<i>
Harnad, S. (1998) Beyond Object Constancy. Institution of Electrical
Engineers (IEE) Seminar on "Self-Learning Robots II:  Bio-Robotics."
Commentary on Pfeifer (1998) "Embodied Cognition: Dynamic and
Information Theoretic Implications of Embodiment"
</i>
<p>
<h3>
<center>
BEYOND OBJECT CONSTANCY
</h3>
<p>
Stevan Harnad
<br>
Cognitive Sciences Centre
<br>
University of Southampton
<br>
Highfield, Southampton
<br>
SO17 1BJ UNITED KINGDOM
<br>
harnad@cogsci.soton.ac.uk
<br>
http://cogsci.soton.ac.uk/~harnad
</center>
<p>
It is surely not irrelevant that such a large proportion of the brain
is devoted to the body and to sensorimotor processing (Jeannerod 1994,
Kaas 1995, Zhang et al. 1997). That should already serve to alert us
that embodiment is likely to be an important factor in cognition.
<p>
Add to that the well-rehearsed advantages of letting the world serve as
its own model (Steels & Brooks 1995), Gibson's (1979) influential
findings on the role of sensorimotor interaction in the detection of
invariants, and perhaps also my own writing on the symbol grounding
problem (Harnad 1990, 1996) and there would appear to be a good deal of
support for Pfeifer's (1998) position.
<p>
I would accordingly like merely to amplify one of his points about
the "fundamental problem of learning category distinctions" (Pfeifer
1998). Object constancy -- the capacity to see an object as being one of
constant size and shape despite variation in retinal position and
distance from the observer -- is certainly one of the most fundamental
category invariants that the brain is capable of detecting. (Moreover, so
important is it, that it is unlikely that object constancy is entrusted
to learning: most of it has probably been "prepared" innately by
evolution.) But the principle of detecting and extracting invariants
under sensorimotor transformations during sensorimotor interactions with
objects can be generalised to more abstract invariants, extracted under
more abstract transformations.
<p>
I am not just referring to the dissociations between self-centred and
object-centred spatial perception, or to higher-order spatial
invariants that have been revealed by neuropsychological testing (the
fact that a patient with left half-field inattention neglects not only
the left half of his retinal field, but also neglects the left half of
objects in his right retinal field -- and, presumably, so on through as
many higher-order fields within fields as the neuropsychologist might
care to test; Corballis 1986). Rather, I am referring to the higher-order
invariants that depend on how we choose (or are constrained by our
physical and social environment to choose) to sort and label all the
kinds of things to which we assign a category name or a descriptive
phrase. Here it is not invariance under sensorimotor transformations
that the brain is detecting, but invariance under within- and
between-category variance.
<p>
If we must learn to call some things "X" and other things "Y," and Xs
and Ys are at first highly interconfusable, then we must somehow modify
our internal representations of Xs and Ys so as to be able to sort and
label them reliably. When this involves changing either the values or
the weighting of the dimensions of the internal representations of Xs
and Ys, the process is called "categorical perception" (Harnad 1987;
Livingston et al 1998; Tijseeling et al. 1997). It is no longer
sensorimotor in the gross motor sense; but as even calling or not
calling something by the same name is a motor act (as surely as
grasping or pointing at it is), naming is still based on a very high-level
sensorimotor invariance when it is based on direct sensorimotor
interaction with Xs and Ys.
<p>
When our interactions with objects become still more abstract, being
based only on the interactions between names and descriptions -- on
"hearsay," so to speak -- then we have arrived at the full power of
natural language, the power of symbolic "theft" over sensorimotor
"toil" (Cangelosi & Harnad, in prep.; Greco, Cangelosi & Harnad, in
prep.). Yet even at such abstract cognitive heights, embodiment is
never escaped, for the power of names and propositions is completely
parasitic on the meanings of those names, and those must all eventually
be grounded in the sensorimotor interactions with the kinds of objects
they designate, and the sensorimotor invariants on the basis of which
the names are assigned (Harnad 1996).

<p> REFERENCES
<p>
Cangelosi, A & Harnad, S. (in prep) On the Virtues of Theft Over Honest Toil:
Grounding Language and Thought in Sensorimotor Categories:  Grounding
Language and thought in Senosimotor Categories.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad96.language.theft.html
<p>
Corballis, M.C. (1986) Fresh fields and postures new: A discussion paper.
Brain & Cognition 5: 240-252.
<p>
Gibson, J. J. (1979) An ecological approach to visual perception.
Boston: Houghton Mifflin
<p>
Jeannerod, M. (1994) The representing brain: neural correlates of
motor intention and imagery. Behavioral and Brain Sciences 17(2) in
press.
<p>
Kaas, J. H. (1995) The reorganisation of sensory and motor maps in
adult mammals. In: The cognitive neurosciences.; Michael S. Gazzaniga,
Ed. MIT Press, Cambridge, MA, US. 1995. p. 51-71.
<p>
Harnad, S. (1987) Psychophysical and cognitive aspects of categorical
perception: A critical overview. In: Harnad 1987.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad87.cpreview.html
<p>
Harnad, S. (1987) The induction and representation of categories. In:
Harnad 1987.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad87.categorization.html
<p>
Harnad, S. (1990) The Symbol Grounding Problem.
Physica D 42: 335-346.  [Reprinted in Hungarian Translation as "A
Szimbolum-Lehorgonyzas Problemaja." Magyar Pszichologiai Szemle
XLVIII-XLIX (32-33) 5-6: 365-383.]
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad90.sgproblem.html
<p>
Harnad, S. (1992) Connecting Object to Symbol in Modeling
Cognition.  In: A. Clark and  R. Lutz (Eds) Connectionism in Context
Springer Verlag, pp 75 - 90.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad92.symbol.object.html
<p>
Hayes, P., Harnad, S., Perlis, D. & Block, N. (1992) Virtual Symposium
on Virtual Mind. Minds and Machines 2: 217-238.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad92.virtualmind.html
<p>
Harnad, S. (1993) Grounding Symbols in the Analog World with Neural
Nets. Think 2(1) 12 - 78 (Special issue on "Connectionism versus
Symbolism," D.M.W. Powers & P.A. Flach, eds.). [Also reprinted in
French translation as: "L'Ancrage des Symboles dans le Monde Analogique
a l'aide de Reseaux Neuronaux: un Modele Hybride." In: Rialle V. et
Payette D. (Eds) La Modelisation. LEKTON, Vol IV, No 2.]
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.html
http://cwis.kub.nl/~fdl/research/ti/docs/think/2-1/index.stm
<p>
Harnad, S. (1993) Artificial Life: Synthetic Versus Virtual.
Artificial Life III. Proceedings, Santa Fe Institute Studies in the
Sciences of Complexity. Volume XVI.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad93.artlife.html
<p>
Harnad, S. (1994) Levels of Functional Equivalence in Reverse
Bioengineering: The Darwinian Turing Test for Artificial Life.
Artificial Life 1(3): 293-301. Reprinted in: C.G. Langton (Ed.).
Artifial Life: An Overview. MIT Press 1995.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/
<p>
Harnad, S. (1994) Computation Is Just Interpretable Symbol
Manipulation: Cognition Isn't. Special Issue on "What Is Computation"
Minds and Machines 4:379-390 [Also appears in French translation in
"Penser l'Esprit: Des Sciences de la Cognition a une Philosophie
Cognitive,"  V. Rialle & D. Fisette, Eds. Presses Universite de
Grenoble. 1996]
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad95.computation.cognition.html
<p>
Harnad, S, (1995) Does the Mind Piggy-Back on Robotic and Symbolic
Capacity? In: H. Morowitz (ed.) "The Mind, the Brain, and
Complex Adaptive Systems." Santa Fe Institute Studies in the
Sciences of Complexity. Volume XXII. P. 204-220.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad95.mind.robot.html
<p>
Harnad, S. (1995) Grounding Symbolic Capacity in Robotic Capacity.
In: Steels, L. and R. Brooks (eds.) The Artificial Life Route to
Artificial Intelligence:  Building Embodied Situated Agents. New
Haven: Lawrence Erlbaum. Pp. 277-286.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad95.robot.html
<p>
Harnad, S. Hanson, S.J. & Lubin, J. (1995) Learned Categorical
Perception in Neural Nets: Implications for Symbol Grounding.
In: V. Honavar & L. Uhr (eds) Symbol Processors and Connectionist
Network Models in Artificial Intelligence and Cognitive Modelling:
Steps Toward Principled Integration. Academic Press.  pp. 191-206.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad95.cpnets.html
<p>
Harnad, S. (1995) Grounding symbols in sensorimotor categories with
neural networks.  In: IEE Colloquium "Grounding Representations:
Integration of Sensory Information in Natural Language Processing,
Artificial Intelligence and Neural Networks" (Digest No.1995/103).
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad95.iee.html
<p>
Harnad, S. (1996) The Origin of Words: A Psychophysical Hypothesis
In Velichkovsky B & Rumbaugh, D. (Eds.) "Communicating Meaning: Evolution
and Development of Language. NJ: Erlbaum: pp 27-44.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad96.word.origin.html
<p>
Tijsseling, A. & Harnad, S. (1997) Warping Similarity Space in Category
Learning by Backprop Nets. In: Ramscar, M., Hahn, U., Cambouropolos, E.
& Pain, H. (Eds.) Proceedings of SimCat 1997: Interdisciplinary
Workshop on Similarity and Categorization. Department of Artificial
Intelligence, Edinburgh University: 263 - 269.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/
<p>
Harnad, S. (1998) Turing Indistinguishability and the Blind
Watchmaker. In: Mulhauser, G. (ed.) "Evolving Consciousness"
Amsterdam: John Benjamins (in press)
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad98.turing.evol.html
<p>
Livingston, K., Andrews, J., & Harnad, S. (In press). Categorical
Perception Effects Induced by Category Learning.  Journal of
Experimental Psychology: Learning, Memory, and Cognition.
http://www.cogsci.soton.ac.uk/~harnad/Papers/Harnad/
<p>
Steels, L. and R. Brooks (1995) (eds.) The Artificial Life Route to
Artificial Intelligence:  Building Embodied Situated Agents. New
Haven: Lawrence Erlbaum.
<p>
Schyns, P., Goldstone. R. & Thibeaut P. (1998 in press) The development of
features in object concepts. Behavioral and Brain Sciences 21.
http://www.cogsci.soton.ac.uk/bbs/Archive/bbs.schyns.html
<p>
Tijsseling, A. & Harnad, S. (1997) Warping Similarity Space in Category
Learning by Backprop Nets. In: Ramscar, M., Hahn, U., Cambouropolos, E.
& Pain, H. (Eds.) Proceedings of SimCat 1997: Interdisciplinary
Workshop on Similarity and Categorization. Department of Artificial
Intelligence, Edinburgh University: 263 - 269.
<p>
Zhang, J. Riehle, A. Requin, J. & Kornblum, S. (1997) Dynamics of
single neuron activity in monkey primary motor cortex related to
sensorimotor transformation. Journal of Neuroscience 17(6): 2227-2246.
<p>