Contents - Konrad Lorenz Institute

Michel A. Hofman
ture of the world than a monkey’s brain and an infant’s
brain maps less than an adult’s brain. But
unlike two-dimensional road maps, the neural
maps of reality will be multi-dimensional, probably
very high-dimensional maps (CHURCHLAND 1996;
GLYMOUR 2001).
The coherence and predictive power that representational
models of reality enjoy is explained by
biological evolution. The better and faster the
brain’s predicitve capacities relative to the organism’s
modus vivendi, the better its chances of survival
and reproduction. In the broadest terms, the
solution found by evolution to the problem of prediction
is to modify execution programs by sensory
information. The value of the sensory input is
greater if it can signal organism-relevant features
and causal regularities between events. To achieve
this, the system needs neural cell clusters that are
interposed between the sensory system and the motor
system to find and embody higher order regularities.
According to CHURCHLAND and CHURCHLAND
(2003), the richer the interposed neural resources,
the more sophisticated the statistical capacities and
the greater the isomorphisms achievable between
the brain’s categorial/causal maps and the world’s
categorial/causal structures. Higher degrees of isomorphism
lead to more reliable models of the
world. As we cannot directly compare representational
models and the world modelled, predictive
success is the measure of fidelity and the guide to
the need for model revision. The reality-appearance
distinction ultimately rests on comparisons between
the predicitve merits of distinct representational
models; the better the model’s predicitve profile
the closer it is to the truth.
Neural Substrates of Intelligence
If we now assume that biological intelligence in
higher organisms is the product of processes of
complex sensory information processing and mental
faculties, responsible for the planning, execution
and evaluation of intelligent behavior, variations
among species in intelligence must in
principle be observable in the neural substrate. Before
attempting to determine the underlying neural
mechanisms of intelligence, we should have in
mind a specific biological entity towards which to
direct our attention. Conceiving biological intelligence
as the capacity of an organism to construct an
adequate model of reality, implies that the spectrum
of inquiry may range from the sensory receptor
system to behavior in its broadest sense (that is
to say, overt activity as well as internal homeostatic
action). Usually, however, valid comparisons at the
extremes of the spectrum i.e., at the level of sense
organs and complex behavior patterns, respectively,
are difficult to make in view of the very great
sensorimotor differences that exist among species
(see, e.g., PIRLOT 1987; MACPHAIL 1993, MACPHAIL/
BOLHUIS 2001; ROTH/WULLIMANN 2001). How to
compare in mammals, for example, the sensory capacity
of diurnal monkeys with stereoscopic vision
with that of nocturnal echolocating bats, or the
learning ability of terrestrial shrews with that of
marine dolphins Differences in intelligence may
in fact be uncorrelated with measurable differences
in overt behavior, nor are such differences implicit
in many learning situations, since both activities
depend on the behavioral potential of the organism
as well as on its internal state (attention, motivation,
etc.; a starving rat, for instance, is probably
not more intelligent than a satiated one!).
To avoid these formal problems, one should instead
investigate the ‘general-purpose’ (neo)cortical
areas, where both perception and instruction take
place. It is the organism’s neural substrate where
the external world is interpreted and modelled,
where concepts are formed and hypotheses tested,
in short, where the physical world interacts with
the mind. Since the primary function of the brain is
to adequately interact with the external world,
brain function can be most readily characterized by
the manner in which the brain senses the physical
environment and how it responds to it by generating
motor actions. From experimental and theoretical
studies it has become evident that the brain is a
distributed parallel processor where most of the
sensory information is analyzed in parallel involving
large neuronal networks (FREEMAN 1975; BAL-
LARD 1986; ZEKI/BARTELS 1999). The principle of parallel
processing implies that activities of ordered
sets of nerve cells can be considered to be mathematical
vectors (or tensors). An important aspect of
this vector approach is that it focuses on the explanation
of brain function in terms of neuronal networks,
and that it is therefore compatible with the
modular and hierarchical organization of the brain.
Despite these major developments to explain
brain function in terms of tensors, the attempts
have so far been confined to sensorimotor operations
(PELLIONISZ 1988), whereas no current theory
successfully relates higher brain functions to details
of the underlying neural structure. This is hardly
surprising in view of the enormous functional complexity
of the brain, especially that of higher verte-
Evolution and Cognition ❘ 180 ❘ 2003, Vol. 9, No. 2