The computational psychiatry of antisocial behaviour and psychopathy

被引:9
|
作者
Pauli, Ruth [1 ]
Lockwood, Patricia L. [1 ,2 ,3 ]
机构
[1] Univ Birmingham, Ctr Human Brain Hlth, Sch Psychol, Birmingham, England
[2] Univ Birmingham, Inst Mental Hlth, Sch Psychol, Birmingham, England
[3] Univ Birmingham, Ctr Dev Sci, Sch Psychol, Birmingham, England
来源
基金
英国惠康基金; 英国医学研究理事会;
关键词
Antisocial; Psychopathy; Conduct disorder; Oppositional defiant disorder; Attention deficit hyperactivity disorder; Machine learning; Computational modelling; Computational psychiatry; CALLOUS-UNEMOTIONAL TRAITS; CONDUCT PROBLEMS; DECISION-MAKING; PERSONALITY-DISORDER; FEARLESS DOMINANCE; BRAIN STRUCTURE; EXPECTED VALUE; GREY-MATTER; REWARD; INDIVIDUALS;
D O I
10.1016/j.neubiorev.2022.104995
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
Antisocial behaviours such as disobedience, lying, stealing, destruction of property, and aggression towards others are common to multiple disorders of childhood and adulthood, including conduct disorder, oppositional defiant disorder, psychopathy, and antisocial personality disorder. These disorders have a significant negative impact for individuals and for society, but whether they represent clinically different phenomena, or simply different approaches to diagnosing the same underlying psychopathology is highly debated. Computational psychiatry, with its dual focus on identifying different classes of disorder and health (data-driven) and latent cognitive and neurobiological mechanisms (theory-driven), is well placed to address these questions. The elucidation of mechanisms that might characterise latent processes across different disorders of antisocial behaviour can also provide important advances. In this review, we critically discuss the contribution of computational research to our understanding of various antisocial behaviour disorders, and highlight suggestions for how computational psychiatry can address important clinical and scientific questions about these disorders in the future.
引用
收藏
页数:11
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