A probabilistic graphical model, Neuronal Unit of Thoughts (NUTs), is proposed in this paper that offers a formalism for the integration of lower-level cognitions. Nodes or neurons in NUTs represent sensory data or mental concepts or actions, and edges the causal relation between them. A node affects a change in the Action Potential (AP) of its child node, triggering a value change once the AP reaches a fuzzy threshold. Multiple NUTs may be crossed together producing a novel NUTs. The transition time in a NUTs, in response to a �surprise,� is characterized, and the formalism is evaluated in the context of a non-trivial application: Autonomous Driving with imperfect sensors. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.