Now, it’s time to say goodbye to that. We are permanently closing Cognitive Daily, and this will be our last post.

While we won’t be here, we’ve seen a number of exceptional psychology blogs join us in sharing the science of psychology with the world, and we encourage you to visit them. Rather than single any of these blogs out, we ask that you visit Dave’s ongoing project, ResearchBlogging.org. There, by clicking on the “Psychology” and “Neuroscience” channels, you can find nearly 100 blogs that regularly discuss peer-reviewed research in the same fields we’ve been covering here. You can also follow dedicated psychology and neuroscience RSS feeds, or the @researchblogs twitter feed, to get an even broader view of what’s going on in the world of science.

We’re grateful to many, many people who have helped make Cognitive Daily great. There are too many to mention by name, but without the many scientists who provided the raw materials, the bloggers who’ve helped share ideas, and the administrators and techies who’ve made it all work, this blog simply couldn’t exist. And, of course, without our readers and commenters, Cognitive Daily probably wouldn’t have been around for more than a few months. You’ve inspired us, motivated us, corrected us, disputed us, informed us, and responded to more polls and surveys than we ever imagined possible. We hope you’ll continue to find Cognitive Daily useful; the archives will remain here for all to see.

What will we do with all that time we’ve freed up? Greta plans to continue her work as Professor of Psychology at Davidson College, teaching and mentoring students, conducting research, and sharing her love of music, literature, and art. Dave will continue as editor of ResearchBlogging.org and weekly columnist for SEEDMAGAZINE.COM, and he’ll maintain his personal blog, Word Munger and his obsessively-updated Twitter account. In addition, Dave’s planning a new project, to be unveiled within the next few weeks. Look for more information about it on Twitter and Word Munger. In addition, Dave’s now launched a new blog, The Daily Monthly. Check there for a new post every day, a new topic each month.

Thanks again for being a part of Cognitive Daily. It’s been an amazing ride.

Update 1/23/2010: Comments have been disabled sitewide. Thanks to everyone for your kind regards.

Update 2/4/2010: Dave’s new project The Daily Monthly is now live. Check there for more writing from Dave.

Aside from the fact that it’s a computer-generated MIDI performance, do you hear anything unusual?

If you’re a non-musician like me, you might not have noticed anything. It sounds basically like the familiar song, even though the synthesized sax isn’t nearly as pleasing as the familiar Nat King Cole version of the song. But most trained musicians can’t listen to a song like this without cringing. Why? Because the music has been made “bitonal” by moving the accompanying piano part up two semitones (a semitone is the difference between a “natural” note and a sharp or flat). Here’s the original, unaltered piece:

Can you tell the difference? A 2000 study led by R.S. Wolpert found that non-musicians couldn’t distinguish between monotonal and bitonal music played side-by-side. Meanwhile musicians found artificially-created bitonal music to be almost unlistenable. For most non-musicians, if they heard anything wrong with the clips, they typically said they were being played too fast, or mentioned some other unrelated concept.

But Mayumi Hamamoto, Mauro Bothelo, and Margaret Munger (AKA Greta) wondered if years of musical training were really necessary for non-musicians to hear bitonal music. Bitonality is actually a bit controversial in the world of music, and it can be a little hard to define. In principle, there’s a difference between bitonality and just playing or singing off-key, but in practice, the difference may not even exist. Advocates of bitonality like to point to the works of composers like Milhaud, Bartók, Prokofiev, and Strauss. These composers deliberately wrote in two different musical keys. But how is that different from occasionally or regularly writing dissonant chords? After all, all the same notes can be written using any musical key. To be truly bitonal, advocates say the two separate parts must unfold independently in different keys. This results in a distinctive “crunch” when the music is played. The separate question is, is this noticeable? Wolpert’s work shows that it is, at least for trained musicians.

Hamamoto’s team replicated Wolpert’s study by playing altered and original clips of familiar songs like the above example to three groups of undergraduates: “Musicians” with more than 5 years of training, “Amateur Musicians” with 1 to 5 years of training, and “Non-Musicians” with less than a year of training. There were 14 students in each group. Musicians were significantly better at noticing that the modified clips were bitonal or “out of tune.”

Next, everyone was given brief training session, where instead of modifying monotonal music to be bitonal, some of Milhaud’s music originally intended to be bitonal was modified to be monotonal. Here’s an example bitonal piece (Milhaud’s “Botafogo”):

After hearing the clip and seeing it identified as bitonal, the students were told

Notice sometimes there is a “crunch” in the sound. This should sound somewhat unpleasant and feel like it shouldn’t be that way.

Then they listened to a manipulated version of the same clip:

Again, they were told this clip was monotonal and directed to notice how the sound seems smoother and more pleasant (to my mind, it’s not nearly as interesting as the original — but that wasn’t part of the study). Next they were trained with feedback, listening and identifying clips until they could accurately label four in a row. This took just a few minutes.

Finally, the respondents were tested on four new clips, all songs by Milhaud. This graph shows the results:

As you can see, for all the songs except “Ipanema,” the students were quite accurate at identifying both bitonal and monotonal songs (error bars are 95 percent confidence intervals). More important, however, was that there was no significant difference in the results for Musicians, Amateur Musicians, and Non-Musicians. All three groups fared equally well.

The authors conclude the identifying bitonal music isn’t a matter of years of musical instruction; it can be achieved with just a brief training session. In fact, the Non-Musicians took no longer than Musicians to complete the training session, so years of experience don’t even help with learning about bitonality.

It also may suggest that the controversy about whether bitonality actually exists may not be warranted. If nearly everyone can hear the difference, then it’s probably a genuine musical phenomenon.

Note: More of the clips from the study can be found on Greta’s website here.

Hamamoto, M., Bothelho, M., & Munger, M.P. (In press). Non-musicians’ and musicians’ perception of bitonality. Psychology of Music. DOI: 10.1177/0305735609351917

But one question researchers haven’t been able to nail down is exactly how synesthesia occurs. Consider the relatively common form of synesthesia, where colors are perceived along with words. One synesthete consistently sees the color green when she hears someone say “neat.” Does the synesthetic experience occur when she first detects the word, or only after she understands its meaning?

A team led by Gary Bargary has figured out a new way to test when a synesthetic experience occurs by relying on the McGurk Effect. In the McGurk effect, the word you “hear” someone saying changes depending on what you see. This movie gives a quick demonstration of the phenomenon:

In the first clip, I superimposed the sound of myself saying “neat neat peat peat” over video of myself saying “neat peat neat peat”. What most people think they hear is “neat meat peat peat.” You can see the actual recording of what I said in the second part of the clip. Because my mouth makes a similar movement when I say “p” and “m”, the combination of the audio “neat” with a video “peat” makes viewers think they heard “meat.” Listeners use both the audio and video information to decide what I’m saying, and they get it wrong! Did you experience the illusion? Let’s make this a poll:

After a few people have responded, we’ll have a good idea of how many people experience the effect.

This offers a unique opportunity to see if synesthesia results from the actual sound a person hears, or their perceived meaning of the word. Bargary’s team assembled a list of words that produce reliable McGurk effects. Here’s a sampling:

Then they played three videos of each word (in completely random order) to 12 synesthetes who had previously demonstrated an association with words and colors. In one, the full-McGurk effect was present (e.g. the visual said “peat” while the audio said “neat”) In one, the viewer saw the video only, and white noise was played. Finally, in one, the face was pixellated and only the audio was heard. On a separate computer monitor was a large array of colors, and after the participants saw each video, they clicked on the color that they experienced with it. Then they told the experimenters the word they thought they had heard/seen. Here’s a typical set of responses:

As you can see, the synesthetes chose very different colors depending on whether or not the McGurk illusion was presented. Respondent S21 perceived light blue when only the visual was shown, green when they only heard the word, and blue when the full illusion was shown. Interestingly, when they saw the visual alone, some of the synesthetes reported no color seen, even when they had identified the word. No one said they saw perceived a color when they couldn’t figure out what word was being said.

But maybe these colors aren’t really that different — you might argue that green and blue are in the same family. So the researchers undertook a systematic analysis of the results, comparing the respondent’s consistency in choosing a color when they didn’t experience the McGurk effect (e.g. they just perceived “neat” both with audio-only and audiovisual) to the times when they did experience the McGurk effect version of the same word (e.g. auditory “bay” compared to audiovisual “day”). Remember, not everyone experiences the effect every time they see it, as (hopefully) our poll above demonstrates. This graph summarizes the results:

The graph plots the difference in colors chosen by nine participants. The researchers calculated the color difference by calculating how far apart the colors were in an RGB color space like this (Source: Wikipedia):

The farther apart the colors, the bigger the color difference. As you can see, for each of these respondents except one, the difference was significant: They chose colors that were more different from each other when the McGurk effect was experienced compared to when it wasn’t experienced.

Bargary’s team says this means that synesthetic perceptions occur late in the process of sensing and perceiving words. The McGurk effect results from integrating inputs from multiple senses (vision and hearing), and the synesthetic perception must occur after that has happened — otherwise, the synesthetes’ responses wouldn’t have been different when they didn’t experience the McGurk effect.

Bargary G, Barnett KJ, Mitchell KJ, & Newell FN (2009). Colored-speech synaesthesia is triggered by multisensory, not unisensory, perception. Psychological science : a journal of the American Psychological Society / APS, 20 (5), 529-33 PMID: 19476587

The host, John Hockenberry of The Takeaway, then introduced the lead author of the study in question, David Dunstan, and me, and asked us to explain how watching TV may or may not result in death. Dunstan’s team’s study, as you might expect, has gotten a lot of media attention. There was a press release, a report on CNN, and many others. It was nearly midnight in Dunstan’s home in Australia, and he had been taking interviews all day.

I had been selected as a commentator because of my column a few weeks ago on SEEDMAGAZINE.COM where I discuss the harms and benefits of TV. So, presumably, my “pro-TV” viewpoint would balance Dunstan’s “anti-TV” research.

But for the most part, science doesn’t lend itself to this sort of position-taking. We can understand the results of a study, and perhaps do a bit of speculating on the implications, but beyond that there really isn’t much room for taking sides. So let’s take a closer look at the study in question.

Dunstan’s team analyzed data from the massive AusDiab study of diabetes and related diseases in Australia. In 1999 and 2000, researchers visited over 28,000 randomly-selected Australian households to gather medical and other data, to be revisited over many years following. For this study, the researchers identified 8,800 adults who met their criteria for participation (basically, they showed no signs of cardiovascular disease, they completed the entire response form and medical tests, and their results fell in a normal range). Then they observed who died over the next six to seven years, a total of 284 individuals.

Since all the respondents in this sample had reported on their TV-watching habits during the week prior to their initial interview, it was a simple statistical calculation to determine more TV-watching was associated with more deaths. Here are those results:

The graph plots mortality rate (per 1000 person-years) against TV viewing-time. The population averaged about 50 years of age, so over 6 or 7 years, you would certainly expect some of them to die, and that’s what you see. The error bars here are 95 percent confidence intervals, which means that plot points are significantly different when they overlap by up to about half the length of the error bars. That means it’s quite clear that people who said they watched four or more hours of TV per day were significantly more likely to die than people who watched no TV. Even when the numbers were adjusted for exercise, age, and waist circumference, TV-watchers were significantly more likely to die during this period than non-TV-watchers (though the relationship was now not quite as strong). Indeed, after these adjustments, there were significant differences in risk of death between the groups who watched 0 to under 2 hours, 2 to under 4 hours, and 4 or more hours of TV per day.

The researchers were careful to only count periods when the respondents were sitting in front of the TV, not while they were working out, or doing household chores, so in a sense what they were really measuring is passive sitting time. Things aren’t looking very good for TV-watchers: even exercising doesn’t seem to counteract the effect of watching of a lot of TV.

But how do these actual results compare to media reports? Take a look at the press release, which says that each hour spent in front of the television daily was associated with:

• an 11 percent increased risk of death from all causes,
• a 9 percent increased risk of cancer death; and
• an 18 percent increased risk of cardiovascular disease (CVD)-related death.

That’s true, but what the release doesn’t say is that these relationships aren’t all statistically significant after adjusting for all experimental factors. Each individual hour of increase isn’t significant either, just the overall pattern. What’s also a little confusing is that these “increased risks” are compared to the baseline of no TV-watching. So watching 6 hours of TV per day doesn’t mean you’re 6 * 18 = 108 percent certain of death! It means roughly 108 percent more than the baseline rate of about 2.5 deaths per 1000 years. And look back at the graph to see the very wide confidence interval suggesting that the actual rate (if the study had surveyed the entire population) is most likely somewhere between 5 and 20 — we don’t know exactly where.

What’s more, all these results are only correlations. We don’t know if TV-watching causes death, because participants weren’t randomly assigned to TV-watching and non-TV-watching groups. That said, the study matches well with a Canadian study that correlated sedentary behavior with high mortality, and with a US study that I discussed in the Seed column, which found that obesity was correlated with TV-watching.

So while this study’s results may not quite match up to the hype, all of the research is consistent with a storyline suggesting that a lot of sedentary behavior is not good for your health, especially cardiovascular health.

So was I able to say anything good about TV in my interview? As I mentioned in my Seed column, there are some benefits to society: In rural India, a study found that treatment of women improved after cable TV was introduced in communities. And people report that they feel less lonely while watching TV, which is definitely better than the alternative. Just don’t do it for six hours a day!

Dunstan, D., Barr, E., Healy, G., Salmon, J., Shaw, J., Balkau, B., Magliano, D., Cameron, A., Zimmet, P., & Owen, N. (2010). Television Viewing Time and Mortality. The Australian Diabetes, Obesity and Lifestyle Study (AusDiab) Circulation DOI: 10.1161/CIRCULATIONAHA.109.894824

The question of how a baseball player knows where to run in order to catch a fly ball has baffled psychologists for decades. (You might argue that a football receiver faces a similar task, but generally in football, the distances involved are much shorter, and most football players aren’t expected to catch passes at all.)

There are three primary possible explanations for how a baseball fielder catches a fly ball:

• Trajectory Projection (TP): The fielder calculates the trajectory of a ball the moment it is hit and simply runs to the spot where it will fall (of course, taking into account wind speed and barometric pressure).
• Optical acceleration cancellation (OAC): The fielder watches the flight of the ball; constantly adjusting her position in response to what she sees. If it appears to be accelerating upward, she moves back. If it seems to be accelerating downward, she moves forward.
• Linear optical trajectory (LOT): The fielder pays attention to the apparent angle formed by the ball, the point on the ground beneath the ball, and home plate, moving to keep this angle constant until she reaches the ball. In other words, she tries to move so that the ball appears to be moving in a straight line rather than a parabola.

In principle, all three of these systems should work. However, TP is probably impossible; our visual system isn’t accurate at determining distances beyond about 30 meters, and outfielders stand up to 100 meters away from home plate. The second system, OAC, might not work because the visual system isn’t actually very sensitive to acceleration. And the third system, LOT, is problematic because it doesn’t predict a unique path for the fielder to take to the ball. Further, the most likely paths a fielder would take to catch a ball wouldn’t be much different under OAC and LOT.

But Philip Fink, Patrick Foo, and William Warren figured out a way to experimentally distinguish between all three models. They had 8 skilled male baseball players and 4 skilled female softball players don VR headsets and attempt to catch virtual balls in a large room. The room was big enough that they could freely move 6 meters in each direction. VR was necessary because the researchers made their virtual balls take paths that aren’t possible in real life:

The players stood about 35 meters from “home plate” and the balls were hit either 4 meters in front or behind them. They were also offset to either side, but this turned out not to matter for the results. Here’s a movie (QuickTime required) showing what a typical player saw in her VR display. And here’s a movie showing what the players actually did.

As the image above shows, half the time the balls took their normal trajectory, but half the time they proceeded in a physically-impossible straight line for the second half of their flight. For the TP model, this shouldn’t matter — players should go straight to the landing point in either case. But with a straight-line motion, OAC and LOT predict very different paths. This graph compares one player’s actual movements with the OAC model’s projections:

The thick lines show the predicted movement if the player was following the OAC model, and the thin lines show the actual movement (tan[alpha] is the acceleration in the change of the angle of the ball relative to the player). As you can see, these patterns match up pretty well. But take a look at this graph:

Here, the thick lines show the predicted movement if the player was following LOT, and the thin lines show the actual movement (again, tan[alpha] is the acceleration in the change of the angle of the ball relative to the player, and tan[beta] is the acceleration in the angle between the ball’s position above the ground and home plate). This time, the model does significantly worse after the ball shifts to a straight trajectory.

The researchers say this is compelling evidence that ball players do rely on the apparent acceleration of the ball’s movement (OAC) in order to track it down and catch it. You’ll notice from the second movie that the player clearly isn’t moving in a straight line to catch the ball, so the TP model is also ruled out. Even though people aren’t very good at detecting acceleration, apparently we’re good enough to catch a fly ball hit 30 to 40 meters (and baseball players routinely shag fly balls hit over 100 meters!).

Fink, P.W., Foo, P.S., & Warren, W.H. (2009). Catching fly balls in virtual reality: A critical test of the outfielder problem Journal of Vision, 9 (13), 1-8 : 10.1167/9.13.14

• Is autism really surging? Michelle Dawson wonders whether the recent rise in autism rates can be traced to methodological differences in studies tracking autism rates.
• We know many men are attracted to younger women, but what does it mean to look younger? Wayne Hooke looks at a recent study and concludes that looking younger may be a matter of looking less masculine.
• Ever had a song that you just can’t get out of your head — an “earworm”? You’d think that psychologists would be all over explaining why that happens. Actually, says Christian Jarrett, there has been little research into the phenomenon. Jarrett discusses one of the few studies shedding light on the phenomenon
• Bronwyn Thompson, the pain-management expert, has recently undergone surgery. Now she’s blogging about her own experiences managing pain during recovery. And as a bonus, she’s discussing a fascinating study about women’s experiences with self-pain-management.
• Finally, Scicurious gives us a holiday-themed post about a fascinating phenomenon: A patient who can remember and work with some numbers, but not others. What’s the difference? Read When Dec 25th Isn’t Christmas Day to find out.

Also, over on Seedmagazine.com, I interview four of our content editors to find out how they select notable posts in each of their areas of expertise. They also look back at their favorite posts of 2009, and give some insight into the future of science online.

Click here to read the whole interview on Seedmagazine.com.

One of the show’s premises is that only highly-trained experts (most importantly, its protagonist, Cal Lightman) are capable of sniffing out a well-schooled liar. This too is based in fact. Most of us are very bad at spotting liars, taking their seemingly earnest facial expressions as the real thing. Ekman’s research, along with many others, has shown that it’s possible to detect subtle differences between inauthentic emotional expressions and the real thing. Since telling a lie invokes its own distinctive emotions, it’s possible to see remnants of these emotions by carefully watching a liar in the act of deceit, even when the liar masks his or her true feelings with a feigned emotion.

But what if there was a shortcut in sniffing out a lie, relying on our own instinctual behavior? Would it be possible to improve the lie-detecting abilities of ordinary people without all that training? A team led by Mariëlle Stel had a hunch that our tendency to mimic the physical and facial expressions of the people we are speaking to might help us to tell when they are lying.

They recruited 92 volunteers to participate in a very short conversation. The volunteers were paired up randomly, and one person from each pair was randomly assigned to be the truth-teller or liar. This person was asked before meeting the other participant if he or she would like to make a donation to Amnesty International, and then, randomly, told to either tell the truth or lie about it, with a one-euro reward if they could convince the partner they were telling the truth.

The partners were divided into three groups. The first group was told to mimic the liar/truth-teller’s facial expressions and body movements. The second group was told NOT to mimic. And a final group was given no instructions about mimicry.

Finally, the partners met, sat down, and talked about why the truth-teller/liar chose to donate (or not donate, depending on what they actually did and the instructions about whether or not to lie). After the conversation, the partner became the lie-detector, and was asked to rate on a scale of 1 to 7 whether they believed the other participant was telling the truth about donating to charity. This brief conversation was recorded on video, and trained coders watched each conversant separately, coding their facial expressions and movements. These were then compared, and if the truth-teller/liar’s movements or expressions were repeated by the lie detector within 10 seconds, it was counted as a match. Did the instructions make a difference? Here are the results:

As you might expect, those instructed to mimic did so significantly more than the other two groups. In fact, the amount of mimicking done by each group was significantly different, reflecting the fact that we do naturally mimic the movements of conversational partners.

So did the mimicking have an effect on ability to detect lies? Here are those results:

As you can see, both when the lie detectors were given instructions to mimic and when they were given no instructions, they rated lying as significantly more truthful than telling the truth (on a scale where 1 = “totally not truthful” and 7 = “totally truthful”). Only when lie detectors were explicitly told not to mimic did their truthfulness ratings actually correspond to the behavior of their partners: they rated lying as significantly less truthful than truth-telling.

So while the dozens of tricks employed in Lie To Me can help true experts detect lies, this simple study seems to show that simply telling interviewers not to mimic the behavior of the people they are talking to can make them much better at detecting lies. Unfortunately, that probably wouldn’t make for as exciting a TV show!

Stel M, van Dijk E, & Olivier E (2009). You want to know the truth? Then don’t mimic! Psychological science : a journal of the American Psychological Society / APS, 20 (6), 693-9 PMID: 19422628

Both Greta and I are big wine fans. Despite Jonah’s recent extremely popular post, I, at least, believe that I can tell the difference between good and bad wines. I’m still convinced that a good wine is more than just an attractive label (though I’m a sucker for labels with Zinfandel puns like “Zen of Zin” or “Amazin”). That said, the research suggesting that labeling has a lot to do with wine preference is also quite convincing.

Several studies suggest that we expect to prefer wines from certain vineyards or regions, and in many cases wine drinkers will actually rate the identical wine higher when it’s presented in a fancier bottle. These results apply not only to wine, but to a variety of foods. Restaurateurs have known this for years, placing special emphasis on the presentation of the food in addition to the actual preparation and ingredients.

So if presentation matters, then perhaps the presentation of wine could actually affect the taste of the food it’s served with. This is the premise of a study by Brian Wansink, Collin Payne, and Jill North.

In their first experiment they served 49 graduate students cheese and one of two types of wine as they arrived at a reception. The wine — in both cases the identical cheap Cabernet — was served in bottles labeled as being from California or North Dakota. Prior to drinking the wine, they rated its expected tastiness on a scale of 1 to 9. After sampling both wine and cheese, they rated both of them for actual taste. Here are the results:

Both expected and actual tastiness ratings were significantly higher for the “California” wine — despite the fact that the wine bottles had identical, professionally-designed labels from the fictitious “Noah’s Winery.” What’s more, the tastiness ratings for the cheese, which was the same, unlabeled mild goat cheese for everyone, matched the wine ratings. When people thought they were drinking better wine, they also liked the cheese more.

In a second experiment, 51 patrons of the Spice Box, the white-tablecloth campus restaurant at the University of Illinois, were served identical prix-fixe meals. When they were seated, the waiter at each table made the following announcement:

Thank you for joining us tonight for this special meal at the Spice Box. Because this is the first meal of this new year, we are offering each person at the table a free glass of this new Cabernet from the state of California [or North Dakota].

The wines were labeled as before, but the patrons were all actually given an identical glass of Charles Shaw Cabernet Sauvignon (the to-my-view undrinkable “Two-Buck Chuck”). Their meal and drink portions were carefully weighed in the kitchen before and after consumption, so that the amount consumed could easily be calculated. Here are the results:

The patrons who thought they were drinking “California” wine consumed significantly more food (p = .02), and marginally more food and wine combined (p = .08). There was no difference in the amount of wine consumed, probably because everyone was limited to one glass.

One possible explanation for the result is social facilitation — diners might have felt compelled to eat more if their table mates were cleaning their plates. However, the researchers controlled for social facilitation and found that the labeling difference still explained the differences in amount of food consumed.

So apparently if we believe we’re drinking better wine — whether or not it’s actually better — we’ll not only think the accompanying food tastes better, we’ll eat more of it too. Maybe the next time I try to lose weight I should start drinking bad wine!

Wansink, B., Payne, C., & North, J. (2007). Fine as North Dakota wine: Sensory expectations and the intake of companion foods. Physiology & Behavior, 90 (5), 712-716 DOI: 10.1016/j.physbeh.2006.12.010

Travis Saunders, a PhD student at the University of Ottawa who studies the impact of sedentary lifestyles, questions whether a little exercise can make up for hours of inactivity. He refers to a study led by G.F. Dunton of the University of Southern California and published in October in the International Journal of Obesity. The researchers conducted a phone survey of 10,000 Americans who ranged from normal weight to obese. As you might expect, people who engaged in a lot of physical activity tended to weigh less than those who did not.

But when the researchers considered how much time these individuals spent watching TV and movies, a different pattern emerged. No matter how much TV they watched, if they didn’t exercise, they had high BMIs (body mass index–a measure of obesity). But even among people who exercised more than an hour a day, those watching more than an hour of TV per day had significantly higher BMIs than those who did not. In fact, for respondents who watched more than an hour of TV, whether or not they exercised no longer predicted BMI.

And there are many other surprising correlations between TV watching and both detrimental and beneficial results. For more, read the whole article.

Clicking on the image below will take you to a short Quicktime movie. Make sure you have your sound turned up, because I’ve recorded a few sentences that play along with the movie. Your job is to determine, as quickly as possible, if each sentence is grammatically correct — while you focus your vision on the animated display.

This demonstration replicates part of an experiment conducted by a group of researchers led by Michael P. Kaschak. The researchers showed similar animations to a group of volunteers and asked them to make similar judgments about spoken language. The question: does our reaction time differ when the animation corresponds to the movement described in language?

In the demonstration you just tried, the first two sentences were distractors. In sentence 1, the motion was “towards,” but the animation was moving down. Sentence 2 was ungrammatical. The two sentences we’re interested in are 3 and 4. Sentence 3, “The leaves fell from the tree,” describes a downward motion, just like the motion in the animation. Sentence 4, “The balloon ascended into the clouds,” describes upward motion, opposite the animation.

Kaschak et al. have good reason to suspect that in the case of these last two sentences, the animation may indeed affect how quickly you can process the sentence. We recently reported on data that suggests that impairment of motor control of the hands may also impair our ability to visualize the same motion. Further, memory for visual objects also appears to make use of the visual system. Kaschak’s team points to other research showing that understanding sentences also involves a “sensorimotor simulation” of the action the sentence describes.

But prior to Kaschak’s team’s experiment, no one had tried to measure how quickly people process language when the motion they are viewing corresponds to an animated display. If viewing motion affects language processing, then there are two possibilities for how the two activities interact. It’s possible that watching motion that corresponds to the motion in a sentence will cause viewers to process language faster (i.e. participants will respond faster to “the leaves fell from the tree” when the animation is moving down). Another possibility is that viewing the animation burdens the same region of the brain that is needed to process the language, so when the animation is moving down, then sentences describing downward motion will be processed slower.

Kaschak et al. showed participants four different animations depicting basic motions: moving lines showing up or down motion, and a spinning spiral that could show movement towards or away from the viewer. During each animation, 10 sentences were read: 2 corresponding to the direction of the movement in the animation, 2 in the opposite direction, and 6 distractors. The test questions were always grammatically correct so that each participant was performing an equivalent task. The distractors included some nongrammatical sentences (to keep the task realistic) and some grammatical sentences describing movement that did not correspond to the animation (like Sentence 1 above).

Respondents took an average of 369 milliseconds to respond to sentences that matched the direction of the animation, but only 330 milliseconds to respond to sentences that described movement in the opposite direction. The difference was statistically significant: people take longer to process sentences that match the movement of an animation than they do to process sentences that don’t match it. Kaschak’s team reasons that we must be using the same region of the brain to process the motion itself as we do to process the language describing that motion.

Note that these results are only for animations showing a very generic sort of motion. There’s little doubt that if we saw an actual leaf falling, or balloon ascending, we’d be able to process that language very quickly. Yet the simple concept of “downward motion” does appear to distract from our ability to process a simple sentence describing a particular sort of downward motion.

We weren’t able to measure how quickly you processed the sentences in our demonstration, but did you notice anything different about trying to assess the “down” sentence compared to the “up” sentence? Let us know in the comments.

Kaschak, M.P., Madden, C.J., Therriault, D.J., Yaxley, R. J, Aveyard, M., Blanchard, A.A., & Zwaan, R.A. (2005). Perception of motion affects language processing. Cognition, 94, B79-B89.