Counting Beans
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Transcript of Counting Beans
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Counting Beans
Dr. Timothy BenderPsychology DepartmentMissouri State University901 S. National AvenueSpringfield, MO 65897
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Counting Beans
In 1871, Jevons wanted to determine how many items he could perceive accurately in one brief glance at a stimulus. Specifically, he wanted to know how many items he could detect without consciously counting them. He assumed that the mind engaged in parallel processing of the number of objects, at least if the number was small enough. At that time, the estimates ranged from 4 to 6 items.
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Counting Beans
In order to study this question, Jevons would throw a handful of beans at a small box. He would then glance swiftly at the box and try to estimate how many beans were in the box. He then counted the actual number and recorded his estimate with the real number. He did this more than 1,000 times!
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Counting Beans
We will try something similar. However we first need to operationally define what a ‘glance’ will be. In this demonstration there are two glance times. Some occur for 167 milliseconds and some for 333 milliseconds. This will allow us to compare a short glance with a slightly longer glance.
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Counting Beans
Please prepare a response sheet consisting of the numbers 1 through 40 written on your paper.
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Counting Beans
For each of the next 40 slides you will see a + sign in the middle of the screen for about 1 second. That will be followed by an image of one or more beans. The image will appear for either 167 milliseconds or 333 milliseconds. Record the number of beans you think you saw.
OPERATING HINT: To continue from trial to trial make sure your cursor is near one side of the slide before you click for the next stimulus.
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Trial 1
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Trial 2
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Trial 3
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Trial 4
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Trial 5
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Trial 6
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Trial 7
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Trial 8
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Trial 9
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Trial 10
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Trial 11
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Trial 12
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Trial 13
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Trial 14
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Trial 15
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Trial 16
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Trial 17
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Trial 18
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Trial 19
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Trial 20
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Trial 21
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Trial 22
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Trial 23
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Trial 24
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Trial 25
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Trial 26
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Trial 27
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Trial 28
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Trial 29
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Trial 30
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Trial 31
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Trial 32
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Trial 33
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Trial 40
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Counting Beans
The following are the correct answers for the 167 millisecond slides. Count the number of times students were correct. Record those scores.1. 2 beans 7. 3 beans 12. 7 beans 22. 5 beans
2. 9 beans 8. 6 beans 15. 7 beans 28. 8 beans
4. 9 beans 9. 3 beans 16. 5 beans 29. 4 beans
5. 10 beans 10. 4 beans 17. 10 beans 30. 8 beans
6. 1 bean 11. 2 beans 21. 1 bean 40. 6 beans
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Counting Beans
The following are the correct answers for the 333 millisecond slides. Count the number of times students were correct for each number. Each student could be correct twice for each. Record those numbers.3. 1 bean 20. 5 beans 27. 3 beans 35. 7 beans
13. 2 beans 23. 9 beans 31. 3 beans 36. 6 beans
14. 1 bean 24. 9 beans 32. 6 beans 37. 4 beans
18. 2 beans 25. 7 beans 33. 10 beans 38. 5 beans
19. 4 beans 26. 8 beans 34. 8 beans 39. 10 beans
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Counting BeansThis table shows Jevons’ percentage correct when the actual number of beans ranged from 3 to 15. (Sorry about that 120 percent . I have no idea how to
tell PowerPoint not to do that!)
0
20
40
60
80
100
120
3 4 5 6 7 8 9 10 11 12 13 14 15
Actual Number of Beans
Perc
en
tag
e o
f C
orr
ect
Esti
mate
s
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Counting Beans
Jevons felt that the number of items a person could perceive in a single glance probably would vary from person to person. However, his own data suggested that an estimate of from 4 to 6 items was likely. His own limit was between 4 and 5, which is very close to the estimate offered by Sperling (1960), based on data collected using the whole report method.
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Counting Beans
Jevons also tended to over-estimate the smaller numbers and under-estimate the larger ones.
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Counting Beans
If you compare the number of times students in class were correct to Jevons’ own data, you will probably find a similar curve. For small numbers, many students were correct. For larger numbers, very few students were correct.
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Counting Beans
Finally, if you compare the data for the 167 millisecond ‘glance’ with those from the 333 millisecond ‘glance,’ you might see better performance for the longer glance. It is possible that with the longer glance, you were able to group the larger sets of beans into smaller sets of 3, 4, or 5. In other words, you had time to engage in some basic chunking of the perceptual information.
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Counting Beans
Data such as these do not tell us how much information we can perceive in a single glance, but they do suggest that we can perceive small numbers of stimuli as a single unit and that we may not have to consciously count the stimuli in order to discriminate quantity.
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References
Jevons, W. S. (1871). The power of numerical discrimination. Nature, 3, 281-282.
Sperling, G. (1960). The information available in brief visual presentations. Psychological Monographs: General and applied, 74, 1-29.