JoVE Science Education
Cognitive Psychology
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JoVE Science Education Cognitive Psychology
Visual Search for Features and Conjunctions
  • 00:00Overview
  • 00:40Stimulus Design
  • 02:13Conducting the Study
  • 03:04Analysis and Results
  • 04:12Applications
  • 05:05Summary

חיפוש חזותי אחר תכונות וצירוף מילים

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Overview

מקור: המעבדה של ג’ונתן פלומבאום – אוניברסיטת ג’ונס הופקינס

איך אנשים מוצאים אובייקטים בסצנות חזותיות עמוסות? חשבו, למשל, לחפש מפתחות על שולחן מבולגן, למצוא את הפרי הבשל ביותר בחנות המכולת, לאתר את המכונית שלכם כשאתם לא ממש זוכרים איפה החניתם אותה, או למצוא חבר ותיק בשער יציאה משדה התעופה. ברור, הבנה של תפיסה חזותית הולכת לשחק תפקיד בכל תשובות, וליתר דיוק, הבנה של תשומת לב חזותית תהיה קריטית.

תשומת לב חזותית מתייחסת ליכולת להתמקד רק בחלק מהתמונה, לאצור את משאבי העיבוד באופן סלקטיבי כדי לקבוע אם הדבר שמחפשים – היעד, בז’רגון הניסויי הסטנדרטי – קיים. כדי לחקור חיפוש ותשומת לב, פסיכולוגים ניסיוניים פיתחו פרדיגמה נפוצה הידועה (באופן לא מפתיע) כחיפוש חזותי.

פסיכולוגים גם הניעו מחקר רב על ידי האינטואיציה כי כל תיאוריה טובה של חיפוש הולך להיות, כדי להסביר מדוע כמה דברים קל למצוא ואחרים קשה למצוא. אז בהקשר של פרדיגמת החיפוש החזותי, פסיכולוגים תפיסתיים התמקדו לעתים קרובות בחיפושים קלים מנוגדים עם אלה קשים יותר. הניגוד המשפיע ביותר הוא בין מה שהחוקרים מכנים חיפוש תכונות לבין חיפוש בשילוב.

Procedure

1. עיצוב גירוי הניסוי כולל שני סוגים של ניסויים. במחצית מהניסויים – ניסויי החיפוש בתכונות – המשתתפים יתבקשו למצוא פס אדום בין הכחולים. אז תעבדו קבוצה של 40 צגים, שממקמים את הפס האדום באופן אקראי בכל אחד מהם, וממקמים באופן אקראי גם 3, 6, 9 או 12 פסים כחולים. מספר הפסים הכחולים הוא עומס מסיח ה?…

Results

Note that response times in feature search trials are relatively unaffected by distractor load (Figure 3). In contrast, conjunction search response times increase linearly. In fact, the slope of that function describes the amount of extra search time it takes, on average, for each additional distractor in the scene. In this case, it looks like about 50 ms per item. Similarly, both searches take about 200 ms with only three distractors present. This suggests that a uniform amount of time is necessary to get a search going and make a response.

The difference between feature and conjunction search suggests how one of the challenges faced by the human visual system involves putting different kinds of information together. Finding a red bar amongst all blue ones is easy—it pops out—because only one kind of information is relevant: color. But finding something that is defined by multiple different kinds of features—in this case, orientation and color—needs focused attention to help bind those features together.

Figure 3
Figure 3: Response times as a function of distractor load in target present trials. Feature search and conjunction search conditions are shown in green circles and yellow triangles, respectively.

Applications and Summary

In the real world, understanding how visual search works has many important applications. For example, major research programs are currently applying an understanding of visual search in the laboratory to understand and improve how doctors search for certain telltale signatures when they look at an x-ray or MRI scan. Similar research programs look at how TSA personnel search through scans of passenger baggage at the airport, and even how athletes locate their teammates on a field.

Transcript

Visual attention refers to the ability to focus in on just a part of an image. To study how people attend to objects in cluttered visual scenes, psychologists use a paradigm known as visual search.

Often, visual search experiments can help researchers explain why some objects are easy to find and others more difficult.

Using the visual search paradigm, this video will demonstrate how to design and identify stimuli for experiments, as well as perform, analyze, and interpret results.

To design the stimuli, compose a pair of conditions that are very similar in terms of display contents, but vary in terms of search difficulty. Consider the classic contrasting example between ‘Feature Search’ and ‘Conjunction Search.’

In the Feature Search condition, design trials in which a single feature distinguishes a target amongst its neighbours, known as the distractors. Here, the target is a red bar, and all the distractors are blue bars. The participant should efficiently find the target, as it “pops out” quickly, even when the distractor load increases from three, six, nine, or 12 blue bars.

In the Conjunction Search condition, design trials in which the target shares similarities with distractors. Here, a red target bar is oriented at -45°, and both red and blue distractors are oriented at +45°. In this case, the participant should find the search more difficult because the similarities don’t provide a “pop out” effect.

Within each search condition, create two sets of 40 trials where the target is present or absent. Make sure to include 10 trials with each distractor load of three, six, nine, or 12 bars. Randomly interleave all trials to guarantee unpredictable sequences for each search type.

To begin the experiment, start by running the Feature Search and Conjunction Search tasks. Use a counterbalanced design, so that some participants will begin with Feature Search, whereas others will complete Conjunction Search first.

With the participant sitting at the computer, assign the ‘M’ key to represent target present responses, and the ‘Z’ key for target absent responses. Indicate to the participant that he or she should press the respective keys to complete each trial as quickly as possible, trying not to make mistakes.

During each trial, capture whether the participant’s response was correct or incorrect, as well as the response time. Output the results into a spreadsheet.

After the participant has completed both search types, examine the overall performance for the target absent trials to make sure the participant was paying attention. Exclude any participant who performs less than 75% correct on these trials.

Once criterion performance has been verified, average together each participant’s response times for all of the target present trials, as a function of search condition (Feature vs. Conjunction) and distractor load.

The data are then graphed by plotting the mean response times across distractor loads for the feature and conjunction search conditions. The response times for the Feature Search task are relatively unaffected by distractor load. In contrast, Conjunction Search response times increase linearly with distractor load. In addition, both searches take about 200 ms with the minimal of three distractors present. This suggests that a uniform amount of time is necessary to start searching and make a response.

Now that you are familiar with setting up a visual search experiment, you can apply this approach to answer more specific research questions.

One of the main challenges faced by our visual system involves the complex integration of multiple visual features. Finding a red bar among all blue ones is easy because only color information is relevant.

However, when finding an object that has several different features, such as orientation and color, more attention must be used to bind those features together.

For example, researchers apply visual search properties to improve how physicians search for certain telltale signs when they look at an x-ray or MRI scan.

In addition, the visual search approach affects how TSA personnel search through scans of passenger baggage at the airport.

You’ve just watched JoVE’s introduction on conducting a visual search experiment. Now you should have a good understanding of how to make visual search stimuli for two different types of visual search conditions, how to conduct the experiment, and finally how to analyze and interpret the results.

You should also have an idea about the type of attention that is required when you are looking for keys on a messy desk or finding the ripest-looking fruit at the grocery store.

Thanks for watching!

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Cite This
JoVE Science Education Database. JoVE Science Education. Visual Search for Features and Conjunctions. JoVE, Cambridge, MA, (2023).