scholarly journals Drift-diffusion explains response variability and capacity for tracking objects

2018 ◽  
Author(s):  
Asieh Daneshi ◽  
Hamed Azarnoush ◽  
Farzad Towhidkhah ◽  
Ali Ghazizadeh
Keyword(s):  
Error Propagation ◽  
Human Subjects ◽  
Error Variance ◽  
Decision Variable ◽  
Drift Diffusion Model ◽  
Multiple Objects ◽  
Drift Diffusion ◽  
Finish Line ◽  
Asymmetric Interference ◽  
Tracking Objects

1.AbstractBeing able to track objects that surround us is key for planning actions in dynamic environments. However, rigorous cognitive models for tracking of one or more objects are currently lacking. In this study, we asked human subjects to judge the time to contact (TTC) a finish line for one or two objects that became invisible shortly after moving. We showed that the pattern of subject responses had an error variance best explained by an inverse Gaussian distribution and consistent with the output of a biased drift-diffusion model. Furthermore, we demonstrated that the pattern of errors made when tracking two objects showed a level of dependence that was consistent with subjects using a single decision variable for reporting the TTC for two objects. This finding reveals a serious limitation in the capacity for tracking multiple objects resulting in error propagation between objects. Apart from explaining our own data, our approach helps interpret previous findings such as asymmetric interference when tracking multiple objects.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

scholarly journals Informing cognitive abstractions through neuroimaging: The neural drift diffusion model.

2015 ◽  
Vol 122 (2) ◽  
pp. 312-336 ◽  
Author(s):  
Brandon M. Turner ◽  
Leendert van Maanen ◽  
Birte U. Forstmann
Keyword(s):  
Diffusion Model ◽  
Drift Diffusion Model ◽  
Drift Diffusion

Incorporating photon recycling into the analytical drift-diffusion model of high efficiency solar cells

2014 ◽  
Vol 116 (19) ◽  
pp. 194504 ◽  
Author(s):  
Matthew P. Lumb ◽  
Myles A. Steiner ◽  
John F. Geisz ◽  
Robert J. Walters
Keyword(s):  
Solar Cells ◽  
Diffusion Model ◽  
High Efficiency ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
High Efficiency Solar Cells ◽  
Photon Recycling

A Discretization Scheme for a Quasi-Hydrodynamic Semiconductor Model

1997 ◽  
Vol 07 (07) ◽  
pp. 935-955 ◽  
Author(s):  
Ansgar Jüngel ◽  
Paola Pietra
Keyword(s):  
Finite Elements ◽  
Mixed Finite Elements ◽  
Exponential Fitting ◽  
Discretization Scheme ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Current Conservation ◽  
Numerical Tests ◽  
Finite Elements Methods ◽  
Degenerate Type

A discretization scheme based on exponential fitting mixed finite elements is developed for the quasi-hydrodynamic (or nonlinear drift–diffusion) model for semiconductors. The diffusion terms are nonlinear and of degenerate type. The presented two-dimensional scheme maintains the good features already shown by the mixed finite elements methods in the discretization of the standard isothermal drift–diffusion equations (mainly, current conservation and good approximation of sharp shapes). Moreover, it deals with the possible formation of vacuum sets. Several numerical tests show the robustness of the method and illustrate the most important novelties of the model.

scholarly journals Speed, Accuracy, and the Optimal Timing of Choices

2018 ◽  
Vol 108 (12) ◽  
pp. 3651-3684 ◽  
Author(s):  
Drew Fudenberg ◽  
Philipp Strack ◽  
Tomasz Strzalecki
Keyword(s):  
Sequential Sampling ◽  
Decision Time ◽  
Optimal Timing ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Prior Beliefs ◽  
Constant Cost ◽  
Speed Accuracy ◽  
Decision Times ◽  
Choice Probabilities

We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly, provided the agent’s prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model (DDM), where the agent knows the utility difference between the choices. (JEL C41, D11, D12, D83)

DISCRETIZATION METHOD OF HYDRODYNAMIC EQUATIONS FOR SIMULATION OF GaN MESFETs

2008 ◽  
Vol 22 (16) ◽  
pp. 1599-1608
Author(s):  
M. REZAEE ROKN-ABADI ◽  
H. ARABSHAHI ◽  
M. R. BENAM
Keyword(s):  
Experimental Methods ◽  
Two Dimensions ◽  
Balance Model ◽  
Discretization Method ◽  
Hydrodynamic Equations ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Transport Models ◽  
Wurtzite Phase ◽  
Inertia Effects

A finite discretization method in two dimensions has been developed and used to model electron transport in wurtzite phase GaN MESFETs. The model is based on the solutions of the highly-coupled nonlinear hydrodynamic partial differential equations. These solutions allow us to calculate the electron drift velocity and other device parameters as a function of the applied electric field. This model is able to describe inertia effects which play an increasing role in different fields of micro and optoelectronics where simplified charge transport models like drift-diffusion model and energy balance model are no longer applicable. Results of numerical simulations are shown for a two-dimensional GaN MESFET device which are in fair agreement with other theoretical or experimental methods.

Sign in / Sign up

Export Citation Format

Share Document