Multitask Learning over Shared Subspaces

Abstract

This paper uses constructs from the field of multitask machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach. We found, as hypothesised, that subject performance was significantly higher on the second task if it shared the same subspace as the first, an advantage that played out most strongly at the beginning of the second task. Additionally, accuracy was positively correlated over subjects learning same-subspace tasks but was not correlated for those learning different-subspace tasks. These results, and other aspects of learning dynamics, were compared to the behaviour of a Neural Network model trained using sequential Bayesian inference. Human performance was found to be consistent with a Soft Parameter Sharing variant of this model that constrained representations to be similar among tasks but only when this aided learning. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning.

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