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Journal Article

A model for learning structured representations of similarity and relative magnitude from experience


Martin,  Andrea E.
Language and Computation in Neural Systems, MPI for Psycholinguistics, Max Planck Society;
FC Donders Centre for Cognitive Neuroimaging , External Organizations;

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Doumas, L. A. A., & Martin, A. E. (2021). A model for learning structured representations of similarity and relative magnitude from experience. Current Opinion in Behavioral Sciences, 37, 158-166. doi:10.1016/j.cobeha.2021.01.001.

Cite as: https://hdl.handle.net/21.11116/0000-0007-B106-A
How a system represents information tightly constrains the kinds of problems it can solve. Humans routinely solve problems that appear to require abstract representations of stimulus properties and relations. How we acquire such representations has central importance in an account of human cognition. We briefly describe a theory of how a system can learn invariant responses to instances of similarity and relative magnitude, and how structured, relational representations can be learned from initially unstructured inputs. Two operations, comparing distributed representations and learning from the concomitant network dynamics in time, underpin the ability to learn these representations and to respond to invariance in the environment. Comparing analog representations of absolute magnitude produces invariant signals that carry information about similarity and relative magnitude. We describe how a system can then use this information to bootstrap learning structured (i.e., symbolic) concepts of relative magnitude from experience without assuming such representations a priori.