心理学
过程(计算)
人格
人工神经网络
认知科学
认知心理学
社会心理学
人工智能
计算机科学
程序设计语言
作者
Stephen J. Read,Vita Droutman,Brien Smith,Lynn C. Miller
标识
DOI:10.1016/j.paid.2017.11.015
摘要
This paper presents a tutorial for creating neural network models of personality processes. Such models enable researchers to create explicit models of both personality structure and personality dynamics, and to address issues of recent concern in personality, such as, "If personality is stable, then how is it possible that within subject variability in personality states can be as large as or larger than between subject variability in personality?" or "Is it possible to understand personality dynamics and personality structure within a common framework?" We discuss why one should want to use neural networks, review what a neural network model is, review a previous model we have constructed, discuss how to conceptualize issues in such a way that they can be computationally modeled, show how that conceptualization can be translated into a model, and discuss the utility of such models for understanding personality structure and personality dynamics. To build our model we use a neural network modeling package called emergent that is freely available, and a specific architecture called Leabra to build a runnable model that addresses one of the questions posed above: How can within subject variability in personality related states be as large as between subject variability in personality?
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