Analyzing internal world models of humans, animals and AI

Internal world models: making predictions based on experience

Humans and animals abstract general laws from everyday experiences. They develop internal models of the world that help them to find their way in unfamiliar contexts. Based on the abstracted models, they can make predictions in new situations and behave accordingly. For example, knowing comparable cities that also have a city centre, pedestrian zones and public transport can help them find their way around a foreign city. Even in social contexts such as dinner in a restaurant, comparable experiences help you to behave appropriately.

World models become more tangible with the help of a new formal description

In order to formalise internal world models across species, the researchers distinguish in their current publication between three abstract spaces that are intertwined: the task space, the neural space and the conceptual space. The task space encompasses everything that an individual experiences. The neuronal space describes the various measurable states of the brain, from the molecular level to the activity of individual neurons through to the activity of entire brain areas. The latter is visualised with the help of a functional magnetic resonance imaging (fMRI) scanner, for example, or measured using techniques such as high-density electrodes or calcium imaging. The equivalent of neural space in AI is the activity of the nodes within the corresponding artificial neural network. The conceptual space consists of pairs of states of the task space and the neural space. These pairs thus represent the status of an individual, which links internal processes with external influences. The current state is constantly changing by transitioning to the next state with a certain probability. These combinations of an individual's experiences on the one hand and the corresponding brain activity on the other, as well as the dynamic transitions, make the individual internal world models scientifically tangible.

Eliminate deficits in internal world models

With the help of the formalised view scientists can now analyse internal world models across disciplinary boundaries and discuss how they arise and evolve. Findings from research on humans and animals, for example, should help to improve AI. For example, current AI systems are not yet able to check the plausibility of their predictions. Even large language models such as ChatGPT have so far only functioned as pattern recognition machines without the ability to actually plan. However, planning is important in order to play through and correct strategies in unknown situations before they are implemented and possibly cause damage. Researchers also suspect that deficits in internal world models could be the cause of some mental illnesses such as depression or schizophrenia. A deeper understanding of world models could help to use medication and therapy in a more targeted way.