Abstract
Higher-order cognition emerges from the interaction of multiple cognitive processes (e.g., perception, attention, inhibition). Yet, these processes remain difficult to measure during ongoing task performance. Traditionally, researchers have studied each cognitive domain in isolation. In contrast, emerging studies have used multiple-task designs from multiple cognitive domains to better understand the relationship between different cognitive processes. These studies, spanning across humans, non-human animals, and artificial neural networks, suggest that cognition is compositional — built from abstract neural representations that generalize across tasks to support flexible behavior. These findings suggest that, with appropriate experimental design, neural signatures associated with a specific cognitive process can be decoded in one task and tested for transfer to another. We term this approach ‘cross-domain transfer.’ Here, we explore the possibility that cognition's compositional architecture, instantiated through abstract neural representations, provides a basis for understanding how reusable cognitive processes can flexibly combine across tasks and contexts. We place cross-domain transfer within the broader evolution of cognitive decoding methods, discuss its theoretical foundations, and synthesize emerging evidence across cognitive domains. Through systematic task selection and validation, this framework provides a way to test competing theories of cognition and develop more interpretable, process-level approaches to measuring and predicting behavior.