The ICSI/Berkeley Neural Theory of Language Project

1. The ICSI/BerkeleyNeural Theory of Language Project • Graduate Students • Leon Barrett (CS) • *Johno Bryant (CS) • *Nancy Chang (CS) • Ellen Dodge (Ling) • Michael Ellsworth (Ling) • Joshua Marker (Ling) • *Eva Mok (CS) • Shweta Narayan (Ling) • *Steve Sinha (CS) • Alumni • Terry Regier (UCB Ling) • David Bailey (Google) • Andreas Stolcke (ICSI, SRI) • Dan Jurafsky (Stanford Ling) • Olya Gurevich (Powerset) • Benjamin Bergen (U. Hawaii Ling) • Carter Wendelken (UCB) • Srini Narayanan (ICSI, UCB) • Gloria Yang (UTD) • Principal investigators • Jerome Feldman (UCB,ICSI) • George Lakoff (UCB Ling) • Srini Narayanan (UCB,ICSI) • Lokendra Shastri (now India) • Affiliated faculty • Chuck Fillmore (ICSI) • Eve Sweetser (UCB Ling) • Rich Ivry (UCB Psych) • Lisa Aziz-Zadeh (USC)

2. Unified Cognitive Science Neurobiology Psychology Computer Science Linguistics Philosophy Social Sciences Experience Take all the Findings and Constraints Seriously

3. Constrained Best Fit in Nature inanimateanimate framing, compromise society, politics

4. 1000 operations/sec 100,000,000,000 units 10,000 connections/ graded, stochastic embodied fault tolerant evolves learns 1,000,000,000 ops/sec 1-100 processors ~ 4 connections binary, deterministic abstract, disembodied crashes frequently explicitly designed is programmed Brains ~ Computers

5. Fast Brain ~ Slow Neurons Mental Connections are Active Neural Connections There is No Erasing in the Brain

6. Constraints on Connectionist Models 100 Step Rule Human reaction times ~ 100 milliseconds Neural signaling time ~ 1 millisecond Simple messages between neurons Long connections are rare No new connections during learning Developmentally plausible

7. Connectionist Models in Cognitive Science Structured PDP Hybrid Neural Conceptual Existence Data Fitting Fast MappingSkill Learning Not discussed in meeting

8. A B C Triangle nodes and McCullough-Pitts Neurons? A B C

9. Representing concepts using triangle nodes

10. Functionalism In fact, the belief that neurophysiology is even relevant to the functioning of the mind is just a hypothesis. Who knows if we’re looking at the right aspects of the brain at all. Maybe there are other aspects of the brain that nobody has even dreamt of looking at yet. That’s often happened in the history of science. When people say that the mental is just the neurophysiological at a higher level, they’re being radically unscientific. We know a lot about the mental from a scientific point of view. We have explanatory theories that account for a lot of things. The belief that neurophysiology is implicated in these things could be true, but we have very little evidence for it. So, it’s just a kind of hope; look around and you see neurons: maybe they’re implicated. Noam Chomsky 1993, p.85

11. Embodiment Of all of these fields, the learning of languages would be the most impressive, since it is the most human of these activities. This field, however, seems to depend rather too much on the sense organs and locomotion to be feasible. Alan Turing (Intelligent Machines,1948) Continuity Principle of the American Pragmatists

12. Learning early constructions (Chang, Mok) The ICSI/BerkeleyNeural Theory of Language Project ECG

13. Ideas from Cognitive Linguistics • Embodied Semantics (Lakoff, Johnson, Sweetser, Talmy • Radial categories (Rosch 1973, 1978; Lakoff 1985) • mother: birth / adoptive / surrogate / genetic, … • Profiling (Langacker 1989, 1991; cf. Fillmore XX) • hypotenuse, buy/sell (Commercial Event frame) • Metaphor and metonymy (Lakoff & Johnson 1980, …) • ARGUMENT IS WAR, MORE IS UP • The ham sandwich wants his check. • Mental spaces (Fauconnier 1994) • The girl with blue eyes in the painting really has green eyes. • Conceptual blending (Fauconnier & Turner 2002, inter alia) • workaholic, information highway, fake guns • “Does the name Pavlov ring a bell?” (from a talk on ‘dognition’!)

14. Cafe Simulation-based language understanding Utterance “Harry walked to the cafe.” Constructions Analysis Process General Knowledge Simulation Specification Schema Trajector Goal walk Harry cafe Belief State Simulation

15. Psycholinguistic evidence • Embodied language impairs action/perception • Sentences with visual components to their meaning can interfere with performance of visual tasks (Richardson et al. 2003) • Sentences describing motion can interfere with performance of incompatible motor actions (Glenberg and Kashak 2002) • Sentences describing incompatible visual imagery impedes decision task (Zwaan et al. 2002) • Simulation effects from fictive motion sentences • Fictive motion sentences describing paths that require longer time, span a greater distance, or involve more obstacles impede decision task (Matlock 2000, Matlock et al. 2003)

16. Neural evidence: Mirror neurons • Gallese et al. (1996) found “mirror” neurons in the monkey motor cortex, activated when • an action was carried out • the same action (or a similar one) was seen. • Mirror neuron circuits found in humans (Porro et al. 1996) • Mirror neurons activated when someone: • imagines an action being carried out (Wheeler et al. 2000) • watches an action being carried out (with or without object) (Buccino et al. 2000)

17. walker at goal energy walker=Harry goal=home Active representations • Many inferences about actions derive from what we know about executing them • Representation based on stochastic Petri nets captures dynamic, parameterized nature of actions • Used for acting, recognition, planning, and language • Walking: • bound to a specific walker with a direction or goal • consumes resources (e.g., energy) • may have termination condition(e.g., walker at goal) • ongoing, iterative action

18. Learning Verb MeaningsDavid Bailey A model of children learning their first verbs. Assumes parent labels child’s actions. Child knows parameters of action, associates with word Program learns well enough to: 1) Label novel actions correctly 2) Obey commands using new words (simulation) System works across languages Mechanisms are neurally plausible.

19. System Overview

20. Learning Two Senses of PUSH Model merging based on Bayesian MDL

21. NTL Manifesto • Basic Concepts are Grounded in Experience • Sensory, Motor, Emotional, Social, • Abstract and Technical Concepts map by Metaphor to more Basic Concepts • Neural Computation models all levels

22. Analyzer: Discourse & Situational Context Simulation based Language Understanding Constructions Utterance incremental, competition-based, psycholinguistically plausible Semantic Specification: image schemas, frames, action schemas Simulation

23. Pragmatics Semantics Syntax Morphology Phonology Phonetics “Harry walked into the cafe.”

24. Pragmatics UTTERANCE Semantics Syntax Morphology Phonology Phonetics “Harry walked into the cafe.”

25. Embodied Construction Grammar • Embodied representations • active perceptual and motor schemas (image schemas, x-schemas, frames, etc.) • situational and discourse context • Construction Grammar • Linguistic units relate form and meaning/function. • Both constituency and (lexical) dependencies allowed. • Constraint-based • based on feature unification (as in LFG, HPSG) • Diverse factors can flexibly interact.

26. Embodiment and Grammar Learning Paradigm problem for Nature vs. Nurture The poverty of the stimulus

27. Embodiment and Grammar Learning Paradigm problem for Nature vs. Nurture The poverty of the stimulus The opulence of the substrate Intricate interplay of genetic and environmental, including social, factors.

28. Embodied Construction GrammarECG(Formalizing Cognitive Linguisitcs) • Linguistic Analysis • Computational Implementation • Test Grammars • Applied Projects – Question Answering • Map to Connectionist Models, Brain • Models of Grammar Acquisition