Linguistics in the context of the computational and cognitive sciences

1. Linguistics in the context of the computational and cognitive sciences Winter 2000 John Goldsmith

2. Logic Computation Mind Machines Language Biq question #1

3. Is the origin of knowledge in the senses, or in the mind? P2 P1 World representation Representation = F(P1, P2) Biq question #2

4. What lessons can we learn from the natural sciences in developing the human sciences? Big question #3

5. Information Sciences The Illusion of Reality, Howard L. Resnikoff(Springer Verlag, 1989) “The history of information science consists of four interwoven strands: • the study of thermodynamics and the theory of measurement in physics; • the study of sensory information processing and knowledge representation in biological systems; • the rise of electrical communication engineering; • and the study of computability and the development of computing machines. In recent years, the common features of these superficially disparate themes have begun to coalesce to form an integrated intellectual discipline whose principal problems blend elements of all four constituent fields.” (p. 3)

6. The Cognitive Sciences • Goal: to study intelligence; intelligent systems; intelligence behavior; • (Looking ahead: is linguistic “behavior” or “knowledge” a form of intelligence?) • Goal: the study of intelligence and its computational processes.

7. What contributes to Cog Sci? • Psychology: • Cognitive psychology (George Miller, J.J. Gibson, U. Neisser) • Psycholinguistics • Use of “information processing” as a point of view without actual computer modeling (simulations).

8. Disciplines in cog sci • Artificial intelligence (as a part of computer science -- computer science as a part of electrical engineering!) • Seymour Papert, Marvin Minsky at MIT; Newell and Simon;

9. Linguistics • From the mid-1950s, staking a claim to psychological reality. • Chomskian program; • Computational linguistics; • Psycholinguistics.

10. (what is computational linguistics?) • Syntactic parsing • Mapping surface structures (linear word order) to semantic slots for data base access. • Automatic speech recognition

11. What is the relationship of language to linguistics withincog sci? • The other disciplines in the cognitive sciences do NOT cede the problem of language to linguistics. In many cases, they almost entirely ignore the current linguistic take on language problems.

12. Contributers to Cog Sci • Philosophy: epistemology (the study of the origin and logical foundations of knowledge) • logic; study of probability; • ?Where to place: the study of Bayesian decision theory.

13. Contributers to Cog Sci • Neuroscience: especially neuroanatomy • Others: Economics (study of rational decision-takers); • Cognitive anthropology, • Cognitive sociology.

14. What is an analysis of a cognitive system? At its most abstract: An analysis of the control and flow of information. I. Memory: how is information retained in a permanent fashion? (long-term); 2. How is new information added (learning)? 3. How is the environment perceived? 4. How is a problem defined and solved?

15. Some typical problems for cognitive science • Vision: perception of objects (in 3D space) • Memory: of events; of information; of language; • Categorization/classification • Planning: how to achieve goals from initial state;

16. Types of models • Simple prose models with a veneer of information processing. • Implemented models (= program written in a programming language) • Mathematical models • Connectionist/neural network models

17. Traditional symbolic models using von Neumann-style computer models Memory accessed through address-labeling; Memory can store both data and program; Input and output devices; Serial computation primarily; Structured linear computational logic. Heavy use of linked lists and associative lists (hashes).

18. Neural network style models Large numbers of simple, identical objects; Each object has an activation level (a real number); Distributed connections among units; Learning is inherent to the system; No centralized control.

19. Coming to grips with the analysis of intelligence: 2 models • Inference versus • Search • Inference: philosophy. Linguistics and psycholinguistics, bayesian decisions, computer science; • Search: traditional AI, much psychology: study of heuristics (=rules of thumb that work most of the time pretty well.)

20. Issues in serialism: • The 100-step limit: • Many interesting cognitive computations take one second or less. • Each machine cycle is about 10 msec; hence no more than 100 cycles are required to accomplish most interesting cognitive tasks. • That’s a serious limit.

21. Some common assumptions in dealing with intelligence: • A large number of rules of the form of productions: • Rule #N: Under conditions C, do P. • What do you do when more than one rule is satisfied at a given time? The control structure. • Linguists know about that problem! But linguists have been remarkably uncreative in considering alternatives.

22. Network models • William James • Donald Hebb • Warren McCulloch (and Pitts) • Frank Rosenblatt • Ross Quillian (spreading activation models) • PDP: McClelland and Rumelhart, back-propagation. • John Hopfield

23. Language -- finally. Is language: a. unique b. what pulls all of cognition together? c. both a and b (NO!) d. neither a nor b.

24. Does what we know about categorization and memory from general functions tell us anything about language? Could language’s approach to the problems of categorization and memory be distinct from other problems? • Does what we know about language inform us about other cognitive functions?

25. Is language unique? • (1) Is language innate? • (2) Does that question bear on whether language is isolated from other functions/deeply linked to other functions? • Does our growing knowledge of localization of functions in the brain help answer questions (1) or (2)?

26. Learning So a big pair of questions is: Whether language is innate; Whether language is integrated vs language is isolated from (other) cognitive functions. Can we shed light on these questions by studying how (if?) language is learned?

27. Should we ask the question: Can language be learned? Are we prepared to take seriously the answer, No, language cannot be learned. If it is known but cannot be learned, then in some sense it must be innate. But are we prepared to consider that possibility?

28. The Chomskian position seems to be clearly, Yes. I think about that view what Hume thought about the view that miracles exist: while we can’t prove it’s not so, there’s essentially no sensible scenario that we could imagine under which a reasonable person would take that hypothesis as the most reasonable explanation.

29. Is knowledge of language a kind of intelligence? • Let’s distinguish between language-learning (= knowledge of how to learn a language) from language-using (= knowing a particular language). • Are either of them a kind of intelligence? • (Related question: is intelligence always adaptive -- a way of responding to the constraints of an external world? If so, that makes it more likely that language is not intelligent/not intelligence.)

30. Is knowledge of language a kind of intelligence?