Recurrence quantification analysis reveals eye-movement behavior differences between experts and novices

Abstract

Understanding and characterizing perceptual expertise is a major bottleneck in developing intelligent systems. In knowledge-rich domains such as dermatology, perceptual expertise influences the diagnostic inferences made based on the visual input. This study uses eye movement data from 12 dermatology experts and 12 undergraduate novices while they inspected 34 dermatological images. This work investigates the differences in global and local temporal fixation patterns between the two groups using recurrence quantification analysis (RQA). The RQA measures reveal significant differences in both global and local temporal patterns between the two groups. Results show that experts tended to refixate previously inspected areas less often than did novices, and their refixations were more widely separated in time. Experts were also less likely to follow extended scan paths repeatedly than were novices. These results suggest the potential value of RQA measures in characterizing perceptual expertise. We also discuss potential use of the RQA method in understanding the interactions between experts' visual and linguistic behavior.

Does Expectation of Abnormality Affect the Search Pattern of Radiologists When Looking for Pulmonary Nodules?

“…Unlike other experiments, which have contrasted experts and naïve observers [11][12][13], this is the first, to the authors' knowledge, to compare the effect of varying clinical information on visual search behaviour in radiology. In this experiment, one of the clinical conditions suggested a low expectation of abnormality (visa application), one suggested a high expectation (previous cancer) and one gave no clinical information at all suggesting an unknown expectation of abnormality.…”

Beyond fixation durations: Recurrence quantification analysis reveals spatiotemporal dynamics of infant visual scanning

“…If refixations happening at later lags are related to a face being present, then the normalized CORM should be higher in comparison to the visual scenes with chairs. Altogether, the normalization process shows how highlevel attentional measures affect the RQA measures and how interpreting the effects of normalization may help to understand in more detail the spatiotemporal properties of fixation sequences (see also Anderson et al, 2013;Vaidyanathan, Pelz, Alm, Shi, & Haake, 2014).…”

Nonlinear Analysis of Eye-Tracking Information for Motor Imagery Assessments

“…Among the large number of nonlinear methods, we do believe Recurrence Quantification Analysis (RQA) is an effective way to extract information from the eye movement dynamics. Indeed, RQA has been previously applied to describe the temporal dynamics of eye movements during picture presentation (Anderson et al, 2013;Vaidyanathan et al, 2014;Farnand et al, 2016), and to study the temporal organization of eye movements during the mental imagery of previously observed pictures. Particularly, high percentage of recurrent fixations and determinism values have been reported during mental imagery.…”