Transcript The language of thought hypothesis

Chapter 6:
The language of thought
hypothesis
Ways of developing PSSH
• The 4 claims are compatible with different ways
of thinking about physical symbol systems and
how the system manipulates them
• diagrammatic symbol structures (e.g.
WHISPER)
• language-like symbol structures (e.g.
language of thought theory)
Motivations for LOTH
• Philosophy
– explaining how causation by content is possible
• Cognitive science
– required for a computational approach to
practical reasoning
perception
concept learning
3 basic claims
• Psychological states are realized by physical states
• applies to both personal-level states (e.g. beliefs)
and subpersonal states (e.g. states of the early visual
system
• Psychological states represent the world
• Psychological states enter into causal relations
• with other psychological states and ultimately
with behavior
The challenge of causation by
content
• The challenge of explaining how psychological states can
enter into causal relations in virtue of their content (how
they represent the world)
• Causal interactions are interactions between physical objects (e.g.
populations of neurons)
• Content properties are not physical properties
• Danger of content properties being epiphenomenal
(soprano example)
Content and vehicle 1
• Philosophers typically distinguish between
• the content of a belief (how the world is
believed to be)
• the vehicle of the belief (the physical object that
realizes the belief in the CNS)
• Analogy between the meaning of a sentence and the the
spoken sounds/written inscription
Content and vehicle 2
• This distinction between content and vehicle applies to
the full range of representations in the CNS
•
personal level representations
beliefs, desires
and other propositional attitudes
•
subpersonal representations
computational
states of modules, individual neurons etc.
Content and structure
• Mental representations have contents that can be either true
or false
• Truth-evaluable contents can be expressed by
declarative sentences
• Declarative sentences report possible states of
affairs
• The content is true just if the possible state of
affairs reported is actual
States of affairs
• States of affairs have structure – e.g.
• An object having a particular property (e.g. St
Louis county has a population of approximately
1M)
• Two object standing in a relation (e.g. St Louis
is east of Kansas City)
Theories of content
Two ways of thinking about the content of mental
representations
Coarse-grained:
Contents correspond to
states of affairs
Fine-grained:
Contents correspond to
ways of thinking about states
of affairs
Either way, contents are typically viewed as having structure
Structure and the LOT
• The LOT hypothesis is a hypothesis about the vehicles of mental
representations
• The vehicles of propositional attitudes have a
structure that is isomorphic to the structure of
contents
their
• The vehicles are isomorphic to the structure of the
sentences that express those contents
• This structure at the level of the vehicle is what explains the
possibility of causation by content
Three claims
(1) Causation through content is ultimately determined by
causal interactions between physical structures
(2) These physical structures have sentence-like structure,
which determines how they are built up and how they
interact with each other
(3) Causal relations betweens sentences in the language of
thought respect logical/rational relations between the
contents of those sentences
Logic and mental causation
• Causation by content exploits rational/logical connections
between contents
Content of desire:
Content of belief:
Content of intention:
I will not lose money
If I buy shares then I
will lose money
I will not buy shares
My belief and desire cause my intention in virtue of logical
relations between the relevant contents
Problem of causation by content
• How do causal interactions between the physical vehicles of
mental representations preserve/exploit logical relations
between the contents of those mental representations?
• LOT answer =
– the LOT is like a formal language
– this allows the LOT to exploit the relation
between syntax and semantics that we
find in a formal language
Formal languages
Examples
•
Propositional calculus
•
Predicate calculus
•
Theories that have predicate calculus as
underlying logic
•
•
Theory of arithmetic
Theory of Turing machines coded into the
theory of arithmetic
Syntax
•Syntax of a formal language
• alphabet of basic symbols of various types
e.g. predicates, names
• rules for combining basic symbols into complex
symbols according to their type
e.g. rules governing wffs
• rules for manipulating those complex symbols
e.g. rules of inference
•Rules identify symbols in terms of their formal (typographic)
features
Semantics
•Semantics provides an interpretation for the
symbols of the formal language
•
•
•
– objects are assigned to names
– sets of objects are assigned to 1-place
relations
– sets of n-tuples of objects are assigned to nplace relations
Two types of logical relation
Logical deducibility
Sentence p is deducible from sentences  iff
there
is a sequence of legitimate symbol
manipulations that
lead from some subset of  to p
Logical consequence
Sentence p is a consequence of sentences  iff there is no
way of interpreting the symbols in 
and p that makes all
of  true and p false
Completeness and soundness
•Two basic results about (first-order) logics (and hence
about theories that can be expressed in first-order logics)
•
Soundness: If p is derivable from  then p is a
consequence of 
•
Completeness: If p is a consequence of  then p
is derivable from 
Implications for LOT
• We can think about logical relations between contents of
mental representations in terms of logical consequence
• We can think about causal relations between the vehicles
of mental representations in terms of logical deducibility
(physical transformations that implement syntactic rules)
• Soundness and completeness ensure that consequence
and deducibility always go together
Example
Symbols
Transformation rule
Meaning/Semantics
1. Fa
Georgina is tall
2. Ga
Georgina has red hair
3. Fa & Ga
If complex symbols ‘S’ and ‘S*’
appear on earlier lines then write
‘S & S*’
Georgina is tall and has
red hair
4. x (Fx & Gx)
If on an earlier line there is a
complex symbol containing a
name symbol then replace the
name symbol by ‘x’ and write ‘x’
in front of the result
Something is tall and has
red hair
Cognitive science arguments
Basic strategy  CogSci treats information-processing as
a form of computation
• we need the LOT as a medium for
computational information-processing
Applications
• practical decision-making
• concept acquisition
• language-learning
LOT and language-learning
Language learning is essentially a process of hypothesis formation
and testing  we need the LOT as a medium for formulating and
modifying the hypotheses
The hypotheses are truth-rules - e.g.
“a is F” is true iff b is G (where a = b and ‘F’ and ‘G’ refer to
the same set of objects)
Means that the LOT must be at least as expressively powerful as the
language being learnt
Problems
• How plausible is it to treat language-learning as a
process of translation?
• How do we learn the leaning of ‘red’?
• ‘a is red’ is true iff a is red*
or
• starting with paradigm cases of red objects and
then learning what other objects are relevantly
similar
Unwelcome implications?
• Fodor argues that most natural language words have
atomistic meanings
• failure of dictionary definitions/analysis of
necessary and sufficient conditions
• This means that there have to be words in the LOT
corresponding to almost all words in, say, English
• This huge LOT vocabulary has to be innate