Kyle Mahowald
University of Texas at Austin, TX·Social Science
Talking about where things are is effortless for people and surprisingly hard for machines. Short words like "this," "that," "here," and "there" let a speaker point out an object, give directions, or share a location, and every human language has words of this kind.
- Fields
Social Science
Computer Science
Psychology
- Topics
spatial language
deixis
demonstratives
- Grants
- 1 known grant, through Jul 2028
What they work on
- Measure how speakers of a variety of languages choose deictic words such as "this," "that," "here," and "there" to refer to objects and locations, using cross-linguistic behavioral experiments.
- Model the trade-off between precision and simplicity in spatial language using the Information Bottleneck framework, an information-theoretic approach developed to explain artificial neural network behavior.
- Identify the cognitive and environmental pressures that shape spatial reference by combining geospatial analysis of speakers' surroundings with machine learning models of word choice.
- Translate the resulting models into insights about how artificial intelligence and robotic systems can interpret and produce spatial language more naturally.
More about the research
But languages divide up space differently, and no one fully understands why they settle on the systems they do. That gap matters for any robot or assistant that has to share a room with a person and interpret or produce instructions about locations.
Why it matters
Spatial language is a persistent weak point for robots and language technologies that must act in physical spaces alongside people. Models that explain why languages carve up space the way they do could make such systems easier and safer to talk to, and the cross-linguistic experimental data would add to understanding of how efficiency pressures shape human language more broadly.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Naomi Shin
University of New Mexico, NM·Social Science
Test whether the listener's location affects a speaker's choice of demonstratives ('this/that'), predicting stronger effects in languages with more demonstrative terms, through experiments that vary where the listener is.
Frederik Hartmann
University of North Texas, TX·Social Science
Build artificial neural networks that learn to reconstruct proto-forms, the ancestor words of a language family, from their descendant words, using large datasets spanning many language families.
Eszter Ronai
Northwestern University, IL·Psychology
Identify the real-time processing signals of focus, the marking of new or emphasized information, in languages with fixed versus flexible word order, using eye-gaze data and statistical modeling.
The school
University of Texas at Austin: 49 labs on PI Pages, $1.1B a year on research, 871 research doctorates a year.
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