https://github.com/princetonuniversity/msddm
Code for the multistage DDM
https://github.com/princetonuniversity/msddm
Last synced: 3 months ago
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Code for the multistage DDM
- Host: GitHub
- URL: https://github.com/princetonuniversity/msddm
- Owner: PrincetonUniversity
- Created: 2015-02-01T10:02:44.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2015-06-05T21:36:22.000Z (over 10 years ago)
- Last Synced: 2025-01-21T09:30:54.058Z (9 months ago)
- Language: Matlab
- Size: 9.18 MB
- Stars: 5
- Watchers: 14
- Forks: 4
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# msddm
This repository contains code for the numerical computation of various performance metrics for a multistage drift diffusion model with time varying piecewise constant drift rate. It is the result of a collaboration between Vaibhav Srivastava, Samuel Feng, and Amitai Shenhav.
The remainder of this file highlights key functions associated with the MSDDM package, including core functions (multi_stage_ddm_metrics.m and multistage_ddm_fpt_dist.m) and wrapper functions (MSDDM_wrapper.m and call_MSDDM_wrapper.m)
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call_MSDDM_wrapper.m
Script describing example calls to MSDDM_wrapper
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MSDDM_wrapper.m
Function for using multi_stage_ddm_metrics and multistage_ddm_fpt_dist to generate expected ER/DT and upper/lower threshold CDFs for analytic MSDDM process, and compare that to MC simulations of the same. Includes rudimentary plots.
*USAGE:
[aRT, aER, aRT_plus, aRT_minus, aCDF_T, aCDF_Y, aCDF_Y_plus, aCDF_Y_minus,simMeanRT, simMeanER,simMeanRT_plus,simMeanRT_minus, simCDF_T, simCDF_Y, simCDF_Y_plus, simCDF_Y_minus] = MSDDM_wrapper(a,s,varthresh,deadlines,thresh,x0,x0dist,runSimulations,doPlots)
*INPUT:
a = vector of drift rates at each stage
s = vector of diffusion rates at each stage
deadlines = vector of times when stages start. Firt entry should be 0.
thresh = vector of thresholds to test (IMPORTANT: these are not thresholds for different *stages* - currently assuming uniform threshold across stages)
x0 = support of initial condition. Equals the initial condition in the deterministic case
x0dist = density of x0. Equals 1 in the deterministic case
runSimulations = binary for whether to perform monte carlo simulations in addition to generating analytic solution
doPlots = binary for whether to generate some relevant plots
*OUTPUT:
aRT, aER = vectors of analytic expected decision time and error rate, one for each threshold being tested [using multi_stage_ddm_metrics]
aCDF_T, aCDF_Y = cell array of analytic CDFs (..._T = RT range, ..._Y = cumul prob), one for each threshold being tested [using multistage_ddm_fpt_dist]
simMeanRT, simMeanER, simCDF_T, simCDF_Y = same as above, but using MC simulations to generate each value estimate
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multi_stage_ddm_metrics.m
Function for generating expected (average) RT/ER overall and mean RTs for upper and lower bounds. Iteratively computes and compiles CDFs for each stage conditional on no threshold crossing in prior stage
*USAGE:
[mean_RT, mean_ER, mean_RT_plus, mean_RT_minus]=multi_stage_ddm_metrics(a ,s, deadlines, thresholds, x0, x0dist)
*INPUT:
a = vector of drift rates at each stage
s = vector of diffusion rates at each stage
deadlines = vector of times when stages start. First entry should be 0.
thresholds = vector of thresholds at each stage.
x0 = support of initial condition. Equals the initial condition in the deterministic case
x0dist = density of x0. Equals 1 in the deterministic case
*OUTPUT:
mean_RT = mean decision time
mean_ER = error rate
mean_RT_plus = mean decision time conditioned on correct decision
mean_RT_minus= mean decision time conditioned on erroneous decision
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multistage_ddm_fpt_dist.m
Function for computing CDF overall (Y) and for upper/lower bounds (Yplus, Yminus) separately. Iteratively computes and compiles CDFs for each stage conditional on no threshold crossing in prior stage
*USAGE:
[T,Y, Yplus, Yminus]=multistage_ddm_fpt_dist(a,s,threshold,x0,x0dist,deadlines,tfinal)
*INPUT:
a = vector of drift rates
s = vector of diffusion rates
z = threshold
x0= discretized initial condition support set
x0dist= discretized pdf of the initial condition (equal to 1 if x0 is deterministic)
deadlines = set of deadlines, first element is zero
tfinal = support for decision time =[0 tfinal]
*OUTPUT:
T = support of decision time
Y = cdf of decision time
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dist_deadline.m
Function for computing particle density, mean, MGF, and no decision probability for a given deadline. (The computed density can be assigned as the density of initial condition for next stage.)
*USAGE:
[x,prob,pnd,mean_dead,mgf_dead,sec_dead] = dist_deadline(a,s,x0, x0dist,deadline,z, theta)
*INPUT:
a = drift rate
s = diffusion rate
x0 = support of initial condition (discretized)
x0dist = density of x0 at points in x0
deadline = change point
z= threshold
theta = moment generating function parameter
*OUTPUT:
x = Support set of distribution at deadline
prob = density of X(deadline) conditioned on decision time greater than deadline
pnd= probability of no decision until deadline
mean_dead= mean value of X(deadline)
mgf_dead= moment generating function of X(deadline)
sec_dead = second moment of X(deadline)
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