ArticleCognition2019
Dimensional attention as a mechanism of executive function: Integrating flexibility, selectivity, and stability.
Article in Cognition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review Exploring the Theories Underlying the Improvement of Balance and Reduction in Falls Following Dual-Task Training among Older Adults.International journal of environmental research and public health · 2022Pooled it
- Infant Habituation Task Performance Is Associated With Familial Risk for Attention-Deficit/Hyperactivity Disorder.JAACAP open · 2026Article
- Dynamic Field Theory of Executive Function: Identifying Early Neurocognitive Markers.Monographs of the Society for Research in Child Development · 2024Article
- The shifting shelf task: a new, non-verbal measure for attentional set shifting.Proceedings. Biological sciences · 2023Article
- To snack or not to snack: Using fNIRS to link inhibitory control to functional connectivity in the toddler brain.Developmental science · 2022Article
- Dimensional label learning contributes to the development of executive functions.Scientific reports · 2022Article
- Epidemiology of body dysmorphic disorder among adolescents: A study of their cognitive functions.Brain and behavior · 2022Article
- A Dynamical Reconceptualization of Executive-Function Development.Perspectives on psychological science : a journal of the Association for Psychological Science · 2021Article
- Exploring the neural basis of selective and flexible dimensional attention: An fNIRS study.Journal of cognition and development : official journal of the Cognitive Development Society · 2020Article
- Not all labels develop equally: The role of labels in guiding attention to dimensions.Cognitive developmentArticle
- A Multi-Method Approach to Addressing the Toddler Data Desert in Attention Research.Cognitive developmentArticle
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Authors and funding
2 authors.
Funding
Abstract
In this report, we present a neural process model that explains visual dimensional attention and changes in visual dimensional attention over development. The model is composed of an object representation system that binds visual features such as shape and color to spatial locations and a label learning system that associates labels such as "color" or "shape" with visual features. We have previously demonstrated that this model explains the development of flexible dimensional attention in a task that requires children to switch between shape and color rules for sorting cards. In the model, the development of flexible dimensional attention is a product of strengthening associations between labels and features. In this report, we generalize this model to also explain development of stable and selective dimensional attention. Specifically, we use the model to explain a previously reported developmental association between flexible dimensional attention and stable dimensional attention. Moreover, we generate predictions regarding developmental associations between flexible and selective dimensional attention. Results from an experiment with 3- and 4-year-olds supported model predictions: children who demonstrated flexibility also demonstrated higher levels of selectivity. Thus, the model provides a framework that integrates various functions of dimensional attention, including implicit and explicit functions, over development. This model also provides new avenues of research aimed at uncovering how cognitive functions such as dimensional attention emerge from the interaction between neural dynamics and task structure, as well as understanding how learning dimensional labels creates changes in dimensional attention, brain activation, and neural connectivity.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.