this README refers to probabilistic programming model for an infant development experiment run by Shari Liu, Tomer Ullman, Josh Tenenbaum & Elizabeth Spelke.
You will need the following things properly installed on your computer:
git clone https://github.com/tomeru/inferReward.gitThe main model is found in rewardInference.txt ; the code there can be directly pasted into a browser implementation of Church, such as in probmods.org.
However, it is advised to reduce the number of mh-samples if using a browser, as inference can take several minutes and cause the browser to crash.
To run the code locally, navigate to your webchurch installiation and run:
church rewardInference.txtIf (infer-agent-reward) is commented in: the output will be a list of pairs of samples from the posterior probability P(Reward(Blue) | Familiarization Action) & P(Reward(Yellow) | Familiarization Action). "Blue" is a stand-in for the agent that the Protagonist in the experiment exerted more effort to reach.
If (predict-agent-action) is commented in: the output will be a list of pairs of actions, sampled from the predicted probability distribution P(Action | Inferred Rewards). Pairs are used as the test stimuli involved pairs of actions.
The raw output can be visualized and analyzed in different ways.
We provide a Python implementation using Seaborn. This code assumes you have run rewardInference and saved the appropriate output.