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A striking limitation of human cognition is our inability to execute sometasks simultaneously. Recent work suggests that such limitations can arise froma fundamental tradeoff in network architectures that is driven by the sharingof representations between tasks: sharing promotes quicker learning, at theexpense of interference while multitasking. From this perspective, multitaskingfailures might reflect a preference for learning efficiency over multitaskingcapability. We explore this hypothesis by formulating an ideal Bayesian agentthat maximizes expected reward by learning either shared or separaterepresentations for a task set. We investigate the agent's behavior and showthat 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 rationalchoices between learning speed and processing efficiency can be examined for avariety of different task environments.

Efficiency of learning vs. processing: Towards a normative theory of multitasking