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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

246
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...
246
Purposive Learning01:22

Purposive Learning

121
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...
121
Associative Learning01:27

Associative Learning

407
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...
407
Observational Learning01:12

Observational Learning

186
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...
186
Instinctive Drift01:05

Instinctive Drift

228
Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
228
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Incremental Incomplete Concept-Cognitive Learning Model: A Stochastic Strategy.

Zhiming Liu, Jinhai Li, Xiao Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |November 24, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel stochastic incremental incomplete concept-cognitive learning method (SI2CCLM) for cognitive computing. The new approach enhances classification accuracy and efficiency by using an order-independent cognitive process.

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    Area of Science:

    • Cognitive Computing
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Concept-cognitive learning (CCL) aims to mimic human cognition for continuous knowledge acquisition.
    • Existing CCL models and algorithms are limited, often depending on attribute order, which impacts classification performance.
    • Current methods fall short of replicating the complexity of the human cognition process.

    Purpose of the Study:

    • To develop a novel concept-cognitive learning method that overcomes the limitations of existing approaches.
    • To introduce a new classification algorithm based on the proposed method.
    • To analyze the parameters and convergence of the new algorithm and demonstrate its cognitive effectiveness.

    Main Methods:

    • A stochastic incremental incomplete concept-cognitive learning method (SI2CCLM) was developed.
    • The SI2CCLM utilizes a stochastic strategy, making the cognition process independent of attribute order.
    • A new classification algorithm was designed and implemented based on the SI2CCLM.

    Main Results:

    • The SI2CCLM demonstrated cognitive effectiveness compared to other CCL methods.
    • The proposed classification algorithm achieved an average accuracy of 82.02% across 24 datasets.
    • The SI2CCLM-based model showed advantages in elapsed time compared to 20 other classification algorithms.

    Conclusions:

    • The SI2CCLM offers a more robust and efficient approach to concept-cognitive learning.
    • The developed classification algorithm provides improved accuracy and speed, surpassing existing methods.
    • This research advances cognitive computing by offering a more sophisticated model of cognitive processes.