Electronic devices pervade modern life. Adults and children alike spend hours every day surfing the web, texting on smartphones and typing and reading on laptops. What impact does this new pattern of human behavior have on our ability to learn? Previous work has observed how e-device usage impacts users’ personality and life goals, but few studies so far have focused on the brain to determine how e-device use affects student learning. In a recent study by our lab1, Neurocognitive Signatures of Naturalistic Reading of Scientific Texts: A Fixation-Related fMRI Study, we attempted to identify the relationships among students’ brain activation patterns while reading scientific texts, their cognitive abilities, reading attitudes and behavior, including e-device usage. Findings from our study have implications for understanding student learning in a population that is increasingly dependent on e-devices.

The Effect of Reading Media

Earlier studies comparing reading on paper versus reading on a digital screen generally point to screen inferiority, suggesting more time is required for screen-based reading to achieve the same level of performance as paper-based reading. Surprisingly, screen inferiority persists with modern e-books even after correcting for physiological causes like visual fatigue and navigation inconvenience. Screen-based reading is also associated with shallower cognitive processing, multi-tasking, discontinuous reading and meta-cognitive overconfidence in reading comprehension. However, researchers have found without time pressure, people who indicated their reading preference for electronic rather than paper platforms could actually show screen superiority2.

The Shallowing Hypothesis

In The Shallows, technology and culture critic Nicholas Carr argued the ultra-brief nature of social media and messaging promotes rapid and shallow thought, which is associated with cognitive and moral shallowness. Furthermore, the frequent usage of ultra-brief media is associated with an increased emphasis on life goals related to hedonism and image3. Carr’s argument has been supported by several studies on university students4–6. Recently, an Amazon Mechanical Turk study with more than 400 participants showed over use of electronic devices is a negative predictor of reading comprehension of expository texts, while reading preference for challenging books is a positive predictor7.

Knowledge Structure and Reader Characteristics

Expository texts, especially those of a scientific nature, contain information and facts about key concepts and how they are connected. Understanding this information is what we call knowledge acquisition. When reading a textbook, for example, students build an abstract knowledge structure (KS), which involves identifying new concepts and their complex relations and comparing these with old or prior knowledge. This KS can be mathematically quantified in a map or network through graph-theoretical measures such as centrality (e.g., optimal KS of a hierarchical concept structure has a medium value of centrality8). Furthermore, we can investigate how this KS is acquired by readers who have different cognitive executive functions, such as working memory, analogical reasoning, evaluative and inferential abilities9 and e-device usage habits. Our neurocognitive findings indicated significant interactions between the KS - text characteristics - and reader characteristics including e-device usage, in both behavior and brain patterns1. For example, when the KS is less than optimal (low centrality value, linear concept structure), readers who report excessive daily use of e-devices tend to rely on different brain regions compared to readers who use e-devices less frequently (see below).

E-device Usage, Attention Switch and Consciousness

We measured fMRI (every 400 ms) and eye-tracking data simultaneously when participants read five scientific texts in a self-paced manner10. Our data suggested that less frequent e-device users recruit more left inferior frontal gyrus and insula for integrative processing during reading. The anterior insula is critical for switching between the default-mode (e.g., ‘mind-wandering’) and central-executive processing (e.g., ‘executive functioning’). We also found that when reading difficult texts with suboptimal KS, heavy e-device users would recruit more temporoparietal junction (TPJ) and less claustrum for integrative processes. The TPJ is important for attention control and integrative processes, while the claustrum has been proposed as the “gate keeper” of neural information for conscious awareness11. These patterns point to an association between habitual e-device usage and individual differences in the neurodynamics of attentional and conscious-modulatory brain areas during scientific text reading. It might be the case that individuals with such neurocognitive patterns prefer electronic media and seek fast and shallow information in the first place.

Written by Chun-Ting Hsu1 and Ping Li2



1 Kokoro Research Center, Kyoto University

2 Department of Psychology, Pennsylvania State University

References

1. Hsu, C.-T., Clariana, R., Schloss, B. & Li, P. Neurocognitive Signatures of Naturalistic Reading of Scientific Texts: A Fixation-Related fMRI Study. Scientific Reports9, 10678 (2019).

2. Sidi, Y., Shpigelman, M., Zalmanov, H. & Ackerman, R. Understanding metacognitive inferiority on screen by exposing cues for depth of processing. Learning and Instruction51, 61–73 (2017).

3. Carr, N. G. The Shallows: What the Internet is Doing to Our Brains. (W.W. Norton, 2010).

4. Ehrenberg, A., Juckes, S., White, K. M. & Walsh, S. P. Personality and Self-Esteem as Predictors of Young People’s Technology Use. CyberPsychology & Behavior11, 739–741 (2008).

5. Correa, T., Hinsley, A. W. & de Zúñiga, H. G. Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior26, 247–253 (2010).

6. Annisette, L. E. & Lafreniere, K. D. Social media, texting, and personality: A test of the shallowing hypothesis. Personality and Individual Differences115, 154–158 (2017).

7. Follmer, D. J., Fang, S. Y., Clariana, R. B., Meyer, B. J. F. & Li, P. What predicts adult readers’ understanding of STEM texts? Read Writ31, 185–214 (2018).

8. Clariana, R. B., Engelmann, T. & Yu, W. Using centrality of concept maps as a measure of problem space states in computer-supported collaborative problem solving. Etr&D-Educ Tech Res61, 423–442 (2013).

9. van den Broek, P. Using texts in science education: cognitive processes and knowledge representation. Science328, 453–6 (2010).

10. Richlan, F. et al.Fixation-related FMRI analysis in the domain of reading research: using self-paced eye movements as markers for hemodynamic brain responses during visual letter string processing. Cerebral Cortex24, 2647–56 (2014).

11. Crick, F. C. & Koch, C. What is the function of the claustrum? Philos T R Soc B360, 1271–1279 (2005).