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Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,The Neural Basis ofThought and Language,Final,Review Session,Administrivia,Final in class next Tuesday, May 9,th,Be there on time!,Format:,closed books, closed notes,short answers, no blue books,And then youre done with the course!,The Second Half,Cognition and Language,Computation,Structured Connectionism,Computational Neurobiology,Biology,Midterm,Final,abstraction,Motor Control,Metaphor,Grammar,Bailey Model,KARMA,ECG,SHRUTI,Bayesian Model of HSP,Bayes Nets,Overview,Bailey Model,feature structures,Bayesian model merging,recruitment learning,KARMA,X-schema, frames,aspect,event-structure metaphor,inference,Grammar Learning,parsing,construction grammar,learning algorithm,SHRUTI,FrameNet,Bayesian Model of Human Sentence Processing,Full Circle,Neural System & Development,Motor Control & Visual System,Spatial Relation,Psycholinguistics Experiments,Metaphor,Grammar,Verbs & Spatial Relation,Embodied,Representation,Structured,Connectionism,Probabilistic,algorithms,ConvergingConstraints,Q & A,How can we capture the difference between,“Harry walked into the cafe.”,“Harry is walking into the cafe.”,“Harry walked into the wall.”,Analysis Process,Utterance,Simulation,Belief State,General Knowledge,Constructions,Semantic,Specification,“Harry walked into the caf.”,The INTO construction,construction INTO,subcase of Spatial-Relation,form,self,f,.orth,“into”,meaning: Trajector-Landmark,evokes Container as cont,evokes Source-Path-Goal as spg,trajector ,landmark cont,cont.interior ,cont.exterior ,The Spatial-Phrase construction,construction S,PATIAL-,P,HRASE,constructional,constituents,sr : Spatial-Relation,lm : Ref-Expr,form,sr,f,before,lm,f,meaning,sr,m,.landmark,lm,m,The Directed-Motion construction,construction DIRECTED-MOTION,constructional,constituents,a : Ref-Exp,m: Motion-Verb,p : Spatial-Phrase,form,a,f,before,m,f,m,f,before,p,f,meaning,evokes Directed-Motion as dm,self,m,.scene dm,dm.agent a,m,dm.motion m,m,dm.path p,m,schema Directed-Motion,roles,agent : Entity,motion : Motion,path : SPG,What exactly is simulation?,Belief update plus X-schema execution,hungry,meeting,cafe,time of day,ready,start,ongoing,finish,done,iterate,WALK,at goal,“Harry walked into the caf.”,ready,walk,done,walker=Harry,goal=cafe,Analysis Process,Utterance,Simulation,Belief State,General Knowledge,Constructions,Semantic,Specification,“Harry is walking to the caf.”,“Harry is walking to the caf.”,ready,start,ongoing,finish,done,iterate,abort,cancelled,interrupt,resume,suspended,WALK,walker=Harry,goal=cafe,Analysis Process,Utterance,Simulation,Belief State,General Knowledge,Constructions,Semantic,Specification,“Harry has walked into the wall.”,Perhaps a different sense of INTO?,construction INTO,subcase of spatial-prep,form,self,f,.orth,“into”,meaning,evokes Trajector-Landmark as tl,evokes Container as cont,evokes Source-Path-Goal as spg, cont,cont.interior ,cont.exterior ,construction INTO,subcase of spatial-prep,form,self,f,.orth,“into”,meaning,evokes Trajector-Landmark as tl,evokes Impact as im,evokes Source-Path-Goal as spg,im.obj1 ,im.obj2 ,“Harry has walked into the wall.”,ready,start,ongoing,finish,done,iterate,abort,cancelled,interrupt,resume,suspended,WALK,walker=Harry,goal=wall,Map down to timeline,S,R,E,ready,start,ongoing,finish,done,consequence,further questions?,What about,“Harry walked into trouble”,or for stronger emphasis,“Harry walked into trouble, eyes wide open.”,Metaphors,metaphors are mappings from a,source domain,to a,target domain,metaphor maps specify the,correlation,between source domain entities / relation and target domain entities / relation,they also allow,inference,to transfer from source domain to target domain (possibly, but less frequently, vice versa), is ,Event Structure Metaphor,Target Domain: event structure,Source Domain: physical space,States are Locations,Changes are Movements,Causes are Forces,Causation is Forced Movement,Actions are Self-propelled Movements,Purposes are Destinations,Means are Paths,Difficulties are Impediments to Motion,External Events are Large, Moving Objects,Long-term Purposeful Activities are Journeys,KARMA,DBN to represent target domain knowledge,Metaphor maps link target and source domain,X-schema to represent source domain knowledge,Metaphor Maps,map,entities and objects,between embodied and abstract domains,invariantly map the,aspect,of the embodied domain event onto the target domain,by setting the evidence for the status variable based on controller state (event structure metaphor),project x-schema,parameters,onto the target domain,further questions?,How do you learn,the meanings of spatial relations,the meanings of verbs,the metaphors, and,the constructions?,How do you learn,the meanings of spatial relations,the meanings of verbs,the metaphors, and,the constructions?,Thats the Regier model.,(first half of semester),How do you learn,the meanings of spatial relations,the meanings of verbs,the metaphors, and,the constructions?,VerbLearn,schema,elbow jnt,posture,accel,slide,extend,palm,6,schema,elbow jnt,posture,accel,slide,extend,palm,8,schema,elbow jnt,posture,accel,slide 0.9,extend 0.9,palm 0.9,6,data #1,data #2,data #3,data #4,schema,elbow jnt,posture,accel,depress 0.9,fixed 0.9,index 0.9,2,schema,elbow jnt,posture,accel,slide 0.9,extend 0.9,palm 0.9,6 - 8,schema,elbow jnt,posture,slide 0.9,extend 0.9,palm 0.7,grasp 0.3,schema,elbow jnt,posture,accel,depress,fixed,index,2,schema,elbow jnt,posture,accel,slide,extend,grasp,2,Computational Details,complexity of model + ability to explain data,maximum a posteriori (MAP) hypothesis,how likely is the data given this model?,penalize complex models those with too many word senses,wants the best model given data,How do you learn,the meanings of spatial relations,the meanings of verbs,the metaphors, and,the constructions?,conflation hypothesis,(primary metaphors),How do you learn,the meanings of spatial relations,the meanings of verbs,the metaphors, and,the constructions?,construction learning,Acquisition,Reorganize,Hypothesize,Production,Utterance,(Comm. Intent, Situation),Generate,Constructions,(Utterance, Situation),Analysis,Comprehension,Analyze,Partial,Usage-based Language Learning,Main Learning Loop,while available and cost stoppingCriterion,analysis = analyzeAndResolve(utterance, situation, currentGrammar);,newCxns = hypothesize(analysis);,if cost(currentGrammar + newCxns) cost(currentGrammar),addNewCxns(newCxns);,if (re-oganize = true) / frequency depends on learning parameter,reorganizeCxns();,Three ways to get new constructions,Relational mapping,throw,the,ball,Merging,throw,the,block,throw,ing the,ball,Composing,throw,the,ball,ball off,you,throw,the,ball off,THROW BALL OFF,THROW OBJECT,THROW BALL,Minimum Description Length,Choose grammar G to minimize,cost(G|D):,cost(G|D) =, size(G) + complexity(D|G),Approximates Bayesian learning; cost(G|D) posterior probability P(G|D),Size of grammar,= size(G) 1/prior P(G),favor fewer/smaller constructions/roles; isomorphic mappings,Complexity of data given grammar, 1/likelihood P(D|G),favor simpler analyses(fewer, more likely constructions),based on derivation length + score of derivation,further questions?,Connectionist Representation,How can entities and relations be represented at the structured connectionist level?,or,How can we represent,Harry walked to the caf,in a connectionist model?,SHRUTI,entity, type, and predicate,focal clusters,An “entity” is a,phase,in the rhythmic activity.,Bindings,are synchronous firings of,role,and,entity,cells,Rules,are interconnection patterns mediated by coincidence detector circuits that allow selective propagation of activity,An episode of reflexive processing is a,transient,propagation of,rhythmic,activity,asserting that walk(Harry, caf),Harry fires in phase with agent role,cafe fires in phase with goal role,+,-,?,agt,goal,+e,+v,?e,?v,+,?,walk,cafe,Harry,type,entity,predicate,“Harry walked to the caf.”,asserting that walk(Harry, caf),Harry fires in phase with agent role,cafe fires in phase with goal role,+,-,?,agt,goal,+e,+v,?e,?v,+,?,walk,cafe,Harry,type,entity,predicate,“Harry walked to the caf.”,Activation Trace for walk(Harry, caf),+: Harry,+: walk,+e: cafe,walk-agt,walk-goal,1,2,3,4,further questions?,Human Sentence Processing,Can we use any of the mechanisms we just discussed,to predict reaction time / behavior,when human subjects read sentences?,Good and Bad News,Bad news:,No, not as it is.,ECG, the analysis process and simulation process are represented at a higher computational level of abstraction than human sentence processing (lacks timing information, requirement on cognitive capacity, etc),Good news:,we can construct bayesian model of human sentence processing behavior borrowing the same insights,Bayesian Model of Sentence Processing,Do you wait for sentence boundaries to interpret the meaning of a sentence? No!,As words come in, we construct,partial meaning representation,some candidate interpretations if ambiguous,expectation for the next words,Model,Probability of each interpretation given words seen,Stochastic CFGs, N-Grams, Lexical valence probabilities,SCFG + N-gram,Reduced Relative,Main Verb,S,NP,VP,D,N,VBN,The,cop,arrested,the,detective,S,NP,VP,NP,VP,D,N,VBD,PP,The,cop,arrested,by,Stochastic CFG,SCFG + N-gram,Main Verb,Reduced Relative,S,NP,VP,D,N,VBN,The,cop,arrested,the,detective,S,NP,VP,NP,VP,D,N,VBD,PP,The,cop,arrested,by,N-Gram,SCFG + N-gram,Main Verb,Reduced Relative,S,NP,VP,D,N,VBN,The,cop,arrested,the,detective,S,NP,VP,NP,VP,D,N,VBD,PP,The,cop,arrested,by,Different,Interpretations,Predicting effects on reading time,Probability predicts human disambiguation,Increase in reading time because of.,Limited Parallelism,Memory limitations cause correct interpretation to be pruned,The horse raced past the barn fell,Attention,Demotion of interpretation in attentional focus,Expectation,Unexpected words,Open for questions,
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