Executive functions (EF), including working memory, inhibition, and cognitive flexibility, vary as a function of socioeconomic status (SES), with children from economically disadvantaged backgrounds having poorer performance than their higher SES peers. Using observational methods, we investigated cognitive stimulation in the home as a mechanism linking SES with EF. In a sample of 101 children aged 60–75 months, cognitive stimulation fully mediated SES‐related differences in EF. Critically, cognitive stimulation was positively associated with the development of inhibition and cognitive flexibility across an 18‐month follow‐up period. Furthermore, EF at T1 explained SES‐related differences in academic achievement at T2. Early cognitive stimulation—a modifiable factor—may be a desirable target for interventions designed to ameliorate SES‐related differences in cognitive development and academic achievement.

The Adolescent Brain Cognitive Development (ABCD) study is an unprecedented longitudinal neuroimaging sample that tracks the brain development of over 10,000 9-10 year olds through adolescence(1). At the core of this study are the three tasks that are completed repeatedly within the fMRI scanner, one of which is the stop-signal task. Laudably, the materials and data from this study are being made openly available to the community. However, in analyzing the available experimental code and data for the stop-signal task, we identified a set of design issues that we believe significantly limit its value. These issues include but are not limited to: variable stimulus durations that violate basic assumptions of dominant stopping models, trials in which stimuli are incorrectly not presented, and faulty stop-signal delays. We present eight issues, show their effect on the existing ABCD data, suggest prospective solutions to the study organizers including task changes for future data collection, and suggest retrospective solutions for data users who wish to make the most of the existing data.The Adolescent Brain Cognitive Development is the largest and most comprehensive long-term study of brain development and child health in the United States(1). The study includes 11,878 youth and their families and aims to understand the environmental, social, genetic and other biological factors that affect brain and cognitive development. study organizers chose to include three cognitive tasks to be presented during fMRI acquisition: the monetary incentive delay task(2), the emotional N-back task(3), and the stop-signal task(4).In this manuscript we focus solely on the stop-signal task.We analyzed behavioral data from baseline scan of 7,321 of the 11,878 participants. This subset resulted from the following exclusion, which attempted to eliminate incomplete data or data that were already flagged as problematic by the ABCD study organizers. First, we attempted to download 8,811 participants from the "FastTrack Recommended Active Series" from the NIMH Data Archive, which has gone through some quality assurance for the associated imaging files by the ABCD study organizers. Of these, 8,776 files were successfully downloaded, but a subset did not include stop-signal data, leaving 7,906 subjects. Of these, only 7,347 included summary scores from the Stop Signal Task in the ABCD Data Release 2.0.Finally, 26 subjects were removed who did not have two complete runs with 180 trials each, leaving us with a total of 7,321 complete datasets.

No abstract

The Adolescent Brain Cognitive Development (ABCD) study is an unprecedented longitudinal neuroimaging sample that tracks the brain development of over 10,000 9-10 year olds through adolescence. At the core of this study are the three tasks that are completed repeatedly within the MRI scanner, one of which is the stop-signal task. In analyzing the available stopping experimental code and data, we identified a set of design issues that we believe significantly compromise its value. These issues include but are not limited to: variable stimulus durations that violate basic assumptions of dominant stopping models, trials in which stimuli are incorrectly not presented, and faulty stop-signal delays. We present eight issues, show their effect on the existing ABCD data, suggest prospective solutions including task changes for future data collection and preliminary computational models, and suggest retrospective solutions for data users who wish to make the most of the existing data.