Causal Structures in Embodied Systems
by George Kampis and Laszlo Gulyas
Embodied cognition is the frontline of cognitive research. The Cognitive Science
Programme at Eötvös University carries out a project in cooperation with SZTAKI and
other institutions. In its current phase the project aims at developing simulations based
on causal principles that unify autonomous robots, evolutionary processes, and cognitive
systems in order to produce open-ended real-world intelligence from interaction.
Embodiment and its Future Challenges
In the last decade it became generally clear that human and animal cognition is based on
embodied functioning rather than abstract representation. To operate an embodied system, it
is sensorimotor loops, learning, and situated actions coordinated by low level control that are
necessary, and not reasoning ability, planning, and knowledge. This recognition has led to a
shift in the emphasis in cognitive systems. Instead of representation, environmental action and
situatedness ( embeddedness in a culture) are believed to supply essential information for the
agent. The new approach is supported by findings of developmental child psychology (in
particular, the motor basis of early concept formation), cognitive linguistics (such the theory
of metaphor) and the dynamic view of cognition (viz. neural networks and dynamic systems).
Most of the current models developed in the new spirit are anti-representational in the same
sense as old cybernetics, where every material function was reduced to feedback
mechanisms and dynamic control. In the embodied framework, research on genuinely
cognitive structures is neglected. An additional problem is that embodiment tends to invite a
controversial focus on internal experience, a factor not accessible to scientific modelling.
Our research started from the hypothesis that intermediate cognitive structures between
situated behavior and mental experience exist, and that by these we can put a handle on
cognitive functions. Recent interest in the points where motor control, neural learning, vision
and symbol systems meet can supply a similar assumption. The paradigm of embodied
cognition must accordingly lift its interest from lower level, insect-like intelligence, which
has dominated earlier research, to vertebrate cognition. Where higher organisms are superior
to insects is the integrated use of their various faculties. We are looking at the basis of how
this happens.
The Primacy of Causality
Expanding the embodied view with insights from developmental studies of the intentional
self, animal imitation research, and models of evolutionary linguistics, suggests that key
elements of embodied cognitive faculty include:
• detection of own actions (“active self”)
• detection of foreign agency and its effects (“mechanisms”)
• episodic memory of action complexes (“narrative”)
• situated social communication in joint activity (“language game”).
The central question is that of causal interaction both at the level of bodily events and
mental models. When looking for the meaning of causality for a real-world body, the usual
conception of causation needs to be extended. Causation is usually assumed to be a
relationship between events. This is a simplification which has its origin in the abstract
representational conception. Any realized action involves activities that take
place in several parallel layers. In other words, causality has “depth”.
One form in which causal depth can be approached is via supervenient levels acting
permanently together. Our interest lies in the more dynamic ways in which various causal
modes influence each other.
An example is the role of different sensory modalities in active perception, where, for
instance, a visually guided mental model can recursively enact actions that approach the
signal and lead to new percepts that help stabilizing the mental model. The underlying
concept is that of the complex body with causal faculties that are context-dependent.
An Application: Evolutionary Technology
The example on which we have chosen to illuminate the framework is evolution. To free
ourselves from biological details the problem we consider is not natural evolution but
evolutionary technology. The ultimate goal is to develop artificial organisms which perform
increasingly complicated tasks. We are currently in the process of developing a simple testbed
of causal principles.
Evolution and cognition have striking parallels. In both cases it is complex bodily properties
of a physically realized agent that collectively determine a historical process of structure
formation, which we often view from the perspective of its end results, i.e., “genetic
information” or “mental representation”. If we want to understand the origin of the inner
structure and the role real-world causality plays in it, evolution is a good starting point.
Evolution is also a useful example because equipping abstract systems with causal powers
may help us understand what makes an evolutionary process work. Selection is only half of
the answer, as was recently demonstrated by studies on robotic as well as software-based
systems, which all failed to improve beyond a point. Looking for the cues, the lack of
feedback from products to the very evolutionary process was recognized by some leading
authors. As a byproduct of the cognitive theory, causality has new suggestions here.
The same causal principle that helps active perception now helps selection. The assumption is
that adaptation that produces new phenotype also switches the interactions between
organisms to a different causal mode, making a new adaptive process possible. Evolutionary
processes, as understood in the model, have the following characteristics:
• phenotypic determination (i.e. interactor-based selection)
• sexual and ecological modes of selection (i.e. preferential mating and food specificity)
• emergent selection modes by phenotype expansion.
Our simulation studies have been using the RePast system of the University of Chicago.
The first results have been recently reported.
Some steps of the ongoing research outlined in this article have been carried out while Prof.
Kampis’s was Fujitsu Visiting Associate Professor at the Japan Advanced Institute of Science
and Technology and during Dr. Gulyas’s visit at Harvard’s Center for Basic Research in the
Social Sciences (CBRSS). A cooperation partner is the Center for Complex Systems Studies
(CCSS) at Kalamazoo College, USA.
Links
http://hps.elte.hu/~kampis/projects/EvoTech.html
http://www.jaist.ac.jp/~g-kampis/
http://www.sztaki.hu/~gulyas/indexE.html
http://www.cbrss.harvard.edu/people/gulyas.htm
http://www.kzoo.edu/physics/ccss/research.html
http://repast.sourceforge.net/
Please contact:
George Kampis, Eötvös University
E-mail: gk@hps.elte.hu