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Related Concept Videos

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Related Experiment Video

Updated: Jun 24, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Explanation strategies in humans versus current explainable artificial intelligence: Insights from image

Ruoxi Qi1, Yueyuan Zheng1,2, Yi Yang2

  • 1Department of Psychology, University of Hong Kong, Hong Kong SAR, China.

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|June 11, 2024
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Explainable AI (XAI) methods show higher similarity to human explanation strategies when highlighting observable causality. This suggests XAI can be more accessible by better aligning with how humans explain their reasoning.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Explainable AI (XAI) methods offer insights into AI decision-making, but their alignment with human explanation processes is not well understood.
  • Comparing XAI outputs with human cognitive strategies is crucial for developing more intuitive and effective AI systems.
  • Current XAI techniques often lack a clear connection to how humans naturally explain their reasoning.

Purpose of the Study:

  • To investigate human attention strategies during image classification and explanation tasks using eye-tracking.
  • To compare human attention patterns with saliency-based explanations generated by current XAI methods.
  • To identify which XAI explanation types best resemble human explanation strategies.

Main Methods:

  • Human participants classified images and explained their classifications.
  • Eye-tracking technology monitored participants' visual attention during both tasks.
  • Saliency maps from XAI methods were compared with human attention data.

Main Results:

  • Humans employed more explorative attention strategies when explaining compared to classifying.
  • Two distinct human explanation strategies emerged: focused scanning with conceptual explanations and explorative scanning with visual explanations.
  • XAI saliency maps most closely resembled human explorative attention strategies.
  • XAI explanations emphasizing observable causality through perturbation aligned better with human strategies than those focusing on internal AI features.

Conclusions:

  • Human explanations integrate both visual and conceptual information, serving distinct functions.
  • XAI methods that highlight observable causality demonstrate greater similarity to human explanation strategies.
  • Aligning XAI with human cognitive processes can enhance user accessibility and understanding of AI.