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Catherine Manning, Udo Boehm, Gaia Scerif, Anthony M Norcia, Eric-Jan Wagenmakers; Age-related changes in perceptual decision-making in children. Journal of Vision 2020;20(11):109. https://doi.org/10.1167/jov.20.11.109.
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Children make better decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. The drift diffusion framework offers the possibility to model accuracy and response-time distributions to decompose performance into separate processing components that can then be linked to neural measures. Within this framework, the decision-making process is modelled as an accumulation of noisy sensory information towards one of two decision bounds. The main parameters are drift-rate (reflecting the rate of evidence accumulation), boundary separation (reflecting response caution), and non-decision time (reflecting sensory encoding and response generation). Here, we collected behavioural and EEG data from 96 children aged 6 to 12 years and 20 adults performing a coherent motion discrimination task. We fitted hierarchical Bayesian drift diffusion models to the behavioural data from each group, allowing drift-rate to vary across the 3 coherence conditions (30%, 50%, 75%), while keeping all other parameters constant across conditions. Older children and adults had higher drift-rates, narrower boundary separations, and shorter non-decision times than younger children. Next, we used Reliable Components Analysis to identify a response-locked EEG component in children and adults that was maximal over centro-parietal electrodes and showed a ramping positivity preceding the response. The rising positivity was steeper in adults than in children. We derived the slope of this activity for each participant and entered it as a regressor in the model. We found that this EEG activity was related to drift-rate in both groups. Our results suggest that age-related improvements in children’s perceptual responses are accompanied by age-related differences in both decisional and non-decisional factors. Furthermore, we report a neural correlate of the decision-making process. These results help to bridge brain and behaviour in understanding the development of perceptual decision-making in children.
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