Abstract
It is often claimed that tools are embodied by their user, but whether the brain actually repurposes its body-based computations to perform similar tasks with tools is not known. A fundamental computation for localizing touch on the body is trilateration Here, the location of touch on a limb is computed by integrating estimates of the distance between sensory input and its boundaries (e.g., elbow and wrist of the forearm). As evidence of this computational mechanism, tactile localization on a limb is most precise near its boundaries and lowest in the middle. Here we show that the brain repurposes trilateration to localize touch on a tool, despite large differences in initial sensory input compared to touch on the body. In a large sample of participants, we found that localizing touch on a tool produced the signature of trilateration, with highest precision close to the base and tip of the tool. A computational model of trilateration provided a good fit to the observed localization behavior. To further demonstrate the computational plausibility of repurposing trilateration, we implemented it in a three-layer neural network that was based on principles of probabilistic population coding. This network determined hit location in tool-centered coordinates by using a tool's unique pattern of vibrations when contacting an object. Simulations demonstrated the expected signature of trilateration, in line with the behavioral patterns. Our results have important implications for how trilateration may be implemented by somatosensory neural populations. We conclude that trilateration is likely a fundamental spatial computation that unifies limbs and tools. Significance StatementIt is often claimed that tools are embodied by the user, but computational evidence for this claim is scarce. We show that to localize touch on a tool, the brain repurposes a fundamental computation for localizing touch on the body, trilateration A signature of trilateration is high localization precision near the boundaries of a limb and low precision in the middle. We find that localizing touch on a tool produces this signature of trilateration, which we characterize using a computational model. We further demonstrate the plausibility of embodiment by implementing trilateration within a three-layer neural network that transforms a tool's vibrations into a tool-centered spatial representation. We conclude that trilateration is a fundamental spatial computation that unifies limbs and tools.
