Similar arguments can be made for attention (L. Whiteley and M. Sahani, 2008, COSYNE, abstract). The notion of suboptimal inference also applies to sensorimotor transformations. To reach for an object in the world, we need to know its position. At the level of the retina, position is specified in eye-centered coordinates but, to be usable to the arm, it must be recomputed in a frame of reference centered on the hand, a computation known as a coordinate transformation. Sober and Sabes (2005) have demonstrated that this coordinate transformation appears to increase positional Cilengitide research buy uncertainty. If there is internal noise in the brain, this makes

perfect sense: the circuits involved in coordinate transformations add noise to the signals, and increase their uncertainty. However, once

again, there is no need to invoke noise. As long as some deterministic approximations are involved in the coordinate transformations, one expects this kind of computation to result in extra behavioral variability and added uncertainty about stimulus location. We have argued that in complex tasks, the main cause of behavioral variability may not be internal noise, but suboptimal inference caused by approximating the generative model of the sensory input. We have also proposed that this suboptimal inference is primarily reflected in the correlations among neurons and their tuning curves. Outside of neuroscience, the conclusion that suboptimal inference Anticancer Compound Library purchase is the main cause of behavioral variability is not particularly original. In fact, this was the conclusion reached a long time ago in fields like machine learning. It is clear, for example, that the main factor that limits the performance of image recognition software is not the amount of internal noise in the camera: most digital cameras have better optics than the human eye and more pixels than we have cones. Nonetheless humans remain extraordinarily better at image recognition

than computers. Instead, almost the bottleneck lies in the quality of the algorithm performing the inference; that, in turn is determined primarily by the severity of the approximations required. In neuroscience, however, we rarely hear the perspective that suboptimal inference may be the major cause of variability. As we saw, many models tend to blame internal variability instead (Deneve et al., 2001; Fitzpatrick et al., 1997; Kasamatsu et al., 2001; Pouget and Thorpe, 1991; Reynolds and Heeger, 2009; Reynolds et al., 2000; Rolls and Deco, 2010; Schoups et al., 2001; Shadlen et al., 1996; Stocker and Simoncelli, 2006; Teich and Qian, 2003; Wang, 2002). In fact, in most of these models, internal variability is the only cause of behavioral variability. A consequence of this conclusion is that internal sources of noise can be large without affecting behavioral performance—so long as their impact on behavioral variability is small compared to the variability introduced by suboptimal inference.