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

Understand

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 tradeoff 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 multitasking 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.