  Real-world generalization, e.g., deciding to approach a never-seen-before
animal, relies on contextual information as well as previous experiences. Such
a seemingly easy behavioral choice requires the interplay of multiple neural
mechanisms, from integrative encoding to category-based inference, weighted
differently according to the circumstances. Here, we argue that a comprehensive
theory of the neuro-cognitive substrates of real-world generalization will
greatly benefit from empirical research with three key elements. First, the
ecological validity provided by multimodal, naturalistic paradigms. Second, the
model stability afforded by deep sampling. Finally, the statistical rigor
granted by predictive modeling and computational controls.
