Compositional learning of functions in humans and machines
Yanli Zhou Brenden M. Lake Adina Williams
The ability to learn and compose functions is foundational to efficient
learning and reasoning in humans, enabling flexible generalizations such as
creating new dishes from known cooking processes. Beyond sequential chaining of
functions, existing linguistics literature indicates that humans can grasp more
complex compositions with interacting functions, where output production
depends on context changes induced by different function orderings. Extending
the investigation into the visual domain, we developed a function learning
paradigm to explore the capacity of humans and neural network models in
learning and reasoning with compositional functions under varied interaction
conditions. Following brief training on individual functions, human
participants were assessed on composing two learned functions, in ways covering
four main interaction types, including instances in which the application of
the first function creates or removes the context for applying the second
function. Our findings indicate that humans can make zero-shot generalizations
on novel visual function compositions across interaction conditions,
demonstrating sensitivity to contextual changes. A comparison with a neural
network model on the same task reveals that, through the meta-learning for
compositionality (MLC) approach, a standard sequence-to-sequence Transformer
can mimic human generalization patterns in composing functions.