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Related Concept Videos

Purposive Learning01:22

Purposive Learning

142
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...
142
Language Development01:22

Language Development

395
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
395
Introduction to Learning01:18

Introduction to Learning

472
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...
472
Cognitive Learning01:21

Cognitive Learning

423
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...
423
Observational Learning01:12

Observational Learning

210
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...
210
Associative Learning01:27

Associative Learning

444
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...
444

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Related Experiment Video

Updated: Jul 19, 2025

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
12:49

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Published on: July 13, 2019

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Clinical Prompt Learning With Frozen Language Models.

Niall Taylor, Yi Zhang, Dan W Joyce

    IEEE Transactions on Neural Networks and Learning Systems
    |August 11, 2023
    PubMed
    Summary

    Prompt learning matches fine-tuning performance for clinical tasks using fewer parameters and less data. This efficient method is ideal for healthcare settings with limited resources and privacy concerns.

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    Area of Science:

    • Artificial Intelligence
    • Natural Language Processing
    • Clinical Decision Support

    Background:

    • Transformer-based language models initially relied on pretraining and fine-tuning for state-of-the-art performance.
    • Recent advancements show prompt learning can match or exceed fine-tuning for certain tasks with minimal parameter updates.

    Purpose of the Study:

    • To evaluate prompt learning's effectiveness on clinical decision tasks.
    • To compare prompt learning directly against traditional fine-tuning methods in a clinical context.

    Main Methods:

    • Investigated prompt learning strategies for clinical decision-making.
    • Directly compared prompt learning performance with traditional fine-tuning approaches.

    Main Results:

    • Prompt learning achieved comparable or superior performance to fine-tuning.
    • Prompt learning utilized significantly fewer trainable parameters (up to 1000x less).
    • Prompt learning required less training time, data, and computational resources.

    Conclusions:

    • Prompt learning presents a highly efficient alternative to fine-tuning for clinical NLP tasks.
    • Its reduced resource demands and data requirements make it suitable for public health providers facing limitations.
    • Patient privacy concerns are better addressed by prompt learning's minimal data usage for fine-tuning.