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Luke Maurits

dblp:80/10451 · DBLP profile ↗
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5ranked-venue papers
2as first author
2since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5 · 2 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 4 · 1 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%
Theoretical computer science
1 paper
Information theory · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities
quantitative linguistics
0.112010
Why are some word orders more common than others? A uniform information density account · NIPS 2010

Methods — techniques the papers use, named apart from their topics

information-theoretic analysis · 0.2
YearPublicationVenuePosition
2024 Can a Causal Relational Matching-to-Sample Task Reveal Abstract Reasoning Abilities in Preschool Children?
Elisa Felsche, Luke Maurits, Daniel Haun
CogSci2
2024 Is magnetoreception experience-dependent in humans?
Yagmur Deniz Kisa, Roman Stengelin, Luke Maurits, Daniel Haun
CogSci3
2014 Cultural evolution with sparse testimony: when does the cultural ratchet slip?
Andrew Whalen, Luke Maurits, Michael Pacer, Thomas L. Griffiths 0001
CogSci2
2012 Connecting input filtering and selection in language evolution
Luke Maurits, Thomas L. Griffiths 0001
CogSci1
2010 Why are some word orders more common than others? A uniform information density account
abstract
Languages vary widely in many ways, including their canonical word order. A basic aspect of the observed variation is the fact that some word orders are much more common than others. Although this regularity has been recognized for some time, it has not been well-explained. In this paper we offer an information-theoretic explanation for the observed word-order distribution across languages, based on the concept of Uniform Information Density (UID). We suggest that object-first languages are particularly disfavored because they are highly non-optimal if the goal is to distribute information content approximately evenly throughout a sentence, and that the rest of the observed word-order distribution is at least partially explainable in terms of UID. We support our theoretical analysis with data from child-directed speech and experimental work.
Luke Maurits, Danielle J. Navarro, Andrew Perfors
NIPS1