Decision-time statistics of nonlinear diffusion models: characterizing long sequences of subsequent trials
type: Item
instance of: scholarly article
title: Decision-time statistics of nonlinear diffusion models: characterizing long sequences of subsequent trials
zbMATH Open document ID: 1457.91158
author: Sebastian Vellmer
author: Benjamin Lindner
published in: Journal of Mathematical Psychology
publication date: 2021-01-06T00:00:00Z
review text: The paper reports on one alternative approach to modeling binary decision making in the diffusion decision model. The authors introduce decision trains, sequence of negative or positive spikes at the decision times The signs of the spikes reflect correctness or incorrectness of the decision. Proper characterization of experiments are established using the decision trains. Relations between the second-order statistics of the decision trains and the response-time densities are derived. Obtained results are compared with direct simulations of the stochastic model. Outlook to further research in the area is placed at the end of the paper.
Mathematics Subject Classification ID: 91B06
Mathematics Subject Classification ID: 91E10
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 7290623
zbMATH Keywords: decision making
zbMATH Keywords: diffusion-decision model
zbMATH Keywords: stochastic decision models
reviewed by: Karel Zimmermann
MaRDI profile type: Publication
full work available at URL: j.jmp.2020.102445
OpenAlex ID: W3085068289
cites work: Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.
cites work: Q3286690
cites work: Q5528194
cites work: The First Passage Problem for a Continuous Markov Process
cites work: Integrate-and-fire neurons driven by asymmetric dichotomous noise
cites work: Sequential Effects in Two-Choice Reaction Time Tasks: Decomposition and Synthesis of Mechanisms
cites work: Q4124657
cites work: Short first-passage times
cites work: The dynamics of choice among multiple alternatives
cites work: Response of Integrate-and-Fire Neurons to Noisy Inputs Filtered by Synapses with Arbitrary Timescales: Firing Rate and Correlations
cites work: Q3592129
cites work: The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks
cites work: A guide to first-passage processes
cites work: On an integral equation for first-passage-time probability densities
cites work: Spike-train spectra and network response functions for nonlinear integrate-and-fire neurons
cites work: The Fokker-Planck equation. Methods of solution and applications
cites work: Statistical structure of neural spiking under non-Poissonian or other non-white stimulation
cites work: Interspike interval correlation in a stochastic exponential integrate-and-fire model with subthreshold and spike-triggered adaptation
cites work: A note on the distribution of response times for a random walk with Gaussian increments
cites work: Stochastic dynamic models of response time and accuracy: A foundation primer
cites work: From Poisson shot noise to the integrated Ornstein-Uhlenbeck process: neurally principled models of information accumulation in decision-making and response time
cites work: The Poisson shot noise model of visual short-term memory and choice response time: normalized coding by neural population size
cites work: Q5574500
cites work: Stochastic models of evidence accumulation in changing environments
DOI: 10.1016/J.JMP.2020.102445
description: scientific article; zbMATH DE number 7290623