study 14: BRAIN DRAIN HYPOTHESIS

Media & Technology

Brain Drain Hypothesis

Ward et alia (2017)

Does the mere presence of our smartphones affect our cognitive performance?

Background

As noted by Ward et alia (2017), “Our smartphones enable—and encourage—constant connection to information, entertainment, and each other. They put the world at our fingertips, and rarely leave our sides. Although these devices have immense potential to improve welfare, their persistent presence may come at a cognitive cost.” More specifically, the Brain Drain Hypothesis posits that “the mere presence of one’s own smartphone may occupy limited-capacity cognitive resources, thereby leaving fewer resources available for other tasks and undercutting cognitive performance.” Indeed, while “individuals are constantly surrounded by potentially meaningful information”, their ability to use this information “is consistently constrained by cognitive systems that are capable of attending to and processing only a small amount of the information available at any given time.”

Thus, the Brain Drain Hypothesis relates to the Working Memory Model (Baddeley and Hitch, 1974), which describes the “cognitive system that supports complex cognition by actively selecting, maintaining, and processing information relevant to current tasks and/or goals.” Because of the “chronic mismatch between the abundance of environmental information and the limited ability to process that information, individuals need to be selective in their allocation of attentional resources”. For instance, “automatic attention generally helps individuals make the most of their limited cognitive capacity by directing attention to frequently goal-relevant stimuli without requiring these goals to be constantly kept in mind.”  Smartphones are a perfect example, as the “increasing integration of these devices into the minutiae of daily life both reflects and creates a sense that they are frequently relevant to their owners’ goals… When these devices are salient in the environment, their status as high-priority (relevant and salient [visible]) stimuli suggests that they will exert a gravitational pull on the orientation of attention.” In certain situations, however, “automatic attention may undermine performance when an environmental stimulus is frequently relevant to an individual’s goals but currently irrelevant to the task at hand”, such as smartphones in an educational setting. This could lead to a decrease in performance, not only if we are distracted by them through automatic attention, but also if we make sure to stay focused, because “inhibiting automatic attention—keeping attractive but task-irrelevant stimuli from interfering with the content of consciousness—occupies attentional resources.” Thus, “the mere presence of one’s smartphone may impose a ‘brain drain’ as limited-capacity attentional resources are recruited to inhibit automatic attention to one’s phone, and are thus unavailable for engaging with the task at hand.”

Participants and Procedures

520 undergraduates from University of California, San Diego.

Participants were first brought to a testing room and, depending on the condition they had been randomly assigned to, were asked to put their smartphone in silent mode and leave it either in an adjacent room, in their pocket/bag, or screen down on their desk. Next, participants were asked to complete two tasks, both intended to measure available cognitive capacity:

  • The Automated Operation Span Task, a measure of working memory assessing the attentional resources available to an individual on a moment-to-moment basis by asking them to keep track of task-relevant information while engaging in complex cognitive tasks. Concretely, participants had to solve a series of math problems while simultaneously updating and remembering a randomly generated letter sequence.
  • A 10-item subset of Raven’s Standard Progressive Matrices, which can be used to measure “fluid intelligence”, i.e. an individual’s capacity for understanding and solving novel problems unrelated to previously accumulated knowledge or domain-specific skill. Concretely, participants were shown incomplete patterns and asked to select the element that best completed them.


Finally, a questionnaire asked participants how often they thought about their phones during the experiment, and to what extent they thought that the location of their phones affected their performance (both on 7-point likert scales).

Findings

As can be seen in the graphs above, participants in the desk condition (high salience) displayed the lowest available cognitive capacity, and those in the other room condition (low salience) displayed the highest available cognitive capacity, while the performance of participants in the pocket/bag condition did not differ significantly from either.

Interestingly, participants did not report different levels of conscious thought about their smartphones in the questionnaire. This is consistent with the hypothesis that the “brain drain” effect occurs even when smartphones do not occupy conscious awareness (distraction). What is more, the overwhelming majority of participants responded that the location of their smartphones has no influence on their performance, which supports the idea that the “brain drain” is mostly unconscious.

IA Tip

In line with IB guidelines, we recommend that students only compare two conditions (e.g., screen down/other room) in their experiment and obtain a single measurable result for each participant in each condition. Doing otherwise would complicate inferential statistics without any benefit as far as the IA is concerned. Of course, students can also  modify the original experiment and use a different measure of cognitive ability/performance. Likewise, this experiment can be replicated online by instructing participants to have their smartphone either screen down in front of them, or in a different room.

Citation

Ward, A. F., Duke, K., Gneezy, A., & Bos, M. W. (2017). Brain Drain: The Mere Presence of One’s Own Smartphone Reduces Available Cognitive Capacity. Journal of the Association for Consumer Research, 2(2), 140-154.

https://doi.org/10.1177/0267323120903686