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That misrepresents our argument. We argue that rich interpretations (contrastive explanantia, qualitative reasoning, and sharpening) are necessary for establishing proper counterfactual dependencies in any type of explanation. The full open-access paper is a better source than just the abstract.

We then provide a positive account of how FC models provide a variety of neuroscientific explanations.

Many neuroscientists and philosophers maintain that because of this, FC models cannot provide explanations. We formulate this problem more precisely and then show that it rests on an impoverished interpretation of scientific models in general and FC models in particular.

These models typically represent time series of recurrent neural activity in conventionally determined spatial regions (as a network’s nodes) and synchronization likelihoods among these time series (as its edges).

This is because without some form of causal grounding, it seems unintelligible why any explanatory relation between these parts and the phenomenon of interest would hold. This problem is particularly pronounced in functional connectivity models (FC) in neuroscience.

Many successful explanations show how causally individuated parts are responsible for the occurrence of the phenomena that scientists seek to explain. On this view, parts that are chosen only by convention, and related only through correlations, cannot possibly figure in successful explanations.

Just as large experimental collaborations transformed physics, we propose a similar collective effort to build AI systems that can deepen our understanding of the universe.

Our vision is that LPMs will act as true collaborators in physics research, helping to generate hypotheses, design experiments, analyze complex data, and open up new directions of inquiry.