NDLI: Learning to learn

Content Provider	IEEE Xplore Digital Library
Author	Butko, N.J. Movellan, J.R.
Copyright Year	2007
Description	Author affiliation: California Univ., San Diego (Butko, N.J.)
Abstract	Empirical evidence shows that infants 10 months of age can learn about 10 times faster than infants 2 months of age that a novel entity is socially contingent. This suggests that during the period from 2 to 10 months of age infants became better learners. One possible explanation for this change is that new brain structures grow, in a genetically predetermined manner, that support more efficient learning. An analogy for this point of view would be the increase in mastication efficiency due to the growth of teeth. An alternative hypothesis is that the increase in learning efficiency is itself the result of a learning process that operates on the time scale of months. Under this view, better learning is the consequence of learning itself. Here we explore the plausibility of the "learning to learn" hypothesis from a computational point of view. We show that with standard reinforcement learning algorithms using an internally generated reinforcement signal it is possible to develop agents that progressively learn to learn within a period of months. The results fit well at a qualitative level empirical evidence regarding the development of social contingency detection in infants. The learning techniques that we explored have potential application for robots that learn to learn on their own.
Starting Page	151
Ending Page	156
File Size	318572
Page Count	6
File Format	PDF
ISBN	9781424411153
DOI	10.1109/DEVLRN.2007.4354070
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-11
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pediatrics Brain Event detection Infomax Control Teeth Infomax Reinforcement Learning (IRL) Social Robotics Helium Learning Probabilistic Robotics Developmental Robotics Temporal Dynamics of Social Interaction Signal generators Cognitive science Probabilistic Functionalism Standards development Social Contingency Robots
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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An Infomax Controller for Real Time Detection of Social Contingency

Learning to learn (2007)

A framework for cognitive robots to learn behaviors through imitation and interaction with humans

An infomax controller for real time detection of social contingency (2005)

Modified reinforcement learning for sequential action behaviors and its application to robotics

Synonyms epigenetic robotics, ontogenetic robotics, cognitive developmental robotics, autonomous mental development definition.

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Infomax control as a model of real time behavior: theory and application to the detection of social contingency.

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