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Experiment exploring whether the visual system does causal inference over latent visual causes. Is there a common cause or is it random noise? Are people Bayes optimal? What's with the pigeons? Let's find out

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WeiJiMaLab/pigeon_inference

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pigeons_inference

Directory

Every repository should have a directory for files describing the structure of the repository and a high-level overview of the contents

pigeon_inference
│   README.md 
│
└─── analysis : all analysis files
│   │
│   └───demos : runnable demo of the experiment itself
│   │
│   └───figures : all figures included in the final paper
│   │
│   └───processed_data : results > results_N where N is the model # (see C_specifymodel_v2)
│   │
│   └───src : analysis code
│
│   
└─── experiment : all experiment files
│   │
│   └───raw_data : unmodified data -- DO NOT EDIT
│   │
│   └───src : experiment code goes here
│
└─── resources : tutorials, presentations, papers, posters, etc.

Installation

You will have to install PsychToolBox to run the experiment.

Note: this code was developed using PsychToolbox version 3.0.14 It is only supported by PsychToolbox versions 3.0.14 and some older versions. It is not supported by v 3.0.15

Usage (Experiment)

To run the full experiment, run pigeon_expt.m

Usage (Analysis)

The analysis files are listed in alphabetical order from A -> F, this is the order in which they should be run.

A_readdata processes the raw data files B_plotsummarystats produces plots of summary statistics (descriptive) C_modelfitting is the main script that calls other model fitting files such as: - C_modelpredictions_v2 which produces the model predictions according to each model specification - C_specifymodel_v2 which has the full list of models and their different parameters, as well as their corresponding model number - Model fitting will result in the processed results found in processed_data/results, where results_N corresponds to the result for model number N D_callplotdatafits calls lower function D_plotdatawithfits which can be used to plot model fits against raw data F_ functions are further analyses based on the log likelihood ratios found in the results_N files

Demo(s)

To run a demo of the experiment, run demo_video.m

Authors and Acknowledgements

Jennifer Lee and Wei Ji Ma

License

Contact lab about licensing code

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Experiment exploring whether the visual system does causal inference over latent visual causes. Is there a common cause or is it random noise? Are people Bayes optimal? What's with the pigeons? Let's find out

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