![]() ![]() Data of this kind are amenable to multivariate pattern analyses (e.g., classification or representational similarity analyses Norman et al., 2006 Kriegeskorte et al., 2008a) or forward encoding analyses using visual (e.g., Nishimoto et al., 2011), semantic (e.g., Huth et al., 2012), or neuromorphic models (e.g., recurrent or deep convolutional neural networks Güçlü and van Gerven, 2015). Furthermore, natural vision paradigms have greater ecological validity ( Felsen and Dan, 2005), and dynamic stimuli have been shown to drive reliable neural responses across individuals ( Hasson et al., 2010 Haxby et al., 2011). They convey rich perceptual and semantic information ( Bartels and Zeki, 2004 Huth et al., 2012) and more fully sample neural representational space than conventional stimuli ( Haxby et al., 2014). There are several benefits to using dynamic, naturalistic stimuli. Participants performed a 1-back category repetition detection task requiring them to attend to either animal behavior or taxonomy. Here we present functional MRI data measured while participants freely viewed brief naturalistic video clips of animals behaving in their natural environments ( Nastase et al., 2017). A growing body of work in both electrophysiology (e.g., Sigala and Logothetis, 2002 Sigala, 2004 Cohen and Maunsell, 2009 Reynolds and Heeger, 2009) and human neuroimaging (e.g., Hon et al., 2009 Jehee et al., 2011 Brouwer and Heeger, 2013 Çukur et al., 2013 Sprague and Serences, 2013 Harel et al., 2014 Erez and Duncan, 2015 Nastase et al., 2017) has suggested mechanisms by which behavioral goals dynamically alter neural representation. In contrast, earlier work emphasized the importance of attention and task demands in actively reshaping representational space ( Shepard, 1964 Tversky, 1977 Nosofsky, 1986 Kruschke, 1992). Current applications of this framework to neural representation (e.g., Kriegeskorte et al., 2008b) often implicitly assume that these neural representational spaces are relatively fixed and context-invariant. This resonates with behavioral and theoretical work describing mental representations of objects and actions as being organized in a multidimensional psychological space ( Attneave, 1950 Shepard, 1958, 1987 Edelman, 1998 Gärdenfors and Warglien, 2012). ![]() The neural representation of a stimulus can be described as a location (i.e., response vector) in a high-dimensional neural representational space ( Kriegeskorte and Kievit, 2013 Haxby et al., 2014). These semantic representations are encoded in the activity of distributed populations of neurons ( Haxby et al., 2001 McClelland and Rogers, 2003 Kriegeskorte et al., 2008b) and command widespread cortical real estate ( Binder et al., 2009 Huth et al., 2012). The human brain rapidly deploys semantic information during perception to facilitate our interaction with the world. ![]()
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