Transcript Review of Fodor’s Modularity Thesis

Modularity of
Mind
(Continued)
&
The Language
of Thought
Hypothesis
Review
Fodor’s Modularity Thesis
Some functions of the brain are modular:
e.g.
Perception
Language acquisition
Language processing
Some functions are not modular but controlled by a
central processing system
Mental modules are characterized as domain specific,
fast, automatic, innate, inaccessible, informationally
encapsulated and realized in a fixed neural architecture.
Objections to modularity
Modularity of mind is a very popular theory among
cognitive scientists, but it is not universally
accepted.
Everyone accepts that the brain has specialized
systems, e.g. the visual cortex. But are they
modules? Do they fit Fodor’s criteria, e.g.
informationally encapsulated, inaccessible, etc.?
In “Is the Mind Really Modular” Jesse Prinz attacks each of
Fodor’s criteria in turn, arguing that some modules may
have some characteristics but Fodor’s criteria do not
usefully describe dedicated systems of the brain.
This article is available at:
www.unc.edu/~prinz/PrinzModularity.pdf
An alternative to modularity: brain areas have
specializations, some innate and some acquired, but
many areas can “moonlight” – contribute to more than
one functional process, and there may be considerable
communication between different functional systems.
Some counter-evidence to modularity:
1) Plasticity of the brain. When one region of the brain is
injured, another region can often take over its function
(particularly in a very young brain). E.g. when a person
becomes blind, the visual cortex can take over some of
the functions of touch perception
2) Apparent communication between modules:
i) sometimes knowing something affects your
perception, if you are expecting to see a cat, you are
more likely to see a hallucination of a cat.
ii) watching someone’s lips while they speak can
help you to hear their words more clearly. In fact, if
you watch lips of someone speaking different words
than you are hearing, your auditory input is
systematically affected. The auditory module seems
to receive input from the visual module.
2) Apparent communication between modules (cont.)
iii) Conscious knowledge or effort can alter your visual
perception, e.g. the duck-rabbit ambiguous figure:
Amazingly, the duck-rabbit can be created in many different
styles
Three possibilities
1) The mind is not modular
2) Low-level systems are modular (e.g.
perceptual systems), but higher-level
systems are not modular (Fodor’s view)
3) The mind is massively modular
How many modules?
Fodor vs. massive modularity
Massive modularity:
•
Promoted by Stephen Pinker
•
The mind is largely or entirely composed of
modules.
•
The “central processing system” is also
modular.
Most cognitive scientists agree that the mind is
modular, but there is much debate over exactly
how many modules there are and how specific
modules are.
Stephen
Pinker
Mind ontologies with partonomies of modules
Alignments are possible between various mind partonomies (often
not separated from brain partonomies), starting from the ‘upper level’:
Mind
hasPart
Sigmund Freud:
Id
Ego
Super-Ego
alignment
Mind
Stephen Pinker:
Specialized Modules
...
...
Central Processing System
...
Modules that have been proposed:
Approximate arithmetic
Logic
Folk physics
Folk psychology -- “mind reading”
Cheater detection
Landscape preference
Natural history
Probabilistic judgment
Etc. etc. etc.
One controversial question: Is there a module for
language acquisition (LAD – language acquisition device,
as proposed by Noam Chomsky) or even some separate
language of thought (LOT) as proposed by Jerry Fodor:
http://host.uniroma3.it/progetti/kant/field/lot.html
Mental Representations
The number four is a concept.
4, 四 and IV are all graphic representations of the
number four.
“Four”, “si” and “quatro” are all spoken
representations of the number four.
We use linguistic representations of concepts
when we speak or write.
We also use other kinds of representations.
Whether in language or image, symbols represent
information.
Computers also use symbols (e.g. 10010) to
represent information
Do our brains also use representations?
If so, what kind?
-- linguistic
-- map
-- pictorial
Different types of representation for different uses
• Visual system interprets input as image-like
representations.
• Somatosensory system
generates body map.
• What about intentional content? How is that
represented in the brain?
Intentional Mental Content
Intentional = aboutness
Intentional mental content is mental content that is
about something.
“I believe this is a pencil.”
Statement about “this” and “pencil” and their
relationship (denotative reference).
Only representations have intentionality.
A pencil is not about something.
The word “pencil”, or a picture of a pencil are about
something, i.e. they are about pencils.
The representational theory of mind posits that all mental
states are intentional. They are all about something.
Beliefs, desires, hopes, fears, pain, perception, imagination,
memory, even hallucinations, are all about something.
(Imagination and hallucinations may be about something
that doesn’t exist. Thus they may be about concepts or
ideas rather than objects.) They are intentional mental
states.
How are intentional mental states represented in the brain?
貓
Thoughts represented
as images
No representation.
Thoughts as brain
states
Thoughts as languagelike representations
How are intentional mental states
represented in the brain? (cont.)
• One possibility: thoughts as images.
• Problem: Too specific (“overspecification”)
• Think: A person killed a tiger.
• Image contains too much information: a
man/woman/child shoots/poisons/strangles a
large/small/orange/white tiger.
Language of Thought Hypothesis
Fodor’s proposal
The Language of Thought Hypothesis (LOTH)
Thinking takes place in a mental language, in
which symbolic representations are manipulated
in accordance to the rules of a combinatorial
syntax (grammar).
Language is composed of semantics and syntax.
Individual words are semantic units – they refer
to things (objects or concepts or ideas). These
semantic units are combined together according
to syntactical laws (grammar) to create
propositions.
E.g.: The horse is brown.
Semantic units: horse, brown
Syntax: the, is, word order
Proposition: a statement about the color of the
horse.
LOTH proposes that thoughts are similarly
constructed.
Thus, thoughts, too, are constructed of
individual semantic units connected with a
combinatorial syntax.
The language of thought need not be exactly
the same as English, or any natural
language, but it should have many of the
same properties as natural languages
have, and have the same expressive
capabilities.
An illustrative example:
@ ^ ** #
@ might mean “horse”
^ might indicate after a noun that a particular instance of the
noun is under discussion
** might indicate that the noun preceding this symbol has the
property indicated by the adjective following this symbol
# might mean “brown”
In logic, ** may become a binary predicate:
**( @ ^ , # )
In Grailog, ** becomes an arc label:
@^
**
#
Or, expanded with node boundary lines:
@^
**
#
In the brain, of course, what is indicated as @ ^ ** and #
would actually be represented as configurations of
neurons.
As in the example above, a language of thought need not
have a grammar exactly like any natural language, but it
should share characteristics that are common to all
natural languages. Thus, it is possible to represent
common nouns, verbs, adjectives, etc. of natural
languages in the language of thought, and the grammar
of the language of thought should have roughly the same
functions as grammars in natural languages have.
How are symbols meaningful?
Where does semantics come from?
Three possibilities:
1)
Meaning is use (Wittgenstein)
2)
Causal theory of meaning
A symbol is meaningful because it has been caused by the
object it represents in the right way.
For Fodor, mental symbols are meaningful because they have
been caused through the process of evolution to truly represent
things in the world.
3)
The inferential theory of meaning
A symbol is meaningful by virtue of the role it plays in thinking
Reasons for LOTH
1)
Semantic parallels between language and thought
Truth value:
•
Some sentences and thoughts have truth values
–
•
Indicative sentences/thoughts: The sky is blue. It is raining.
Some sentences and thoughts don’t have truth values
–
Interrogative sentences (questions), imperative sentences
(orders); e.g. Is it raining? Go to class!
–
Hopes, fears, desires; e.g. It shall not rain tonight.
•
Note: the sentence “I hope it shall not rain tonight” has a
truth value, since it may be true or false that I hope it; but my
hope “It shall not rain tonight” does not have a truth value.
2)
Syntactic Parallels between language and thought
•
Systematicity. Both language and thought are systematic.
There is a systematic relationship among
thoughts/sentences.
Because of combinatorial syntax, the form of a sentence is
distinct from its meaning. As a result, if someone, in any
language, can make a meaningful sentence with a certain
form of syntax, they can also use the same syntactic form to
make a new sentence with a different meaning.
For example, anyone who can say: “the dog is under the
bed”, can say “the bed is under the dog.” Likewise with
thoughts. Anyone who can think, “flowers smell sweeter
than fruits” can think, “fruits smell sweeter than flowers.”
• Productivity
Both language and thought is productive (generative): an
infinite number of sentences or thoughts can be produced
using combinatorial syntax.
(Of course, there are practical limitations to the number and
length of sentences/thoughts that anyone can produce).
E.g.
The cat sat on the mat.
The black cat sat on the yellow mat.
The black cat, which ate my sausage yesterday, sat on
the old yellow mat that I inherited from my grandmother.
There is no limit to how long and complicated I could make this
sentence (or how many sentences I can create starting with
“the cat…”.)
Would LOT be a natural language or
mentalese?
Two possibilities:
1) The language of thought (LOT) is a natural language,
e.g. English, Chinese, etc., and it is learned.
2) LOT is an innate mental language, mentalese.
Learning a natural language is a process of translation:
translating English, etc., into mentalese.
Fodor’s view
The language of thought (LOT) is not a natural language.
LOT is mentalese and is universal and innate.
All people (and, to some extent, higher animals) think in mentalese.
Mentalese contains all basic concepts.
Thus, all basic concepts are innate.
All complex concepts are (ultimately) built up from basic concepts
(intermediate complex concepts can be used).
Example:
The complex concept of “bull” can be composed of the basic concepts
of “cow” and “male”. The concepts of “cow” and “male” are innate, and
thus we have the innate capacity to understand the concept of “bull”.
Learning the word “bull” just makes our thinking more efficient.
Thus, according to Fodor, every normal human being that
has ever existed has had the conceptual capacity to
understand computers, quarks, communism, cancer, etc.
Even prehistorical hunter-gatherers had these concepts
(or the ability to form these concepts through the
combination of their innate concepts).
We do not learn new concepts, we just learn to put familiar
innate concepts together into new combinations.
Learning a natural language is learning to identify the words
of the natural language with our innate concepts.
Basic concepts in LOT can be seen as picture producers in
Roger Schank’s conceptual dependency theory (CDT).
For a detailed LOT/CDT alignment see Charles Dunlop:
http://www.springerlink.com/content/m0347214863t3260/
Arguments for mentalese
1) Translatability of propositions
The same proposition can be stated in different
languages.
“It is raining” and “下 雨 了” express exactly the same
proposition.
Therefore, the proposition does not depend on the
words of the natural language.
Thus, the proposition is encoded in our brains in
mentalese.
2) Concept learning/language learning
We cannot acquire the word for a concept without already
having the concept (in mentalese).
E.g. how does a child learn the word “bird”. You point to
birds and say “bird”. But how does he figure out what is a
bird and what isn’t?
The hypothesis and test theory: he already has several
concepts, e.g. flying things, brown things, etc. and
guesses which one you mean, until he hits on the
concept of “bird”, tests it out and confirms it.
What is a “blaggort”? Try to learn it!
Blaggort
Blaggort
Blaggort
Not a blaggort
Not a blaggort
Not a blaggort
Not a blaggort
Blaggort
You start with some hypotheses, e.g. man, woman,
old, young, dark-haired, light-haired, wearing
glasses, not wearing glasses. Then you test and
confirm positive and negative examples.
You are more likely to start with ideas like “man” or
“woman” than to start with ideas like “left-facing”,
against a light background, looking at the
camera, etc.
Ideas like “formally dressed”, “wearing a hat” and
“wearing glasses” are somewhere in between.
We test for concepts we already have and finally
understand the new concept (“blaggort”) by
conjoining two of the old concepts
(“man” and “wearing glasses”).
The new concept need not have an (artificial) name
(“blaggort”): mentalese allows anonymous concepts.
Both order-sorted logic and description logic (DL)
permit such class intersection / concept conjunction,
indicated by a ‘squared’ intersection symbol, :
“man”
“wearing glasses”
(cf. http://dl.kr.org)
In Grailog, intersection equivalently becomes a
‘blank node’ that is a subclass of both concepts
and inherits from both (multiple inheritance):
Man
Wearing Glasses
subClassOf
What is a “bladdink”? Try to learn it!
Not a bladdink
Bladdink
Not a bladdink
Not a bladdink
Bladdink
Not a bladdink
Not a bladdink
Not a bladdink
New concept (“bladdink”) disjoins two concepts
(“wearing a bow tie” or “wearing a hat”).
The new concept need not have a name:
mentalese allows taking the anonymous union.
In Grailog, union yields a ‘blank node’ that has both
concepts as subclasses (cf. Taxonomic RuleML):
subClassOf
Wearing a Bow Tie
Wearing a Hat
What is a “blannuls”? Try to learn it!
Not a blannuls
Not a blannuls
Not a blannuls
Blannuls
Blannuls
Blannuls
Not a blannuls
Not a blannuls
New concept (“blannuls”) negates a concept,
i.e. takes complement (not “wearing glasses”).
The new concept need not have a name:
mentalese allows anonymous negation.
In Grailog, complement becomes a ‘dashed node’ that
contains the class to be negated:
Wearing Glasses
negation
What is a “blakkuri”? Try to learn it!
Not a blakkuri
Not a blakkuri
Not a blakkuri
Not a blakkuri
Blakkuri
Blakkuri
Not a blakkuri
Not a blakkuri
New concept (“blakkuri”) conjoins two negated
concepts, i.e. two complemented concepts
(not “man” and not “wearing glasses”).
It uses anonymous negation and conjunction.
In Grailog, intersection of complements is a
composition of these earlier constructs:
Man
Wearing Glasses
subClassOf
negation
What is a “blappenu”? Try to learn it!
Not a blappenu
Blappenu
Not a blappenu
Not a blappenu
Not a blappenu
Blappenu
Blappenu
Not a blappenu
New concept (“blappenu”) conjoins a negated
concept (not “wearing a hat”) and the
disjunction of a concept (“wearing a bow tie”)
with a negated concept (not “man”).
In Grailog, the embedded disjunction becomes a
complex anonymous concept:
Wearing a Bow Tie
Wearing a Hat
Man
subClassOf
negation
A child just learning to speak also starts with the
assumption that some hypotheses are more likely than
others.
E.g.
when you point to this:
and say “niu”, a
child is more likely to
think that you are
naming the concept of “bird”, than to think you are
naming the concept of sparrow, or brown things, or
things with small heads.
When you say, “that’s the wind”, how does a child know
what you’re referring to? He has to make a likely guess.
He can do this because he already has the capacity to
think about the wind (i.e. he already has a concept of
wind).
How does a child learn to understand and use the word
“beautiful” correctly? Again, he must have a concept of
beauty.
Arguments for mentalese (cont.)
3) Ambiguity in natural language
The word “bank” has two meanings. With the sentence
“I’m going to the bank”, I can think two different thoughts:
I’m going to the financial institution.
I’m going to the river’s edge.
going
I
going
The surface language is ambiguous, but our thoughts are
not confused. They can distinguish these deep sentences:
I’m going to the financial-bank going(I, financial-bank)
I’m going to the river-bank
going(I, river-bank)
Arguments for mentalese (cont.)
4) Tip-of-the-tongue phenomenon
We know what we want to say, but we can’t think of
the word – i.e. we have the concept in mentalese
but we can’t think of the word in English or in
whatever our native natural language is.
5) Non-linguistic thought
Higher animals, pre-linguistic children and children
that have never been able to learn a language can
still think, and still have concepts.
Arguments for LOT being a natural language
Simplicity:
• No need to posit two systems: mentalese and natural language
• No need for innate concepts
Intuitive:
• It seems like we think in English/Chinese/other natural language
(but then there would be as many LOTs as there are NLs!)
Evidence from non-linguistic children
• Pre-linguistic children and children deprived of the chance to
learn a language appear to be constrained in their ability to think
and have poor conceptual abilities.
A hybrid theory
Sterelny upholds a hybrid theory. People
start with a basic language-like system
with few simple concepts. In learning a
natural language, they develop greater
language abilities and develop the vast
majority of their concepts. People then
think in their natural language.
Readings
Optional Readings:
Churchland, Paul (1981) “Eliminative Materialism and the Propositional
Attitudes” The Journal of Philosophy, Vol. 78, No. 2 (Feb., 1981), pp.
67-90,
Dennett, Daniel (1991) “Two Contrasts: Folk Craft versus Folk Science
and Belief versus Opinion” in Brainchildren
More optional readings:
(On modularity)
Prinz, Jesse, (2006), “Is the mind really modular?”
www.unc.edu/~prinz/PrinzModularity.pdf