It is common in machine-learning research today for scientists to design and train models to perform cognitive capacities, such as object classification, reinforcement learning, navigation and more. Neuroscientists compare the processes of these models with neuronal activity, with the purpose of learning about computations in the brain. These machine-learning models are constrained only by the task they must perform. Therefore, it is a worthwhile scientific finding that the workings of these models are similar to neuronal activity, as several prominent papers reported. This is a promising method to understanding cognition. However, I argue that, to the extent that this method’s aim is to explain how cognitive capacities are performed, it is likely to succeed only when the capacities modelled with machine learning algorithms are the result of a distinct evolutionary or developmental process
