Antisocial Learning: Using Learning Window Width to Model Callous-Unemotional Traits?. Issue 1 (31st May 2021)

• Record Type: Journal Article
• Authors: Moul, Caroline; Robinson, Oliver J.; Livesey, Evan J.
• Is Part Of: Computational psychiatry. Volume 5:Issue 1(2021)
• Page Start: 54
• Page End: 59
• Publication Date: 2021-05-31
• Subjects: psychopathy -- callous-unemotional -- outcome expectancies -- associative learning -- learning window width; Psychiatry -- Mathematical models -- Periodicals; Psychiatry -- Data processing -- Periodicals; Computational neuroscience -- Periodicals; 616.89140285
• DOI: 10.5334/cpsy.68
• Languages: English
• ISSNs: 2379-6227

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.