Egocentric value maps of the near-body environment

Body-part centric response fields are pervasive: they are observed in single neurons, fMRI, EEG, and multiple behavioural measures. This prevalence across scales and measures makes them excellent candidates for studying systems-level neuroscience. Nonetheless, they remain poorly understood because we lack a unifying formal explanation of their origins and role in wider brain function. Here, we provide such explanation. We use reinforcement learning to analytically explain the existence of body-part centric receptive fields, also known as peripersonal field. We then simulate multiple experimental findings considered foundational in the peripersonal space literature. Our results demonstrate that peripersonal fields naturally arise from two simple and plausible assumptions about living agents: 1) they experience reward when they contact objects in the environment, and 2) they act to maximise reward. These simple assumptions are enough to explain empirical findings on stimulus kinematics, tool use, valence, and network-architecture. Our explanation provides further insight. First, it offers multiple empirically testable predictions. Second, it offers a formal description of the notion that the world-agent state is encoded in parieto-premotor cortices, using motor primitives: peripersonal fields provide building blocks that together create a short-term model of the world near the agent in terms of its future states; a successor representation. This short-term, close-range egocentric peripersonal map is analogous to the long-term, long-range allocentric spatial map of place and grid cells, which underlie locomotion and navigation to reach distant objects. Together, these allocentric and egocentric maps allow efficient interactions with a changing environment across multiple spatial and temporal scales. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes