A recent associative model [Miller, N.Y., Shettleworth, S.J., 2007. Learning about environmental geometry: an associative model. J. Exp. Psychol. Anim. B 33, 191-212] is an influential mathematical account of how agents behave when reorienting to previously learned locations in spatial arenas. However, it is mathematically and empirically flawed. Here, we explore these flaws, including its inability to properly predict geometric superconditioning. We trace the flaws to the model’s mathematical structure and how it handles inhibition. We then propose an operant artificial neural network model that solves these problems with inhibition and can correctly model both reorientation and superconditioning.