Cognitive Daily reports nearly every day on fascinating peer-reviewed developments in cognition from the most respected scientists in the field.
Greta Munger is Associate Professor of Psychology at Davidson College whose works include The History of Psychology: Fundamental Questions. Dave Munger is a writer whose works include Researching Online and The Pocket Reader. And yes, he is married to Greta.
I'm usually disappointed when I try to take a picture of a steep precipice—it never seems as impressive in the photo as it did when I was standing right there. Take this photo, for example. It's a nice shot of my daughter Nora, taken on our hike in the Great Smoky Mountains this past summer, but you just don't get much sense of the dizzying precipice she's standing on the edge of.
Later that summer, on Lake Powell, Utah, I finally managed to get a shot that conveyed some drama:
But even this shot doesn't really show the paralyzing terror Nora felt as she peered over the 100-foot drop-off to her left (okay, it was probably more my terror as her father, but you get the idea).
One reason for this is that humans routinely overestimate the steepness of slopes when they're actually standing on them—guessing that a 10° pitch is actually 30°. Something about physically dealing with the environment exaggerates our perception of its characteristics. On our weekend hike the Smokeys, laden with backpacks, it sure felt like we had hiked a lot more than three miles each way! But is that just the exhaustion talking, or is our perception really affected by the burdens we carry?
A group led by Dennis Proffitt conducted a clever set of experiments to answer that question. In their first experiment, they asked participants to make repeated judgments about the distance a construction cone placed from 2 to 14 meters away. Though they weren't allowed to walk to the cone, participants were given a ruler to give a frame of reference. The trick was, half the participants wore backpacks laden with one-fifth of their body weight. Here are the results:
People wearing backpacks consistently estimated the distances as longer than people not wearing packs. But maybe these results could be explained by the fact that participants were wearing backpacks. Even though they weren't required to actually walk the distance, they may have gotten some clues as to what the experiment was about when the researchers asked them to guess distances while wearing backpacks.
So Proffitt's team devised a subtler way of testing the same thing. A few years earlier, Frank Durgin had led a group doing research on a treadmills. If volunteers were shown a virtual-reality display indicating they were actually moving while walking on a treadmill, then afterwards, off the treadmill, they were able to march in place (wearing a blindfold) better than another group who had watched only a still image on the treadmill. The people who hadn't been exposed to motion on the treadmill marched forward a bit when told to march in place, as if to compensate for the lack of motion on the treadmill.
Proffitt's group replicated Durgin's work, but then added another dimension to the study. Both before and after the treadmill exercise, participants were led blindfolded to a hallway where they performed the same distance-judgment task that the backpack group had done earlier. Those who had seen a virtual reality display indicating they were moving estimated the distance to be about ten percent shorter than they had before the treadmill exercise. Those who had only seemed to be walking in place estimated the distance to be about ten percent longer than they had before. Proffitt et al. argue that the non-moving group felt like they needed to expend more effort to go the same distance—because all the exertion on the treadmill had "gotten" them nowhere. The group that saw the VR motion, by contrast, was amply rewarded for its work, and so felt that the distance seemed shorter than it had before.
Proffitt, D. R., Stefanucci, J., Banton, T., & Epstein, W. (2003). The role of effort in perceiving distance. Psychological Science, 14(2), 106-112.
If you're a perception teacher, a great way to show how the vision system adapts is to use prism glasses to shift a volunteer's vision. While various types of glasses are available (the most common is designed to allow a person to read a book lying on her back), the most effective for this demo is a pair that makes the world appear shifted about ten degrees to one side—so what was directly in front of your victim now appears ten degrees to the left (or right, depending on the particular pair of glasses). The best volunteers are athletes—quarterbacks or pitchers. Suppose the starting softball pitcher is in your class. You get her to throw plush balls at the you while wearing the glasses—initially, she will miss by several feet. It doesn't take long to adapt, however, and soon the volunteer has learned to hit you with every pitch. Then you ask her to remove the glasses and try again: for the first several tosses, the throws miss in the opposite direction (for true hilarity, get the coach to show up at this point and threaten to drop her from the team!). Fortunately, the victim soon adapts, throwing strikes again and saving her full-ride scholarship.
This demo only works if the volunteer can't see her hand and the target at the same time. If you have a new volunteer sit at a table wearing the glasses and attempt to reach for an object a few feet away, he'll easily reach for it, just as quickly as if his vision wasn't distorted at all (provided his hand and the target are both visible).
Curiously, if you try to duplicate this second demo using a video monitor, the results are rather different. Consider the following setup:
The volunteer is behind a screen and can't see his hand, only a TV monitor displaying a picture of his hand and the target, but rotated by 40 degrees. It's a slightly different setup from the prism glasses, because the camera is showing a view from above, but the problem is essentially the same—how to adapt when the input you see is different from the real world. Yet with this setup, the participant never completely adapts—though he does get a bit better, he's never perfect. He keeps making the same mistake, over and over again. Instead of moving in a straight line to the target, the hand starts off in the wrong direction, and the path must be constantly corrected in order to reach the target. But even after 40 trials, it doesn't completely return to normal. When the image is rotated back to normal, the mistake is made in reverse. With prism glasses, as long as both his hand and the target are in view, these transitions are managed with ease, and he never makes a mistake.
So is viewing your movement on a video screen inherently different than using prism glasses? There are many cases when people need to use a video screen to monitor their work—robotic surgery, handling hazardous substances, remote control of probes, and so on. If motion errors in these tasks are unavoidable, it's an important issue to study.
A group led by Isabel Pennel devised an experiment to attempt to uncover just what's different about video monitors compared to prism glasses. They noted that most prism studies involved rotations of much less than 40 degrees—generally, they were closer to 10 degrees. So perhaps if the video image was rotated less, observers would make errors and adapt in a pattern similar to what earlier research had found for the prism glasses.
Pennel's team tested three groups of people. The first group saw images rotated by 40 degrees; the second, 10 degrees; and the third's images weren't rotated at all. All groups started with a pre-test, where the camera was not rotated. In this part, everyone was able to reach directly for the target. Next, during the testing phase, the camera was rotated by 10, 40, or 0 degrees. Finally, during the post-test, the camera was again set to 0 degrees for all groups. Here's a summary of the results:
In the testing phase, as expected, participants in the 40° condition were initially off by a lot—over 20 degrees. After 5 trials, their results stabilized, but they were still not accurate. However, participants in the 10° condition were also inaccurate at the start. Their results, too, stabilized after 5 trials, but they were as accurate as the 0° condition. So, unlike wearing prism glasses, when a video monitor is used to skew vision, an initial period of adaptation is required before people can accurately reach for a target.
In the posttest phase, those in the 40° condition again required a few trials before their responses were accurate. The 10° condition was statistically indistinguishable from the 0° condition, but even this group had some problem adapting to "normal" vision: when the room was darkened and they were asked to point straight ahead relative to their body position, responses were off by several degrees.
So something about a remote monitor is fundamentally different than using prism glasses. It can't be that accurate reaching is impossible, because those in the 0° condition performed just fine. Pennel et al. believe that the different viewing angle on the TV monitor may be partly to blame. If a camera angle is dramatically skewed, adapting in a short period of time is nearly impossible. As technology offers more opportunities for remote controlled devices, engineers will need to carefully test them to make sure humans are capable of safely operating them. And a remote-controlled softball pitcher is probably out of the question.
Pennel, I., Coello, Y., & Orliaguet, J.P. (2003). Visuokinesthetic realignment in a video-controlled reaching task. Journal of Motor Behavior, 35(3), 274-284.
This weekend, robot cars competed in a challenge that most humans would find trivial: drive 132 miles in 12 hours without crashing. Yet crash, they do. The difficult part isn't so much the steering and acceleration, it's determining the difference between an obstacle you must navigate around and a benign shadow on the road; it's deciding whether that dark patch ahead is open roadway or deep water. These things are so easy for humans that we take them for granted, yet for a machine it's a task literally in its infancy.
By the time a child is 2, it can easily tell the difference between a shadow and a real object, walk through an environment crowded with obstacles, do all the things that robots find most difficult. So how do babies learn these critical perceptual tasks that are so hard for computers? A team led by Paul Quinn investigated one aspect of this problem—how babies group similar objects.
Grouping objects is one way we are easily able to navigate through an environment: why, for example, do we see hundreds of leaves on a tree and recognize they are part of a solid object, but at the same time understand that the dapples of sunlight they filter onto the ground are not? One reason is that the leaves are all similar in shape, but the light admitted through the spaces between the leaves is not. You've probably heard the term "Gestalt," which refers to the set of principles we use to make visual sense of the world. The principles are simple: similarly shaped objects should be grouped together, items moving together are probably part of the same object, things closer together are likely to be connected. Somehow, however, while humans and animals are great at putting these rules together in order to function successfully, the challenge of programming a computer to do the same thing is a daunting task.
What Quinn's team wanted to study is whether babies are born with all of the Gestalt rules intact, or whether some of them are learned or acquired as they grow. A different team led by Quinn had established that babies as young as three months old are able to group objects that are similar in brightness or darkness. Now they wanted to examine whether babies can also group objects based on shape.
So how do you test such a thing in a baby too young to speak? Quinn et al. used a familiarization procedure: you show a baby one item until she's bored with it. Then show her a two new items—one is the same, and one is different—and see how which one seems to hold her interest more. In their first experiment, they used arrays of Xs and Os:
The researchers reasoned that if babies could successfully group the shapes, they would be more interested in a different pattern of blocks than a similar one. Two groups of babies were tested: 3- to 4-month-olds, and 6- to 7-month-olds. The younger babies stared at both patterns of blocks for an equal amount of time. Older babies, however, looked at the different pattern (horizontal blocks after being familiarized to vertical rows, or vertical blocks after horizontal rows) significantly more of the time—57.58 percent, compared to 42.42 percent for the similar pattern.
But what if babies weren't actually grouping the objects, but rather simply observing that the overall pattern had rotated? To address this concern, the team conducted a new experiment. In the first part, the procedure was the same, except that now babies were tested on patterns of Xs and Os instead of blocks:
But for the second part, babies were shown random patterns of Xs and Os:
The 3- to 4-month-olds again did not prefer either pattern, whether organized in rows or columns, or random. However, the 6- to 7-month-olds, as in the first experiment, preferred the rotated pattern of columns and rows (59.76 percent to 40.24 percent), but were ambivalent about the rotated random patterns, suggesting that the difference between the babies is really a difference in grouping ability.
But perhaps the problem wasn't that 3- to 4-month-olds couldn't group by shape, but that they couldn't distinguish between Xs and Os. So they gave one final test:
Now both younger babies (62.95 to 37.05 percent) and older babies (66.50 to 33.50 percent) preferred the solid pattern to the identical pattern. Clearly, both groups of babies can tell the difference between an X and an O, so the evidence really does seem to indicate that older babies have learned to group by shape.
When the Gestalt principles were being uncovered in the 1920s, researchers argued that the rules were innate. But Quinn and his colleagues argue that their experiment shows that babies actually learn how to group objects based on shape sometime between 3 and 6 months of age—so it's not an innate ability. With dozens of other principles to learn, and with an even more complex array of rules governing how the principles are applied, it's astounding to realize that babies acquire them so quickly, while scientists struggle to duplicate the same tasks.
Eventually robots will be able to drive trucks through the desert, but it will have taken much more than two years to accomplish—and hundreds of other human problems, such as understanding language, creating art, and feeling true love, will remain unsolved.
Quinn, P.C., Bhatt, R.S., Brush, D., Grimes, A., & Sharpnack, H. (2002). Development of form similarity as a Gestalt grouping principle in infancy. Psychological Science, 13(4), 320-328.
The Parthenon in Nashville, Tennessee, is a full-scale reconstruction of the rather more famous monument atop the Acropolis in Athens, Greece. We visited it with our daughter Nora a few years back:
As you can see, it's a dramatic building, dominating the landscape of the otherwise ordinary city park in which it sits. So, when we're confronted with such a massive landmark, do we use it to organize the surrounding area as well? Several studies have shown that we do pay attention to the surroundings of objects in order to remember their location. If we memorize the locations of a number of objects in a room, and later are asked to imagine ourselves back in a particular spot in the room, we're more accurate pointing in the direction of one of the objects if it's in front of us, rather than behind. If the room is rectangular, we're more accurate pointing to objects that are oriented in a direction parallel to the walls of the room—even if we memorized the objects when we were facing in an oblique direction.
So both the orientation of the viewer and the surrounding area can impact our memory for object locations. But what about when we're outside? Can features in the landscape similarly influence our memory? A team led by Timothy McNamara conducted a study in Centennial Park where the Nashville Parthenon is located to investigate these issues. Since the Parthenon is such a large, regular object, the team suspected it might influence how people's memory for other objects in the park. Volunteer participants in the experiment were blindfolded and driven to the park. The blindfolds were removed and the participants were led on one of two paths through the park. They were instructed to memorize the location of eight objects along the path. The key to the experiment was the orientation of the paths: one path was aligned with the shape of the Parthenon, but the second one was misaligned—rotated by 45 degrees, so participants were always walking at diagonals compared to the monument. I've created a diagram of the area using a satellite image from Terraserver (that's the Parthenon in the middle of the picture):
Notice that the items the participants memorized were located at the intersections of both of the paths. Participants were told that they were allowed to stop and turn their heads as they walked, but they should keep their bodies facing forward along the path at all times. They were led through the path twice, and by the end of the learning phase, they could recall the order in which they had seen each of the objects.
Next, they returned to the laboratory and were tested on the relative locations of the objects using a series of questions, all in the same format. For example, they might be instructed to "imagine you are standing by the bench and facing the tree. Point to the frame."
The results were as follows:
This chart compares the direction people imagined themselves heading to the errors they made pointing to the locations of other objects. Note that higher values on the chart correspond to less accurate location memory. Participants who followed the misaligned path were nearly always less accurate than those who walked the path aligned with the Parthenon—the only exceptions were facing 45 and 225 degrees, where the results were statistically indistinguishable. Why not in those cases? It may be that participants were using other landmarks to frame their memories—for example, the lake, which cuts at about a 45-degree angle on the lower right of the map.
The participants following the aligned path did the best when their imagined views aligned with both their path of travel and the walls of the Parthenon. There was not a comparable effect for those who followed the misaligned path.
McNamara et al. claim that theirs is the first experiment examining real-world outdoor frames of reference. They argue when a viewer's path is aligned with a landmark, the landmark becomes an important aid to navigation and memory. Even when recalling a view that was directly along their path of travel, the misaligned group was nearly always less accurate than the aligned group. But the wide disparity in accuracy throughout the various viewing angles suggests that many factors are involved. Other landmarks such as the lake probably also influenced the results.
McNamara, T.P., Rump, B., & Werner, S. (2003). Egocentric and geocentric frames of reference in memory of large-scale space. Psychonomic Bulletin and Review, 10(3), 589-595.
There are two different ways we might navigate from place to place: we either remember landmarks along the way, or we note how far we go in each direction, and what turns we've made along the way. The landmark system doesn't work very well in nondescript landscapes or in the dark, and the second system—which mariners term "dead reckoning," is susceptible to increasing errors as the distance we travel increases. So in day-to-day life, walking or driving around town, which method do we use?
A team led by Florence Gaunet explored this issue using a driving simulator. The participants in the experiment "drove" through virtual city streets in one of three ways. The active group actually controlled their path using a joystick, following directions given to them by an experimenter. The passive group took the same path, but had no control over their direction; instead the simulator simply propelled them along. Finally, the snapshot group was shown static images from along the journey, roughly every 2 seconds, approximating the same speed of travel as the other groups.
Gaunet and her colleagues tested how well participants knew where they had gone in three different ways: by showing snapshots from along the journey (these weren't necessarily the identical views the snapshot group had seen) and asking whether they had passed through that spot; by asking them to point back towards the starting position of their journey, and by asking them to draw the path of their journey on a piece of blank paper.
The first two tests revealed no differences between any of the groups. The group that had only seen snapshots was as accurate as the other groups, even when the snapshot they viewed wasn't one they had seen before. All three groups were equally accurate when asked to use the joystick to orient the simulator so that the virtual observer was facing towards the starting point.
It was only the drawings were analyzed that differences between the groups began to emerge. There was no significant difference between the maps drawn by either active or passive observers of the smooth animation. However, the snapshot viewers made large errors in their maps, transforming gradual corners into sharp ones, modifying irregular angles to 90-degree turns, and making significant mistakes in the total distance traveled.
Gaunet et al. don't claim that their data provides a definitive answer as to whether we navigate by landmarks or dead-reckoning; however, it is clear that when we only see a slide show of a trip, our ability to make a map is impaired—thus, we're getting important information about our location from the sensation of motion, suggesting that dead-reckoning is an important aspect of how we get around.
Gaunet, F., Vidal, M., Kemeny, A., & Berthoz, A. (2001). Active, passive and snapshot exploration in a virtual environment: Influence on scene memory, reorientation and path memory. Cognitive Brain Research, 11, 409-420.
All this talk about stereotypes can get you thinking. Perhaps some stereotypes reflect actual differences. Take color vision, for example: men often refer to themselves as "color-impaired," letting the women in their lives make home design decisions and even asking them to match clothing for them. Maybe they're just behaving in accordance with traditional stereotypes ... but maybe there's something more to it.
In the 1980s, vision researchers began to find some real physical differences between the eyes of many women and those of most men. "Normal" color vision is possible because we have three different types of cone cells in our eyes, each of which responds to a different wavelength of light. The process is basically the reverse of how a TV set or computer monitor works: on a TV, there are three different colored dots—red, green, and blue—and the millions of "colors" we see are based on mixtures of different proportions of those colors. In the eye, cone cells can have three different photopigments. These are usually generalized as red, green, and blue, but their actual values are closer to yellowish green, green, and bluish violet. To avoid confusion, psychologists typically refer to them to long-, medium, and short-wavelength sensitive cones. Supposing we're looking at a yellowish-green thing, the long-wavelength cones are stimulated the most, the medium-wavelength cones are stimulated a bit, and the short-wavelength cones are not stimulated at all, and the appropriate signal is sent along the optic nerve to the brain, which then recognizes the color as "yellowish-green."
What the researchers were finding when they actually looked at the structure of the eye is that many women—perhaps over fifty percent—possessed a fourth photopigment. Was this merely a genetic anomaly? Would the brain even be able to process this fourth input? The early research suggested that it would not. Women were no better at determining whether two very similar color patches were actually the same. They were only slightly better than men at detecting subtle spots of red light, a fact researchers attributed to individual difference.
However, Kimberly Jameson, Susan Highnote, and Linda Wasserman were not convinced by this evidence. Five- and six-year-old girls are better at naming colors than boys, and grown men are not as good at color-naming compared to women. They felt the existing measures of color sensitivity and color-matching did not capture all the differences between men and women, and devised a new experiment that they felt was more representative of real-world vision.
It's quite difficult to examine an eye to determine if it has the fourth photopigment—the process generally involves removing the eye itself. Jameson and her colleagues might have had just a bit of difficulty recruiting volunteers to participate in an experiment requiring such extreme measures, so instead they used a genetic test to determine how many different photopigments participants were likely to possess (they estimate this process to be about 90 percent accurate—biologists will recognize this as the genotype versus phenotype problem). Of 64 participants in the study, 23 were women with 4 photopigments, 15 were women with 3 photopigments, 22 were men with 3 photopigments, and 4 were men with 2 photopigments (this is commonly called "color-blindness," but most people with 2 photopigments can still distinguish between many colors).
Next, participants viewed a spectrum projected on a lucite window covered with tracing paper. Over the next hour and a half, they performed an array of tasks, including marking the edges of the visible rainbow, marking the locations of the "best example" of each of the major colors, and marking the edges of each "band" of color in the rainbow. Between each task, a camera flash was set off to mask the previous spectrum example, and the experimenter mounted a new sheet of tracing paper on the panel.
The most compelling results came from the number of spectral bands task:
Type of participant
Average number of spectral bands
Number of participants
Four-pigment females
10
23
Three-pigment females
7.6
15
Three-pigment males and females
7.3
37
Two-pigment males
5.3
4
Four-pigment females perceived significantly more bands of color than both three-pigment males and females. Further, three-pigment males and females are statistically indistinguishable, suggesting that the result is not due to some cultural difference between men and women.
So why were others unable to find significant results in a color-matching task when we see such dramatic results here? Jameson et al. suggest that there may be two (or more) different modes of seeing color, each processed differently in the brain. The brain may use the data from all four photopigments for some processes, but not for others. But this is still supposition. What's clear from this study is that the stereotype of women being better with color may reflect real differences between men and women.
Jameson, K. A., Highnote, S. M., & Wasserman, L. M. (2001). Richer color experience in observers with multiple opsin genes. Psychonomic bulletin and review, 8, 244-261.
There is considerable evidence that using a cell phone impairs driving ability. The research has even reached the popular consciousness: hosts of radio call-in shows ask cell-phone callers to pull over before making their comments; drivers give wide berths to people who are obviously talking while they drive.
All this knowledge begs the question: If drivers are aware of the dangers of cell phone use, can they compensate for their weaknesses and effectively negate any problems from driving with a phone? Mary Lesch and Peter Hancock had been part of a 2003 team that had found drivers reacted slower to a stoplight when distracted with a simulated cell-phone dialing task. In a new article, they took another look at the data from that study to see if they could answer this secondary question: can drivers effectively compensate for the distraction of a phone?
In the 2003 study, participants drove cars around an outdoor test track and were asked to stop as quickly as possible when a red light flashed outside the car. Their reaction time, stopping time, and and stopping distance were measured. To simulate using a cell phone, participants had to indicate whether a digit flashed on a small monitor inside the car matched a phone number they had memorized before the task began. Participants were slower to react and more likely to go through the red light when doing the cell phone task.
The same participants had also been asked to rate how confident they were about dealing with the distraction, but this data was not analyzed in the original study. In 2004, Lesch and Hancock returned to this data with a new analysis. They divided the respondents into two age groups, older and younger, and analyzed the data for men separately from women. They then looked at confidence levels and reaction time. Here is the result:
Drivers with high confidence are those who indicated they were "comfortable" or "very comfortable" dealing with the distraction while driving. Low confidence drivers rated themselves "uncomfortable" or "very uncomfortable." The change in reaction time reflects how much slower a driver reacted to a traffic light when distracted compared to driving without a distraction—so a taller bar indicates poorer performance driving with a cell phone. If drivers were able to compensate for their weaknesses, we might expect that more confident drivers would show a smaller change in reaction time. This holds true for male drivers, but women—especially older women—tend to react just as slowly, whether or not they believe they are comfortable handling the distraction.
The participants were also asked to rate how demanding the task was, and women rated it as significantly less demanding than men—despite the fact that women's overall performance was not significantly different from that of men.
Though they consider this research to be "exploratory" due to the small number of participants (36), Lesch and Hancock argue that these results suggest that individuals are unable to assess the danger of driving with a cell phone. A common argument against banning cell phone use while driving is that drivers are aware of the dangers and can use their judgment to decide when it's safe to make a phone call. If nothing else, these results certainly call that line of reasoning into question.
Lesch, M.F, & Hancock, P.A. (2004). Driving performance during concurrent cell-phone use: are drivers aware of their performance decrements? Accident Analysis and Prevention, 36, 471-480
We know that "average" faces are judged to be more attractive than the faces of the individuals making up the average. But this doesn't tell us what the mechanism for judging attractiveness is. Do we judge faces to be attractive because they are potential mates, or is there some other reason for perceiving attractiveness?
Jamin Halberstadt and Gillian Rhodes came up with a novel way to try to answer that question: instead of faces, they asked participants to rate other things. If we rate average birds as more attractive than actual examples of birds, then this could suggest that we have a general mechanism for judging attractiveness—after all, we can be reasonably certain that most people aren't seeking out birds as potential mates.
Halberstadt and Rhodes actually tested three different kinds of objects: birds, fish, and cars. For the bird experiment, they took drawings of 14 different passerines—robins, sparrows, and their cousins—and used software to generate a picture of an "average" passerine. Next, they took the drawings of the individual birds and distorted them in two different directions. They made the drawings more average by distorting them towards the average passerine. Next, they made exaggerated "caricatures" of the birds by emphasizing the differences between the original drawing and the average drawing. Thus, for each bird, a set of seven drawings was generated:
Participants rated each of these images on a scale of 1 to 10 for familiarity, averageness, or attractiveness. In every case, the more average birds were rated as more familiar, more average, and more attractive.
For the fish and car experiments, Halberstadt and Rhodes used a different technique. Starting with 32 fish drawings, they took two fish and averaged them together. Then they took two of these drawings and averaged them to create an average of four fish, continuing until they made one overall average fish comprising all 32 species. Again, participants rated each of these fish for attractiveness, familiarity, or averageness. Individual fish were rated as less attractive than two fish averaged together, and two-fish averages were less attractive than four-fish averages, but after that the pattern flattened out. 32-fish averages were no more attractive than 2-fish averages. There were similar results in the car experiment. Halberstadt and Rhodes reason that these larger groups of averages aren't different enough to show different levels of attractiveness, and that the difference between individuals and 2-item averages shows that averageness is a component of attractiveness not only for faces, but for other items as well.
Halberstadt and Rhodes conducted one more analysis of their data—they factored out the effect of familiarity. When this was done, for birds and fish, there was still a relationship between averageness and attractiveness. But there was no link in the case of cars, suggesting that we may judge attractiveness in living organisms differently than we do for artifacts.
Halberstadt, J. & Rhodes, G. (2003). It's not just average faces that are attractive: Computer-manipulated averageness makes birds, fish, and automobiles attractive. Psychonomic Bulletin and Review, 10(1), 149-156.
Click on the image below to be taken to a quicktime movie showing 9 different faces. When the movie is finished playing, drag the slider back and forth to pick the face you think is the most attractive.
The faces are composite images—"average" faces made by morphing together 48 different photos. Previous research has shown that people typically perceive average faces as more beautiful than unusual faces (and here we've written about how easy it is to change our conception of "average"). But what about people from different racial groups? Would a Caucasian perceive an "average" South Asian composite face as more or less beautiful than a composite from their own race? The example movie above was made from photos used by Gillian Rhodes and a team of colleagues to try to answer that same question. The first frame in the movie is a "Super-Caucasian" created by exaggerating the features that distinguish Caucasian men from Japanese men by 50 percent. The Caucasian-ness is gradually diminished—the middle frame in the movie a 50 percent Caucasian-Japanese blend, and the final frame is a 50 percent "Super-Japanese." One quarter of the way through the movie, you see a 100-percent Caucasian composite, and three-quarters of the way through, a 100-percent Japanese composite.
You might expect that Caucasians would prefer the average Caucasian face and Japanese would prefer the Japanese face. But the results found by Rhodes' team were rather different. They presented cards with each of these images (sorted in a random order) to Caucasian college students. They asked the participants to select the the most attractive card from the stack and rate it for attractiveness on a scale of 1 to 10. The process was repeated until all cards were rated. Here's a summary of the results:
While there was a trend, especially among females, to rate the Caucasian male faces as more attractive, the face rated most attractive of all was the combined average of both Caucasian and Japanese faces. When participants rated female faces, the average faces were favored to an even larger degree. What's going on here? Why should we prefer combined races over those more similar to us?
Rhodes and her team speculated that the preference for composite race faces may be related to health. People of mixed-race ancestry do appear to have a larger variety of genes, and on the the other end of the scale, when close relatives have children together, they are susceptible to a variety of ailments. In a separate experiment, the team examined not only composite faces, but also faces of Eurasian people (adult children of one Asian parent and one Caucasian parent). Again, participants were asked to rate the faces for attractiveness, but they also rated the faces for how "healthy" they appeared. Here are the results for male faces.
The Eurasian faces achieved a whopping advantage over both Caucasian and Asian faces, for both health and attractiveness ratings. Again, similar results were found for female faces. Rhodes et al. argue that this supports their health hypothesis—that humans select mates based on their perceived health level. However, they are also careful to note some potential problems with their research. By using composite faces, they show us faces no real human actually possesses (a separate portion of their experiment used individual faces, and similar results were found, but individual faces offer problems of their own, because participants may be judging other factors such as perceived wealth). Cultural differences such as a tendency not to smile for photos might also play a role. And it's entirely possible that "health" ratings may simply be an artifact of "attractiveness" ratings—wishful thinking that a pretty face is also a healthy one.
All those caveats aside, the basic finding—that we tend to find both mixed-race composites and actual people of mixed race more attractive—is surprising and interesting on its own.
Did your own result from the quicktime example above match the research findings? Let us know in the comments.
Rhodes, G., Lee, K., Palermo, R., Weiss, M., Yoshikawa, S., Clissa, P., Williams, T., Peters, M., Winkler, C., & Jeffery, L. (2005). Attractiveness of own-race, other-race, and mixed-race faces. Perception, 34, 319-340.
Taste is a notoriously difficult sense to study. My son Jim can't stand baked potatoes, but I can't get enough of them. I don't like watermelon, but the rest of my family gobbles it up. Even more perplexingly, I do like watermelon candy. With all the individual differences in taste, how can scientists learn anything specific about how the sense works?
The difficulties in taste study are compounded by the fact that taste is intimately associated with the sense of smell. Every kid knows to plug his nose when trying a food he or she doesn't like. Researchers must be constantly aware that differences in taste may also be due to smell. So when they want to answer a question like "how does the texture of a food impact its taste?", they know it will not be a simple matter to explore.
A group of scientists led by David Cook wanted to examine just that question. Initially, the problem seems simple enough: just cook up a bunch of food with different textures but the same amount of flavoring, and see how people perceive the taste. But then the problems come in: how do you vary texture? Will the texturing agents themselves change the food's flavor? What about individual differences between tasters? Do foods with different textures smell different?
The team started by trying two different flavors: sugar and iso-amyl acetate—a banana flavoring. They diluted each flavor in water, and then progressively varied the viscosity of each solution by mixing with three different tasteless thickening agents: guar gum, carageenan, and hydroxypropylmethyl cellulose (HPMC). (Now you know what about half the ingredients listed on your Hostess Twinkie are for!)
First they controlled for smell having taster smell and taste each sample and then place their nostrils on a device that physically measured the concentration of odorant that remained in their breath. In this way, they could determine how much odorant was present while the samples were being consumed, instead of relying on the tasters' perception of smell. They found no systematic difference in odor across the range of samples.
Next, a set of tasters who were trained and had two or more years' experience tasting food tried each sample, cleansing their palate with crackers and water between tastings, and rating them separately for "banana flavor" and "sweetness." As previous research had indicated, perception of flavor decreased as the solutions thickened. But why? Earlier studies had suggested that flavor diminished in proportion to the concentration of the thickening agent. While Cook et al.'s data confirmed this, they obtained different results for each thickener:
As the concentration of thickener increased, the perceived sweetness of solutions thickened with carrageenan decreased less than the other solutions. Perhaps concentration of thickener was not the best way model the impact of a thickener. So the team turned to the model of Jozef Kokini, who in the 1970s and 1980s developed a mathematical representation of how the mouth determines the viscosity of a substance. There's no reason to believe humans have detectors for carrageenan concentration in their mouths, but we do have nerves that can detect the sensation of touch. Perhaps we approximate viscosity by pressing food against the roof of our mouths with our tongues. Kokini developed a complex mathematical formula to model this method of determining "oral shear stress," or the amount of force it takes us to compress a thick liquid in our mouths. Cook et al. then applied this formula to their solutions and compared it to perceived sweetness:
Now, the results for each thickener follow a nearly identical path. Similar results were obtained for the banana flavoring.
Cook et al. conclude that we not only consider information from our taste buds and sensory organs in our noses, but also the feeling of the food in our mouths to determine flavor. While this research doesn't explain why Jim doesn't like potatoes, it does get us closer to understanding the many processes involved in the sensation of taste.
Cook, D. J., Hollowood, T. A., Linford, R., & Taylor, A. J. (2003). Oral shear stress predicts flavour perception in viscous solutions. Chemical Senses, 28, 11-23.
The human brain is incredibly specialized. There are individual neurons for recognizing faces, edges of objects, and specific sounds. One fruitful area of research recently has been to determine precisely how specialized the brain really is. Here's one example. The image below links to an animated movie. Click on it and see how quickly you can determine which direction the rectangles are moving:
If you're like most adults, you're able to determine the correct direction very quickly.
Now, take a look at this animation and try it again:
This one should have taken somewhat longer, even though it was moving the same direction as the first one. What's the difference? In the second animation, only the changing colors cue us to the direction of motion; in the first, we get an additional cue because the yellow rectangles only move a half-step between each frame. This and other research has led psychologists to believe that color and motion are processed separately in adults. The reason it takes longer to process the second animation is because the color information is processed first, then other parts of the brain must interpret that color change as motion.
Karen Dobkins and Christina Anderson wondered whether we're born with brains that process color separately from motion, or if this is a later development. To answer their question, they showed adults and babies animations similar to the ones above, but with a twist. When they showed the sharply contrasting black and yellow animation, they presented this animation with several different levels of contrast, gradually decreasing the contrast until it was just as difficult to determine the direction of the yellow rectangles as it was for the colored ones. Here's a summary of their results:
The vertical axis is the percentage of contrast compared to the original, 100 percent yellow-and-black animation in order for participants to have the same difficulty seeing motion as they did in the color animation (a rate of 75 percent correct). For adults, it was just a tiny fraction of the original level of contrast, but for the youngest babies, equivalence was reached at 10 percent of the original contrast level (I don't have Dobkins and Anderson's graphics, but here's a guess at what that might have looked like).
Dobkins and Anderson argue that this means that babies have not yet separated the processes of color detection and motion detection. If the colored animation gives adults so much difficulty that an extremely low-contrast black and yellow animation—more than 100 times lower than for babies—is its equivalent, then it must be because babies are processing the information differently from adult. Babies are better than adults at perceiving motion from color differences.
(A side note: How do you tell if a baby perceives motion? Watch their eyes. An independent observer, unaware of the particular animation the baby was watching, observed the baby's eyes to see which direction they were moving. When the eyes moved in the same direction as the animated motion, the researchers determined that the baby perceived the motion. The same method was used with the adults in this experiment.)
Dobkins, K.R., & Anderson, C.M. (2002). Color-based motion processing is stronger in infants than in adults. Psychological Science, 13(1), 76-80.
Perceiving motion creates a fascinating problem for psychologists. Physicists for centuries have devised a whole set of rules describing how objects actually move. These rules are so precise and accurate that it's tempting to say that the human perceptual system simply integrates them into motion we see, so that our mental representation of what we see is identical to what's actually going on in the world.
Some research, such as this article we reported on last month, supports that notion. Since we expect objects to keep on moving (the physical principle of momentum), then our representation of an object presented onscreen that suddenly disappears will continue to move. If we're tricked into thinking an object is moving faster, our representation takes even longer to stop.
But other explanations for this error are also possible. Scott Jordan and Günther Knoblich, for example, call this same phenomenon by a different name: "displacement of the perceived vanishing point." The traditional name for the effect, "representational momentum," may have a bit more of a ring to it, but Jordan and Knoblich have a point: are we really talking about people having the physical concept of momentum in their heads, or is this phenomenon more related to where we're planning for an object to go, regardless of the physical principles involved?
To test this notion that control is more important than physics, they devised a simple experiment. Participants would use buttons to control a small dot appearing on screen. Pressing one button caused it to accelerate to the left, and the other button cause acceleration in the opposite direction. By pressing the button repeatedly, they could move it quickly from side to side. Their task was to move it back and forth from one edge of the screen to the other, as rapidly as possible. Then suddenly the dot would disappear, and the task would change: now participants had to identify the spot where the dot disappeared. As expected based on the research of Hubbard (and pioneered by Jennifer Freyd and Ronald Finke), participants made consistent errors—on average, they thought the dot had disappeared farther along its path than it really had.
But what would happen if participants were given more—or less—control over the object? If Jordan and Knoblich are right in their explanation of the phenomenon, when people have more control over the object, the size of the error should change based on what they are planning to do next. So Jordan and Knoblich set up their experiment to change the way the objects were controlled. First, they varied the amount each button-click accelerated the object: for half of the trials, button-clicks had a low impact, and for the other half, they had a high impact. Since high-impact clicks impacted the motion of the dot to a greater degree, users felt they had more control.
To give participants less control, some of them were placed in pairs. Instead of using both buttons, they were only allowed to use one—for example, one partner might only use the left key and the other only use the right key. Sometimes the dot disappeared while the participant was in control, actively pressing his or her button, but sometimes it disappeared while the partner was in control. These paired participants also had high-impact and low-impact conditions.
The key to the experiment was this: regardless of the condition: paired, individual, low-impact, or high-impact, the task was designed so that the dots disappeared only when participants were trying to slow them down, and they always disappeared while traveling at the exact same velocity. With these variables eliminated, the results reflect only the level of control each participant had. Here's a chart summarizing the results:
Since participants were always trying to slow the dots down, we would expect their memory errors to be smaller when they have more control—and indeed, this is borne out by the results. The biggest errors occurred when the object was under the control of the partner and the button-click impact was low. Individual control led to the smallest errors (though, interestingly, the when the paired participants were in control, on the high-impact condition, their results were indistinguishable from the full-time individuals), and the paired individuals, when they were in control, were in the middle.
From a physics perspective, each of these objects had the same momentum when it disappeared, yet the phenomenon that Hubbard, Freyd, and many others call "representational momentum" varied. So clearly, whatever is going on inside of our heads isn't exactly the same as classical physics. This makes a certain amount of sense, because to make accurate predictions about the world, we need to not only incorporate the laws of physics, but also the intentions of ourselves and others—and physics says nothing about that.
Jordan, J.S., & Knoblich, G. (2004), Spatial perception and control. Psychonomic Bulletin and Review, 11(1), 54-59.
One of Jean Piaget's most famous observations is the phenomenon of "object permanence"—the idea that babies younger than eight months old have no conception of an object once it's hidden from view. It's easy to see how he came to this conclusion. Click on the picture of my daughter Nora at six months of age to see a video of her spectacularly failing the object permanence test. Once the object is hidden under a napkin, she seems to lose all interest in it.
But does she really not understand that the object is still there, or is she simply interested in other things? A team of researchers led by Yuyan Luo developed a different methodology to answer this question (Yuyan Luo, Renée Baillargeon, and Laura Breuckner, University of Illinois; and Yuko Munakata, University of Colorado, "Reasoning about a Hidden Object After a Delay: Evidence for Robust Representations in 5-Month-Old Infants," Cognition, 2003).
Their procedure involved dividing 5-month-old babies into two groups. The first group was shown a box which was placed behind a screen on a small stage. The experimenter showed the baby that there was no room in the six inches between the screen and the back wall of the stage to fit any objects other than the box. Next, with the screen up, the babies were shown a cylinder that was taller than the screen. The babies were distracted for a few minutes, then presented with the testing phase. In this phase, the cylinder was moved behind the screen—right where the baby had only ever known to be filled with a box. If the baby remembered that the box was there, Luo et al. reasoned, then she would be surprised to see the cylinder move through the space that should have been occupied by the box.
Of course, five-month-olds can't tell us if they're surprised, so typically "surprise" is measured by how long the baby looks at the object which is supposed to surprise them. Babies did indeed stare at the cylinder for up to a full 60 seconds after it stopped moving.
The second group was shown the identical procedure, except that the box behind the screen was only a half-inch deep, leaving plenty of room for the cylinder to pass unimpeded. While this group also stared at the cylinder for a long period of time, they tended to lose interest about 20 seconds quicker than the first group. It's difficult to come up with any explanation for this difference other than that the babies remember that the object behind the screen shouldn't block the path of the cylinder. The reason they stared at all is simply because they hadn't seen the cylinder move on its own before.
So apparently babies do remember objects, even several minutes after they are hidden. In the video, Nora may have remembered the rattle after it was placed under the napkin but simply lost interest now that it was no longer moving.
Rummaging through your bag in search of keys, it's clear that you can recognize objects using just your fingers. But is it easier to recognize the keys if you feel them as if you were going to open to door, or if you encounter the key's teeth in some odd orientation--like pointed straight up? Consider the following pictures. Which object looks more familiar?
Indeed, it is easier to recognize objects visually when you see them in familiar orientations. Could this orientation effect extend to objects we touch? Fiona Newell and her colleagues used little LEGO towers to ask this question, and (with the help of my local LEGO expert, Nora) we've recreated a couple of their stimuli. Notice that these objects are only slightly different from one another, making this a pretty tough task.
Newell et al mounted their towers on stands, so they could not be moved, and presented four towers for participants to learn. The participants were instructed to either visually inspect the towers, or used their hands to explore the objects presented behind a curtain. When participants thought they could identify each object, the researchers presented eight towers that included the original set plus four new ones. Half of the time the towers were in the same orientation that participants had learned, but half of the time towers had been rotated about the horizontal axis (180°, like doing a headstand). In addition to the orientation change, half of the time participants identified the towers using the same modality they had used at study--if you had visually inspected, you would still be visually identifying the objects. For the other half of the trials, modality was switched--if you had visually studied the towers, you would now be feeling the towers behind the curtain. With this design, Newel et al. get to answer two questions: does orientation matter for touch (if yes, the 180° rotation should be harder), and how readily can we transfer visual and touch information (changing modalities from study to test)?
You can see that when the towers have done a headstand (a 180° rotation about the x-axis), it's significantly harder to recognize them (p < .05). Orientation matters for touch, as well as vision. Remember that there are 8 towers to choose from, so participants are well above chance.
But something cool is going on when you switch modalities; now participants are better when objects are presented in the 180° rotation. In other words, if you studied an object with your fingers, you have an easier time identifying it by sight if it's upside down! What is going on?
Newell et al suggest that we think about what part of these towers would be easiest to feel. As you reach behind the curtain to learn the towers, the most natural grasp would involve you thumb on the front of the tower with fingers behind, perhaps leading to a much more detailed representation of the back of the object. With visual inspection, however, you'd know a lot more about the front of the object. So, if exploration by touch emphasizes the back of the object, and you are then trying visually identify it, a view of the back, thanks to a 180° horizontal rotation/headstand, would be most helpful.
This suggests that any rotation that switched the front and back views of the towers would help cross-modally (switching from vision to touch, or vice versa). And, a rotation that did not switch back and front would not help. Specifically, a rotation about the z-axis, or a cartwheel, should not lead to improved identification at 180°, because the front is the same at study and test (and so is the back). Here are the results for this transformation:
Again, percent correct are presented for each study-test pattern and orientation, but now it doesn't help to have the 180° rotation when you switch modalities. In fact, here it's easier to identify the object with either modality when it hasn't been rotated at all.
Looking for your keys, it's going to be easier to identify them from a familiar angle both by sight and by touch. However, visually you know a lot about the front of an object, while you know more about it's back if you've been grasping it. So if you need to identify which of two apparently identical paper coffee cups is yours, it would probably help to look at its back to see just where that seam you've been fiddling with is located.
Newell, F. N., Ernst, M. O., Tjan, B. S. & Bülthoff, H. H. (2001). Viewpoint dependence in visual and haptic object recognition. Psychological Science, 12, 37-42.
Imagine sitting in a coffee shop, having a nice conversation with your friend Dave. If Dave looks at something, your eyes will reflexively move to look at the same item. This is actually quite convenient, because it may help you figure out what Dave is talking about, or what he might comment on next. How much of this joint attention reflex depends on Dave's face? You'll do this even if he only moves his eyes, without turning his head, so it might be that what you're reacting to isn't so much his face, but the movement of his pupils.
What we know about certain brain mechanisms makes this question even more interesting: different regions of our brain are involved with processing faces (areas in the temporal lobe) and directing spatial attention (areas in the parietal lobe). In fact, face processing is so specialized there are specific sites within the right temporal lobe just for recognizing and identifying upright faces. It's much harder to recognize an upside down face, in part because the specialized area of the right hemisphere isn't used the same way.
Just a quick note about the hemispheres: you have two, and any time we say things like "language is in the left hemisphere" or "face processing is in the right hemisphere" we are talking about the brains of typical right-handed people. This is not because we have anything against left-handed people! It's just that most people are right-handed and so most of the work has been done with right-handed people. As far as the brains of left-handed people go, it does not seem to be the case that it's simply reversed, and there seem to be different kinds of left-handedness (to make things even more complicated!)
To figure out if joint attention depends on faces and what parts of the brain are involved, Alan Kingstone, Chris Kelland Friesen and Michael Gazzaniga conducted a series of experiments on a very special individual, "J.W." Because of severe epilepsy that was not responsive to drugs, J.W. had surgery to cut his corpus callosum, the large bundle of neurons that connect the two hemispheres. This greatly reduces the seizures of epilepsy, but it also means that information cannot be shared between J.W.'s hemispheres: what the right hemisphere does is completely isolated from what the left hemisphere does.
By using stimuli that are briefly presented, researchers can know which hemisphere has what information. When J.W. is looking at center of the computer screen, images presented on the left half of the computer screen will be processed by the right hemisphere; the left hemisphere will process images presented on the right half of the computer screen. This is true for everyone (not just J.W.) and is called contralateral projection--the left hemisphere receives input from the right side of the world (vision, sound and touch) and controls the right side of your body; the right hemisphere does the same for the left. When J.W. keeps his eyes focused on the center of the screen, we know what each hemisphere is seeing (we know this for other people, too, but their hemispheres are connected and so information passes from one to the other). Using two very simple faces and a target detection task, Kingstone et al. can find out how much the joint attention reflex depends on the face by manipulating whether or not the right hemisphere (the one with specialized face processing) is presented with the target.
The task is really simple: keep your eyes on the cross, and quickly identify where the asterisk appears. Half the time it appears on the left, and half on the right. To see if joint attention matters, two simple faces are presented, and right before the target appears, pupils are added to both faces. Half of the time the pupils mean that the faces are looking where the target is about to appear, but half of the time they look the wrong way.
In three different experiments, Kingstone and his team presented J.W. with faces, inverted faces and only the eyes. When the target was on the right (and so processed by the left hemisphere), J.W. was equally fast at detecting any target for all three kinds of pictures. However, when the target was on the left (and so processed by the right hemisphere) the faces mattered. Here's a graph depicting how quickly targets on the left were detected in congruent and incongruent trials.
Targets appearing on the left, and so processed by the right hemisphere, take longer to find when the face looks the wrong way. It takes longer because attention is reflexively directed where the face is looking, and that's not where the target appears. The face didn't matter at all when the left hemisphere was detecting the target (times were equally fast, around 450 ms), so this gaze effect has something to do with face processing of the right hemisphere. You can see that in the graph when you consider the inverted face condition: now it doesn't matter where the face is looking, the right hemisphere is fast for both congruent and incongruent trials. Face processing is best with upright faces, and the specialized areas of the right hemisphere aren't used the same way when the face is inverted (they might not be used at all). How much of the face do we need for a gaze effect to occur? In fact, the eyes alone are enough to redirect attention for the right hemisphere, as you can see in the graph.
Now, back to your conversation with Dave in the coffee shop--the fact that your eyes reflexively follow his does have to do with his face and eyes, not just the fact that some movement was occurring. If it was just movement, both hemispheres would show the gaze effect, not just the one with specialized face processing.
Kingstone, A., Friesen, C. K. & Gazzaniga, M. S. (2000). Reflexive joint attention depends on lateralized cortical connections. Psychological Science, 11, 159-166.
We've posted on boundary extension before, here, here, and here, but we've never written about boundary extension and kids. Boundary extension is when we remember more of a picture than was actually shown to us, as if our mind is actively creating a portion of the image we didn't see, beyond its boundaries. A 2002 team led by John Seamon found that people of all ages experience boundary extension.
Some research has found evidence that boundary extension doesn't work for all images. We reported on a study by Andrew Mathews and Bundy Mackintosh suggesting that for emotional, arousing images, people with higher anxiety levels have less boundary extension than those with lower anxiety levels. Perhaps children also experience higher levels of anxiety when viewing emotional images. A team led by Ingrid Candel published the results of their study to test this hypothesis in the American Journal of Psychology.
They showed ten- to twelve-year-olds either neutral pictures (like bananas or an old tire) or emotional pictures (like a shark or a gun), then asked them to draw those pictures from memory. The redrawn pictures were carefully measured to see if their boundaries were larger or smaller than the originals (this is the method used by Helene Intraub and Michael Richardson in their pioneering 1989 study on the subject). Unlike Mathews and Mackintosh, Candel et al. found boundary extension for both emotional and neutral pictures—an average of 48 percent the size of the original for neutral pictures, and 46 percent the size of the original for emotional pictures.
Does this mean children don't restrict their focus on emotional pictures? There were a few differences between Candel et al.'s study and that of Mathews and Mackintosh. First of all, Candel et al. made no attempt to distinguish between mildly emotional and extremely emotional images. They did not separate the kids into high- and low-anxiety groups. And they used a different method—drawing, rather than choosing between different pictures. It's possible that one or more of these differences is what accounts for the disparity between results. It's also possible that kids simply react differently to emotional pictures than adults do.
Mathews and Mackintosh speculate that the lower boundary extension they found for high-anxiety people viewing extremely negative pictures was due to their inability to avert their eyes from the threatening picture. Perhaps children behave more like the low-anxiety group in Mathews and Mackintosh's study, and avert their eyes, thereby exploring more of the boundaries of the picture. It's also possible that Candel et al.'s pictures simply weren't negative enough for them to observe the effect. The disparity between these two results highlights the difficulty in psychological research, particularly when complex phenomena such as emotions, anxiety, or childhood are being studied.
Candel, I. Merckelbach, H., Houben, K., and Vandyck, A. (2004). How children remember neutral and emotional pictures: Boundary extension in children's scene memories. American Journal of Psychology 117(2), 249-257.
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