Maturational Biases and Encapsulation in Cognitive Development
Kazuo Hiraki and Akio Sashima
Presto,JST / Electrotechnical Laboratory, MITI
1-1-4 Umezono Tsukuba-shi, Ibaraki, 305 Japankhiraki@etl.go.jp, firstname.lastname@example.org
Most system designers of complex artificial systems
explicitly/implicitly use a design principle, in which higher-level
functions are composed of encapsulated lower-level modules. Modularity
is seen as a necessary feature of large-scale software design.
However, in human cognitive development, it is unlikely that there
exists an executive function, analogous to a systems analyst,
at work in the modularization of cognitive function.
In this research, we address the question: ``If higher-level
cognitive functions are organized based on encapsulated lower-level
modules, how are these modules created in the course of development?''
Elman (1993) suggested that a developmental increase in
working memory capacity promotes encapsulation, and improves learning.
We claim that ``maturational biases'' such as body growth also
promotes encapsulation. To elaborate upon this hypothesis, we have been
using autonomous robots as the subject of cognitive development, and
constructing computer programs by which robots can behave analogously to
infants (Hiraki,Sashima and Phillips 1997; Hiraki,Sashima and Phillips
in press). The following summarizes the result of a simulation
focusing on the relationship between spatial development and changes
of degrees of freedom (DOF) with body growth.
Psychological experiments on children's development of spatial
knowledge suggest experience at self-locomotion with visual tracking
as important factors. Yet, the mechanism underlying development is
unknown. We have proposed a robot that learns to ``mentally track''
a target object (i.e., maintaining a representation of an object's
position when outside the field-of-view) as a model for spatial
development. Mental tracking is considered as prediction of an
object's position given the previous environmental state and motor
commands, and the current environment state resulting from
movement. Following Jordan and Rumelhart's (1992) forward modeling
architecture, the system consists of two components: an inverse model
of sensory input to desired motor commands; and a forward model of
motor commands to desired sensory input (goals).
In order to get a understanding of the relationship between
body growth and spatial development, we compared the robot under
stepwise and non-stepwise conditions. In the stepwise development
condition, we simulated three stages of a child's development of motor
skills with the robot by varying its permitted actions (DOF). In stage 1,
the robot is only permitted head rotation. In stage 2, the robot can
rotate both head and body. Finally, in stage 3, the robot is also
permitted self-locomotion, whereas in stages 1 and 2, locomotion was
performed by an external agent.
In the non-stepwise condition, the robot
commences training at stage 3 (i.e., all actions permitted).
In each condition, the robots were tested on the ``three cups''
paradigm (where children are required to select the cup containing the
hidden object under various movement conditions). The result showed
that there was a faster decrease in error under the stepwise condition
than under the non-stepwise condition in stage 3 for both forward and
inverse models. The simulation results suggested that gradual growth
of body might help spatial development by limiting degrees of freedom
that infants must control.
Elman, J. (1993).
Learning and development in neural networks: The importance
of starting small.
Cognition, 48. 71-99.
Hiraki, K., Sakima, A., Phillips, S. (1997).
Mental Tracking: A Computational Model of Spatial Development.
Proc. of International Joint Conference of Artificial Intelligence.
301-307, Morgan Kaufmann.
Hiraki, K., Sakima, A., Phillips, S. (in press).
From Egocentric to Allocentric Spatial Behavior: A Computational
Model of Spatial Development,
ADAPTIVE BEHAVIOR, MIT Press.
Jordan, M.I. and Rumelhart, D.E. (1992).
Forward Models: Supervised learning with
a distal teacher,
Cognitive Science, 16,