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REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

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Page 1: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

REMERGE: A new approach to the neural basis of generalization and

memory-based inference

Dharshan Kumaran, UCLJay McClelland, Stanford University

Page 2: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Proposed Architecture for the Organization of Semantic Memory

McClelland, McNaughton & O’Reilly, 1995

colorform

motion

action

valance

Temporal pole

name

Medial Temporal Lobe

Page 3: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Two Questions

• If extraction of generalizations depends on gradual learning, how do we form generalizations and inferences shortly after initial learning?

• Why do some studies find evidence consistent with the view that an intact MTL facilitates certain types of generalization in memory?

Page 4: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Relational Theory of Memory (Eichenbaum & Cohen)

• Proposes that elements of related memories become linked within the same memory trace, and that the formation of such linkages is a critical function of the MTL.

Page 5: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

REMERGE: Recurrence and Episodic Memory Results in Generalization

• Holds that several MTL based item representations may work together through recurrent activation

• Draws on classic exemplar models (Medin & Shaffer, 1978; Nosofsky, 1984)

• Extends these models by allowing similarity between stored items to influence performance, independent of direct activation by the probe (McClelland, 1981)

• Demonstrates the strong dependence of some forms of generalization and inference on the strength of learning for trained items

Page 6: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Phenomena Considered

• Benchmark Simulations– Categorization– Recognition memory

• Acquired Equivalence• Associative Chaining– In paired associate learning– In hippocampal reactivation during sleep

• Transitive Inference– Effects of increasing study– Effects of sleep

Page 7: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Acquired Equivalence(Shohamy & Wagner, 2008)

• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4

• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?

Page 8: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

F1 S1 F2 S2 F3 S3 F4 S4

Acquired Equivalence(Shohamy & Wagner, 2008)

• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4

• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?

Page 9: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

F1 S1 F2 S2 F3 S3 F4 S4

Acquired Equivalence(Shohamy & Wagner, 2008) S1 S2 S3 S4

• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4

• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?

Page 10: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

F1 S1 F2 S2 F3 S3 F4 S4

Acquired Equivalence(Shohamy & Wagner, 2008) S1 S2 S3 S4

• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4

• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?

Page 11: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Acquired Equivalence(Shohamy & Wagner, 2008)

• Study:– F1-S1; – F3-S3;– F2-S1; – F2-S2;– F4-S3; – F4-S4

• Test:– Premise: F1: S1 or S3?– Inference: F1: S2 or S4?

Page 12: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Associative Chaining• Study:

– AB, XY– BC, YZ

• Test:– A: B or Y– A: C or Z

A B C X Y Z

Page 13: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Hippocampal Reactivation After Maze Exploration

Replays in Remerge:

Forward: 51%Backward: 31%Crossed: 18%Disjoint: <1%

Page 14: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Growth in Generalization with Increasing Premise Strength

Page 15: REMERGE: A new approach to the neural basis of generalization and memory-based inference Dharshan Kumaran, UCL Jay McClelland, Stanford University

Discussion• As we’ve known for quite some time

– Generalization and Inference can be supported by exemplar models• Should we, then, simply abandon the complementary learning

theory, and just make it exemplars all the way down?• I think not –

– Cortical learning supports changes in the ‘features’ that serve as the basis for exemplar learning

– And clearly, retrograde amnesia studies support = an MTL basis for recent memory= a neocortical basis for remote memory

• A future challenge is to develop an fully integrated neuro-computational theory of memory integrating MTL and neocortical influences