Random-effects analyses (i.e., a t test comparing win versus loss responses) were performed on the resulting tables. Whole-brain GLM was restricted INCB024360 molecular weight to the HRF model. For whole-brain analyses, contrast maps were produced from the first-level analysis described above. The resulting statistical maps were normalized and sampled to a standard (MNI) space with 2 mm resolution, and then each voxel was subjected to a random-effects contrast of

the voxelwise response to wins versus losses. This study was supported by the Department of Neurobiology and Kavli Institute for Neuroscience, Yale University School of Medicine. We thank Matt Kleinman, Zhihao Zhang, and Sam Cartmell for assistance with data collection, and Brice Kuhl for useful discussions. “
“The process by which the brain transforms sensory inputs into perceptual events often begins before physical contact with the sensory stimulus (Freeman, 1979, Friston, 2005a, McMains et al., 2007, Mesulam, 2008, Mumford, 1992, Sylvester et al., 2009 and Wald and Wolfowitz, 1949). Knowledge

and experience set expectations for what is likely—but not yet—to be encountered, helping to augment the speed and accuracy of subsequent perceptual judgments. These predictive representations confer distinct behavioral PFT�� advantages upon organisms aiming to Non-specific serine/threonine protein kinase survive in complex, noisy, and unpredictable sensory environments. How predictive perceptual information is encoded in the brain is not well understood. Several influential models of sensory perception have centered on the idea that the brain generates stimulus templates or “search images” in advance of a stimulus encounter (Freeman,

1981, Friston, 2003 and Mumford, 1992). According to such mechanisms, a sensory percept is instantiated through an interaction between the prestimulus template and the incoming sensory input. Multiple studies have found effects of anticipatory attention in the visual and auditory systems (Kastner et al., 1999, Kumar and Sedley, 2011, Luck et al., 1997 and Ress et al., 2000), and more recently, effects of anticipatory attention or top-down search have also been found for specific visual objects in higher-order visual cortex (Esterman and Yantis, 2010, Peelen et al., 2009, Stokes et al., 2009 and Summerfield et al., 2006). By comparison, research on predictive coding in the olfactory system has been scant at best. Given that odor objects are routinely experienced within a highly odiferous background (Gottfried, 2010 and Stevenson and Wilson, 2007), it follows that mechanisms to steer selective attention toward an odor of interest, and away from an odor of no interest, would be essential for overcoming sensory noise in the olfactory environment.