A neural mechanism for compositional generalization of structure in humans

An exceptional human ability to adapt to the dynamics of novel environments relies on abstracting and generalizing past experiences. While previous research has examined how humans generalize isolated sequential processes, we know little concerning the neural mechanisms that enable adaptation to the more complex dynamics that govern everyday experience. Here, we deployed a novel sequence learning task based on graph factorization, coupled with simultaneous magnetoencephalography (MEG) recordings, to ask whether reuse of experiential “building blocks” provides an abstract structural scaffolding that enables inference and generalization. We provide behavioral evidence that participants decomposed task experience into subprocesses, abstracted dynamical subprocess structures away from sensory specifics, and transferred these to a new task environment. Neurally we show this transfer is underpinned by a representational alignment of abstract subprocesses across task phases, where this included enhanced neural similarity among stimuli that adhered to the same subprocess, a temporally evolving mapping between predictive representations of subprocesses and a generalization of the precise dynamical roles that stimuli occupy within graph structures. Crucially, decoding strength for dynamical role representations predicted behavioral success in transfer of subprocess knowledge, consistent with a role in supporting behavioral adaptation in new environments. We propose a structural scaffolding mechanism enables compositional generalization of dynamical subprocesses that facilitate efficient adaptation within new contexts.