Neural and behavioural differences in multisensory statistical and reinforcement learning across development and task...

The development and maturation of multisensory processing and integration depend on the brain's ability to detect patterns and adjust behaviour based on feedback. However, the developmental dynamics of these mechanisms are not yet fully understood. This study examines neural and behavioural differences in multisensory statistical and reinforcement learning processes by comparing adults and children, and further investigates these processes across two learning tasks of varying difficulty in children. Using a discriminative choice and a match recognition multisensory associative learning task with embedded statistical stimulus regularities, 28 adults (19.0-30.9 years) and 2 groups of children (N = 28 and N = 29, 8.5-12.8 years) learned novel, non-linguistic associations of audio-visual or tactile-visual stimuli. Behaviourally, accuracy increased and reaction time decreased over time in all groups, and these learning effects were stronger in adults than in children and for audio-visual than for tactile-visual learning. Computational modelling revealed that adults employed more advanced learning strategies, with greater sensitivity to value differences, and more deterministic choice patterns. Behaviourally, reaction times were slower for more surprising trials across all groups. Neuroimaging showed that statistical surprise modulated activity in the lateral prefrontal cortex, intraparietal sulcus, and anterior cingulate cortex, while reward prediction errors were linked to striatal, medial frontal, and hippocampal regions. Reward prediction errors were consistent between adults and children and between task variants, whereas neural surprise processing emerged only in the match recognition task and showed developmental differences, indicating immaturity in children's ability to detect statistical regularities. These findings highlight the importance of task design when comparing learning differences across developmental groups and showcase how reinforcement and statistical learning can be studied in parallel.