Neural computation as a tool to differentiate perceptual from emotional processes: the case of anger superiority effect.

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Coarse scales are sufﬁcient for efﬁcient categorization of emotional facial expressions: Evidence from neural computation (unknown): “We have used the KDEF and POFA not only because they are two commonly employed databases, but also because they consist of very carefully controlled pictures selected for important neuroimaging, behavioral and connection-ist modeling papers [8,17,24,38] in the ﬁeld of EFE categorization.”
Computational Evidence That Frequency Trajectory Theory Does Not Oppose But Emerges From Age-of-Acquisition Theory (2012, unknown): “…processes such as language (Ellis & Lambon Ralph, 2000), as well as memory (French, 1999), visual recognition (Mermillod, Bonin, Mondillon, Alleysson, & Vermeulen, 2010; Mermillod, Vermeulen, Lundqvist, & Niedenthal, 2009), and semantic categorization (McClelland, McNaughton, & O’Reilly, 1995).”
Coarse scales are sufficient for efficient categorization of emotional facial expressions: Evidence from neural computation (2010, background): “…other words, we assume that the phylogenetic development of the human neural structures dedicated to categorization of an EFE may be such as to provide faster access to coarse scale information conveyed by magnocellular neurons, because this information is more efficient for categorization of EFE.”