The nervous system uses internal models to achieve sensory integration.

All linear accelerometers measure gravito-inertial force, which is the sum of gravitational force (tilt) and inertial force due to linear acceleration (translation). Neural strategies must exist to elicit tilt and translation responses from this ambiguous cue. To investigate these neural processes, we developed a model of human responses and simulated a number of motion paradigms used to investigate this tilt/translation ambiguity. In this model, the separation of GIF into neural estimates of gravity and linear acceleration is accomplished via an internal model made up of 3 principal components: 1) the influence of rotational cues (e.g., semicircular canals) on the neural representation of gravity, 2) the resolution of gravito-inertial force into neural representations of gravity and linear acceleration, and 3) the neural representation of the dynamics of the semicircular canals. By combining these simple hypotheses within the internal model framework, the model mimics human translation and tilt responses for preliminary data from one human subject. These modeled response characteristics are consistent with preliminary data and with the hypothesis that the nervous system uses internal models to estimate tilt and translation in the presence of ambiguous sensory cues.