Excessive interaction with e-devices may be related to weaker activity in the brain

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Tract-based spatial statistics (TBSS) group comparison of fractional anisotropy (FA; red–yellow: control > smartphone dependence (SPD)). Significantly lower FA was found in SPD in the highlighted areas including right superior longitudinal fasciculus (SLF), superior corona radiata (SCR), anterior limb of the internal capsule (ALIC), posterior limb of the internal capsule (PLIC), external capsule, sagittal stratum, fornix/stria terminalis, bilateral cerebral peduncle, superior and middle cerebellar peduncles, medial lemniscus and pontine crossing tract.

Overuse of electronic devices is no “LOL” – laughing out loud – matter, according to researchers, who suggest that excessive interaction with e-devices may be related to weaker activity in areas of the brain that are key to understanding scientific texts.

In a study that used neuroimaging to explore brain activity, the researchers found the frequent e-device use – such as texting on a smart phone or reading on a tablet – was negatively correlated with activity in brain areas that are critical for integrating multiple sources of information, the researchers said.

Expository texts, such as articles in a science textbook, typically use inter-connected information, which means that material in one part of the text must be linked to information found in another part of the text, especially when reading for the purpose of understanding inter-connected concepts.

“In this case, if people use electronic devices excessively on a daily basis, that could possibly impair their ability to acquire hierarchical order – or structure – of scientific concepts,” said Ping Li, associate director emeritus of Penn State’s Institute for CyberScience and professor of psychology, linguistics, and information sciences and technology.

Li added that readers often must integrate many pieces of information that are arranged in a hierarchy in the article. Just like a person exercises to strengthen certain muscles, the researchers suggest that by using certain areas of the brain – and ignoring others – those sections may become stronger, or weaker.

People who scan, or write bits of information too often, then, may not be building up the brain power to absorb multiple concepts stretched over long articles.

“If you cannot acquire – or understand – this hierarchy, then you aren’t acquiring the essence of the concepts,” said Li.

“Understanding science isn’t randomly putting sentences or bits of texts together, it is putting the key concepts in these sentences together in a hierarchical structure – which is something a lot of students are having trouble with today.”

The researchers, who report their findings in a recent issue of Scientific Reports, used fMRI – functional Magnetic Resonance Imaging – technology that monitors brain activity to seek underlying brain patterns that may be associated with integrating information during reading. They simultaneously tracked students’ eye-movement patterns to better understa

nd how the readers followed scientific texts.

A total of 51 participants took part in the study. Participants in the study read five scientific articles in the MRI scanner.

The articles covered STEM topics including math, GPS, Mars, electric circuits and the environment.

The researchers found, across all texts, a negative correlation between the self-reported frequency of electronic device usage and activity in brain regions referred to as left insula and the inferior frontal gyrus, or IFG.

These brain areas are among the most important parts of the brain for information processing, such as paying attention and understanding language, according to Li.

“For example, we know that the IFG is very important for language understanding, for understanding semantics, or the meaning of words and grammar, and we see that this area becomes less active among people who use more electronic devices,” said Li.

“The insula is an area involved in cognitive tasks like attention switching. So, let’s say you’re daydreaming while you’re reading a text and then a teacher suddenly tells you to pay attention to a certain part of the text, that’s when you are switching attention.”

The current study builds on previous research.

In a 2017 study, the researchers found that people who often used e-devices were less successful on reading comprehension tests after reading scientific texts.

The team recruited more than 400 participants from Amazon’s mTurk, an online crowdsourcing site, for that study.

Li cautions that much work still remains.

“It’s important to note that what we are showing here isn’t causation. At this point, we are just showing correlation between these brain areas and excess electronic device usage,” said Li.

According to the researchers, future research may focus on the effect that e-device use has on certain age groups. Middle school educators, in particular, may be interested in the ramifications of e-devices on learning.

“Our work may have implications for education,” said Li. “Our goal was to look for a neural basis for reading comprehension. Middle school, for example, is a time when kids are starting to read about science – very basic information in the STEM fields.

Right now, we have very little knowledge about how a middle school student’s brain is responding when they are trying to understand these very basic scientific concepts.”


Smartphones are becoming a vital part of our lives.

These portable “mini-computers” help keep us connected, organized and entertained anywhere and anytime (Samaha and Hawi, 2016).

Smartphones allow us to make calls, check and send emails, use chat applications for texting or voice calls, manage calendars, map driving or walking routes, take/edit/store/share videos and photos, play games or music, and more (Long et al., 2016).

Smartphones have been changing the way we live – how we feel, how we behave, how we communicate and how we age (Dennison et al., 2013; Sarwar and Soomro, 2013; Abu-Shanab and Haddad, 2015).

However, accompanying the popularization of smartphones, their overuse has become an epidemic around the world. In the USA, 64% of American adults owned a smartphone in 2014 according to a survey by the Pew Research Center, up from 35% in 2011.

As many as 15% of American young adults between 18 years and 29 years of age were found to be heavily dependent on smartphones (Smith, 2015; Long et al., 2016).

In China, the Ministry of Industry and Information Technology announced that smartphone ownership had exceeded 1.3 billion by 2016, which is 95.5% of the total population (Long et al., 2016).

The prevalence of problematic smartphone use among Chinese adolescents and undergraduates was estimated to be 21.3% to 26.2% (Long et al., 2016; Tao et al., 2016). Smartphone ownership initiation also exhibits a younger tendency.

In Spain, the proportions of children who own a cell phone at age 10, 12 and 14 are 30%, 70% and 83%, respectively. Furthermore, Spanish children habitually access such devices beginning from 2–3 years of age (Protegeles, 2014).

Smartphone dependence (SPD), also called smartphone addiction or problematic mobile phone use, is regarded as a behavioral addiction, as opposed to a substance addiction, such as drug or alcohol abuse.

An increasing number of studies have been conducted on behavioral addictions to videogames (Fisher, 1994), food (Oxford, 2001), shopping (O’Guinn and Faber, 1989; Clark and Calleja, 2008), work (Andreassen et al., 2010), online sex (Griffiths, 2004; Young, 2008) and the Internet (Young, 2004; Beard, 2005).

As a subset of addictive behaviors, SPD may share many common properties with other kinds of addiction disorders (Bianchi and Phillips, 2005; Billieux, 2012), especially Internet addiction.

For example, they may present similar problematic manifestations (De-Sola Gutiérrez et al., 2016), including conscious use in dangerous situations or prohibited contexts (Billieux et al., 2008), loss of interest in other activities (Griffiths, 2000), physical and mental impairment (Chóliz, 2012), social and familial interruptions (Sansone and Sansone, 2013), and anxiety and loneliness with restricted usage (Ha et al., 2008).

Studies on Internet addiction have focused on the behaviors it involves, its influence on daily life and social performance, clinical diagnosis and treatment (Caplan, 2002; Shaw and Black, 2008; Tao et al., 20102016; The Ministry of Industry and Information Technology of China, 2016), as well as effects on brain structures and functions (Yuan et al., 2011; Lin et al., 2012; Wee et al., 2014; Sepede et al., 2016).

High-resolution magnetic resonance imaging has allowed enhanced sensitivity and power to detect morphological, functional, regional and network changes in the brain, which has facilitated exploration of the neural substrates of addiction.

Evidence has shown altered gray matter density (Yuan et al., 2011; Weng et al., 2013), cortical thickness (Hong et al., 2012; Yuan et al., 2013), cerebral blood flow (Feng et al., 2013), and functional connectivity (Ding et al., 2013; Hong et al., 2013) in regions involved in addictive or compulsive behaviors in subjects with Internet addiction. Changes in white matter tracts (Yuan et al., 2011; Dong et al., 2012; Lin et al., 2012) were also detected by diffusion tensor imaging (DTI), a sensitive tool that reflects the water diffusion characteristics of white matter.

We hypothesized that due to its similarities to other behavioral addictions, SPD may also be associated with impairments of white matter fibers (Wang et al., 2016).

In this study, we investigated white matter integrity in young adults with SPD using observer-independent tract-based spatial statistics (TBSS) analysis with DTI and linked the quantitative variables of brain structures to the behavioral measures of SPD.


More information: Chun-Ting Hsu et al. Neurocognitive Signatures of Naturalistic Reading of Scientific Texts: A Fixation-Related fMRI Study, Scientific Reports (2019). DOI: 10.1038/s41598-019-47176-7

Journal information: Scientific Reports
Provided by Pennsylvania State University

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