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Related Concept Videos

Retrieval01:12

Retrieval

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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
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Elaborative Rehearsals01:07

Elaborative Rehearsals

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Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
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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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Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

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Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
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Observational Learning01:12

Observational 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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Implicit Memories01:24

Implicit Memories

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Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...
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Related Experiment Video

Updated: Jan 19, 2026

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
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The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

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Cross-Batch Reference Learning for Deep Retrieval.

Huei-Fang Yang, Kevin Lin, Ting-Yen Chen

    IEEE Transactions on Neural Networks and Learning Systems
    |September 24, 2019
    PubMed
    Summary
    This summary is machine-generated.

    A new training method called cross-batch reference (CBR) optimizes deep networks for image retrieval. This approach enhances feature learning for better retrieval performance by enabling interbatch information passing.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep learning significantly improves visual recognition tasks.
    • Deep features from classification tasks are generic but suboptimal for image retrieval.
    • Current deep features are optimized for classification, not retrieval criteria.

    Purpose of the Study:

    • To introduce a novel training mechanism for optimizing deep networks for image retrieval.
    • To enhance the performance of deep representations in image retrieval applications.

    Main Methods:

    • Introduced cross-batch reference (CBR), a training mechanism for deep networks.
    • Enabled interbatch information passing in stochastic gradient descent (SGD) training.
    • Derived a differentiable lower bound for optimizing mean average precision (mAP) loss.

    Main Results:

    • CBR training enhances deep networks with a retrieval criterion.
    • Learned features effectively discriminate between relevant and irrelevant samples.
    • Experiments demonstrate favorable performance improvements on several datasets.

    Conclusions:

    • CBR is an effective training mechanism for improving deep network performance in image retrieval.
    • The proposed method optimizes deep networks for retrieval criteria, outperforming classification-optimized features.
    • CBR offers a viable approach for learning discriminative features for image retrieval tasks.