Although not all researchers agree on the exact bounds of scientific discovery, theory formation is clearly at the core of the domain. Relevant AI research done in scientific discovery includes Kocabas (1992); Karp (1989); Prager, Belanger, and De Mori (1989); Kulkarni and Simon (1988); and Langley et al. (1987). I consider model-based discovery to be a diagnosis and design problem. More precisely, modelbased-theory refinement can be seen as a four-step process: (1) gather data, (2) compare the data to model-based predictions, (3) identify the sources of discrepancies between the predictions and the field data, and (4) fix these discrepancies by modifying the model. The first three steps are traditionally addressed by diagnosis systems, but the fourth step requires design techniques.

The human orientation system is a complex system in which the brain merges information from a variety of sensors to help maintain a coherent interpretation of body position and movement. I designed a model of this system based on the observer theory model (OTM), which was developed by Merfeld (1990) for the orientation system of the squirrel monkey. Under this scheme, the central nervous system has an internal representation of the sensor organs and tries to minimize the error between its estimate of the sensory afferent signals and the actual afferent signals. It works iteratively until the results of the proposed experiment can be modeled.

The human orientation system is a complex system in which the brain merges information from a variety of sensors to help maintain a coherent interpretation of body position and movement. These sensors include the semicircular canals and the otolith organs located in the inner ear as well as vision and somatosensory perception. I designed a model of this system based on the observer theory model (OTM), which was developed by Merfeld (1990) for the orientation system of the squirrel monkey. Under this scheme, the central nervous system has an internal representation of the sensor organs and tries to minimize the error between its estimate of the sensory afferent signals and the actual afferent signals. As designed, MARIKA's goal is to classify the vestibular system of the subject as normal or abnormal and propose a corresponding model. It works iteratively until the results of the proposed experiment can be modeled. Additional experiments can be presented in succession to the same model.