#bayesianism #cognitive-science #computation #computational-psychology
Finally, probabilistic models can be used to advance and perhaps resolve some of the great theoretical debates that divide tradi- tional approaches to cognitive science. The history of computational models of cogni- tion exhibits an enduring tension between models that emphasize symbolic represen- tations and deductive inference, such as first-order logic or phrase structure gram- mars, and models that emphasize continu- ous representations and statistical learning, such as connectionist networks or other as- sociative systems. Probabilistic models can be defined with either symbolic or continu- ous representations, or hybrids of both, and help to illustrate how statistical learning can be combined with symbolic structure. More generally, we think that the most promis- ing routes to understanding human intelli- gence in computational terms will involve deep interactions between these two tradi- tionally opposing approaches, with sophis- ticated statistical inference machinery oper- ating over structured symbolic knowledge representations.
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- (no access) - [Ron_Sun]_The_Cambridge_Handbook_of_Computational.pdf, p77
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