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Related Concept Videos

Purposive Learning01:22

Purposive Learning

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 bonus...
Introduction to Learning01:18

Introduction to Learning

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...
Cognitive Learning01:21

Cognitive Learning

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.
Tolman introduced the idea that behavior is influenced by...
Observational Learning01:12

Observational Learning

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 because...
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Associative Learning01:27

Associative Learning

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.
Classical conditioning, also known...

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Related Experiment Video

Updated: May 18, 2026

Long-term Sensory Conflict in Freely Behaving Mice
06:12

Long-term Sensory Conflict in Freely Behaving Mice

Published on: February 20, 2019

Self-consistent learning of the environment.

Kukjin Kang1, Shun-ichi Amari

  • 1BSI, RIKEN, Hirosawa, Wako, Saitama, Japan. kkang@brain.riken.jp

Neural Computation
|September 14, 2012
PubMed
Summary

This study explores Bayesian inference for environmental feature estimation. It finds that correlated features improve accuracy, and self-consistent learning with maximum a posteriori probability (MAP) or stochastic Bayesian estimation (SBE) methods can adapt to these correlations.

Area of Science:

  • Computational neuroscience
  • Machine learning
  • Statistical inference

Background:

  • Accurate environmental feature estimation is crucial for intelligent systems.
  • Perception accuracy is often underestimated due to ignoring feature correlations.
  • Bayesian inference provides a framework for updating beliefs with new evidence.

Purpose of the Study:

  • To investigate how estimation error in Bayesian processes depends on prior feature distributions.
  • To explore methods for learning prior distributions of correlated environmental features from experience.
  • To analyze the impact of different Bayesian estimation techniques on accuracy and learning dynamics.

Main Methods:

  • Developed a self-consistent learning process to jointly estimate environmental features and their correlations.

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Last Updated: May 18, 2026

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  • Applied maximum a posteriori probability (MAP) estimation to reduce feature vector dimensionality.
  • Investigated stochastic Bayesian estimation (SBE) and a variant (SBE2) for robust learning under noise.
  • Analyzed learning dynamics, including hysteresis, under varying noise levels.

    • Main Results:

    • Ignoring feature correlations leads to underestimated perceptual accuracy.
    • MAP estimation in self-consistent learning can exhibit hysteresis at critical noise levels.
    • Stochastic Bayesian estimation (SBE) ensures convergence to the true distribution regardless of noise, albeit with lower accuracy than MAP.
    • SBE2 offers improved accuracy over SBE without introducing hysteresis.

    Conclusions:

    • Self-consistent learning is essential for accurately modeling correlated environmental features.
    • MAP and SBE/SBE2 offer distinct trade-offs between accuracy, robustness to noise, and learning stability.
    • The choice of Bayesian estimation method impacts the reliability and efficiency of environmental feature inference.