The Neural Basis of Thought and Language Final Review Session Administrivia Final in class next Tuesday, May 9th Be there on time! Format: closed books, closed notes short answers, no blue books And then youre done with the course! The Second Half abstraction
Motor Control Bayes Nets Grammar Metaphor Cognition and Language Computation Bailey KARMA Model Structured Connectionism ECG
SHRUTI Bayesian Model of HSP Computational Neurobiology Biology Midterm Final Overview Bailey Model Grammar Learning feature structures
parsing Bayesian model merging construction grammar recruitment learning learning algorithm KARMA SHRUTI X-schema, frames FrameNet
aspect Bayesian Model of Human Sentence Processing event-structure metaphor inference Full Circle Neural System & Development Metaphor Psycholinguistics Experiments Grammar
Embodied Representation Structured Connectionism Probabilistic algorithms Converging Constraints Motor Control & Visual System Verbs & Spatial Relation Spatial Relation 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. Harry walked into the caf. Utterance Constructions General Knowledg e Belief State Analysis Process Semantic
Specificatio n Simulation The INTO construction construction INTO subcase of Spatial-Relation form selff .orth into meaning: Trajector-Landmark evokes Container as cont evokes Source-Path-Goal as spg trajector spg.trajector landmark cont cont.interior spg.goal cont.exterior spg.source The Spatial-Phrase construction
construction SPATIAL-PHRASE constructional constituents sr : Spatial-Relation lm : Ref-Expr form srf before lmf meaning srm.landmark lmm The Directed-Motion construction construction DIRECTED-MOTION constructional constituents a : Ref-Exp m: Motion-Verb p : Spatial-Phrase form af before mf
mf before pf meaning evokes Directed-Motion as dm selfm.scene dm dm.agent am dm.motion mm dm.path pm schema Directed-Motion roles agent : Entity motion : Motion path : SPG What exactly is simulation? Belief update plus X-schema execution at goal ready time
of day hungry start ongoing meeting iterate cafe WALK finish done
Harry walked into the caf. ready walker=Harry walk done goal=cafe Harry is walking to the caf. Utterance Constructions General
Knowledg e Belief State Analysis Process Semantic Specificatio n Simulation Harry is walking to the caf. suspended interrupt ready abort
walker=Harry start cancelled resume ongoing finish done iterate WALK goal=cafe
Harry has walked into the wall. Utterance Constructions General Knowledg e Belief State Analysis Process Semantic Specificatio n Simulation
Perhaps a different sense of INTO? construction INTO subcase of spatial-prep form selff .orth into meaning evokes Trajector-Landmark as tl evokes Container as cont evokes Source-Path-Goal as spg tl.trajector spg.trajector tl.landmark cont cont.interior spg.goal cont.exterior spg.source construction INTO subcase of spatial-prep form selff .orth into meaning
evokes Trajector-Landmark as tl evokes Impact as im evokes Source-Path-Goal as spg tl.trajector spg.trajector tl.landmark spg.goal im.obj1 tl.trajector im.obj2 tl.landmark Harry has walked into the wall. suspended interrupt ready abort walker=Harry start
cancelled resume ongoing finish done iterate WALK goal=wall Map down to timeline ready
start ongoing finish done consequence E S R 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
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 1. map entities and objects between embodied and abstract domains 2. 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) 3. 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 0.9 extend 0.9 palm 0.7 0.9 - 8] [6 grasp 0.3 data #1 data #2 data #3
data #4 schema elbow jnt posture accel depress 0.9 fixed 0.9 index 0.9 
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 argmax P(m | D) wants the best model given data m
argmax P( D | m) P(m) by Bayes' rule m how likely is the data given this model? penalize complex models those with too many word senses 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 Usage-based Language Learning (Utterance, Situation) Reorganiz e (Comm. Intent, Situation) Constructions Generate
Analyze PartialAnalysis Hypothesize Comprehension Acquisition Utterance Production 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 THROW < BALL Merging throw the block throwing the ball THROW < OBJECT Composing
throw the ball ball off you throw the ball off THROW < BALL < OFF 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 Harry walked to the caf. entity type Harry + +e +v cafe ?
?e ?v asserting that walk(Harry, caf) Harry fires in phase with agent role cafe fires in phase with goal role predicate + -
walk ? agt goal Harry walked to the caf. entity type Harry + +e +v cafe
? ?e ?v asserting that walk(Harry, caf) Harry fires in phase with agent role cafe fires in phase with goal role predicate + -
walk ? agt goal Activation Trace for walk(Harry, caf) +: walk walk-agt walk-goal +: Harry +e: cafe 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 S Stochastic CFG NP NP VP NP D The VP
N VBN cop arrested D the detective The VP N VBD cop arrested
PP by SCFG + N-gram Reduced Relative Main Verb S S NP NP VP NP D
The VP N VBN cop arrested D the detective N-Gram The VP N
VBD cop arrested PP by SCFG + N-gram Different Interpretations Main Verb Reduced Relative S S
NP NP VP NP D The VP N VBN cop arrested D
the detective The VP N VBD cop arrested PP by 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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