How the Systematicity of Relational Language Affects the Learning of a Compositional System via Changing Attention

It is well established that relational language with different structures (e.g., "top, middle, bottom" used together are more systematic than "on, in, under") can lead to differences in learners' relational representations. However, the underlying mechanism or processes are less specified. We advance the "language systematizes attention hypothesis" through two eye-tracking experiments in which undergraduate students learn to map novel spoken artificial language (object names + relational terms) to novel visual configurations (shapes + relative locations). Participants either heard more systematic relational terms (consistently referring to relative spatial locations) or less systematic relational terms (probabilistically referring to relative spatial locations). Results confirmed that more systematic relational language elicits more selective and sustained attention patterns in learners, compared to less systematic language. However, the benefit in behavioral accuracy depends on the task difficulty. Findings have implications for how to structure language to guide attention and enhance learning outcomes.

Publication Details

Journal
eScholarship (California Digital Library)
Published
2025-01-01
Primary Topic
Assisted Reproductive Technology and Twin Pregnancy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

How the Systematicity of Relational Language Affects the Learning of a Compositional System via Changing Attention

eScholarship (California Digital Library)
Assisted Reproductive Technology and Twin Pregnancy
article

How the Systematicity of Relational Language Affects the Learning of a Compositional System via Changing Attention

article en

Abstract

It is well established that relational language with different structures (e.g., "top, middle, bottom" used together are more systematic than "on, in, under") can lead to differences in learners' relational representations. However, the underlying mechanism or processes are less specified. We advance the "language systematizes attention hypothesis" through two eye-tracking experiments in which undergraduate students learn to map novel spoken artificial language (object names + relational terms) to novel visual configurations (shapes + relative locations). Participants either heard more systematic relational terms (consistently referring to relative spatial locations) or less systematic relational terms (probabilistically referring to relative spatial locations). Results confirmed that more systematic relational language elicits more selective and sustained attention patterns in learners, compared to less systematic language. However, the benefit in behavioral accuracy depends on the task difficulty. Findings have implications for how to structure language to guide attention and enhance learning outcomes.

eScholarship (California Digital Library)
Quality Education
Openalex Percentile: Top 39%
Assisted Reproductive Technology and Twin Pregnancy
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.

How the Systematicity of Relational Language Affects the Learning of a Compositional System via Changing Attention · eScholarship (California Digital Library) (2025) | TGRS Research Map | TGRS