Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

44.8K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
44.8K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Associative Learning01:27

Associative Learning

1.3K
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...
1.3K
Purposive Learning01:22

Purposive Learning

513
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...
513
Observational Learning01:12

Observational Learning

1000
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...
1000
Learning Disabilities01:25

Learning Disabilities

626
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
626

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ego4D: Around the World in 3,600 Hours of Egocentric Video.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

A domain-agnostic approach for characterization of lifelong learning systems.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Learning Spherical Convolution for 360<sup>°</sup> Recognition.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Tracking.

Clinical chemistry·2021
Same author

Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Dynamics Tracking.

medRxiv : the preprint server for health sciences·2021
Same author

Emergence of exploratory look-around behaviors through active observation completion.

Science robotics·2020
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

7.3K

End-to-End Policy Learning for Active Visual Categorization.

Dinesh Jayaraman, Kristen Grauman

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

    Autonomous agents can improve visual recognition by learning motion policies and predicting how movement affects their view. This "active recognition" approach enhances performance by enabling agents to actively seek better perspectives.

    More Related Videos

    Immunohistochemical Visualization of Hippocampal Neuron Activity After Spatial Learning in a Mouse Model of Neurodevelopmental Disorders
    07:43

    Immunohistochemical Visualization of Hippocampal Neuron Activity After Spatial Learning in a Mouse Model of Neurodevelopmental Disorders

    Published on: May 12, 2015

    11.8K
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.6K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
    07:31

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

    Published on: February 8, 2019

    7.3K
    Immunohistochemical Visualization of Hippocampal Neuron Activity After Spatial Learning in a Mouse Model of Neurodevelopmental Disorders
    07:43

    Immunohistochemical Visualization of Hippocampal Neuron Activity After Spatial Learning in a Mouse Model of Neurodevelopmental Disorders

    Published on: May 12, 2015

    11.8K
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.6K

    Area of Science:

    • Computer Vision
    • Robotics
    • Machine Learning

    Background:

    • Autonomous agents require robust visual recognition in unconstrained environments.
    • Moving to acquire new views (active vision) offers a performance improvement opportunity.

    Purpose of the Study:

    • To develop an end-to-end trainable system for active recognition using recurrent neural networks.
    • To investigate if reasoning about motion's effect on visual input enhances active vision capabilities.

    Main Methods:

    • Trained a recurrent neural network for end-to-end learning of motion policies for active recognition.
    • Incorporated a module to forecast the impact of agent motion on its environmental representation.
    • Evaluated the system on three challenging datasets.

    Main Results:

    • The end-to-end system successfully learned effective motion policies for active category recognition.
    • "Learning to look ahead" by forecasting motion effects significantly improved recognition performance.
    • The proposed active vision approach demonstrated robust performance across diverse datasets.

    Conclusions:

    • End-to-end learning of motion policies is feasible for active recognition in autonomous agents.
    • The capacity to reason about the effects of motion is crucial for advanced active vision.
    • Forecasting visual consequences of movement enhances the performance of autonomous visual recognition systems.