To appear in The Innate Mind: Structure and
Content. Edited by Peter
Carruthers, Stephen
Laurence, & Stephen Stich
Dan Sperber
Modularity and
relevance: How can a massively modular mind be flexible and context-sensitive?[1][1]
Let me start
with a quotation from Randy Gallistel (echoing Chomsky 1975):
Adaptive
specialization of mechanisms is so ubiquitous and so obvious in biology, at
every level of analysis, and for every kind of function, that no one thinks it
necessary to call attention to it as a general principle about biological
mechanisms.
In this light, it is odd but true
that most past and present contemporary theorizing about learning does not
assume that learning mechanisms are adaptively specialized for the solution of
particular kinds of problems. Most theorizing assumes that there is a
general-purpose learning process in the brain, a process adapted only to
solving the problem of learning. […] From a biological perspective, this
assumption is equivalent to assuming that there is a general-purpose sensory
organ that solves the problem of sensing (Gallistel 1999:1179).
Gallistel’s
remark can be extended to cognition in general. It is odd but true that most
past and present contemporary theorizing about cognition does not assume
that cognitive mechanisms are adaptively specialized for the solution of
particular kinds of problems. There is indeed a great divide today between a
minority of cognitive scientists for whom mind-brains are best viewed as
articulations of specialised modules, and a majority for whom at least the human mind-brain is largely non-modular.
I belong to the minority and have argued the case for massive modularity
elsewhere.[2][2]
What I want to do here is answer two questions: How can a massively modular
mind be flexible? And: How can a massively modular mind be context-sensitive?
The two questions are related: the context of cognitive processes is changing
every fraction of a second, if only because it is modified by these very
processes. In verbal comprehension, for
instance, the interpretation of every utterance modifies the context in which
the next utterance is interpreted. Context-sensitivity is the ability to take
this ever changing context into account. “Flexibility” (or “plasticity”) is a
metaphor that is best unpacked as meaning context-sensitivity in the longer
run. An individual cognitive system is flexible if it can modify itself on the
basis of experience. When humans in general are described as a particularly
flexible species, it is even longer-term context-sensitivity that is involved:
over historical time, humans have adapted to very diverse natural and
human-made environments and have, for this, developed novel cognitive
competencies. Clearly, a system that is flexible is in a better position to
exhibit context-sensitivity in the short run.
1. Cognitive modules are a
type of biological modules
Given that
discussions of cognitive modularity often get bogged down in tedious
terminological arguments, I might have been tempted to avoid the term “module”
altogether if it were not that that there is much recent relevant work on
biological modularity (e.g. Callebaut & Rasskin-Gutman, forthcoming), of
which cognitive modularity is best seen, I want to argue, as a special case. It
is hardly controversial that complex organisms are systems made up of many
distinct sub-systems—including but not limited to classical “organs”—now often
called “modules”—that may differ from one another functionally, structurally,
ontogenetically, and phylogenetically. A modular organisation is an effect of biological
evolution, which responds in a piecemeal fashion to challenges presented by the
environment. Arguably, modularity is also a condition of evolvability (Wagner
& Altenberg 1996). Because they are opportunistic responses to a great
variety of problems and opportunities, it is in the nature of modules to be
quite diverse in form, size, and function. Hence, one cannot both appreciate
the role of modularity in biological systems and ask for a precise and rich
definition of what a module is, or insist that a genuine module should resemble
some prototype. Let me repeat, if you insist that a module should be defined in
a narrow and rigid way, you are ignoring the evolutionary dimension of
modularity.
Biological
modules can be articulated in a variety of ways, and can, in particular,
contain sub-modules. For instance the vertebrate digestive system is itself a
complex module and contains as sub-modules various portions of the digestive
tract such as the pharynx, the stomach or the large intestine, glands such as
the salivary glands or the liver, chemical modules such as hormones and enzymes
produced by the glands, and so on. Inherited modules can evolve and both turn
into and generate new modules in the lifetime of the organism. For instance B
lymphocytes are inherited cell-sized modules that evolve within the organism
and generate antibodies, i.e. new protein-sized modules the function of which
is to bind to, and thereby neutralize specific antigens. It may not be obvious
at first to think of modules the size and character of freely moving
short-lived cells and proteins, but, again, the point about a modular
organisation is that it may contain as modules any autonomously functioning
device with a phylogenetic or ontogenetic history of its own.
If cognitive
modules are real components of the cognitive system and not mere boxes in a
nominalist flow-chart model, then they are a sub-type of biological modules.
They are characterised in particular by specific input conditions and by
proprietary resources used to process inputs that meet these conditions. The
inputs that happen to meet the input conditions of a given module constitute
what I have called its actual domain
(Sperber 1994). In most cases, these input conditions are an imperfect but
effective way of picking out items that belong to some objective category or
domain of items in the environment. This objective domain then is the proper domain of the module. The
function of the module is to inform the organism about items in its proper
domain. It is with reference to such a proper domain that a module can be said
to be domain-specific. A module might, for instance, accept as inputs sounds
exhibiting specific structural patterns, when, in the environment where this
module operates, such sound patterns almost always correspond to speech in a
given natural language. Then the proper domain of this module would be speech
in that language (even if it might be activated by some
non-genuinely-linguistic sound pattern à la Jabberwocky).
A cognitive
module has its own procedures and may also have a data-base of its own. A face
recognition module, for instance, has both data about the faces it is capable
of recognising and dedicated procedures to match perceptual inputs to these
data. The fact that a module can draw only on a limited data-base, if any, to
process its inputs is what Fodor (1983, 2001) calls “informational
encapsulation,” one of several criteria for modularity in his Modularity of Mind (1983) and the only
one that plays a significant role in his The
Mind Doesn’t Work That Way (2001). Because an informationally encapsulated
device only has access to limited information, excluding some information that
might in principle be pertinent to its producing the right outputs and that
might be available elsewhere in the organism, it fails to exhibit the
context-sensitivity that is characteristic of human cognition as a whole.
Paradigm examples are provided by perceptual illusions: I (that is, a whole person) have
the information that the two lines in the Müller-Lyer illusion are equal (say,
I have measured them), but my visual perceptual device has no access to this
information and keeps “seeing” them as unequal. Cognitive reflexes are, in
this respect, extreme cases of encapsulation: given the proper input, they
immediately deliver their characteristic output, whatever its appropriateness
in the context.
It is
important to distinguish domain-specificity
from encapsulation. A device is domain-specific if its function is to process only inputs belonging
to some specific empirical domain (even if its input conditions do not
perfectly pick out all and only items in this domain, so that there is a degree
of mismatch between its proper and its actual domain). For instance, a face
recognition device has as its function to process faces (even if its operation
can also be triggered by merely face-like stimuli, e.g. masks). An encapsulated device is one that uses a
limited data-base to process its inputs. A word recognition device, for
instance, takes as characteristic inputs phonetic representations of speech and
uses as a database a dictionary. It is plausible that there are domain-general
mental devices. Working memory, for instance, might be seen as a domain-general
device that processes inputs whatever their contents, and manages their level
of activation for the benefit of other, inferential devices. I cannot think, on
the other hand, of a plausible example of a non-encapsulated mental device,
that is, of a device that would use the whole mental encyclopaedia as its
database. Non-encapsulation is, tautologically, a property of the mind as a
whole, but it does not seem to be a property of any autonomous sub-component of
it.[3][3]
What a
cognitive module does at a given time (if it does anything at all) is
determined by the inputs it is processing, by its procedures, and by its
data-base, if any. It is not directly
governed by what other modules of the cognitive system are doing, and does not directly draw on the informational
resources available to these other components. I stress “directly” because
there are, of course, indirect ways in which modules affect one another. Apart
from sensory organs, all components of the cognitive system get their inputs
from other components: roughly speaking, face recognition gets its input from
visual perception, pragmatic interpretation of utterances gets part of its input from
linguistic decoding, and so on. So, a module’s operations are typically
triggered by being fed as input the output of some other module. Moreover, the
triggering input typically has been informed by the procedures and data of the
feeder module. Still, once it is performing its function, a module works on its
own and is unable to take advantage of information that might be present in the
system as a whole but that is found neither in the input nor in the proprietary
data-base of the module.
Isn’t there a
risk, though, when allowing for a great variety of modules networked in complex
ways, of trivialising the notion of
modularity to the point of confusing modules with the boxes used in
diagrams representing the flow of information in cognitive processes? The risk
is avoided, I maintain, by the modularist’s commitment to biologically
realistic interpretation of the boxes. A boxological flow chart can be
interpreted as a mere algorithmic representation of a complex cognitive process
showing how, in principle, the process could be materially realised, but
carrying no commitment regarding its actual implementation in mind-brains. The
true modularist is interested in “boxes” that correspond to neurologically
distinct devices. A neurologically distinct device, or module, need not occupy
a single and continuous brain location all by itself, its boundaries need not
be sharp, but still, it must be distinguishable not just functionally but also
neurologically. This presupposes that a module has a distinct history in the
development of the individual brain, and this in turn presupposes some genetic
and evolutionary story about the conditions that make such an individual
development possible.
The issue
now is whether such an articulation of biologically real cognitive modules
could exhibit the flexibility and context-sensitivity exhibited by the human
mind as a whole.
2. Modularity and
flexibility
Modules are
“rigid”. The human mind, on the other hand, is “flexible”. Since both “rigid”
and flexible” are metaphors, this raises not so much a serious objection to a
modularist view of the human mind as an interesting question: How could flexibility be
achieved in such a modular system? The answer is that most innate[4][4]
cognitive modules are domain-specific learning
mechanisms (“learning instincts” [Marler 1991], or “module templates” [Sperber
1994]) that generate the working modules of acquired cognitive competence.
Even though
the existence and many characteristics of mental modules are explained by
biological evolution, this does not imply that modules are simply phenotypic
expressions of genes, or that the development of each and every module is
strongly canalised. On the contrary, it would be in the nature of modules to
vastly differ from one another in this as in other respects. For some of the
problems cognitive modules handle, “pre-wiring” may be appropriate. For other problems,
an effective modular solution may involve adding data to the proprietary
data-base of an otherwise predetermined module. In other cases still, the
development of a module may involve drawing on information picked up from the
environment not just to enrich the data-base but also to shape procedures.
There is, in
fact, a full range of cases from innately specified modules to brain tissues
that are merely ready to modularise competencies of a specific type. Here are
five examples across this range:
· Avoidance of vertical drops: Human infants (and other baby
animals also) perceive and avoid vertical drops in terrain, even if they have
had no experience of falling before, as demonstrated by means of the well-known
“visual cliff” experiments initiated by Gibson & Walk (1960). This is an
obvious modular adaptation to a serious hazard facing animals moving on the
ground. To be efficient, this particular module had better not depend on
learning. It is as good an example of an innate cognitive module as one may
ever hope to find.
· The Garcia effect (Garcia & Koelling 1966): Rats and other
animals are innately equipped to develop an aversion to whatever type of food
seems to have made them sick. This is a highly specialised one-pass-learning
module. The outcome of such learning is a novel capacity, that of reacting with
aversion to a specific kind of food. If the rat develops, say, three such
aversions, then it has three distinct abilities. It could be that the learning
process and each specific aversive reaction are all carried out by the same
module: learning consisting in adding to the initially empty proprietary
data-base of the module data about specific foods to be avoided. Or it could be
that the learning process results each time in the setting up of a new module
or sub-module dedicated to a specific aversive food. So, which is it: one
general food-aversion module with a growing data-base, or a learning module
producing as many micro-modules as there are aversions? This is an empirical
issue that might be decided by answering questions such as the following: Do
aversive reactions to different foods employ different detection procedures (as
opposed to the same procedure using different data)? Does a new aversion
recruit distinct brain tissues? Can the more general ability to generate new
aversions and each of the more specific aversions be selectively impaired?
Positive answers to such questions would suggest that to each new aversion
corresponds a new mini-(sub)-module.
· Face recognition: I assume that face recognition is modular
(which is controversial, but see Kanwisher & Moscovitch 2000). If so, we
are dealing, as in the case of the Garcia effect, with two types of modular
abilities: a general learning ability
to form specific abilities to detect
specific faces. Is there a general face-recognition module that performs both
functions or are individual-face-detectors developed as autonomous
mini-(sub)-modules? This is an empirical question to which we do not have an
answer. As in the case of the Garcia effect, these are nevertheless genuinely
distinct possibilities involving subtle differences in the way these abilities
may be carried out and impaired.
· Language faculty and linguistic competences: The language faculty is a complex learning
module that, given proper linguistic and contextual inputs, yields one or, in
the case of plurilinguals, several mental grammars. Each of these grammars is itself a complex
module subserving both verbal coding and decoding in a given language. Each
mental grammar has a distinct developmental story, and can selectively decay or
be impaired. It is plausible that, say, the two mental
grammars of a bilingual individual are sub-modules of a more general
mental universal grammar and, as such, share some resources (Dehaene et al.
1997, Kim & al. 1997).
·
With many
innate modules being learning modules generating further modules, with brain
areas ready to modularize, one may envisage that the human mind is
characterised not only by massive modularity, but also by teeming modularity. A great many highly specialized procedures, the
size, say, of a specific concept or even of a particular inference rule, may be
modular in the intended sense. That is, there may be a plethora of distinct
biological devices emerging on some innate basis in the course of cognitive
development, and functioning with a certain degree of autonomy in cognitive
activity (a similar view, based on an analogy between cognitive modules and
enzymes, is developed by Clark Barrett, forthcoming). I hope these remarks help
understand how a massively modular mind may indeed be flexible, even if the
detailed ways in which such flexibility is achieved obviously are a matter for
empirical research.
3. How can a massively
modular mind be context-sensitive?
According to
Fodor, in human cognition, only peripheral input systems are modular. One of
the distinctive properties of modular input systems, he argues, is that their
operations are mandatory. Supporters of the idea of massive modularity, not
just at the input level, but at all levels of cognitive activity, shouldn’t
lightly accept the idea that mandatoriness characterizes modular operations. If
all the modules of a massively modular mind mandatorily processed any input
available to them (including the outputs of other modules that meet their input
conditions) there would be a computational explosion. Even if such a system could
work at all, it is hard to see how it could exhibit the kind of
context-sensitivity characteristic of human cognition. Every input would be
processed in the same way in every situation. Of course, some limited
context-sensitivity could still be built into such a system. The output of a
given module could inhibit the operations of another module: the standard
violent response to an apparently aggressive movement, for instance, can be
inhibited by the perception of signs of playfulness. A danger detection module,
acting as “and-gate,”
may accept only complex inputs such as pairs of more elementary
inputs, for instance a sound and a
visual signal. In such cases, there is an in-built context-dependency, but it
remains quite local, unlike the context-dependency displayed by ordinary human
cognition, for instance in verbal comprehension.
It one takes
for granted that modularity implies mandatoriness, then
one should reject the massive modularity hypothesis. My strategy will be to examine and question
the idea that the operation of modules must be mandatory—even in the case of
Fodorian input modules. I will then argue that the system as a whole exhibits
context-sensitivity through the allocation of energy among modules.
There are
two senses in which a cognitive procedure might be said to be mandatory. In a
first sense—the only one in which I will use the term—, a procedure is
mandatory if, given the appropriate input, it will follow its course and
produce its output whatever the rest of the mind/brain is doing (except in
cases of pathological or accidental impairments). In other words, the procedure
is mandatory in the sense that an appropriate input is sufficient to trigger it
in such a manner that it will run its course (and not just to give it some
initial activation). In a second sense, a procedure is “mandatory” if it cannot
be voluntarily willed or blocked (except in an indirect way, for instance by
acting on the availability of the inputs rather than on the procedure
itself)—for this I will just use “involuntary”. When Fodor argues that the
operations of mental modules are “mandatory,” he seems to have both senses in
mind. It is self-evident that a procedure that is mandatory in the first sense,
i.e. automatically stimulus-triggered, would be “mandatory” in the second
sense, i.e. involuntary. There are procedures that are indeed both mandatory
(in the first sense) and involuntary. For instance, perceiving an object as
coloured is automatically triggered by the stimulus and cannot be willed or
blocked. Similarly, being presented with a pair of numbers such as 50 and 100
automatically triggers (in a person familiar with numbers) a comparison of
their size, before any decision could be taken to perform or not to perform
such a comparison. Still, the two properties, that of being mandatory, i.e.
input-triggered, and that of being involuntary, are far from being
co-extensive. There are many cognitive procedures over which the individual has
no voluntary control and that, in the course of ordinary cognitive activity,
may be inhibited or enhanced both by mind-internal factors such as expectations
and by mind-external factors such as distracting stimuli. These procedures are
neither voluntary nor mandatory.
If I see
just in front of me, in broad daylight, the face of my
The
operations of input modules seem mandatory when you just consider cases where
the stimulus is, and stays long enough, at fixation, and the perceiver is not
actively tracking some other stimulus. Striking experimental demonstration of
this is provided by work on “inattentional blindness.” For instance, Simons
& Chabris (1999) found that about 50% of participants asked to monitor a
basketball passing event on a screen failed to notice a gorilla who walked
across the screen in full view, stopped in the middle of the players as the
action continued all around it, turned to face the camera, thumped its chest,
and then resumed walking. There are many, more banal cases, with most if not
all input modules, where a stimulus is well within the field of perception but
either is not in a focal position or is not sufficiently attended to, where the
resources of the mind are invested in processing other competing stimuli, or
inner thoughts, and where the module fails to process the stimulus (or at least
fails to process it sufficiently): the familiar face is not recognised, the
sentence structure is not parsed, the gorilla walks unnoticed. Let me insist, I
am talking about cases where the psychophysical perceptual conditions for the
operation of the module are satisfied and where, with less competition from
other stimuli or other thoughts, or with appropriate expectations facilitating
the process, the stimulus would have been processed. At least some of the
procedures involved in perceiving the gorilla are not mandatory. There may well
be an initial activation of the relevant procedures, but, when an individual’s
attention is focused on something else, they may not run their full course. I
take it that the idea that visual perception is modular is not put in jeopardy
by such data. Then however, mandatoriness cannot be a defining trait of
modules. (By the way, I am not trying to make a terminological, but a
substantive point. If these perceptual procedures that fail to deliver their
expected output in the inattentional blindness experiments mentioned above are
still “mandatory” by your definition, so be it. What matters here is that the
availability of an appropriate input is not sufficient to cause these
procedures to run their full course. The interesting issue then becomes: what
other factors determine which procedures follow their course?)
The general
point I am stressing here is this: mental modules in humans compete for
energetic resources. Not all of them can operate simultaneously. This is true
at all levels: perceptual, conceptual, and psychomotor. Contrast humans with
simpler cognitive systems in this respect. Take a frog (or at least the
idealised frog of philosophers—I am not making zoological claims). Here it sits
waiting for a fly moving within reach. No fly movement, no cognitive process
other than the low level monitoring of the visual field necessary to activate
the get-the-fly module when appropriate. Is this a case of a wholly stimulus-driven
module with mandatory operations? Almost, but not quite.
Presumably the frog is also monitoring for possible predators and other
dangers, and if a fly and a predator are sighted simultaneously, the operations
of the get-the-fly module are pre-empted by those of the escape-the-predator
module. This priority of the escape-the-predator module over all others
(feeding and also mating modules) is clearly adaptive and is presumably built
in. So, the operations of the escape-the-predator module are fully mandatory,
and those of the get-the-fly module are mandatory unless pre-empted. Frogs may
well have a few more cognitive modules. Even so, it is plausible that the
operations of each of them are mandatory except in the case of pre-emption, and
that the order in which modules may pre-empt one another is fixed in the frog’s
nervous system. Moreover, cases of actual modular pre-emption are likely to be
relatively rare (it is not that often that a frog is simultaneously presented
with a possible prey, a possible predator, and a possible mate). The human
predicament is quite different. If, as I have suggested, the human mind is
teeming with modules, then, at all times, a number of modules have inputs
available and must be competing for brain power to process them. Rather than a
fixed and global pre-emption order, which would not be adaptive in this case,
some flexible, context-sensitive energy allocation procedure must be at work.
What should
this energy allocation procedure be doing, that is, how might it contribute to
the efficiency of the human cognitive system as a whole? Again, contrast with
(philosophers’) frogs. Presumably there are just a few categories of stimuli,
such as flies, that frogs can discriminate, and only in restricted conditions.
They monitor their environment to check whether any of these categories happen
to be instantiated and then produce the prewired behavioural response. Humans
can discriminate tens of thousands of categories in their environment, very few
of which trigger automatic behavioural responses. At any one moment, humans are
monitoring their environment through all their senses and establish perceptual
contact with a great many potential inputs for further processing. Frogs have
no memory to speak of. Humans have vast amounts of information stored in
memory. When processing a new input, humans bring some of this stored
information to bear on it. Attending to a given stimulus, activating memorised
information, bringing the two together and drawing inferences are
effort-demanding mental activities. Effort is a cost that should be incurred
only in the expectation of a benefit. Different trains of thought involve quite
different evolving allocations of efforts and may produce quite different
cognitive benefits.
What are the
benefits of cognitive activity? The reply that comes most readily to mind is
that cognition helps the organism recognise and react to opportunities and
problems present in its environment; a more precise answer would consist in
describing in much greater detail the various kinds of opportunities and
problems that cognition helps the organism cope with. In the human case, a
massive investment is made in cognition, and much knowledge is gathered,
updated and corrected without any specific practical goal. Presumably, what
looks like—and often is—the pursuit of knowledge for its own sake helps prepare
for an open range of future contingencies. Of course, knowledge is not equally
pursued in all directions. Humans develop interests that guide their cognitive
investments. Again, it seems, spelling out the benefit of cognition for humans
would amount to describing in detail these diverse interests and possibly to
explaining what makes their pursuit worth the effort. So, whereas it is natural
to think of mental energy or effort in quantitative terms, one tends to
approach cognitive benefit in qualitative terms. A philosopher might want to
leave the matter there, but a psychologist cannot. The brain can be expected to
allocate its energetic resources, not in a random, but in a beneficial way. To
achieve this, it does not have to be able to attribute an absolute value to the
expected cognitive benefit of the processing of all available inputs, but it
must be able to select, among the inputs and procedures actually competing for energy,
some with relatively higher expected benefits.
Cognitive
efficiency is a matter of investing effort in processing the right inputs. What
are the right inputs? Do they have a characteristic property that the
mind/brain can use in order to select them? Deirdre Wilson and I have argued
that they do, and that this property is relevance, in a precise sense that we
have tried to define and that I will briefly outline here (Sperber & Wilson
1995, Wilson & Sperber 2004).
Relevance is
a property of inputs to cognitive processes. At a fairly abstract level,
relevance can be defined relative to an
inferential procedure and a context: a piece of information is relevant in
a context for a given inferential procedure, if processing the piece of
information and the context together yields different conclusions than would be
obtained by processing them separately. A bit more technically, a piece of
information is relevant in a context for a given inferential procedure, just in
case the set of conclusions that the inferential procedure derives from the
union of this piece of information and the context, taken together as a single
set of premises, is different from the union of the two sets of conclusions the
inferential procedure would derive separately from the piece of information, on
the one hand, and from the context, on the other. For instance, if the
procedure instantiates the elimination rules of propositional calculus, then
(a) but not (b) is relevant in context (c):
(a) p and r
(b) q and r
(c) {if p then s, if s then t}
As can
be easily verified, (a) in the context of (c) yields the two conclusions s and t, which are derivable neither from (a) alone nor from (c) alone,
whereas (b) in the context of (c) yields no conclusions other than those
derivable from (b) alone and from (c) alone.
This
abstract definition is useful as a step towards defining relevance in a
psychologically more pertinent way. A piece of information is relevant to an individual at a time only if there
is a procedure and a context available to the individual at that time, relative
to which the piece of information is relevant in the sense proposed above (this
is just a necessary condition—for a fuller definition, see Sperber & Wilson
1995, chapter 3).
Relevance is
a property easily achieved: practically any new piece of information that
connects, however weakly, with what the individual already knows will be
relevant by our definition. Relevance, however, is a matter of degree.
Cognitive efficiency is not just a matter of processing relevant inputs; it is
a matter of processing the most relevant inputs available. Everything else
being equal, the greater the cognitive benefit yielded by the processing of an
input, the greater its relevance. Also—and this is quite specific to the
approach taken by relevance theory—everything else being equal, the greater the
cost of processing an input, the lesser its relevance. Here is a short
artificial illustration. Being told by the doctor “you have flu” is likely to
carry more cognitive effects, and therefore be more relevant, than being told
“you are ill.” Being told “you have flu” is also likely to be more relevant
than being told “you have a disease spelled with the sixth, twelfth, and
twenty-first letters of the alphabet”, because the first of these two statements
would yield the same cognitive effects as the second, but for less processing
effort.
Cognitive
efficiency, then, is a matter of maximising the relevance of the inputs
processed. There may well not be a unique way to maximise relevance and
therefore to optimise cognitive efficiency. One input may be preferable to
another in terms of benefits, the other in term of costs, and, in the absence
of a common metric, there is no obvious way to decide between the two. Still,
as long as some inputs are clearly more relevant and therefore preferable to
others, it should be possible to enhance cognitive efficiency through input
selection. In other words, we should not expect the system to do more than tend
to optimise. But how can even this be achieved? To try
and answer, I will look first at costs, then at benefits, and then will put the
two together.
How can the
brain optimally allocate energy? The solution could, in principle, be a
cognitive one. That is, the brain could represent its own energy consumption, compute
the expected cost of various procedures, and use this as a criterion in
deciding how much to invest in each procedure. In other terms, the brain might
be automatically taking, every fraction of a second, decisions similar to those
we consciously take once in a while when, for instance, we choose to save our
effort by using a pocket calculator rather than perform a mental calculation.
Note, however, that this cognitive way of minimising the energetic costs of
cognitive processes would involve a significant cost of its own, which might
make it self-defeating.
Are there
non-cognitive ways of minimising effort in mental processes? Consider the
comparable problem of minimising energy consumption in muscular movement.
Muscles get their energy from chemical reactions. This energy can be converted
into work or into heat. The efficiency of the process (except when the function
of the movement is to provide heat, as when shivering) depends on letting as
little energy as possible degrade into heat. These local chemical reactions
depend on a supply of oxygen and nutrient by blood vessels, a supply which has
its own energy costs and which can be insufficient or excessive for optimal
efficiency. Blood vessels also have the function of removing carbon dioxide and
waste products such as lactate. The removal of lactate from the muscle is
slower than its production, causing, in case of prolonged use of the muscle, a
perception of fatigue. Only above this threshold is muscular effort represented in the cognitive system—and
even then in a very coarse manner—, often inducing intentional reallocation of
muscular effort. The regulation of effort—the production of the right quantity
of energy in muscle tissue, the adjustment of blood flow and so on—is otherwise
achieved not through computations over representations, but through
non-cognitive physiological procedures which, one may assume, are to a very
large extent genetically specified. I suggest that the regulation of effort in
cognitive processes is likewise achieved, for the most part, through
non-cognitive brain processes that are also largely genetically specified.
That the
flow of energy in the brain is guided by non-cognitive mechanisms may seem easy
enough to accept. Isn’t it just an
aspect of the neurological implementation of cognitive processes? How could
this be relevant to an understanding of cognition at a computational or
algorithmic level, to use Marr’s popular distinction? Well, I will argue that
the regulation of this energy flow has cognitive, and even epistemic,
consequences.
Understanding
how the brain is sensitive to the cost of various procedures may be difficult.
Even more difficult is understanding how the brain
could be sensitive to the size of the cognitive benefits resulting from the
processing of various inputs.
To begin
with, how can the brain distinguish, among all the cognitive changes that might
be brought about by cognitive operations, those which are beneficial, and those
which are not, and which may even be costly (for instance, mistaken
inferences)? Well, the brain has no other choice than to trust itself and be, so to speak, optimistic about its own
procedures. That is, it should behave in a way consistent with the presumption
that, in general, its perceptions are veridical and its inferences rational. In
normal conditions, the processing of new inputs yields positive cognitive
effects, that is, it results in an improvement of the individual’s knowledge of
her world, be it by adding new pieces of knowledge, updating or revising old
ones, updating degrees of subjective probability in a way sensitive to new
evidence, or merely reorganising existing knowledge so as to facilitate future
use. There are many exceptions, of course—cases where less processing would
have resulted in better knowledge—, but procedures that have tended to produce
more negative than positive cognitive effects are likely to have been selected
out. The relevance of this is that the brain would be roughly right in treating
any and every cognitive effect as a positive effect, in other terms, as a
cognitive benefit.
But then
what? Supposing it treats all cognitive effects as cognitive benefits, how
could the brain then calculate the size of these cognitive effects? Should it
count the number of conclusions arrived at? Should it treat the value of each
conclusion as depending on its complexity? Should it multiply the value of each
conclusion by its subjective probability? Should it give
greater value (and how much greater?) to conclusions that have practical
consequences, or relate to standing interests? How should it evaluate
revisions of previous beliefs? And so on. Or are these even the right
questions? Actually, it is not at all obvious that the brain should calculate the size of cognitive effects.
There may be physiological indicators of the size of cognitive effects in the
form of patterns of chemical or electrical activity at specific locations in
the brain. A module receives some degree of activation form other modules with
which it is connected. It is activated by upstream feeder modules that present
it with inputs. It may be activated by downstream client modules that are
already mobilised and that would benefit from receiving new or further inputs
from it. Suppose that these physiological indicators locally determine the
ongoing allocation of brain energy to the processing of specific inputs. These
indicators may be coarse. Nevertheless, they may be sufficient to cause energy
to flow towards those processes likely to generate relatively greater cognitive
effects at a given time. In other words, just as effort need not be computed,
cognitive effect need not be computed either, and both effort and effect
factors may steer the train of our thoughts without themselves being thought
about at all.
Someone
might object: suppose there are physiological indicators of effort and effect.
All they can indicate are past or current effort and effect, whereas what
should guide the allocation of brain resources is expected effort and effect.[5][5]
Answer: It is not true that indicators can only indicate past and present
states of affairs. Dark clouds may indicate that rain is probable. The current
level of lactate concentration in a muscle may indicate that it cannot continue
for long to perform the same amount of work. The differences in the patterns of
activity of two competing cognitive processes may indicate which has the
highest expected cognitive utility. Suppose the processing of inputs A and B
are both currently producing the same level of effect, but the processing of A
is producing these effects with greater effort. Or suppose the processing of
inputs A and B are both currently requiring the same level of effort, but the
processing of B is resulting in greater effect. Of course, it is impossible to
be sure how things would evolve, but in both cases, a greater cognitive utility
should be expected from the continued processing of B rather than A. A better
indication still may be given by the direction in which levels of effect and
effort are moving. If the processing of inputs A and B are producing the same
amount of effect for the same amount of effort, but the amount of effect
produced by the processing of A is on the decrease whereas that of B is
constant or on the increase, or if the amount of effort required by the
processing of A is on the increase and that of B constant or on the decrease,
then again greater cognitive utility should be expected from the continued
processing of B rather than A.
If we look
at the issue in an evolutionary perspective, what does all this mean? Imagine a
species investing more and more in cognition, monitoring in a more and more
fine-grained way more and more aspects of the environment, constructing an ever
richer memory, and achieving this by use of an ever greater variety of
perceptual and conceptual modules. The result would be a kind of attentional
bottleneck: only very few of the available inputs could be treated
attentionally, and only very limited background information could be brought to
bear on the treatment of these inputs. This bottleneck would in turn create a
strong and constant selective pressure for optimising the choice of inputs to
be processed, which, in the picture I am presenting, is equivalent to
optimising the allocation of energy to modules. Such a selective pressure
should result in the evolution of a variety of traits contributing to an
optimal allocation. I am not excluding the possibility that, among these
traits, there may be mental devices directly involved in internal
administration of resources, but I find it implausible, both for evolutionary
and efficiency reasons, to imagine that this allocation of resources might be
wholly or even mostly controlled by some central specialised device. For the
same kind of reasons that, whether we like it or not, market economies work
better than centrally managed ones, a competition for resources among modules
seems more likely to yield good results than a centrally controlled allocation.
There is a
great variety of small changes in the functioning and articulation of modules
that may each have contributed to improving the allocation of resources in
evolutionary time, or that may contribute to it in cognitive development. These
include, as I have already suggested, the use of simple and approximate
indicators of the ongoing and expected expenditure of energy, and of the
ongoing and expected cognitive impact of specific procedures.
Different
modules may be more or less easily mobilised in a way that reflects their
general contribution to relevance. Modules specialised in processing inputs
with high cognitive impact in the history of the species (and in particular
with high practical impact) should be given a greater initial claim on brain
resources, with the possibility of pre-empting other procedures in a bottom-up
fashion (as we know from the literature on attention is typically the case, for
instance with potential danger signals). (Incidentally, given that the human
environment changes much faster than the human genome, this may occasionally
have counter-adaptive results. For instance, people living in an urban
environment are uselessly startled by all too frequent sudden loud noises that
would have deserved immediate attention in an ancestral environment.)
Inputs pertaining to an area of stable interest developed by the
individual benefit from richer intra-modular data-bases and from richer
inter-modular connections (the two ways in which richer background information
is realised in a modular system). Modules processing such
inputs should therefore be given a greater claim on energetic resources
and mobilise more easily.
Inputs
pertaining to ongoing cognitive processes also benefit, ceteris paribus, from a
greater claim on resources, this time because of quantitative factors on the
effort side: the devices and data needed to process these inputs are already
mobilized, and therefore their processing is less costly than the processing of
inputs for which inactive or less active devices must be given energy. Thus
relevance to current cognitive activity is, ceteris paribus, greater relevance.
More
generally, there are many different ways, some obvious, others still to be
discovered, in which a massively modular system might improve the allocation of
its energetic resources among its modules, doing much better than a random
allocation. Some of the traits of the human cognitive organisation that tend to
optimise relevance have emerged in the evolution of the species. Others emerge
in cognitive development and throughout the cognitive life of the individual.
These lifetime improvements are themselves made possible by the flexibility of
the evolved modular system of human cognition. This flexibility, therefore,
should not be seen as a mere ability to adjust cognitive capacities to the
demands and opportunities of different environments. It is also helps maximise
the relevance achieved by ongoing cognitive processes. Flexibility, i.e.
long-term context-sensitivity, makes a critical contribution to short-term
context-sensitivity.
4. Conclusion
The claim
that the human cognitive system tends to allocate resources to the processing
of available inputs according to their expected relevance is at the basis of
relevance theory (where it constitutes the first, cognitive principle of
relevance).[6][6]
The main thesis of this chapter has been that this allocation can be achieved
without computing expected relevance. When an input meets the input condition
of a given modular procedure, it gives this procedure some initial level of
activation. Input-activated procedures are in competition for the energy
resources that would allow them to follow their full course. What determines
which of the procedures in competition get sufficient resources to trigger
their full operation is the dynamics of their activation.
This dynamics depend both on the prior degree of mobilisation of a modular
procedure and on the activation that propagates from other active modules. It
is quite conceivable also that the mobilisation of some procedures has
inhibitory effects on some others. The relevance-theoretic claim is that, at
every instant, this dynamics of activation provides rough physiological
indicators of expected relevance. The flow of energy in the system is locally
regulated by these indicators. As a result, those input-procedure combinations
that have the greatest expected relevance are the more likely ones to receive
sufficient energy to follow their course. This is just a tendency, but it is
strong enough to yield the kind of context-sensitivity that humans actually
exhibit in their individual cognitive processes.[7][7]
I am well
aware of the vague and speculative nature of the view outlined in this chapter.
It calls both for greater empirical anchoring and for formal modelling. I feel
nevertheless justified in putting forward this view as it is by, paradoxically,
an argument of Fodor himself. He writes: “Turing’s idea that mental processes
are computations […], together with Chomsky’s idea that poverty of the stimulus
arguments set a lower bound to the information a mind must have innately, are
half of the New Synthesis. The rest is the “massive modularity” thesis and the
claim that cognitive architecture is a Darwinian adaptation. […] there are some
very deep problems with viewing cognition as computational, but […] these
problems emerge primarily in respect to mental problems that aren’t
modular. The real appeal of the massive modularity thesis is that, if it is
true, we can either solve these problems, or at least contrive to deny them
center stage pro tem” (Fodor 2001: 23). This should be a strong vindication of
the massive modularity thesis. Fodor, however, goes on to say: “The bad news is
that, since massive modularity thesis pretty clearly isn’t true, we’re
sooner or later going to have to face up to the dire inadequacies of the only
remotely plausible theory of the cognitive mind that we’ve got so far” (ibid.).
His main reason for claiming that the thesis is not true is the alleged
inability of a massively modular system to exhibit context-sensitivity. This is
why it seemed worth explaining, however tentatively, how such a system might be
context-sensitive, contrary to Fodor’s claim. Since the massive modularity
thesis might be true, we can keep exploring “the only remotely plausible
theory of the cognitive mind that we’ve got so far,” and that, surely, is good
news.
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