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Piaget's Stage 2 of Cognitive Development01:14

Piaget's Stage 2 of Cognitive Development

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The preoperational stage, the second of Jean Piaget's four stages of cognitive development, spans approximately ages 2 to 7 and is characterized by the emergence of symbolic thinking. During this stage, children use language, images, and symbols to represent objects and concepts, enabling them to engage in imaginative and pretend play. This symbolic thinking supports children's ability to perform make-believe actions, such as imagining a broom as a horse or their hand as a phone, blending...
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Purposive Learning01:22

Purposive Learning

395
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...
395
Observational Learning01:12

Observational 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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Steps in the Modeling Process01:14

Steps in the Modeling Process

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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
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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.
Tolman introduced the idea that behavior is influenced by...
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Piaget's Stage 1 of Cognitive Development01:14

Piaget's Stage 1 of Cognitive Development

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The sensorimotor stage, the initial phase of Jean Piaget's theory of cognitive development, spans the first two years of a child's life. During this period, infants actively engage with their surroundings, building cognitive awareness through direct interaction with the world. This interaction is primarily based on sensory perception and motor actions, allowing infants to gradually understand basic physical properties and predict how objects interact within their environment.
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Related Experiment Video

Updated: Jan 5, 2026

Practical Methodology of Cognitive Tasks Within a Navigational Assessment
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Active Exploration for Learning Symbolic Representations.

Garrett Andersen1, George Konidaris2

  • 1PROWLER.io, Cambridge, United Kingdom.

Advances in Neural Information Processing Systems
|October 29, 2019
PubMed
Summary
This summary is machine-generated.

This study presents an active exploration algorithm for efficient learning of symbolic environment models. The algorithm guides agents to uncertain areas, outperforming random and greedy methods in games.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Data-efficient learning is crucial for agents operating in complex environments.
  • Current exploration strategies often lack efficiency in model learning.
  • Symbolic models offer a powerful abstraction for understanding environment dynamics.

Purpose of the Study:

  • To introduce an online active exploration algorithm for data-efficient symbolic model learning.
  • To guide agent exploration towards areas of high model uncertainty.
  • To evaluate the algorithm's performance against baseline exploration policies.

Main Methods:

  • An online active exploration algorithm with two parts: intermediate Bayesian symbolic model generation and uncertainty-guided exploration.
  • Utilizing collected data to build an initial symbolic model.
  • Directing future exploration based on the model's uncertainty.

Main Results:

  • The proposed algorithm significantly outperforms random and greedy exploration policies.
  • Demonstrated effectiveness in two distinct computer game domains: an Asteroids-inspired game and the Treasure Game.
  • Achieved data-efficient learning of abstract symbolic models.

Conclusions:

  • The developed algorithm enables efficient learning of symbolic environment models through active exploration.
  • The approach is robust across environments with varying dynamics and logical complexity.
  • This work contributes to advancing data-efficient reinforcement learning and model-based AI.