A Simple(r) Tool For Examining Fixations

This short paper describes an update of A Simple Tool For Examining Fixations (ASTEF) developed for facilitating the examination of eye-tracking data and for computing a spatial statistics algorithm that has been validated as a measure of mental workload (namely, the Nearest Neighbor Index: NNI). The code is based on Matlab® 2013a and is currently distributed on the web as an open-source project. This implementation of ASTEF got rid of many functionalities included in the previous version that are not needed anymore considering the large availability of commercial and open-source software solutions for eye-tracking. That makes it very easy to compute the NNI on eye-tracking data without the hassle of learning complicated tools. The software also features an export function for creating the time series of the NNI values computed on each minute of the recording. This feature is crucial given that the spatial distribution of fixations must be used to test hypotheses about the time course of mental load.

[1]  R. Groner,et al.  Looking at Faces: Local and Global Aspects of Scanpaths , 1984 .

[2]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[3]  J. Beatty.  Task-evoked pupillary responses, processing load, and the structure of processing resources. , 1982, Psychological bulletin.

[4]  Joel S. Warm,et al.  The Sustained Attention to Response Task (SART) Does Not Promote Mindlessness During Vigilance Performance , 2014, Hum. Factors.

[5]  Michela Terenzi,et al.  ASTEF: A simple tool for examining fixations , 2008, Behavior research methods.

[6]  J. Stern.  Theoretical and applied aspects of eye movement research A. G. Gale and F. Johnson, (Elsevier Science Publishers B.V., Amsterdam, 1984) pp. xiii + 565, Dfl. 185 , 1985, Biological Psychology.

[7]  Ivan Burmistrov,et al.  Flat Design vs Traditional Design: Comparative Experimental Study , 2015, INTERACT.

[8]  Francesco Di Nocera,et al.  Implementing dynamic changes in automation support using ocular-based metrics of mental workload: a laboratory study. , 2015 .

[9]  Michela Terenzi,et al.  Effects of Temporal and Spatial Demands on the Distribution of Eye Fixations , 2008 .

[10]  Nadine B. Sarter,et al.  Clutter in Electronic Medical Records , 2015, Hum. Factors.

[11]  Ronald Ranvaud,et al.  Spatial Pattern of Eye Fixations and Evidence of Ultradian Rhythms in Aircraft Pilots. , 2015, Aerospace medicine and human performance.

[12]  Ned Levine,et al.  CrimeStat IV: A Spatial Statistics Program for the Analysis of Crime Incident Locations, Version 4.0 , 2013 .

[13]  J. Beatty.  Task-evoked pupillary responses, processing load, and the structure of processing resources. , 1982 .

[14]  Alan Kennedy,et al.  Book Review: Eye Tracking: A Comprehensive Guide to Methods and Measures , 2016, Quarterly journal of experimental psychology.

[15]  Michela Terenzi,et al.  A Random Glance at the Flight Deck: Pilots' Scanning Strategies and the Real-Time Assessment of Mental Workload , 2007 .

[16]  Byron Nakos,et al.  EyeMMV toolbox: An eye movement post-analysis tool based on a two-step spatial dispersion threshold for fixation identification , 2014 .

[17]  Sebastiaan Mathôt,et al.  PyGaze: An open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments , 2014, Behavior research methods.

[18]  Paul M. Fitts,et al.  Eye movements of aircraft pilots during instrument-landing approaches. , 1950 .