Social creatures can infer the mental states of others. This cognitive ability, called mentalizing, can be considered a process of inferring others’ hidden states behind their actions from partially observable sensory information. The purpose of this review is to propose the computational mechanisms of mentalizing and review the neural substrates that might underlie each computational process. In fact, inference about hidden states is a ubiquitous task in many sensory systems, and this can be achieved under the predictive coding framework in which the brain probabilistically estimates a latent state that most likely causes the observed sensory events by minimizing errors between the actual and predicted sensory signals. We argue that what might be unique to mentalizing is not merely the representation of others’ internal states in an arbitrary latent space but also the capacity to represent them in a mental space that one can experience subjectively. This function can solve the so-called symbol grounding problem. Further, the use of symbol grounding makes the inference system efficient and reliable by reducing the cost to learn de novo the latent representations of others’ mental states. On the basis of a preliminary simulation, we demonstrate that an artificial mentalizing system with a symbol grounding function performs better in predicting the actions of virtual agents than a pure Bayesian observer without the symbol grounding function. Emerging novel paradigms integrating artificial and biological neural networks will enable a better understanding of the neural algorithms and computational processes for complex social cognition including mentalizing.
Kaneko et al. (Wed,) studied this question.