TY - JOUR
T1 - The role of (bounded) optimization in theory testing and prediction
T2 - In response to: Suboptimality in perceptual decision making
AU - Howes, Andrew
AU - Lewis, Richard L.
PY - 2019/1/10
Y1 - 2019/1/10
N2 - We argue that a radically increased emphasis on (bounded) optimality can contribute to cognitive science by supporting prediction. Bounded optimality (computational rationality), an idea that borrowed from artificial intelligence, supports a priori behavioral prediction from constrained generative models of cognition. Bounded optimality thereby addresses serious failings with the logic and testing of descriptive models of perception and action.
AB - We argue that a radically increased emphasis on (bounded) optimality can contribute to cognitive science by supporting prediction. Bounded optimality (computational rationality), an idea that borrowed from artificial intelligence, supports a priori behavioral prediction from constrained generative models of cognition. Bounded optimality thereby addresses serious failings with the logic and testing of descriptive models of perception and action.
UR - https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/role-of-bounded-optimization-in-theory-testing-and-prediction/9CE101CC58A9D987D11407336DBEDDAA/core-reader#
U2 - 10.1017/S0140525X18001486
DO - 10.1017/S0140525X18001486
M3 - Comment/debate
SN - 0140-525X
VL - 41
JO - Behavioral and Brain Sciences
JF - Behavioral and Brain Sciences
M1 - e232
ER -