The Buddhist idiom “monkey mind” does not require years of contemplation to understand. It explains itself quickly to anyone who attempts the most basic meditative practice: closing the eyes and concentrating solely on the breath. By the second or third exhalation, your attention will have shifted -- if not to an itch, or the aftertaste of your most recent meal, then to some memory, plan, song lyric, etc., and then to another, until you remember to focus on the flow of the breath.
Whereupon it will all start up again. The human mind, in the Buddha’s words, moves “just as a monkey swinging through the trees grabs one branch and lets it go, only to seize another….” The simile is all the more fitting given that he spent years meditating in the forest. (I take it by implication that the mind also makes shrill noises and scratches itself a lot.)
Twenty-five hundred years and a good deal of laboratory research later, Michael Corballis’s The Wandering Mind: What the Brain Does When You’re Not Looking (University of Chicago Press) has little to say about taming, much less transcending, the restless mind. Corballis, a professor emeritus of psychology at the University of Auckland, New Zealand, wants to reconcile us to the mental flux through a review of scientific research on the neurobiology behind ordinary awareness. From his perspective, wandering attention is necessary and even beneficial for humankind, in spite of the disapproval of authority figures for countless generations.
Central to the author’s approach is what he calls “mental time travel” -- meaning, in part, the human ability to remember the past and anticipate the future, but also (more importantly, perhaps) our capacity to shift attention away from immediate experience for considerable periods while focusing on our memories, plans and worries.
This power is a blessing and a curse, and Nietzsche suggested that it gives us reason to envy the beast of the field, which “springs around, eats, rests, digests, jumps up again, and so from morning to night and from day to day, with its likes and dislikes closely tied to the peg of the moment, and thus is neither melancholy nor weary.”
But that’s just human vanity talking. A variety of methods are available to record the flow of blood and bursts of neural activity within the brain -- and some can be used on lab animals as well as hospital patients. Corballis reports on experiments with rats that have learned their way through a maze to a feeding spot. The effort sets off “sharp-wave ripples” among the brain cells dedicated to tracking a rat’s location. But the activity may continue even after the rat is done, “as though the animal is mentally tracing out a trajectory in the maze,” the author says.
Perhaps this is not so surprising, since “for a laboratory rat, being in a maze is probably the most exciting event of the day.” But there’s more:
“These mental perambulations need not correspond to the paths that the rat actually traversed. Sometimes the ripples sweep out in a path that is precisely the reverse of the one the rat actually took. It may be a path corresponding to a section of the maze the rat didn’t even visit, or a shortcut between locations that wasn’t actually traversed. One interpretation is that the ripples function to consolidate the memory for the maze, laying down a memory for it that goes beyond experience, establishing a more extensive cognitive map for future use. But mind wandering and consolidation may be much the same thing. One reason that we daydream -- or even dream at night -- may be to strengthen memories of the past, and allow us, and the rat, to envisage future events.”
On that point, at least, our difference from the humble rodent is one of degree and not of kind: the human brain undertakes (and absorbs information from) a much wider range of activity, but the same part of the brain -- the hippocampus -- serves as the hub for the neural networks that enable “mental time travel.”
What does distinguish us, of course, is language, which among other things enables storytelling and more complex forms of social organization than those possible for even the most sophisticated chimpanzee community. So the human brain finds itself navigating any number of mazes, many of its own creation. Zoning out while someone is speaking, then, is not a solely a function of overburdened powers of attention reaching their limit. The wandering mind is part of a range of phenomena that includes dreaming, fantasy, hallucination and creativity -- all of them products of the brain’s constant obligation to shift between levels of experience and directions of “time travel.”
Corballis makes the point with a range of biological, medical and anthropological references in a casual style that sometimes just barely holds things together. One or two chapters might have been removed without it making much difference, as would the jocular bits about whether the reader is still paying attention. (“Yes,” reads my note in the margin, “because irritation wonderfully concentrates the mind.”)
While interesting on the whole, the book leaves completely unaddressed the question of whether there is any difference between a mind wandering under its own powers, so to speak, and one that’s grown accustomed to constantly increasing bombardment. Where the monkeys used to swing from vine to vine, they now run the risk of colliding in midair, distracted by all the beeps and buzzes coming from their smartphones.
When Rowland Hussey Macy opened his namesake store in 1858, understanding consumer behavior was largely a matter of guessing. Retailers had little data to assess what customers wanted or how variables like store hours, assortment or pricing might impact sales. Decision making was slow: managers relied on manual sales tallies, compiled weekly or annually. Dozens of stores failed, including several of Macy’s original stores.
Predictive analytics, in the early days of retail, were rudimentary. Forward-thinking retailers combined transactional data with other types of information -- the weather, for example -- to understand the drivers of consumer behavior. In the 1970s, everything changed. Digital cash registers took hold, allowing companies to capture data and spot trends more quickly. They began A/B testing, piloting ideas in a test vs. control model, at the store level to understand the impact of strategy in near real time.
In the early days of AOL, where I worked in the 1990s and early 2000s, we were quick to recognize the risk to brick-and-mortar stores, as online retailers gathered unprecedented data on consumer behavior. Companies like Amazon could track a customer’s movements on their site using click-stream data to understand which products a customer was considering, or how long they spent comparing products before purchasing. Their brick-and-mortar counterparts, meanwhile, were stuck in the 1800s.
Unexpected innovations, however, have a funny way of leveling the playing field. Today, broadband ubiquity and the proliferation of mobile devices are enabling brick-and-mortar stores to track cell phone signals or use video surveillance to understand the way consumers navigate a store, or how much time they spend in a particular aisle. Sophisticated multichannel retailers now merge online behavior with in-person information to piece together a more holistic picture of their consumers, generating powerful data that drive changes in layout, staffing, assortment and pricing. A recent study found that 36 percent of in-store retail purchases -- worth a whopping $1.1 trillion -- are now influenced by the use of digital devices. Retailers who leverage online research to drive brick-and-mortar sales are gaining a competitive advantage.
The use of big data and predictive analytics in higher education is nascent. So-called disrupters often claim that the lecture hasn’t changed in 150 years, and that only online learning can drive transformative, game-changing outcomes for students. Of course, these claims ring hollow among today’s tech-savvy professors.
Since my transition into higher education, I have been struck by the parallel journey retailers and educators face. Both have been proclaimed obsolete at various points, but the reality is that the lecture, like the retail experience, has and will continue to evolve to meet the new demands of 21st-century users.
Like brick-and-mortar stores, lectures were once a black box -- but smart faculty members are beginning to harness the presence of mobile devices to capture unprecedented levels of data in traditional classrooms. And smart institutions are combining real-time engagement data with historic information to spot challenges early and change the academic trajectory for students.
Historical sources of student data (FAFSA, GPA, SAT, etc.) have predictive validity, but they are a bit like the year-over-year data retailers used: limited in depth and timeliness. The heart of a higher education institution is its professors -- and its classes. In addition to professors being experts in their fields, providing unique learning opportunities to their students, studies have shown that when professors have positive relationships with students, it leads to greater student success.
Some of the most interesting early data are coming from the big, first-year lecture courses. While most students experience these as a rite of passage, they also hold great potential as models of how behavioral data can improve engagement and completion rates for students. Faculty are no longer powerless in the face of larger classes and limited insight into their students' learning behavior. They can track how well students are engaging in traditional lecture classes and intervene with students who aren’t engaged in the behaviors (note taking, asking questions and attendance) that correlate with success.
Historically, professors have relied on piecemeal solutions to gather insights on student behavior. So-called student-response systems and learning management software, like digital cash registers in the ’70s, provide useful data -- but they don’t provide the sort of real-time analytics that can inform an instructor’s practice or to identify students in need of additional support and coaching.
A more recent brand of solutions -- in full disclosure, including ours at Echo360 -- are designed to work in conjunction with great teaching, while providing instructors with the tools to track and measure student engagement: Are students taking notes? Are they asking questions? These tools give administrators and instructors insight into how students are interacting and participating both in class, as well as with content or readings before and after class. No more waiting for summative tests to demonstrate that a student misunderstood a concept weeks or months earlier.
The analogy between retail and education has its limitations. The mission and objectives in education are more nuanced, and frankly, more important. However, education, like every sector, has what we call a moment of truth.
For retailers, that moment of truth is centered around the purchase decision. Sophisticated marketers and retailers have used behavioral data to become incredibly skilled at understanding and shaping that purchase decision to achieve extraordinary results.
It’s time to use those learnings for a higher calling. The explosion of digital devices in the classroom allows us to understand the learning process wherever it is happening on campus, and to support education’s vital moment of truth -- a transaction of knowledge between professors and students.
Frederick Singer is CEO and founder of Echo360, which provides active learning and lecture capture services to more than 650 higher ed clients in 30 countries.
In hiring of tenure-track STEM instructors, female, black and Latino academics have an edge, while Asians are at a disadvantage. But the picture for tenure is more nuanced -- and women with young children lose out.