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Related Concept Videos

Purposive Learning01:22

Purposive Learning

188
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Observational Learning01:12

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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 Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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Nonconscious Mimicry01:13

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Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
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Introduction to Learning01:18

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Socially situated artificial intelligence enables learning from human interaction.

Ranjay Krishna1, Donsuk Lee1, Li Fei-Fei1

  • 1Computer Science Department, Stanford University, Stanford, CA 94305.

Proceedings of the National Academy of Sciences of the United States of America
|September 19, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) agents can now learn from social interactions to ask better questions in new situations. This socially situated learning approach improves AI

Keywords:
computer visionhuman-centered AIhuman–computer interactionsocially situated learning

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Artificial intelligence (AI) agents struggle with novel situations not present in training data.
  • Socially situated learning, acquiring information from others, is crucial for human development but challenging for AI.
  • Existing AI methods often assume human availability and willingness to answer any query.

Purpose of the Study:

  • To formalize socially situated learning for AI as a reinforcement learning problem.
  • To develop an AI agent that learns to ask informative natural language questions to acquire missing information.
  • To enable AI agents to adapt their social interaction strategies based on observed human norms.

Main Methods:

  • Framed socially situated learning as a reinforcement learning task.
  • Developed an interactive agent that learns to ask natural language questions about images.
  • Deployed the agent on a social network, using observed social interactions as rewards.

Main Results:

  • The agent improved its visual recognition performance by 112% over an 8-month deployment with 236,000 users.
  • A controlled experiment showed the agent outperformed an active-learning baseline by 25.6%.
  • The agent successfully adapted its question-asking behavior based on user engagement norms.

Conclusions:

  • Socially situated learning is a viable approach for enhancing AI adaptability in open environments.
  • AI agents can learn to interact socially to acquire necessary information, respecting human interaction norms.
  • This research opens avenues for continuously improving AI agents through social learning.