Mobile phones influence concentration


As smart phones have become a pervasive part of daily life over the last decade or so, they’ve changed the way people socialize and communicate.

They’re always around and always within reach, or nearly always.

So what happens to people’s brains and bodies when their phones are out of reach, or within reach but not usable?

That’s what Dave Markowitz, assistant professor in the School of Journalism and Communication, and colleagues sought to find out in a recent study published in PLOS One, a peer-reviewed, open-access scientific journal.

Markowitz is interested in understanding the psychology of communication behavior, including language patterns and how media affects social and physical processes.

As part of his doctoral thesis at Stanford University, he devised a study examining how subjects responded when exercising self-control with their phones.

He recruited 125 participants for the study, who were assigned to one of three groups and then directed to sit alone in an empty room for six minutes, though they weren’t told the duration. Here’s how the groups were divided up:

  • Members of one group were told to entertain themselves with their mobile phone, except no phone calls and no texting.
  • Members of the second group were told to leave their phones outside the room, sit alone without their device and entertain themselves with their thoughts.
  • Members of the third group were allowed to keep their phones but told to turn them face down on the table in front of them and not use them. They were also told to entertain themselves with their thoughts.

A fingertip device was used to measure skin conductance, an indicator of arousal. Participants’ level of enjoyment, concentration difficulty, mind wandering and general mood were measured using post-study questionnaires.

Markowitz and colleagues found that participants without their phones had more difficulty concentrating and more mind wandering compared to those who used their phone.

And those who had to resist using their phone had greater perceived concentration abilities than those who sat without their phone.

“The surprising finding for me was the reduction in concentration difficulty when people had to resist” using the phone, Markowitz said.

One possible reason that resisting the phone led to perceived improvement concentration?

Most people think phones are valuable and seeing it front of them, even though they could not use it, offered something to think about compared to sitting without their phone, he said

“At least having it front of you was psychologically better than not having it all,” he said. “Having some form of external stimulation, even if it wasn’t used, I think that can focus the mind a bit.

It suggests having the phone present is better than not, but what’s not clear is whether the phone is special, or if the participants would have reacted the same way with a book in front of them that they weren’t allowed to read or pick up, he said.

Markowitz is interested in understanding the psychology of communication behavior, including language patterns and how media affects social and physical processes.

As part of his doctoral thesis at Stanford University, he devised a study examining how subjects responded when exercising self-control with their phones.

Markowitz’s findings fit with research by Tim Wilson at the University of Virginia, who found that when people were given time for “just thinking,” they experience psychological consequences — less enjoyment, more difficulty concentrating, more mind wandering — compared to if they had some form of external stimulation.

“The mind can wander and lose focus when you’re not given a thinking aid,” which can be less psychologically positive for people, he said.

Markowitz said his study also fits in a framework of trying to understand if technology, or media in general, are mirrors or modifiers of human behavior.

If technology is a mirror, then mediated experiences reflect how people also act offline. If technology is a modifier, then in some cases it’s changing the way we behave, think and feel in the world, he said.

“That’s still really an open question,” he said.

“There are some cases where mediated and nonmediated experiences show consistencies in behavior, but other cases where mediation plays a crucial, modifying role. I’m interested in exploring these boundaries.”

What Is Intelligence?

Intuitively we all know what it is to be intelligent, although definitions of intelligence can be very diverse. It is something that helps us plan, reason, solve problems, quickly learn, think on our feet, make decisions and, ultimately, survive in the fast, modern world.

To capture this elusive trait, cognitive tests have been designed to measure performance in different cognitive domains, such as processing speed and language.

Very soon it became clear that the results of different cognitive tests are highly correlated and generate a strong general factor that underlies different capabilities – general intelligence or Spearman’s g (Spearman, 1904).

One of the most used tests nowadays to estimate Spearman’s g is the Wechsler Adult Intelligent Scale (WAIS).

This test combines results of multiple cognitive tests in one measurement, full-scale IQ score.

Are the tests able to measure human intelligence and does expressing it in a single number—IQ score—make sense? Despite critiques of this reductionist approach to intelligence, the tests have proven their validity and relevance.

First, results of IQ tests strongly correlate with life outcomes, including socioeconomic status and cognitive ability, even when measured early on in life (Foverskov et al., 2017).

The increasing complexity and technology-dependent society imposes ever growing cognitive demands on individuals in almost every aspect of everyday life, such as banking, using maps and transportation schedules, reading and understanding forms, interpreting news articles.

Higher intelligence offers many seemingly small advantages, but they accumulate to affect overall chances in life of individuals (Gottfredson, 1997).

These are beneficial to socioeconomic status, education, social mobility, job performance, and even lifestyle choices and longevity (Lam et al., 2017).

Second, intelligence turns out to be a very stable trait from young to old age in the same individual. In a large longitudinal study of English children, a correlation of 0.81 was observed between intelligence at 11 years of age and scores on national tests of educational achievement 5 years later.

This contribution of intelligence was evident in all 25 academic disciplines (Deary et al., 2007). Even at much later age, intelligence remains stable: a single test of general intelligence taken at age 11 correlated highly with the results of the test at the age of 90 (Deary et al., 2013).

Finally, one of the most remarkable findings of twin studies is that heritability of intelligence is extraordinarily large, in the range 50%–80% even reaching 86% for verbal IQ (Posthuma et al., 2001).

This makes human intelligence one of the most heritable behavioral traits (Plomin and Deary, 2015). Moreover, with every generation, assortative mating infuses additive genetic variance into the population, contributing to this high heritability (Plomin and Deary, 2015).

Thus, despite its elusiveness in definition, intelligence lies at the core of individual differences among humans.

It can be measured by cognitive tests and the results of such tests have proven their validity and relevance: intelligence measures are stable overtime, show high heritability and predict major life outcomes.

Biological Basis of Intelligence: A Whole-Brain Perspective

Are Bigger Brains Smarter?

A question that has puzzled scientists for centuries is that of the origin of human intelligence. What makes some people smarter than others?

The quest to answer these questions has started as early as 1830s in Europe and Russia where the brains of deceased elite scientists and artists were systematically collected and meticulously studied (Vein and Maat-Schieman, 2008).

However, all the attempts to dissect the exceptional ability and talent did not reveal much at that time.

The reigning hypothesis of the past century was that smarter people have bigger brains. With the advances in neuroimaging techniques this hypothesis was put to test in many studies.

Indeed, a meta-analysis of 37 studies with over 1,500 individuals of the relationship between in vivo brain volume and intelligence found a moderate, yet significant positive correlation of 0.33 (McDaniel, 2005).

A more recent meta-study of 88 studies with over 8,000 individuals again reported a significant, positive, slightly smaller correlation coefficient of 0.24.

One of the conclusions of this study was that the strength of the association of brain volume and IQ seems to be overestimated in the literature but remains robust after accounting for publication bias (Pietschnig et al., 2015).

Thus, overall bigger brain volume, when analyzed across multiple studies, is associated with higher intelligence.

Which Brain Areas Are Important for Intelligence?

Brain function is distributed across various areas that harbor specific functions. Can intelligence be attributed to one or several of these areas?

Structural and functional brain imaging studies focused on locating general intelligence within the brain and linking specific types of cognition to specific brain areas (Deary et al., 2010).

Early imaging studies associating intelligence to brain structure showed that full-scale IQ scores, a measure of general intelligence, showed a widely distributed pattern of correlations with brain structures: IQ scores correlated with intracranial, cerebral, temporal lobe, hippocampal, and cerebellar volumes (Andreasen et al., 1993), that together encompass almost all brain areas.

Voxel-based morphometry (VBM), a neuroimaging analysis technique that allows estimation of focal differences in brain structure, makes it possible to test whether any such areas are clustered together or distributed throughout the brain.

Application of VBM to brain imaging data revealed that positive correlations between intelligence and cortical thickness are located primarily in multiple association areas of frontal and temporal lobes (Hulshoff Pol et al., 2006; Narr et al., 2007; Choi et al., 2008; Karama et al., 2009).

Based on 37 neuroimaging studies, Jung and Haier (2007) put forward that in particular the structure of frontal Brodmann areas 10, 45–47, parietal areas 39 and 40, and temporal area 21 positively contribute to IQ scores (Jung and Haier, 2007).

This model was extended by later studies to frontal eye field, orbitofrontal area, as well as a large number of areas in temporal lobe – inferior and middle temporal gyrus, parahippocampal cortex and auditory association cortex (Narr et al., 2007; Choi et al., 2008; Colom et al., 2009Figure 1).

Brain Structure Changes

Brain structure is not fixed at one particular developmental time point and then remains unaltered for the rest of our lives.

Gray matter volume changes throughout childhood as well as adulthood (Gogtay et al., 2004) and is influenced by learning, hormonal differences, experience and age. Gray matter changes may reflect rearrangements of dendrites and synapses between neurons (Gogtay et al., 2004).

When people acquire a new skill, for instance juggling, transient and selective structural changes are observed in brain areas that are associated with the processing and storage of complex visual motion (Draganski et al., 2004).

Similarly, sex differences and age differences are important factors that influence brain structure and can affect which cortical areas associate with intelligence.

Substantial sex differences were reported in the pattern of correlations between intelligence and regional gray and white matter volumes (Haier et al., 2005; Narr et al., 2007; Yang et al., 2014; Ryman et al., 2016), but the reports do not fully agree on the brain areas showing sex differences or their association with cognitive performance.

Haier et al. (2005) reported correlations of IQ with parietal and frontal regions in males, whereas women showed correlations mainly within the frontal lobe (Haier et al., 2005).

Similar results were obtained by Ryman et al. (2016) in males – fronto-parietal gray matter was more significantly related to general cognitive ability. However, in females the results indicated associations with intelligence in white matter efficiency and total gray matter volume (Ryman et al., 2016).

Yet different conclusions were drawn by Narr et al. (2007), where women showed significant associations in gray matter thickness in prefrontal and temporal association cortices, whereas men show associations primarily in temporal-occipital association cortices (Narr et al., 2007).

Finally, in a recent study where surface-based morphometry (SBM) was applied instead of VBM, substantial group differences in brain structure were found between sexes but cognitive performance was unrelated to brain structural variation within and between sexes (Escorial et al., 2015).

What the studies do agree on is that substantial sex differences exist in brain structure, but that these differences not always underlie variation in cognitive performance.

For example, one of the well-established sex differences in brain structure is the increased cortical thickness of males compared to females (Lüders et al., 2002), but relationships between full-scale IQ score and brain tissue volumes do not differ between men and women (Narr et al., 2007; Escorial et al., 2015).

Age Matters

In addition to sex differences, gray matter volume shows dramatic changes during lifetime that are part of normal development (Gogtay et al., 2004).

The initial increase at earlier ages is followed by sustained thinning around puberty. This developmental change is thought to be a result of overproduction of synapses in early childhood and increased synaptic pruning in adolescence and young adulthood (Bourgeois et al., 1994).

Furthermore, different areas have their own timeline of maturation: higher-order association cortices mature only after lower-order somatosensory and visual cortices (Gogtay et al., 2004). Correlations with intelligence follow a similar developmental curve.

The strongest correlations between gray matter volume and intelligence have been found for children around the age of 10 years (Shaw et al., 2006; Jung and Haier, 2007).

However, at age 12, around the start of cortical thinning, a negative relationship emerges (Brouwer et al., 2014). Moreover, it seems that the whole pattern of cortical maturation unfolds differently in more intelligent children.

Children with higher IQ demonstrate a particularly plastic cortex, with an initial accelerated and prolonged phase of cortical increase and equally vigorous cortical thinning by early adolescence (Shaw et al., 2006).

Brain Specialization to Different Types of Intelligence

In addition to associations of cortical structure with intelligence, imaging studies have revealed correlations of functional activation of cortical areas with intelligence.

Psychology distinguishes between two types of intelligence that together comprise Spearman’s g: crystallized and fluid intelligence.

Crystallized intelligence is based on prior knowledge and experience and reflects verbal cognition, while fluid intelligence requires adaptive reasoning in novel situations (Carroll, 1993; Engle et al., 1999).

Multiple studies imply that fluid intelligence relies on more efficient function of distributed cortical areas (Duncan et al., 2000; Jung and Haier, 2007; Choi et al., 2008).

In particular, lateral frontal cortex, with its well-established role in reasoning, attention and working memory, seems to support fluid intelligence, but also the parietal lobe is implicated. One of the earlier studies of fluid intelligence using Raven’s Advanced Progressive Matrices by Haier et al. (1988) demonstrated activation of several areas in the left-hemisphere, in particular posterior cortex.

Cognitive performance showed significant negative correlations with cortical metabolic rates, suggesting more efficient neural circuits (Haier et al., 1988). In later studies, fluid intelligence was strongly linked to both function and structure of frontal lobe regions (Choi et al., 2008).

When participants perform verbal and nonverbal versions of a challenging working-memory task, while their brain activity is measured using functional magnetic resonance imaging (fMRI), individuals with higher fluid intelligence are more accurate and have greater event-related neural activity in lateral prefrontal and parietal regions (Gray et al., 2003).

Also in a PET-scan study, participants showed a selective recruitment of lateral frontal cortex during more complicated cognitive tasks compared to easier tasks (Duncan et al., 2000).

In a more recent report, the measurements of gray matter volume of two frontal areas – orbito-frontal (OFC) and rostral anterior cingulate cortices (rACC) – were complemented by white matter connectivity between these regions.

Together, left gray matter volume and white matter connectivity between left posterior OFC and rACC accounted for up to 50% of the variance in general intelligence. Thus, especially in prefrontal cortex, structure, function and connectivity all relate to general intelligence, specifically to reasoning ability and working memory (Ohtani et al., 2014).

Crystallized intelligence that largely relies on verbal ability, on the other hand, depends more on the cortical structure and cortical thickness in lateral areas of temporal lobes and temporal pole (Choi et al., 2008; Colom et al., 2009).

While parietal areas (Brodman area 40) show overlap in their involvement in crystallized and other types of intelligence, temporal Brodman area 38 is exclusively involved in crystallized intelligence.

These findings harmonize well with the function of the temporal lobe – it is thought to be responsible for integrating diverse semantic information from distinct brain regions.

Studies of patients with semantic dementia support the role of temporal lobe in semantic working memory as well as memory storage (Gainotti, 2006).

Thus, subdividing Spearman’s g reveals distinct cortical distributions involved in subdomains of intelligence.

It is likely that further subdividing fluid and crystallized intelligence, for instance in verbal comprehension, working memory, processing speed, and perceptual organization, may result in a more defined map of cortical regions on left and right hemisphere that relate to these subdomains of intelligence (Jung and Haier, 2007).

White Matter and Intelligence

Not only gray matter, but also white matter volumes show an association with intelligence that can be explained by common genetic origin (Posthuma et al., 2002).

White matter consists of myelinated axons transferring information from one brain region to another and integrity of the white matter tracts is essential for normal cognitive function. Thus, specific patterns of white matter dysconnectivity are associated with heritable general cognitive and psychopathology factors (Alnæs et al., 2018).

For example, Yu et al. (2008) found that mental retardation patients show extensive damage in the integrity of white matter tracts that was assessed by fractional anisotropy. IQ scores significantly correlated with the integrity of multiple white matter tracts in both healthy controls and mental retardation patients (Yu et al., 2008).

This correlation was especially prominent in right uncinate fasciculus that connects parts of temporal lobe with the frontal lobe areas (Yu et al., 2008).

These results support previous findings on the association of particularly temporal and frontal lobe gray matter volume and intelligence (Hulshoff Pol et al., 2006; Narr et al., 2007; Choi et al., 2008; Karama et al., 2009) and emphasize that intact connectivity between these areas is important for intelligence.

Longitudinal studies that track changes in white matter across development and during aging also show that changes in white matter are accompanied by changes in intelligence.

During brain maturation in children, white matter structure shows associations with intelligence.

In a large sample (n = 778) of 6- to 10-year-old children, white matter microstructure was linked to non-verbal intelligence and to visuospatial ability, independent of age (Muetzel et al., 2015).

In another study, where white matter was studied in typically-developing children vs. struggling learners, the white matter connectome efficiency was strongly associated with intelligence and educational attainment in both groups (Bathelt et al., 2018).

Also at later stages in life, changes in white matter microstructure are coupled with changes in intelligence (Ritchie et al., 2015).

Substantial correlations of 12 major white matter tracts with general intelligence were found in older individuals (Penke et al., 2012).

Subsequent analysis showed that lower white matter tract integrity exerts a substantial negative effect on general intelligence through reduced information-processing speed (Penke et al., 2012).

Thus, structurally intact axonal fibers across the brain provide the neuroanatomical infrastructure for fast information processing within widespread brain networks, supporting general intelligence (Penke et al., 2012).

Conclusions on Gross Brain Distribution of Intelligence

Thus, both functional and structural neuroimaging studies show that general intelligence cannot be attributed to one specific region. Rather, intelligence is supported by a distributed network of brain regions in many, if not all, higher-order association cortices, also known as parietal-frontal network (Jung and Haier, 2007Figure 1).

This network includes a large number of regions—the dorsolateral prefrontal cortex, the parietal lobe, and the anterior cingulate, multiple regions within the temporal and occipital lobes and, finally, major white matter tracts.

Some limited division of function can be observed, implicating frontal and parietal areas in fluid intelligence, temporal lobes in crystallized intelligence and white matter integrity in processing speed.

Although brain imaging studies have identified anatomical and functional correlates of human intelligence, the actual correlation coefficients have consistently been modest, around 0.15–0.35 (Hulshoff Pol et al., 2006; Narr et al., 2007; Choi et al., 2008; Karama et al., 2009).

There are most likely various reasons for this, but an important conclusion is that human intelligence can only partly be explained by brain structure and functional activation of cortical areas observed in MRI.

There are other factors contributing to intelligence that have to be considered. To put it in an evolutionary perspective, the human brain has outstanding cognitive capabilities compared to other species, that include many specific human abilities – abstract thinking, language and creativity.

However, human brain anatomy is not that distinct from other mammalian species and it cannot satisfactorily account for a marked evolutionary jump in intelligence. Both in its size and neuronal count, the human brain does not evolutionary stand out: elephants and whales have larger brains (Manger et al., 2013) and long-finned pilot whale cortex contains more neurons (37 billion) than that of humans (19–23 billion; Pakkenberg and Gundersen, 1997; Herculano-Houzel, 2012; Mortensen et al., 2014).

Especially the brains of our closest neighbors on the evolutionary scale, non-human primates, show remarkable resemblance. In fact, the human brain is anatomically in every way a linearly scaled-up primate brain (Herculano-Houzel, 2012), and appears to have little exceptional or extraordinary features to which outstanding cognitive abilities can be attributed.

Thus, answers to the origins of human intelligence and its variation between individuals most probably do not lie only in the gross anatomy of the brain, but rather should be sought at the level of its building blocks and computational units—neurons, synapses and their genetic make-up.

University of Oregon


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