Multitasking Capability Versus Learning Efficiency in Neural Network Architectures

Sebastian Musslick, Andrew M. Saxe, Kayhan Özcimder, Biswadip Dey, Greg Henselman, Jonathan D. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

One of the most salient and well-recognized features of human goal-directed behavior is our limited ability to conduct multiple demanding tasks at once. Previous work has identified overlap between task processing pathways as a limiting factor for multitasking performance in neural architectures. This raises an important question: insofar as shared representation between tasks introduces the risk of cross-talk and thereby limitations in multitasking, why would the brain prefer shared task representations over separate representations across tasks? We seek to answer this question by introducing formal considerations and neural network simulations in which we contrast the multitasking limitations that shared task representations incur with their benefits for task learning. Our results suggest that neural network architectures face a fundamental tradeoff between learning efficiency and multitasking performance in environments with shared structure between tasks.

Original languageEnglish (US)
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages829-834
Number of pages6
ISBN (Electronic)9780991196760
StatePublished - 2017
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: Jul 26 2017Jul 29 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period7/26/177/29/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • capacity constraint
  • cognitive control
  • learning
  • multitasking
  • neural networks

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