Stimuli, data, and model predictions for the paper titled "Modeling human intuitions about liquid flow with particle-based simulation" (Bates, Yildirim, Battaglia, Tenenbaum) forthcoming in PLOS Computational Biology.Content DescriptionStimuli:Exp1_images: Images for all stimuli in Experiment 1Exp2_images: Images for all stimuli in Experiment 2Exp1_movies: Videos used for practice trials in Experiment 1Exp2_movies: Videos used for practice trials in Experiment 2Data:responses.zip: Subject responses, indexed by stimulus name, in Pickle format. These can be loaded in Python using:import picklewith open([file name], 'rb') as fid: data = pickle.load(fid)The resulting format will be a Python dictionary.Model Predictions:(Note: load using Python NumPy, e.g.import numpy as npdata = np.load('path_to_file')All files are simple, numpy arrays containing model predictions for each set of parameter values. See notes below for description of each dimension. Parameter values along a dimension always go from low to high.)SPH ground truth:exp1_grnd_water_200.npyexp1_grnd_stickyhoney_100.npyexp2_grnd_water_200.npyexp2_grnd_stickyhoney_100.npyDimensions: (scenes)SPH Intuitive Fluids Engine:exp1_damping_alpha_0.01_0.2_2.0_sticky_N_1_5_10_15_25_50_75_100.npyexp2_damping_alpha_0.01_0.2_2.0_sticky_N_1_5_10_15_25_50_75_100.npyDimensions: (scenes, alpha, zeta, # particles)SPH Intuitive Fluids Engine (Position Noise, from Bates et al. 2015, referenced in text):exp1_position_noise_alpha_0.01_0.2_2.0_sticky_noiselevels_5_N_1_5_10_15_25_50_75_100_samples_16.npyexp2_position_noise_alpha_0.01_0.2_2.0_sticky_N_noiselevels_5_1_5_10_15_25_50_75_100_samples_16.npyDimensions: (scenes, alpha, noise magnitudes, # particles, noisy samples)SimpleSim and Gravity Heuristic:exp1_heuristic.npyexp2_heuristic.npyDimensions: (scenes, g, m)Convolutional Neural Networks:convnet_exp1_water.npyconvnet_exp1_honey.npyconvnet_exp2_water.npyconvnet_exp2_honey.npyDimensions: (scenes)