  Artificial Intelligence (AI) has been rapidly advancing and has demonstrated
its ability to perform a wide range of cognitive tasks, including language
processing, visual recognition, and decision-making. Part of this progress is
due to LLMs (Large Language Models) like those of the GPT (Generative
Pre-Trained Transformers) family. These models are capable of exhibiting
behavior that can be perceived as intelligent. Most authors in Neuropsychology
consider intelligent behavior to depend on a number of overarching skills, or
Executive Functions (EFs), which rely on the correct functioning of neural
networks in the frontal lobes, and have developed a series of tests to evaluate
them. In this work, we raise the question of whether LLMs are developing
executive functions similar to those of humans as part of their learning, and
we evaluate the planning function and working memory of GPT using the popular
Towers of Hanoi method. Additionally, we introduce a new variant of the
classical method in order to avoid that the solutions are found in the LLM
training data (dataleakeage). Preliminary results show that LLMs generates
near-optimal solutions in Towers of Hanoi related tasks, adheres to task
constraints, and exhibits rapid planning capabilities and efficient working
memory usage, indicating a potential development of executive functions.
However, these abilities are quite limited and worse than well-trained humans
when the tasks are not known and are not part of the training data.
