시각적 통계 학습
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Overview
출처: 조나단 플롬바움 연구소 -존스 홉킨스 대학
시각적 환경에는 공간과 시간의 개체 간의 관계와 관련된 방대한 양의 정보가 포함되어 있습니다. 특정 개체는 다른 개체 부근에 나타날 가능성이 높습니다. 이러한 규칙성을 학습하면 개체 인식을 비롯한 다양한 시각적 처리를 지원할 수 있습니다. 당연히, 인간은 이러한 규칙들을 자동으로, 신속하고, 의식하지 않고 배우는 것처럼 보입니다. 이러한 유형의 암시적 학습에 대한 이름은 시각적 통계 학습입니다. 실험실에서는 부수적인 인코딩 패러다임으로 연구됩니다 : 참가자는 말도 안되는 물체의 흐름을 관찰하고 스트림의 기본 통계 구조와 관련이없는 작업인 커버 작업을 완료합니다. 그러나 통계 구조가 존재하고, 짧은 노출 기간 의 후속 – 일부 실험에서 10 분으로 짧은 – 친숙성 테스트는 참가자에 의해 학습의 정도를 보여줍니다.
이 비디오는 시각적 통계 학습을 유도하고 테스트하는 표준 방법을 보여줍니다.
Procedure
Results
Because each familiarity test includes one triplet and one foil (a randomly generated non-triplet), chance performance overall is 50%. Score each trial in terms of whether the participant chose the triplet or the foil as more familiar, and selecting triplets more than half of the time constitutes a demonstration of statistical learning. After testing 10-20 participants, average together the rate of choosing the familiar triplet among all the participants. A simple bar graph is a good way to visualize the main effect (Figure 3).
Figure 3. Statistical learning rate as measured by triplet identification in a familiarity test. 50% is chance performance, equal familiarity with both triplets and foils.
Applications and Summary
Visual statistical learning has been utilized as a starting point for investigating a variety of issues in learning, perception, and memory. These include the influence and necessity of attention for learning, the brain areas involved in implicit visual memory and object recognition, as well as differences and similarities in learning about spatial vs. temporal structure. Visual statistical learning is also thought to be an example of a broader class of statistical learning mechanisms, including auditory statistical, which is thought to support early language learning in infants and children because the sounds and letters in a language tend to appear with highly reliable statistical relationships.
Transcript
Learning regularities-the statistical structures of objects in our external world-is an important part of visual processing.
Objects in our visual environment occur in the dimensions of space and time. Certain objects are more likely to appear near others, like a coffee cup next to a computer.
Such occurrences provide regularities that support predictable object recognition, in which humans learn automatically and quickly, without conscious awareness.
This video demonstrates how to setup and conduct a visual statistical learning experiment using an incidental encoding paradigm, as well as how to analyze the data and interpret the results.
In this experiment, the stimuli-sets of nonsense objects grouped into triplets-are displayed on the center of a computer screen, one at a time, for 250 ms each.
To establish regularities-the statistical structure-the objects within a triplet are always shown in the same sequence, but the order of triplets is presented at random.
Thus, the transitional probability between elements of a given triplet is always 1, whereas the transitional probabilities between unrelated elements are considerably lower.
In order to unknowingly expose participants to the object sequences, a cover task is used with colored objects. In this case, participants are asked to make a response when the objects are gray and to withhold when the objects randomly appear red.
After the cover task is completed, participants are given a familiarity task to test the extent of encoding the previous nonsense objects. During each familiarity trial, previously viewed triplets are randomly presented along with newly generated triplets, referred to as foils.
Now, participants are asked to identify which set is more familiar. The dependent variable then is the number of times participants correctly identify the prior triplets as most familiar, rather than the foils.
If no learning occurs during the initial encoding phase, the actual and foil triplets would be picked the same number of times. On the other hand, if learning occurred, the actual triplets will be picked more frequently than the foils.
Before the participant arrives, verify that the stimuli and coding parameters to be used have been generated.
To begin the experiment, greet the participant in the lab and explain the general procedures that will be used for the task.
Have the participant sit comfortably in front of the computer monitor and keyboard. Explain that when a gray object appears on the screen they should press the ‘J’ key, and when red objects appear they should withhold responses.
Once the participant understands the task rules, start the first portion of the experiment, the incidental encoding phase. Expose the participants to the object sequences for 10 min.
After the short exposure period, explain that there is one more task to complete in a 5 min period. Instruct the participant that they will now see two sets of triplets and should press the 1-key or the 2-key to indicate which grouping looks more familiar. Tell them that they should guess if they don’t recognize either.
After confirming that the participant is ready to begin, start the 30 familiarity trials.
To analyze the data in the familiarity phase, score each trial where the participant chose the familiar triplet as correct and the foil triplet as incorrect.
To visualize the results, graph the mean of the percent correct responses across participants. Because chance performance is 50%, note that visual statistical learning occurred, as participants correctly identified the familiar objects around 70% of the time.
Now that you are familiar with the methods of inducing and testing visual statistical learning, let’s take a look at other ways experimental psychologists use statistics to investigate learning.
The paradigm can be translated into broader classes of sensory learning mechanisms that include the auditory domain. For example, infants and children use auditory statistics in early language formation because the sounds and letters in a language tend to appear with highly reliable statistical relationships.
In another experiment, researchers investigated how the associations between letters and colors can be unknowingly or implicitly learned. Over several days, participants read texts that were customized to have four distinctly colored letters.
When asked to identify the color of letters presented on a screen, participants were faster and more accurate for letter-color pairs that were the same as in the customized text. This suggests that the statistical structure of the colored letters was implicitly learned.
You’ve just watched JoVE’s introduction to visual statistical learning. Now you should have a good understanding of how to setup and perform the experiment, as well as analyze and assess the results.
Thanks for watching!