Hooked on Bytes: Exploring the Psychology Behind Addictive Technology

“It is okay to own a technology, what is not okay is to be owned by technology.” ― Abhijit Naskar, Mucize Insan: When The World is Family

In today’s digital age, addictive technology is pervasive and has been linked to several negative impacts on mental health, relationships, and productivity (Alter, 2017).

Hook Model and Variable Rewards

One of the primary psychological concepts that can explain addictive technology is the “Hook Model” proposed by Nir Eyal (2014). This model suggests that technology products are designed to be addictive by incorporating triggers, actions, variable rewards, and investments. The variable rewards component is particularly relevant to addiction, as it is based on the idea of intermittent reinforcement (Skinner, 1957). According to Skinner’s operant conditioning theory, unpredictable rewards stimulate dopamine release in the brain, leading to a stronger association between action and reward, and ultimately, addiction (Sapolsky, 1999).

Addictive Technology and the Dopamine Effect

The role of neurotransmitters, specifically dopamine, in addictive technology use is pivotal. Dopamine is a chemical messenger in the brain that plays a significant role in reward and pleasure-seeking behaviors (Blum et al., 2000). The anticipation of a potential reward or enjoyable experience triggers the release of dopamine, creating a sense of pleasure and satisfaction (Schultz, 2000). When a user receives a notification, likes, or comments on social media, or achieves a new level in a game, this acts as a reward, triggering a dopamine release. Over time, these dopamine-fueled experiences can lead to an addiction to technology (Lanaj, Johnson, & Barnes, 2014).

Social Media and Fear of Missing Out (FOMO)

Social media platforms utilize variable rewards to a great extent, fueling the addiction to technology (Alter, 2017). One of the main psychological factors driving this addiction is the Fear of Missing Out (FOMO). FOMO arises from the need to stay connected and informed about others’ experiences, which can lead to compulsive social media usage (Przybylski, Murayama, DeHaan, & Gladwell, 2013). This fear of missing out often results in users spending excessive amounts of time online, which may, in turn, negatively impact mental health, sleep, and relationships (Woods & Scott, 2016).

Smartphone Addiction

Smartphones represent another major source of addictive technology. Smartphones offer constant connectivity, allowing users to access social media, games, and other apps anytime and anywhere. This constant accessibility can lead to addictive behaviors, such as checking the phone obsessively, even when there are no new messages or updates (Elhai, Dvorak, Levine, & Hall, 2017). Some users may also develop a “phantom vibration syndrome,” where they perceive their phone vibrating even when it is not, indicating a high level of dependency on their smartphone (Rosen, Whaling, Rab, Carrier, & Cheever, 2013).

Gaming Addiction and Flow Theory

Gaming addiction is another example of addictive technology, with video games designed to engage players for extended periods. This engagement is often facilitated by the “flow” state, a psychological concept proposed by Csikszentmihalyi (1990). Flow is a state of deep absorption, characterized by the perfect balance between challenge and skill, leading to intense focus and enjoyment. Game developers design games to maximize flow experiences, creating highly immersive and addictive environments (Hamari, Shernoff, Rowe, Coller, Asbell-Clarke, & Edwards, 2016).

Negative Consequences of Addictive Technology

The consequences of addictive technology use can be far-reaching, affecting mental health, relationships, and productivity. Numerous studies have demonstrated links between excessive social media usage and increased rates of anxiety, depression, and loneliness (Keles, McCrae, & Grealish, 2020). Additionally, gaming addiction has been associated with sleep disturbances, lower academic performance, and social isolation (King, Delfabbro, & Griffiths, 2011). Furthermore, addictive technology usage can exacerbate existing mental health issues or even lead to the development of new ones (Alter, 2017).

“We all need a technological detox; we need to throw away our phones and computers instead of using them as our pseudo-defence system for anything that comes our way. We need to be bored and not have anything to use to shield the boredom away from us. We need to be lonely and see what it is we really feel when we are. If we continue to distract ourselves so we never have to face the realities in front of us, when the time comes and you are faced with something bigger than what your phone, food, or friends can fix, you will be in big trouble.” ― Evan Sutter, Solitude: How Doing Nothing Can Change the World

Mitigating Addictive Technology Use: Strategies and Interventions

Given the negative consequences associated with addictive technology use, there is a pressing need to develop effective strategies and interventions. The principles of Cognitive-Behavioral Therapy (CBT) can be applied to help individuals manage their technology use (Young, 2011). This approach involves identifying and challenging maladaptive thoughts related to technology use and developing healthier behaviors.

Digital detoxes, where individuals deliberately refrain from using digital devices for a certain period, can also be beneficial (Sampasa-Kanyinga & Lewis, 2015). These detoxes can help users break the cycle of compulsive checking and regain control over their technology use. However, long-term behavior change often requires more comprehensive interventions that target the underlying psychological factors contributing to addiction.

At the societal level, there are increasing calls for technology companies to take responsibility for the addictive nature of their products (Alter, 2017). This can involve designing products that encourage healthier usage patterns, implementing features that allow users to monitor their usage, and educating users about the potential risks of excessive use. In schools, digital literacy programs that educate students about the risks and benefits of technology use can be beneficial. These programs can empower students to use technology in a balanced and responsible manner.

The psychology of addictive technology provides vital insights into how digital devices and platforms can lead to compulsive usage patterns. Understanding these psychological mechanisms is crucial for developing effective strategies and interventions to promote healthier technology use. As technology continues to evolve, ongoing research and advocacy will be needed to ensure that digital innovations contribute to, rather than detract from, human well-being.

References

Alter, A. (2017). Irresistible: The rise of addictive technology and the business of keeping us hooked. Penguin.

Blum, K., Braverman, E. R., Holder, J. M., Lubar, J. F., Monastra, V. J., Miller, D., Lubar, J. O., Chen, T. J., & Comings, D. E. (2000). Reward deficiency syndrome: a biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors. Journal of psychoactive drugs, 32 Suppl, i–112. https://doi.org/10.1080/02791072.2000.10736099

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper & Row.

Elhai, J. D., Dvorak, R. D., Levine, J. C., & Hall, B. J. (2017). Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. Journal of Affective Disorders, 207, 251–259. https://doi.org/10.1016/j.jad.2016.08.030

Eyal, N. (2014). Hooked: How to build habit-forming products. Penguin.

Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170–179. https://doi.org/10.1016/j.chb.2015.07.045

Keles, B., McCrae, N., & Grealish, A. (2020). A systematic review: The influence of social media on depression, anxiety, and psychological distress in adolescents. International Journal of Adolescence and Youth, 25(1), 79–93. https://doi.org/10.1080/02673843.2019.1590851

King, D., Delfabbro, P., & Griffiths, M. (2011). The role of structural characteristics in problematic video game play: An empirical study. International Journal of Mental Health and Addiction, 9(3), 320–333. https://doi.org/10.1007/s11469-010-9289-y

Lanaj, K., Johnson, R. E., & Barnes, C. M. (2014). Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organizational Behavior and Human Decision Processes, 124(1), 11–23. https://doi.org/10.1016/j.obhdp.2014.01.001

Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848. https://doi.org/10.1016/j.chb.2013.02.014

Rosen, L., Whaling, K., Rab, S., Carrier, L., & Cheever, N. (2013). Is Facebook creating “iDisorders”? The link between clinical symptoms of psychiatric disorders and technology use, attitudes and anxiety. Computers in Human Behavior, 29(3), 1243–1254. https://doi.org/10.1016/j.chb.2012.11.012

Sampasa-Kanyinga, H., & Lewis, R. F. (2015). Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents. Cyberpsychology, Behavior, and Social Networking, 18(7), 380–385. https://doi.org/10.1089/cyber.2015.0055

Sapolsky, R. M. (1999). The physiology and pathophysiology of unhappiness. In D. Kahneman, E. Diener, & N. Schwarz (Eds.), Well-being: The foundations of hedonic psychology (pp. 453–469). Russell Sage Foundation.

Schultz, W. (2000). Multiple reward signals in the brain. Nature Reviews Neuroscience, 1(3), 199–207. https://doi.org/10.1038/35044563

Skinner, B. F. (1957). Verbal behavior. Appleton-Century-Crofts.

Woods, H. C., & Scott, H. (2016). #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression, and low self-esteem. Journal of Adolescence, 51, 41–49. https://doi.org/10.1016/j.adolescence.2016.05.008

Young, K. S. (2011). CBT-IA: The first treatment model for internet addiction. Journal of Cognitive Psychotherapy, 25(4), 304–312. https://doi.org/10.1891/0889-8391.25.4.304