Claude Shannon introduced a scientifically important notion of “information” in his 1948 article “A Mathematical Theory of Communication”. The intuitive idea is that information measures reduction in uncertainty, where reduced uncertainty manifests as an altered probability distribution over possible states. Shannon codified this idea within a rigorous mathematical framework, laying the foundation for information theory (Cover and Thomas 2006). Shannon information is fundamental to modern engineering. It finds fruitful application within cognitive science, especially cognitive neuroscience. Does it support a convincing analysis of computation as “information-processing”? Consider an old-fashioned tape machine that records messages received over a wireless radio. Using Shannon’s framework, one can measure how much information is carried by some recorded message. There is a sense in which the tape machine “processes” Shannon information whenever we replay a recorded message. Still, the machine does not seem to implement a non-trivial computational  model.[6]  Certainly, neither the Turing machine formalism nor the neural network formalism offers much insight into the machine’s operations. Arguably, then, a system can process Shannon information without executing computations in any interesting sense.