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Related Concept Videos

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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Labeling Emotion01:20

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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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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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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Introduction to Learning01:18

Introduction to Learning

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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.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Cognitive Learning01:21

Cognitive Learning

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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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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Robust-EQA: Robust Learning for Embodied Question Answering With Noisy Labels.

Haonan Luo, Guosheng Lin, Fumin Shen

    IEEE Transactions on Neural Networks and Learning Systems
    |April 7, 2023
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    Summary
    This summary is machine-generated.

    This study introduces a robust learning algorithm to improve embodied question answering (EQA) systems facing noisy data. The novel method enhances agent performance in complex environments by effectively filtering inaccurate labels.

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    Area of Science:

    • Artificial Intelligence
    • Robotics
    • Computer Vision

    Background:

    • Embodied question answering (EQA) involves agents exploring environments to answer user queries, with applications in robotics and personal assistants.
    • High-level visual tasks like EQA are vulnerable to noisy inputs due to complex reasoning processes.
    • Robustness against label noise is crucial for practical EQA applications.

    Purpose of the Study:

    • To propose a novel label noise-robust learning algorithm specifically designed for the embodied question answering (EQA) task.
    • To enhance the reliability and performance of EQA systems in the presence of noisy visual and navigation data.

    Main Methods:

    • A joint training co-regularization method was developed for noise-robust filtering within the visual question answering (VQA) module.
    • A two-stage hierarchical robust learning algorithm was implemented to filter noisy navigation labels at both trajectory and action levels.
    • A joint robust learning mechanism was employed to integrate purified labels and coordinate the EQA system.

    Main Results:

    • The proposed algorithm demonstrated superior robustness in deep learning models compared to existing EQA models under noisy conditions.
    • Effectiveness was validated in extremely noisy environments (45% noisy labels) and low-level noisy environments (20% noisy labels).

    Conclusions:

    • The developed label noise-robust learning algorithm significantly improves the performance of embodied question answering systems.
    • The findings pave the way for more reliable and practical applications of EQA in real-world scenarios.