Advancing knowledge of cognitive development sequences in infancy

Frank Guerin

Research output: Contribution to conferencePaper

Abstract

Human adults have relatively sophisticated cognitive
abilities, and manage vast quantities of diverse knowledge. The
amount of data in the genome, and the differences with other species,
show that a relatively small amount of information must code a system
that can bootstrap itself to this high level of sophistication. How
this bootstrapping process works remains largely a mystery. The
technique of computational modelling opens up the possibility of developing
a complete model of this bootstrapping process which could
allow us to understand the whole developmental sequence, starting
with infancy. Existing computational models of infant development
typically only model one episode in one area of competence, and
these individual episodes have not been linked up; yet, the process
by which new developments build on earlier achievements is central
to the bootstrapping, and remains a mystery. This paper looks at what
work needs to be done to take forward the idea of attempting to explain
cognitive development at a level which could account for long
sequences of development. We argue that in order to understand the
developmental processes underlying longer sequences we first need
to determine what these possible sequences are, in detail; existing
knowledge of such sequences is quite sketchy. The paper identifies
the need to discover a directed graph of behaviours describing all
the ancestors of sophisticated behaviours. The paper outlines some
experiments that may help to discover such a graph.
Original languageEnglish
Pages29-36
Number of pages8
Publication statusPublished - 2010
EventInternational Symposium on AI-Inspired Biology - Leicester, United Kingdom
Duration: 29 Mar 20101 Apr 2010

Conference

ConferenceInternational Symposium on AI-Inspired Biology
CountryUnited Kingdom
CityLeicester
Period29/03/101/04/10

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  • Cite this

    Guerin, F. (2010). Advancing knowledge of cognitive development sequences in infancy. 29-36. Paper presented at International Symposium on AI-Inspired Biology, Leicester, United Kingdom.