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

Observational Learning01:12

Observational Learning

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 because...
Purposive Learning01:22

Purposive Learning

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 bonus...
Associative Learning01:27

Associative Learning

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...
Introduction to Learning01:18

Introduction to Learning

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...
Cognitive Learning01:21

Cognitive Learning

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.
Tolman introduced the idea that behavior is influenced by...
Steps in the Modeling Process01:14

Steps in the Modeling Process

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...

You might also read

Related Articles

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

Sort by
Same author

Toddlers' Active Gaze Behavior Supports Self-Supervised Object Learning.

Developmental science·2026
Same author

Re-assessing the Evidence for MR Abilities in Children Using Computational Models.

Developmental science·2026
Same author

Efficient coding in active perception: A developmental perspective on autonomous control.

Advances in child development and behavior·2026
Same author

Infants and Mobiles: Developing an Understanding of Cause and Effect.

Developmental science·2026
Same author

Simulated Cortical Magnification Supports Self-Supervised Object Learning.

IEEE International Conference on Development and Learning. IEEE International Conference on Development and Learning·2026
Same author

Performance of the Verily Study Watch for measuring sleep compared to polysomnography.

Frontiers in sleep·2025

Related Experiment Videos

Goal-directed learning of features and forward models.

Sohrab Saeb1, Cornelius Weber, Jochen Triesch

  • 1Frankfurt Institute for Advanced Studies, Goethe University, Frankfurt am Main, Germany. saeb@fias.uni-frankfurt.de

Neural Networks : the Official Journal of the International Neural Network Society
|July 21, 2009
PubMed
Summary

This study introduces a novel brain model incorporating feature detection and memory for improved reinforcement learning. The model enhances performance by filtering irrelevant data and predicting future states, linking reward systems to cortical circuits.

Related Experiment Videos

Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Cognitive Science

Background:

  • The brain constructs internal world representations for action, traditionally attributed to unsupervised learning from sensory input.
  • Emerging evidence points to the dopaminergic system's role, modelable via reinforcement learning (RL).
  • Standard RL models lack explicit feature detection and memory integration.

Purpose of the Study:

  • To develop an enhanced RL model integrating feature detection and a memory layer for state representation.
  • To investigate how this integrated model handles task-irrelevant information and sensory input loss.
  • To explore the emergence of goal-directed forward models within this framework.

Main Methods:

  • A novel neural network architecture combining a feature detection stage and a memory layer (previous state and action).
  • A temporal difference-based learning rule for training weights connecting memory inputs to the state layer.
  • Evaluation of network performance under conditions with irrelevant features and absent sensory input.

Main Results:

  • The enhanced model maintained performance despite task-irrelevant features and sensory input invisibility.
  • A goal-directed forward model emerged, selectively representing task-relevant state-action pairs.
  • The model demonstrated a link between RL, feature detection, and forward models.

Conclusions:

  • The proposed model offers a mechanism for how reward systems utilize cortical circuits for goal-directed feature detection and prediction.
  • Integrating feature detection and memory enhances RL agent's robustness and predictive capabilities.
  • This work bridges RL, predictive processing, and the understanding of neural computation in goal-directed behavior.