Publikation
Fodor and Pylyshyn's Legacy - Still No Human-like Systematic Compositionality in Neural Networks
Tim Woydt; Moritz Willig; Antonia Wüst; Lukas Helff; Wolfgang Stammer; Constantin A. Rothkopf; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2506.01820, Pages 1-18, Computing Research Repository, 2025.
Zusammenfassung
Strong meta-learning capabilities for systematic compositionality are emerging as
an important skill for navigating the complex and changing tasks of today’s world.
However, in presenting models for robust adaptation to novel environments, it is
important to refrain from making unsupported claims about the performance of
meta-learning systems that ultimately do not stand up to scrutiny. While Fodor
and Pylyshyn famously posited that neural networks inherently lack this capacity
as they are unable to model compositional representations or structure-sensitive
operations, and thus are not a viable model of the human mind, Lake and Baroni
recently presented meta-learning as a pathway to compositionality. In this position
paper, we critically revisit this claim and highlight limitations in the proposed
meta-learning framework for compositionality. Our analysis shows that modern
neural meta-learning systems can only perform such tasks, if at all, under a very
narrow and restricted definition of a meta-learning setup. We therefore claim
that ‘Fodor and Pylyshyn’s legacy’ persists, and to date, there is no human-like
systematic compositionality learned in neural networks.