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Perceptual decision-making in children: Age-related differences and EEG correlates

Dr. Catherine Manning
University of Oxford ~ Department of Experimental Psychology
Eric-Jan Wagenmakers
University of Amsterdam ~ Psychological Methods
Anthony M. Norcia
Stanford University, USA
Gaia Scerif
University of Oxford, United Kingdom
Dr. Udo Boehm

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. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 six- to twelve-year-olds and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet model comparisons suggested that the best model of children’s data included age effects only on drift rate and boundary separation (not non-decision time). Next we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children - and to uncover processing differences which are not apparent in the response time and accuracy data alone.



perceptual development


Decision Making
Perception and Signal Detection
Accumulator/Diffusion models
Cognitive Neuromodeling
Non-decision time not varying with age Last updated 3 years ago

Thank you for your really clear and interesting talk. I am really surprised that DIC, despite being biased toward more complex models, did not favor the model with non-decision time varying with age. In our lab we do have evidence that the motor time as recorded with EMG is decreasing with age (I can ask for the publication if you're interested...

Dr. Gabriel Weindel 0 comments
within-individual differences? Last updated 3 years ago

Nice talk! If I understand correctly, you did not find that the EEG provided much additional information about the decision process beyond age. I was wondering whether the situation changes if you look within-individual. I have found in my own work that the within-individual comparison (i.e., between different conditions) tends to work much better.

Dr. Marieke Van Vugt 1 comment

Thank you for your talk Dr. Manning. It was very clear! Is it possible that your component 2 is a Readiness Potential (RP), in which case it could reflect the motor system preparing itself for response? We found in our preprint (link below) that the Readiness Potential can track decision-time. The *downward* slope of Component 2 before its negative...

Dr. Michael D. Nunez 1 comment
CPP component Last updated 3 years ago

Great talk! Do you think the difference of activity level of component 1 at time 0 reflects the difference between both groups in the boundary separation parameter of the DDM? The question is related to a study by McGovern et al. 2018, who described the activity level at the time of response as reflecting the threshold (if I recall that correctly).

Mr. Peter Maximilian Kraemer 1 comment
Cite this as:

Manning, C., Wagenmakers, E.-J., Norcia, A., Scerif, G., & Boehm, U. (2020, July). Perceptual decision-making in children: Age-related differences and EEG correlates. Paper presented at Virtual MathPsych/ICCM 2020. Via