Computational neurodevelopment: infant decision-making in changing environments

Abstract

In recognition of the fact that most psychiatric conditions have neurodevelopmental origins, there is an increasing interest in applying the methodological and conceptual approaches from computational psychiatry to developmental cohorts. However, the challenge of acquiring and modelling behavioural responses in very young infants has thus far proven difficult to overcome. To address this we developed a novel gaze-contingent, cued-reversal paradigm that allowed 6-10 month old infants to make overt behavioural responses to assess learning of expectations and updating of behaviour in response to change. We then fit computational models to infant behaviour and, for the first time, were able to validate the winning model to the same standards as would be expected of adults (e.g. good parameter recoverability, model identifiability and simulated behavioural responses). Similar to prior findings in adults, model-based prediction error measures correlated with post-switch increases in pupil size; consistent with noradrenalines hypothesised role in learning about change. Data-driven clustering based on model parameters revealed two infant behavioural subtypes hidden within the data; one with a perseverating profile and the other with a more exploratory decision-making pattern. This approach sheds new light on the classic finding that all infants under 12 months tend to perseverate. Crucially, there were no significant differences in age between the clusters, but differences in terms of adaptive skills and temperament measured via gold-standard developmental assessments. These results prime the field for infant computational psychiatry, demonstrating that we can reliably fit models to infant data and that the parameters from such models can identify subgroups with distinct cognitive profiles that are superior to those derived from the behavioural data alone.