 Computational model of cognition

A computational model of cognition is an explicit set of mathematical or algorithmic rules connecting inputs and previous experience to predictions about behaviour. Its variables can represent internal quantities, while its predictions concern observable choices, reaction times or other measurements. This makes a proposed cognitive mechanism precise enough to simulate and test.^[ref]

A simple reinforcement learning example updates a predicted value $Q_t$ after a reward $r_t$:The learning rate $\alpha$ controls the contribution of the reward prediction error $r_t-Q_t$. With $Q_0=0$, constant reward $r_t=1$ and $\alpha =1/2$, successive values are $1/2$, $3/4$ and $7/8$. Equivalently, $Q_t=1-2^{-t}$. These are model predictions for constructed inputs, not measured human responses.^[ref]

Predicting choices additionally requires a rule linking values to actions. Parameter estimation can fit such a model, but a good fit alone does not establish that its internal variables are uniquely recoverable. Parameter recovery tests this by simulating data from known parameters and checking whether the fitting procedure retrieves them.^[ref] This distinguishes a useful formal account from a merely suggestive verbal description.