Cognitive deficits and enhancements in youth from adverse conditions: An integrative assessment using Drift Diffusion Modeling in the ABCD study

Stefan Vermeent*, Ethan Scott Young, Meriah Lee DeJoseph, Anna-Lena Schubert, Willem Frankenhuis

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Childhood adversity can lead to cognitive deficits or enhancements, depending on many factors. Though progress has been made, two challenges prevent us from integrating and better understanding these patterns. First, studies commonly use and interpret raw performance differences, such as response times, which conflate different stages of cognitive processing. Second, most studies either isolate or aggregate abilities, obscuring the degree to which individual differences reflect task-general (shared) or task-specific (unique) processes. We addressed these challenges using Drift Diffusion Modeling (DDM) and structural equation modeling (SEM). Leveraging a large, representative sample of 9–10 year-olds from the Adolescent Brain Cognitive Development (ABCD) study, we examined how two forms of adversity—material deprivation and household threat—were associated with performance on tasks measuring processing speed, inhibition, attention shifting, and mental rotation. Using DDM, we decomposed performance on each task into three distinct stages of processing: speed of information uptake, response caution, and stimulus encoding/response execution. Using SEM, we isolated task-general and task-specific variances in each processing stage and estimated their associations with the two forms of adversity. Youth with more exposure to household threat (but not material deprivation) showed slower task-general processing speed, but showed intact task-specific abilities. In addition, youth with more exposure to household threat tended to respond more cautiously in general. These findings suggest that traditional assessments might overestimate the extent to which childhood adversity reduces specific abilities. By combining DDM and SEM approaches, we can develop a more nuanced understanding of how adversity affects different aspects of youth's cognitive performance. Research Highlight: To understand how childhood adversity shapes cognitive abilities, the field needs analytical approaches that can jointly document and explain patterns of lowered and enhanced performance. Using Drift Diffusion Modeling and Structural Equation Modeling, we analyzed associations between adversity and processing speed, inhibition, attention shifting, and mental rotation. Household threat, but not material deprivation, was mostly associated with slower task-general processing speed and more response caution. In contrast, task-specific abilities were largely intact. Researchers might overestimate the impact of childhood adversity on specific abilities and underestimate the impact on general processing speed and response caution using traditional measures.

Original languageEnglish
Article numbere13478
Number of pages17
JournalDevelopmental Science
Volume27
Issue number4
Early online date6 Feb 2024
DOIs
Publication statusPublished - Jul 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors. Developmental Science published by John Wiley & Sons Ltd.

Funding

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study ( https://abcdstudy.org ), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal‐partners.html . A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/ . ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from Data Release 4.0 (DOI: https://doi.org/10.15154/1523041 ). WEF's contributions have been supported by the Dutch Research Council (V1.Vidi.195.130) and the James S. McDonnell Foundation (https://doi.org/10.37717/220020502) MLD was supported by the National Institute of Child Health and Human Development (NICHD) (1F32HD112065‐01).

FundersFunder number
National Institutes of HealthU01DA051039, U01DA051016, U01DA051038, U01DA051018, U01DA051037, U01DA041106, U01DA041117, U01DA041028, U01DA041148, U24DA041147, U01DA041048, U01DA041025, U24DA041123, U01DA041134, U01DA041156, U01DA041089, U01DA041022, U01DA041120, U01DA041174, U01DA050987, U01DA050988, U01DA050989, U01DA041093
National Institutes of Health
James S. McDonnell Foundation
Nederlandse Organisatie voor Wetenschappelijk OnderzoekV1.Vidi.195.130
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

    Keywords

    • Adolecent Brain Cognitive Development (ABCD) Study
    • Adversity
    • Cognitive deficits
    • Cognitive enhancements
    • Drift Diffusion Modeling

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