Research Papers

Eye-Tracking Data Predict Importance of Product Features and Saliency of Size Change

[+] Author and Article Information
Ping Du

Department of Mechanical Engineering,
Iowa State University,
Ames, IA 50011
e-mail: pdu@iastate.edu

Erin F. MacDonald

Assistant Professor
Department of Mechanical Engineering,
Iowa State University,
Ames, IA 50011
e-mail: erinmacd@iastate.edu

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 5, 2013; final manuscript received March 26, 2014; published online June 2, 2014. Assoc. Editor: Jonathan Cagan.

J. Mech. Des 136(8), 081005 (Jun 02, 2014) (14 pages) Paper No: MD-13-1245; doi: 10.1115/1.4027387 History: Received June 05, 2013; Revised March 26, 2014

Features, or visible product attributes, are indispensable product components that influence customer evaluations of functionality, usability, symbolic impressions, and other qualities. Two basic components of features are visual appearance and size. This work tests whether or not eye-tracking data can (1) predict the relative importances between features, with respect to their visual design, in overall customer preference and (2) identify how much a feature must change in size in order to be noticeable by the viewer. The results demonstrate that feature importance is significantly correlated with a variety of gaze data. Results also show that there are significant differences in fixation time and count for noticeable versus unnoticeable size changes. Statistical models of gaze data can predict feature importance and saliency of size change.

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Fig. 1

The Tobii T120 eye tracker (left) and the associated control computer (right)

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Fig. 3

Design pool for varied features

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Fig. 4

An illustration of the experiment flow (demonstrated by the SBS condition)

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Fig. 2

Sample pairs used in the experiment (section II size variants are headlight (15%), side mirror (20%), seat (15%), and cargo box (10%), from top to bottom)

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Fig. 5

Screens from experiment section II (images from SBS condition, with enlarged text)

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Fig. 6

Car feature-importance-rating survey question screen (rearview mirror referred to as side mirror in this paper)

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Fig. 7

An example of the AOIs generated for a car

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Fig. 12

Trends of average fixation time spent on a feature as its importance varies are similar for the car and the electric bicycle (section II—SBS condition); error bars indicate ±1 standard errors

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Fig. 8

Average fixation time spent on a feature increases with its importance rating (section I); error bars indicate ±1 standard errors (the two series of data are nudged along the horizontal axis to avoid overlapping of the error bars)

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Fig. 9

Average first-located time on a feature varies with importance ratings (section I); error bars indicate ±1 standard errors

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Fig. 10

Average fixation time spent on a feature increases with its importance rating (section II); error bars indicate ±1 standard errors

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Fig. 11

Average first-located time on a feature decreases with its importance rating (section II); error bars indicate ±1 standard errors



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