Perceptual decision-making in children: Age-related differences and EEG correlates
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 pr...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer
2020
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| _version_ | 1797073990218416128 |
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| author | Manning, C Wagenmakers, E-J Norcia, A Scerif, G Boehm, U |
| author_facet | Manning, C Wagenmakers, E-J Norcia, A Scerif, G Boehm, U |
| author_sort | Manning, C |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T23:29:52Z |
| format | Journal article |
| id | oxford-uuid:6ba8a72c-fe6c-4d9d-8280-4058b2c74320 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:29:52Z |
| publishDate | 2020 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:6ba8a72c-fe6c-4d9d-8280-4058b2c743202022-03-26T19:05:31ZPerceptual decision-making in children: Age-related differences and EEG correlatesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6ba8a72c-fe6c-4d9d-8280-4058b2c74320EnglishSymplectic ElementsSpringer2020Manning, CWagenmakers, E-JNorcia, AScerif, GBoehm, UChildren 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. |
| spellingShingle | Manning, C Wagenmakers, E-J Norcia, A Scerif, G Boehm, U Perceptual decision-making in children: Age-related differences and EEG correlates |
| title | Perceptual decision-making in children: Age-related differences and EEG correlates |
| title_full | Perceptual decision-making in children: Age-related differences and EEG correlates |
| title_fullStr | Perceptual decision-making in children: Age-related differences and EEG correlates |
| title_full_unstemmed | Perceptual decision-making in children: Age-related differences and EEG correlates |
| title_short | Perceptual decision-making in children: Age-related differences and EEG correlates |
| title_sort | perceptual decision making in children age related differences and eeg correlates |
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