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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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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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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 learning is based on purposive behavior, incidental learning, and insight learning.
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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Related Experiment Video

Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings.

Zhuofeng Wu1, Chaowei Xiao2, Vg Vinod Vydiswaran1

  • 1University of Michigan, Ann Arbor.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing
|December 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces HiCL, a hierarchical contrastive learning framework that improves text representation by considering both local and global relationships. HiCL enhances training efficiency and effectiveness for semantic textual similarity (STS) tasks.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Traditional sequence encoding methods often overlook local text features, hindering generalization to shorter texts.
  • Existing approaches face challenges in balancing training efficiency and the effectiveness of representation learning.

Purpose of the Study:

  • To propose HiCL, a novel hierarchical contrastive learning framework.
  • To enhance training efficiency and effectiveness in text representation learning.
  • To improve performance on semantic textual similarity (STS) tasks.

Main Methods:

  • HiCL employs a hierarchical approach, processing text at both local segment-level and global sequence-level.
  • It utilizes contrastive learning for both segment and sequence representations.
  • Efficient encoding is achieved by processing short segments first and then aggregating them, addressing the quadratic complexity of transformers.

Main Results:

  • HiCL significantly enhances the performance of the SNCSE model on seven STS tasks.
  • An average improvement of +0.2% on BERTlarge and +0.44% on RoBERTalarge was observed.
  • The framework demonstrates superior effectiveness and efficiency compared to traditional methods.

Conclusions:

  • HiCL offers an effective and efficient approach to text representation learning.
  • The hierarchical contrastive learning strategy successfully models both local and global text relationships.
  • This framework provides a strong foundation for advancing semantic textual similarity research.