The application of eye-movement technique in researching individual differences in cognitive abilities

Subjects: Psychology >> Cognitive Psychology Subjects: Psychology >> Psychological Measurement submitted time 2020-03-03

陆润豪 张兴利 施建农

Abstract： Eye-movement technique has been widely used in the studies of cognitive psychology. The application of eye-movement technique in researching individual differences in cognitive ability has also attracted researchers' attention. Previous studies have successfully identified individuals with psychological disorders (such as autism spectrum disorder and schizophrenia) by using eye-movement analysis. Whether eye-movement characteristics can accurately reflect the cognitive ability of normal people is worth exploring. In this review, we first introduced the good reliability of individual’s eye-movement characteristics. Studies indicated that eye-movement characteristics have good test-retest reliability and internal consistency reliability, which lays the foundation for using eye-movement characteristics to measure cognitive abilities. Subsequently, we systematically summarized the relationship between eye-movement characteristics and cognitive abilities from three aspects: fixation and saccade related indicators, scan patterns, and pupil size. These studies provided substantial evidence that eye-movement characteristics could reflect individual’s cognitive abilities such as intelligence and working memory. To be specific, individuals with high cognitive abilities show more efficient information processing in eye-movement tasks such as visual searching and reading, reflecting in longer saccade amplitude and lower proportion of long fixation duration. In intelligence tests, individuals with high cognitive abilities tend to select the more efficient strategies according to test’s rules. Take Raven's advanced progressive matrices test as an example, high intelligence individuals spend longer time on encoding the problem and demonstrate less toggling rate between problem area and response alternatives area. In addition, studies focusing on scan patterns developed powerful quantitative analysis tools in recent years, which brings new vitality to qualitative scan patterns. These studies found that indicators based on quantitative scan patterns perform better than conventional fixation or saccade indicators in predicting individual’s cognitive abilities. Moreover, pupil size, a more physiological indicator indirectly reflecting the activity of locus coeruleus norepinephrine (LC-NE) system, also has close relations with cognitive abilities. However, the correlation between cognitive abilities and tonic (baseline) pupil size remained controversial for several reasons, such as the diverging definition of baseline and different measurements for cognitive abilities. The correlation between phasic (task-related) pupil size and cognitive abilities was close to agreement according to the control hypothesis. For tasks that require more exploration, individuals with high cognitive abilities tend to fully utilize their cognitive resources to complete these tasks, causing their phasic pupil size to dilate more that low cognitive abilities individuals. In contrast, for tasks that require more exploitation, they tend to utilize fewer resources to efficiently complete these tasks, causing their phasic pupil size to dilate smaller than individuals with low cognitive abilities. Studies have revealed the close relationship between various eye-movement characteristics and cognitive abilities, indicating eye-movement technique is a promising tool for measuring individual cognitive differences with good reliability and validity. Future studies need to further explore the causal relationship, internal cognitive mechanism, and underlying neurobiological basis between eye-movement characteristics and cognitive abilities. Also, the influences of possible moderating variables (e.g., age, task type, task difficulty and physical properties of stimulus materials) should be fully considered in relative studies. Furthermore, it is worth exploring to develop reliable and valid cognitive ability tests combined with artificial intelligence algorithm based on eye-movement characteristics.

Peer Review Status:Awaiting Review

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