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

arXiv: https://arxiv.org/abs/2007.03124 · HTML: https://mlsanity.com/paper/2007.03124/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Neural population geometry and optimal coding of tasks with shared latent structure](https://mlsanity.com/paper/2402.16770/) (2024)
- [Meta-Learning Strategies through Value Maximization in Neural Networks](https://mlsanity.com/paper/2310.19919/) (2023)
- [A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents](https://mlsanity.com/paper/2510.22052/) (2025)

## Abstract (arXiv)

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. 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.