Antisocial Learning: Using Learning Window Width to Model Callous-Unemotional Traits?

Computational psychiatry (Cambridge, Mass.) Pub Date : 2021-05-31 eCollection Date: 2021-01-01 DOI:10.5334/cpsy.68
Caroline Moul, Oliver J Robinson, Evan J Livesey
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引用次数: 0

Abstract

Psychopathic traits and the childhood analogue, callous-unemotional traits, have been severely neglected by the research field in terms of mechanistic, falsifiable accounts. This is surprising given that some of the core symptoms of the disorder point towards problems with basic components of associative learning. In this manuscript we describe a new mechanistic account that is concordant with current cognitive theories of psychopathic traits and is also able to replicate previous empirical data. The mechanism we describe is one of individual differences in an index we have called, "learning window width". Here we show how variation in this index would result in different outcome expectations which, in turn, would lead to differences in behaviour. The proposed mechanism is intuitive and simple with easily calculated behavioural implications. Our hope is that this model will stimulate discussion and the use of mechanistic and computational accounts to improve our understanding in this area of research.

反社会学习:用学习窗口宽度来模拟冷酷无情的性格特征?
在机械的、可证伪的描述中,研究领域严重忽视了精神病态特征和童年的类似物——冷酷无情的特征。这是令人惊讶的,因为这种障碍的一些核心症状指向了联想学习的基本组成部分的问题。在这份手稿中,我们描述了一个新的机制帐户,这是与当前的心理变态特征的认知理论一致,也能够复制以前的经验数据。我们描述的机制是我们称之为“学习窗口宽度”的指标中的个体差异之一。在这里,我们展示了该指数的变化如何导致不同的结果预期,进而导致行为的差异。所提出的机制直观、简单,具有易于计算的行为含义。我们的希望是,这个模型将激发讨论和使用机械和计算帐户,以提高我们对这一研究领域的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.30
自引率
0.00%
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审稿时长
17 weeks
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