Cognitive science

Cognitive science investigates the mind and intelligence by combining methods from disciplines including psychology, neuroscience, linguistics, computer science and artificial intelligence. Its scientific questions concern mechanisms of perceiving, learning, reasoning and acting, rather than just naming mental abilities.^[ref]

A computational model of cognition makes an account precise enough to compare with observations. For example, consider two hypotheses about people performing synchronous speech. One predicts that a designated follower always trails a leader by a constant interval $d$. Another allows both people to adjust their timing. Recording corresponding speech landmarks produces a sequence of signed timing differences; this can test whether the proposed constant-follower account fits the task.

Fred Cummins's paired-reading experiments supply a concrete connection between linguistics and quantitative behavioural measurement: corresponding speech events can be closely aligned, and both speakers adjust their timing relative to individual readings.^[ref]

A successful behavioural prediction does not uniquely identify a neural mechanism. Combining task performance, explicit models and independent physiological measurements helps distinguish explanations that agree on one observable. The field's different methods constrain one another without requiring every question to be answered at the same descriptive level.

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.