Issue #101
by Gary Marchionini (UNC School of Information & Library Science)
A bit over 40 years ago I career transitioned from mathematics education to information science and joined the faculty at UMD. To gain some perspective, I sat in on the information science seminar taught by Lawrence Heilprin. One large impression I took away from Dr. Heilprin was his statement that ‘compression is a fundamental problem of information science.’ He was referring to indexing and abstracting as library functions that mapped long-form information onto compressions that are quickly consumable by humans and to serve as the basis for selective dissemination services. I took this to mean how do we learn what we need effectively and efficiently. How do humans thrive in an environment that offers (or increasing assaults us with) enormous volumes of stimuli, yet we are somehow able to accept and process the specific signals that help us understand and solve our problems? Today we use metaphors like ‘drinking from the information firehose’ to describe the information overload problem and ‘sensemaking’ to describe the compression of those streams into thoughts that comprise our understandings and actions. This life-long grapple with the problem of getting the right information to the right person at the right time when faced with massive candidate information streams is especially poignant in today’s world of social media, global information networks, and artificial tools and agents.
Responsibility for meeting this challenge has historically been the purview of expert humans in fields like library and information science, however, new tools and new kinds of human behavior make it increasing necessary to supplement human expertise with technological aids and also to segment and assign some of the workflow to automation. A flurry of new research papers illustrates this trend and raises questions about what information processing tasks are best assigned to machines and what tasks require human execution. Two threads in recent papers illustrate our growing effort to understand and meet this challenge.
First, there is continued evidence of human capabilities and limitations in working in high-intensity information environments. A recent paper titled “The unbearable slowness of being: Why do we live at 10 bits/s?”¹ in the January 2025 issue of Neuron reviews decades of research on how rapidly humans perceive and process information. Zheng and Meister examine the slowness of human action across many kinds of tasks ranging from reading, listening, speaking, and typing to memorization, object recognition, and skilled game execution. They use information-theory metrics to conclude that humans execute these tasks in the 5 to 50 bits/second range - i.e., 10 bps order of magnitude across tasks. They compare this to the gigabit per second processing of the human perceptual system and state “our brain will never extract more than 10 bps of that giant bitstream.” They discuss different hypotheses for the size (100,000,000) of what they term the ‘sifting number’ (filter) defined as the ratio of sensory information rate to behavioral throughput rate, as well as hypotheses of storage capacity, evolutionary biology and neural architecture, and brain-computer interfaces. They propose an ‘inner brain’ and ‘outer brain’ architecture that explains our species’ speed of being. While examining the various theoretical explanations for these throughput differences between perception and action, the authors point out that machines operate at much higher rates on both perception and action dimensions and suggest that there are ecological niches (e.g., transportation) where humanity might be better served by getting out of the way and assigning such tasks to machines. Humans will always want to be the ones to decide which tasks get assigned.
The range of human performance rates has been studied in the human-factors literature for decades. Dennis Egan had a chapter in the first edition of the Handbook of Human-Computer Interaction (1988)² in which he reviewed studies of performance ranges for different tasks (e.g., text editing, information search, and programming) with the largest ratios from fastest to slowest of 7:1 for text editing, 16:1 for search, and 28:1 for programming (experts) and discussed how complexity of task increased the range between fastest and slowest performance. These studies will help us in segmenting and assigning tasks to machines. The respective capability and limitation tradeoffs of humans and machines are now far beyond those expressed in the 19th century folklore of John Henry racing the steam engine to drill rock.
Second, there is increasing evidence that humans are learning and behaving in new ways. New longitudinal studies of how adolescents use social media and smart phones and what are some of the correlational physiological and behavioral effects raise related questions about human roles in information processing, learning, and living. Several research groups have been collecting data from cohorts of sometimes hundreds of adolescents’ use of smartphones over multiple years using mixed methods that include surveys, interviews, logging, and fMRI scans. One paper, Maza et.al published in the Journal of the American Medical Association-Pediatrics³ followed adolescents’ use of 3 social media platforms over two years and found “different neural patterns based on their social media checking behavior.” Other work by this group (Haag et.al., 2024)⁴ with the same cohort of 170 adolescents focused on anxiety, stress, and depression and found that these adolescents used their smart phones for large portions of their day (median use 543 minutes per day— 9 hours a day!), however, negative effects were not related to total amount of use but rather to the kinds of smartphone use (e.g., social media use to seek social and mental support). Another research group’s recent paper on smartphone use in schools with 117 students reported a median of 5.5 hours per day of smartphone use with a median of 2 of those hours taking place while in school (Christakis et.al, 2025)⁵. Clearly, we are learning that our large portions of our time at early stages of brain and psychological development are devoted to new kinds of mediated interactions that influence our information seeking and consumption practices and physical development. These results have implications for how we evolve with technologies that have fundamentally different information processing and physical action performance ranges.
The grand challenge of the right information to the right person at the right time is increasingly influenced by information tools and services that may be inextricably connected to our information needs and the external repositories and streams of potential resources to meet those needs. Human information processing practices are evolving, and information scientists and professionals are engaged in understanding and hopefully guiding these practices.
1: Zheng, J., & Meister, M. (2025). The unbearable slowness of being: Why do we live at 10 bits/s? Neuron, Volume 113(Issue 2), S. 192-204. doi: https://doi.org/10.1016/j.neuron.2024.11.008
2: Egan, D. (1988). Chapter 24. Individual Differences in Human-Computer Interaction. In M. Helander (Hrsg.), Handbook of Human-Computer Interaction (S. 543-568). Amsterdam: North-Holland.
3: Maza MT, Fox KA, Kwon S, et al. Association of Habitual Checking Behaviors on Social Media With Longitudinal Functional Brain Development. JAMA Pediatr. 2023;177(2):160–167. doi:10.1001/jamapediatrics.2022.4924
4: Haag, A-C., Nick, E.A., Chen, M.S., Telzer, E.H., Prinstein, M.J., & Bonanno, G.A. (in press). Investigating risk profiles of smartphone activities and psychosocial factors in adolescents during the COVID-19 pandemic. Journal of Research on Adolescence, 35, 1-17
5: Christakis DA, Mathew GM, Reichenberger DA, Rodriguez IR, Ren B, Hale L. Adolescent Smartphone Use During School Hours. JAMA Pediatr. Published online February 03, 2025. doi:10.1001/jamapediatrics.2024.6627
Feature Stories solely reflect the opinion of the author.
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