There are many ways to be rich: Effects of three measures of semantic richness on visual word recognition

Previous studies have reported that semantic richness facilitates visual word recognition (see, e.g., Buchanan, Westbury, & Burgess, 2001; Pexman, Holyk, & Monfils, 2003). We compared three semantic richness measures--number of semantic neighbors (NSN), the number of words appearing in similar lexical contexts; number of features (NF), the number of features listed for a word's referent; and contextual dispersion (CD), the distribution of a word's occurrences across content areas-to determine their abilities to account for response time and error variance in lexical decision and semantic categorization tasks. NF and CD accounted for unique variance in both tasks, whereas NSN accounted for unique variance only in the lexical decision task. Moreover, each measure showed a different pattern of relative contribution across the tasks. Our results provide new clues about how words are represented and suggest that word recognition models need to accommodate each of these influences.; Previous studies have reported that semantic richness facilitates visual word recognition (see, e.g., Buchanan, Westbury, & Burgess, 2001; Pexman, Holyk, & Monfils, 2003). We compared three semantic richness measures—number of semantic neighbors (NSN), the number of words appearing in similar lexical contexts; number of features (NF), the number of features listed for a word’s referent; and contextual dispersion (CD), the distribution of a word’s occurrences across content areas—to determine their abilities to account for response time and error variance in lexical decision and semantic categorization tasks. NF and CD accounted for unique variance in both tasks, whereas NSN accounted for unique variance only in the lexical decision task. Moreover, each measure showed a different pattern of relative contribution across the tasks. Our results provide new clues about how words are represented and suggest that word recognition models need to accommodate each of these influences.; Previous studies have reported that semantic richness facilitates visual word recognition (see, e.g., Buchanan, Westbury, & Burgess, 2001; Pexman, Holyk, & Monfils, 2003). We compared three semantic richness measures-number of semantic neighbors (NSN), the number of words appearing in similar lexical contexts; number of features (NF), the number of features listed for a word's referent; and contextual dispersion (CD), the distribution of a word's occurrences across content areas-to determine their abilities to account for response time and error variance in lexical decision and semantic categorization tasks. NF and CD accounted for unique variance in both tasks, whereas NSN accounted for unique variance only in the lexical decision task. Moreover, each measure showed a different pattern of relative contribution across the tasks. Our results provide new clues about how words are represented and suggest that word recognition models need to accommodate each of these influences. [PUBLICATION ABSTRACT