Generalized Drift-diffusion Models Research Articles

Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers.

We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. The drivers' probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles.

Giant orbital Hall effect and orbital-to-spin conversion in 3d , 5d , and 4f metallic heterostructures

The orbital Hall effect provides an alternative means to the spin Hall effect to convert a charge current into a flow of angular momentum. Recently, compelling signatures of orbital Hall effects have been identified in $3d$ transition metals. Here, we report a systematic study of the generation, transmission, and conversion of orbital currents in heterostructures comprising $3d$, $5d$, and $4f$ metals. We show that the orbital Hall conductivity of Cr reaches giant values of the order of $5\ifmmode\times\else\texttimes\fi{}{10}^{5}$ $[\frac{\ensuremath{\hbar}}{2e}]$ ${\mathrm{\ensuremath{\Omega}}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\mathrm{m}}^{\ensuremath{-}1}$ and that Pt presents a strong orbital Hall effect in addition to the spin Hall effect. Measurements performed as a function of thickness of nonmagnetic Cr, Mn, and Pt layers and ferromagnetic Co and Ni layers reveal how the orbital and spin currents compete or assist each other in determining the spin-orbit torques acting on the magnetic layer. We further show how this interplay can be drastically modulated by introducing $4f$ spacers between the nonmagnetic and magnetic layers. Gd and Tb act as very efficient orbital-to-spin current converters, boosting the spin-orbit torques generated by Cr by a factor of 4 and reversing the sign of the torques generated by Pt. To interpret our results, we present a generalized drift-diffusion model that includes both spin and orbital Hall effects and describes their interconversion mediated by spin-orbit coupling.

Transient neuronal suppression for exploitation of new sensory evidence

In noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify and respond via saccade to the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals’ behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset but sensitive to stimulus strength. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is important for dynamic perceptual decision making.

Effects of Altered Excitation-Inhibition Balance on Decision Making in a Cortical Circuit Model.

The synaptic balance between excitation and inhibition (E/I balance) is a fundamental principle of cortical circuits, and disruptions in E/I balance are commonly linked to cognitive deficits such as impaired decision-making. Explanatory gaps remain in a mechanistic understanding of how E/I balance contributes to cognitive computations, and how E/I disruptions at the synaptic level can propagate to induce behavioral deficits. Here, we studied how E/I perturbations may impair perceptual decision-making in a biophysically-based association cortical circuit model. We found that both elevating and lowering E/I ratio, via NMDA receptor (NMDAR) hypofunction at inhibitory interneurons and excitatory pyramidal neurons, respectively, can similarly impair psychometric performance, following an inverted-U dependence. Nonetheless, these E/I perturbations differentially alter the process of evidence accumulation across time. Under elevated E/I ratio, decision-making is impulsive, overweighting early evidence and underweighting late evidence. Under lowered E/I ratio, decision-making is indecisive, with both evidence integration and winner-take-all competition weakened. The distinct time courses of evidence accumulation at the circuit level can be measured at the behavioral level, using multiple psychophysical task paradigms which provide dissociable predictions. These results are well captured by a generalized drift-diffusion model (DDM) with self-coupling, implementing leaky or unstable integration, which thereby links biophysical circuit modeling to algorithmic process modeling and facilitates model fitting to behavioral choice data. In general, our findings characterize critical roles of cortical E/I balance in cognitive function, bridging from biophysical to behavioral levels of analysis.SIGNIFICANCE STATEMENT Cognitive deficits in multiple neuropsychiatric disorders, including schizophrenia, have been associated with alterations in the balance of synaptic excitation and inhibition (E/I) in cerebral cortical circuits. However, the circuit mechanisms by which E/I imbalance leads to cognitive deficits in decision-making have remained unclear. We used a computational model of decision-making in cortical circuits to study the neural and behavioral effects of E/I imbalance. We found that elevating and lowering E/I ratio produce distinct modes of dysfunction in decision-making processes, which can be dissociated in behavior through psychophysical task paradigms. The biophysical circuit model can be mapped onto a psychological model of decision-making which can facilitate experimental tests of model predictions.

Confluence of Timing and Reward Biases in Perceptual Decision-Making Dynamics.

Although the decisions of our daily lives often occur in the context of temporal and reward structures, the impact of such regularities on decision-making strategy is poorly understood. Here, to explore how temporal and reward context modulate strategy, we trained 2 male rhesus monkeys to perform a novel perceptual decision-making task with asymmetric rewards and time-varying evidence reliability. To model the choice and response time patterns, we developed a computational framework for fitting generalized drift-diffusion models, which flexibly accommodate diverse evidence accumulation strategies. We found that a dynamic urgency signal and leaky integration, in combination with two independent forms of reward biases, best capture behavior. We also tested how temporal structure influences urgency by systematically manipulating the temporal structure of sensory evidence, and found that the time course of urgency was affected by temporal context. Overall, our approach identified key components of cognitive mechanisms for incorporating temporal and reward structure into decisions.SIGNIFICANCE STATEMENT In everyday life, decisions are influenced by many factors, including reward structures and stimulus timing. While reward and timing have been characterized in isolation, ecologically valid decision-making involves a multiplicity of factors acting simultaneously. This raises questions about whether the same decision-making strategy is used when these two factors are concurrently manipulated. To address these questions, we trained rhesus monkeys to perform a novel decision-making task with both reward asymmetry and temporal uncertainty. In order to understand their strategy and hint at its neural mechanisms, we used the new generalized drift diffusion modeling framework to model both reward and timing mechanisms. We found two of each reward and timing mechanisms are necessary to explain our data.

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.

Simultaneous resolution of the micromagnetic and spin transport equations applied to current-induced domain wall dynamics

In this paper, we use simulations to study current-induced domain wall dynamics by simultaneously resolving the spin transport and micromagnetic equations for a three-dimensional ferromagnetic strip. In contrast to local approaches, which neglect mutual interaction between spins and magnetic moments, our approach recalculates the spin distribution at each time step using the generalized drift diffusion model, which takes the transverse spin absorption phenomenon into account. We quantified the differences between a local approach and treatment based on a self-consistent method by plotting the domain wall velocity as a function of the domain wall width. We also characterized the domain wall velocity and the Walker breakdown condition as a function of the transverse spin absorption length ${l}_{\ensuremath{\perp}}$, which plays a crucial role in domain wall dynamics.

Modeling of the quantum dot filling and the dark current of quantum dot infrared photodetectors

A generalized drift-diffusion model for the calculation of both the quantum dot filling profile and the dark current of quantum dot infrared photodetectors is proposed. The confined electrons inside the quantum dots produce a space-charge potential barrier between the two contacts, which controls the quantum dot filling and limits the dark current in the device. The results of the model reasonably agree with a published experimental work. It is found that increasing either the doping level or the temperature results in an exponential increase of the dark current. The quantum dot filling turns out to be nonuniform, with a dot near the contacts containing more electrons than one in the middle of the device where the dot occupation approximately equals the number of doping atoms per dot, which means that quantum dots away from contacts will be nearly unoccupied if the active region is undoped.

Physical Foundation of a Recently Proposed Schottky-Contact Model

The goal of this correspondence is to supplement the Schottky-contact model proposed in “A Generalized Drift-Diffusion Model for Rectifying Schottky Contact Simulation,” IEEE Transactions on Electron Devices, vol. 57, no. 7, July 2010, with a solid physical foundation by rigorously rederiving it from an analytical solution of Boltzmann's equation in the boundary layer.