“Economies of Experience”—Disambiguation of Degraded Stimuli Leads to a Decreased Dispersion of Eye-Movement Patterns

Magdalena Król , Michał Król


We demonstrate “economies of experience” in eye-movement patterns—that is, optimization of eye-movement patterns aimed at more efficient and less costly visual processing, similar to the priming-induced formation of sparser cortical representations or reduced reaction times. Participants looked at Mooney-type, degraded stimuli that were difficult to recognize without prior experience, but easily recognizable after exposure to their undegraded versions. As predicted, eye-movement dispersion, velocity, and the number of fixations decreased with each stimulus presentation. Further analyses showed that this effect was contingent on recognition, and the selection of information from the stimulus could be informed by the identity of the presented object. Finally, our study demonstrates that after exposure to the undegraded version of the stimulus, eye-movement patterns associated with its degraded and undegraded versions become more similar. This suggests that eye-movement patterns can evolve to facilitate the optimal processing of a given stimulus via experience-driven perceptual learning.
Author Magdalena Król (Filia we Wrocławiu / II Wydział Psychologii we Wrocławiu)
Magdalena Król,,
- II Wydział Psychologii we Wrocławiu
, Michał Król
Michał Król,,
Journal seriesCognitive Science, ISSN 0364-0213, e-ISSN 1551-6709, (A 35 pkt)
Issue year2018
Publication size in sheets1.4
Keywords in EnglishEye movements; Object recognition; Top-down; Visual attention; Perceptual learning; Visual processing
ASJC Classification1702 Artificial Intelligence; 2805 Cognitive Neuroscience; 3205 Experimental and Cognitive Psychology
URL http://onlinelibrary.wiley.com/doi/10.1111/cogs.12566/full
Languageen angielski
Economies of Experience_tekst.pdf 1.13 MB
Additional file
Economies of Experience_oświadczenie.pdf 196.66 KB
Score (nominal)35
Score sourcejournalList
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.365; WoS Impact Factor: 2017 = 2.617 (2) - 2017=3.177 (5)
Citation count*3 (2020-11-28)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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