  Deep-learning models can extract a rich assortment of features from data.
Which features a model uses depends not only on \emph{predictivity} -- how
reliably a feature indicates training-set labels -- but also on
\emph{availability} -- how easily the feature can be extracted from inputs. The
literature on shortcut learning has noted examples in which models privilege
one feature over another, for example texture over shape and image backgrounds
over foreground objects. Here, we test hypotheses about which input properties
are more available to a model, and systematically study how predictivity and
availability interact to shape models' feature use. We construct a minimal,
explicit generative framework for synthesizing classification datasets with two
latent features that vary in predictivity and in factors we hypothesize to
relate to availability, and we quantify a model's shortcut bias -- its
over-reliance on the shortcut (more available, less predictive) feature at the
expense of the core (less available, more predictive) feature. We find that
linear models are relatively unbiased, but introducing a single hidden layer
with ReLU or Tanh units yields a bias. Our empirical findings are consistent
with a theoretical account based on Neural Tangent Kernels. Finally, we study
how models used in practice trade off predictivity and availability in
naturalistic datasets, discovering availability manipulations which increase
models' degree of shortcut bias. Taken together, these findings suggest that
the propensity to learn shortcut features is a fundamental characteristic of
deep nonlinear architectures warranting systematic study given its role in
shaping how models solve tasks.
