Discerning Quantity: Numerosity in Two Embodied Machine Learning Agents

As artificial intelligence systems continue to overcome evermore challenging tasks, researchers have suggested that the time is ripe to begin evaluating these systems along more psychologically inspired lines. This study seeks to build upon these recommendations by evaluating two machine learning models, A-Learning and Proximal Policy Optimisation, for the cognitive capability known as numerosity. In our experiment, these two models were embodied in a three-dimensional virtual environment, known as Animal-AI, and tested in a psychologically inspired numerosity experiment. In contrast to previous research, A-Learning failed to reliably express numerosity capabilities, as did Proximal Policy Optimisation. Both models displayed a tendency to overfit to the first policy that provided rewarding feedback. These results suggest that predicting the cognitive capabilities of machine learning models once embodied is non-trivial, and confounding factors such as environmental properties and perceptual processes complicate the expression of numerosity capabilities. Building on these findings, it is suggested that future researchers pay greater attention to the influence of environmental factors and perceptual mechanisms on the machine learning models they are developing, especially if such models are to be embodied in a virtual- or real-world environment.