What deep learning can tell us about higher cognitive functions like mindreading?
This addresses the challenge of understanding complex brain functions for cognitive science and AI, but it is incremental as it builds on existing critiques without new empirical results.
The paper examines whether deep learning can elucidate the computations behind higher cognitive functions like Theory of Mind, concluding that scaling current algorithms is unlikely to achieve human-level performance.
Can deep learning (DL) guide our understanding of computations happening in biological brain? We will first briefly consider how DL has contributed to the research on visual object recognition. In the main part we will assess whether DL could also help us to clarify the computations underlying higher cognitive functions such as Theory of Mind. In addition, we will compare the objectives and learning signals of brains and machines, leading us to conclude that simply scaling up the current DL algorithms will most likely not lead to human level Theory of Mind.