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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.
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Cognitive Models for Machine Theory of Mind.

Christian Lebiere1, Peter Pirolli2, Matthew Johnson2

  • 1Department of Psychology, Carnegie Mellon University.

Topics in Cognitive Science
|December 1, 2024
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Summary
This summary is machine-generated.

Cognitive models can achieve machine theory of mind (MToM) by replicating human behavior and personalizing predictions. This approach enhances AI by understanding and shaping human actions in complex tasks.

Keywords:
ACT‐RCognitive modelsHuman–machine teamingInstance‐based learningIntelligent agentsModel tracingPersonalizationTheory of mind

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • A true machine theory of mind (MToM) requires replicating human cognition, personalizing models from limited data, and explaining predictions via cognitive processes.
  • Current AI lacks the nuanced understanding of human behavior necessary for true MToM.

Purpose of the Study:

  • To propose and demonstrate a class of cognitive models capable of achieving MToM.
  • To show how these models can optimize AI agents through personalized understanding of human behavior.
  • To explore applications in collective human-machine intelligence.

Main Methods:

  • Utilizing cognitive architectures (e.g., ACT-R) for mechanistic grounding of behavior.
  • Employing instance-based learning to capture behavioral diversity from individual experiences.
  • Implementing knowledge tracing and model tracing for personalized model alignment with behavior.

Main Results:

  • Demonstrated a cognitive model for decision-making in a Minecraft search and rescue task.
  • Showcased personalized cognitive models that enable AI to diagnose, predict, and manage human behavior.
  • Generated outputs such as cognitive load, error probability, self-efficacy, and trust calibration predictions.

Conclusions:

  • Cognitive models offer a viable path toward achieving MToM.
  • Personalized cognitive models can significantly enhance AI capabilities in human-AI collaboration.
  • This research has implications for future AI development and collective intelligence systems.