Ease of Learning Explains Semantic Universals
This repository accompanies the paper:
- Shane Steinert-Threlkeld and Jakub Szymanik, "Ease of Learning Explains Semantic Universals", under revision at Cognition. https://semanticsarchive.net/Archive/zM5ZGIxM/EaseLearning.pdf
We generate artificial color naming systems by partitioning the CIELab color space, then train neural networks to learn those systems. We show that the ability of networks to learn these color systems is well-explained by their degree of convexity, supporting a semantic universal for color terms across language.
Python 2.7, TensorFlow 1.10, Numpy 1.13, Pandas
(NB: the code should be compatible with Python 3, but has not been tested with it. Later versions of TF and NP will also likely work, but no promises.)
will run the main experiment reported in the paper. In the output directory
trial, the following will be output:
results.csv: each row is one trial, recoring the color system generating algorithm's parameter values, degree of convexity, network accuracy, as well as other geometric variables, and the actual partition
points.npy: a numpy array, with the CIELab space points
The algorithm for generating artificial color naming systems (and some utility methods for it) are in
Analyzing the Data
The regression and commonality analysis reported can be run via
This will also produce
The cluster analysis is handled by
clusters.R, an will produce the cluster PNG files.