# Characterizing the role of affective information in early word learning using observational and experimental designs

> **NIH NIH F32** · STANFORD UNIVERSITY · 2024 · $73,408

## Abstract

PROJECT SUMMARY
 Children learn language in the context of complex and multidimensional social interactions. Affective
information is a prominent yet, underexplored feature of children's early learning context despite its effects on
infants' attention and memory systems.1–3 Understanding how children’s affective environments shape learning
is an important public health issue, as approximately a third of mothers in the US experience depression in the
first three years of a child’s life.4 The proposed research aims to fill this knowledge gap by capturing
theoretically important dimensions of affect (such as valence and arousal5) in children’s real-world early
environments, with the ultimate translational goal of understanding the genesis of variation in language and
well-being. Central to this goal will be evaluating how children adapt their use of affective information in
families with depressed caregivers. We will quantify the affective context of learning events using multiple
methods, including experimental studies and descriptive computer vision and machine learning analyses of a
large dataset of egocentric-view videos of children’s home environment.
 In Aim 1, we will ask whether the structure of affective information in caregiver input predicts the age at
which children produce different types of words (Aim 1a) and how children adapt to the affective cues of
caregivers with depression symptoms (Aim 1b). We will use automatically coded affective features from videos
across three modalities (facial, vocal, and semantic) to predict the age at which children produce different
words depending on the severity of their caregivers’ depression symptoms. In Aim 2, we build on the
correlational, naturalistic evidence from Aim 1 by taking an experimental approach, allowing us to uncover
causal links between affect and word learning. Specifically, in three novel word-learning studies, we will
disentangle which dimensions of affect (e.g., valence and arousal) are most impactful.
 Together, this research will provide insight into the affective mechanisms that promote word learning in
child-caregiver interactions, with the ultimate goal of improving resilience and language development in
households with atypical affective dynamics. Through these aims, the applicant will build on her background in
affective development and gain training in computational methods and theoretical knowledge in understanding
clinical variation in affect across households. The sponsor, Dr. Michael Frank, has deep expertise in early
learning and extensive mentorship experience in developmental and computational methods. The co-sponsor,
Dr. Ian Gotlib, is an expert in the study of caregiver depression. The unique training environment at Stanford
University provides the resources necessary to successfully complete the proposed work, as well as additional
opportunities for mentorship and professional development. In sum, the proposed project and training plan will
support the applican...

## Key facts

- **NIH application ID:** 10901497
- **Project number:** 1F32HD115513-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Mira L. Nencheva
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $73,408
- **Award type:** 1
- **Project period:** 2024-08-05 → 2027-08-04

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10901497

## Citation

> US National Institutes of Health, RePORTER application 10901497, Characterizing the role of affective information in early word learning using observational and experimental designs (1F32HD115513-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10901497. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*