Revisiting Menu Design Through the Lens of Implicit Statistical Learning

Emmanouil Giannisakis*, Evanthia Dimara, Annabelle Goujon, Gilles Bailly

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Implicit Statistical Learning (ISL) studies how exposing individuals
to repeated statistical patterns can help develop skills in the absence
of conscious awareness, such as learning a language or detecting
familiar shapes. This paper transposes ISL in the context of menu
design learnability. Our analysis of menu patterns in various applications from the 80s to today reveals a consistent linear pattern
with command names on the left and keyboard shortcut cues aligned
on the right. We then develop a design space of menu patterns by
manipulating two factors of ISL theory, spatial proximity (distance)
and relative positioning between commands and shortcut cues. We
empirically compare four menu patterns of this design space on
whether they can improve keyboard shortcut adoption through two
controlled experiments. Results did not capture clear effects among
the menu patterns, suggesting that ISL in the context of HCI might
involve more complex factors than initially anticipated, such as the
time the users are exposed to the menu pattern. We reflect on the
challenges in applying theories from cognitive science to HCI and
hope that our systematic methodology and experiment designs will
serve as a basis for encouraging more studies in the area.
Original languageEnglish
Title of host publicationAVI 2022: Proceedings of the 2022 International Conference on Advanced Visual Interfaces
PublisherAssociation for Computing Machinery (ACM)
Pages1-9
Number of pages9
ISBN (Print)978-1-4503-9719-3
DOIs
Publication statusPublished - 6 Jun 2022

Keywords

  • Implicit Statistical Learning
  • spatial relationships
  • GUI
  • menu

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