# The neurocognitive dynamics of learning and executive function

> **NIH NIH R01** · UNIVERSITY OF TENNESSEE KNOXVILLE · 2020 · $206,521

## Abstract

Project Summary
 Executive function (EF) refers to the collection of processes that control and regulate
cognitive functioning to achieve flexible or goal-directed behavior. EF influences diverse
developmental outcomes, including quality of life measures and academic achievement.
In this way, EF can serve as a leverage point for improving general aspects of cognitive
functioning through interventions, ultimately enhancing physical and financial health and
reducing crime rates. However, there is a lack of theoretical models that can be used to
explain EF processes and develop well-grounded interventions. This proposal aims to
test a learning mechanism that we have previously proposed to explain central aspects
of EF development: the dimensional label learning hypothesis. According to this
hypothesis, learning labels for visual features and dimensions (e.g, color or shape)
structures frontal-posterior cortical connectivity. These connections can then be used to
guide cognitive processing toward task-relevant features of the visual world through the
activation of labels. To shed light on this process, we will follow children longitudinally
from ages 2 to 5. A battery of tasks will be administered to assess dimensional label
learning and dimensional attention. Neural activity will be measured from frontal,
temporal, and parietal cortices using functional near-infrared spectroscopy. Finally,
dynamic neural field (DNF) simulations will be used to interpret the neural and
behavioral data. Such models can be used to implement specific hypotheses about
neurocognitive functioning and learning to assess model fit in an iterative fashion. In this
way, a model can be developed to explain behavioral and neural data observed as
children learn dimensional labels and attentional skills. This model can then provide an
arena to test different hypotheses about the learning processes that give rise to
changes in EF. We will use these data to examine: (1) whether dimensional label
learning predicts the development of dimensional attention, (2) the neural basis of
dimensional label learning, (3) whether the neural dynamics during dimensional label
comprehension and production predict neural activation in dimensional attention tasks,
(4) whether the learning and neurocognitive processes implemented by the DNF model
explain the association between behavioral and neural data. The DNF model can be
used to make predictions about the role of dimensional label learning in aspects of EF
development.

## Key facts

- **NIH application ID:** 9948495
- **Project number:** 5R01HD092485-03
- **Recipient organization:** UNIVERSITY OF TENNESSEE KNOXVILLE
- **Principal Investigator:** Aaron T Buss
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $206,521
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9948495

## Citation

> US National Institutes of Health, RePORTER application 9948495, The neurocognitive dynamics of learning and executive function (5R01HD092485-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9948495. Licensed CC0.

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