Abstract
Eye movements help to infer the cognitive strategy that a person uses in fluid intelligence tests. However, intelligence tests demand different relations/rules tokens to be solved, such as rule direction, which is the continuation, variation or overlay of geometric figures in the matrix of the intelligence test. The aim of this study was to understand whether eye movements could predict the outcome of an intelligence test and in the rule item groups. Furthermore, we sought to identify which measure is best for predicting intelligence test scores and to understand if the rule item groups use the same strategy. Accordingly, 34 adults completed a computerized intelligence test with an eye-tracking device. The toggling rate, that is, the number of toggles on each test item equalized by the item latency explained 45% of the variance of the test scores and a significant amount of the rule tokens item groups. The regression analyses also indicated toggling rate as the best measure for predicting the score and that all the rule tokens seem to respect the same strategy. No correlation or difference were found between baseline pupil size and fluid intelligence. Wiener Matrizen-Test 2 was demonstrated to be a good instrument for the purpose of this study. Finally, the implications of these findings for an understanding of cognition are discussed.
Highlights
Research has shown that psychophysiological measures can be employed as predictors of fluid intelligence (Schlagenhauf et al, 2013; Finn et al, 2015; Friedal et al, 2015), including aspects of ocular movement analysis such as pupillometry (Hayes and Petrov, 2016), scanpath (Hayes et al, 2015) and strategy (Vigneau et al, 2006)
The results suggested that Matrix Time Distribution Index (MTDI), a measure of matrix inspection, would be the best predictor for high scores in the matrix-based intelligence test
The aim of this study was to understand whether eye movements and strategy can predict the outcome of an intelligence test and in particular, in the rule tokens item groups
Summary
Research has shown that psychophysiological measures can be employed as predictors of fluid intelligence (Schlagenhauf et al, 2013; Finn et al, 2015; Friedal et al, 2015), including aspects of ocular movement analysis such as pupillometry (Hayes and Petrov, 2016), scanpath (Hayes et al, 2015) and strategy (Vigneau et al, 2006). Eye movement analysis during Gf tasks has helped in understanding problem-solving (Thibaut and French, 2016; Vendetti et al, 2017). This type of data is very beneficial for understanding the strategies individuals employ to solve a task and identify the best strategy (Bethell-Fox et al, 1984; Hayes et al, 2015). Eye gaze behavior can reflect a cognitive strategy (Snow, 1980; Bethell-Fox et al, 1984)
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