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- W4366506398 abstract "This chapter reviews contemporary computational models of psychological development in a historical context, including those based on symbolic rules, artificial neural networks, dynamic systems, robotics, and Bayesian ideas. Emphasis is placed on newer work and the insights that simulation can provide into developmental mechanisms. Within space limitations, coverage is both sufficiently broad to provide a general overview of the field and sufficiently detailed to facilitate understanding of important techniques. Prospects for integrating the dominant approaches of neural networks and Bayesian methods are explored. There is also speculation about how deep-learning networks might begin to impact developmental modeling by increasing the realism of training patterns, particularly in visual perception." @default.
- W4366506398 created "2023-04-22" @default.
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- W4366506398 date "2023-05-11" @default.
- W4366506398 modified "2023-09-27" @default.
- W4366506398 title "Computational Models of Developmental Psychology" @default.
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- W4366506398 doi "https://doi.org/10.1017/9781108755610.028" @default.
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