Computational Analysis of Human Thinking Processes (Invited Paper)


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Introduction to Human Behavior

For example, Angie believes that David stole a candy bar if and only if there is a belief relation between Angie and a mental representation, the content of which is David stole a candy bar. Thus, R1 is a schema. For example, the case of belief is as follows:. RTM is a species of intentional realism —the view that propositional attitudes are real states of organisms, and in particular that a mature psychology will make reference to such states in the explanation of behavior.

For debate on this issue see for example Churchland , Stich , Dennett One important virtue of RTM is that it provides an account of the difference between the truth and falsehood of a propositional attitude in particular, of a belief. On that account, the truth or falsehood of a belief is inherited from the truth or falsehood of the representation involved. If the relationship of belief holds between Angie and a representation with the content David stole a candy bar , yet David did not steal a candy bar, then Angie has a false belief.

Computational Linguistics (Stanford Encyclopedia of Philosophy)

RTM, LOTH, and CSMP was inspired on one hand by the development of modern logic, and in particular by the formalization of logical inference that is, the development of rules of inference that are sensitive to syntax but that respect semantic constraints. These two developments led to the creation of the modern digital computer, and Turing argued that if the conversational behavior via teletype of such a machine was indistinguishable from that of a human being, then that machine would be a thinking machine.

It is the idea that the mind is a computer, and that thinking is a computational process. The importance of CTM is twofold. First, the idea that thinking is a computational process involving linguistically structured representations is of fundamental importance to cognitive science. It is among the origins of work in artificial intelligence, and though there has since been much debate about whether the digital computer is the best model for the brain see below many researchers still presume linguistic representation to be a central component of thought.

Second, CTM offers an account of how a physical object in particular, the brain can produce rational thought and behavior. The answer is that it can do so by implementing rational processes as causal processes. This answer provides a response to what some philosophers—most famously Descartes , have believed: that explaining human rationality demands positing a form of existence beyond the physical. It therefore stands as a major development in the philosophy of mind. Explaining rationality in purely physical terms is one task for a naturalized theory of mind.

Computational Models of Cognition: Part 1

Still, CTM lends itself to a physicalist account of intentionality. There are two general strategies here. Internalist accounts explain meaning without making mention of any objects or features external to the subject. For example, conceptual role theories see for instance Loar explain the meaning of a mental representation in terms of the relations it bears to other representations in the system.

Externalist accounts explicitly tie the meaning of mental representations to the environment of the thinker. For example, causal theories see for instance Dretske explain meaning in terms of causal regularities between environmental features and mental representations. For example, on a dark evening, someone might easily mistake a cow for a horse; in other words, a cow might cause the tokening of a mental representation that means horse. But if, as causal theories have it, the meaning of a representation is determined by the object or objects that cause it, then the meaning of such a representation is not horse , but rather horse or cow since the type of representation is sometimes caused by horses and sometimes caused by cows.

That is, if the representation was not caused by horses, then it would not sometimes be caused by cows.

But this dependence is asymmetric: if the representation was not ever caused by cows, it would nevertheless still be caused by horses. As all of the above examples explain meaning in physical terms, the coupling of a successful CTM with a successful version of any of them would yield an entirely physical account of two of the most important general features of the mind: rationality and intentionality. LOTH then, is the claim that mental representations possess combinatorial syntax and compositional semantics—that is, that mental representations are sentences in a mental language.

This section describes four central arguments for LOTH. Fodor argued that LOTH was presupposed by all plausible psychological models. Fodor and Pylyshyn argue that thinking has the properties of productivity, systematicity, and inferential coherence, and that the best explanation for such properties is a linguistically structured representational system. In short, the argument was that the only game in town for explaining rational behavior presupposed internal representations with a linguistic structure.

The development of connectionist networks —computational systems that do not presuppose representations with a linguistic format—therefore pose a serious challenge to this argument. In the s, the idea that intelligent behavior could be explained by appeal to connectionist networks grew in popularity and Fodor and Pylyshyn argued on empirical grounds that such an explanation could not work, and thus that even though linguistic computation was no longer the only game in town, it was still the only plausible explanation of rational behavior.

Their argument rested on claiming that thought is productive , systematic , and inferentially coherent. Productivity is the property a system of representations has if it is capable, in principle, of producing an infinite number of distinct representations.

Introductory Video

For example, sentential logic typically allows an infinite number of sentence letters A, B, C, Thus the system is productive. The system is not productive. Productivity can be achieved in systems with a finite number of atomic representations, so long as those representations may be combined to form compound representations, with no limit on the length of the compounds.

That is, productivity can be achieved with finite means by employing both combinatorial syntax and compositional semantics. Fodor and Pylyshyn argue that mental representation is productive, and that the best explanation for its being so is that it is couched in a system possessing combinatorial syntax and compositional semantics.

They first claim that natural languages are productive.

Introductory Video

For example, English possesses only a finite number of words, but because there is no upper bound on the length of sentences, there is no upper bound on the number of unique sentences that can be formed. More specifically, they argue that the capacity for sentence construction of a competent speaker is productive—that is, competent speakers are able to create an infinite number of unique sentences.


  1. North American Chapter of the Association for Computational Linguistics () - ACL Anthology.
  2. Computational Thinking in Life Science Education.
  3. The Last Days of Jesus: His Life and Times.

Of course, this is an issue in principle. No individual speaker will ever construct more than a finite number of unique sentences. Nevertheless, Fodor and Pylyshyn argue that this limitation is a result of having finite resources such as time. The argument proceeds by noting that, just as competent speakers of a language can compose an infinite number of unique sentences, they can also understand an infinite number of unique sentences.

Fodor and Pylyshyn write,. However, this unbounded expressive power must presumably be achieved by finite means. The way to do this is to treat the system of representations as consisting of expressions belonging to a generated set. More precisely, the correspondence between a representation and the proposition it expresses is, in arbitrarily many cases, built up recursively out of correspondences between parts of the expression and parts of the proposition. But, of course, this strategy can only operate when an unbounded number of the expressions are non-atomic.

So linguistic and mental representations must constitute [systems possessing combinatorial syntax and compositional semantics]. In short, human beings can entertain an infinite number of unique thoughts. But since humans are finite creatures, they cannot possess an infinite number of unique atomic mental representations. Thus, they must possess a system that allows for construction of an infinite number of thoughts given only finite atomic parts.

The only systems that can do that are systems that possess combinatorial syntax and compositional semantics.

Thus, the system of mental representation must possess those features. Systematicity is the property a representational system has when the ability of the system to express certain propositions is intrinsically related to the ability the system has to express certain other propositions where the ability to express a proposition is just the ability to token a representation whose content is that proposition. For example, sentential logic is systematic with respect to the propositions Bill is boring and Fred is funny and Fred is funny and Bill is boring , as it can express the former if and only if it can also express the latter.

Similarly to the argument from productivity, Fodor and Pylyshyn argue that thought is largely systematic, and that the best explanation for its being so is that mental representation possesses a combinatorial syntax and compositional semantics. The argument rests on the claim that the only thing that can account for two propositions being systematically related within a representational system is if the expressions of those propositions within the system are compound representations having the same overall structure and the same components, differing only in the arrangement of the parts within the structure, and whose content is determined by structure, parts, and arrangement of parts within the structure.

That is, they are both conjunctions, they have the same components, they only differ in the arrangement of the components within the structure, and the content of each is determined by their structure, their parts, and the arrangement of the parts within the structure. But, the argument continues, any representational system that possesses multiple compound representations that are capable of having the same constituent parts and whose content is determined by their structure, parts and arrangement of parts within the structure is a system with combinatorial syntax and compositional semantics.

Hence, systematicity guarantees linguistically structured representations. Fodor and Pylyshyn argue that, if thought is largely systematic, then it must be linguistically structured. They argue that for the most part it is, pointing out that anyone who can entertain the proposition that John loves Mary can also entertain the proposition that Mary loves John.

What explains that is that the underlying representations are compound, have the same parts, and have contents that are determined by the parts and the arrangement of the parts within the structure. But then what underlies the ability to entertain those propositions is a representational system that is linguistically structured.

See Johnson for an argument that language, and probably thought as well, is not systematic. A system is inferentially coherent with respect to a certain kind of logical inference, if given that it can draw one or more specific inferences that are instances of that kind, it can draw any specific inferences that are of that kind. Here A is a logical conjunction, and B is the first conjunct.

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A system that can draw the inference from A to B is a system that is able to infer the first conjunct from a conjunction with two conjuncts, in at least one instance. A system may or may not be able to do the same given other instances of the same kind of inference. It may not for example be able to infer Bill is boring from Bill is boring and Fred is funny.

If it can infer the first conjunct from a logical conjunction regardless of the content of the proposition, then it is inferentially coherent with respect to that kind of inference. As with productivity and systematicity, Fodor and Pylyshyn point to inferential coherence as a feature of thought that is best explained on the hypothesis that mental representation is linguistically structured. The argument here is that what best explains inferential coherence with respect to a particular kind of inference, is if the syntactic structure of the representations involved mirrors the semantic structure of the propositions represented.

For example, if all logical conjunctions are represented by syntactic conjunctions, and if the system is able to separate the first conjunct from such representations, then it will be able to infer for example, Emily is in Scranton from Emily is in Scranton and Judy is in New York , and it will also be able to infer Bill is boring from Bill is boring and Fred is funny , and so on for any logical conjunction.

Thus it will be inferentially coherent with respect to that kind of inference. If the syntactic structure of all the representations matches the logical structure of the propositions represented, and if the system has general rules for processing those representations, then it will be inferentially coherent with respect to any of the kinds of inferences it can perform.

Representations whose syntactic structure mirrors the logical structure of the propositions they represent, however, are representations with combinatorial syntax and compositional semantics; they are linguistically structured representations. Thus, if thought is inferentially coherent, then mental representation is linguistically structured.

And Fodor and Pylyshyn claim,. In short, human thought is inferentially coherent. Any example of inferential coherence is best explained by appeal to linguistically structured representations. Hence, inferential coherence in human thought is best explained by appeal to linguistically structured representations. There are important problems for, and objections to, LOTH. The first is the problem of individuating the symbols of the language of thought, which if unsolvable would prove fatal for LOTH, at least insofar as LOTH is to be a component of a fully naturalized theory of mind, or insofar as it is to provide a framework within which psychological generalizations ranging across individuals may be made.

The second is the problem of explaining context-dependent properties of thought, which should not exist if thinking is a computational process. The third is the objection that contemporary cognitive science shows that some thinking takes place in mental images, which do not have a linguistic structure, so LOTH cannot be the whole story about rational thought.

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INTRODUCTION

The fourth is the objection that systematicity, productivity, and inferential coherence may be accounted for in representational systems that do not employ linguistic formats such as maps , so the arguments from those features do not prove LOTH. The fifth is the argument that connectionist networks, computational systems that do not employ linguistic representation, provide a more biologically realistic model of the human brain than do classical digital computers.

The last part briefly raises the question whether the mind is best viewed as an analog or digital machine. An important and difficult problem concerning LOTH is the individuation of primitive symbols within the language of thought, the atomic mental representations. There are three possibilities for doing so: in terms of the meaning of a symbol, in terms of the syntax of a symbol where syntactic kinds are conceived of as brain-state kinds , and in terms of the computational role of the symbol for example, the causal relations the symbol bears to other symbols and to behavior.

Computational Analysis of Human Thinking Processes (Invited Paper) Computational Analysis of Human Thinking Processes (Invited Paper)
Computational Analysis of Human Thinking Processes (Invited Paper) Computational Analysis of Human Thinking Processes (Invited Paper)
Computational Analysis of Human Thinking Processes (Invited Paper) Computational Analysis of Human Thinking Processes (Invited Paper)
Computational Analysis of Human Thinking Processes (Invited Paper) Computational Analysis of Human Thinking Processes (Invited Paper)
Computational Analysis of Human Thinking Processes (Invited Paper) Computational Analysis of Human Thinking Processes (Invited Paper)
Computational Analysis of Human Thinking Processes (Invited Paper) Computational Analysis of Human Thinking Processes (Invited Paper)
Computational Analysis of Human Thinking Processes (Invited Paper) Computational Analysis of Human Thinking Processes (Invited Paper)
Computational Analysis of Human Thinking Processes (Invited Paper) Computational Analysis of Human Thinking Processes (Invited Paper)
Computational Analysis of Human Thinking Processes (Invited Paper)

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