  The integration of behavioral phenomena into mechanistic models of cognitive
function is a fundamental staple of cognitive science. Yet, researchers are
beginning to accumulate increasing amounts of data without having the temporal
or monetary resources to integrate these data into scientific theories. We seek
to overcome these limitations by incorporating existing machine learning
techniques into an open-source pipeline for the automated construction of
quantitative models. This pipeline leverages the use of neural architecture
search to automate the discovery of interpretable model architectures, and
automatic differentiation to automate the fitting of model parameters to data.
We evaluate the utility of these methods based on their ability to recover
quantitative models of human information processing from synthetic data. We
find that these methods are capable of recovering basic quantitative motifs
from models of psychophysics, learning and decision making. We also highlight
weaknesses of this framework, and discuss future directions for their
mitigation.
