Is inferring readers' literacy skills possible by analyzing their eye movements during text reading? This study used Support Vector Machines (SVM) to analyze eye movement data from 61 undergraduate students who read a multiple-paragraph, multiple-topic expository text. Forward fixation time, first-pass rereading time, second-pass fixation time, and regression path reading time on different regions of the text were provided as features. The SVM classification algorithm assisted in distinguishing high-literacy-skilled readers from low-literacy-skilled readers with 80.3 % accuracy. Results demonstrate the effectiveness of combining eye tracking and machine learning techniques to detect readers with low literacy skills, and suggest that such approaches can be potentially used in predicting other cognitive abilities.
Eye movements are one of the most fundamental behaviors during reading. A growing number of Chinese reading studies have used eye-tracking techniques in the last two decades. The accumulated data provide a rich resource that can reflect the complex cognitive mechanisms underlying Chinese reading. This article reports a database of eye-movement measures of words during Chinese sentence reading. The database contains nine eye-movement measures of 8,551 Chinese words obtained from 1,718 participants across 57 Chinese sentence reading experiments. All data were collected in the same experimental environment and from homogenous participants, using the same protocols and parameters. This database enables researchers to test their theoretical or computational hypotheses concerning Chinese reading efficiently using a large number of words. The database can also indicate the processing difficulty of Chinese words during text reading, thus providing a way to control or manipulate the difficulty level of Chinese texts.
Impoundment of the Three Gorges Reservoir (TGR) has dramatically influenced the riparian environment and shaped a new drawdown zone, which has experienced long‐term winter conditions and short periods of summer flooding. The community structure and diversity of arbuscular mycorrhizal (AM) fungi (AMF) were investigated in three areas with different fertilization histories [Area A (5 years of fertilization), Area B (3 years of fertilization) and Area C (no fertilization)] in the drawdown zone of the TGR. Altogether, 50 AMF species were identified; the genera Acaulospora, Funneliformis and Glomus were predominant. The AM fungal community differed among areas A, B and C. A higher isolation frequency and relative abundance of Acaulospora, Ambispora, Entrophospora and Paraglomus were observed in areas A and B; however, Claroideoglomus, Diversispora, Sclerocystis and Septoglomus were more abundant in Area C. The highest spore density occurred in Area C, and was slightly lower in Area A and lowest in Area B. Conversely, species richness and diversity indices (Shannon–Wiener and evenness indices) were the highest in Area A, followed by areas C and B. Based on nonmetric multidimensional scaling analyses, the distribution of AMF was influenced by plant host, fertilization practice and environmental factors. Among them, the soil physicochemical properties were the main drivers affecting AMF, in which three edaphic attributes (carbon/nitrogen ratio, available phosphorus and potassium content) were significantly correlated (P < 0.001) with the AM fungal community composition in the three areas of the drawdown zone of the TGR.
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