April 27, 2013
March 17, 2013
March 15, 2013
FROM FACEBOOK 15/03/2013
SI LO DICEN LAS ABUELAS, VA A MISA:
“Buenos Aires, 13 de marzo de 2013.
Bergoglio declaró como testigo en el juicio por Plan Sistemático de apropiación y en la megacausa ESMA.
Abuelas de Plaza de Mayo hace pública la declaración del Papa electo Jorge Bergoglio, en el juicio por Plan Sistemático de Apropiación de Menores. Allí se lo citó para declarar acerca del caso de la detenida desaparecida embarazada Elena de la Cuadra, cuya familia acudió a él, en vano, en busca de ayuda. En ese entonces Bergoglio recibió al padre de Elena en dos oportunidades, derivándolo al Arzobispo de La Plata, Mario Picchi. Èste le confirmó que Elena había dado a luz a una niña a la que llamó Ana Libertad y que estaba en manos de una familia: “A la nena la cría una familia bien, lo de Elena es irreversible”, le explicó. Ana libertad continúa desaparecida.
También transcribimos un fragmento relativo a apropiación de niños
BR- ¿Usted cuándo tomó conocimiento de que había niños que estaban siendo apropiados en la dictadura?
B- Eso recientemente… Bah, recientemente, hará diez años.
BR- En el año 199x y algo sería?
B- Quizás en el tiempo del Juicio a las Juntas. Por ahí.
BR- Un poco antes, entonces.
B- Un poco antes. Por ahí, más o menos, empecé a enterarme de eso.
BR- Hemos hablado en diversas partes de documentación que podría ser aportada o no en el proceso. Me gustaría, para finalizar, que se acuerde de qué manera el tribunal puede hacerse de esta valiosa documentación que es de público y notorio que la Iglesia contiene gran parte de la documentación. Esto consta en diversos testimonios, incluso testimonios que se han recibido aquí mismo en este juicio. Que antes de terminar la audiencia se acuerde y se determine de qué manera y a la brevedad se puede el tribunal hacer de toda esa valiosa documentación de archivos.
Presidente: Pídalo, doctora.
BR- Estoy preguntando si va a haber una manera acordada que podamos ir a buscar y a ver esa documentación.
P- Entonces la pregunta sería si el señor testigo se compromete a hacer revisar archivos.
B- Sí, no tengo problema. Voy a encargar a los custodios de los archivos que lo hagan. De hecho, a propósito de otros juicios sobre el mismo tema hemos recibido pedidos de documentación y se les ha enviado la que había, toda la que había.”
February 19, 2013
Environmental Complexity and the Evolution of Cognition – Peter Godfrey-Smith
Department of Philosophy
In R. Sternberg and J. Kaufman (eds.) The Evolution of Intelligence.
Mahwah: Lawrence Erlbaum, 2002, pp. 233-249
1. Starting Simple
2. The Environmental Complexity Thesis
3. On Complexity
4. The ECT as a Component in Many Evolutionary Scenarios
1. Starting Simple
One problem faced in discussions of the evolution of intelligence is the need to get a precise fix on what is to be explained. Terms like “intelligence,” “cognition” and “mind” do not have simple and agreed-upon meanings, and the differences between conceptions of intelligence have consequences for evolutionary explanation. I hope the papers in this volume will enable us to make progress on this problem. The present contribution is mostly
focused on these basic and foundational issues, although the last section of the paper will
look at some specific models and programs of empirical work.
Some people have a very demanding picture of what is required for intelligence,
thinking that it always involves such sophisticated skills as planning, language-use, and
perhaps even some sort of consciousness. To these people, intelligence is to be contrasted
with instinct. Perhaps in this rich sense of the term, intelligence is even to be contrasted with
the simpler types of learning, such as learning through reinforcement (operant
conditioning). From this first point of view, the problem of explaining the evolution of
intelligence is explaining why instinct and other simple behavioral capacities were not
enough; why evolutionary processes took a few organisms so far beyond these basic
Another approach uses terms like “intelligence” and “cognition” in much less
demanding senses. On this second approach, intelligence is not restricted to a few
exceptional cases in the biological world — humans and perhaps some primates. Rather,
intelligence exists to some degree in a huge range of living systems. Humans have a lot
more of it than cockroaches do, but cockroaches do have some of it.
According to this second view, all the mechanisms that enable organisms to
coordinate their behavior with conditions in the world involve some degree of intelligence.
So there is no opposition between intelligence and what is often referred to as “instinct.” An
instinctive behavior can involve perception, and a good deal of processing and feedback to
ensure the right match between behavior and circumstances. According to this second view,
that is a low-level variety of intelligence.
Maybe it is difficult to motivate the second, less demanding approach with the term
“intelligence” which has such definite common sense usages. Perhaps it is better to present
this view with the aid of a less everyday term such as “cognition.” So in this paper I will
mostly discuss the “evolution of cognition” rather than the evolution of intelligence. The
term “mind” is another broad one, but it has its own capacity to mislead, as for many people
it is closely linked with consciousness and a sense of self.
Given this, we can describe the second approach by saying that a great range of
living things have some cognitive capacities. In many cases these capacities are extremely
limited. The capacities we habitually refer to as “intelligent” in humans, such as the
capacities for planning and conscious reflection, comprise one type of cognition. But when
a fish negotiates its way around a reef, or a rat finds its way back to a food source, the
internal processes responsible for these behaviors are varieties of cognition as well.
Does it matter which of these two general approaches to the evolution of cognition
we take? Certainly it does not much matter what we decide to refer to with the term
“intelligent” or “cognitive.” Either way, the problem remains of explaining how nervous
systems evolved at all, and the problem remains of explaining how humans became so much
smarter, in many respects, than other organisms. But I think it often does make a difference
how we view and describe the continuities between human and non-human psychological
capacities. A great deal depends on how much significance we place on the distinction
between animals that use language and those that don’t, for example. Views about non-
human cognition often have ethical consequences, and consequences for a range of issues in
the philosophy of mind.
In any case, my own approach is very much along the lines of the second option
described above. I approach the problem with a broad and very undemanding concept of
“cognition.” My aim is to set out with a broad concept of cognition and ask: can we
formulate a generalization about why these sorts of capacities will tend to evolve? Because I
use “cognition” to refer to such a broad class of capacities, cognition is not a single
evolutionary “discovery,” restricted to a single lineage of organisms. Cognition of various
kinds has been discovered and rediscovered by evolution many times, just as eyes and
wings have been discovered independently several times. And just as we find with eyes,
cognitive machinery is very diverse. There are lots of ways to process information and
control behavior; a central nervous system is one way, but not the only way. An important
feature of this view, which will be discussed in more detail below, is that cognition “shades
off” into other kinds of biological capacities and processes. In some cases it is hard to
distinguish cognition from other control systems in the body, and hard to distinguish
behavior from such things as growth, development and the regulation of metabolism.
Cognition is diverse, but it might be possible to find a common type of evolutionary
story that applies in all or most of these diverse cases. With such a general framework in
hand, we can then ask more specific questions about why certain types of cognition evolved.
When is learning favored over less flexible strategies of dealing with the world? And if one
is to learn, when is learning through individual trail-and-error better than learning by
imitating a parent? What brings about the transition to a planning intelligence? When is
what primatologists refer to as “theory of mind” (taking other individual organisms to have
a mind) favored? And what on earth lay behind the explosion of mental capacities found in
the evolution of humans?
It might turn out that all these explanations are so diverse that it is pointless to try to
link them under a general principle. I do not deny that possibility. But my own approach
here is to outline and cautiously defend one possible generalization about the adaptive value
of cognition. The generalization is intended to be a fairly obvious one, something that has
been expressed in partial or imperfect ways dozens of times before, dating back to the 19th
century. My aim here is more to make a vague existing idea into a precise one, than to
present an novel idea. I do think it could be of considerable help to discussions about the
evolution of cognition if this underlying idea, and its possible rivals, were made explicit and
precise in people’s minds.
2. The Environmental Complexity Thesis
Here is my proposal for a general “first principle” about the evolution of cognition:
Environmental Complexity Thesis (ECT):
The function of cognition is to enable the agent to deal with environmental complexity.
Each of the key terms in the ECT requires a good deal of clarification. The term “function”
is understood here in a strong sense. To ascribe a function in this sense is to offer an
evolutionary hypothesis. The function of a trait or structure is the effect or capacity it has
which has been responsible for its success under a regime of natural selection. When we
say that the function (in this strong sense) of the thorns on a plant is to deter herbivores
from eating the plant, we are not just saying that the thorns help the plant by deterring
herbivores. We are saying that thorns were selected for in evolutionary processes because
they tended to have the effect of deterring herbivores. The ECT makes a similar claim about
The ECT is a broader and more abstract claim than the one about the thorns, but
very abstract functional claims can certainly be made. Eyes have evolved many times, and
they make use of various different types of mechanisms. But a general claim can be made
about their evolutionary function: the function of eyes is to respond in discriminative ways
to light, and hence to enable the organism to make use of information about the world that is
carried in light (Gibson 1966, Dretske 1981). We can also formulate an even more general
thesis about the function of perceptual mechanisms: they all respond, with some degree of
sensitivity or discrimination, to some physical or chemical variables that impinge causally on
the organism, in such a way as to enable the organism to make use of information about the
world that is carried by these variables.
Some might say that when we formulate a broad generalization like this, we are
saying something so obvious or empty as to make it not worth the effort of saying. I
disagree. First, it is important to be able to embed specific functional claims made about
biological structures within a more general picture of what the organism as a whole is doing.
Second, it is actually quite hard to get these generalizations right, and lots of puzzling
questions can get raised along the way. For example, in the claims about perception I made
above, I used the concept of information. Although lots of people, including scientists, talk
about information in a casual and unreflective way, it is a subtle and difficult concept. The
philosopher Fred Dretske (1981) developed a detailed theory of where information is found
in the physical world, and of how everyday talk about information is related to the technical
discussions of “information theory” in engineering. Information, for Dretske, is found
where there is contingency and correlation. Any variable in the world which has a range of
possible states is a source of information. When the state of a source of information is
correlated with the state of another variable, as a consequence of physical laws, the second
variable carries information about the source. For Dretske, information is a resource that
organisms use to make their way through the world; cognitive systems are information
consuming, or information exploiting, systems.
Without careful, explicit discussions like Dretske’s, it would be unclear whether or
not it is really justifiable to use the concept of information when making generalizations
about the function of eyes and other perceptual systems. Whenever I use the term
“information” in this paper, I have Dretske’s sense in mind. Below I will discuss the concept
of “environmental complexity” in some detail; this is another concept which it can be easy to
throw around without having a clear idea of what is being said.
So far I have said that the ECT is an attempt to give a general functional explanation
of cognition. Functional explanations have received a lot of attention in the philosophical
literature, especially over the past 20 years or so. (For a collection of classic and recent
articles, see Allen, Bekoff and Lauder 1998) In this paper I will not discuss the many issues
that have arisen in these debates, as the present topic is not functional explanation in general
but evolutionary hypotheses about cognition. All that is important for present purposes is
the idea that functional explanations are attempts to describe, in a shorthand way, the
processes of mutation and natural selection that were responsible for the origination and
maintenance of biological structures. Functional explanations are attempts to isolate the
effects or dispositions of a structure which were responsible for the natural selection of that
structure. So functional claims are “teleological” only in this specific Darwinian sense.
In the first section I said I would be using a broad and undemanding sense of the
term “cognition.” But how broad? What exactly is cognition? What is the set of organisms
in which it is found?
I understand cognition as a collection of capacities which, in combination, allow
organisms to achieve certain kinds of coordination between their actions and the world. This
collection typically includes the capacities for perception, internal representation of the
world, memory, learning, decision-making and the production of behavior. This set of
capacities, according to the ECT, has the function of making possible patterns of behavior
which enable organisms to effectively deal with complex patterns and conditions in their
So although the ECT is expressed as a connection between cognition and
environmental complexity, it really embeds two separate claims. One is the claim that the
immediate role of cognition is to control behavior. The other is the claim that the point of
this control of behavior is to deal with environmental complexity.
Think of cognition here not as a single type of process, but as a biological “tool-kit”
used to direct behavior. There is no single list of tools found across all the organisms with
cognitive capacities; different organisms have different collections of tools, according to
their circumstances and history. So when I listed “perception, internal representation of the
world, memory, learning (etc.)” above, it should not be thought that this has to be a
description of a set of recognizable and distinct “modules” common to all cognitive
systems. Rather, I am referring to a set of capacities which are realized in very different
ways in different organisms, a set of capacities which shade into each other and shade off
into other, non-cognitive parts of the biological machinery. Also, the capacity for “internal
representation of the world” is one about which there is enormous disagreement within
philosophy and cognitive science (Stich and Warfield 1994). Here again, some people
understand this ability in a very demanding way, perhaps requiring language, while others
view a very wide range of mechanisms for registering external events as representational in
some sense. Here I assume a very loose and undemanding view of representation, but the
issues surrounding that concept are too complex to go into here.
In the list of basic cognitive capacities given above, some are more fundamental than
others. Perception is very fundamental; learning is somewhat less so. It is true that all
macroscopic animals are thought to be able to “learn,” in at least a minimal sense. Bees have
been shown to have quite impressive ways of learning the location of food sources, and fruit
flies have been conditioned to exhibit avoidance behaviors in some of the same sorts of
ways found in more celebrated learners such as rats and pigeons. The neural basis for the
most minimal kinds of learning is often studied using sea slugs. Still, learning is not in
principle essential to cognition. A behavioral pattern which is completely insensitive to any
refinement through learning, but which does involve the coordination of actions with
perceived environmental conditions, does display a minimal type of cognition.
But if learning is not essential, where does cognition stop? Do plants have it? What
about bacteria? By any normal standard, plants and bacteria do not have minds and do not
exhibit cognition. But my suggestion is that cognition shades off into other kinds of
biological processes. There is not much point in trying to draw an absolute line. Plants and
bacteria do exhibit some capacities for flexible response to environmental conditions, using
environmental cues to control development and metabolism. These are low-level cases of the
same types of capacities that, in more elaborate cases, do constitute cognition.
Many bacteria can adjust in adaptive ways to changing circumstances around them.
Dretske (1986) discusses aquatic bacteria which use little internal magnets to track the
distinction between north and south, enabling them to move towards water with their
required chemical properties. Bacteria also make use of external cues to adjust their
metabolic activity. A famous case is the lac operon system in e. coli bacteria. These bacteria
can respond to a change in local food type through processes in which the availability of a
nutrient affects the regulation of genes which code for enzymes able to digest that nutrient.
Plants are able to direct a range of their activities with the aid of cues from the
external world. Within “activities” here I include some cases which fall naturally under the
headings of growth or individual development, and others which might be distinguished
from development and considered genuine behavior. Silvertown and Gordon (1989) argue
at length that plants can behave, but they use an extremely broad conception of behavior. I
suggest a narrower construal; one rough way to distinguish plant behavior from plant
development is to say that behavioral changes must be reasonably rapid and also reversible.
Then there will be a large range of cases where plants adaptively control individual
development with environmental cues, and a smaller range of cases where they control
behavior. Genuine plant behaviors include the behaviors of Venus Fly Traps, and a large
range of reversible responses to local light conditions.
Some of the ways in which plants use environmental cues are quite sophisticated.
For example, many plants can determine not just that they are being shaded, but that they are
being shaded by other plants. This is done by detecting the wavelength properties of
reflected light. The plants respond to shading by growing longer shoots (Silvertown and
Lovett Doust 1993 pp. 11-12). Lest I leave out the least glamorous biological kingdom,
some soil fungi have a reflex which enables them to trap and digest tiny wandering worms.
Though it makes sense to distinguish control of plant behavior from control of plant
development, for many theoretical purposes these can be seen as similar capacities. From
the point of view of theoretical modeling, control of developmental processes and control of
behavior have much in common (see section 4 below). And the temptation to use intentional
and cognitivist terms when describing control of plant development in more informal ways
can be strong. David Attenborough begins his book The Private Life of Plants as follows:
“Plants can see. They can count and communicate with one another” (1995 p. 7). Most of
the phenomena Attenborough is referring to here involve control of growth and
development, such as tactile exploration by a young vine, looking for a tree to climb.
I said that plants use a lot of their “smarts” for controlling growth and development,
rather than behavior. In fact this phenomenon is not restricted to plants, but is found in
vertebrate animals as well. In certain fish, capacities for perception and information-
processing are used in directly regulating central aspects of development. These fish
determine whether they will develop as male or female via perception of their relative size
within the population (Francis and Barlow 1993). So when I said earlier that the ECT links
cognition first to behavior, and then to environmental complexity, this was a slight
oversimplification. Even in animals, sometimes cognition controls things other than
I have spent some time discussing capacities for flexible response which do not
constitute genuine cognition on any normal standard. My aim in discussing these cases is to
suggest that cognition shades off into other kinds of biological processes; even though
plants do not exhibit cognition and people do, there is no single scale between them and us
with a threshold marking a transition to genuine cognition. Rather, all or practically all living
organisms have some capacities for responding to environmental changes and conditions.
Sometimes environmental cues are used to control metabolic processes or development,
sometimes they are used to consciously choose where to plant crops. In ordinary talk and in
theoretical discussion, we habitually pick out only some of these capacities as “intelligent”
or “cognitive,” and the decision to do so can be guided by a mixture of criteria. Complexity
and flexibility play a role, but so does the time-scale at which responses occur. There is
nothing wrong with that; my point is just that there are some fundamental similarities
between real cognition and much simpler capacities for control of biological processes, and
there is no reason to seek a sharp cut-off between the two classes.
As some terminology might be useful here, I will say that plants and bacteria have a
number of “proto-cognitive” capacities. These are capacities for controlling individual
growth, development, metabolism and behavior by means of adaptive response to
environmental information. The term “development” refers here only to processes within an
individual lifetime; evolutionary change is not classified as proto-cognitive. Complex multi-
cellular organisms like ourselves also contain “subpersonal” systems with some of the
proto-cognitive capacities of simpler whole organisms. The vertebrate endocrine and
immune systems are examples. In this paper I will not discuss the very difficult questions
raised by the attribution to proto-cognitive capacities to higher-level systems, such as ant
In stressing that cognition shades off into other proto-cognitive biological processes,
I am asserting a version of what is sometimes called a “continuity” assumption about
cognition. The simplest biological capacities that we might consider proto-cognitive are
cases of flexibility in behavior or development (etc.) controlled by a fixed response to a
physically simple environmental cue, but where the nature of the response is not determined
directly by the physical properties of the cue. (There has to be some “arbitrariness” in how
the cue affects the system, to use a term due to Levins 1968.) As we add different types of
flexibility of response, and different kinds of inner processing of the output of perceptual
mechanisms, we reach clearer and clearer cases of cognition. But there is no single path that
takes us from the simplest cases to the most elaborate. There are various ways of adding
sophistication to the mechanisms of behavioral control, ways which will be useful to
different organisms according to their circumstances. The ability to expand or contract the
range of stimuli coupled to a given response is one important sophistication (Sterelny
1995). The ability to learn through reinforcement is another. Yet another is the ability to
construct a “cognitive map” of spatial structure in the environment (this case will be
discussed further below). It is an error to try to describe a single hierarchy of cognitive
skills, from simplest to most complex. Here as elsewhere, there are many distinct kinds of
Compare two imaginary organisms which both have good spatial memory. One is
“more sophisticated” than the other because it can remember more features of the
environment, and can use its knowledge to find novel routes to where it wants to go. But this
first organism can only acquire this spatial information by first-hand experience, by
laboriously traveling and remembering the terrain. The second organism has a more limited
capacity to remember features and to manipulate its internal model of the world, but it can
acquire its knowledge in a richer variety of ways. It can infer spatial structure from the
behavior of other organisms. In some respects the first organism is smarter, but in other
respects the second is.
As we add sophistication to the tool-kit of behavior-guiding capacities, we eventually
reach clear, unmistakable cases of cognition. And these clear cases do not all involve
humans. Some birds, such as Clark’s nutcrackers in the south-west of the US, hide stores of
food when supplies are good, and retrieve it in times of scarcity. To do this requires a
sophisticated combination of perceptual abilities and spatial memory. In the sense in which I
am using the term “cognition,” there is nothing marginal about such cognitive capacities.
So I claim that we reach uncontroversial cases of cognition before we reach
language use. And I have left out all mention of the “qualitative,” first-person, “how it feels”
side of mental life. My overall position is that we do have reasonably good evidence to posit
rich qualitative states in non-human animals. But that is a separate point which does not
matter to the task at hand.
To use a very broad sense of “cognition,” as I do here, does not require postulation
of fundamental similarity in the cognitive processes in all these diverse cases. Indeed, I have
been stressing the opposite — the diversity of ways in which cognitive and proto-cognitive
capacities are realized. Many people have given general overarching theories of how all
cognitive processes work. Examples include the general theories of learning that dominated
psychology in the middle decades of this century. If one has an overarching theory of this
kind, then one will want to have a broad term like “cognition” to capture what one is
generalizing about. (Although many of the behaviorist psychologists who defended general
theories of learning would not have liked the term “cognition.”) I am skeptical about those
overarching theories of cognitive processes and mechanisms, and I use a broad sense of
“cognition” for what might be called more “ecological” reasons. There is a certain kind of
job that the collection of processes I am calling “cognitive” performs. For one reason or
another, organisms acquire capacities for behavior and machinery to control this behavior.
The behavioral machinery acquired is diverse, and so are the mechanisms used to control
this behavior. Our sophisticated human mental abilities are one instance of this evolutionary
phenomenon, but the abilities of bees and jaguars are as well. Understanding the evolution
of cognition is understanding this whole domain of evolution’s products.
3. On Complexity
The ECT claims that the function of cognition is to enable organisms to deal with
environmental complexity. But what exactly is environmental complexity? There is a good
deal of unpacking to do here (see also Godfrey-Smith 1996.)
I suggest that the most useful concept of complexity here is a simple one.
Complexity is heterogeneity. Complexity is variety, diversity, doing a lot of different things
or having the capacity to occupy a lot of different states.
There are many different kinds of heterogeneity, hence many kinds of complexity. It
is not just unnecessary, but positively mistaken, to try to devise a single scale to order all
environments from the least to the most complex. Rather, any environment will be
heterogeneous in some respects, and homogeneous in others. Environments can be
heterogeneous in space and in time, and spatial and temporal heterogeneity exists at many
different scales. An environment with a large number of different possible states which
come and go over time is a complex environment, in that respect. So is an environment
which is a patchwork of different conditions across space. The heterogeneity property is not
the same in these two cases, but in both cases heterogeneity can be opposed to
homogeneity. A complex environment is in different states at different times, rather than the
same state all the time; a complex environment is different in different places, rather than the
same all over. Whether a particular type of complexity is relevant to an organism will
depend on what the organism is like — on the organism’s size, physiology, needs and habits.
The heterogeneity properties of environments are objective, organism-independent
properties, but among the countless ways in which an environment is structured and
patterned, only some will be relevant to any given organism. (See Levins 1968 for a classic
discussion of some of these issues.)
In the ECT I said that cognition enables agents to “deal” with environmental
complexity. That terminology suggests that environmental complexity is seen as posing
problems for organisms. Often this is so, but I do not want to put too much weight on the
concept of a “problem” here (Lewontin 1983). In some cases, environmental complexity
provides what would normally be called an “opportunity” rather than a problem. A
population might be located in a fairly benign set of circumstances, but one where tracking
and adapting to environmental complexity makes it possible for some individuals gain a
reproductive advantage over others. Natural selection works in a comparative way; the
absolute level of hardship is in general not important in understanding evolutionary
processes within a population. So while I will often write of the “problems” posed by
environmental complexity in this paper, occasionally I will use the term “opportunities” as
well. The distinction between the terms is mostly an everyday one which should not be
taken too seriously in this context.
If we think of complexity just as heterogeneity, this concept of complexity can be
applied to organisms as well as to environments. An organism is complex to the extent that
it is heterogeneous. Here again, there are different kinds of heterogeneity; an organism can
be heterogeneous in many different respects. (For different concepts of organismic
complexity, see McShea 1991.)
Cognitive capacities themselves are complex, so the ECT can be seen as claiming
that one kind of organic complexity has been produced by evolution to enable organisms to
deal with environmental complexity. Dealing with complex problems by means of
perception and action can be seen as a special case of a more general phenomenon: dealing
with environmental complexity by means of flexibility.
This way of looking at the ECT is illustrated by the “proto-cognitive” capacities that
were discussed in the previous section of this paper. When a plant has the ability to
adaptively alter its development to suit its environment, this is a case of complexity in the
plant’s developmental capacities which enables the plant to adapt to heterogeneity in its
environment. Similarly, why do e. coli bacteria have their lac operon system of gene
regulation? The preferred food of e. coli bacteria is glucose, but sometimes glucose is not
available while other sugars are. The variability in the availability of different sugars is one
type of environmental complexity faced by bacteria. Metabolic machinery is expensive, and
e. coli have apparently been selected to economize in their production of enzymes. So the
enzyme needed to digest lactose is not produced in the absence of lactose. Instead, the
production of the enzyme is controlled by an environmental cue. Here the cue used is the
presence of lactose itself (and also the amount of glucose available to the cell — see Lodish
et al. 1995 pp. 421-22). The system of gene regulation used by the bacteria here constitutes
one kind of complexity in these organisms, and this complex mechanism has a functional
explanation, of the strong type discussed earlier. The function of the lac operon system is to
enable e. coli bacteria to deal effectively with one type of environmental complexity –
variation in the availability of different sugars.
As I said earlier, I do not claim that bacteria exhibit cognition; this is at most a case
of proto-cognition. However, the ECT claims that the explanations for more complex and
genuinely cognitive capacities tend to have a similar general shape as this explanation for a
property of bacteria. The point of acquiring complex systems for behavioral control is to
enable the organism to deal with variation in what the environment confronts the organism
with, and variation in the opportunities the environment offers.
Environmental complexity figures in evolutionary processes that give rise to cognition.
But where does environmental complexity itself come from? And what should we make of
cases where environmental complexity is itself the product of organisms and their activities?
Environmental complexity itself has many sources. For the purposes at hand I will
make a loose distinction between two main categories. One source is the class of physical
processes which are more or less independent of the activities of the organisms under
consideration. Seasonal cycles provide an obvious example. And many resources that are
relevant to an organism’s well-being will be scattered through space in a way that is largely
independent of the organism’s own actions and properties.
When some type of organism acquires, through evolutionary processes, a way of
tracking and dealing with environmental complexity of this first kind, the explanatory
pattern described by the ECT has a straightforward causal directionality. But there are other
cases in which the situation is more complicated. These are cases where the environmental
complexity that organisms must deal with is either a causal product of, or is constituted by,
the activities of other organisms within the same population. Then we have a situation that
can exhibit feedback, or a “coupling” of organism and environment. (Lewontin 1983,
The most graphic examples are probably those that involve competitive interactions
between animals. If the only way for you to obtain and hold a resource is by winning
contests with other individuals in the same population, then these other organisms constitute
a key part of your environment. Their behavioral complexity constitutes part of the
environmental complexity you must deal with, so the behavioral capacities of organisms
similar to yourself are the source of a crucial kind of complexity in your own environment.
In behavioral ecology, contests of this kind are modeled with game theory (Krebs and
Davies 1987). Most mathematical game theory models only remain simple enough to be
comprehensible when many idealizations are made. Surrounding a few well-understood
cases explicitly modeled with game theory, there is now a great deal of informal verbal
“modeling” (in scare quotes) and computer simulation of these sorts of interactions going
on. In fact, some have claimed that feedback processes of this kind are the key to
understanding the evolutionary transition to genuine human intelligence. Those suggestions
will be discussed in my final section below.
My present point is that these phenomena are not incompatible with the ECT. The
ECT need not be understood in a way in which the processes generating environmental
complexity are casually autonomous, or independent of the activities of the evolving
population in question. The ECT is compatible with the view that a centrally important
aspect of environmental complexity for many organisms is complexity that is made up by,
or caused by, the activities of other organisms of the same species. In those cases, the ECT
describes one part of a larger causal “cycle” — the part in which environmental complexity
puts selective pressure on organisms’ cognitive capacities. The other part of the “cycle” is
where the behaviors of organisms influence or determine the relevant patterns of
So some environmental complexity for a given organism is made up by the activities
of other organisms in that population. What about organisms from other species, which
constitute sources of food, or sources of danger, for the organisms we are concerned with? I
stress again that my two-way distinction here is rough and ready. To the extent that the
relevant activities of other species are causally influenced by the properties and activities of
the population under consideration, we have a case of the second “coupled” type. Predator-
prey interactions are a classic example. In general descriptions of ecological relationships,
people often stress that every species is connected to virtually ever other, through direct or
indirect causal chains. Clearly however this is a matter of degree. It is an error to over-
generalize about the richness of inter-specific connections, just as it is an error to treat
organisms as if all they ever have to deal with is an independent, causally autonomous,
I have been contrasting relatively simple cases in which organisms are responding to
environmental complexity that is causally independent of them, and more complicated cases
where the environmental complexity itself depends on the organisms in significant ways.
But even in the “simpler” class of cases, it should not be thought that I am suggesting that
the evolutionary processes themselves are simple and predictable. Much environmental
complexity is not relevant to any given organism, and the factors that contribute to some
aspect of complexity posing a problem are diverse and subtle. Suppose you move through
the world like a monkey, swinging from tree branches. Then the relevance of diversity in the
size and strength of these branches depends a great deal on your size. If you are small, most
branches will support you and in any case a fall is unlikely to lead to serious harm. If you
are larger, paths through the forest must be chosen with care and pose a significant
information-processing problem because of the causal role of your own weight (Povinelli
and Cant 1995). Some branches will break, leading to a dangerous fall, while others will
bend, in ways that affect your possible next moves. Heterogeneity in the properties of tree
branches is thus relevant in different ways, and in different degrees, to differently sized
The mere presence of environmental complexity that is relevant for a given type of
organism does not automatically generate cognition, or even natural selection for it. The
consequences of relevant environmental complexity also depend on many other features of
the organism and its ecology. The fact that the ECT is expressed as a simple generalization
should not be taken to downplay the role for “architectural constraints” in explaining why
evolution takes the course it takes in a particular case. (These constraints are famously
discussed in Gould and Lewontin 1979.) For some organisms, getting smart is not really an
evolutionary option, as a consequence of their basic biological lay-out, their characteristic
developmental sequence, or their overall ecology. Even for those that could, in principle, start
to respond to environmental variation by tracking and behaviorally adapting to it, the
appropriate genetic variation has to arise, and there will be costs associated with the
machinery required to take a smart approach.
It is also well understood that some kinds of environmental complexity can be
effectively dealt with by buffering it or blocking it out. One can respond to a threat by being
smart, but also by becoming impervious to it, via a strong shell or via sheer size. Some
organisms, including many insects, deal with certain kinds of environmental complexity with
an “r-selected” strategy for reproduction, in which there is massive reproduction in good
times, and little activity in bad times. To take this strategy it is necessary to be able to
produce huge numbers of quickly-maturing offspring when times are good. All this makes
for a lack of cognitive machinery in r-selected organisms.
So whether evolution takes a lineage of organisms down a path towards increased
cognitive capacity is contingent on a great range of factors, many of them having to do with
the “raw materials” that evolution has to work with in that particular case. But this fact does
not make the ECT false or naive. The ECT, when it applies to some particular case, is one
part of a more complicated and detailed explanation.
4. The ECT as a Component in Many Evolutionary Scenarios
So far this paper has discussed the ECT in extremely general terms. In this final section I
will look at some specific models and programs of empirical work. I will discuss four
examples, each focused on understanding a specific type of cognition (or proto-cognition). I
suggest that the ECT is one component in many diverse scenarios that have been discussed
in connection with the evolution of cognition.
(i) Phenotypic plasticity
I have said several times in this paper that cognition, understood in my broad way, shades
off into other biological processes, especially those that use signals (from the environment
or elsewhere) to control adaptive responses. One important class of cases in the category I
have been calling “proto-cognitive” is the phenomenon of phenotypic plasticity, especially in
In the paradigm cases, phenotypic plasticity is a phenomenon in which a single plant
genotype can produce variety of forms (phenotypes) or can take a variety of developmental
paths, where the “choice” is determined by an environmental cue transduced by the plant
(Bradshaw 1965, Sultan 1987, Schlichting and Pigliucci 1998). A plant might have a wet-
environment and a dry-environment phenotype, for example, or might alter its form
according to altitude and accompanying climatic conditions, as in the classic experiments of
Clausen, Keck and Hiesey (1948). So in these cases the plant has some mechanism for
transducing an environmental cue, and of controlling development as a function of the state
of the cue. The cue, the plant phenotype, and the environmental variables that are being
adapted to might be discrete or continuous. (When the organism’s response is a discrete
choice this is sometimes called “polyphenism,” but I will not make that terminological
No nervous system is involved in these cases, and in general it is growth and
development, rather than behavior, that is being controlled. But this type of phenomenon is a
useful “zero order” case for discussions of models of adaptive response to environmental
conditions. There are formal similarities between these capacities and cases of real
cognition. Indeed, in the 1990s two mathematically identical evolutionary models were
published independently (Moran 1992, Sober 1994). One was presented as a model of the
advantages of learning (Sober), while the other was presented as a model of the advantages
of plastic control of development (Moran).
How do the models and theoretical discussions look? Let us be very abstract.
Assume that an organism confronts an environment which has a range of alternative
possible states. The organism itself has a range of possible developmental options. The
alternative environmental states have consequences for the organism’s chances of surviving
and reproducing, and the best developmental option for one environmental state is not the
best choice for another. The organism receives imperfect information about the actual state
of the environment, as a consequence of correlations between environmental conditions
which matter to it and environmental conditions which directly affect its periphery. There are
several ways to respond to the problem. One way is to be able to buffer out the
environmental variation — perhaps by being big, or strong, or restricting exposure in some
way. Another way is to adapt to the most common or the most critically important
environmental state. But yet another way is to use a flexible strategy — to use environmental
information to determine the organism’s phenotype in accordance with how the environment
is perceived to be.
For example, Drew Harvell (1986) investigates defences against predators produced
by colonial marine invertebrate animals called “bryozoans,” or sea moss. The bryozoans
Harvell studies are able to detect the presence of predatory sea slugs, making use of a water-
borne chemical cue. When sea slugs are around, the bryozoans produce spines. The spines
have been shown to effectively reduce predation, but also to incur a significant cost in terms
of growth, so they are detrimental when sea slugs are not around.
Here we have a rudimentary form of perception. The bryozoans show sensitivity to an
environmental cue which is not itself practically important, but which carries information
about a more important state of the world, the presence of predators. The organism use a
cue to produce an adaptive response to a more important “distal” environmental state.
In cases like these, a complete explanation for the organism evolving a “smart” or
proto-cognitive capacity includes a description of the problem posed by environmental
complexity — the fact that predators are sometimes, but not always, present. But the
explanation includes much more as well. We need to also know the reasons for a proto-
cognitive response being favored over buffering, adaptation to the most common condition,
or some other “dumb” strategy.
When will the proto-cognitive strategy be favored? There is no simple answer, but
many models developed by biologists and others can be pieced together to give a partial
answer (Godfrey-Smith 1996, chapters 7-9). Some parts of the story are intuitive. To use a
proto-cognitive strategy, the organism needs a suitable signal from the environment. If there
is no way of tracking the relevant states of the environment, it is better to produce a single
“cover-all” phenotype, or to adapt to the single most common or important environmental
state. Parts of information theory and signal detection theory can be used to describe exactly
what sorts of properties an environmental cue must have, in order for it to be worth using.
When do you want to choose a flexible strategy over an inflexible one? Only when your
environmental cue is good enough so you don’t make too many of the wrong kinds of
errors. Even you can track the world with some reliability, if one type of wrong decision is
sufficiently disastrous, it may be best never to behave in a way which risks this error.
Principles like those are close to common sense. But these models also have a number of
more subtle features. For example, it can matter a great deal how payoffs from individual
encounters or “trials” are related to each other in their effects on overall fitness — whether
payoffs are summed or multiplied (Levins 1968, Seger and Brockman 1987). The principles
discussed by these models of plasticity cast light on both proto-cognition and genuine
cognition as well. The models describe the first step towards cognition — opting for a
flexible response to a heterogeneous environment.
(ii) The evolution of associative learning.
The second example I will discuss is a computer simulation of the evolution of associative
learning, due to Todd and Miller (1991). This simulation explores the evolution of the
architectural properties of simple networks of neurons, using what is known as the “genetic
algorithm.” The aim is to see when evolution will select for organisms that exhibit one of the
simplest kinds of learning — classical conditioning.
The neural networks can usefully be imagined as embodied in simple marine
animals which are born in the open sea, but which settle down to an immobile life feeding
on passing food particles. Once an individual has settled, its only problem is to decide
whether to feed or not feed, when presented with each item of possible food. The
environment contains both food and also inedible or poisonous particles, in equal
proportions. When food is eaten the organism gains an energetic benefit, and when poison
is eaten the organism pays a cost, though the error is not fatal.
Particles of possible food have two sorts of properties that the organisms can
perceive — color and smell. Food smells sweet and poison smells sour, but in this turbulent
environment smells can mislead. The probability of a sweet smell, given the presence of
food, is 0.75. The probability of a sour smell, given poison, is also 0.75.
The color of food is not affected by turbulence, but color is unpredictable in a
different respect. In half of the population’s environment food is red and poison is green,
but in the other half the colors are reversed. Within each of these two micro-environments,
color is 100% reliable.
Each generation contains a large number of individuals of different types, which
settle at random in the two different micro-environments. At the end of a fixed period they
reproduce (sexually) according to their accumulated fitness, with the possibility of mutation
and recombination of genes. The new generation then floats about and settles in the
environment at random and the cycle begins again.
The neural networks placed in this scenario are constrained to have three “units,” or
nodes, only. These nodes are like idealized nerve cells. Despite these limited resources there
are lots of possibilities for the networks’ architectures, and these architectures evolve in the
model by natural selection across generations. Units can function as input devices of
various kinds (red-detectors, green-detectors, sweet-detectors or sour-detectors). There is
just one type of output unit (eating), and a “hidden” unit, which mediates between a detector
and a motor unit, is also a possibility. The range of units an individual has, and which ones
are connected to which, are determined by its genetic make-up.
Connections between units can be hard-wired with either an excitatory or inhibitory
one-way connection, or they can be plastic and altered by the individual’s experience. If the
genotype specifies a plastic connection, then the connection is shaped over time by what is
known as a “Hebbian” learning rule. If those two units tend to fire at the same time in the
individual’s experience, they acquire a positive connection between them — one unit comes
to have a (one-way) excitatory connection to the other. If they do not tend to fire together,
the connection becomes negative or inhibitory. The question the model is intended to
address is: when and in what ways will individuals with the ability to learn evolve in the
population? The question is interesting because Hebbian learning is discussed a good deal
by neuroscientists, and they hunt for Hebbian learning in the synapses of the brain. But
from the evolutionary point of view, it is often not obvious what use Hebbian learning has.
If two neurons tend to fire together, what is the point of also making one excite the other?
At the start of a “run” of the Todd and Miller simulation, the population consists of
randomly configured individuals, most of which do not fare well. For example, some will
not have a motor unit and will never eat, or will have a motor unit connected to an input unit
which has the wrong setting — it might tell the organism to always eat when the present food
particle smells sour. Another type of miswiring might be called “the academic.” An
individual can have two input units and a motor unit, but only learnable connections between
all three units, connections which are initially set at zero. Suppose such a creature lands in a
patch where food is red. Then it will learn the statistical association between redness and a
sweet smell — the red-color input unit will tend to be on at the same time as the sweet-smell
unit. But nothing is inducing the individual to eat. The motor unit will never be turned on,
and its knowledge of the world will not do the individual any good, as far as nutrition is
Two kinds of wiring do work well for the organism though. One has a fixed positive
connection between a sweet-smell sensor and a motor unit, and nothing else which
influences behavior. This organism will generally eat when there is food present — in the
present case, it will make the right decision 75% of the time. After a short period, these
individuals tend to proliferate in the population.
The best possible wiring is a variant on this one, which has a fixed connection
between a sweetness sensor and a motor unit (as above), but also a learnable connection
between a color sensor and the motor unit. From the start this individual will tend to eat
when there is food, as the smell sensor is controlling the motor unit. But in addition there
will be a correlation between eating and some particular state of the color sensor. If the
micro-environment is a red-food one, then when the organism eats it will also tend to be
seeing red. This correlation establishes a connection between the color sensor and the motor
unit, and (given the right initial settings) this connection will eventually be strong enough to
control the motor unit by itself. Then the eating behavior will be controlled by a 100%
reliable cue for the remainder of the individual’s life. Typical runs of the simulation begin
with the fairly rapid evolution of the simple, hard-wired smell-guided networks, and some
time afterwards learners appear and take over.
This simulation illustrates the advantages associated with two kinds of behavioral
complexity. Consider first the contest between individuals which always eat every particle
that drifts by, and individuals which use smell as a cue. If everything in the environment was
food, there would be little point in controlling behavior with perception, especially with an
only partially reliable environmental cue. But the environment used by Todd and Miller is
one where food and poison drift by with equal frequencies. This is one type of
environmental complexity, and it has the consequence that a permanently-eating architecture
will have low fitness when compared to an individual that uses smell as a cue. A different
type of environmental complexity, and a different reliability relationship between a cue and
the world, explains the evolution of learning. The total environment in which these
organisms live is spatially heterogeneous — in half the environment food is red and in the
other half food is green. If food was red in the whole environment, it would not be worth
taking the time to learn that food is red, and a network with a red-detector hard-wired to the
output unit would be optimal. But Todd and Miller use an environment which is
heterogeneous in this respect as well. And although the color of food is not predictable in
advance, the past experience of an individual is a good guide to the future. That is what is
needed for learning to be more useful than an inflexible behavioral program.
(iii) Spatial memory and cognitive maps
For a mobile animal, one very important kind of environmental heterogeneity is
heterogeneity in the distribution of resources, dangers and other factors in space. Spatial
structure plays a very different role for a plant, of course, or for an animal like a clam which
does not move around the world. But once an organism is on the move, as most terrestrial
animals must sometimes be, spatial structure in the environment is of prime importance.
Here as in general, some of this environmental heterogeneity can be dealt with by
various forms of buffering. But evolutionary responses to the problem of dealing with space
have produced some impressive and complicated forms of cognition, even in small and
otherwise behaviorally simple animals. The mechanisms associated with bee dances, which
direct workers from the hive to sources of food, are one famous example, but it has turned
out that bees as individuals also show good spatial skills. They can learn to reliably
associate a source of food with either single landmarks or with geometrical structure in a set
of landmarks. In recent years a lot of attention has been directed on spatial memory in food
storing birds, such as the Clark’s nutcracker in the south-western US, and marsh tits in
England. Clark’s nutcrackers hide thousands of pine seeds as a food source for the winter.
It appears that these birds have specialized spatial memory abilities which do not extrapolate
(as far as has been determined) to superiority over other birds in non-spatial memory tasks.
(See Roberts 1998 chapter 7 for the bee and bird examples in this paragraph.)
Back in 1948, E. C. Tolman suggested that both animals and humans make their
way through space by using “cognitive maps,” or rich internal representations of spatial
structure in the environment. After initial controversy and some decades of neglect during
the heyday of strict forms of behaviorism, the concept of a cognitive map is again being
used by ethologists and comparative psychologists (Tolman 1948, Thinus-Blanc 1988,
Roberts 1998). The concept of a cognitive map is controversial in a number of respects.
First, there is a good deal of vagueness and ambiguity in how it is applied by different
researchers (Bennett 1996). Some use the term to refer specifically to postulated internal
structures which work in psychological processing in ways reminiscent of ordinary, external
maps. In this narrow sense, the hypothesis that an animal has a “cognitive map” requires, at
a minimum, a capacity to devise novel detours and shortcuts in response to obstruction of
more familiar paths. But the term is sometimes used more broadly, to refer to almost any
kind of spatial memory. Tolman, for example, distinguished “strip maps” and
“comprehensive maps” within the more general category of cognitive maps. Strip maps
represent only a path to a goal; they are dependent on the starting point of the animal.
Comprehensive maps are richer representations of the overall spatial structure in some
domain, so they can used despite variation in starting points, new obstacles and so on. But it
can be argued that the sort of behavior associated with “strip maps” is easily explained
without talking of inner “maps” at all; the animal is just executing a sequence of behaviors
in response to a set of cues or landmarks. Some sophistication in memory is clearly
involved, but there is no need to postulate an inner map-like structure.
However, there are experimental results which do justify a richer interpretation of
some animals’ inner processing of spatial information. A simple and striking case is found
in an experiment by Tolman and Honzik (1930). Rats were trained in a maze that has three
different paths to a single supply of food. Path 1 is shorter than path 2, and path 2 is shorter
than path 3. The rats were easily able to learn to prefer the best available path. After a few
days of training, path 1 was almost always chosen first. If path 1 was blocked (at an early
point), they would go back and take path 2. If path 2 was also blocked, they would settle for
path 3. So far, this only shows a fairly routine (but very useful) type of reinforced learning.
The impressive behavior resulted when path 1 was blocked in a novel way for the first time.
Path 1 has its final section in common with path 2, but path 3 reaches the food
independently of this common section. So when this final part of path 1 is blocked, that has
the effect of making path 2 useless as well. What will the rats do when path 1 is blocked in
this novel way? Their history of conditioning has taught them that when path 1 is blocked,
path 2 is the next choice. But if the rats are smart enough to realize the consequences of this
novel way of blocking path 1, they should choose path 3 directly and not waste time on path
2. In Tolman and Honzik’s experiment, a large majority of rats, on encountering the novel
obstruction on the later part of path 1 for the first time, returned to the junction point of the
three paths and immediately chose path 3. They did not follow their failure on path 1 with
an attempt at path 2; they had somehow been able to represent the new obstacle as rendering
path 2 useless as well.
Tolman and Honzik interpreted this as showing “insight” on the part of the rats,
following the gestalt psychologist Köhler, who had found similar results with chimps.
Tolman did not, for some reason, use this experiment in the famous 1948 discussion which
introduced the concept of a “cognitive map.” Instead he used results which, to my mind,
were a good deal less convincing than his 1930 “insight” experiment. (Thinus-Blanc 1988
erroneously reports Tolman 1948 as actually discussing the “insight” experiment in support
of the cognitive map concept, an interesting case of wishful thinking, or post-hoc
improvement of Tolman’s paper!) Perhaps Tolman did not think of the “insight” experiment
as showing specifically spatial cognitive skills, but a more general capacity to draw
conclusions and reason beyond the immediate lessons of conditioning. However the
“cognitive map” concept is used, this 1930 experiment does appear to show a capacity to
construct and manipulate some sort of internal model of spatial structure in the environment.
In qualification of this, I should note that Tolman and Honzik found it quite tricky to devise
a maze in which most of their rats would consistently show this spatial “insight.” For
example, insight was consistently shown only when the maze was made of elevated tracks,
Another kind of experiment designed to investigate innovative behavior based on
representation of spatial structure studies the use of short cuts. Both dogs and chimps can
be shown hidden pieces of food in an environment, with the order of their exposure to the
food corresponding to an inefficient path from food item to food item. Once released, the
animals in both cases are able to find the food items and move from item to item using a
path that is new and more direct and efficient than the one they were trained on. (In effect,
the chimps do a reasonable job at what mathematicians refer to as a “traveling salesman
problem.”) Here again, the behavior produced does not correspond to any motor routine or
action pattern that the animal was trained on, and these experiments also control for such
possibilities as olfactory detection of the food (Roberts 1998, Menzel 1997).
All of these experiments are associated with some controversy. For example, it can
be argued that to the extent that animals are simply moving from one memorized landmark
(or point specified by its relation to a set of landmarks) to another, there is justification for
attributing memory to the animal but not a cognitive map (Bennett 1996). Menzel (1997)
found deviations from “traveling salesman” optimality when macaques were tested on
whether they chose the optimal path from item to item, or simply went towards the closest
piece of food at each decision point. But it is noteworthy that the best of this work, such as
the Tolman and Honzik “insight” experiment, does involve behavior which appears to justify
the postulation of internal representation of the world, and fairly complex use of the
representations in guiding behavior, without the animal having a capacity for public
language. There is a tradition in philosophy of denying that any animal which lacks
language can properly be said to “think” or “represent.” In recent years, Davidson (1975)
has been the most influential defender of this view. It was held in a different form by Dewey
(1929) and is often associated with Wittgenstein (1953) and his followers. Such views
struggle to make sense of the skills in animal path-choice discussed above. Whether or not
these animals are able to “represent” the world in the richest, most philosophically loaded
sense of “represent,” they do seem to be doing some kind of representation or mapping of
the spatial structure of their environment (see also Allen and Bekoff 1997). Problems and
opportunities associated with spatial structure in environments have apparently generated a
range of sophisticated cognitive skills.
(iv) Social intelligence models
It has usually been thought not too hard to explain the evolution of such capacities as
learning (Example (ii) above) and the ability to represent spatial structure in an environment
(Example (iii)). But it is a different matter to explain the highly developed and distinctive
cognitive capacities of human beings and non-human primates. Over 20 years ago, Nicholas
Humphrey suggested that primates seem to have too much brain-power to be explained by
the demands of such activities as foraging for food. He suggested that the problems
primates are using this intelligence to deal with stem from the social complexity of their
environments (Humphrey 1976). In particular, much primate life is concerned with the
formation and maintenance of alliances, the policing of dominance hierarchies, and a variety
of other social tasks that involve a mixture of competition and cooperation. Humphrey’s
suggestion (which had been partially anticipated by others) was that high primate
intelligence evolved in response to the problem of dealing with this kind of complexity. As
each individual primate comprises part of the environment for the others in a population, we
have the ingredients here for a process of feedback, in which each increase in intelligence
produced by evolution adds to the complexity of the social environment that individuals
This idea has come to be known as the “Machiavellian intelligence hypothesis”
(Byrne and Whiten 1988, Whiten and Byrne 1997). Here I use the term “social
intelligence” rather than “Machiavellian intelligence.” As Whiten and Byrne say, the term
“Machiavellian” should not be taken to suggest that all the behaviors involved in the
hypothesis are manipulative and deceitful. They mean to include “cunning cooperation”
which contributes to individual reproductive success (Whiten and Byrne 1997 pp. 12-13).
Whiten and Byrne do mean to exclude, however, any suggestion of natural selection
operating at the level of groups rather than individuals — they mean to exclude the idea that
intelligent cooperation could evolve “for the good of the group.” As I do think the term
“Machiavellian intelligence” continues to mislead, suggesting the darker side of social
behavior, and I also do not want to rule out some role for group selection in these processes,
I prefer to use the term “social intelligence hypothesis.” Like Gigerenzer (1997), I reserve
“Machiavellian” as a narrower term, specifically for behaviors involving exploitation rather
The social intelligence hypothesis is compatible with the ECT; it is a specific
instance of the ECT which involves a special kind of environment. In the previous section I
argued that the ECT need not be understood in a way that requires the environment in
question to be independent of the population that is evolving. In any social animal, a key
part of an individual’s environment is made up of the other members of the social group,
with all their behavioral capacities. The same is true to a lesser extent with many non-social
animals. In cases like these, the ECT describes one particular explanatory arrow within a
larger explanatory structure, a structure which links cognitive capacities and environmental
complexity in a “coupled” way. At any given time, the individuals in a social population face
environmental complexity in the form of the behavioral patterns of the other local members
of the population. This environmental complexity may (or may not) give more intelligent
individuals an advantage over less intelligent individuals, by some specific measure of
intelligence relevant to the situation. If intelligence is favored, and if this kind of intelligence
is inherited, then over time intelligence will increase in the population. If the intelligent
individuals themselves display more complex patterns of behavior than others in the
population, then this increase in intelligence will in turn entail an increase in the complexity
of the social environment faced by later individuals. This process may or may not have a
“runaway,” positive-feedback character. One should not assume that a runaway process is
the only outcome. For example, other constraints and costs might start to assume a larger
role once the individuals reach a certain level of intelligence.
The social intelligence hypothesis has been developed in a number of different
specific versions. Stronger versions claim that social complexity has been the key factor in
producing the high levels of primate intelligence; weaker versions see this as one
explanatory factor which might work in conjunction with others. For example, some suggest
a role for special problems associated with foraging for the ripe fruits favored by primates –
it might be that primates require especially sophisticated cognitive maps of their (non-social)
environments. (Whiten and Byrne 1997 contains discussions of a range of alternatives to
the social intelligence hypothesis.) Different versions of the hypothesis also stress different
aspects of social living — direct competition between males for mates, dealing with
dominance hierarchies, cooperative foraging and so on.
As Gigerenzer (1997) notes, the social intelligence hypothesis is sometimes
associated with the suggestion that (i) the overall degrees of complexity in social and non-
social environments (or social and non-social aspects of an environment) can be compared,
and (ii) social environments are more complex. As I said back in section 3 of this paper, I
am a skeptic about the project of giving overall measures of complexity across
environments; any environment is complex in some respects and simple in others.
Gigerenzer is similarly skeptical about these complexity measures. But where Gigerenzer
appears to think that this problem makes it pointless to generalize about the role of
complexity in the evolution of cognition, I think the environmental complexity thesis is a
useful general principle despite the absence of a unitary scale of environmental complexity.
As I understand them, most but not all of the specific hypotheses discussed under
the general category of “social intelligence” can be seen as applications of the ECT. The
versions that do fall under the ECT are those that stress the role of cognition in dealing with
the behavioral complexity of other individuals within a social group. An example of a
hypothesis in this general area that does not fall under the ECT is one version of the
“protean behavior” hypothesis, discussed by Geoffrey Miller (1997). Miller suggests that
animals like primates have been selected to be able to produce genuinely unpredictable
behavior. Unpredictable behavior has fairly obvious advantages when an animal is escaping
from predators, and more subtle advantages in situations involving conflict and bluffing, as
discussed in game theory. In those cases, producing unpredictable behavior itself requires
no special cognitive sophistication. But Miller also suggests that capacities for novel,
creative behavior will help individuals attract mates, especially in situations of female choice,
and these behavioral capacities are facilitated by a large brain. There are several ways in
which this scenario might work, but consider one case. Suppose there has been selection for
behavioral novelty in males, and suppose these novel mating displays succeed by taking
advantage of a general feature of perceptual and cognitive mechanisms — the fact that
novelty attracts attention. Then any evolved increase in cognitive sophistication due to this
process cannot be seen as an application of the ECT. A complex social environment might
be created by male behaviors in this case, but cognition is not being selected as a way of
dealing with complexity. On the other hand, if females are being strongly selected for the
capacity to see through all this behavioral noise and make adaptive choices, there will be
selection, compatible with the ECT, for cognitive sophistication in females.
All these “sex-specific” hypotheses about the advantages of cognition in primates
have a problem stemming from the basic similarity between male and female brains in the
most intelligent primates (as Richard Francis stressed to me). If selection only favors
elaborate cognition in one sex, then the other sex will have much of its brain treated by the
theory as a mere byproduct. Big brains are too expensive to be treated like male nipples; big
brains as byproducts would be analogous to peacock tails on peahens. If both sexes are
being selected to be smart, but in very different ways, then the problem posed by the lack of
obvious sexual dimorphism is more subtle and hard to assess. In any case, I introduce these
speculations about unpredictable behavior not to endorse them, but to illustrate the fact that
while the ECT is very broad, it does not trivially encompass any possible explanation for the
evolution of cognition. The ECT only covers cases where environmental complexity
(whether social or nonsocial) creates a problem (or an opportunity) for some type of
organism, and the problem leads to natural selection favoring individuals with an ability to
use cognition to coordinate behavior with the state of the environment.
In earlier discussions of social intelligence, there was sometimes the appearance of a
sharp “either-or” characteristic to the debates about social and non-social complexity; either
primates became smart for social reasons, or they became smart for reasons having to do
with non-social aspects of their ecology. But as Byrne (1997) argues, there is plenty of
room for mixed explanations, in which a number of factors have a role. Byrne himself
posits three distinct evolutionary transitions in the evolution of intelligence in monkeys, apes
and humans. In this scenario, the evolutionary branch containing monkeys and apes
(haplorhines) became smarter than its relatives because of selection for social intelligence.
But the “Great Apes” (chimps, bonobos, humans, gorillas, orangutans) branched off from
this group and became smarter because of selection for “technical intelligence,” which
involves planning and sophisticated tool-use in activities such as foraging. And then the
branch that led to modern humans was perhaps again the subject of selection for social
intelligence, in part because of larger group size. This scenario is very speculative, as Byrne
stresses, but it provides a good example of a “mixed” story about the evolution of human
cognition. It would be a mistake to only pursue “pure” social intelligence hypotheses, out of
an overly strong attachment to explanatory simplicity.
In closing, I will try to give a more “fleshed out” summary of what the ECT claims.
Environmental Complexity Thesis (more detailed version):
The basic pattern found in the evolution of cognition is a pattern in which individual
organisms derive an advantage from cognitive capacities in their attempts to deal with
problems and opportunities posed by environmental complexity of various kinds. Cognitive
capacities confer this advantage by enabling organisms to coordinate their behavior with the
state of the environment. Cognition itself should be thought of as a diverse “tool-kit” of
capacities for behavioral control, including capacities for perception, internal representation
of the world, memory, learning, and decision-making. These capacities vary across different
types of organism and are not sharply distinguished from other biological capacities, some
of which have a “proto-cognitive” character. The “environment” referred to in the ECT
includes the social environment, and there are some reasons to believe that problems posed
by social complexity have been very important in the evolution of primate and human
intelligence. Many specific evolutionary scenarios that have been discussed as possible
explanations of particular cognitive capacities are instances of the ECT, or have the ECT as a
Thanks to Richard Francis, Lori Gruen and Kim Sterelny for discussions and
correspondence on these issues.
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Max Raphael by Max Pechstein 1910
“The Demands of Art”. By Max Raphael. With an introduction by Herbert Read. Translated by Norbert Gutterman. Princeton University Press (Bollingen Series), 1968. 258 pp.
Some fight because they hate what confronts them; others because they have taken the measure of their lives and wish to give meaning to their existence. Max Raphael was a very pure example of the second type.
He was born was born near the Polish-German border in 1889. He studied philosophy, political economy and the history of art in Berlin and Munich. His first work was published in 1913. He died in New York in 1952. In the intervening forty years he thought and wrote incessantly. Only a fraction of his work has been published and most of it is out of print. He left thousands of pages of manuscript which his widow and friends are ordering and hoping to publish. Their subject matter ranges from paleontology to classical architecture, from Gothic sculpture to Flaubert, from modern city planning to epistemology.
For five years I tried to interest European publishers in his work. In vain. A fact which I mention only because in a few decades it may be hard to remember how unknown and unrecognized Max Raphael still was in 1968.
His life was austere. He held no official academic post. He was forced several times to emigrate. He earned very little money. He wrote and noted without cease. As he traveled, small groups of friends and unofficial students collected around him. By the cultural hierarchies he was dismissed as an unintelligible but dangerous Marxist; by the party communists as a Trotskyist.
To appreciate the possible role of the book under review demands a clarity about the present situation of the arts. (Nobody who is not prepared to grapple with fundamentals should approach the book.) It is a situation of extreme crisis. The validity of art itself is in question. Many significant artists in the world question whether art is justified – not on account of the quality of a particular talent but on account of the relevance of art to the demands of the time in which we live.
Raphael quotes a remark by Cézanne (in the context of a quite different analysis):
“I paint my still lifes, these natures mortes, for my coachman who does not want them, I paint them so that children on the knees of their grandfathers may look at them while they eat their soup and chatter. I do not paint them for the pride of the Emperor of Germany or the vanity of the oil merchants in Chicago. I may get ten thousand francs for one of these dirty things, but I’d rather have the wall of a church, a hospital, or a municipal building.”
Since 1848, artists unready to be mere paid entertainers have tried to resist the embourgeoisement of their finished work, the transformation of the spiritual value of their work into property value. This regardless of individual political opinions as such. In the years after Cézanne, the resistance of artists became more active and more violent – in that it was built into their work. What Constructivism, Dadaism, Surrealism, etc., all shared was their opposition to art as a cultural alibi for existing society. In the last decade the tactics of resistance have changed. Less frontal confrontation. Instead, infiltration. Irony and philosophic skepticism. The consequences in Tachism, Pop Art, Minimal Art, Neo-Dada, etc. But art is still transformed into the property of the property-owning class. In the case of the visual arts the property involved is physical; in the case of the other arts it is moral property.
Art historians with a social or Marxist formation have interpreted the art of the past in terms of class ideology. They have shown that a class, or groups in a class, tended to support and patronize art which to some degree reflected or furthered their own class values and views. It now appears that in the later stages of capitalism this has ceased to be generally true. Art is treated as a commodity whose meaning lies only in its rarity value and its functional value as a stimulant of sensation. Works of art become objects whose essential character is like that of diamonds or sun-tan lamps. The determining factors of this development – internationalism of monopoly, powers of mass media communication, level of alienation in consumer societies – need not concern us here. But the consequence does. ART CAN NO LONGER OPPOSE WHAT IS. The faculty of proposing an alternative reality has been reduced to the faculty of designing – more or less well – an object. Hence the imaginative doubt in all artists worthy of their category. Hence the fact that the militant young begin to use “art” for more direct action.
One might argue that artists should continue, regardless of society’s immediate treatment of their work: that they should address themselves to the future, as all artists after 1848 have had to do. But this is to ignore the world-historical moment at which we have arrived. Imperialism, European hegemony, the moralities of capitalist-Christianity and state communism, the practice of constructing “humanist” cultures on a basis of monstrous exploitation – this entire interlocking system is now being challenged: a world struggle is now being mounted against it. Those who envisage a different future are obliged to define their position toward this struggle, obliged to choose. Such a choice tends to lead them to impotent despair or to the conclusion that world liberation is the pre-condition for any new valid cultural achievement. (I simplify and somewhat exaggerate the positions for the sake of brevity.) Either way their doubts about the value of art are increased.
In this present crisis, is it any longer possible to speak of the revolutionary meaning of art? This is the fundamental question. It is the question that Max Raphael begins to answer in “The Demands of Art.”
The book is based on some lectures that Raphael gave in the early thirties to a modest adult education class in Switzerland under the title “How Should One Approach a Work of Art?“ He chose five works and devoted a chapter of extremely thorough and varied analysis to each. The works are: Cézanne’s “Mont Sainte-Victoire” of 1904-06 (the one in the Philadelphia Museum), Degas’ etching of “Madame X Leaving Her Bath,” Giotto’s “Lamentation Over the Dead Christ” (Padua) compared with his later “Death of Saint Francis” (Florence), a drawing of Rembrandt of “Joseph Interpreting Pharaoh’s Dreams,” and Picasso’s “Guernica.” (The chapter on Guernica was of course written later.) These are followed by a general chapter on “The Struggle to Understand Art,” and an appendix consisting of an unfinished but extremely important essay entitled “Towards an Empirical Theory of Art,” written in 1941.
The editing, production and translating of the present volume – under the direction of two of Raphael’s friends – are a model of an efficient labor of love.
I shall not discuss Raphael’s analyses of the five individual works. They are brilliant, long, highly particularized and dense. The most I can do is attempt a crude outline of his general theory.
A question that Marx posed but could not answer: If art, in the last analysis, is not a superstructure of the economic base, why does its power to move us endure long after the base has been transformed? Why, asked Marx, do we still look toward Greek art as an ideal? He began to answer by speaking about the “charm” of “young children” (the young Greek civilization), and then broke off the manuscript and was far too occupied ever to return to the question.
“A transitional epoch,” writes Raphael, “always implies uncertainty: Marx’s struggle to understand his own epoch testifies to this. In such a period two attitudes are possible. One is to take advantage of the emergent forces of the new order with a view to undermining it, to affirm it in order to drive it beyond itself: this is the active, militant, revolutionary attitude. The other clings to the past, is retrospective and romantic, bewails or acknowledges the decline, asserts that the will to live is gone – in short, it is the passive attitude. Where economic, social and political questions were at stake Marx took the first attitude; in questions of art he took neither.” He merely reflected his epoch.
Just as Marx’s taste in art – the classical ideal excluding the extraordinary achievements of paleolithic, Mexican, African art – reflected the ignorance and prejudice of art appreciation in his period, so his failure to create (though he saw the need to do so) a theory of art larger than that of the superstructure theory was the consequence of the continual, overwhelming primacy of economic power in the society around him. In view of this lacuna in Marxist theory, Raphael sets out to develop a theory of art that he calls empirical “because it is based on a study of works of art from all periods and nations. I am convinced that mathematics, which has travelled a long way since Euclid, will some day provide us with the means of formulating the results of such a study in mathematical terms.” And he reminds the skeptical reader that before infinitesimal calculus was discovered even nature could not be studied mathematically:
“Art is an interplay, an equation of three factors – the artist, the world and the means of figuration.”
Raphael’s understanding of the third factor, the means or process of figuration, is crucial. For it is this process which permits him to consider the finished work of art as possessing a specific reality of its own:
“Even though there is no such thing as a single, uniquely beautiful proportion of the human body or a single scientifically correct method of representing space, or one method only of artistic figuration, whatever form art may assume in the course of history, it is always a synthesis between nature and (or history) and the mind, and as such it acquires a certain autonomy vis-à-vis both these elements. This independence seems to be created by man and hence to possess a psychic reality; but in point of fact the process of creation can become an existent only because it is embodied in some concrete material.”
The artist chooses his material – stone, glass, pigment or a mixture of several. He then chooses a way of working it – smoothly, roughly, in order to preserve its own character, in order to destroy or transcend it. What is represented is materialized in the worked, raw material; whereas the worked raw material acquires an immaterial character through its representations and the UNNATURAL unity which connects and binds them together. “Artistic” material , so defined, a substance half material and half spiritual, is an ingredient of the material of figuration.
The two processes which produce the material of figuration (the process of transforming raw material into artistic material and that of transforming the matter of sensation into means of representation) are continually interrelated. Together they constitute what might be called the matter of art.
Figuration begins with the separate long drawn-out births of ideas and motif, and is complete when the two are born and indistinguishable from one another.
“The characteristics of the individual idea are: (1) It is simultaneously an idea and a feeling. (2) It contains the contrasts between the particular and the general, the individual and the universal, the original and the banal. (3) It is a progression toward ever deeper meanings. (4) It is the nodal point from which secondary ideas and feelings develop.”
“The motif is the sum total of line, colour and light by means of which the conception is realized.” The motif begins to be born apart from, but at the same time as the idea, because “only in the act of creation does the content become fully conscious of itself.”
What is the relation between the pictorial (individual) idea and nature? “The pictorial idea separates usable from unusable elements of natural appearances and, conversely, study of natural appearances chooses from among all possible manifestations of the pictorial idea the one which is most adequate. The difficulty of the method comes down to proving what one believes – proof here consisting in this, that the opposed methodological starting points (experience and theory) are unified, brought together in a reality of a special kind, different from either, and that this reality owes its pictorial life to a motif adequate to the conception and developed compositionally.”
What are the methods of figuration? (1) The structuring of space. (2) The rendering of forms within that space EFFECTIVE. The structuring of space has nothing to do with perspective: its tasks are to dislocate space so that it ceases to be static (the simplest example is that of the forward-coming relaxed leg in standing Greek figures) and to divide space into quanta so that we become conscious of its divisibility and thus cease to be the creature of ITS continuity (e.g., the receding planes parallel to the picture surface in late Cézannes).
“To create space is to penetrate not only into the depth of the picture but also into the depths of our intellectual system of coordinates (which matches that of the world). Depth of space is depth of essence or else it is nothing but appearance and illusion.”
The distinction between actual form and effective form is as follows:
“Actual form is descriptive; effective form is suggestive, i.e., through it the artist, instead of trying to convey the contents and feelings to the viewer by fully describing them, provides him only with as many clues as he needs to produce these contents and feelings within himself. To achieve this the artist must act not upon individual sense organs but upon the whole man, i.e., he must make the viewer live in the work’s own mode of reality.”
What does figuration with its special material (see above) achieve?
“Intensity of figuration is not display of the artist’s strength; not vitality, which animates the outer world with the personal energies of the creative artist; not logical or emotional consistency, with which a limited problem is thought through or felt through to its ultimate consequences. What it does denote is the degree to which the very essence of art has been realized: the undoing of the world of things, the construction of the world of values, and hence the constitution of a new world. The originality of this constitution provides us with a general criterion by which we can measure intensity of figuration. Originality of constitution is not the urge to be different from others, to produce something entirely new; it is (in the etymological
sense) the grasping of the origin, the roots of both ourselves and things.”
One must distinguish here between Raphael’s “world of values” and the idealist view of art as a depository of transcendental values. For Raphael the values lie IN THE ACTIVITY revealed in the work. The function of the work of art is to lead us from the work to the process of creation which it contains. This process is determined by the material of figuration and it is within this material, which Raphael discloses and analyses with genius, that mathematics my one day be able to discover precise principles. The process is directed toward creating within the work a synthesis of the subjective and the objective, of the conditional and the absolute within a totality governed by its own laws of necessity. Thus the world of things is replaced within the work by a hierarchy of values created by the process it contains.
I can give no indication here of the detailed, specific and unabstract way in which Raphael applies his understanding to the five works he studies. I can only state that his eye and sensuous awareness were as developed as his mind. Reading him one has the impression, however difficult the thought, of a man of unusual and stable balance:
“Since we cannot know ourselves directly, but only through our actions, it remains more than doubtful whether our idea of ourselves accords with our real motives. But we must strive unremittingly to achieve this congruence. For only self-knowledge can lead to self-determination, and false self-determination would ruin our lives and be the most immoral action we could commit.”
To return to our original question: what is the revolutionary meaning of art? Raphael shows that the revolutionary meaning of a work of art has nothing to do with the subject matter in itself, or with the functional use to which the work is put, but is a meaning continually awaiting discovery and release.
“However strong a given historical tendency may be, man can and has the duty to resist it when it runs counter his creative powers. There is no fate which decrees that we must be victims of technology or that art must be shelved as an anachronism: the ‘fate’ is merely misuse of technology by the ruling class to suppress the people’s power to make its own history. To a certain extent it is up to every individual, by his participation in social and political life, to decide whether art shall or shall not become obsolete. The understanding of art helps raise this decision to its highest level. As a vessel formed by the creative forces which it preserves, the work of art keeps alive and enhances every urge to come to terms with the world.”
“We have said that art leads us from the work to the process of creation. This reversion, outside the theory of art, will eventually generate universal doubt about the world as given, the natural as well as the social. Instead of accepting things as they are, of taking them for granted, we learn, thanks to art, to measure them by the standard of perfection. The greater the gulf between the ideal and the real, the more inescapable is the question: Why is the existing world the way it is? How has the world come to be the way it is? De omnibus rebus dubitandum est! Quid certum? We must doubt all things! What is certain? (Descartes). It is the nature of the creative mind to dissolve seemingly solid things and to transform the world as it is into a world in process of becoming and creating. This is how we are liberated from the multiplicity of things and come to realize what it is that all conditional things ultimately possess in common. Thus, instead of being creatures isolated among other isolated creatures we become part of the
power that creates all things.”
Raphael did not, could not, make our choices for us. Everyone must resolve for himself the conflicting demands of his historical situation. But Raphael does show, as no other writer has ever done, the revolutionary meaning of the works inherited from the past – and the works that will be eventually created in the future. And this he shows without rhetoric, without exhortation, modestly and with reason. His was the greatest mind yet applied to the subject.
February 9, 2013
February 5, 2013
Dear Friends and colleagues,
I have recently self-published a novel titled “Foreigner”.
Hard copies can be bought at Amazon:
or at Lulu publishers:
If you would like to help me selling it I would be very grateful.
Perhaps you could forward this note to your friends?
I can certainly do the same for you!
Have a nice day!
PS. Any feedback would be greatly appreciated!