Jove
Visualize
Contact Us

Related Concept Videos

Modeling in Therapy01:26

Modeling in Therapy

141
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
141
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

146
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
146
Associative Learning01:27

Associative Learning

548
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...
548
Steps in the Modeling Process01:14

Steps in the Modeling Process

297
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
297
Labeling Emotion01:20

Labeling Emotion

231
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...
231
Observational Learning01:12

Observational Learning

292
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...
292

You might also read

Related Articles

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

Sort by
Same author

Long Distance Non-Contact Dermoscopy: Technological Foundations, Clinical Applications, and Future Directions.

International journal of dermatologyยท2026
See all related articles
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 Experiment Video

Updated: Sep 3, 2025

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

531

Modeling Subjective Affect Annotations with Multi-Task Learning.

Hassan Hayat1, Carles Ventura1, Agata Lapedriza1

  • 1Department of IT, Multimedia and Telecommunications (IMT), Universitat Oberta de Catalunya, 08018 Barcelona, Spain.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

Aggregated annotations in affect modeling can bias results. A Multi-Task (MT) deep learning architecture, which jointly models individual and aggregated annotations, outperforms traditional Single-Task (ST) models trained only on aggregated data.

Keywords:
aggregated annotationsemotion modelingmultitask learningsubjective labelssupervised learning

More Related Videos

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
12:55

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

8.5K
Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

15.8K

Related Experiment Videos

Last Updated: Sep 3, 2025

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

531
Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
12:55

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

8.5K
Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

15.8K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Affective Computing

Background:

  • Supervised learning relies on dataset annotations for model generalization.
  • Current practice aggregates multiple annotator labels (e.g., average, majority vote) for training.
  • This aggregation can obscure subjective information crucial for tasks like emotion modeling, introducing annotation bias.

Purpose of the Study:

  • To investigate the limitations of using aggregated annotations in affect modeling.
  • To propose and evaluate a novel Multi-Task (MT) deep learning architecture for improved affect recognition.
  • To compare the MT architecture against a Single-Task (ST) architecture trained on aggregated data.

Main Methods:

  • Developed and compared two deep learning architectures: Single-Task (ST) and Multi-Task (MT).
  • The ST architecture models single emotional perceptions.
  • The MT architecture jointly models individual annotator perceptions and aggregated annotations simultaneously.

Main Results:

  • The MT approach demonstrated superior performance in modeling both individual and aggregated annotations compared to methods trained solely on aggregated data.
  • The MT architecture achieved state-of-the-art results on established benchmarks: COGNIMUSE, IEMOCAP, and SemEval_2007.
  • The study highlights the weakness of relying exclusively on aggregated annotations for subjective tasks.

Conclusions:

  • Jointly modeling individual and aggregated annotations via a Multi-Task architecture is more effective for affect recognition.
  • The proposed MT approach mitigates information loss and bias inherent in aggregated annotations.
  • This method offers a significant advancement for modeling subjective human experiences in machine learning.