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Related Concept Videos

Purposive Learning01:22

Purposive Learning

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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...
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Cognitive Learning01:21

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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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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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Observational Learning01:12

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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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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.
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Related Experiment Video

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The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
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Implicit learning is order dependent.

Randall K Jamieson1, John R Vokey2, D J K Mewhort3

  • 1Department of Psychology, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada. randy.jamieson@umanitoba.ca.

Psychological Research
|October 22, 2015
PubMed
Summary
This summary is machine-generated.

Implicit learning shows order dependence. While training order didn't affect overall grammar recognition, it influenced judgments on specific items, suggesting a need for item-level analysis in implicit learning theories.

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Area of Science:

  • Cognitive Psychology
  • Learning Sciences
  • Artificial Grammar Learning

Background:

  • Implicit learning is crucial for acquiring complex information without conscious awareness.
  • Artificial grammar tasks are widely used to study implicit learning mechanisms.
  • Existing models primarily focus on category-level discrimination.

Purpose of the Study:

  • To investigate order dependence in implicit learning within the artificial-grammar task.
  • To examine the impact of training string order on both category and item-level discrimination.
  • To propose refinements to existing theories of implicit learning.

Main Methods:

  • Two experiments were conducted using the artificial-grammar task.
  • Participants were exposed to grammatical training strings in varying orders.
  • Performance was assessed via discrimination of grammatical vs. ungrammatical test strings and judgments on specific test items.

Main Results:

  • Training string order did not influence participants' ability to distinguish grammatical from ungrammatical strings (category-level).
  • However, the order of training strings significantly affected participants' judgments about specific test strings (item-level).
  • This indicates order dependence operates at the item-level of analysis.

Conclusions:

  • Implicit learning in artificial grammar tasks is sensitive to the order of presented information.
  • Current theories need to incorporate item-level analysis to fully account for implicit learning.
  • The findings offer a novel approach for evaluating and advancing implicit learning models.