness between 40% to 70% of a Si cell would have a max PCE. The ratio depended on the tandem cell thickness. We conclude that the lattice-matched GaNAsP/Si tandem cell has potential for ultrathin thin Si-based solar cell applications.

Methodological issues with value-based decision-making (VBDM) tasks: does trial wording matter?

Objective: Most value-based decision-making (VBDM) tasks instruct people to make value judgements about stimuli using wording relating to consumption, however in some contexts this may be inappropriate. This study aims to explore whether variations of trial wording capture a common construct of value. Method: Pre-registered, within-subject design. Fifty-nine participants completed a two-alternative forced choice task where they chose between two food images. Participants completed three blocks of trials: one asked which they would rather consume (standard wording), one asked which image they like more, and one asked them to recall which image they rated higher during a previous block. We fitted a drift-diffusion model to the reaction time and choice data to estimate evidence accumulation (EA) processes during the different blocks. Results: There was a highly significant main effect of trial difficulty, but this was not modified by trial wording (F = 2.00, p = .11, np2 = .03, BF10 = .05). We also found highly significant positive correlations between EA rates across task blocks (rs > .44, ps < .001). Conclusions: Findings provide initial validation of substitute wording for VBDM tasks that can be used in contexts where it may be undesirable to ask participants to make consummatory judgements.

Computational modeling of the N-Back task in the ABCD study: associations of drift diffusion model parameters to polygenic scores of mental disorders and cardiometabolic diseases

Background. Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with PRS for mental disorders and comorbid cardiometabolic diseases. Methods. We used the drift diffusion model to infer latent computational processes underlying decision-making and working memory during the N-back task in 3707 children aged 9-10 from the ABCD Study. SNP-based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated N-back task performance and neurocognitive assessments. PRS was calculated for Alzheimer’s disease (AD), bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia and type 2 diabetes. Results. Heritability estimates of cognitive phenotypes ranged from 12 to 39%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRS for CAD and schizophrenia. Longer non-decision time was associated with higher PRS for AD and lower PRS for CAD. Narrower decision threshold was associated with higher PRS for CAD. Load-dependent effects on non-decision time and decision threshold were associated with PRS for AD and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRS for any of the mental or cardiometabolic phenotypes.Conclusions. We identified distinct associations between computational cognitive processes to genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.

Motor response vigour and fixations reflect subjective preferences during intertemporal choice

Value-based decision-making is of central interest in cognitive neuroscience and psychology, as well as in the context of neuropsychiatric disorders characterised by decision-making impairments. Studies examining (neuro-)computational mechanisms underlying choice behaviour typically focus on participants' decisions. However, there is increasing evidence that option valuation might also be reflected in motor response vigour and eye movements, implicit measures of subjective utility. To examine motor response vigour and visual fixation correlates of option valuation in intertemporal choice, we set up a task where the participants selected an option by pressing a grip force transducer, simultaneously tracking fixation shifts between options. As outlined in our preregistration (https://osf.io/k6jct), we used hierarchical Bayesian parameter estimation to model the choices assuming hyperbolic discounting, compared variants of the softmax and drift diffusion model, and assessed the relationship between response vigour and the estimated model parameters. The behavioural data were best explained by a drift diffusion model specifying a non-linear scaling of the drift rate by the subjective value differences. Replicating previous findings (Green et al., 1997; Wagner et al., 2020a), we found a magnitude effect for temporal discounting, such that higher rewards were discounted less. This magnitude effect was further reflected in response vigour, such that stronger forces were exerted in the high vs. the low magnitude condition. Bayesian hierarchical linear regression further revealed higher grip forces, faster response times and a lower number of fixation shifts for trials with higher subjective value differences. Our data suggest that subjective utility or implicit valuation is reflected in response vigour during intertemporal choice. Taking into account response vigour might thus provide deeper insight into decision-making, reward valuation and maladaptive changes in these processes, e.g. in the context of neuropsychiatric disorders.

Modeling Nanoscale III–V Channel MOSFETs with the Self-Consistent Multi-Valley/Multi-Subband Monte Carlo Approach

We describe the multi-valley/multi-subband Monte Carlo (MV–MSMC) approach to model nanoscale MOSFETs featuring III–V semiconductors as channel material. This approach describes carrier quantization normal to the channel direction, solving the Schrödinger equation while off-equilibrium transport is captured by the multi-valley/multi-subband Boltzmann transport equation. In this paper, we outline a methodology to include quantum effects along the transport direction (namely, source-to-drain tunneling) and provide model verification by comparison with Non-Equilibrium Green’s Function results for nanoscale MOSFETs with InAs and InGaAs channels. It is then shown how to use the MV–MSMC to calibrate a Technology Computer Aided Design (TCAD) simulation deck based on the drift–diffusion model that allows much faster simulations and opens the doors to variability studies in III–V channel MOSFETs.

The critical role of interference control in metaphor comprehension evidenced by the drift–diffusion model

We address the question of, among several executive functions, which one has a strong influence on metaphor comprehension. To this end, participants took part in a metaphor comprehension task where metaphors had varying levels of familiarity (familiar vs. novel metaphors) with different conditions of context (supporting vs. opposing contexts). We scrutinized each participant’s detailed executive functions using seven neuropsychological tests. More interestingly, we modelled their responses in metaphor comprehension using the drift–diffusion model, in an attempt to provide more systematic accounts of the processes underlying metaphor comprehension. Results showed that there were significant negative correlations between response times in metaphor comprehension and scores of the Controlled Oral Word Association Test (COWAT)-Semantic, suggesting that better performances in comprehending metaphors were strongly associated with better interference control. Using the drift–diffusion model, we found that the familiarity, compared to context, had greater leverage in the decision process for metaphor comprehension. Moreover, individuals with better performance in the COWAT-Semantic test demonstrated higher drift rates. In conclusion, with more fine-grained analysis of the decisions involved in metaphor comprehension using the drift–diffusion model, we argue that interference control plays an important role in processing metaphors.