“In this paper, a simple neural network model will be used to simulate the development of children’s ability to solve equivalence problems. The model treats algebraic problem solving as an acquired skill, emerging slowly from practice solving example problems… In summary, a recurrent neural network can serve as a useful model of how we learn mathematical equivalence… We investigated the strategies of the model, and found that it adopted an add-all strategy like many children do before mastering equivalence problems” A neural network model of learning mathematical equivalence Kevin W. Mickey, James L. McClelland

“We explore a recurrent neural network model of counting based on the differentiable recurrent attentional model of Gregor et al. (2015). Our results reveal that the model can learn to count the number of items in a display, pointing to each of the items in turn and producing the next item in the count sequence at each step, then saying ‘done’ when there are no more blobs to count. The model thus demonstrates that the ability to learn to count does not depend on special knowledge relevant to the counting task. We find that the model’s ability to count depends on how well it has learned to point to each successive item in the array, underscoring the importance of coordination of the visuospatial act of pointing with the recitation of the count list… Yet the errors that it makes have similarities with the patterns seen in human children’s counting errors, consistent with idea that children rely on graded and somewhat variable mechanisms similar to our neural networks.” Can a Recurrent Neural Network Learn to Count Things? Mengting Fang, Zhenglong Zhou, Sharon Y. Chen, James L. McClelland

“Over the course of development, humans learn myriad facts about items in the world, and naturally group these items into useful categories and structures. This semantic knowledge is essential for diverse behaviors and inferences in adulthood. How is this richly structured semantic knowledge acquired, organized, deployed, and represented by neuronal networks in the brain? We address this question by studying how the nonlinear learning dynamics of deep linear networks acquires information about complex environmental structures. Our results show that this deep learning dynamics can self-organize emergent hidden representations in a manner that recapitulates many empirical phenomena in human semantic development. Such deep networks thus provide a mathematically tractable window into the development of internal neural representations through experience.” “In addition to matching neural similarity patterns across subjects, experiments using fMRI and single-unit responses have also documented a correspondence between neural similarity patterns and behavioral similarity patterns (21).” A mathematical theory of semantic development in deep neural networks Andrew M. Saxe, James L. McClelland, and Surya Ganguli

I personally think there are plenty of examples out there in neuroscience and computer science papers let alone what other fields are starting to discover with the use of AI. In my opinion it should be of no surprise and quite clear how replicating a mechanism of self-adapting logic would create behaviours that we can find directly within ourselves.

Let me know if this is enough to prove my point, but I think I’m tired of reading papers for a bit.