Review of Blind Spot: the Hidden Biases of Good People by Mahzarin R. Banaji and Anthony G. Greenwald

Considerable ambiguity still surrounds the exact circumstance under which Michael Brown was shot dead by a police officer in the summer of 2014 in Ferguson, Missouri. One thing is certain; it has spurred mountains of media coverage, energized protests across the country, and gave many Americans enough pause to rethink their views on the treatment of Black Americans in this country, particularly when it comes to law enforcement. But will merely thinking about the disadvantages experienced by millions of Black Americans and other minority groups move us toward a more egalitarian society? Mahzarin R. Banaji and Anthony G. Greenwald paint a more complex picture in their provocative and timely book, “Blind Spot.” The book sheds light on the social-cognitive science behind hidden biases that may help explain discrimination and persistent gaps in health care, housing, employment, and law enforcement experienced by minorities.

Over the last fifty to seventy years there has been a sharp decline in the public expression of prejudice. In the early 1960’s roughly forty percent of White Americans still favored racial segregation in schools. By 1995 that figure dropped to nearly zero. Toeing the line, American governments and institutions dramatically shifted policies to align with normative racial attitudes, evidenced by Brown v. Board of Education, to name just one example. However, despite the decline of explicitly prejudiced attitudes and policy, Black disadvantage persists. Banaji and Greenwald point to national audit studies conducted in 1989 and 2000 in which Black and White actors were paired for similarity in appearance, education, and socioeconomic status, and then asked to apply for mortgage loans, purchase insurance, or secure an apartment lease. Results consistently demonstrate racial disadvantage. For example, in 2000, White homebuyers and apartment seekers were favored eight percent more often than Blacks. Similar employment audits reveal a sixteen percent favoring of White over Black job applicants. Other forms of “un-obtrusive” measures also point to persistence of racial discrimination despite what people report on questionnaires. For example, the lost letter technique is an un-obtrusive method researchers use to measure peoples’ attitudes. An exemplar of this method involves a stamped and addressed envelope that is left open in a public place. The envelope contains a graduate school application and a photo of either a White or Black applicant. One iteration of this study found that the letter was mailed 45 percent of the time for White applicants and only 37 percent of the time for Black applicants.

In light of results from audit studies and the lost letter technique, keen readers may be asking themselves why Black Americans continue to experience clear forms of discrimination despite changes in public policy and reported attitudes of Americans. Covering the last two decades of innovative research on what social cognitive scientists such Banaji and Greenwald call implicit bias, “Blind Spot” attempts to answer this difficult and pressing question. The tone of the book is careful and professional but also provocative and personal. It is filled with interesting anecdotes on the path to scientific discovery, candid self-analyses, and difficult lines of questioning that provide a rare window into the minds of two gifted psychological scientists. In “Blind Spot” Banaji and Greenwald accomplish the exceptional feat of conveying real hard-nosed science in way that makes the science feel real.

On an intuitive level, we all understand that what we do, say, believe and feel are not always guided by what we consciously think. We have all driven home on a regular commute only to realize upon arrival that we have no recollection of the drive. Or we have prepared cereal for breakfast only to return the first bite to the bowl out of shock at the sour taste of orange juice. Just as driving a car or pouring cereal can be done without conscious awareness so too can we hold beliefs or attitudes without conscious awareness. The attitudes we hold have a reflective, a conscious, or an explicit form as well as an automatic, an unconscious, or an implicit form. This is not a new idea. Philosophers and scientist have been writing about the two-sided nature of the mind for hundreds of years and thanks to Freud the unconscious has become something everyone and their grandmother is familiar with. The novel idea that Banaji and Greenwald outline in “Blind Spot” is at its core a technique, not an idea. Measuring the unconscious has been an exceptionally challenging task for scientists, but as two of the leading researchers on implicit social cognition, Banaji and Greenwald developed a creative way to measure how our unconscious beliefs guide our behavior, using what they call the Implicit Association Task (IAT). The most basic form of the IAT measures how long it takes people to organize pictures, objects, or names with words that are either positive or negative. If it takes longer to press a key to confirm a match between say a picture of a Black person and a positive word than it does to press a key to confirm a match between a picture of a White person and positive word; and it also takes a relatively shorter length of time to confirm Black with negative words than White with negative words, then that person holds a cognitive bias that associates White with good and Black with bad. By measuring the time it takes people to make associations, the IAT is capitalizing on how the brain stores information. Concepts (such as Black and good) that are more highly associated with one another can be retrieved faster, and thus result in faster reaction times. The underlying assumption of the IAT is that concepts that are closely associated in the brain are in essence preferred and more swiftly accessed, or at least have been reinforced over time perhaps through the mountains of media and cultural images that associate White with good and Black with bad. The IAT is a breakthrough because it quantifies implicit cognitive biases that are particularly hard to capture through traditional self-report measures for attitudes, such as racial prejudice, that people are highly motivated to hide or regulate. As a result, scientists can now measure attitudes in a way that reveals similar estimates of discrimination as those found in more cumbersome audit studies or un-obtrusive techniques. Based on millions of responses to the race IAT, scientists now know that seventy-five percent of Americans display implicit preference for White relative to Black (take the test here).

One critique of Banaji and Greenwald’s book is their use of the word bias. After all, the IAT is a measure of an implicit association and not an implicit bias. Bias is a loaded term and its use in this context of the race IAT implies racial prejudice. The Oxford dictionary defines bias as a “prejudice in favor of or against one thing, person or group compared with another, usually in a way considered to be unfair.” The critical component of this definition is that the preference is unfair. This begs the question: is it unfair for a person to hold an unconscious cognitive preference for “Black is bad” when they honestly do not hold that belief in an explicit sense? Stated another way: when is it reasonable to conclude that an implicit association is an implicit bias? I like to define fairness as everyone getting what she or he needs. Using this definition of fairness we can conclude that when the needs of people are disregarded, overlooked, or suppressed as a result of the mere association between Black and bad, and White and good it is reasonable to call this association a bias. In light of this logic, there is ample evidence that scores on the IAT do in fact result in behaviors that prevent people from getting what they need. It might be hard for people unfamiliar with the scientific literature to see how differences on the order of hundredths of seconds on the IAT can lead to real-world discrimination, however, Banaji and Greenwald point to numerous studies in which the IAT predicts differences in actual discriminatory behavior. For example, the race IAT predicted voting for John McCain rather than Barack Obama after controlling for a number of other factors. This said, it is important to note that most of these studies are correlational. That is, it is possible the relationship between IAT and discriminatory behavior is reverse such that discriminatory behaviors lead people to have a Black is bad IAT result. In order to examine the causal relationship between IAT and discriminatory behavior we need to randomly assign people to have high or low race IAT scores. Clearly this is not possible, but there are a few studies that attempt to experientially manipulate the IAT through priming people with positive Black role models (e.g., Michael Jordan) and negative White role models (e.g., the shoe bomber) and then measure the effect on behavior. Banaji and Greenwald point to one or two such studies which provide preliminary evidence that experimental changes in IAT do in fact lead to the expected changes in behavior, however the issue was largely avoided in their book. Perhaps the authors decided this is an issue best left to scientific journals, and that the current weight of evidence points in the hypothesized direction. I agree, but it is worth a note of caution to the reader.

Even with evidence that implicit association leads to real-world discrimination it is easy to assume that this is only for people who secretly harbor prejudiced beliefs. However, this is not the case. It is possible to hold an explicit attitude that conflicts with an implicit attitude. In fact, the title of Banaji and Greenwald’s book is inspired by the idea that many good people who hold egalitarian views and have good intentions, also have a blind spot (that is they hold implicit attitudes) that can prevent them from acting in line with their egalitarian values. The authors skillfully lay out an often-understated way in which implicit biases, such as the black is bad association, lead to hidden biases in the way well-intentioned people act. For example, non-action, selective helping, and in-group favoritism which is, in its worst form, nepotism, can also be as innocent as giving to a charitable organization that primarily assists needy people who happen to be White. By contributing to such a charity, people are not directly harming minority racial groups, but they are contributing to the relative advantage of White communities. In this way, “intergroup discrimination is less and less likely to involve explicit acts of aggression toward the out-group and more likely to involve everyday acts of helping the in-group… [which] may be the largest contributing factor to the relative disadvantages experienced by Black Americans and other already disadvantaged groups.”

This is hard pill for well-intentioned White Americans to swallow. It is difficult for people to identify their non-actions let alone feel guilty about them. As the sociologist Peggy McIntosh put it, White privilege is “an invisible weightless knapsack of assurances, tools, maps, guides, codebooks, passports, visas, clothes, compass, emergency gear, and blank checks.” Taking a close look at what it means to be advantaged or to have White Privilege involves unpacking that knapsack. Unpacking the knapsack requires a very difficult kind of self-reflection that highlights the fact that the things we may feel we have earned are in fact gifts we were given for being born of certain color. Further, we know from studies on cognitive dissonance theory that becoming aware of hidden biases or implicit attitudes that conflict with our beliefs and actions violates the natural human striving for mental harmony, or consonance. It produces discomfort and striving to align the discordant parts, which begs the question: how can we bring our explicit and implicit attitudes into alignment?

If there is one part of “Blind Spot” that leaves readers wanting more it is Banaji and Greenwald’s answer to the question: “how can I reduce my implicit biases?” The science of changing or avoiding the traps of implicit biases is nascent. To the authors’ credit, they were careful with their language and conclusions, staying as close as possible to evidence for which there is scientific consensus throughout the book. This is refreshing and appreciated considering how most popular science books reach for grand truths and easy solutions, but by sticking to their scientific guns in “Blind Spot,” the book has the unpleasant consequence of leaving the reader feeling hopeless. Implicit attitudes run deep. They are resistant to interventions, and remain relatively stable over time. Current scientific work is exploring the bounds of this stability and more intervention research is needed to explore how implicit attitudes can be shaped over time. In the meantime, the best way to prevent implicit biases from guiding our behavior is to “outsmart the machine.” That is, we can develop strategies that reduce the likelihood that implicit biases play a role in health care, employment, or housing loan decisions. For example, the National Heart, Lung, and Blood Institute has drafted guidelines for cholesterol screening at certain ages to prevent the providers from forgoing cholesterol screening for women on the basis that they are less likely to develop heart disease. While women have a lower-risk than men of developing heart disease, they are still at risk. The guidelines help ensure providers do not rely on their (correct but biased) gut feeling that women probably do not need the screening as much as men. The result has been higher quality care for women and catching risk for heart disease earlier. An important lesson from “Blind Spot” is that it can be a remarkably fruitful and worthwhile exercise to explore ways to outsmart your own machinery in order to reduce biases in your actions. If you are in a position that judges the merits of others (e.g., an employer), blind yourself to information such as name, ethnicity, gender, etc. as much as possible before passing judgment.

If outrage at police violence against young black men and the subsequent political frenzy that ensues after each incident are any indication of the beliefs most Americans hold toward Black people, there is no better time to read “Blind Spot.” Banaji and Greenwald shed light on a question running through the minds of many well-intentioned Americans: “Why is this still happening?” Perhaps if we put some thought into how the way we think influences our behavior, we can devise more inventive and pervasive mechanisms for avoiding the trips of implicit biases. Only then will we be able to act in ways that reflect the true nature of who we say we are and want to be.

I’ll close with a hopeful video to lighten the mood.


News Release: How to donate your body to science, without having to die: Launch of Open Humans Network

Open Humans Blog

“Open Humans” project backed by Knight and Robert Wood Johnson Foundation invites individuals to share their most personal health information to accelerate medical breakthroughs.

Note: embeddable videos, photos and graphics are available at

BOSTON (March 24, 2015) A group of top university scientists just launched a project to build a community of researchers and participants who want to benefit medical progress – by using technology to open up health data.

The “Open Humans Network,” created by researchers from Harvard, New York University and the University of California San Diego, is backed by a $1 million investment from the John S. and James L. Knight Foundation and the Robert Wood Johnson Foundation, each of which contributed $500,000 in separate grants.

The project aims to break down barriers that make it difficult for willing individuals to access and share their data with researchers. To this end, the Open Humans…

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Where are data on gun violence?

Much of the recent coverage of gun violence in this country points to a lack of data available on the topic. The absence of these data, or at least the inaccessibility of them, points to inherent prejudice. In an age where we collect data on literally everything and use it daily to help explain phenomena and change our world it is telling that it is hard to find good data on gun violence, particular gun violence as it relates to race, sex, age and mental health.

There are some projects working to remedy this. I’d like to see the gun violence archive project expanded. The project started in 2014 as an offshoot of a crowdsourced initiative by Slate, which documented incidents of gun violence after Newton. We need a tool on this website to visualize the data they collect. Maps of incidents that can be tabulated by different variables would help bring to light the normality of gun violence and the prevalence of racially charged incidents. In light of recent events it is noteworthy that this project collects data on “officer involved shootings”. However the project fails to capture officer involved shootings of unarmed person(s). Instead the project counts the following categories under “officer involved shootings”…

  1. Officer shot
  2. Officer killed
  3. perpetrator shot
  4. perpetrator killed
  5. perpetrator suicide at standoff

This is problematic because the method of collection presumes that someone shot or killed by an officer is a perpetrator (someone who has committed a crime). While the project has an “armed” category described in their glossary it doesn’t collect data on “unarmed” incidents. Further, race/ethnicity, age, sex, and mental health status are conspicuously absent from the glossary for this project. These data should be collected!

The data we collect and how we collect it tells us a lot about what we value.

We need to value data on gun violence with an eye toward race, sex, age, and mental health. We need to translate data into graphics and stories to help explain what the heck is going on. And we need to use data and story to inform how we change. Otherwise, I’m afraid outrage will fade, and the status quo will resume until the next everyday tragedy goes viral.

FDA’s Proposed Rules on Food Labeling

The Food and Drug Administration (FDA) of the US Department of Health and Human Services (HHS) has extended the commenting period to August 1st, 2014 for the proposed rules on food labeling (Docket ID: FDA-2012-N-1210).

I’ve written previously on the proposed rules. Here is a quick summary:

I applauded 3 major components of the ruling:

  1. Label added sugars in addition to natural sugars
  2. Addition of column on label to include both per serving and per package
  3. Highlight “calories”, “serving size” and “percent of daily value” through changes to the size, style, and position of font

And I questioned 1 component of the ruling:

  1. Revision of serving sizes of foods that can reasonably be consumed at one-eating occasion and updating, modifying, and establishing certain reference amounts customarily consumed. For example, this component would require that both a 12 oz bottle of soda and a 20 oz bottle of soda be labelled as a single serving.

I argued that this is a mistake for the following reasons:

  1. Research shows that people consume more when the container is larger. If we use the quantity of food or drink that is “reasonably consumed at one-eating occasion” or “customarily consumed” as a measure of what is safe, as is the implicit role of the FDA, then we fail to consider how a larger container may increase what is ordinarily consumed to levels that pose a health risk.

I suggested that new rule should reflect what is safe rather than what is “customarily consumed”. And, if the FDA insists that we use the quantity an average person consumes as a measure of what is safe, then we should at the least account for external factors (such as container size) that increase the point at which our body tells us we’re satisfied.

I pointed to 2 potential unintended consequences of this rule:

  1. Companies might discontinue smaller container sizes
  2. Consumers might choose larger containers over smaller containers with increased frequency

I’m revisiting this issue today after scanning the comments submitted thus far.

I found one submission from Behavioral Science and Regulation Group at Harvard that resonates with my concern. We both note that “serving size” is an implicit endorsement to consumers of what is an appropriate or healthy quantity. While I suggest the FDA use a different measure of “serving size” that is more in line with a healthful serving, the Harvard group suggests to change the wording of “serving size” to reduce implicit endorsement of healthfulness:

As the FDA acknowledges in its proposed rule, more than half of consumers perceive the term “serving size” to be a recommended serving size, not an amount customarily consumed. For those people that would, in the absence of a serving size, have eaten a small portion, the inclusion of a perceived serving size recommendation could lead them to eat more than they otherwise would. This is because these consumers believe that the FDA has implicitly endorsed the serving size as healthy. Consuming larger portion sizes is related to increased calorie consumption. While the rule’s revision of the serving size volumes and increased use of “whole package” labeling is appropriate and important, it will also exacerbate this problem, because the perceived recommendations will typically be for even larger portions […]

We suggest that the word “serving” and the phrase “serving size” be changed to avoid an implied endorsement. Changing “serving” to a word that does not suggest the context of a meal, like “unit” or “quantity,” may mitigate the endorsement effect.

This group from Harvard also endorses the FDA’s changes that use visual cues to increase clarity for the consumer:

The result is a nutrition label that behavioral science indicates will decrease the time consumers spend finding information, improve readability, focus attention on the most important information, and make information easier to process and remember.


And they make a fabulous recommendation on how to help consumers “avoid too much” of ingredients that are known to pose a health risk:

The FDA can better communicate product healthfulness by grouping nutrients into mutually-exclusive evaluative categories and using color to highlight healthful ingredients or particularly high or low nutrient levels.

The comment is worth reading in its entirety (see comment  from Behavioral_Science and Regulation Group here).

It was not surprising to find that the comment period was probably extended in response to the numerous requests from industries that anticipate adverse affects from the ruling: Juice Product Association, Specialty Food Association, American Beverage Association, Council for Responsible Nutrition, Snack Food Association, and Grocery Manufacturers Association to name a few. Though there was one notable exception in the Academy of Nutrition and Dietetics. Many of these industry representatives jumped in to voice their concern. It seems the cranberry industry is particularly concerned about requirements to label added sugars. There were several representatives from various companies including Gary Dempze of Gaynor Cranberry Co., Inc. who says, “unlike other fruit, cranberries have little natural sugar and, therefore, have a uniquely tart taste. Cranberry products need to be sweetened so consumers can enjoy their health benefits.”

Other supportive comments come from Weight Watchers, the American Diabetes Association, the National Alliance for Hispanic Health, and the American Dental Association to name a few of the big ones.

All-in-all it is fun to read through comments and see where different institutions fall on the issue. Give it a whirl.


Temporal Self-Regulation Theory: Why we keep trying (and failing) to go for that early morning run.

keep-the-dream-alive Last night in a burst of optimism I set my alarm for 5:30 AM. I thought I would sneak in an early morning run around the neighborhood before work. But as bells rang at that un-godly hour, I cracked an eye to a dark, cold room and groped for the snooze button. Ten minutes later, with a slight increase in clarity, I delayed once more “today, sleep is more important”…snooze again. As you might have guessed, I didn’t wake up in time to run.

We’ve all had a similar experience. Our preconceived intentions to engage in healthy behavior too often fail to come to fruition when it’s time to act. But we also intuit that our intentions are somehow linked to our behavior.

Most of the prevailing theoretical models of health behavior such as the Theory of Planned Behavior (Ajzen & Madden, 1986, request pdf), posit that intentions in combination with a number of other factors, such as behavioral beliefs, can predict likelihood of behavior. And these theories do predict behavior reasonably well (see Godin and Kok, 1996), but they fail to explain why large increases in intention only lead to small changes in behavior (see review by Webb and Sheeran, 2006). In this way these theories fail to fully explain health behavior.

Hall and Fong (2007), developed Temporal Self-Regulation Theory to help explain why, when it comes to health-related actions, the intention–behavior link may break down. They postulate that perhaps our intentions sometimes fail to lead to behavior because,

[many health behaviors are] associated with a characteristic set of contingencies whose valence changes dramatically depending on the temporal frame.

I’ve added emphasis to the quote to help break it down. In generally when psychologists talk about behavioral “contingencies” they are referring to if-then conditions that create potential for the occurrence of certain behavior and its consequences. Using the running example above, one behavioral contingency could be stated, “if I run in the morning, then I might be healthier when I’m older”. The “valence” of this contingency is positive—who doesn’t want to live a long and healthful life? “Temporal frame” refers to the very human capacity to think not only in the present moment or short-term, but also to weigh long-term consequences of our actions. Our example contingency has a long-term orientation. The authors contend that valence of the contingency changes with temporal frame, so let’s say I am thinking in the short-term, the behavioral contingency could then be stated, “if I run in the morning, then I might be tired for the rest of the day”. This is, of course, negative in valence. So the theory predicts that I will be more likely to create an intention to run in the morning if I’m focussed on the long-term as opposed to the short-term. This helps explain why it’s so hard to engage in health protective behaviors (such as running) and dis-engage in health risk behaviors (such as smoking). It is hard to delay gratification and most health risk behaviors are satisfying in the short-term and unsatisfying in the long-term, while most protective behaviors are predominately unsatisfying in the short-term and satisfying in the longer-term .

So, back to why my intention to run in the morning failed to lead to running after the alarm went off.

Last night when I set my alarm for 5:30 AM I was thinking about my long-term health, “I’ll look and feel so good in my summer swimsuit after working out” or “I’ll be less prone to disease when I’m older”.  Further the immediate costs of setting the alarm were low—I only had to click few buttons. In contrast, while reaching for the snooze, the costs of running were more immediate and the short-term consequence were salient, “I’m tired now, and I’ll be too sleepy to be productive today if I run”.

These tables and figures from Hall and Fong (2007) demonstrates how protective and risky health behavior have the opposite contingency valence with respect to time orientation. As depicted in the table 1, participants in this study estimated the point in time at which they would notice the benefit/cost of health protective behaviors (e.g. exercise and dieting), and health risk behaviors (e.g. smoking and drinking).

temporal proximity measure Hall and Fong (2007) Sticking with our morning run example, Figure 1 below demonstrates that people don’t notice the cost of running when thinking about rising at the crack of dawn for a run (question #1) or when deciding to run by setting the alarm an hour early (question #2). We start to feel the cost when the alarm goes off and we have to get out of bed and dress (questions #3). The perceived cost continues to grow as we run and after we’ve successfully run once (questions #4 and #5). We start to feel the cost less once we’ve made this morning run a regular routine for a week (question #6). As we continue to engage in our morning run routine the perceived cost continues to decrease, completely disappearing after a several years (question #9).

Now, what about the benefit of running early in the morning? Figure 1 indicates that we don’t feel the benefit of our run until we’ve done it regularly for a week (question #6), at which point the benefits grow exponentially for a year (question #8) and then decreases toward zero as we approach a decade (question #10).

These results provide evidence that the perceived benefit of running occurs well after the initial behavior occurs, while the perceived cost is felt just before, during  and a short while after the behavior initiates.

So when we are making the decision to set the alarm early for tomorrow’s run costs are low and abstract, so we are focusing on the long-term. When the alarm goes off and we are engaging in the behavior the costs are high and concrete, so we are focusing on the short-term. 

Before looking at Figure 1 below, notice that numbers 0 through 9 on the x-axis correspond to questions 1 through 10 in Table 1 pictured above. This is because academics like to make things more complicated than they need to be :). Screen Shot 2014-03-20 at 9.00.55 AM Figure 2 shows that the same trend holds for another health protective behavior (dieting). Screen Shot 2014-03-20 at 9.01.08 AMAs expected, the authors found the opposition result for health risk behavior—costs come after engaging in behavior and benefits occur before/during, see Figures 3 and 4 below. Screen Shot 2014-03-20 at 9.01.19 AM Screen Shot 2014-03-20 at 9.01.31 AM

So how does Temporal Self-Regulation Theory help me running in the morning? It suggests that on thing that might help is to try to minimize the short-term costs and maximize the short-term benefit. This can be hard, but may be as simple as rewarding yourself with a favorite breakfast if you complete the morning run.

Obviously, perceived temporal proximity with regard to behavior is only part of the picture. The authors introduce a working model (below) to illustrate Temporal Self-Regulation Theory more fully, which I’ve enhanced with definitions of each component. The model introduces two factors, behavioral prepotency and self-regulatory capacity that (1) influence (or moderate) the link between intentions and behavior; and (2) directly influence behavior in the absence of intentions. Health behaviors are complex and theories require continuous testing and refinement but Temporal Self-Regulation Theory adds an interesting new component to existing theories that is surely worth further consideration and testing.

Enhanced schematic representation of Temporal Self-Regulation Theory


Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22, 453-474.

Godin, G., & Kok, G. (1996). The theory of planned behavior: A review of its applications to health-related behaviors. American Journal of Health Promotion, 11, 87-98.

Hall, P. a., & Fong, G. T. (2007). Temporal self-regulation theory: A model for individual health behavior. Health Psychology Review, 1(1), 6–52. doi:10.1080/17437190701492437

Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132, 249-268. doi: 10.1037/0033-2909.132.2.249


Peter McGraw and Joel Warner on Humor

I have a shameless plug to make. My first interview for The Society Page’s Humor-Code-Book-Coverpodcast, Office Hours hit the airwaves today (subscribe on iTunes). Dr. Peter McGraw and Joel Warner were kind enough to chat with me at Portland’s Bridgetown Comedy Festival on their new book, The Humor Code. We talked (and laughed) about Benign Violation Theory and their travels around the world in search of what makes things funny (listen here).

Peter McGraw (@PeterMcGraw) is a marketing and psychology professor at the University of Colorado Boulder and founder of the Humor Research Lab (aka HuRL). Joel Warner (@joelmwarner) is a journalist, writing for many prominent publications including Wired, The Boston Globe, and Slate.


links that tickled me