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Conference Papers Year : 2022

Revisiting Menu Design Through the Lens of Implicit Statistical Learning

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.
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Dates and versions

hal-03669928 , version 1 (02-06-2022)

Identifiers

  • HAL Id : hal-03669928 , version 1

Cite

Emmanouil Giannisakis, Evanthia Dimara, Annabelle Goujon, Gilles Bailly. Revisiting Menu Design Through the Lens of Implicit Statistical Learning. 2022 International Conference on Advanced Visual Interfaces (AVI 2022), Jun 2022, Roma, Italy. ⟨hal-03669928⟩
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