Computational mechanisms underpinning greater exploratory behaviour in excess weight relative to healthy weight adolescents

Jocelyn Halim, Alex H. Robinson, Juan F. Navas, Cristina Martin-Perez, Raquel Vilar-Lopez, Trevor T.J. Chong, Antonio Verdejo-Garcia

Research output: Contribution to journalArticleResearchpeer-review


Obesity in adolescence is associated with cognitive changes that lead to difficulties in shifting unhealthy habits in favour of alternative healthy behaviours, similar to addictive behaviours. An outstanding question is whether this shift in goal-directed behaviour is driven by over-exploitation or over-exploration of rewarding outcomes. Here, we addressed this question by comparing explore/exploit behaviour on the Iowa Gambling Task in 43 adolescents with excess weight against 38 adolescents with healthy weight. We computationally modelled both exploitation behaviour (e.g., reinforcement sensitivity and inverse decay parameters), and explorative behaviour (e.g., maximum directed exploration value). We found that overall, adolescents with excess weight displayed more behavioural exploration than their healthy-weight counterparts – specifically, demonstrating greater overall switching behaviour. Computational models revealed that this behaviour was driven by a higher maximum directed exploration value in the excess-weight group (U = 520.00, p =.005, BF10 = 5.11). Importantly, however, we found substantial evidence that groups did not differ in reinforcement sensitivity (U = 867.00, p =.641, BF10 = 0.30). Overall, our study demonstrates a preference for exploratory behaviour in adolescents with excess weight, independent of sensitivity to reward. This pattern could potentially underpin an intrinsic desire to explore energy-dense unhealthy foods – an as-yet untapped mechanism that could be targeted in future treatments of obesity in adolescents.

Original languageEnglish
Article number106484
Number of pages9
Publication statusPublished - 1 Apr 2023


  • Addictive eating
  • Adolescent obesity
  • Computational modelling
  • Decision-making
  • Explore/exploit

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