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Matthew Termuende

dblp:415/3550 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0000-9269-7703ORCID · reported

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 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.

Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 44% Interaction techniques and input · 44% Accessibility and assistive technology · 13%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
text visualization
1.012026
The Impact of Visual Segmentation on Lexical Word Recognition · IEEE Trans. Vis. Comput. Graph. 2026
Usability and user experience research › user performance
reading performance
1.012026
The Impact of Visual Segmentation on Lexical Word Recognition · IEEE Trans. Vis. Comput. Graph. 2026
Interaction techniques and input
word recognition
1.012026
The Impact of Visual Segmentation on Lexical Word Recognition · IEEE Trans. Vis. Comput. Graph. 2026
Accessibility and assistive technology
reading assistance
0.312026
The Impact of Visual Segmentation on Lexical Word Recognition · IEEE Trans. Vis. Comput. Graph. 2026

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

lexical decision experiment · 2.0preregistered study · 1.0pre-registered study · 1.0
YearPublicationVenuePosition
2026 The Impact of Visual Segmentation on Lexical Word Recognition
abstract
When a reader encounters a word in English, they split the word into smaller orthographic units in the process of recognizing its meaning. For example, "rough", when split according to phonemes, is decomposed as r-ou-gh (not as r-o-ugh or r-ough), where each group of letters corresponds to a sound. Since there are many ways to segment a group of letters, this constitutes a computational operation that has to be solved by the reading brain, many times per minute, in order to achieve the recognition of words in text necessary for reading. In English, the irregular relationships between groups of letters and sounds, and the wide variety of possible groupings make this operation harder than in more regular languages such as Italian. If this segmentation takes a significant amount of time in the process of recognizing a word, it is conceivable that providing segmentation information in the text itself could help the reading process by reducing its computational cost. In this paper we explore whether and how different visual interventions from the visualization literature could communicate segmentation information for reading and word recognition. We ran a series of pre-registered lexical decision experiments with 192 participants that tested five main types of visual segmentations: outlines, spacing, connections, underlines and color. The evidence indicates that, even with a moderate amount of training, these visual interventions always slow down word identification, but each to a different extent (between 32.7ms-color technique-and 70.7ms-connection technique). These findings are important because they indicate that, at least for typical adult readers with a moderate amount of specific training in these visual interventions, accelerating the lexical decision task is unlikely. Importantly, the results also offer an empirical measurement of the cost of a common set of visual manipulations of text, which can be useful for practitioners seeking to visualize alongside or within text without impacting reading performance. Finally, the interaction between typographically encoded information and visual variables presented unique patterns that deviate from existing theories, suggesting new directions for future inquiry.
Matthew Termuende, Kevin Larson, Miguel A. Nacenta
IEEE Trans. Vis. Comput. Graph.1