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

TL;DR

This paper proposes a normative framework analyzing the tradeoff between learning speed and multitasking capacity in cognitive architectures, suggesting that humans prioritize quick learning over multitasking ability due to shared representations.

Contribution

It introduces a Bayesian model and a mathematical framework to explain how and why learning efficiency is favored over multitasking in cognitive systems.

Findings

Agents prefer shared representations for faster learning

Tradeoff leads to reduced multitasking capacity

Framework applies across diverse task environments

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 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.