Sounds were presented through Sensimetric MRI Compatible Insert Earphones (www.sens.com/s14/index.htm). To set the volume levels in the scanner, a functional run Metabolism inhibitor was started and the volume of the stimuli was slowly increased until the participant pressed a button indicating they could hear the stimuli clearly. Before the experiment,

observers were given detailed instructions that they should imagine only isolated objects, and that “giant” versions of small objects should be imagined “as having the same size as a car or piano” while tiny versions of large objects should be imagined “as having the same size as a matchbox or something that could fit in your hand.” Observers then were given a short practice

run outside the scanner in which they heard the names of small objects, big objects, tiny versions of big objects, and giant versions of small objects, following the same timing as in the experimental runs. None of these practice object stimuli were used in the main experiment. Functional data were preprocessed using Brain Voyager QX software (Brain Innovation, Maastricht, Netherlands). Preprocessing included slice scan-time correction, 3D motion correction, linear trend removal, temporal high-pass filtering (0.01 Hz cutoff), spatial smoothing (4 mm FWHM kernel), and transformation into Talairach coordinates. For the ROI overlap computations, analyses were performed on unsmoothed functional data in ACPC space (no Talairach transform). Statistical analyses were based Raf phosphorylation on the general linear model. All GLM analyses included regressors for each experimental condition, defined as square-wave regressors for each stimulus presentation time convolved with a gamma-function to approximate the idealized hemodynamic response. A whole-brain, random-effects group average analysis was conducted on data from the Big versus Small Object Experiment (E1). A contrast was performed at an uncorrected threshold of p < 0.001 (with an additional cluster threshold of 10 mm3 applied) to test for regions more active to small

versus big objects and vice-versa. To obtain Farnesyltransferase size-preference maps for each subject, an object-responsive mask was computed by taking all voxels posterior to Y = −19 (to isolate the occipital-temporal lobes) that were active in either the Small > Rest or the Big > Rest contrast at T > 2.0. The preference map shows the t values of the small object versus big object contrast, within this object-responsive mask. To compute the group size-preference map, the time series of each subject was concatenated and a fixed-effects GLM analysis was run on the group data (see Hasson et al., 2003 and Levy et al., 2001), and the same procedure as in the single subject case was subsequently followed. To obtain regions-of-interest from the Big and Small Object experiment, whole-brain GLMs were conducted for each individual.