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Related Concept Videos

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...
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...
Nonconscious Mimicry01:13

Nonconscious Mimicry

Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
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...
Implicit Memories01:24

Implicit Memories

Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...
Control Systems01:10

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...

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Related Experiment Video

Updated: Jun 21, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

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Published on: May 8, 2021

Biomimetic approach to tacit learning based on compound control.

Shingo Shimoda1, Hidenori Kimura

  • 1RIKEN Brain Science Institute (BSI)-Toyota Collaboration Center, Nagoya 463-0003, Japan. shimoda@brain.riken.jp

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|August 5, 2009
PubMed
Summary

Biological systems adapt using unique learning mechanisms. This study statistically links individual computational media activities to emergent global behaviors like autonomous rhythm generation and balanced postures.

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Robotics

Background:

  • Living organisms exhibit remarkable adaptation to novel environments through learning mechanisms distinct from current artificial intelligence.
  • Biological control systems, such as neural networks and intracellular processes, rely on computational media with simple rules for emergent complex behaviors.
  • Previous work demonstrated bipedal walking using this biological control paradigm without explicit robot models or trajectory planning.

Purpose of the Study:

  • To elucidate the underlying principles connecting individual computational media activities to emergent global behaviors in adaptive systems.
  • To statistically explain how simple, localized rules can lead to complex, optimized global functions.
  • To address the puzzle of how individual element activities achieve global coordination in machine learning inspired by biology.

Main Methods:

  • A statistical approach was employed to analyze the relationship between individual computational media activities and global network behaviors.
  • The study focused on understanding emergent properties without relying on global performance indices.
  • The methodology aimed to bridge the gap between micro-level element interactions and macro-level system functionality.

Main Results:

  • The research demonstrates that individual activities within computational media can generate optimized global behaviors.
  • Autonomous rhythm generation was observed as an emergent property from localized computational element interactions.
  • The study successfully showed the learning of balanced postures without explicit global optimization criteria.
  • A statistical framework was established to link micro-level activities to macro-level network functions.

Conclusions:

  • Individual activities of computational media, governed by simple rules, can lead to sophisticated, adaptive global behaviors.
  • The statistical approach provides a theoretical foundation for understanding emergent properties in biologically inspired machine learning.
  • This research offers insights into creating more adaptive and robust artificial systems by mimicking biological learning principles.