How Cognition Could Be Computing
Semiotic Systems, Computers, & the Mind
William J. Rapaport
Department of Computer Science & Engineering,
Department of Philosophy, Department of Linguistics,
and Center for Cognitive Science
rapaport@buffalo.edu
http://www.cse.buffalo.edu/~rapaport
Summary
• Computationalism = cognition is computable.
– Mental processes can be the result of algorithmic procedures…
– …that can be affected by emotions/attitudes/individual histories.
• Computers that implement these (cognitive) procedures
really exhibit those mental processes.
– They are “semiotic” (= sign-using) systems.
– They really think.
• “Syntactic semantics” explains how all this is possible.
What Is “Computationalism”?
• “Computationalism” =? cognition is computation
– Hobbes, McCulloch/Pitts, Putnam, Fodor, Pylyshyn, …
– interesting, worth exploring, possibly true
– BUT:
• Not what “computational”/“computable”
usually mean!
• What should “computationalism” be?
• Must preserve crucial insight:
– cognition is explainable via mathematical theory of computation
“Computable”
•
Task / goal / field of study G is computable
iff
 algorithm(s)formal for G
The Proper Treatment of Computationalism
•
Computationalism ≠ Cognition is computation
The Proper Treatment of Computationalism
•
Computationalism = Cognition is computable
–
i.e.,  algorithm(s) that compute cognitive functions
a) Working assumption of computational cognitive science:
• All cognition is computable
b) Basic research question of computational cognitive science:
• How much of cognition is computable?
c) …
Proper Treatment of Computationalism
c) Implementational implication
(multiple realization):
•
•
If cognition is computable, then:
anything that implements cognitive computations
would be cognitive
(would really think)
even if humans don’t do it that way!
–
Turing:

–
Piccinini:


–
brain might be analog, but digital computer can still pass TT
neural spike trains are not representable as digit strings;
 not computational
/ brain does not compute
BUT:
 cog. functions whose O/P they produce are computable
: human cognition is computable but not computed
II. Syntactic Semantics
as a theory underlying computationalism
1. Cognition is internal
•
Cognitive agents have direct access
only to internal representatives of external objects
2. Semantics is syntactic
•
 Words, meanings, & semantic relations between them
are all syntactic items
3. Understanding is recursive
•
Recursive Case:
–
•
We understand one thing in terms of
another that must already be understood;
Base Case:
–
We understand something in terms of itself
(syntactic understanding)
Syntactic Semantics
1. Internalism:
Cognitive agents have direct access only
to internal representatives of external objects
–
A cognitive agent understands the world by
“pushing the world into the mind” (Jackendoff 2002)
– “Output of sensory transducers is the only contact
the cognitive system ever has with the environment” (Pylyshyn 1985)
 Both words & their meanings (including external objects)
are represented internally in a single LOT
•
•
Humans:
biological neural network
Computers:
–
–
artificial neural network
symbolic knowledge-representation & reasoning system
Syntactic Semantics
2. (Internalism ) Syntacticism
 Words, meanings, & semantic relations
between them are all syntactic
•
syntax ⊋ grammar
Syntactic Semantics
2. (Internalism ) Syntacticism
 Words, meanings, & semantic relations
between them are all syntactic
•
syntax =
–
•
set of signs / uninterpreted marks / neuron firings / …
semantics = study of relations between members of two sets
–
–
–
study of relations among members of a single set
set of signs / marks / neuron firings / …
& set of (external) meanings / …
(with its own syntax!)
“Pushing” meanings into same set as symbols for them
allows semantics to be done syntactically
•
•
•
turns semantic relations between 2 sets (internal signs, external meanings)
into relations among the marks of a single (internal) LOT/ syntax
e.g.:
truth tables & formal semantics are both syntactic
e.g.:
neuron firings representing both signs & external meanings
 Symbol-manipulating computers
can do semantics by doing syntax
SYN
DOM
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Syntax
SYN
DOM
SYN
DOM
•
•
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Syntax
SEM
DOM
•
•
Semantics
SYN
DOM
SYN
DOM
Syntax
•
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SEM
DOM
•
•
•
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Semantics
•
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•
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Syntactic
semantics
Syntactic Semantics
3. Understanding must be understood recursively:
– Recursive cases:
•
•
We understand a syntactic domain (SYN-1) indirectly
by interpreting it in terms of a semantic domain (SEM-1)
–
e.g.) understanding relevance logic in terms of Routley-Meyer ternary relation on points.
–
but SEM-1 must be antecedently understood
SEM-1 can be understood by considering it as a syntactic domain SYN-2
interpreted in terms of yet another semantic domain
–
e.g.) understanding RM ternary relation in terms of situation semantics
– which also must be antecedently understood, etc.
– Base case:
• A domain that is understood directly (i.e., not “antecedently”)
– in terms of itself (in terms of relations among its symbols)
– i.e., syntactically & holistically
III. Rapaport’s Thesis
 Syntax suffices for semantic cognition
 cognition is computable
&  computers are capable of thinking
James H. Fetzer’s Thesis
•
It doesn’t,
– it isn’t,
 & they aren’t
Fetzer’s Thesis
• Computers differ from cognitive agents in 3 ways:
1. statically (symbolically)
2. dynamically (algorithmically)
3. affectively (emotionally)
• Simulation is not the real thing
Fetzer’s Static Difference
ARGUMENT 1: Computers are mark-manipulating systems,
minds are not.
Premise 1: Computers manipulate marks on the basis of their size, shapes,
and relative locations.
Premise 2: (a) These shapes, sizes, and relative locations exert causal influence
upon computers
but (b) do
not stand for anything for those systems.
Premise 3: Minds operate by utilizing signs that stand for other things in some
respect or other for them as sign-using (or “semiotic”) systems.
__________________________________________________________________
Conclusion 1: Computers are not semiotic (or sign-using) systems.
___________________________________________________________________
Conclusion 2:
Computers are not the possessors of minds.
Figure 10. The Static Difference
Fetzer |- Computers Are Not Semiotic Systems
1. In a “semiotic system” (e.g., a mind):
–
a)
b)
c)
something (S) is a sign of something (x) for somebody (z)
x “grounds” sign S
x “is an interpretant w.r.t. a context” to sign-user z
S is in a “causation” relation with z
Fetzer |- Computers Are Not Semiotic Systems
2. In a computer (I/O) system:
a) input i (playing role of sign S)
is in a “causation” relation with
computer c (playing role of sign-user z)
b) output o (playing role of thing x)
is in an “interpretant” relation with computer c
c) BUT: No “grounding” relation between i & o
Fetzer |- Computers Are Not Semiotic Systems
3.  Computers only have causal relationships,
no mediation between I/P & O/P (?!)
4. But semiotic systems require such mediation
•
Peirce:
interpretant is “mediately determined by” the sign
– [ “interpretant” is really the sign-user’s mental concept
of the thing x (!!) ]
5.  Computers are not semiotic systems
6. But minds are.
7.  Minds are not computers
& computers can’t be minds.
Three Arguments against Static Difference
A. Incardona |- Computers are semiotic systems!
1. X is a semiotic system iff X carries out a process that
mediates between a sign & its interpretant
–
Semiotic systems interpret signs
2. Algorithms describe processes
that mediate between I/Ps & O/Ps
–
–
An algorithm’s O/P is an interpretation of its I/P
Algorithms ground the I/O relation
3. Computers are algorithm machines
 Computers are semiotic systems
Three arguments against the Static Difference
B. Argument that computers are semiotic systems
from embedding in the world:
– Fetzer’s (counter?)example:
•
“A red light at an intersection stands for
applying the brakes and coming to a complete halt,
only proceeding when the light turns green,
for those who know ‘the rules of the road’.”
– Can such a red light stand for
applying the brakes, etc., for a computer?
•
It could, if the computer “knows the rules of the road”
– But a computer can “know” those rules…
•
•
if it has those rules stored in a knowledge base
and if it uses those rules to drive a vehicle
–
cf. 2007 DARPA Urban Grand Challenge
»
Parisien & Thagard 2008, “Robosemantics:
How Stanley Represents the World”, Minds & Machines 18
Three Arguments against the Static Difference
C. Goldfain |- Computer’s marks stand for something for it
•
Does a calculator that computes GCDs understand them?
–
•
Fetzer & Rapaport:
No
Could a computer that computes GCDs understand them?
–
–
Fetzer:
Goldfain & Rapaport:


No
Yes, it could…
as long as it had enough background / contextual / supporting info
a computer with a full-blown theory of math
at the level of an algebra student learning GCDs
could understand GCDs as well as the student
Summary: No “Static Differences”
• Both computers & minds manipulate marks
• The marks can “stand for something”
for both computers & minds
• Computers (and minds) are “semiotic systems”
• Computers can possess minds
Fetzer’s Dynamic Difference
ARGUMENT 2: Computers are governed by algorithms, but
minds are not.
Premise 1: Computers are governed by programs,
which are causal models of algorithms.
Premise 2: Algorithms are effective decision procedures for arriving at definite
solutions to problems in a finite number of steps.
Most human thought processes, including dreams, daydreams,
and ordinary thinking, are not procedures for arriving at solutions
to problems in a finite number of steps.
Premise 3:
______________________________________________________________________
Conclusion 1:
Most human thought processes are not governed by
programs as causal models of algorithms.
_______________________________________________________________________
Conclusion 2:
Minds are not computers.
Figure 11. The Dynamic Difference
The Dynamic Difference
• Premises 1 & 2:
– Def of ‘algorithm’ is OK
– But algorithms may be the wrong entity
• may need a more general notion of “procedure”
(Shapiro)
• like an algorithm, but:
– need not halt
– need not yield “correct” output
– can access external KB (Turing “oracle” machine)
The Dynamic Difference
•
Premise 3: Most human thinking is not algorithmic
– Dreams are not algorithms
– Ordinary stream-of-consciousness thinking is not “algorithmic”
• BUT:
– Some human thought processes may indeed not be algorithms
• Consistent with “proper” treatment of computationalism
– Real issue is…
• Could there be algorithms/procedures that produce these
(or other mental states or processes)?
– If dreams are our interpretations of random neuron firings during sleep,
as if they were due to external causes…
• …then: if non-dream neuron-firings are computable
(& there’s every reason to think they are)
then so are dreams
– Stream of consciousness might be computable
• e.g., via spreading activation in a semantic network
The Dynamic Difference
• Whether a mental state/process is computable
is at least an empirical question
– Must avoid the Hubert Dreyfus fallacy:
• one philosopher’s idea of a non-computable process
is another computer scientist’s research project
• what no one has yet written a program for
is not thereby necessarily non-computable
– In fact:
Mueller, Erik T. (1990), Daydreaming in Humans & Machines:
A Computer Model of the Stream of Thought (Ablex)
• Cf. Edelman, Shimon (2008), Computing the Mind (Oxford)
–  burden of proof is on Fetzer!
The Dynamic Difference
• Dynamic Conclusion 2:
– Are minds computers?
• Maybe, maybe not
• I prefer to say (with Shimon Edelman, et al.):
– The (human) mind is a virtual machine,
computationally implemented
(in the nervous system)
Summary: No “Dynamic Difference”
• All (human) thought processes are/might be
describable by algorithms/procedures
= computationalism properly treated
Fetzer’s Affective Difference
ARGUMENT 3: Mental thought transitions are affected by emotions, attitudes,
and histories, but computers are not.
Premise 1: Computers are governed by programs,
which are causal models of algorithms.
Premise 2: Algorithms are effective decisions,
which are not affected by emotions, attitudes, or histories.
Premise 3: Mental thought transitions are affected by values of variables
that do not affect computers.
_____________________________________________________________________
Conclusion 1:
The processes controlling mental thought transitions are
fundamentally different than those that control
computer procedures.
_____________________________________________________________________
Conclusion 2:
Minds are not computers.
Figure 12. The Affective Difference
Contra Affective Premises 2 & 3:
• Programs can be based on (idiosyncratic)
emotions, attitudes, & histories
– Rapaport-Ehrlich contextual vocabulary acquisition program
• Learns a meaning for an unfamiliar word from:
– the word’s textual context
– integrated with the reader’s idiosyncratic …
» “denotations”, “connotations”,
» emotions, attitudes, histories,
» & prior beliefs
– Sloman, Picard, Thagard
• Developing computational theories of affect, emotion, etc.
• Emotions, attitudes, & histories can affect computers that model them.
Summary: No “Affective Differences”
• Processes controlling mental thought transitions
are not fundamentally different from
those controlling algorithms/procedures.
• Algorithms can take emotions/attitudes/histories
into account.
• Both computers & minds can be affected by
emotions/attitudes/histories
The Matter of Simulation
ARGUMENT 4: Digital machines can nevertheless simulate thought processes
and other forms of human behavior.
Premise 1: Computer programmers and those who design the systems that they
control can increase their performance capabilities,
making them better and better simulations.
Premise 2: Their performance capabilities may be closer and closer
approximations to the performance capabilities of human beings
without turning them into thinking things.
Premise 3:
Indeed, the static, dynamic, and affective differences that
distinguish computer performance from human
performance preclude them from being thinking things.
______________________________________________________________________________
Conclusion: Although the performance capabilities of digital machines can
become better and better approximations of human behavior,
they are still not thinking things.
Argument from Simulation
• Agreed:
A computer that “simulates” some process P
is not necessarily “really” doing P
– But what is “really doing P” vs. “simulating P”?
– What is the “scope” of a simulation?
• Computer simulations of hurricanes
don’t get real people really wet
– Real people are outside the scope of the simulation
– BUT: a computer simulation of a hurricane could get
simulated people simulatedly wet
• Computer simulation of the daily operations of a bank
is not thereby the daily operations of a (real) bank
– BUT: I can do my banking online
– Simulations can be used as if they were real
Argument from Simulation
• Some simulations of Xs are real Xs:
– scale model of a scale model of X is a scale model of X
– Xeroxed/faxed/PDF copies of documents
are those documents
– A computer that simulates an “informational process”
is thereby actually doing that informational process
• Because
a computer simulation of information is information…
Argument from Simulation
• Computer simulation of a picture is a picture
– digital photography
• Computer simulation of language is language
– computers really do parse sentences (Woods)
– IBM’s Watson really answers questions
• Computer simulation of math is math
– “A simulation of a computation and the computation itself are
equivalent: try to simulate the addition of 2 and 3, and the
result will be just as good as if you ‘actually’ carried out the
addition—that is the nature of numbers” (Edelman)
• Computer simulation of reasoning is reasoning
– automated theorem proving, computational logic,…
Argument from Simulation
• Computer simulation of cognition is cognition
– “if the mind is a computational entity,
a simulation of the relevant computations
would constitute its fully functional replica”
(Edelman)
– cf. “implementational implication”
Summary:
Simulation Can Be(come) the Real Thing
• Close approximation to human thought processes
can turn computers into thinking things
– actually?
– only asymptotically?
– merely conventionally?
• Turing said…
• “the use of words and general educated opinion
will alter so much
that one will be able to speak of machines thinking
without expecting to be contradicted.” (Turing 1950)
– “general educated opinion”
• changes when we abstract & generalize
– “the use of words”
• changes when reference shifts
from word’s initial / narrow application
to more abstract / general phenomenon
– cf. “fly”, “compute”, “algorithm”
– ditto for “cognition” / “think”
Summary
• Computers are “semiotic (sign-using) systems”.
• Computationalismproperly treated = cognition is computable…
• …not necessarily computational.
• Any non-computable residue will be negligible
– Mental processes are describable (governable) by algorithmic procedures…
– …that can be affected by emotions/attitudes/individual histories.
– Computers that implement these cognitive procedures
really exhibit those cognitive behaviors.
• They really think.
– Computers can possess minds.
• “Syntactic semantics” explains how all this is possible.
• Rapaport, William J. (2012),
– “Semiotic Systems, Computers, and the Mind:
How Cognition Could Be Computing”,
– International Journal of Signs and Semiotic Systems
2(1) (January-June): 32–71.