@inproceedings{cb8d8ade56ed473588063d2e775e33ea,
title = "Efficiency of learning vs. processing: Towards a normative theory of multitasking",
abstract = "A striking limitation of human cognition is our inability to execute some tasks simultaneously. Recent work suggests that such limitations can arise from a fundamental trade-off in network architectures that is driven by the sharing of representations between tasks: sharing promotes quicker learning, at the expense of interference while multitasking. From this perspective, multitasking failures might reflect a preference for learning efficiency over parallel processing capability. We explore this hypothesis by formulating an ideal Bayesian agent that maximizes expected reward by learning either shared or separate representations for a task set. We investigate the agent's behavior and show that over a large space of parameters the agent sacrifices long-run optimality (higher multitasking capacity) for short-term reward (faster learning). Furthermore, we construct a general mathematical framework in which rational choices between learning speed and processing efficiency can be examined for a variety of different task environments.",
keywords = "Bayesian inference, capacity constraints, cognitive control, multitasking",
author = "Yotam Sagiv and Sebastian Musslick and Yael Niv and Cohen, {Jonathan D.}",
note = "Publisher Copyright: {\textcopyright} 2018 Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. All rights reserved.; 40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018 ; Conference date: 25-07-2018 Through 28-07-2018",
year = "2018",
language = "English (US)",
series = "Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018",
publisher = "The Cognitive Science Society",
pages = "1002--1007",
booktitle = "Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018",
}