  Large Language Models (LLMs) are capable of displaying a wide range of
abilities that are not directly connected with the task for which they are
trained: predicting the next words of human-written texts. In this article, I
review recent research investigating the cognitive abilities developed by LLMs
and their relation to human cognition. I discuss the nature of the indirect
process that leads to the acquisition of these cognitive abilities, their
relation to other indirect processes, and the implications for the acquisition
of integrated abilities. Moreover, I propose the factors that enable the
development of abilities that are related only very indirectly to the proximal
objective of the training task. Finally, I discuss whether the full set of
capabilities that LLMs could possibly develop is predictable.
