Semantic Context Boosts Word Learning from Low-Informative Events

When a toddler hears a new word, its meaning is rarely transparent from the situational context in which the word occurred. Prior work has illustrated the challenge of learning from these low informative naming events (Cartmill et al., 2013; Medina et al., 2011). We investigate whether another feature of the learning environment – the rich semantic structure in which words occur (Tamis-LeMonda et al. 2019; Custode & Tamis-LeMonda 2020) – may help learners overcome low informativity of individual naming events. To test this, we designed a modified version of the Human Simulation Paradigm (HSP) in which adult participants (N = 48) were tasked with learning a target word meaning from six low informative scenes. Importantly, half of the participants learned the target word while also learning words from the same semantic category; the other half learned the same target word from the same low informative scenes while learning words from different semantic categories. Participants were better at learning the target word when the low informative scenes occurred in a consistent semantic context, suggesting that semantic structure alleviates the challenge of learning from low informative naming events. Thus, the rich broader semantic context that has been reported in several recent studies (see Roy et al., 2015; Tamis-LeMonda et al., 2019) may prove key to children’s rapid word learning and to resolving the long-standing paradox of children’s word learning.

Publication Details

Journal
OpenCommons - UConn (University of Connecticut)
Published
2025-05-01
Primary Topic
Child and Animal Learning Development
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Semantic Context Boosts Word Learning from Low-Informative Events

OpenCommons - UConn (University of Connecticut)
Child and Animal Learning Development
article

Semantic Context Boosts Word Learning from Low-Informative Events

article en

Abstract

When a toddler hears a new word, its meaning is rarely transparent from the situational context in which the word occurred. Prior work has illustrated the challenge of learning from these low informative naming events (Cartmill et al., 2013; Medina et al., 2011). We investigate whether another feature of the learning environment – the rich semantic structure in which words occur (Tamis-LeMonda et al. 2019; Custode & Tamis-LeMonda 2020) – may help learners overcome low informativity of individual naming events. To test this, we designed a modified version of the Human Simulation Paradigm (HSP) in which adult participants (N = 48) were tasked with learning a target word meaning from six low informative scenes. Importantly, half of the participants learned the target word while also learning words from the same semantic category; the other half learned the same target word from the same low informative scenes while learning words from different semantic categories. Participants were better at learning the target word when the low informative scenes occurred in a consistent semantic context, suggesting that semantic structure alleviates the challenge of learning from low informative naming events. Thus, the rich broader semantic context that has been reported in several recent studies (see Roy et al., 2015; Tamis-LeMonda et al., 2019) may prove key to children’s rapid word learning and to resolving the long-standing paradox of children’s word learning.

OpenCommons - UConn (University of Connecticut)
Quality Education
Openalex Percentile: Top 42%
Child and Animal Learning Development
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.