Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study

Understand

Deep neural networks (DNNs) have achieved unprecedented performance on a wide range of complex tasks, rapidly outpacing our understanding of the nature of their solutions.

• This has caused a recent surge of interest in methods for rendering modern neural systems more interpretable.
• In this work, we propose to address the interpretability problem in modern DNNs using the rich history of problem descriptions, theories and experimental methods developed by cognitive psychologists to study the human mind.
• To explore the potential value of these tools, we chose a well-established analysis from developmental psychology that explains how children learn word labels for objects, and applied that analysis to DNNs.