Gaze Transition Entropy

Krzysztof Krejtz , Andrew Duchowski , Tomasz Szmidt , Izabela Krejtz , Fernando González Perilli , Ana Pires , Anna Vilaro , Natalia Villalobos


This article details a two-step method of quantifying eye movement transitions between areas of interest (AOIs). First, individuals’ gaze switching patterns, represented by fixated AOI sequences, are modeled as Markov chains. Second, Shannon’s entropy coefficient of the fit Markov model is computed to quantify the complexity of individual switching patterns. To determine the overall distribution of attention over AOIs, the entropy coefficient of individuals’ stationary distribution of fixations is calculated. The novelty of the method is that it captures the variability of individual differences in eye movement characteristics, which are then summarized statistically. The method is demonstrated on gaze data collected from two studies, during free viewing of classical art paintings. Normalized Shannon’s entropy, derived from individual transition matrices, is related to participants’ individual differences as well as to either their aesthetic impression or recognition of artwork. Low transition and high stationary entropies suggest greater curiosity mixed with a higher subjective aesthetic affinity toward artwork, possibly indicative of visual scanning of the artwork in a more deliberate way. Meanwhile, both high transition and stationary entropies may be indicative of recognition of familiar artwork.
Author Krzysztof Krejtz (Wydział Psychologii)
Krzysztof Krejtz,,
- Wydział Psychologii
, Andrew Duchowski
Andrew Duchowski,,
, Tomasz Szmidt
Tomasz Szmidt,,
, Izabela Krejtz (Wydział Psychologii)
Izabela Krejtz,,
- Wydział Psychologii
, Fernando González Perilli
Fernando González Perilli,,
, Ana Pires
Ana Pires,,
, Anna Vilaro
Anna Vilaro,,
, Natalia Villalobos
Natalia Villalobos,,
Journal seriesACM Transactions on Applied Perception, ISSN 1544-3558, (A 25 pkt)
Issue year2015
Publication size in sheets0.3
Keywords in EnglishEye movement transitions, Markov chain, entropy
ASJC Classification3205 Experimental and Cognitive Psychology; 1700 General Computer Science; 2614 Theoretical Computer Science
Languageen angielski
2015-TAP-Krejtz-Gaze-Entropy.pdf 1.27 MB
Additional file
BROWN_IR-ADV5255_16_0106_001-45.pdf 17.92 KB
BROWN_IR-ADV5255_16_0106_001-50.pdf 37.52 KB
Score (nominal)35
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2015 = 0.896; WoS Impact Factor: 2015 = 0.561 (2) - 2015=1.193 (5)
Citation count*78 (2021-02-23)
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