Wearing Masks and Asserting Meaning: Insights from the Neurocognitive Science of Cool

By Will McCreadie & Dr. James Giordano, PhD, MPhil. This piece was originally published by Georgetown’s Pellegrino Center for Clinical Bioethics on May 18th, 2020.

Four months ago, people wearing masks stood out. Now it’s those who don’t that often catch a sideways glance. Yet, despite the ongoing risk of infection amidst calls and efforts for relaxing social restrictions, some people are rebelling against wearing protective gear. Just this past weekend, when one maskless family was asked on the street about their lack of PPE, they responded, almost in unison “masks aren’t cool”. At the same time, A-list celebrities like Jennifer Lopez and Alex Rodriguez have been “corona-shamed” and labeled arrogant for not wearing masks. Why the discrepancy?

Research in neurocognitive science suggests that sentiments of “cool” are actually a complex combination of feelings of fear and aspiration. It combines the desire to be differentiated with the need to feel accepted. Studies indicate that deciding something is cool draws on two functional systems of the brain: the default mode network (DMN), and the salience network (SN). The DMN is linked to introspection and the determination of value – the rewards associated with being “cool”, while the SN plays a role in fear (often seeking to balance fears of both the behavior in question, and of being ostracized).

Such patterns of thought, emotion, and behavior are the focus of somewhat new disciplines of neuroeconomics and neuromarketing. The use of masks provides a perfect natural experiment to gauge how “cool” works, because they haven’t been common in our society since the 1918 flu pandemic, and have been now thrust into the social-spotlight.

When deciding if something is “cool,” our brains calculate the relative benefits and costs of that choice. We rarely need to ponder this judgement; we just “feel it”. The human brain takes less than 300 milliseconds to form an opinion, assessing events and consequences in our past, with the current situation, and making predictions about the near-term and future consequences of our decisions and actions. This is the phenomenon of automatic valuation.

Deciding whether something is cool, and worth the “investment” in terms of benefit, burden, and risk, comes down to figuring out what maximizes its – and your – utility. Our brains go through a rapid series of inquiry: Will this choice help me or hurt me? In the near term, or in the future? Is it the best of my available options? People aren’t perfectly rational, so we tend to base decisions and actions upon our beliefs and experience of what’s most useful.

In the case of masks, the obvious tradeoff is freedom versus safety; but considering “cool” in the equation demonstrates that other forces are also at work. What we find “cool” and feel good about depends on the image of ourselves we want to convey. We are strongly social creatures, who are sensitive to the ways we’re regarded. Whether or not you wear a mask conveys a signal (even if you don’t realize it). Social signaling plays a significant role in what we wear, and do.

This partly explains the divide in public stances on PPE. In neuroeconomic terms, masks are an identity good. People who wear masks (or dress up their Twitter profiles with mask pics) may hope to signal their virtue and intelligence, by highlighting the relative sacrifice of their comfort, both for the good of others (and for their own good – both to prevent infection, and to be perceived as socially responsible). People making unusual homemade masks may seek to highlight their resourcefulness and creativity. Those without masks are signaling something else: confidence, rebelliousness, bravery, or foolishness and selfishness — depending on your perspective.

There is also a status system in mask culture. Any recollection of middle school will surely bring to mind the in-group/out-group dynamic that plays a substantive part in determining what we find cool: with people on the “outside” aspiring to copy and outdo people on the “inside” to gain acceptance. Primate studies show this hierarchical behavior to be a side-effect of evolution. Status, and belonging to an in-group were valuable for our ancestors because the chances of survival were higher for a group member than an outcast. This primal need to conform may be a one of the factors in seeking to be “cool”. At the same time, no one wants to feel like a faceless member of the herd. To be cool, we strive for acceptance without homogeneity, and differentiation without alienation from the group.

Almost overnight COVID-19 has created a new in-group: people wearing masks. Like any major trend, there are subgroups within the mask-wearing set. The professional grade mask signals that you either are a “front-line” worker, that you have enough money to afford a scarce item, or that you’ve got good connections. People in different age groups also try to gain status by signaling different things. Teens, for example, may want branded masks (searches for designer masks increased 100-fold from mid-February to mid-April). Part of the reason why a teen covets a Supreme face mask ($450 online if you can get it), while their parents would never wear one, is that their peer groups value different things. Teens tend to want to be edgy and unique.

A significant element of cool in the age of COVID-19 is competitive – and reciprocal – altruism, which is another form of social signaling. Whether it’s the CEO of Flexport sending 3 million masks to Amsterdam, or the CEO of Twitter giving away almost a billion dollars and tracking it in a Google spreadsheet, people and companies are vying to be the most creative and effective responders to the virus. If selflessness wasn’t a valuable social signal, people would make these donations anonymously. Our research has shown that every altruistic act has an egoistic component. It’s “cool to be kind”, and as a result such acts of altruism make the actor feel good.

For many people, masks are an entirely new form of self-expression whose usefulness goes beyond their protective benefit. Simply put, as we strive to re-start our socio-economic engines for the benefit of both individuals and the population at large, masks are a currency of capability and cool. So, whether it’s making a statement of individualism, asserting acts of altruism, or evidencing a stance of responsibility, masks are a medium to represent ourselves in a commitment to each other.

What Changes When No One Needs to Drive?

Imagine trying to ride a horse on the highway.  It’s legal, but quasi-insane.  In the future, we’ll likely feel the same way about letting humans drive cars (although we may still race cars on tracks).  Nearly 25,000 people die in car accidents each week worldwide, the equivalent of a 9/11 scale tragedy every day. Autonomous vehicles (AVs) are projected to decrease auto fatality rates by 80%. The economic arguments for AVs are also compelling.  According to research from Columbia University, if even 10% of driving switches to AVs, $250 billion of annual economic output could be unlocked. 

Combine the potential to save millions of lives with the massive financial rewards of automating transportation (a case of Gekko’s Law) and the multibillion dollar race for AVs makes perfect sense. With that in mind, here are a few ways the world may change when we make the shift.

Space

The magical thing about Uber is not that you don’t need to drive, it’s that you don’t need to park.  Ride-sharing companies like Uber and Lyft provide an excellent roadmap for understanding the AV revolution. Picture Uber without the driver, and you have a window to the future. Many leaders in autonomous driving are working on shared autonomous vehicles (SAVs) to do exactly this.

Parking spaces typically take up 50-60% of the land area of American downtown areas.  Worse, the average personal car is unused for 95% of the day.  With a network of SAVs, you could use far fewer cars and drive constantly, freeing up space used for parking.  This means wider walkways, larger lawns, more parks, and major new urban development opportunities.  

Parking needs for SAVs awaiting maintenance will be minimal. UBS analysts project car ownership will decline by 70% as people join AV ridesharing networks.  If you’re skeptical, check how many DVD’s you’ve purchased since subscribing to Netflix. 

The increase in downtown space will also allow new homes to be built, increasing supply and making cities more affordable.  55% of the world’s population already lives in cities, and the UN projects that will jump to 68% by 2050, making any extra urban space vital. 

Congestion 

While there are likely to be fewer cars, congestion may worsen as SAVs remove the hassle and expense of transportation.  The San Francisco County Transport Authority concluded that half of the 60% increase in the city’s traffic between 2010 and 2016 came from Uber and Lyft.  Ridesharing is cheap and easy, so more people opt for a private car, rather than walk or use public transit.  

On the other hand, congestion may be mitigated by connectivity, our next topic.

Connectivity & Edge Computing

Self driving cars have the potential to be a city’s nervous system.  While most of the focus today is on cloud computing, AVs will depend on edge computing.  With edge computing, thousands of small servers on street corners or in cars themselves will process the data AVs generate as they drive.  It takes far less time to send data to a nearby server than to upload it to the cloud. As anyone who’s tried to stop a car on ice knows, braking a second later could mean stopping twenty feet farther down the road.  Simply put, AVs can’t afford to wait for the cloud. 

Connectivity also enables cars to communicate with each other and the environment.  Vehicle to vehicle (V2V) communication will allow cars to travel faster and know exactly when a light is going to change, whether the car in front intends to turn, and if there is a faster route to take.  Picture Waze on steroids for the entire city, and you have a rough idea of how this will feel. As with the current version of Waze, this may have the unintended consequence of disrupting previously quiet streets with diverted traffic.  

Besides allowing cars to perform rapid calculations, edge computing networks will also enable cities to absorb the staggering amount of data that AVs generate.  Current AV prototypes can generate up to 20 TB of data per day. 20 TB of text, if printed out on single-sided pages, would form a 40 mile high stack (over 7 times the height of Mt. Everest). 

AV data could be used to take the pulse of an urban area: block by block variations in weather and air quality are possible.  It could also be used to track population movements and economic activity. Imagine if Amazon makes a deal with Lyft to get the data from its SAV network, and then serves you a Whole Foods ad.  Its algorithms can then track car movements to see which users and demographics the ad resonated with, and better target you in the future.  

Even if data is anonymized, population movements can be tracked in aggregate.  For example, as David Zipper has suggested, police departments could see a large cluster of people traveling to a house they don’t usually frequent to plan a protest, and then reverse engineer where potential dissidents lived.  Questions like these are already emerging in the legislative battle over the Mobility Data Specification (MDS), a California law which gives cities wide access to rideshare data. 

Other Economic Impacts

Aside from the economic benefits already mentioned, SAVs have several other implications for the economy.  It’s still unclear whether traditional car companies (OEMs) will create separate self-driving systems, or if they’ll all buy AV capability from a software provider like Google or Uber.  It’s also unclear if people will opt to buy their own cars or share them. A network of SAVs seems more likely given the long-term trend away from ownership (which I’ll cover in detail in an upcoming post) and towards services. 

SAVs are also much more economically rational for consumers. 

This chart from ARK reveals two interesting (and horrifying) stats: 

  1. Taxis are almost three times as expensive per mile as private planes. 
  2. Shared autonomous vehicles are projected to be cheaper than walking. 

The projections compare the cost per mile of SAVs to the cost of the calories in a Big Mac that would fuel a human to walk the same distance.  Even if SAVs are twice as expensive as ARK projects, they’ll still beat walking. Some people may still elect to own personal cars, the way some people still download rather than stream songs and movies, but the majority will use SAVs. 

The shift to SAVs will affect more than just OEMs, it also threatens car insurance industry.  KPMG predicts that autonomous cars will lead to a 90% reduction in accident frequency by 2050, resulting in a $137 billion decrease in premiums. 

AVs also promise to disrupt millions of jobs.  There are 3.5 million registered truck drivers in the US, another 5.2 million people who work to support the trucking industry, and an estimated 1.5 million rideshare drivers.  If all of these jobs were eliminated (unlikely, but possible), roughly 6% of the U.S. workforce would be unemployed. 

Some of these are service jobs that would still exist without truck driver, including restaurants that cater primarily to truckers, roadside motels, and mechanics.  However, the above does not include delivery jobs, which are also likely to be automated. 

If labor statistics aren’t your thing, this 2014 chart from NPR helpfully shows the most common job in each state:

While economists often point to the new jobs created after factory work displaced artisans during the Industrial Revolution, finding new, fulfilling work for disrupted drivers remains a poorly addressed challenge. 

Environmental Impact

The environmental impact of self-driving cars rests on whether we make the switch to electric vehicles. Currently, cars looking for streetside parking travel two thirds of the length of the US per parking space per year.  If an all-electric future becomes a reality, the emissions cut by not hunting for parking spaces are enormous. However, if regulation and economics don’t bring an electric AV future, we face a massive increase in pollution as self-driving cars drive nonstop.   

Urban sprawl is another consideration often downplayed by AV enthusiasts.  When transportation becomes easier, urban sprawl increases. For an example, check out this timelapse of Paris’s footprint as road networks develop. The same effect has been shown with the spread of the interstate highway system in the US. 

While long commutes have made us desire downtown real estate close to career opportunities and urban amenities, people may opt to live further away if they don’t need to do the driving.  

Finally, even if AVs are fully electric, current battery technology has a significant environmental impact because of lithium mining.  Alternate batteries will need to be developed to ensure a sustainable future.  

Conclusion 

Given the undeniable potential that self-driving cars have to save lives and increase work and leisure time, all signs point towards the widespread use of AVs. We need to begin designing infrastructure and policy to meet the needs of an autonomous future. 

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