The ultimate goal of reading is to extract meaning from visually printed words. However, the mechanisms that mediate orthography and semantics are not well-understood, and have rarely been implemented in computational models. To address this puzzle, one of the strategies cognitive scientists have begun to use is to examine semantic richness effects. Semantic richness effects refer to the finding that words associated with relatively more semantic information are recognized faster and more accurately, due to their possessing richer, better-specified semantic representations. Importantly, semantic richness is not a unitary concept. Instead, it draws on various theoretical perspectives and can vary along multiple dimensions. Indeed, the definition of richness depends on the particular model of semantic memory one adheres to. For instance, if one assumes an embodied framework, then richer concepts could be defined as those which are associated with more sensory/perceptual or sensorimotor experience. Alternatively, if one assumes that semantic memory is structured by lexical co-occurrence, then richer concepts might be those that are associated with denser semantic neighborhoods. Finally, if semantic memory reflects episodic memory, then richness could be indexed by the number of contexts a concept is associated with. In addition to the dimensions described above, semantic richness has also been captured by a concept’s number of features, number of associates, and number of senses, and new dimensions are sure to emerge. Evidence is accumulating that various semantic richness dimensions explain variance in lexical processing, both behaviorally and in the brain. Critically, if a given richness dimension can be shown to predict language processing, it provides support for the model or perspective from which that dimension is derived. Thus, the study of semantic richness effects has the potential to yield important new insights into how meaning is extracted from print. The goal of this Special Topic is to highlight the latest findings regarding semantic richness and theoretical developments on the issue of semantic processing. We welcome submissions on any semantic richness manipulation, using behavioral and/or neuroimaging paradigms. We also welcome submissions reporting new theoretical and/or modeling work, or methodological advances in norms, corpora, etc. Our hope is to provide a forum for state-of-the-art research in this field, and to foster new theoretical advances.
The ultimate goal of reading is to extract meaning from visually printed words. However, the mechanisms that mediate orthography and semantics are not well-understood, and have rarely been implemented in computational models. To address this puzzle, one of the strategies cognitive scientists have begun to use is to examine semantic richness effects. Semantic richness effects refer to the finding that words associated with relatively more semantic information are recognized faster and more accurately, due to their possessing richer, better-specified semantic representations. Importantly, semantic richness is not a unitary concept. Instead, it draws on various theoretical perspectives and can vary along multiple dimensions. Indeed, the definition of richness depends on the particular model of semantic memory one adheres to. For instance, if one assumes an embodied framework, then richer concepts could be defined as those which are associated with more sensory/perceptual or sensorimotor experience. Alternatively, if one assumes that semantic memory is structured by lexical co-occurrence, then richer concepts might be those that are associated with denser semantic neighborhoods. Finally, if semantic memory reflects episodic memory, then richness could be indexed by the number of contexts a concept is associated with. In addition to the dimensions described above, semantic richness has also been captured by a concept’s number of features, number of associates, and number of senses, and new dimensions are sure to emerge. Evidence is accumulating that various semantic richness dimensions explain variance in lexical processing, both behaviorally and in the brain. Critically, if a given richness dimension can be shown to predict language processing, it provides support for the model or perspective from which that dimension is derived. Thus, the study of semantic richness effects has the potential to yield important new insights into how meaning is extracted from print. The goal of this Special Topic is to highlight the latest findings regarding semantic richness and theoretical developments on the issue of semantic processing. We welcome submissions on any semantic richness manipulation, using behavioral and/or neuroimaging paradigms. We also welcome submissions reporting new theoretical and/or modeling work, or methodological advances in norms, corpora, etc. Our hope is to provide a forum for state-of-the-art research in this field, and to foster new theoretical advances.
