Friday gorilla spotting

It’s Friday. Time for some gorilla spotting. You’d be amazed where you can find gorillas once you start looking.

This should give conspiracy theorists something to ponder.

moon landing with gorilla

gorillas are not alone

be aware of invisibility

I’ve been discovering that the more you look for invisible gorillas, the more you have to worry about other invisible things. If these warning signs are any indication, other people have started searching for the invisible too…

Apparently, we gorillas aren’t the only invisible critters:

sign stating "beware of invisible cows"

sign from Hawaii by skelekitty

engrish funny invisible fish

Apparently, objects can be invisible too.

signpost saying "missing invisible bicycle"

missing invisible bicycle

Imagine what other invisible things you hairless apes might notice if you weren’t always distracted by talking on cell phones or counting basketball passes

When less is more (memory limits and correlations)

Here’s a fairly geeky post connecting a fact about statistical distributions to a possible benefit of having limited working memory abilities.

I recently ran across a series of articles by Yaakov Kareev from the mid-late 1990s showing something remarkable: People with less working memory capacity are better able to detect moderately strong correlations (Kareev, 1995; Kareev et al, 1997; Kareev, 2000). Understanding why requires a bit of a digression into statistics. For those of you familiar with statistical distributions, skew, and correlations, just skip to the end.

A brief discussion of correlations: Correlations express the relationship between two variables and can range from -1.0 to 1.0. Imagine that height and weight were perfectly correlated. A perfect positive correlation means that you could line up all the people in the world from tallest to smallest, and the tallest person would also be the heaviest, the next tallest would be the next heaviest, and so on down to the smallest person who would be the lightest of all. Of course, in the real world, correlations are rarely perfect. There are plenty of heavy short people and light tall people. The correlation coefficient (r) gives a way to express the degree to which two variables tend to vary together. The higher the absolute value of the correlation, the better you can predict one variable from the other. If the two variables are completely independent, they will have a correlation of 0, and knowing the value of one variable tells you nothing about the value of the other.

Technical bits about sampling distributions: Because correlations can’t be any bigger than 1.0 (or smaller than -1.0), the distribution of possible correlations is truncated. You can’t do any better than a perfect ability to predict one variable based on values of another. That’s different from a normal or bell-shaped distribution which continues to infinity in either direction.

Figure illustrating normal distribution

normal distribution from Nusha at sl.wikipedia

If a variable is normally distributed, then if you test any person at random, they are equally likely to have a value for that variable that is above or below the average value for the population. If you take a sample of 10 random people and average their scores, the average will be equally likely to fall above or below the population average. Most of the values will fall closer to the average, with extreme values being more rare (hence the peak at the center of the distribution). If you did that repeatedly and then averaged all of the averages, the grand average would be close to the population average. If you took a large enough sample, then the average of that sample would be close to the average for the population as well.

Correlations don’t work that way, though, because their distribution is truncated. If the correlation in the population is r=.60, the distribution will have a big peak higher than .60 and a long tail below .60. It has what is known as a negatively skewed sampling distribution.

figure illustrating skewed distributions

Figure from Kareev et al (1997)

This property of the sampling distribution for correlations means that if you sample 10 people at random from the population and compute the correlation for those 10 people, you are more likely to find a correlation that is bigger than the true correlation in the population than one that is smaller than the true correlation. The smaller the sample, the more likely it is that the correlation in your sample will be bigger than the correlation in the true population value. In the figure from Kareev et al (1997) above, you can see that the shape of the distribution with small samples has most of its mass to the right of the vertical line that shows the true population correlation. Only with large samples does the peak of the distribution shift closer to the true population value.

Okay. Enough of the stats. What does this all mean? It means that if you are only able to take small samples, you are likely to perceive real correlations to be stronger than they actually are. And, it turns out that this distortion might be a good thing. People are built as pattern detectors, and it’s important that we successfully detect relationships that actually exist in the world. It’s important to know whether a particular type of cloud is associated with rain, for example. We can’t readily measure every possible example of an association in the world (we don’t have access to all cases in which that cloud type appeared and whether or not it rained). Instead, pattern perception is driven by anecdotes or collections of anecdotes—we have access to only a small number of examples. And, if there actually is a relationship between two variables in the world, we’ll be more likely to detect it with small samples than with large samples because small samples tend to suggest a stronger association than is actually present in the world.

Here’s where Kareev’s findings are particularly noteworthy: People who have low working memory capacity tend to perceive real correlations to be stronger than do people with high working memory capacity. In essence, people with less memory available can keep fewer examples in mind when checking whether an association exists, and as a result, they are more likely to have an inflated estimate of the actual association. That is, they’re more likely to see the correlation as really strong and are less likely to miss a moderate correlation in the world. Having less working memory available makes you better able to detect the presence of an association when you’re looking for one.

This sort of “less is more” idea has been used to explain the ease with which children can acquire language (e.g., Newport, 1988). It might also help to explain the ease with which people form stereotypes (but only when those stereotypes are actually true). This is a beautiful example of taking a simple, unremarkable fact about statistical distributions and using it to predict something remarkable about how people perceive the world.

Sources cited:

Kareev, Y. (1995). Through a narrow window: working memory capacity and the detection of covariation Cognition, 56 (3), 263-269 DOI: 10.1016/0010-0277(95)92814-G

Kareev, Y., Lieberman, I., & Lev, M. (1997). Through a narrow window: Sample size and the perception of correlation. Journal of Experimental Psychology: General, 126 (3), 278-287

Kareev Y (2000). Seven (indeed, plus or minus two) and the detection of correlations. Psychological review, 107 (2), 397-402 PMID: 10789204

Newport, E. (1988). Constraints on learning and their role in language acquisition: Studies of the acquisition of American sign language Language Sciences, 10 (1), 147-172 DOI: 10.1016/0388-0001(88)90010-1

Silly humans miss the point of movies

I just ran across this article at the Wall Street Journal about hairless apes who think the point of watching a movie is to look for editing mistakes. Yeah, if you’re forced to watch a terrible movie and can’t leave because your date thinks it’s fabulous, you might as well look for errors. And you’ll almost certainly find some if you try.

You hairless apes have limits on how much information you can focus attention on at once — you can’t take in everything. Most people don’t notice errors because they are paying attention to what matters: the story! If you are catching errors, that means you are focusing all of your limited resources on irrelevant details like the shape of a bite taken out of an apple rather than on the important stuff.

Every movie has errors, and the odds are good that the script supervisors and editors knew about all of them—but they decided to use those shots anyway. Why? Because fixing an error might require them to use a shot with a weaker acting performance, and that might cause normal viewers to lose interest in the story. And the point of good filmmaking is telling a convincing and compelling story, not making sure that every trivial detail matches perfectly in every single shot.

But when normal viewers (people who don’t spend their free time viewing movies frame-by-frame) spot errors, then the filmmakers probably haven’t done their job; errors don’t jump out at you if you are actually interested in the story. Experiments show that even when people intentionally look for changes, they tend not to notice them, and when they’re not looking for changes, they virtually never see them (a phenomenon known as “change blindness”). If people who compulsively search for errors find some, it means nothing about the quality of the moviemaking.

So, a big chest thump and five bananas to you error sleuths. You’ve uncovered “mistakes” that the filmmakers likely knew about already, and you’ve proven your ability to focus on irrelevant details rather than watching movies for the reasons most people do: entertainment and storytelling. Of course, if your idea of entertainment is to watch movies frame-by-frame and search for errors, then more power to you. Apparently, you have a lot of company among your fellow hairless apes. And if you truly can keep your attention focused on that level of detail for a couple of hours straight, you might consider a career in airport security.

Soup dé jà vu — a short film by Michael Tamburro

About 15 years ago, when I was a graduate student at Cornell University, I had the privilege to work with a terrific undergraduate, Michael Tamburro. For someone interested in studying change detection in movies, Michael was the ideal student to work with. He had a background in psychology, film, and computer science and was interested in making a short film as part of his film class. We thought it would be fun to make a longer film that would illustrate the principles of change blindness in motion pictures that Daniel Levin and I had been exploring for a few years (I’ll post about that work soon). I’ve talked about his film many times to other researchers, but few have seen it. Until now—yesterday Michael gave me permission to post it to YouTube. So, without further ado, here it is:

Technical note: The film was created using one of the first Sony digital video cameras, but the editing was done largely with linear editing using tape deck. It’s amazing how much easier it is to do this sort of editing and filming now.

Inattentional blindness for sword swallowing

Chris and I shared the 2004 Psychology Ig Nobel award for “demonstrating that when people pay close attention to something, it’s all too easy to overlook anything else — even a woman in a gorilla suit.” You can read more about our study at

Marc Abrahams, the organizer of the Ig Nobel awards, just emailed us this video from 2007 Ig Nobel winner Dan Meyer (shared with Brian Witcombe). They won the prize in medicine for their article on “sword swallowing and its side effects.” In the video, Dan Meyer swallows a sword in front of the Cavern Club, a famous pub in Liverpool where the Beatles played. Tourists apparently walked by to photograph the Cavern Club didn’t notice the person swallowing a sword in front of the entrance. You can see a couple of the oblivious tourists at the end of the video.

You can read Marc Abraham’s original post here.

Sources Cited:
Simons DJ, & Chabris CF (1999). Gorillas in our midst: sustained inattentional blindness for dynamic events. Perception, 28 (9), 1059-74 PMID: 10694957

Witcombe B, & Meyer D (2006). Sword swallowing and its side effects. BMJ (Clinical research ed.), 333 (7582), 1285-7 PMID: 17185708

friday gorilla spotting

It’s Friday.  Time for some gorilla spotting.  You’d be amazed where you can find gorillas once you start looking.  Here’s a gorilla taking his rightful place in history.

mount rushmore with gorilla

Mount Rushmore -- improved

A weight lifted?

Imagine the biggest hill in your home town. Now try to guess how steep it is, where 0° would be completely flat and 90° would be vertical. (In Champaign, that’s easy — we have no hills.) Go ahead. Make an estimate before reading further.

What did you guess? 60°? 45°? 30°? The odds are good that you massively overestimated the steepness of the hill (Proffitt et al, 1995). People judge 5° slopes to be more than 20°. Our intuitive judgments about absolute distances and slopes are terribly inaccurate.

The steepest road in the United States, Canton Street in Pittsburgh, has a slope of approximately 20° (37ft of elevation change for every 100ft of distance — trig finally comes in handy).

view of Canton Road

Canton Road in Pittsburgh (from Lildobe at en.wikipedia)

If you’ve run the Boston marathon, you might think Heartbreak Hill is huge, but it ascends at just over 2.5 degrees relative to a completely flat road (about 27m elevation change over 600m distance).

image of heartbreak hill

Heartbreak Hill photo by

The famed Alpe d’Huez climb in the Tour de France averages just 7.8°, but it’s particularly tortuous because of it’s length and the altitude at the finish. The hills seem much bigger because we’re fatigued.

Research over the past 15 years by Dennis Proffitt and colleagues examined just that experience: When we’re tired, hills seem steeper (see Bhalla & Proffitt, 1999). People judge hills to be steeper when wearing a heavy backpack or after jogging. Proffitt and colleagues argue that these overestimates are actually misperceptions—we really see the hill differently when we’re fatigued, and our physiological state at least partly determines our conscious perception. A jogger viewing a 5° hill before a run estimates it to be just over 21°. After jogging, they judge it to be nearly 28°.

But do people really perceive the hill differently or do they just say that they do? People intuitively understand the Heartbreak Hill effect—we know that climbing hills is harder when we’re tired or when we’re carrying a heavy bag.

A recent series of experiments suggests that the effect of backpacks on slope judgments was due to such intuitions rather than to an effect of physiological changes on conscious perception (Durgin et al, 2009). As in the earlier studies by Proffitt and colleagues, Durgin et al had subjects wear a backpack and estimate the slope of a ramp. In the standard backpack condition, subjects were left to their own devices to guess why they were being asked to carry a weight. Unlike the earlier studies, Durgin et al added a critical control condition: Subjects were told that the backpack contained electromyographic equipment designed to measure their ankle muscles. To make the deception complete, they attached electrodes to the subjects’ ankles had had a fan noise come from the backpack. With this explanation for the backpack, subjects no longer need to wonder why the experimenter made them wear a backpack, so they would be less likely to look for some reason.

If slope estimates are due to the effect of physiology on conscious judgments, then it shouldn’t matter what explanation subjects were given—the backpack weighed the same in both conditions. But Durgin et al found that those who fell for the ruse gave estimates no different from those who weren’t wearing a backpack at all. In other words, the effect of wearing a backpack was due to the intuitions of the subjects that weight should affect slope judgments and not due to the weight itself. When they had no reason to guess that the experimenters were interested in the effect of weight on slope judgments, their judgments were unaffected by the weight. Although other evidence provides some support for the effects of physiological state on judgments, in this case, the effect appears to be one of experimental and social context on judgments.

Proffitt, D. R. P., Bhalla, M., Gossweiler, R., & Midgett, J. (1995). Perceiving geographical slant. Psychonomic Bulletin & Review, 2, 409-428

Bhalla M, & Proffitt DR (1999). Visual-motor recalibration in geographical slant perception. Journal of experimental psychology. Human perception and performance, 25 (4), 1076-96 PMID: 10464946

Durgin, F., Baird, J., Greenburg, M., Russell, R., Shaughnessy, K., & Waymouth, S. (2009). Who is being deceived? The experimental demands of wearing a backpack Psychonomic Bulletin & Review, 16 (5), 964-969 DOI: 10.3758/PBR.16.5.964

Estimating a renowned man's character

Have you ever wondered how politicians, athletes, and public figures can get away with scandal after scandal for most of their careers and then receive laudatory obituaries when they die? How do they resurrect their reputations following major ethical breaches? One way appears to be via a death-bed conversion. In a recent study by George Newman, Kristi Lockhart, and Frank Keil (2010), people judged the moral character of a person based on a brief description of their good and bad activities throughout their lives. You might think that reputations would be made based on the sum total of a person’s life work, but it turns out that their actions just before their death are the most important. A person who was a louse for most of their life but turned good at the end is judged to be more moral than someone who was good throughout their life but turned to the dark side at the end. That’s why Star Wars fans have a more positive view of Darth Vader—he helped Luke in the end. This end-of-life bias suggests that we treat a person’s most recent actions as the best estimate of their true underlying nature. The bias is so strong that people who were good at the beginning of their life but bad thereafter are judged to be less moral than people who were genuinely bad throughout their life! Unfortunately for slimy politicians, the redemption effect seems to depend at least partly on dying just after seeing the light.

Source cited:
Newman GE, Lockhart KL, & Keil FC (2010). “End-of-life” biases in moral evaluations of others. Cognition, 115 (2), 343-9 PMID: 20138612

(The title of this post is an allusion to Mark Twain: “To arrive at a just estimate of a renowned man’s character one must judge it by the standards of his time, not ours.” It seems that the judgment comes when the man’s time is up …)

I see gorillas

For reasons I don’t quite understand, hairless primates regularly see religious figures in common objects.  This lady sees the Virgin Mary in the wood grain of her door.
image of woman looking at door

image of woman looking at sexy gorilla on door

What’s wrong with these hairless primates?  Why don’t they see the gorillas?  All the sexy, sexy gorillas.
gorilla toast

gorilla toast