The following article appeared recently in the New York Times. It describes how police in Mumbai, India, undertook an experiment to control the excessive blowing of car horns by drivers caught in what must be nightmarish traffic in that largest of Indian cities. As in most urban environments, if traffic isn’t moving, at some point the horns start to blow. So at some critical intersections, devices were installed that activated to keep a traffic light red for some period following its activation, and it is activated with each horn honk it detects. (I was going to say, “that it hears,” but that is way too anthropomorphic for me!) However, the astute student of behavior analysis will note that my statement still describes the device as an agent, that, once started, resets with each subsequent honk.

The video, available here, shows that a digital display indicates the decibel level at the intersection and another such display warns motorists of the consequences of honking: “Honk More—Wait More.”  Aside from generating the above video clip of driver behavior at one such intersection that went viral, evidence of its effectiveness in noise abatement was not commented on in the article.

Two features in particular of the contingency piqued my interest. First, the device’s operation is very familiar as an example of a differential-reinforcement-of-zero responding, or DRO, schedule (see “The Term DRO: Bad or Possibly Redeemable Label?”). Assuming a green traffic light is the reinforcer, the only way to get it is by not engaging in horn honking for a specified time. Any behavior other than horn blowing will turn on the green light and allow traffic to flow.  Actually, the arrangement is a little more complicated than that because DRO schedules are in effect for horn blowing by drivers of cars going north and south and east and west. In the absence of horn blowing, the light will change according to its regular cycle.

So two conditions must be met for the light to change from red to green: the period of green-light time allowed for traffic flowing across the intersection (say N-S traffic) must pass and the drivers waiting for the light to change to green to allow E-W traffic to move must not blow their horns. But what happens if N-S has the green light and a N-S driver honks the horn? Does this stop all traffic? Or does it stop the E-W red light from changing to green? If the latter, there could be a problem: they who have the green light could then keep it green by continuing to honk. This isn’t mentioned in the article.

Second, the no-honk contingency is unusual because it applies not to individual responding, but to the behavior of a large group of drivers. It is reminiscent of a demonstration reported in the 1960s by B. F. Skinner. In it, two pigeons were placed side by side in a special operant conditioning chamber (shown in the accompanying photograph.) For either pigeon to get food, each had to peck the same of three, colored response keys within a half-second of one another. This cooperative contingency resulted in the pigeons coordinating their pecks over time. This is exactly the same contingency in effect in Mumbai. The light turns green—the presumed reinforcer—only if not just two people, but multiple people do not engage in a particular response—sitting on the horn—simultaneously.

Engineering the control of large numbers of people by redesigning environments is commonplace. Signage is a simple example, although rules don’t always control behavior as well as do the consequences backing up those rules/signs: hence, fines for speeding.  I first thought about the engineering of crowd control years ago when my family went to Disneyland. There, they masterfully employed twisting, turning queues where visitors stand waiting for rides, a technology that is now ubiquitous in airport security lines and other such venues. Traffic lights themselves, invented in 1923 by African American inventor Garrett Morgan, bring the driving behavior of billions of people around the world under the discriminative-stimulus control of three, colored lights. A striking aspect of the Mumbai project is that it is a group contingency in which the behavior of each driver affects the outcome for many other people, like Skinner’s experiment but on a far-grander scale. Whether it really works or whether people figure out ways to circumvent it (like the N-S traffic continuing to blow horns to keep their light green) are open questions. Nonetheless, it was a clever idea that fits well into a behavior-analytic understanding of crowd behavior. The police probably are not aware of Skinner’s experiments or of DRO schedules, but that never has been a criterion for good behavior-analytic practices.

P.S. Something to think about: A “Behavior Analysis Outside-the-Box Award” to recognize innovations incorporating sound behavior-analytic principles into people management, even though the solutions are not framed from a behavior-analytic perspective. It might take some digging around to find examples, but, just as the Mumbai experiment, they undoubtedly are out there awaiting discovery. 

Happiness pervades modern life. It is a major topic of talk-show interviews, best-selling books, psychotherapeutic interactions, everyday gossip (“How can she really be happy with him?”), and personal ruminations. Poets, cartoonists, and novelists have done as good a job as psychologists in understanding it. I personally have always preferred Charles Shultz’s (the creator of the comic strip “Peanuts”) rather structural definition of happiness as “a warm puppy.”

In psychology and other forums, happiness has been suggested to be best understood contextually, with some, both East and West, going so far as to say that happiness can be experienced only in contrast to unhappiness, that the two go together like yin and yang. Despite all the writing and discussing of the topic, happiness remains elusive—both personally for many, and conceptually for at least as many others. And yet, at least in Western culture, its achievement is of major concern to many, maybe even most, people.

The immediate concern of this commentary is how behavior analysis might consider happiness. Conventionally, happiness is treated as a state, and in particular an emotional state, like hope or fear. Behavior analysts recognize these latter constructs for what they are: verbal labels attached to characteristic ways of responding in certain contexts. A person jumps for joy and we are likely to say it is because they are so happy. And how do we know they are happy? Because they are jumping for joy, rendering the concept logically circular: she jumps because she is happy and is happy because she jumps. This won’t work. Happiness is not a state, trait, or emotion; it is at best an inference from observed behavior.

Does happiness cause behavior? This is the tip of an iceberg of controversy in psychology about whether emotions cause behavior or are caused by behavior. Remember the James-Lange theory from your history of psych class? That theory turned emotions upside down and said that happiness doesn’t cause us to behave, but rather we behave and that causes the emotional state of happiness. This is better, but still no cigar, because it leaves emotion as a state, just not one that causes behavior.

Behavior analysts take happiness to the next level beyond James-Lange by observing that not only is happiness not the cause of behavior, it is not a state at all. Rather, it is simply behavior controlled by past and present circumstances. A series of failures—personal, familial, or societal —may “make someone unhappy” and a series of successes may “make someone happy.” Happiness, or the lack thereof, in these instances is determined by circumstances, reactions to circumstances, and what people say—the verbal labels attached to those circumstances—about the confluence of the first two. We learn to attach labels not only to other people’s behavior, but to our own, and those labels, whatever their source, can have major effects on future actions. So such actions are not determined by whether we are “happy” or not, but by our history of reinforcement and the presence of labels of our behavior (and hence “us”) that identify it as reflecting being happy or not.

It’s a complicated topic, indeed. Maybe we should stick with warm puppies.

The A-B-A, or reversal, design is one of the most recognized, single-case experimental designs in both research and practice (although in practice, the return to baseline is followed by a return to the treatment, or B, phase). In non-experimental settings, A-B, or non-reversal designs, occur often. Sometimes this is in the form of a singular life-changing event, but more often is just a part of everyday living. An example of the former might be the sudden death of a very close friend or relative and of the latter, starting an exercise program. Departed loved ones cannot be replaced, but one could stop exercising. But, hey, if it is working why would you want to do that? So most people skip the return to A, or at least try to, once they make a positive life change.
 

Sometimes A-B-A designs are just a part of the normal course of daily events. The toaster is working on Monday—the “A” or baseline phase—but on Tuesday the lever won’t stay in the “toast” position. So, we try to fix it—the intervention, or B phase. After fixing it, if the lever stays down and we can have our toast spread with apricot preserves (I prefer Bon Maman brand) and Roquefort cheese (Société brand is, in my opinion, the best), then the baseline is restored.

Although A-B-A designs in everyday personal lives are pretty common, at a cultural level it isn’t often that A-B-A designs appear naturally, without being “set up” by someone. In the case of the COVID-19 virus in the United States and elsewhere around the world, the presence of such a design allows us the rare opportunity to not only see if what we are doing is working, but also what happens if we stop doing what was working.

Look at the data. By late March, the end of what we can label the “A” phase, the number of cases was on a grim but clear upward trend. As the economy shut down and people stayed in their homes—the “B” phase—the upward trend reversed, and the curve was headed down. As state governments and governors decided it was time to re-open, albeit with admonishments about social distancing—the return to baseline or second “A” phase—the number of cases clearly increased again. 
 

The reasons for the return to the baseline are pretty clear, in a general sense: economic and social pressures have combined to push medical and administrative leaders to re-open an endangered economy. And that is what they did. One can take issue with both why, and, even more importantly, how it was done, but the data are pretty clear: social distancing and related measures keep the spread of COVID-19 to lower levels than does its absence. Are people social distancing in the absence of the earlier, tighter management of the situation? The reversal to baseline suggests not. Reminds me of the old 1960s antiwar song with the famous line, “When will they ever learn?” When will we ever learn?