Steeve Laquitaine & Justin L. Gardner
Analyse and model human inference behavior in motion direction and spatial location estimation tasks in which optimality requires exploiting knowledge of the statistical distribution of the stimuli.
See also website.
To run the task git clone the project and follow the instructions given in .../task/README.txt
-
Set your screen parameters (will run on screenNumber = 1, you can change the screen number in taskDotDir.m)
-
Open runTask
Run an 10º prior
taskDotDir('steeve_exp12_metho_Pstd010_mean225_coh006012024_dir36_t107_073_033perCoh_130217')or a 80º prior
taskDotDir('steeve_exp12_metho_Pstd080_mean225_coh006012024_dir36_t106_075_034perCoh_130217')%dropped frames: 0.00 %donenote : taskDotDir.m loads the stimulus parameters (each trial's motion direction and coherence and a few stats) stored in a file like (e.g., 10º prior) :
steeve_exp12_metho_Pstd010_mean225_coh006012024_dir36_t107_074_033perCoh_controlRndInitPosSymPrior_131224.mat
There is one file per prior.
- When the task is running to enter a response move your mouse to the desired direction, then press 1 on keyboard (make sure mglEditScreenParameters takes "1" as input)
- Download the dataset from Mendeley into your project path e.g.,
proj_path = "Desktop/project"
Run from the terminal:
curl https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/nxkvtrj9ps-1.zip --output dataset.zipThe dataset should look like this:
Desktop/project/
- data/mat/
- data01_direction4priors/ # four-prior motion experiment
- data/
- sub01/ # subject 1
- steeve_exp12_data_sub01_sess06_run26_Pstd040_mean225_coh006_012_024_dir36_randInitPosSymPrior80_140106.mat # task & behavioral data
- steeve_exp12_data_sub01_sess06_run26_Pstd040_mean225_coh006_012_024_dir36_randInitPosSymPrior80_140106.edf # eye tracking data
...
- sub12/
- data02_direction1prior/ # one-prior motion experiment
- data/
- sub01/
- steeve_data_sub01_sess01_run01_Pstd080_mean225_coh006_012_024_dir36_170704.mat
- steeve_data_sub01_sess01_run02_Pstd080_mean225_coh006_012_024_dir36_170704.edf
...
- sub06/
- data03_orientation/ # four-prior location experiment
- data/
- sub01/
- steeve_exp04_data_sub01_sess01_run01_Pstd080_mean225_con010_0156_1_loc36perCon_151208.mat
- steeve_exp04_data_sub01_sess01_run01_Pstd080_mean225_con010_0156_1_loc36perCon_151208.edf
...
- sub09/ - Install the
mglpackage for data extraction. Task and behavioral data are saved in the.mat. You can extract them withgetTaskParameters('..mat'): Clone themglsoftware and add it to your matlab path. In Matlab:
# move to project path
cd Desktop/project
# clone package
git clone https://github.com/justingardner/mgl.git mgl
# add package to path
addpath(genpath('Desktop/project/mgl'))
# test your installation
help mgl
ans =
MGL library functions
Main screen functions
mglOpen : Opens the screen
mglFlush : Flips front and back buffer
mglClose : Closes the screen- Extract the data from each file (see documentation in 1). The extracted
datais a matlab structure with two elements. The data are contained in the second element:
data = getTaskParameters('data/mat/data01_direction4priors/data/sub01/steeve_exp12_data_sub01_sess06_run26_Pstd040_mean225_coh006_012_024_dir36_randInitPosSymPrior80_140106.mat')
data{2}
ans =
struct with fields:
nTrials: 202 # trial count
trialVolume: [1x202 double]
trialTime: [1x202 double]
trialTicknum: [1x202 double]
trials: [1x202 struct]
blockNum: [1x202 double]
blockTrialNum: [1x202 double]
response: [1x202 double]
reactionTime: [1x202 double] # reaction time is in seconds relative to the trial segment start
originalTaskParameter: [1x1 struct]
originalRandVars: [1x1 struct]
responseVolume: [1x202 double]
responseTimeRaw: [1x202 double]
randVars: [1x1 struct]
parameter: [1x1 struct]
data{2}.reactionTime
ans =
Columns 1 through 14
2.1582 2.5499 1.7992 2.6472 1.9805 1.9602 1.1360 1.9403Get subject's estimated directions (Cartesian coordinates):
data{2}.randVars.prodcoorGet the task's displayed directions (degree):
data{2}.randVars.myRandomDirGet the task's displayed direction coherences (dot ratio):
data{2}.randVars.myRandomCohGet the response arrow's initial position/directions (degree):
data{2}.randVars.myRandomCoh@article{laquitaine2018switching,
title={A switching observer for human perceptual estimation},
author={Laquitaine, Steeve and Gardner, Justin L},
journal={Neuron},
volume={97},
number={2},
pages={462--474},
year={2018},
publisher={Elsevier}
}
(1) https://gru.stanford.edu/doku.php/mgl/taskreference?s[]=reactiontime
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