Understanding Antisocial Behaviour in Childhood
Can developments in data science help to provide new insights and treatments?
Updated February 27, 2026 | Reviewed by Abigail Fagan
This post was written by Elizabeth Berry, BSc, and Patricia Lockwood, Ph.D.
Adolescence is a time of rapid change, biologically, emotionally, and socially. While most young people are able to navigate complex social interactions during this transition, approximately 5% go on to show repetitive patterns of aggressive, defiant, or antisocial behaviour known as Conduct Disorder (CD). When these concerning behaviours become persistent, severe, or disruptive to a young person’s personal, social, or academic life, it may be an indication of an underlying psychiatric disorder. New developments in data science, where we build mathematical models of child behaviour, may provide unexpected answers to how, when, and why adolescents behave antisocially.
What Is Conduct Disorder?
Conduct Disorder (CD) is the most common antisocial disorder in childhood and adolescence. It is characterised by these repetitive and persistent patterns of behaviour whereby a young person consistently violates the rights of others or disregards age-appropriate societal norms. These behaviours can include physical aggression towards others or animals, destruction or theft of others' personal property, and deceitfulness.
Unlike the occasional misbehaviour seen in many adolescents, behaviours associated with CD are more socially disruptive. They tend to reflect a pervasive pattern rather than isolated incidents, often highlighting a notable disregard for others’ rights and a distinct lack of remorse. This is especially true for a subgroup of young people with CD who display what are known as Callous-Unemotional (CU) traits. Recent research has highlighted the importance of recognising these traits, with the DSM-5 introducing a “limited prosocial emotions” specifier, which applies to children and adolescents who display characteristics such as a lack of remorse or guilt , shallow or deficient emotional responses, and reduced empathy. These are considered key risk factors for more severe and persistent antisocial behaviour and are associated with significant long-term consequences, including an increased risk of academic failure, often due to truancy, suspension or expulsion, and an increased likelihood of developing antisocial personality disorder or psychopathic traits in adulthood.
Computational Approaches to Understanding Antisocial Behaviour and Conduct Disorder
Through behavioural studies, researchers have highlighted key components of the CD profile, including the aggressive, rule-breaking, and impulsive behaviours outlined previously in this post. However, while identification of these traits is useful, these studies offer little explanation as to why individuals with CD show these behaviours, and what differences may be occurring at a mechanistic level for them to arise. In recent years, researchers have more frequently used a computational approach from data science, which we have recently reviewed , to understand the complex psychological and neural mechanisms underlying these antisocial behaviours.
A key feature of this approach is using gamified tasks to measure behaviour during complex social interactions or while making social choices. One such example is a simple interactive task that captures how individuals decide whether to rely on others. In this game, a young person is given a small amount of money or points and must choose how much to “invest” in a partner. Whatever they send is multiplied, and the partner then decides how much to return. Across repeated rounds, this setup provides a controlled way to observe cooperation , reciprocity, and reactions to unfaithfulness. Computational models are then used to analyse these choices, allowing researchers to estimate processes such as expectations of trustworthiness, how quickly beliefs update after fair or unfair behaviour, and sensitivity to social reward or loss. In young people with Conduct Disorder, especially those with high levels of CU traits, these models often reveal reduced trust, weaker learning from others’ positive actions, and a tendency to prioritise self-interest.
One of the major strengths of these task-based approaches is that they allow researchers to connect observable behaviours, such as the choices an individual makes or how quickly they respond, to the underlying cognitive and neural processes that drive them. By fitting mathematical models to these smaller constituent parts of behaviour, such as choices, response times, or patterns of social interaction, and translating them into formal, quantitative terms, estimates of the underlying cognitive processes that best explain those patterns can be inferred .
This, in turn, reveals how quickly each individual learns from outcomes, how they weigh risks and rewards, or how they interpret others’ intentions, which cannot be directly observed from behaviour alone.
Examples of this approach in antisocial behaviour research take parameters like learning rate, effort discounting, or reward sensitivity to reveal how quickly individuals with or without these traits update their expectations, how much effort they are willing to invest, or how strongly they react to different outcomes. By uncovering these mechanisms, researchers can bring new insights into the processes driving behaviour, add to the foundation of behavioural descriptions of antisocial behaviours, and gain an understanding of why individuals differ in their social decision-making in the first place.
This kind of modelling approach provides a more precise understanding of CD, not simply as a collection of symptoms, but as a set of identifiable disruptions in learning and decision-making processes. Ultimately, this shift from descriptive to mechanistic understanding represents a notable step toward understanding, predicting, and potentially treating antisocial behaviour.
Hsieh, Y., Berry, E., Vogel, T. A., & Lockwood, P. (2025, November 7). A Systematic Review of Computational Approaches to Understanding Social Decision-Making in Conduct Disorder and Psychopathy. https://doi.org/10.31234/osf.io/w982f_v1
Konovalov, A., & Ruff, C. C. (2022). Enhancing models of social and strategic decision making with process tracing and neural data. Wiley Interdisciplinary Reviews: Cognitive Science , 13 (1), e1559.
Lockwood, P. L., & Klein-Flügge, M. C. (2021). Computational modelling of social cognition and behaviour—a reinforcement learning primer. Social cognitive and affective neuroscience , 16 (8), 761-771.
Pauli, R., & Lockwood, P. L. (2023). The computational psychiatry of antisocial behaviour and psychopathy. Neuroscience & Biobehavioral Reviews , 145 , 104995.
Share this post Facebook Bluesky Linkedin Email
There was a problem adding your email address. Please try again.
By submitting your information you agree to the Psychology Today Terms & Conditions and Privacy Policy
Patricia Lockwood, Ph.D., is a Wellcome Trust/Royal Society Sir Henry Dale Fellow, Associate Professor. Jo Cutler, Ph.D., is a Postdoctoral Research Fellow in the Social Decision Neuroscience Lab at the University of Birmingham.
Get the help you need from a therapist near you–a FREE service from Psychology Today.
This article is part of the Bringwise Psychology Journal — daily insights on human behavior, mental health, and personal growth.