Figure 7. A neural network learns the 3d Mind Model from dynamics alone.

A) Transition probabilities between 60 human mental states were used to generate a sequence of mental states with naturalistic dynamics. This sequence was used to train neural networks in Studies 3a and 3b with a singular goal: to predict the next state from the current state. B) The resulting dimensions learned by the networks were aligned with conceptual structures from the literature, including the 3d including the 3d Mind Model in Study 3a, via cross-validated Procrustes transformations. The aligned dimensions were then correlated with one another to test whether the network had learned a human-like representational space. C) The networks spontaneously learned 1d, 2d, and 3d conceptual spaces. The 3d network in Study 3a recover the conceptual dimensions of rationality, social impact, and valence from the transition dynamics. These results indicate that mental state dynamics – and the goal of predicting them – suffice to explain the structure of mental state concepts.