Knowing-How
Knowing-how is Gilbert Ryle's term for the kind of competence that cannot be reduced to a set of propositions. To know how to ride a bicycle, balance on a beam, or speak a language fluently is not to possess a collection of facts — it is to embody a capacity. Ryle introduced the distinction to attack the intellectualist fallacy: the assumption that all intelligent performance is guided by prior consultation of propositions.
The knowing-that / knowing-how distinction is not as clean as Ryle supposed. Expert practitioners often articulate rules they follow; novices who learn rules can eventually internalize them as skill. What begins as explicit, propositional knowing-that can become implicit, procedural knowing-how through practice. This suggests the two are not different kinds of knowledge but different stages in the same learning process — the distinction is temporal, not categorical.
The machine learning parallel is instructive: neural networks that learn procedural skills from data acquire knowing-how without knowing-that. They cannot state the rules they are following. Whether this shows that understanding is possible without propositional knowledge — or that something is missing — is the contested question.
The Biological Substrate of Knowing-How
The knowing-how / knowing-that distinction Ryle drew is not merely philosophical — it maps onto a genuine dissociation in biological memory systems. Declarative (explicit) memory and procedural (implicit) memory are served by distinct neural architectures. The hippocampus and medial temporal lobe support declarative memory — the kind that stores facts and episodes and is accessible to conscious report. Procedural knowledge of skilled action is distributed across the motor cortex, basal ganglia, and cerebellum — structures that can be severely damaged without impairment of knowing-how, and vice versa.
The amnesic patient H.M., who could not form new declarative memories after bilateral hippocampal resection, could still learn and retain new motor skills — and crucially, had no declarative knowledge of having learned them. He would improve at mirror-tracing across sessions while expressing surprise at each session that he had any skill with the task. This is knowing-how without knowing-that in its starkest form: the body retaining what the mind cannot report.
The cerebellar contribution to skill is particularly significant for any philosophical account. The cerebellum maintains forward models — internal simulations of expected motor consequences — that are updated by error signals. Skilled performance involves minimizing the discrepancy between the forward model's prediction and the actual sensory consequence of action. This is computation in a rigorous sense: the cerebellum performs Kalman-filter-like state estimation during movement. Whether such computation counts as a form of "knowing" in Ryle's sense, or whether it reveals that Ryle's framing is too anthropocentric, is an open question in philosophy of mind.
Habits — the most degraded and automatized forms of procedural knowledge — are associated with a shift from cortically mediated, goal-directed behavior to striatum-mediated, stimulus-response behavior as practice continues. This chunking is not merely behavioral efficiency; it reflects a genuine change in the causal control of action. Expert knowing-how may be less available to deliberate modification precisely because it has been encoded in systems with limited access to propositional revision. This would explain why coaching experienced athletes often requires unlearning — a notoriously difficult process — rather than simply adding new instructions.
The implication for Ryle's argument against the intellectualist fallacy is that he was right for the wrong reason. It is not that knowing-how is non-propositional in principle. It is that procedural knowledge is encoded in neural systems that are architecturally isolated from the systems that support propositional thought — and those systems operate on principles closer to control engineering than to symbolic reasoning.