Perceptual Decision-Making in Children: Age-Related Differences and EEG Correlates

Abstract

Children make faster and more accurate 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 6-to 12-year-old children 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 inapparent in the response time and accuracy data alone.

Prior information use and response caution in perceptual decision-making: No evidence for a relationship with autistic-like traits

“…The diffusion model provides greater insight regarding the cognitive processes underlying task performance than traditional accuracy and RT measures, and can provide alternative explanations of the behavioural results. For example, in our own work the diffusion model has allowed us to conclude that inattentive children's reduced task accuracy was due to less efficient information processing rather than a speed accuracy trade-off (Retzler et al, 2020), and that age-related differences in children's motion sensitivity are driven by changes in both speed-accuracy trade-offs and efficiency of information processing (Lerche & Voss, 2016;Manning et al, 2020).…”

“…The drift rate δ, boundary separation α and non-decision time τ were estimated for each participant with population means μδ, μα, μτ and variances σδ 2 , σα 2 , στ 2 . The priors for the group-level distributions were based on previous work (Manning et al, 2020;Matzke & Wagenmakers, 2009) and participantlevel prior distributions were truncated to ensure plausibility. Following Mulder et al (2012), the starting point for the neutral cue trials (condition c = 1; βp1) was fixed at 0.5 (i.e., exactly halfway between the correct and incorrect boundary).…”

Visual motion and decision-making in dyslexia: Evidence of reduced accumulation of sensory evidence and related neural dynamics

“…Children completed motion coherence and direction integration tasks within childfriendly games (based on Manning et al, 2019Manning et al, , 2021. Using animations, participants were told that fireflies were escaping from their viewing boxes, and they were asked to tell the zookeeper which way the fireflies were escaping.…”

“…As pre-registered, the first author (CM) ran a default Bayesian t-test using the BayesFactor R package (Morey & Rouder, 2018) which revealed weak, inconclusive evidence for the absence of group differences in age (BF in support of group differences = 0.33; Jeffreys, 1961). As we know that diffusion model parameters change with age (Manning et al, 2021), and as we couldn't conclusively rule out group differences in age, we also ran models which partialled out the effects of age from all of the parameters (using the residuals from the line of best fit between age and each of the parameters), in addition to our standard models. In our pre-registered analysis plan we decided not to control for performance IQ as it may relate to both group membership and decision-making in cognitively relevant ways (Dennis et al, 2009).…”

Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review

“…The adult BCI focus is at least partially attributable to the relative ease of acquiring from this population, robust brain signals that can be well-characterized. While the findings of adult studies are promising, BCI algorithms optimized for adults cannot be directly applied to pediatric users due, in part, to agerelated differences in the brain responses of interest (Volosyak et al, 2017;Manning et al, 2021). For example, compared to adults, children exhibit less language lateralization (Holland et al, 2001), attenuated movement-related cortical potentials (MRCPs) (Pangelinan et al, 2011), and greater attentional effects on the latencies of auditory evoked potentials (Choudhury et al, 2015).…”