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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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Associative Learning01:27

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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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Introduction to Cognitive Psychology01:20

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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Real-World Application of Classical Conditioning01:15

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Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
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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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Behaviorism01:28

Behaviorism

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The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
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Related Experiment Video

Updated: Dec 29, 2025

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
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The Emergence of Stimulus Relations: Human and Computer Learning.

Chris Ninness1, Sharon K Ninness2, Marilyn Rumph1

  • 1Behavioral Software Systems, 2207 Pinecrest Dr, Nacogdoches, TX 75965 USA.

Perspectives on Behavior Science
|February 1, 2020
PubMed
Summary
This summary is machine-generated.

Connectionist models (CMs) simulate human learning by deriving novel stimulus relations, offering a powerful tool for behavior analysis research. These artificial neural networks show symbiotic potential with human studies for understanding learning processes.

Keywords:
Connectionist modelContextual controlEpochsMomentumStimulus equivalence

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Area of Science:

  • Behavior Analysis
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Traditional stimulus equivalence research relies on human participants.
  • Connectionist models (CMs) are emerging as computational analogues for human learning.
  • Existing CMs like RELNET simulate stimulus relations using matching-to-sample procedures.

Purpose of the Study:

  • To provide an overview of connectionist models in behavior analysis.
  • To explore the application of CMs in simulating derived stimulus relations.
  • To highlight the symbiotic potential between human and simulated investigations.

Main Methods:

  • Review of existing connectionist models (e.g., RELNET, compound stimuli algorithms).
  • Discussion of derived stimulus relations in human academic remediation.
  • Introduction of a working example: Emergent Virtual Analytics (EVA) neural network.

Main Results:

  • CMs demonstrate the capacity to approximate human acquisition of stimulus relations.
  • Neural networks learn to derive novel or untrained stimulus relations over training epochs.
  • EVA model illustrates how CMs can simulate and predict human-derived stimulus relations.

Conclusions:

  • Connectionist models offer a valuable tool for understanding, simulating, and predicting human learning.
  • The symbiotic relationship between human and artificial investigations enhances behavior analysis.
  • Emerging neural network approaches like EVA advance practical and experimental behavior analysis.