A Neurally Plausible model of Reasoning.ppt
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1、A Neurally Plausible model of Reasoning, Lokendra Shastri ICSI, Berkeley,Lokendra Shastri International Computer Science Institute Berkeley, CA 94704,Five levels of Neural Theory of Language,Cognition and Language,Computation,Structured Connectionism,Computational Neurobiology,Biology,SHRUTI,abstrac
2、tion,“John fell in the hallway. Tom had cleaned it. He got hurt.”,Tom had cleaned the hallway.The hallway floor was wet.John slipped and fell on the wet floor.John got hurt as a result of the fall.,such inferences establish referential and causal coherence.,Reflexive Reasoning,UbiquitousAutomatic, e
3、ffortlessExtremely fast - almost a reflex response of our cognitive apparatus,Reflexive Reasoning,Not all reasoning is reflexive Contrast with reflective reasoningdeliberateinvolves explicit consideration of alternativesrequire props (paper and pencil)e.g., solving logic puzzles differential equatio
4、ns,How fast is reflexive reasoning?,We understand language at the rate of 150-400 words per minuteReflexive inferences required for establishing inferential and causal coherence are drawn within a few hundred millisecond, Lokendra Shastri ICSI, Berkeley,How can a system of slow and simple neuron-lik
5、e elements,encode a large body of semantic and episodic knowledge and yet perform a wide range of inferences within a few hundred milliseconds?, Lokendra Shastri ICSI, Berkeley,Characterization of reflexive reasoning?,What can and cannot be inferred via reflexive processes?, Lokendra Shastri ICSI, B
6、erkeley,Shruti,http:/www.icsi.berkeley.edu/shastri/shrutiLokendra Shastri V. Ajjanagadde (Penn, ex-graduate student) Carter Wendelken (UCB, ex-graduate student) D. Mani (Penn, ex-graduate student) D.J. Grannes (UCB, ex-graduate student) Jerry Hobbs, USC/ISI (abductive reasoning) Marvin Cohen, CTI (m
7、etacognition; belief and utility) Bryan Thompson, CTI (metacognition; belief and utility),Lokendra Shastri ICSI, Berkeley,Reflexive Reasoning representational and processing issues,Activation-based (dynamic) representation of events and situations (relational instances),Dynamic representation of rel
8、ational instances,“John gave Mary a book”,giver: John recipient: Mary given-object: a-book,*,Reflexive Reasoning,Expressing dynamic bindings Systematically propagating dynamic bindings Computing coherent explanations and predictions evidence combination dynamic instantiation and unification of entit
9、ies,Requires compatible neural mechanisms for:,All of the above must happen rapidly,Learning,one-shot learning of events and situations (episodic memory) gradual/incremental learning of concepts, relations, schemas, and causal structures,Relation focal-cluster,+ - ? fall-pat fall-loc,FALL,Entity, ca
10、tegory and relation focal-clusters,+ - ? fall-pat fall-loc,FALL,Entity, category and relation focal-clusters,+ - ? fall-pat fall-loc,FALL,Functional nodes in a focal-cluster collector (+/-), enabler (?), and role nodes may be situated in different brain region,Focal-cluster of a relational schema,FA
11、LL,focal-clusters of motor schemas associated with fall,focal-clusters of lexical know- ledge associated with fall,focal-clusters of perceptual schemas and sensory representations associated with fall,focal-clusters of other relational schemas causally related to fall,episodic memories of fall event
12、s,Focal-clusters,Nodes in the fall focal-cluster become active whenperceiving a fall event remembering a fall event understanding a sentence about a fall event experiencing a fall event,A focal-cluster is like a “supra-mirror” cluster,Focal-cluster of an entity,John,+ ?,focal-clusters of motor schem
13、as associated with John,focal-clusters of lexical know- ledge associated with John,focal-clusters of perceptual schemas and sensory representations associated with John,focal-clusters of other entities and categories semantically related to John,episodic memories where John is one of the role-filler
14、s,+ - ? fall-pat fall-loc,Fall,+ ?,+ ?,Hallway,John,“John fell in the hallway”,+ - ? fall-pat fall-loc,Fall,+ ?,+ ?,Hallway,John,“John fell in the hallway”,+ - ? fall-pat fall-loc,Fall,+:Fall,+:John,fall-pat,fall-loc,+:Hallway,“John fell in the hallway”,Encoding “slip = fall” in Shruti,SLIP,FALL,+ -
15、 ? fall-pat fall-loc,Such rules are learned gradually via observations, by being told ,“John slipped in the hallway”,Slip,+ - ? slip-pat slip-loc,Fall,+ - ? fall-pat fall-loc,mediator,r2,r1,?,+, “John fell in the hallway”,A Metaphor for Reasoning,An episode of reflexive reasoning is a transient prop
16、agation of rhythmic activity Each entity involved in this reasoning episode is a phase in this rhythmic activity Bindings are synchronous firings of cell clusters Rules are interconnections between cell-clusters that support propagation of synchronous activity,Focal-clusters with intra-cluster links
17、,John,+ ?,+ - ? fall-pat fall-loc,FALL,+e +v ?v ?e,Person,Shruti always seeks explanations,Encoding “slip = fall” in Shruti,SLIP,FALL,+ - ? fall-pat fall-loc,+ ? r1 r2,mediator,Linking focal-clusters of types and entities,John,+e +v ?v ?e,+e +v ?v ?e,+e +v ?v ?e,Hallway,Location,Man,Person,Focal-clu
18、sters and context-sensitive priors (T-facts),+ ?,+ - ? fall-pat fall-loc,+e +v ?v ?e,*,context-sensitive priors,*,* cortical circuits,entities and types,entities and types,John,FALL,Person,Focal-clusters and episodic memories (E-facts),+ ?,+e +v ?v ?e,episodic memories,e-memories,e-memories,from rol
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