1. Emergent Semantics: Meaning and Metaphor Jay McClelland Department of Psychology and Center for Mind, Brain, and Computation Stanford University

2. Representation is a pattern of activation distributed over neurons within and across brain areas. Bidirectional propagation of activation underlies the ability to bring these representations to mind from given inputs. The knowledge underlying propagation of activation is in the connections. Experience affects our knowledge representations through a gradual connection adjustment process language The Parallel Distributed Processing Approach to Semantic Cognition

3. Distributed Representations: and Overlapping Patterns for Related Concepts dog goat hammer

4. Responses of Four Neurons to Face and Non-Face Stimuli

5. Emergence of Meaning and Metaphor Learned distributed representations that capture important aspects of meaning emerge through a gradual learning process in simple connectionist networks Metaphor arises naturally as a byproduct of learning information in homologous domains in models of this type

6. Emergence of Meaning: Differentiation, Domain-Specificity, and Reorganization

8. The Rumelhart Model

9. The Training Data: All propositions true of items at the bottom level of the tree, e.g.: Robin can {grow, move, fly}

10. Target output for ‘robin can’ input

11. ajaj aiai w ij net i =  a j w ij w ki Forward Propagation of Activation

12.  k ~ (t k -a k ) w ij  i ~   k w ki w ki ajaj Back Propagation of Error () Error-correcting learning: At the output layer:w ki =  k a i At the prior layer: w ij =  j a j … aiai

15. ExperienceExperience Early Later Later Still

16. Why Does the Model Show Progressive Differentiation? Learning is sensitive to patterns of coherent covariation. Coherent Covariation: –The tendency for properties of objects to co-vary in clusters

17. Waves of differentiation reflect coherent covariation of properties across items. Patterns of coherent covariation are reflected in the principal components of the property covariance matrix. Figure shows attribute loadings on the first three principal components: –1. Plants vs. animals –2. Birds vs. fish –3. Trees vs. flowers Same color = features covary in component Diff color = anti-covarying features What Drives Progressive Differentiation?

18. Sensitivity to Coherence Requires Convergence A A A

19. Conceptual Reorganization (Carey, 1985) Carey demonstrated that young children ‘discover’ the unity of plants and animals as living things with many shared properties only around the age of 10. She suggested that the coalescence of the concept of living thing depends on learning about diverse aspects of plants and animals including –Nature of life sustaining processes –What it means to be dead vs. alive –Reproductive properties Can reorganization occur in a connectionist net?

20. Conceptual Reorganization in the Model Suppose superficial appearance information, which is not coherent with much else, is always available… And there is a pattern of coherent covariation across information that is contingently available in different contexts. The model forms initial representations based on superficial appearances. Later, it discovers the shared structure that cuts across the different contexts, reorganizing its representations.

22. Organization of Conceptual Knowledge Early and Late in Development

23. Proposed Architecture for the Organization of Semantic Memory color form motion action valance Temporal pole name Medial Temporal Lobe

24. Metaphor in Connectionist Models of Semantics By metaphor I mean: the application of a relation learned in one domain to a novel situation in another

25. Hinton’s Family Tree Network Person 1 Relation Person 2

27. Understanding Via Metaphor in the Family Trees Network Marco’s father is Pierro. Who is James’s father? Christopher’s daughter is Victoria. Who is Roberto’s daughter?

28. Emergence of Meaning and Metaphor Learned distributed representations that capture important aspects of meaning emerge through a gradual learning process in simple connectionist networks Metaphor arises naturally as a byproduct of learning information in homologous domains in models of this type