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.

在研究中，我们通过探索人类和神经网络模型在学习和推理组合函数方面的能力，发现人类能够在不同的交互条件下对视觉函数组合进行零样本泛化，比较同一任务中的神经网络模型发现，通过元学习组合性（MLC）方法，标准序列到序列的转换器能够模仿人类在函数组合中的泛化模式。