Meta-analysis of the implied distribution of callous-unemotional traits across sampling methods and informant.

Callous-unemotional (CU) traits have been measured in a variety of sample-types (e.g., community or forensic) and from the perspective of different informants (e.g., self-report or parent-report) using the inventory of callous-unemotional traits total score (ICU-T). Although the positive association between CU traits and antisocial behavior is uncontroversial, the degree to which sample-types are different from each other has received little attention despite such knowledge being important for generalization and interpretation of research findings. To address this gap in the literature, we estimated the implied distribution of the ICU-T across sample-types, informants, and their interaction using meta-analytic models of sample means and variances. In unconditional models, we found that sample-type significantly moderated mean ICU-T scores but not variance, while informant significantly moderated the variance of ICU-T scores but not means. There was also a significant interaction between sample-type and informant. Mean parent-reported ICU-T scores were significantly lower than self-reported scores in community samples, but not significantly different in samples with elevated levels of antisocial behavior. Implications of our findings include improved research efficiency, the need for different ICU-T norms across informants, and greater understanding of informant biases.

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

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.