Category: General

What can be done to reduce the time spent on our devices has become a very public question. The issue has resulted in K12 schools being legislated in many areas to prevent students from bringing their phones to class and concerns are common that the rest of us should also cut back. I have written previously about the issue when present in young phone users and whether heavy use should be characterized as a physical addiction or a bad habit. 

The present post originated from my continued reading on this topic and an article I read related to the bad habit perspective proposing that adolescent screen time could be reduced if adolescents became more aware of their screen time and begin to think of it in terms of time not spent on other activities they value. This article described a program called Project Reboot and the Clearspace app. The app allows a user to set specific limits and informs the user when limits are being exceeded. The notion of improving mindfulness and the exertion of personal control seemed related to the bad habit perspective. Is awareness enough or should use simply be prevented?

Research on apps that intervene

It seemed likely that researchers would have investigated digital strategies to address a problem that originates with digital devices and I located a meta-analysis of such strategies (Tahmillah and colleagues, 2023). I recommend anyone interested in this topic take a look at this article because it does a great job of categorizing apps and the approaches taken to impact screen time. Just the list of apps was informative (Clearspace was not included) if for no other reasons than many may be unaware that such interventions exist and because the costs vary greatly. Identifying the apps proven to have an impact on behavior in combination with the mechanisms employed by such apps was informative. Simply blocking the use of an app or taking phones away limits screen time, but technology in general and most apps specifically have both opportunities and limitations so most of us in the long run would be better off making decisions about when and for how long to use our apps. 

The classification system

Rahmillah and colleagues proposed the following categories:

1. Block
2. Self-tracking
3. Goal advancement
4. Reward/punishment

Categories were further differentiated by underlying processes. For example, goal advancement, the approach that most interested me, included among the processes the opportunity to set a goal and a goal warning that the limit was approaching. 

The idea of the meta-analysis was to identify the apps that had demonstrated success in reducing screen time and then consider the underlying processes used by the more successful apps. In reviewing the studies found to impact screen time, I discovered the built-in screen time capabilities of iOS were listed (example of iOS screentime study available online). Given the costs I had found associated with other apps, this seemed important to me as users might be reluctant to consider costly apps this seemed important. 

Description of iOS Screen Time Controls

iOS Screen Time offers capabilities that seem examples of the goal-setting and goal-warning processes of a Goal Advancement approach. The following describes how to set up an iOS device to provide these functions.

The following image shows important iOS settings for limiting use. These features are available from the System settings. The Screen Time button (left-hand panel) provides access to multiple settings in the right-hand panel. Goals can often be accomplished in multiple ways and I am describing just one sequence here.

From the following image, note the following. The settings you can implement can be protected by a password (red box near bottom of image). If you wanted to set controls on a child’s phone to prevent access or limit the time on an app, you might want to protect the setting with a password. For your own use, you might not want to use a password offering you a way to ignore the notice that you had reached the limit you had established. You can set limits in several ways – individually or by category. If you decide to set limits by category, you might want to exclude some apps from that category. Always allowed provides a way to do this. 

The approach I am taking here makes use of the See All Apps and Website Activity option (Green box). 

The Apps activity option shows time spent by app or by category. You can toggle between the two presentations by selecting the button shown in the smaller green box (this would take you to the app view from the category view). Toward the right end of a category, you should see the > symbol. Selecting this symbol will reveal the apps associated with that category. 

You can now select the category or individual apps to set limits. I have selected Instagram. 

I can then set the maximum time per day and customize the time during a week should I want to do something more complex such as extend the limit for the weekend. 

What happens when you exceed your goal? You will encounter the following Time Limit Screen. If you have a password set, you (or someone with the password) would have to enter the password to provide you more time. If no password has been set, you see the following options and can make an informed selection to continue.

Summary

Apps are available to help individuals manage their screen time and research indicates some apps do produce improvement. Of course, research studies do not claim every individual will respond in this manner. Some techniques allow individuals to set goals and inform the user when they have spent the maximum time they intended to spend. This approach is based on the assumption that the lack of awareness is a reason many exceed the amount of time individuals intend to spend and behavior will change when a method of improving awareness is provided. iOS has screen time controls built in that allow for goal setting and goal awareness to be provided. Because these functions come with the operating system on Apple devices a user does not have to spend money on additional capabilities. This post explains how to set goals in iOS. 

Reference

Rahmillah, F. I., Tariq, A., King, M., & Oviedo-Trespalacios, O. (2023). Evaluating the effectiveness of apps designed to reduce mobile phone use and prevent maladaptive mobile phone use: multimethod study. Journal of Medical Internet Research, 25, e42541.

I have resisted the complaints that reading from a screen leads to poorer comprehension for some time. Much of my own research and writing have promoted the educational benefits of technology and the multiple studies and meta-analyses reporting that comprehension is adversely impacted when reading on a computer or tablet seemed a challenge to my message. A recent post on Reading Rockets by Timothy Shanahan identified several new studies I had not read and encouraged another look and another post.

When I first wrote about educational applications of technology, my approach was strongly influenced by David Jonassen’s mid-1990s book Mindtools. Rather than focusing on programming, computer literacy, or drill software, Jonassen proposed that educators consider how using productivity tools (e.g., word processing) could change how learners explored ideas. Again using word processing as an example, how writing to learn with a computer might expand the cognitive benefits of the writing experience beyond that allowed by pencil and paper.

A more recent version of the “tool perspective” (e.g., Etchells, 2024) offers a more nuanced view and notes that tools are in many ways neutral and users determine whether tools will be used effectively or detrimentally. The ills associated with technology are challenging because technology offers opportunities and challenges. However, Etchells proposes that the challenges are not due to addictions or biological triggers. The challenges are often best described as bad habits. This creates a difficult situation for some with the tendency to promote but also allows others to blame the tool as bad and ignore the control and responsibility of the user. With a specific focus on reading from digital devices which of these perspectives makes the most sense? If I maintain a position of advocacy, can I focus on the responsibility of the learner in a way that offers potential solutions rather than blaming the victim?

My “out”

First, I have no concerns about my own nearly total reliance on reading from digital devices. Even if there is a minor hit to my comprehension, my digital reading is part of a process of getting from a review of the ideas of others to my own written output. Digital reading allows me to efficiently highlight and annotate and export these personalizations of my understanding into external storage that I have accumulated over the years and use months and sometimes years later as background for what I write. This is one of those situations in which I cannot direct others to research evaluating the relative effectiveness of this approach to knowledge work, but trying to approximate my process on paper seems pointless. Put another way, reading comprehension is just one component in combination with long-term storage, multi-document processing, organization, retrieval, the flexible connection of stored elements, etc. that are involved in the writing and teaching I do.

Shallow Comprehension

I am starting to find the redundancy of the basic research studies and meta-analyses that have filled journal space in recent years to be mildly irritating. Replication is important in science, but at some point, it is time to move on to replication and extension. The studies that are attempting to study the process of comprehension when reading from a screen or paper are an example of what I mean by extension. Because it is not obvious why reading text on paper or on a screen involves the cognitive processes involved in reading, repetition of differences in the product (answers to test questions) has a decreasing value.

It would seem that the existing research demonstrates an issue, but not an explanation. Reading from a screen and paper would seem to involve the same cognitive subtasks, but the results are not equivalent. The explanation that seems to have gained the widest acceptance (my judgment) is commonly called the “shallowing hypothesis. I attribute this claim originally to Nicholas Carr who wrote a popular book titled “The Shallows”. As I understand the argument, the reading we tend to do online mostly involves what we might commonly describe as skimming. This experience leads to a bad habit that is activated when we read material from a screen even with content we might read more carefully under other circumstances. The reading habits of doomscrolling are proposed to generalize to reading books.

Eye movement research offers a way to watch the process of reading. We know that reading on a screen moves faster and readers overestimate their understanding level. Speed to complete the reading of a passage and requests to estimate the level of understanding by asking for prediction of performance areas variables that are easy enough to collect. I studied the phenomenon of failed reader understanding of their level of understanding and recognized the issue as a problem of comprehension monitoring resulting in poorer calibration. Calibration is simply the accuracy with which you can estimate the quality of a future performance. I always explained it as the decision that a student makes the night before an examination. Can they stop studying now and go out with friends or should they keep working to get the exam score they want? I was interested in calibration as a challenge for less capable readers reasoning that simple fix-up problems such as stopping and rereading sections that were poorly understood was the basic adjustment less capable readers were unable to make. In reflecting on the challenge in the context of college student study behavior, I tended to think of the problem as a matter of efficiency. Students used to tell me they had read chapters several times in preparation for an exam. Rereading is a common strategy among the motivated, but less capable learner. It would be far better to recognize the topics you did not understand and focus on these topics. It would be even better to notice this immediately while reading and while being more aware of the context of the material being read. This would be the ideal time to reread a paragraph or two.

When I was actively involved in this research, this challenge was sometimes studied by inserting different types of errors in text and determining if readers could identify the problems. Create circumstances that should result in a failure of comprehension and see if a reader would recognize the problem. For example, a statement that existing knowledge should indicate was inaccurate or a conflict between two sentences in the same sentence. Better readers were better at identifying these inserted errors. The technique was criticized because it placed readers in the role of proofreaders which is not the way we actually read. At the time, I tried to investigate the issue of monitoring skills by using a similar technique without informing the participants in my research and using eye-movement recordings to determine what happened when readers encountered what should have been confusing sentences. At the time the technique was tricky to use and intrusive as readers had to maintain their heads in a fixed position by bracing their chin on a bar. My research traded one form of artificiality for another.

The sophistication of the equipment has drastically improved and eye movement techniques are being used to compare participants reading from printed material and from a screen. For example, a recent study compared 8th graders in Norway (Jensen and colleagues, 2024) reading items from the national reading assessment exam from paper and screen while recording eye movements. As part of the preparation for the actual experiment, the researchers asked about their experience with reading from paper and screen and determined that many students proposed they comprehended better when reading from screens.

The reading examination itself involved passages of text each followed by several questions. Readers could refer to the written material when answering the questions. The eye movements indicated that readers referred back and forth between text and questions more times while reading from the screen to answer the questions. The researcher found that the readers had greater difficulty understanding when reading from a screen. This makes sense, but the readers still performed more poorly on the questions which would seem to indicate they were aware that they did not understand and could not completely remedy the difficulty.

Solutions

I don’t see online reading as a behavior that will disappear or diminish in frequency. I wonder about the hypothesis that poor habits are developed from heavy exposure to the scanning behaviors that many apply in texting and scrolling through social media feeds such as Twitter, Mastodon, and Blue Sky. I admit my perspective differs because I am a reader of books and journal articles on digital devices. I try to remember that my age means this style of reading (long form) predated any exposure to reading on a device. So my habits could be different allowing strategies of reading to differ between long-form and the types of text more suited a superficial approach.

Shanahan proposes that if technology environments encourage maladaptive behaviors why couldn’t they be engineered to encourage more effective behaviors? I think there are already such opportunities with longer-form content, but the opportunities are both ignored by most readers and not taught in educational settings. The exporting of annotations and highlights I rely on is seldom a part of the reading process of most learners. Here is another example of the transfer of a habit. Younger students are not allowed to highlight or write in their print books and the highlighting behavior of college students has frequently been criticized. I have written extensively about a variety of ways in which educators and learners can increase their active engagement with digital content. I call this collection of techniques layering because the strategies are external to the text itself and yet provide opportunities for processing text (and video) in more cognitively active ways.

Maybe improved awareness is also important. If skimming can be understood as a habit sometimes applied inappropriately in some settings, greater metacognitive awareness of goals and content would seem to be important. Concepts such as deep reading versus recreational reading are not new and the concern for shallow reading on devices is just another version of this older way of thinking about what learning from reading requires.

Sources

Carr, N. (2020). The shallows: What the Internet is doing to our brains. WW Norton & Company.

Etchells, P. (2024). Unlocked: the real science of screen time (and how to spend it better). Hachette UK

Jensen, R. E., Roe, A., & Blikstad-Balas, M. (2024). The smell of paper or the shine of a screen? Students’ reading comprehension, text processing, and attitudes when reading on paper and screen. Computers & Education, 219, 105107.

Jonassen, D. H., & Carr, C. S. (2020). Mindtools: Affording multiple knowledge representations for learning. In Computers as cognitive tools (pp. 165–196). Routledge.

Singer, L. M., & Alexander, P. A. (2017). Reading on paper and digitally: What the past decades of empirical research reveal. Review of Educational Research, 87(6), 1007–1041. https://doi.org/10.3102/0034654317722961.

The benefits and harms done by our devices and the ways we use them is one of those topics likely of at least marginal interest to most people. We wonder whether we have become too dependent and whether existing interests and capabilities have deteriorated. If we are a parent or educator we likely have additional unique interests. Have I messed up my kid by letting him or her use a phone without supervision? How do I keep my students on track in the classroom and are they using AI to complete my assignments quickly without any actual educational benefits? 

My personal interest goes a bit further in that I spent my career preparing educators to use technology in their classrooms and I by training feel responsible for understanding the science behind recommendations made to others. So, I review the research on many topics. From this perspective, I would offer the following observation – it is seldom as simple as it seems. I know this to be true in the social sciences which would be the field covering phone use and the impact on development and learning. This is the case for many reasons and far from a topic I can thoroughly consider here. One comment I used to make to my own students may be helpful. For context, I was trained as a biologist who became a psychologist. My comment was – students tend to see a discipline like chemistry as more sophisticated and complicated than psychology. Consider that the chemicals in that beaker don’t decide whether they feel like reacting today. People are different. 

Back to the issue of our devices and the research on how we are being impacted. I recently read a book by Jonathan Haidt titled The Anxious Generation. Many people must have read this book because it topped the New York Times best-seller list for several weeks which I interpret to mean many parents and educators are concerned and were attracted by the topic. Recommendations made in the book, for example, no phones in schools, have been implemented in many schools. I read Haidt’s book in response to such changes concerned my pro-technology advocacy should be tempered. I admit my initial reaction was that Haidt made a reasoned and evidence-based case. However, I have learned that such secondary sources need to be vetted and I was concerned by some of Haidt’s rationale which I would describe as “what else besides iPhones and Instagram” could explain the mental health issues of adolescent females. The “what else could it be” logic just seems weak. In reaction, I read a related, but less well-known book by Pete Etchells and as I suspected the issue is complicated. 

What follows is my summary of the two books with some related comments. 

Comparing Perspectives on Screen Time and Mental Health

Haidt’s “The Anxious Generation”

In “The Anxious Generation,” Jonathan Haidt really covers two broad problems he contends have changed the mental health development of children and adolescents. Children have been overprotected in their daily lives resulting in a lack of play and autonomy involving risks and consequences. In contrast, the author argues that the rise of smartphones and social media have been largely unmonitored and unrestricted with negative consequences to mental health. I will comment on both claims, but emphasize technology use. 

Haidt highlights the following key points:

Slow-Growth Childhood: Human children have an extended childhood compared to other mammals, providing them with time to learn through play and social interactions. This extended childhood is crucial for emotional and cognitive development.

Importance of Play: Play-based childhoods are essential for healthy development, fostering skills such as emotional regulation, social competence, and creativity.

Safetyism: Haidt argues that the overprotective parenting style prevalent in the 1990s, termed “safetyism,” limited children’s opportunities for risk-taking and independence, further contributing to anxiety. Haidt uses example older folks will find familiar – e.g., playground equipment that is no longer allowed for safety reasons and reduced independence in moving about the neighborhood or in solo trips to the store.

Negative Impacts of Smartphones: These problems include:

Social deprivation: Replacing face-to-face interaction with screen time.

Sleep deprivation: Late-night phone use cutting into sleep at ages when necessary. The early school start for adolescents is an example of when this becomes a problem.

Attention fragmentation: Constant notifications and multitasking impairing focus.

Addiction: The design of social media platforms promoting addictive behaviors. Etchells will reject this notion if understood as a physiological addition as in drug use.

Puberty as a Sensitive Period: Puberty is a critical period for brain development, making adolescents particularly vulnerable to the negative effects of social media. Hence, experiences allowed at other ages may do less damage.

What evidence suggests that puberty is a “sensitive period” for social media use?

Brain Development: Puberty to a greater degree than the earlier period of childhood involves the pruning of neurons and the strengthening of connections, processes influenced by experiences. This heightened neuroplasticity implies that experiences during puberty, including social media use, can have a greater and more enduring impact on brain structure and function.

Orben’s Research: Amy Orben, found that negative correlations between social media use and life satisfaction were more pronounced in the 10-15 age group, encompassing puberty, compared to older individuals (16-21). This finding directly supports the idea of puberty as a sensitive period for social media’s influence on well-being.

Social Learning and Conformity: Adolescents are conformers, a process that social media platforms can strongly influence. Puberty marks a time when individuals are particularly sensitive to social cues and peer influence, seeking to establish their identity and social standing. Social media platforms, with their emphasis on likes, followers, and influencers, can hijack this natural drive for social learning, potentially leading to distorted perceptions and unhealthy comparisons. Giirls are most vulnerable to social media’s negative effects between 11 and 13, while boys experience this heightened vulnerability between 14 and 15. This timeframe aligns with typical puberty onset, further suggesting that the hormonal and social changes during this period make adolescents more susceptible to the pressures and comparisons prevalent on social media.

In conclusion, the sources provide converging evidence pointing to puberty as a “sensitive period” for social media use. This sensitivity stems from the interplay of rapid brain development, heightened social awareness, and the drive for conformity, all characteristic of adolescent development. This information suggests that social media use during puberty requires particular attention and potentially different approaches compared to other age groups.

Haidt makes use of historical data linking changes in mental health data over the years to changes in the technology available to young people.  Much of Haidt’s argument about the damage done by access to the Internet is made by tracking mental health outcomes against dates associated with changes in technology. Gen Z started to reach puberty in 2009. iPhones became available in 2009. Social media capabilities such as retweets and likes became available in 2009. Front facing cameras in 2010. Facebook acquired Instagram in 2012. His challenge to critics is what else could have accounted for the changes in adolescent mental health that correspond to this time frame.

Recommendations: Haidt proposes four solutions to address this issue: no smartphones before high school, no social media before age 16, phone-free schools, and promoting more unsupervised play and childhood independence.

Etchells’ “Unlocked”

Pete Etchells, in his book “Unlocked: The Real Science of Screen Time (and how to spend it better)” offers a more nuanced view of screen time and its relationship to mental health. 

Etchells first notes that the consequences of technology and social media likely include both positive and negative impacts so explanatory models must take into account both possible outcomes. Etchells’ key arguments include:

Screen Time is Complex: Etchells argues simply measuring the total amount of screen time is insufficient to understand its impact on well-being. What matters is how screen time is used, the specific apps and content consumed, and the context of use.

Limited Evidence for Strong Negative Effects: While some studies have shown correlations between screen time and mental health issues, the evidence is mixed, with many studies showing small or inconsistent effects. The existence of correlations even if the correlation reflected a causal relationship with negative changes in mental health as a consequence do not necessarily show large effects. The impact is typically small. 

Etchells critiques the reliance on anecdotal evidence and highlights methodological limitations in much of the research. One of the most important methodological limitations appears to be whether self-reports versus verifiable behavioral data are used. Even though digital devices are great at recording data on use (consider your iPhone and your daily report of time spent on different apps) many studies ask users to estimate their time spent and activities experienced. When studies using self-report data are compared with studies using the more accurate data collected by the devices used, significant relationships are typically found only with the self-report data. Etchells speculates that user awareness of screen time issues and potential negative consequences results in participants in these studies interpreting negative personal experiences as an indication of too much screen time. How individuals feel about their screen use and their perceived self-control are more strongly related to well-being than objective measures of screen time.

Importance of Habits over Addiction: Etchells argues that framing excessive screen use as “addiction” is unhelpful, as it focuses on abstinence as the only solution. Instead, he advocates for viewing screen use as a set of habits that can be modified to promote well-being. The author argues that the assumption of a physiological explanation (probably dopamine) within the framework of addictions such as drug abuse is what readers of many of the negative books assume. Addiction tends to ignore agency and motivation. This is where the notion of habits also fits and solutions that are more nuanced with a focus on decision-making and awareness.

Oversimplification of “Screen Time”: Etchells argues that simply measuring the total amount of time spent in front of screens is too crude a metric to be meaningful. He emphasizes that “screen time” encompasses a vast range of activities, from educational apps to social media to video games, each with potentially different effects on well-being. He contends that focusing solely on duration ignores the crucial factors of content, context, and purpose of use.

Reliance on Anecdotal Evidence: Etchells criticizes the prevalent use of anecdotal stories to support claims about the negative impacts of screen time. While these stories can be compelling and relatable, he argues they often lack scientific rigor and can lead to biased conclusions. We like stories because we often can relate to such examples and do not necessarily make the same connections with the data in graphs and statistics. Of course, coming up with examples that fit a perspective does not necessarily fit what is most common in a sample of participants. He points out that reliance on anecdotes is particularly problematic in a relatively new field like digital technology research, where robust, longitudinal data is still limited.

Methodological Issues in Correlational Studies: Much of the research on screen time relies on correlational studies, which can only demonstrate associations, not causal relationships. Etchells highlights the problem of “third variables,” unaccounted-for factors that might influence both screen time and mental health, leading to spurious correlations. For example, pre-existing mental health conditions, family dynamics, or socioeconomic factors could contribute to both increased screen time and negative well-being outcomes, creating the illusion of a direct link where none exists. Correlations showing relationships do not necessarily convey the magnitude of an effect. A significant correlation in a large population may be associated with a very small impact.

Lack of Theoretical Framework: Etchells argues that the field lacks a robust theoretical framework to guide research and interpret findings. He suggests that without clear theoretical models, researchers are left to “grasp at straws,” making tenuous connections between screen time and a wide range of outcomes without a solid foundation for understanding the underlying mechanisms. This lack of theoretical grounding makes it difficult to develop testable hypotheses and draw meaningful conclusions. Haidt’s “what else could it be” argument fits here.

Ambiguous Terminology: Etchells points out the imprecise use of terms like “addiction” and “attention” in screen time research. He criticizes the tendency to label excessive screen use as “addiction” without sufficient evidence of a true physiological dependence. He also argues that “attention” is often used interchangeably with “self-control,” leading to conceptual confusion and misinterpretations of research findings.

Insufficient Attention to Positive Effects: Etchells argues that the focus on potential negative consequences of screen time has overshadowed research on its potential benefits. He acknowledges that excessive or problematic screen use can be detrimental, but he emphasizes the importance of recognizing the many ways in which technology can enhance learning, communication, and social connection. He encourages a more balanced approach that considers both the positive and negative aspects of screen time.

In conclusion, Etchells urges caution against drawing sweeping conclusions about the impact of screen time based on the existing research. He advocates for more nuanced investigations that consider the complexities of technology use, adopt rigorous methodologies, and develop strong theoretical frameworks to guide future research.

Comparing Haidt and Etchells

While both Haidt and Etchells acknowledge the potential downsides of excessive screen time, they differ significantly in their overall perspectives and conclusions:

Emphasis on Negative Effects: Haidt emphasizes the negative impacts of smartphones and social media, attributing a rise in mental health problems directly to these technologies. Etchells, on the other hand, acknowledges the potential for harm but argues that the evidence for strong negative effects is weak and often overstated.

Role of Social Media: Haidt sees social media as a particularly harmful force, promoting conformity, comparison, and addiction. Etchells recognizes these potential issues but maintains that social media can also have positive benefits, depending on how it is used. The existence of both positive and negative outcomes argues against simplistic abstinence/avoidance and urges more careful personal awareness and control. 

Solutions: Haidt proposes strict limitations on smartphone access and social media use, advocating for a return to a more play-based childhood with less adult supervision. Etchells focuses on promoting healthy screen habits, emphasizing individual agency and self-regulation rather than strict restrictions.

Conclusion

Haidt’s “The Anxious Generation” presents a compelling, albeit alarming, argument about the potential downsides of technology for young people. However, Etchells’ “Unlocked” offers a more balanced perspective, urging caution against oversimplifying the complex relationship between screen time and mental health. The key takeaway is that understanding the nuances of screen use and focusing on developing healthy habits is crucial for mitigating potential harms and maximizing the benefits of technology. Both books highlight the importance of critical thinking and evidence-based approaches when evaluating the impact of technology on our lives.

Sources:

Etchells, P. (2024). Unlocked: the real science of screen time (and how to spend it better). Hachette UK

Haidt, J. (2024). The anxious generation: How the great rewiring of childhood is causing an epidemic of mental illness. Random House.

I have been reading Robert Haidt’s “The Anxious Generation”. This post is not a description of that book, but the book’s focus and content were the origin of my present topic. The book, which has topped the New York Times best-seller list for some weeks, focuses on the mental health damage of the way kids are raised in combination with the negative impact of social media. Haidt is particularly concerned about young girls and their susceptibility to the negative consequences of what becomes a damaging addiction to social media. While male and 75, I had an uneasy feeling Haidt could have been describing me and my preoccupation with a different online environment. Before I get to my personal insight, allow me to describe the characteristics Haidt argues drive the general problem that is the focus of his book.

Haidt proposes that the writings of B.J. Fogg have served as the bible guiding many social media entrepreneurs. I usually read the core literature associated with what I write, but Fogg’s book was priced at textbook levels and I decided I was very familiar with the central content (a different source). Fogg successful persuasion can be accomplished based on behavioral principles any college student who has taken the Introduction to Psychology course will recognize. His terminology is a little different. When I taught the Intro course, I described the process Fogg and Haidt emphasize as operant conditioning. A very simple version of the way operant conditioning changes behavior is captured in the sequence – stimulus – response – consequences. Behavior changes (response) are encouraged by positive consequences. When used to account for what seems an addiction to online social media in children, the response might be frequent checking of a social media account and the positive reinforcers (likes, comments, attention, etc.). Access to a phone (the stimulus) triggers the initial behavior generating the consequences. 

The Fogg version is described in the following model. 

Fogg uses a little different vocabulary. The external trigger is his term for stimulus and action is the behavior. Variable reinforcement is another concept from behavioral psychology that translates as a situation in which a behavior does not produce a consequence every time it is produced. This unpredictability increases the frequency of behavior. A common example is the way a slot machine works. A gambler would soon quit if previous experiences were always wins and now the machine stops paying out. You tend quit putting coins into a vending machine if the first attempt or maybe the second produces no soda or candy bar. If however, a slot machine generates wins now and then people keep feeding their coins.

Fogg and Haidt add one additional component to the model – investment. Social media often has another characteristic increasing holding power. The example of investments are everywhere online. Do you play that online game where you have to guess the spelling of a mystery word within so many tries? If you have a streak of days going, you have an investment that makes it very likely you will not miss a day. Do you take photos or use AI to create photos to embellish your posts? You are making an investment. Do you pay to add weapons or clothing (skins) in an online game? You are investing. When your identity becomes part of your participation in an activity, you are heavily invested. I would suggest making political comments on social media are a good example of being invested. Our political affiliations are part of our identities. Haidt argues that Instagram has such a powerful impact on young females (often negative) because the photo-heavy nature of the platforms triggers the role appearance has in the identity of young women.

Social media involves a switch from external triggers to internal triggers. Once you are involved, you don’t have to be sent a message that you have a new like or comment. You don’t have to see your phone sitting on your desk. Your thoughts lead you to get your phone out of your pocket or purse to check your accounts to see if anything new has shown up. 

Haidt emphasizes the powerful impact of social media on the attention and mental health in children and adolescents. I saw a similarity in the application of the model and the arguments made to adults who write on blogs, Substack and Medium. I don’t think the negative impact holds in the same way because of the life experiences and brain maturity (frontal cortex and metacognition) of adults and draw the following parallels more out of amusement than concern. If there was one insight that triggered me to write this post, it was the recognition of identity in motivating behavior more so than the behavioral explanation. For those who write, writing for public consumption is part of personal identity. I think the behavioral impact on behavior is more powerful because of this self view. 

Here is my attempt to apply  the cycle of engagement (Haidt and Fogg) to writing:

1. Trigger

The cycle starts with a trigger that prompts users to take an action. Triggers can be:

• External triggers: Notifications, emails, messages, ads, or reminders that tell users to open an app or take action. For example, a push notification letting you know someone liked your post.
• Internal triggers: Emotions, thoughts, or desires that come from within, like boredom, curiosity, just wondering if there is something there. These feelings push users to check their phone or social media without an external prompt.

2. Action

After the trigger, the user performs an action. The action may be simple such as seeing if anyone has read your post or liked your content. There may be statistics or charts to check. The actions that are triggered can be more involved such as researching and writing another post. 

3. Variable Reward

Once the action is taken, the user receives a reward, but to keep engagement high, the reward is often variable or unpredictable. This uncertainty makes the reward more powerful or resistant to extinction in behavioral terms. 

• Social rewards: Users may get more or fewer likes, comments, or shares on their posts each time, keeping them hooked. In some writing platforms, there is financial compensation to check.

4. Investment

The final stage of the cycle is investment, where the user puts something of value back into the platform. This investment can be time, effort, data, or emotional input, and it increases the likelihood that the user will return to the platform. Examples include:

• Creating content: Posting a photo, video, or comment.
• Personalization: Customizing a profile or favoriting the work of others.
• Building relationships: Adding friends, replying to messages, or participating in communities.This investment helps users feel more connected to the platform because they’ve contributed, and it primes them for future triggers. As they invest more, they become more likely to engage again, as their investment increases the value of the platform to them
• Financial commitment: Payment of a fee to participate.
• Affirmation of identity: The content I generate and share demonstrates I am a writer.

5. Repeat

Once users have invested in the platform, they receive new triggers, starting the cycle all over again. Over time, this cycle builds a habit, with users increasingly relying on internal triggers, such as boredom or curiosity, to engage with the platform.

If you are a writer who contributes content online, you may see yourself in this description. With social media, such models are used to describe how participants are drawn into spending more and more time in such environments sometimes with negative emotional consequences. I will leave it to your own analysis to determine whether being drawn into a platform to which you contribute your writing impacts your emotional well-being.

Reference

Fogg, B.J. (2003). Persuasive technology: Using computers to change what we think and do (Interactive Technologies).