Understanding • I Hear and I Forget • I See and I Remember • I Do and I Understand Attributed to Confucius, ~500 BCE H E G F D A B C An ambiguous image; A or G can be closer to you How could a mass of chemical cells produce language and thought? Will computers think and speak? How much can we know about our own experience? How do we learn new concepts? Does our language determine how we think? Is language Innate? How do children learn grammar? How did languages evolve? Why do we experience everything the way that we do? Constrained Best Fit in Nature inanimate physics chemistry biology vision language animate lowest energy state molecular minima fitness, MEU Neuroeconomics threats, friends errors, NTL 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) Artist’s Rendition of a Typical Cell Membrane Pre-Natal Tuning: Internally generated tuning signals • But in the womb, what provides the feedback to establish which neural circuits are the right ones to strengthen? – Not a problem for motor circuits - the feedback and control networks for basic physical actions can be refined as the infant moves its limbs and indeed, this is what happens. – But there is no vision in the womb. Recent research shows that systematic moving patterns of activity are spontaneously generated prenatally in the retina. A predictable pattern, changing over time, provides excellent training data for tuning the connections between visual maps. • The pre-natal development of the auditory system is also interesting and is directly relevant to our story. – Research indicates that infants, immediately after birth, preferentially recognize the sounds of their native language over others. The assumption is that similar activity-dependent tuning mechanisms work with speech signals perceived in the womb. Post-natal environmental tuning • The pre-natal tuning of neural connections using simulated activity can work quite well – – a newborn colt or calf is essentially functional at birth. – This is necessary because the herd is always on the move. – Many animals, including people, do much of their development after birth and activity-dependent mechanisms can exploit experience in the real world. • In fact, such experience is absolutely necessary for normal development. • As we saw, early experiments with kittens showed that there are fairly short critical periods during which animals deprived of visual input could lose forever their ability to see motion, vertical lines, etc. – For a similar reason, if a human child has one weak eye, the doctor will sometimes place a patch over the stronger one, forcing the weaker eye to gain experience. Tinbergen’s Four Questions How does it work? How does it improve fitness? How does it develop and adapt? How did it evolve? 5 levels of Neural Theory of Language Spatial Relation Motor Control Metaphor Grammar Cognition and Language abstraction Computation Structured Connectionism Neural Net Triangle Nodes SHRUTI Computational Neurobiology Biology Neural Development Quiz Midterm Finals Brains ~ Computers • 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 • crashes • designed, programmed Freud’s Original Connectionist Model Connectionist Model of Word Recognition (Rumelhart and McClelland) Interactive Activation Reading Model Modeling lexical access errors • • • • Semantic error Formal error (i.e. errors related by form) Mixed error (semantic + formal) Phonological access error Phonological access error: Selection of incorrect phonemes Syl FOG f r d Onsets DOG k CAT m ae RAT MAT o t Vowels On Vo Co g Codas Adapted from Gary Dell, “Producing words from pictures or from other words” Representing concepts using triangle nodes Connectionist Circuit for Gender(RD5) = female RD5 2 gender female Also shown as a triangle node How does activity lead to structural change? • The brain (pre-natal, post-natal, and adult) exhibits a surprising degree of activity dependent tuning and plasticity. • To understand the nature and limits of the tuning and plasticity mechanisms we study – How activity is converted to structural changes (say the ocular dominance column formation) • It is centrally important for us to understand these mechanisms to arrive at biological accounts of perceptual, motor, cognitive and language learning – Biological Learning is concerned with this topic. Long Term Potentiation (LTP) • These changes make each of the winning synapses more potent for an intermediate period, lasting from hours to days (LTP). • In addition, repetition of a pattern of successful firing triggers additional chemical changes that lead, in time, to an increase in the number of receptor channels associated with successful synapses - the requisite structural change for long term memory. – There are also related processes for weakening synapses and also for strengthening pairs of synapses that are active at about the same time. During normal low-frequency trans-mission, glutamate interacts with NMDA and nonNMDA (AMPA) and metabotropic receptors. With highfrequency stimulation The ICSI/Berkeley Neural Theory of Language Project Rods and Cones in the Retina http://www.iit.edu/~npr/DrJennifer/visual/retina.html Color Naming Basic Color Terms (Berlin & Kay) Criteria: 1. Single words -- not “light-blue” or “blue-green” 2. Frequently used -- not “mauve” or “cyan” 3. Refer primarily to colors -- not “lime” or “gold” 4. Apply to any object -- not “roan” or “blond” © Stephen E. Palmer, 2002 The WCS Color Chips • Basic color terms: – – – – Single word (not blue-green) Frequently used (not mauve) Refers primarily to colors (not lime) Applies to any object (not blonde) FYI: English has 11 basic color terms Results of Kay’s Color Study Stage I II IIIa / IIIb IV V VI VII W or R or Y W W W W W W Bk or G or Bu R or Y R or Y R R R R Y Y Y Y G or Bu G G G Bk Bu Bu Bu Bk Bk Bk Y+Bk (Brown) Y+Bk (Brown) Bk or G or Bu G or Bu Bk W R Y R+W (Pink) Bk or G or Bu R + Bu (Purple) R+Y (Orange) B+W (Grey) If you group languages into the number of basic color terms they have, as the number of color terms increases, additional terms specify focal colors 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’!) Concepts are not categorical Radial Structure of Mother Genetic mother Stepmother Unwed mother Surrogate mother Biological mother Adoptive mother Central Case Foster mother Birth mother Natural mother The radial structure of this category is defined with respect to the different models Language, Learning and Neural Modeling www.icsi.berkeley.edu/AI • Scientific Goal Understand how people learn and use language • Practical Goal Build systems that analyze and produce language • Approach Embodied linguistic theories with advanced biologically-based computational methods General and Domain Knowledge • Conceptual Knowledge and Inference – – – – Embodied Language and Domain Independent Powerful General Inferences Ubiquitous in Language • Domain Specific Frames and Ontologies – Framenet (www.icsi.berkeley.edu/framenet) • Metaphor links domain specific to general – E.g., France slipped into recession. Image schemas • Trajector / Landmark (asymmetric) – The bike is near the house – ? The house is near the bike TR • Boundary / Bounded Region – a bounded region has a closed boundary LM boundary bounded region • Topological Relations – Separation, Contact, Overlap, Inclusion, Surround • Orientation – Vertical (up/down), Horizontal (left/right, front/back) – Absolute (E, S, W, N) Learning System dynamic relations (e.g. into) structured connectionist network (based on visual system) We’ll look at the details next lecture Simulation-based language understanding “Harry walked to the cafe.” Utterance Constructions Analysis Process General Knowledge Belief State Schema walk Trajector Harry Cafe Goal cafe Simulation Specification Simulation Simulation Semantics • BASIC ASSUMPTION: SAME REPRESENTATION FOR PLANNING AND SIMULATIVE INFERENCE – Evidence for common mechanisms for recognition and action (mirror neurons) in the F5 area (Rizzolatti et al (1996), Gallese 96, Boccino 2002) and from motor imagery (Jeannerod 1996) • IMPLEMENTATION: – x-schemas affect each other by enabling, disabling or modifying execution trajectories. Whenever the CONTROLLER schema makes a transition it may set, get, or modify state leading to triggering or modification of other x-schemas. State is completely distributed (a graph marking) over the network. • RESULT: INTERPRETATION IS IMAGINATIVE SIMULATION! 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 walker at goal energy walker=Harry goal=home 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 Learning Verb Meanings David 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. cow apple ball juice bead girl bottle truck baby w oof yum go up this no m ore m ore spoon ham m er shoe d ad d y m oo w hee get out there bye banana box eye m om y uhoh sit in here hi cookie horse d oor boy choochoo boom oh open on that no food toys yes misc. people d ow n sound emotion action prep. demon. social Words learned by most 2-year olds in a play school (Bloom 1993) System Overview Learning Two Senses of PUSH Model merging based on Bayesian MDL Training Results David Bailey English • 165 Training Examples, 18 verbs • Learns optimal number of word senses (21) • 32 Test examples : 78% recognition, 81% action • All mistakes were close lift ~ yank, etc. • Learned some particle CXN,e.g., pull up Farsi • With identical settings, learned senses not in English Task: Interpret simple discourse fragments/ blurbs France fell into recession. Pulled out by Germany US Economy on the verge of falling back into recession after moving forward on an anemic recovery. Indian Government stumbling in implementing Liberalization plan. Moving forward on all fronts, we are going to be ongoing and relentless as we tighten the net of justice. The Government is taking bold new steps. We are loosening the stranglehold on business, slashing tariffs and removing obstacles to international trade. Probabilistic inference – Filtering • P(X_t | o_1…t,X_1…t) • Update the state based on the observation sequence and state set – MAP Estimation • Argmaxh1…hnP(X_t | o_1…t, X_1…t) • Return the best assignment of values to the hypothesis variables given the observation and states – Smoothing • P(X_t-k | o_1…t, X_1…t) • modify assumptions about previous states, given observation sequence and state set – Projection/Prediction/Reachability • P(X_t+k | o_1..t, X_1..t) Metaphor Maps • Static Structures that project bindings from source domain f- struct to target domain Bayes net nodes by setting evidence on the target network. • Different types of maps – PMAPS project X- schema Parameters to abstract domains – OMAPS connect roles between source and target domain – SMAPS connect schemas from source to target domains. • ASPECT is an invariant in projection. Results • Model was implemented and tested on discourse fragments from a database of 50 newspaper stories in international economics from standard sources such as WSJ, NYT, and the Economist. • Results show that motion terms are often the most effective method to provide the following types of information about abstract plans and actions. – Information about uncertain events and dynamic changes in goals and resources. (sluggish, fall, off-track, no steam) – Information about evaluations of policies and economic actors and communicative intent (strangle-hold, bleed). – Communicating complex, context-sensitive and dynamic economic scenarios (stumble, slide, slippery slope). – Commincating complex event structure and aspectual information (on the verge of, sidestep, giant leap, small steps, ready, set out, back on track). • ALL THESE BINDINGS RESULT FROM REFLEX, AUTOMATIC INFERENCES PROVIDED BY X-SCHEMA BASED INFERENCES. Embodied Construction Grammar ECG 1. Linguistic Analysis 2. Computational Implementation a. Test Grammars b. Applied Projects – Question Answering 3. Map to Connectionist Models, Brain 4. Models of Grammar Acquisition 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. “Harry walked into the cafe.” Pragmatics Semantics Syntax Morphology Phonology Phonetics “Harry walked into the cafe.” Pragmatics Semantics Syntax Morphology Phonology Phonetics U T T E R A N C E ECG Structures • Schemas – image schemas, force-dynamic schemas, executing schemas, frames… • Constructions – lexical, grammatical, morphological, gestural… • Maps – metaphor, metonymy, mental space maps… • Spaces – discourse, hypothetical, counterfactual… Embodied schemas schema name schema Source-Path-Goal roles source path goal trajector role name schema Container roles interior exterior portal boundary Boundary Source Trajector Interior Portal Goal Path Exterior These are abstractions over sensorimotor experiences. Embodied constructions Form Meaning construction HARRY form : /hEriy/ meaning : Harry Harry cafe ECG Notation CAFE construction CAFE form : /khaefej/ meaning : Cafe Constructions have form and meaning poles that are subject to type constraints. Simulation-based language understanding construction WALKED form selff.phon [wakt] meaning : Walk-Action constraints selfm.time before Context.speech-time selfm..aspect encapsulated “Harry walked into the cafe.” Utterance Analysis Process Constructions General Knowledge Semantic Specification Belief State CAFE Simulation Simulation specification The analysis process produces a simulation specification that •includes image-schematic, motor control and conceptual structures •provides parameters for a mental simulation Summary: ECG • Linguistic constructions are tied to a model of simulated action and perception • Embedded in a theory of language processing – Constrains theory to be usable – Frees structures to be just structures, used in processing • Precise, computationally usable formalism – Practical computational applications, like MT and NLU – Testing of functionality, e.g. language learning • A shared theory and formalism for different cognitive mechanisms – Constructions, metaphor, mental spaces, etc. State of the Art Natural Language Understanding • Limited Commercial Speech Applications transcription, simple response systems • Statistical NLP for Restricted Tasks tagging, parsing, information retrieval • Template-based Understanding programs expensive, brittle, inflexible, unnatural • Essentially no NLU in QA, HCI systems Key ideas for a NT of language acquisition Nancy Chang • Opulence of the substrate – Prelinguistic children already have rich sensorimotor representations and sophisticated social knowledge • Basic Scenes – Simple clause constructions are associated directly with scenes basic to human experience (Goldberg 1995, Slobin 1985) • Verb Island Hypothesis – Children learn their earliest constructions (arguments, syntactic marking) on a verb-specific basis (Verb Island Hypothesis, Tomasello 1992) Early talk about throwing Transcript data, Naomi 1;11.9 Sample input prior to 1;11.9: don’t throw the bear. don’t throw them on the ground. Nomi don’t throw the books down. what do you throw it into? Sample tokens prior to 1;11.9: throw throw off I throw it. I throw it ice. (= I throw the ice) Par: Par: Child: Child: Par: Par: Child: Child: Child: Par: Child: they’re throwing this in here. throwing the thing. throwing in. throwing. throwing the frisbee. … do you throw the frisbee? do you throw it? throw it. I throw it. … throw frisbee. she’s throwing the frisbee. throwing ball. Sachs corpus (CHILDES) How do they make the leap? 0-9 months 18-24 months • Smiles • Responds differently to intonation • Responds to name and “no” • 9-18 months • • First words • Recognizes intentions • Responds, requests, calls, greets, protests • agent-object – Daddy cookie – Girl ball • agent-action – Daddy eat – Mommy throw action-object – Eat cookie – Throw hat entity-attribute – Daddy cookie • entity-locative – Doggie bed Correlating forms and meanings FORM (sound) “you” lexical constructions you MEANING (stuff) Qu ic kTi me™ a nd a TIFF (U nc omp res se d) de co mpre ss or are n ee de d to se e thi s p i cture . Human “throw” throw Throw thrower throwee “ball” “block” ball block Object Learning-Analysis cycle 1. Learner passes input (Utt, Sitn) Reorganize and current CxnSet to Analyzer. (Utterance, Situation) 2. Analyzer produces SemSpec Constructions and Constructional Analysis. Analyze Semantic Specification, Constructional Analysis 3. Learner updates CxnSet: Hypothesize a. Hypothesize new map. b. Reorganize CxnSet (merge or compose). c. Reinforce (based on usage). How could a mass of chemical cells produce language and thought? Will computers think and speak? How much can we know about our own experience? How do we learn new concepts? Does our language determine how we think? Is language Innate? How do children learn grammar? How did languages evolve? Why do we experience everything the way that we do? Forthcoming Book From Molecule to Metaphor: The Neural Basis of Language and Thought June 2006