Naselaris et al. (2009) used a model similar to the one described for the Kay et al. (2008) study to attempt to reconstruct images from brain activation. They found that the reconstructions find more provided by the basic model were not better than chance with regard to their accuracy. However, by using a database of six million randomly selected natural images as priors, it was possible to create image reconstructions that had structural accuracy substantially better than chance. Furthermore, using a hybrid model that also included semantic labels for the images, the reconstructions also had
a high degree of semantic accuracy. Another study by Pereira et al. (2011) used a similar approach to generate concrete words from brain activation, using a “topic model” trained on corpus of text from Wikipedia. These studies highlight the utility of model-based decoding, which provides much more powerful decoding abilities via the use of computational models that better characterize mental processes along with statistical information mined from large online databases. The foregoing examples of successful decoding are impressive, but each is focused on decoding between different stimuli (images or concrete words) for which the relevant representations are located within a circumscribed set of brain areas at a relatively small spatial scale (e.g., Ibrutinib cost cortical columns). In
these cases, decoding likely relies upon the relative activity of specific subpopulations of neurons within those relevant cortical regions or the
fine-grained vascular architecture in those Mannose-binding protein-associated serine protease regions (see Kriegeskorte et al., 2010 for further discussion of this issue). In many cases, however, the goal of reverse inference is to identify what mental processes are engaged against a much larger set of possibilities. We refer to this here as “large-scale” decoding, in which “scale” refers to both the spatial scale of the relevant neural systems and the breadth of the possible mental states being decoded. Such large-scale decoding is challenging because it requires training data acquired across a much larger set of possible mental states. At the same time, it is more likely to rely upon distributions of activation across many regions across the brain and thus has a greater likelihood of generalizing across individuals compared to the decoding of specific stimuli, which is more likely to rely upon idiosyncratic features of individual brains. Although most previous decoding studies have examined generalization within the same individuals, a number of previous studies has shown that it is possible to generalize across individuals (Davatzikos et al., 2005, Mourão-Miranda et al., 2005 and Shinkareva et al., 2008). In an attempt to test the large-scale decoding concept, we (Poldrack et al.