behavior and have lapsed back into mere redescription.

Rather than simply assuming that contemporary DNNs implement classical
architectures or searching for some _a priori_ guarantee, what we need to do
is try to _find out_ whether or not they are doing so. The burgeoning field of
“mechanistic interpretability” is making this increasingly feasible (Elhage et
al.,, 2021; Olsson et al.,, 2022; Merullo et al.,, 2024; Wu et al.,, 2023).
Though interpretability work is often raised in the context of AI safety
considerations (Rudner and Toner,, 2021), it can also play a crucial role in
informing cognitive theory, allowing us to extract higher-level, functionally
relevant descriptions of these models’ internal representations that we can
put into contact with existing cognitive theory (McGrath et al., 2023b, ;
Millière,, 2024; Millière and Buckner, 2024b, ). In principle, this process
could point us towards eliminativism, if successful models’ representations
bear no resemblance to classical constructs, or mere implementationism, if
they exactly recapitulate the structure of a fully specified symbolic theory.
In the cases where this work has been pursued, however, it seems likely that
these models land somewhere in between, recapitulating certain aspects of
classical theory, but diverging in interesting ways (Baroni,, 2022; Chen et
al.,, 2024; Manning et al.,, 2020; McGrath et al., 2023a, ). In such cases, we
have suggested that it may be worth preserving key terms to encompass these
behaviors (McGrath et al., 2023a, ; McGrath and Russin,, 2024), as long as it
does not lose track of the fact that they contain new insights. In a slogan,
not all systems that we may count as implementations are ‘mere’—informative
implementations can point us to new insights into previously postulated
structures (Smolensky,, 1988; Smolensky and Legendre,, 2011). Though
interpretive work specifically focused on evaluating the compositional models
reviewed here is in its initial stages, it is a promising line of future
investigation (Andreas,, 2019; Lepori et al.,, 2023; Lewis et al.,, 2023;
McCoy et al.,, 2019; Russin et al., 2021a, ; Soulos et al.,, 2020; Todd et
al.,, 2024). From a philosophical perspective, further progress on deeper
issues about the nature of explanation and implementation will put us in a
position to use these findings to resolve the mere implementation objection
once and for all.

###  5.4 Conclusion

In this article, we have traced the notion of compositionality from its
emergence in early analytic philosophy to its present status as a key
explanatory construct in contemporary cognitive science. We have emphasized
the role that compositionality plays in debates over the explanatory adequacy
of neural network models of human cognition and reviewed a range of recent
results suggesting that current deep neural networks are capable of
replicating some of the behavioral signatures that have motivated the
ascription of internal compositional systems to human beings. In particular,
these recent results suggest that architectural inductive biases and
metalearning (or large-scale pretraining) are viable approaches to reproducing
these behavioral signatures with strictly neural architectures. We have
discussed how, when taken seriously as approaches to modeling human
compositionality, these frameworks generate concrete predictions about its
underlying neural mechanisms and about its development. This recent success
also reopens key philosophical questions about the theoretical significance
and proper interpretation of neural networks—whether they should be understood
as merely implementing classical compositional mechanisms, or may generate
novel insights and motivate revisions to classical theories. These pressing
questions take us beyond asking what neural networks can do—instead asking
what they _mean_ for our understanding of the human mind. We expect that
resolving them will require an interdisciplinary approach and increased
philosophical engagement.

###  5.5 Acknowledgments

We would like to thank Richard Kimberley Heck, Josh Schechter, Christopher
Hill, Elizabeth Miller, Roman Feiman, Ellie Pavlick, Michael J. Frank, Randall
O’Reilly, Edouard Machery, Suraj Anand, Steven Frankland, Taylor Webb, Stavros
Orfeas Zormpalas, and the participants in Brown University Dissertation
Workshop and the Philosophy and Cognitive Science of Deep Learning reading
group for helpful comments and discussions. JR was supported by NIH NIGMS
COBRE grant GR5271961.

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