Regions in the human Lateral Occipital Complex (LOC) show fMRI responses to illusory surfaces. We show that the LOC activation is due to the globally completed region and occurs even when the region is not bounded by illusory contours (ICs). Kanizsa-type stimuli were modified by rounding the corners of the "pacmen" inducers and misaligning them slightly. The impression of an enclosed, salient region (SR) remained, although ICs were no longer perceived (psychophysical data). fMRI activity was elevated for both the IC and SR stimuli, compared to their control stimuli. The LOC response to salient regions may be the result of fast but crude region-based segmentation processes, which are useful for selecting parts of cluttered images for more detailed, computationally intensive processing.

Trust lies at the heart of every social interaction. Each day we face decisions in which we must accurately assess another individual's trustworthiness or risk suffering very real consequences. In a global marketplace of increasing heterogeneity with respect to nationality, race, and multiple other social categories, it is of great value to understand how implicitly held attitudes about group membership may support or undermine social trust and thereby implicitly shape the decisions we make. Recent behavioral and neuroimaging work suggests that a common mechanism may underlie the expression of implicit race bias and evaluations of trustworthiness, although no direct evidence of a connection exists. In two behavioral studies, we investigated the relationship between implicit race attitude (as measured by the Implicit Association Test) and social trust. We demonstrate that race disparity in both an individual's explicit evaluations of trustworthiness and, more crucially, his or her economic decisions to trust is predicted by that person's bias in implicit race attitude. Importantly, this relationship is robust and is independent of the individual's bias in explicit race attitude. These data demonstrate that the extent to which an individual invests in and trusts others with different racial backgrounds is related to the magnitude of that individual's implicit race bias. The core dimension of social trust can be shaped, to some degree, by attitudes that reside outside conscious awareness and intention.

The parahippocampal place area (PPA) has been demonstrated to respond more strongly in fMRI to scenes depicting places than to other kinds of visual stimuli. Here, we test several hypotheses about the function of the PPA. We find that PPA activity (1) is not affected by the subjects' familiarity with the place depicted, (2) does not increase when subjects experience a sense of motion through the scene, and (3) is greater when viewing novel versus repeated scenes but not novel versus repeated faces. Thus, we find no evidence that the PPA is involved in matching perceptual information to stored representations in memory, in planning routes, or in monitoring locomotion through the local or distal environment but some evidence that it is involved in encoding new perceptual information about the appearance and layout of scenes.

We propose a method for the statistical analysis of fMRI data that tests cluster units rather than voxel units for activation. The advantages of this analysis over previous ones are both conceptual and statistical. Recognizing that the fundamental units of interest are the spatially contiguous clusters of voxels that are activated together, we set out to approximate these cluster units from the data by a clustering algorithm especially tailored for fMRI data. Testing the cluster units has a two-fold statistical advantage over testing each voxel separately: the signal to noise ratio within the unit tested is higher, and the number of hypotheses tests compared is smaller. We suggest controlling FDR on clusters, i.e. the proportion of clusters rejected erroneously out of all clusters rejected, and explain the meaning of controlling this error rate. We introduce the powerful adaptive procedure to control the FDR on clusters. We apply our cluster based analysis (CBA) to both an event-related and a block design fMRI vision experiment, and demonstrate its increased power over voxel-by-voxel analysis in these examples as well as in simulations.

To test whether the human fusiform face area (FFA) responds not only to faces but to anything human or animate, we used fMRI to measure the response of the FFA to six new stimulus categories. The strongest responses were to stimuli containing faces: human faces (2.0% signal increase from fixation baseline) and human heads (1.7%), with weaker but still strong responses to whole humans (1.5%) and animal heads (1.3%). Responses to whole animals (1.0%) and human bodies without heads (1.0%) were significantly stronger than responses to inanimate objects (0.7%), but responses to animal bodies without heads (0.8%) were not. These results demonstrate that the FFA is selective for faces, not for animals.