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It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach

Dewhurst, Richard LU ; Nyström, Marcus LU orcid ; Jarodzka, Halszka ; Foulsham, Tom ; Johansson, Roger LU orcid and Holmqvist, Kenneth LU (2012) In Behavior Research Methods
Abstract
Eye movement sequences---or scanpaths---vary depending on stimulus characteristics and task (Foulsham \& Underwood, 2008; Land, Mennie, \& Rusted, 1999). Common methods for comparing scanpaths, however, are limited in their ability to capture both the spatial and temporal properties of which a scanpath consists. Here we validate a new method for scanpath comparison based on geometric vectors, which compares scanpaths over multiple dimensions retaining positional and sequential information (Jarodzka, Holmqvist, \& Nyström, 2010). `MultiMatch' was tested in two experiments and pitted against ScanMatch (Cristino, Mathôt, Theeuwes, \& Gilchrist, 2010), the most comprehensive adaptation of the popular Levenshtein method.... (More)
Eye movement sequences---or scanpaths---vary depending on stimulus characteristics and task (Foulsham \& Underwood, 2008; Land, Mennie, \& Rusted, 1999). Common methods for comparing scanpaths, however, are limited in their ability to capture both the spatial and temporal properties of which a scanpath consists. Here we validate a new method for scanpath comparison based on geometric vectors, which compares scanpaths over multiple dimensions retaining positional and sequential information (Jarodzka, Holmqvist, \& Nyström, 2010). `MultiMatch' was tested in two experiments and pitted against ScanMatch (Cristino, Mathôt, Theeuwes, \& Gilchrist, 2010), the most comprehensive adaptation of the popular Levenshtein method. Experiment 1 used synthetic data, demonstrating the greater sensitivity of MultiMatch to variations in spatial position. In experiment 2 real eye movement recordings were taken from participants viewing sequences of dots, designed to elicit scanpath pairs with commonalities known to be problematic for algorithms (for example, when one scanpath is shifted in locus, or fixations fall either side of an AOI boundary). Results illustrate the advantages of a multidimensional approach, revealing how two scanpath differ. For instance, if one scanpath is the reverse copy of another the difference is in direction but not the position of fixations; or if a scanpath is scaled down, the difference is in the length of saccadic vectors but not overall shape. As well as having enormous potential for any task in which consistency in eye movements is important (e.g. learning), MultiMatch is particularly relevant for "eye movements to nothing" in mental imagery research and embodiment of cognition, where satisfactory scanpath comparison algorithms are lacking. (Less)
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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
vectors, Scanpaths, MultiMatch, ScanMatch
in
Behavior Research Methods
publisher
Springer
external identifiers
  • wos:000314573200018
  • scopus:84864473838
  • pmid:22648695
ISSN
1554-3528
DOI
10.3758/s13428-012-0212-2
project
Thinking in Time: Cognition, Communication and Learning
language
English
LU publication?
yes
id
f1e66b75-bf2c-47c2-ba81-5b220852ee3d (old id 2540206)
date added to LUP
2016-04-01 09:50:22
date last changed
2023-02-02 02:31:57
@article{f1e66b75-bf2c-47c2-ba81-5b220852ee3d,
  abstract     = {{Eye movement sequences---or scanpaths---vary depending on stimulus characteristics and task (Foulsham \& Underwood, 2008; Land, Mennie, \& Rusted, 1999). Common methods for comparing scanpaths, however, are limited in their ability to capture both the spatial and temporal properties of which a scanpath consists. Here we validate a new method for scanpath comparison based on geometric vectors, which compares scanpaths over multiple dimensions retaining positional and sequential information (Jarodzka, Holmqvist, \& Nyström, 2010). `MultiMatch' was tested in two experiments and pitted against ScanMatch (Cristino, Mathôt, Theeuwes, \& Gilchrist, 2010), the most comprehensive adaptation of the popular Levenshtein method. Experiment 1 used synthetic data, demonstrating the greater sensitivity of MultiMatch to variations in spatial position. In experiment 2 real eye movement recordings were taken from participants viewing sequences of dots, designed to elicit scanpath pairs with commonalities known to be problematic for algorithms (for example, when one scanpath is shifted in locus, or fixations fall either side of an AOI boundary). Results illustrate the advantages of a multidimensional approach, revealing how two scanpath differ. For instance, if one scanpath is the reverse copy of another the difference is in direction but not the position of fixations; or if a scanpath is scaled down, the difference is in the length of saccadic vectors but not overall shape. As well as having enormous potential for any task in which consistency in eye movements is important (e.g. learning), MultiMatch is particularly relevant for "eye movements to nothing" in mental imagery research and embodiment of cognition, where satisfactory scanpath comparison algorithms are lacking.}},
  author       = {{Dewhurst, Richard and Nyström, Marcus and Jarodzka, Halszka and Foulsham, Tom and Johansson, Roger and Holmqvist, Kenneth}},
  issn         = {{1554-3528}},
  keywords     = {{vectors; Scanpaths; MultiMatch; ScanMatch}},
  language     = {{eng}},
  publisher    = {{Springer}},
  series       = {{Behavior Research Methods}},
  title        = {{It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach}},
  url          = {{http://dx.doi.org/10.3758/s13428-012-0212-2}},
  doi          = {{10.3758/s13428-012-0212-2}},
  year         = {{2012}},
}