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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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 of...
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...
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...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Outracing champion Gran Turismo drivers with deep reinforcement learning.

Nature·2022
Same author

Phoneme restoration and empirical coverage of Interactive Activation and Adaptive Resonance models of human speech processing.

The Journal of the Acoustical Society of America·2016
Same author

Optimal Curiosity-Driven Modular Incremental Slow Feature Analysis.

Neural computation·2016
Same author

Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory.

Frontiers in psychology·2014
Same author

Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots.

Frontiers in psychology·2013
Same author

Incremental slow feature analysis: adaptive low-complexity slow feature updating from high-dimensional input streams.

Neural computation·2012

Related Experiment Videos

An intrinsic value system for developing multiple invariant representations with incremental slowness learning.

Matthew Luciw1, Varun Kompella, Sohrob Kazerounian

  • 1IDSIA/SUPSI/USI Lugano-Manno, Switzerland.

Frontiers in Neurorobotics
|June 12, 2013
PubMed
Summary
This summary is machine-generated.

Curiosity Driven Modular Incremental Slow Feature Analysis (CD-MISFA) uses artificial curiosity to learn sensory representations. This intrinsic motivation drives agents to simplify complex inputs by learning features efficiently.

Keywords:
exploration-exploitationintrinsic motivation systemsneuromodulationnorepinephrineslow feature analysis

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Robotics

Background:

  • Intrinsically-motivated learning is crucial for agents to develop representations.
  • Artificial curiosity provides a framework for unsupervised representation learning.

Purpose of the Study:

  • To introduce and analyze the Curiosity Driven Modular Incremental Slow Feature Analysis (CD-MISFA) model.
  • To explore the role of intrinsic motivation in learning multiple sensory representations.
  • To investigate neurophysiological analogs of CD-MISFA.

Main Methods:

  • Utilizing the slowness principle for unsupervised representation learning.
  • Generating intrinsic reward signals based on learning progress.
  • Balancing exploration and exploitation for efficient learning.
  • Testing the model on synthetic data and the iCub robot.

Main Results:

  • The intrinsic value system is essential for effective representation learning.
  • CD-MISFA successfully learns multiple feature sets for perceptual simplification.
  • Learned representations follow an order from least to most costly, aligning with curiosity theory.

Conclusions:

  • CD-MISFA provides a computational model for intrinsically-motivated invariance learning.
  • The findings support the theory of curiosity in guiding representation acquisition.
  • The model has implications for artificial intelligence and understanding biological learning systems.