Less screen time and more green time are associated with better psychological outcomes among children and adolescents, according to a study published September 2 in the open-access journal PLOS ONE by Tassia Oswald of the University of Adelaide, and colleagues.
The prevalence of mental illness among children and adolescents is increasing globally. Technological developments in recent decades have increased young people’s engagement with screen-based technologies (screen time), and a reduction in young people’s contact with nature (green time) has been observed concurrently.
This combination of high screen time and low green time may affect mental health and well-being.
But research investigating the psychological impacts of screen time or green time typically considers each factor in isolation and fails to delineate the reciprocal effects of high technology use and low contact with nature on mental health and cognitive outcomes.
To address this question, Oswald and colleagues analyzed the findings of 186 studies to collate evidence assessing associations between screen time, green time, and psychological outcomes (including mental health, cognitive functioning, and academic achievement) for children and adolescents.
Young people from low socioeconomic backgrounds were underrepresented in the literature overall and may be disproportionately affected by high screen time and low green time, making this a priority group for future research.
However, additional longitudinal studies and RCTs are needed to determine whether decreasing screen time and increasing green time would improve psychological outcomes.
According to the authors, preliminary evidence suggests that green time could potentially buffer the consequences of high screen time, meaning nature may be an under-utilized public health resource to promote youth psychological well-being in a high-tech era. Investment in more rigorous research is needed to explore this.
Oswald adds: “This systematic scoping review highlights that nature may currently be an under-utilised public health resource, which could potentially function as an upstream preventative and psychological well-being promotion intervention for children and adolescents in a high-tech era.
However, robust evidence is needed to guide policies and recommendations around appropriate screen time and green time at critical life stages, to ultimately ensure optimal psychological well-being for young people.”
Tablets and smartphones (i.e., mobile technology) as learning tools for school use is on the rise worldwide (Norris and Soloway, 2015). The technology is reported to impact positively on learning outcomes (Major et al., 2017), by facilitating contextual and situated learning (Brown and Mbati, 2015).
For instance, mobile devices are thought to stimulate personalized and informal learning by corroborating and adapting to the interests, preferences, and competencies of learners (Traxler and Wishart, 2011), while affording personal publishing and sharing (Mbati, 2017).
However, exposure to screens may also have more undesirable side-effects of concern to formal and informal learning. In so-called iPad schools where books are switched for iPads in class, play during break-time shifts from physical to more sedentary activities (Schilhab, 2017a).
Crudely put, engagement with the external world of concrete phenomena and spontaneous events is switched for engagement with the mediated world of smart technology, where children watch and share YouTube videos, read Wikipedia, and are exposed to vast amounts of information from others (Holloway et al., 2013; Duarte Torres et al., 2014).
Hence, along with the increased use of mobile technology come attentional and cognitive shifts pertaining to the learning and development of the individual (e.g., Ward et al., 2017). Numerous studies have demonstrated that smart technology use influences attentional and cognitive processes in unexpected ways.
For instance, it has been reported that devoting attention to mobile phones voluntarily or involuntarily changes the content and dynamics of conversations (Turkle, 2015), resulting in shallower content (Przybyliski and Weinstein, 2013) and lower levels of reported empathic concerns among interlocutors (Misra et al., 2016).
It has also been argued that smart technology’s capacity as information store has profound consequences on how we manipulate and memorize learned material (Sparrow et al., 2011; Barr et al., 2015; Dong and Potenza, 2017), although the actual effects on learning are also disputed (Aagaard, 2015; Heersmink, 2016).
In meta-cognitive research, on-screen readers performed worse than print readers when tested in connection with self-regulated reading of expository texts (Ackerman and Goldsmith, 2011), suggesting that screen reading alters the recruitment of mental efforts (Lauterman and Ackerman, 2014).
In comparison, the natural world seems to engage attentional and cognitive processes differently. Following Attention Restoration Theory (ART, e.g., Kaplan, 1995) in opposition to screen watching (e.g., television), unthreatening greenish outdoor environments typically accessible to both urban and country dwellers stimulate by so-called soft fascination (Kaplan and Berman, 2010).
Please note that threatening greenish outdoor environments may have more intrusive, yet desirable cognitive effects (e.g., Kahn et al., 2009). Accordingly, resting in green environments enhances so-called executive functioning (Bratman et al., 2012) in use when concentrating and thinking, and is therefore central for academic success (Diamond, 2013). Arguments for exposing students to nature are partly based on this effect (Matsuoka, 2010; Kuo et al., 2017). Although the restorative effect of soft nature on cognitive functioning, as proposed by ART, is persuasive with respect to promoting nature interventions in school, another much more profound effect of relevance to success in school and life not addressed by ART has gone largely unnoticed.
We advocate that the mental work occurring during restoration of executive functioning, so-called mind wandering, e.g., off-task thoughts that occur either with or without intention (Smallwood and Schooler, 2006), is crucially important in its own right.
Given that screen watching and screen use is more likely to affect attentional and cognitive processes by hard fascination (Kaplan and Berman, 2010), to an extent that sometimes renders mobile technology use addictive (e.g. Rosen et al., 2013; Billieux et al., 2015), thus tapping into self-regulatory processes (Schilhab, 2017b), nature’s facilitating effect on mind wandering becomes noteworthy.
In what follows, we (a) highlight how nature-induced soft fascination leaves room for spontaneous thoughts, which are under increased pressure from the mobile technology-induced hard fascination and more controlled thoughts and (b) emphasize the need for research relating green environments, open monitoring and divergent thinking.
Forming part of executive functions (Engle, 2002; Posner et al., 2013), attentional control is closely related to success in school (Diamond, 2011). James (1892) famously distinguished between involuntary and voluntary attention, also known as stimulus-dependent and directed attention (e.g., Chun et al., 2011).
The former refers to attention that requires no effort, such as when something dangerous, pleasurable or novel occurs (e.g., Sood and Jones, 2013) whereas the latter refers to the kind of attention employed when something is not particularly interesting and therefore requires a good deal of mental effort (Kaplan and Berman, 2010).
Thus, stimulus-dependent attention often depends on external sense activity that drives learning automatically and bottom-up, whereas directed attention is independent of stimulus characteristics and works top-down (Wilson, 2002).
As noted by Kaplan and Berman (2010), James (1892, p. 88) pointed to “strange things, moving things, wild animals, bright things, pretty things, metallic things, words, blows, blood, etc. etc. etc.” as engaging stimulus-dependent attention.
In this understanding, mobile technology seems entirely unmatched in its ability to “call” up the attention of its user. Mobile technology affords immediate access to pleasure, and unexpected and novel stimuli and thus taps heavily into our attentional resources (Lee et al., 2014; Li et al., 2015) combatting e.g. social anxiety and boredom (Elhai et al., 2017) or feeding attentional impulsiveness (Roberts et al., 2015).
Even long-term attentional effects, the so-called phantom vibration and phantom ringing hallucinations, seem to occupy the mind of heavy mobile technology users (Lee et al., 2014; Tanis et al., 2015).
Nature-Induced Soft Fascination
Conversely, natural stimuli seem to capture attentional processes in an opposing way, although it is worth noting that “untrammeled” and unmanaged wild nature is likely to have different attentional effects (Davis and Gatersleben, 2013). ART suggests that non-threatening natural environments are experienced with less cognitive effort, because they are “softly fascinating” with no elements that compete with each other for attentional focus (Kaplan, 1995).
ART predicts that perceiving natural stimuli will allow finite cognitive capacities, such as focusing attention, to restore, alleviating the individual from cognitive fatigue that is experienced when these capacities are overused (Kaplan, 1995; Berman et al., 2008). Indeed, there is an existing research base that supports the notion that exposure to nature can be beneficial to cognitive processes (for review, see Ohly et al., 2016).
We suggest that non-threatening natural environments that softly fascinate have positive effects on cognition through the facilitation of spontaneous thought processes.
According to ART, nature-bound stimuli are less likely to signify a sense of immediate danger or otherwise pull attention along particular thought paths. Hence, engaging with nature-bound stimuli involves comparably fewer symbolic associations than engaging with smart technology.
A pond full of carp signifies nothing or very little beyond itself. Carp swimming just “are”—the observation does not trigger a sense of danger, hard fascination difficult to disengage from or intentions to act, whereas a picture of carp as in advertisements normally signifies or stands for something different that instigates serial thought processes calling upon directed attention.
It is likely that the “closed signification,” which is the fact that nature’s stimuli point to themselves and not away from themselves to something beyond, provides nature with the strength to decelerate or even obliterate thought processes (Schilhab, 2017c).
In a study that illustrated how the brain processes natural and non-natural stimuli differently, Berto et al. (2008) used eye-tacking technology to investigate how participants viewed two types of scenes.
They found that viewing natural scenes was associated with greater exploration and fewer fixations; however, when viewing urban scenes, participants were more likely to fixate on certain stimuli. Greater scene exploration suggests greater fascination that is not cognitively demanding, whereas frequent and longer fixation suggests that attention is more readily captured by these stimuli that they are more cognitively demanding to process (Berto et al., 2008).
Being in a safe natural environment, where the surrounding stimuli have no intrinsic threat, goal, or task associated with them, may benefit non-perceptual cognitive processes important for learning. An environment with no goal-directed or task-positive stimuli may also be associated with activation of task-unrelated neural networks, such as the default mode network (DMN) (Andrews-Hanna et al., 2014).
The DMN is associated with autobiographical memory and mind-wandering and has been shown to be separately involved in the maintenance phase of working memory alongside task-positive networks (Piccoli et al., 2015). Maintaining and remembering newly acquired information is one of the most commonly demonstrated cognitive benefits of exposure to natural stimuli (Ohly et al., 2016).
Moreover, the cognitive load in working memory tasks can be predictive of the impact of natural stimuli, where the harder the task conditions, the greater the cognitive restoration associated with exposure to nature (e.g., Dadvand et al., 2015). This suggests that the harder the brain is working to shield memorized information from external and internal distraction, the greater the impact a natural environment will have on restoration of this and associated abilities.
The reduced pull on thought processes facilitates more self-generated thoughts where the mind “move[s] hither and thither without fixed course or certain aim” (Christoff et al., 2016, p. 719). Such episodes are considered adaptive since they allow individuals to, for instance, prepare for future events, to sustain a sense of self-identity and to re-interpret social encounters (Andrews-Hanna et al., 2014).
Spontaneous thought processes associated with the reduced external pull on thoughts also loosely resemble divergent thinking processes stimulating creative thinking and abilities to think “out of the box” (Colzato et al., 2012)1.
This might suggest that in contrast to smart technology use, nature-bound stimuli are more likely to endorse so-called open monitoring mind states prevalent in certain meditative traditions (Tang and Posner, 2009; Howell et al., 2011; Lebuda et al., 2016).
Following Hommel and Colzato (2017), focused-attention meditation (FAM) differs from open-monitoring meditation (OMM) and have different and sometimes even opposite impacts on cognitive processes.
Whereas FAM traditionally trains directed attention capacities by sustained attention on a specific object, OMM “sustains attentive monitoring of anything that might occur in experience without focusing on any explicit object” (ibid. p. 115; see also Lutz et al., 2008).
In the current context, we suggest that nature-bound stimuli are likely to induce open-monitoring mental states that typically promotes the divergent thinking style that allows many new ideas to be generated (Leong et al., 2014; Colzato and Hommel, 2017; Colzato et al., 2017).
Studies examining the impact of acute moderate and intense physical exercise on convergent and divergent thinking in athletes and non-athletes (S Colzato et al., 2013) or the effect of walk (Keinänen, 2016; Zhou et al., 2017) could form the backdrop for a future research design to test the impact of nature-bound vs. mobile technology-bound stimulation (for the distinction between the effect of the outdoors and physical activity, see Oppezzo and Schwartz, 2014).
Ackerman, R., and Goldsmith, M. (2011). Metacognitive regulation of text learning: on screen versus on paper. J. Exp. Psychol. Appl. 17, 18–32. doi: 10.1037/a0022086 – PubMed Abstract | CrossRef Full Text | Google Scholar
Andrews-Hanna, J. R., Smallwood, J., and Spreng, R. N. (2014). The default network and self–generated thought: component processes, dynamic control, and clinical relevance. Ann. N. Y. Acad. Sci. 1316, 29–52. doi: 10.1111/nyas.12360- PubMed Abstract | CrossRef Full Text | Google Scholar
Barr, N., Pennycook, G., Stolz, J. A., and Fugelsang, J. A. (2015). The brain in your pocket: evidence that smartphones are used to supplant thinking. Comput. Hum. Behav. 48, 473–480. doi: 10.1016/j.chb.2015.02.029 – CrossRef Full Text | Google Scholar
Berman, M. G., Jonides, J., and Kaplan, S. (2008). The cognitive benefits of interacting with nature. Psychol. Sci. 19, 1207–1212. doi: 10.1111/j.1467-9280.2008.02225.x – PubMed Abstract | CrossRef Full Text | Google Scholar
Berto, R., Massaccesi, S., and Pasini, M. (2008). Do eye movements measured across high and low fascination photographs differ? Addressing Kaplan’s fascination hypothesis. J. Environ. Psychol. 28, 185–191. doi: 10.1016/j.jenvp.2007.11.004 – CrossRef Full Text | Google Scholar
Billieux, J., Maurage, P., Lopez-Fernandez, O., Kuss, D. J., and Griffiths, M. D. (2015). Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Curr. Addict. Rep. 2, 156–162. doi: 10.1007/s40429-015-0054-y – CrossRef Full Text | Google Scholar
Bratman, G. N., Hamilton, J. P., and Daily, G. C. (2012). The impacts of nature experience on human cognitive function and mental health. Ann. N. Y. Acad. Sci. 1249, 118–136. doi: 10.1111/j.1749-6632.2011.06400.x – PubMed Abstract | CrossRef Full Text | Google Scholar
Brown, T. H., and Mbati, L. S. (2015). Mobile learning: moving past the myths and embracing the opportunities. Int. Rev. Res. Open Distributed Learn. 16, 115–135. doi: 10.19173/irrodl.v16i2.2071 – CrossRef Full Text | Google Scholar
Christoff, K., Irving, Z. C., Fox, K. C., Spreng, R. N., and Andrews-Hanna, J. R. (2016). Mind-wandering as spontaneous thought: a dynamic framework. Nat. Rev. Neurosci. 17, 718–731. doi: 10.1038/nrn.2016.113 – PubMed Abstract | CrossRef Full Text | Google Scholar
Chun, M. M., Golomb, J. D., and Turk-Browne, N. B. (2011). A taxonomy of external and internal attention. Annu. Rev. Psychol. 62, 73–101. doi: 10.1146/annurev.psych.093008.100427 – PubMed Abstract | CrossRef Full Text | Google Scholar
Colzato, L. S., and Hommel, B. (2017). “Meditation,” in Theory-Driven Approaches to Cognitive Enhancement, eds L. Colzato (Cham: Springer), 226–235. – Google Scholar
Colzato, L. S., Ozturk, A., and Hommel, B. (2012). Meditate to create: the impact of focused-attention and open-monitoring training on convergent and divergent thinking. Front. Psychol. 3:116. doi: 10.3389/fpsyg.2012.00116 – PubMed Abstract | CrossRef Full Text | Google Scholar
Colzato, L. S., Szapora, A., Lippelt, D., and Hommel, B. (2017). Prior meditation practice modulates performance and strategy use in convergent-and divergent-thinking problems. Mindfulness 8, 10–16. doi: 10.1007/s12671-014-0352-9 – CrossRef Full Text | Google Scholar
Dadvand, P., Nieuwenhuijsen, M. J., Esnaola, M., Forns, J., Basagaña, X., Alvarez-Pedrerol, M., et al. (2015). Green spaces and cognitive development in primary schoolchildren. Proc. Natl. Acad. Sci. U.S.A. 112, 7937–7942. doi: 10.1073/pnas.1503402112 – PubMed Abstract | CrossRef Full Text | Google Scholar
Davis, N., and Gatersleben, B. (2013). Transcendent experiences in wild and manicured settings: the influence of the trait “connectedness to nature.” Ecopsychology 5, 92–102. doi: 10.1089/eco.2013.0016 – CrossRef Full Text | Google Scholar
Diamond, A. (2011). Biological and social influences on cognitive control processes dependent on prefrontal cortex. Prog. Brain Res. 189, 319–339. doi: 10.1016/B978-0-444-53884-0.00032-4 – PubMed Abstract | CrossRef Full Text | Google Scholar
Dong, G., and Potenza, M. N. (2017). Internet searching and memory processing during a recollection fMRI task: evidence from pseudo recollected trials. J. Technol. Behav. Sci. 1, 32–36. doi: 10.1007/s41347-016-0002-2 – CrossRef Full Text | Google Scholar
Duarte Torres, S., Weber, I., and Hiemstra, D. (2014). Analysis of search and browsing behavior of young users on the web. ACM Trans. Web. 8:7. doi: 10.1145/2555595 – CrossRef Full Text
Elhai, J. D., Vasquez, J. K., Lustgarten, S. D., Levine, J. C., and Hall, B. J. (2017). Proneness to boredom mediates relationships between problematic smartphone use with depression and anxiety severity. Soc. Sci. Comput. Rev. 1–14. doi: 10.1177/0894439317741087 – CrossRef Full Text | Google Scholar
Engle, R. W. (2002). Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11, 19–23. – Google Scholar
Holloway, D., Green, L., and Livingstone, S. (2013). Zero to Eight. Young Children and Their Internet Use. London: LSE; EU Kids Online. – Google Scholar
Howell, A. J., Dopko, R. L., Passmore, H. A., and Buro, K. (2011). Nature connectedness: associations with well-being and mindfulness. Pers. Individ. Dif. 51, 166–171. doi: 10.1016/j.paid.2011.03.037 – CrossRef Full Text | Google Scholar
James, W. (1892). Text-Book of Psychology. London: Macmillan.
Kahn, P. S. Jr., Severson, R. L., and Ruckert, J. H. (2009). The human relation with nature and technological nature. Curr. Dir. Psychol. Sci. 18, 37–42. doi: 10.1111/j.1467-8721.2009.01602.x – CrossRef Full Text | Google Scholar
Kaplan, S., and Berman, M. G. (2010). Directed attention as a common ressource for executive functioning and self-regulation. Perspect. Psychol. Sci. 5, 43–57. doi: 10.1177/1745691609356784 – PubMed Abstract | CrossRef Full Text | Google Scholar
Keinänen, M. (2016). Taking your mind for a walk: a qualitative investigation of walking and thinking among nine Norwegian academics. High. Educ. 71, 593–605. doi: 10.1007/s10734-015-9926-2 – CrossRef Full Text | Google Scholar
Kuo, M., Browning, M. H., and Penner, M. L. (2017). Do lessons in nature boost subsequent classroom engagement? Refueling students in flight. Front. Psychol. 8:2253. doi: 10.3389/fpsyg.2017.02253 – PubMed Abstract | CrossRef Full Text | Google Scholar
Lauterman, T., and Ackerman, R. (2014). Overcoming screen inferiority in learning and calibration. Comput. Hum. Behav. 35, 455–463. doi: 10.1016/j.chb.2014.02.046 – CrossRef Full Text | Google Scholar
LeBlanc, A. G., and Chaput, J. P. (2017). Pokémon GO: a game changer for the physical inactivity crisis? Prev. Med. 101, 235–237. doi: 10.1016/j.ypmed.2016.11.012 – PubMed Abstract | CrossRef Full Text | Google Scholar
Lebuda, I., Zabelina, D. L., and Karwowski, M. (2016). Mind full of ideas: a meta-analysis of the mindfulness–creativity link. Pers. Individ. Dif. 93, 22–26. doi: 10.1016/j.paid.2015.09.040 – CrossRef Full Text | Google Scholar
Lee, Y. K., Chang, C. T., Lin, Y., and Cheng, Z. H. (2014). The dark side of smartphone usage: psychological traits, compulsive behavior and technostress. Comput. Hum. Behav. 31, 373–383. doi: 10.1016/j.chb.2013.10.047 – CrossRef Full Text | Google Scholar
Leong, L. Y. C., Fischer, R., and McClure, J. (2014). Are nature lovers more innovative? The relationship between connectedness with nature and cognitive styles. J. Environ. Psychol. 40, 57–63. doi: 10.1016/j.jenvp.2014.03.007- CrossRef Full Text | Google Scholar
Li, J., Lepp, A., and Barkley, J. E. (2015). Locus of control and cell phone use: implications for sleep quality, academic performance, and subjective well-being. Comput. Hum. Behav. 52, 450–457. doi: 10.1016/j.chb.2015.06.021 – CrossRef Full Text | Google Scholar
Lutz, A., Slagter, H. A., Dunne, J. D., and Davidson, R. J. (2008). Attention regulation and monitoring in meditation. Trends Cogn. Sci. 12, 163–169. doi: 10.1016/j.tics.2008.01.005 – PubMed Abstract | CrossRef Full Text | Google Scholar
Major, L., Haßler, B., and Hennessy, S. (2017). “Tablet use in schools: impact, affordances and considerations,” in Handbook on Digital Learning for K-12 Schools, eds A. Marcus-Quinn and T. Hourigan (Cham: Springer International Publishing), 115–128. – Google Scholar
Matsuoka, R. H. (2010). Student performance and high school landscapes: examining the links. Landsc. Urban Plan. 97, 273–282. doi: 10.1016/j.landurbplan.2010.06.011 – CrossRef Full Text | Google Scholar
Mbati, L. (2017). Creating awareness around rhizomatic principles in mlearning: a means to improving practice. Int. J. Mobile Blended Learn. 9, 74–87. doi: 10.4018/IJMBL.2017040105 – CrossRef Full Text | Google Scholar
Misra, S., Cheng, L., Genevie, J., and Yuan, M. (2016). The iPhone effect the quality of in-person social interactions in the presence of mobile devices. Environ. Behav. 48, 275–298. doi: 10.1177/0013916514539755 – CrossRef Full Text | Google Scholar
Norris, C. A., and Soloway, E. (2015). Mobile technology in 2020: predictions and implications for K-12 education. Educ. Technol. 55, 12–19. – Google Scholar
Ohly, H., Gentry, S., Wigglesworth, R., Bethel, A., Lovell, R., and Garside, R. (2016). A systematic review of the health and well-being impacts of school gardening: synthesis of quantitative and qualitative evidence. BMC Public Health 16, 286. doi: 10.1186/s12889-016-2941-0 – PubMed Abstract | CrossRef Full Text | Google Scholar
Oppezzo, M., and Schwartz, D. L. (2014). Give your ideas some legs: the positive effect of walking on creative thinking. J. Exp. Psychol. Learn. Mem. Cogn. 40, 1142–1152. doi: 10.1037/a0036577 – PubMed Abstract | CrossRef Full Text | Google Scholar
Piccoli, T., Valente, G., Linden, D. E., Re, M., Esposito, F., Sack, A. T., et al. (2015). The default mode network and the working memory network are not anti-correlated during all phases of a working memory task. PLoS ONE 10:e0123354. doi: 10.1371/journal.pone.0123354 – PubMed Abstract | CrossRef Full Text | Google Scholar
Posner, M. I., Rothbart, M. K., and Tang, Y. (2013). Developing self-regulation in early childhood. Trends Neurosci. Educ. 2, 107–110. doi: 10.1016/j.tine.2013.09.001 – PubMed Abstract | CrossRef Full Text | Google Scholar
Przybyliski, A. K., and Weinstein, N. (2013). Can you connect with me now? How the presence of mobile communication technology influences face-to-face conversation quality. J. Soc. Pers. Relat. 30, 237–246. doi: 10.1177/0265407512453827 – CrossRef Full Text | Google Scholar
Roberts, J. A., Pullig, C., and Manolis, C. (2015). I need my smartphone: a hierarchical model of personality and cell-phone addiction. Pers. Individ. Dif. 79, 13–19. doi: 10.1016/j.paid.2015.01.049 – CrossRef Full Text | Google Scholar
Rosen, L. D., Whaling, K., Rab, S., Carrier, L. M., and Cheever, N. A. (2013). Is Facebook creating “iDisorders”? The link between clinical symptoms of psychiatric disorders and technology use, attitudes and anxiety. Comput. Hum. Behav. 29, 1243–1254. doi: 10.1016/j.chb.2012.11.012 – CrossRef Full Text | Google Scholar
Ruiz-Ariza, A., Casuso, R. A., Suarez-Manzano, S., and Martínez-López, E. J. (2018). Effect of augmented reality game Pokémon GO on cognitive performance and emotional intelligence in adolescent young. Comput. Educ. 116, 49–63. doi: 10.1016/j.compedu.2017.09.002 – CrossRef Full Text | Google Scholar
S Colzato, L., Szapora, A., Pannekoek, J. N., and Hommel, B. (2013). The impact of physical exercise on convergent and divergent thinking. Front. Hum. Neurosci. 7:824. doi: 10.3389/fnhum.2013.00824 – PubMed Abstract | CrossRef Full Text | Google Scholar
Schilhab, T. (2018). Can your child’s phone bring them closer to nature? ScienceNordic. Available online at: http://sciencenordic.com/can-your-child%E2%80%99s-phone-bring-them-closer-nature (Accessed January 10, 2018).
Sood, A., and Jones, D. T. (2013). On mind wandering, attention, brain networks and meditation. Explore 9, 136–141. doi: 10.1016/j.explore.2013.02.005 – PubMed Abstract | CrossRef Full Text | Google Scholar
Sparrow, B., Liu, J., and Wegner, D. M. (2011). Google effects on memory: cognitive consequences of having information at our fingertips. Science 333, 776–778. doi: 10.1126/science.1207745 – PubMed Abstract | CrossRef Full Text
Tang, Y. Y., and Posner, M. I. (2009). Attention training and attention state training. Trends Cogn. Sci. 13, 222–227. doi: 10.1016/j.tics.2009.01.009 – PubMed Abstract | CrossRef Full Text | Google Scholar
Tanis, M., Beukeboom, C. J., Hartmann, T., and Vermeulen, I. E. (2015). Phantom phone signals: an investigation into the prevalence and predictors of imagined cell phone signals. Comput. Hum. Behav. 51, 356–362. doi: 10.1016/j.chb.2015.04.039 – CrossRef Full Text | Google Scholar
Traxler, J., and Wishart, J. (2011). Making Mobile Learning Work: Case Studies of Practice. Bristol: ESCalate, HEA Subject Centre for Education, University of Bristol.
Turkle, S. (2015). Reclaiming Conversation: The Power of Talk in a Digital Age. Penguin. New York, NY: Penguin Books.
Ward, A. F., Duke, K., Gneezy, A., and Bos, M. W. (2017). Brain drain: the mere presence of one’s own smartphone reduces available cognitive capacity. J. Assoc. Consum. Res. 2, 140–154. doi: 10.1086/691462 – CrossRef Full Text | Google Scholar
Zhou, Y., Zhang, Y., Hommel, B., and Zhang, H. (2017). The impact of bodily states on divergent thinking: evidence for a control-depletion account. Front. Psychol. 8:1546. doi: 10.3389/fpsyg.2017.01546 – PubMed Abstract | CrossRef Full Text | Google Scholar
More information: Oswald TK, Rumbold AR, Kedzior SGE, Moore VM (2020) Psychological impacts of “screen time” and “green time” for children and adolescents: A systematic scoping review. PLoS ONE 15(9): e0237725. doi.org/10.1371/journal.pone.0237725