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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.
Tolman introduced the idea that behavior is influenced by...
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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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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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Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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Associative Learning01:27

Associative Learning

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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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How do students improve their value-based learning with task experience?

Sarah DeLozier1, John Dunlosky

  • 1a Department of Psychology , Colorado State University , Fort Collins , CO , USA.

Memory (Hove, England)
|July 24, 2014
PubMed
Summary
This summary is machine-generated.

Students improve value-based learning with experience, but not by selectively encoding high-reward items. Overall recall increases, not strategic focus, drive score improvements in reward-based learning tasks.

Keywords:
Learning how to learnMetamemorySelective encodingValue-based learning

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Area of Science:

  • Cognitive Psychology
  • Educational Psychology
  • Neuroscience

Background:

  • Learners often improve performance with experience in tasks involving variable rewards.
  • It is debated whether this improvement stems from enhanced memory for high-value items or general memory enhancement.

Purpose of the Study:

  • To investigate if learning improvements in reward-based tasks are driven by selective encoding of high-value items.
  • To determine the mechanisms behind performance gains in value-based learning.

Main Methods:

  • Four experiments involved participants studying and recalling words with assigned point values across multiple trials.
  • Study conditions varied, including self-pacing, experimenter-pacing, time penalties, and value-emphasized instructions.
  • Performance was measured by points earned (based on correct recall of valued items) and overall recall rates.

Main Results:

  • Participants consistently earned more points across initial trials, indicating learning.
  • Performance improvements were linked to increased overall recall, not to a greater focus on encoding high-value words.
  • Neither explicit instructions nor varying study conditions promoted selective encoding of high-value items.

Conclusions:

  • Metacognitively-driven selective encoding is not the primary mechanism for improvement in value-based learning.
  • Performance gains appear to be driven by general memory enhancements rather than strategic item selection.
  • Further research should explore alternative explanations for improved value-based learning outcomes.