Even very sophisticated ideas about causality, such as Bayesian networks, require an intuitive notion of causality to provide a scaffolding for how variables are related to each other. Brains meld this preverbal sensory-motor notion with later linguistic representations to provide a highly useful, neurally encoded concept of causality that supplies the basis for the explanatory relation that holds between hypotheses and evidence. Thus we are beginning to glimpse the neural mechanisms that allow brains to represent hypotheses and concepts, including explanation and causality, using patterns of neural activity that constitute both verbal and multimodal representations. I suspect that human understanding of time, like that of space and causality, is often difficult to put into words because its neural encoding is partly dependent on physiological rather than verbal representations.