Efficiency of learning vs. processing: Towards a normative theory of multitasking
- Yotam Sagiv, Princeton Neuroscience Institute, Princeton, New Jersey, United States
- Sebastian Musslick, Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States
- Yael Niv, Princeton Neuroscience Institute, Princeton, New Jersey, United States
- Jonathan Cohen, Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States
AbstractA 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.
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