Face processing in humans is compatible with a simple shape-based model of vision

Understanding how the human visual system recognizes objects is one of the key challenges in neuroscience. Inspired by a large body of physiological evidence (Felleman and Van Essen, 1991; Hubel and Wiesel, 1962; Livingstone and Hubel, 1988; Tso et al., 2001; Zeki, 1993), a general class of recognition models has emerged which is based on a hierarchical organization of visual processing, with succeeding stages being sensitive to image features of increasing complexity (Hummel and Biederman, 1992; Riesenhuber and Poggio, 1999; Selfridge, 1959). However, these models appear to be incompatible with some well-known psychophysical results. Prominent among these are experiments investigating recognition impairments caused by vertical inversion of images, especially those of faces. It has been reported that faces that differ "featurally" are much easier to distinguish when inverted than those that differ "configurally" (Freire et al., 2000; Le Grand et al., 2001; Mondloch et al., 2002) ??finding that is difficult to reconcile with the aforementioned models. Here we show that after controlling for subjects' expectations, there is no difference between "featurally" and "configurally" transformed faces in terms of inversion effect. This result reinforces the plausibility of simple hierarchical models of object representation and recognition in cortex.

A Privacy Conscious Bluetooth Infrastructure for Location Aware Computing

We present a low cost and easily deployed infrastructure for location aware computing that is built using standard Bluetooth® technologies and personal computers. Mobile devices are able to determine their location to room-level granularity with existing bluetooth technology, and to even greater resolution with the use of the recently adopted bluetooth 1.2 specification, all while maintaining complete anonymity. Various techniques for improving the speed and resolution of the system are described, along with their tradeoffs in privacy. The system is trivial to implement on a large scale – our network covering 5,000 square meters was deployed by a single student over the course of a few days at a cost of less than US$1,000.