Contextual Diversity, Not Word Frequency, Determines Word-Naming and Lexical Decision Times

By James S. Adelman, Gordon D. A. Brown, Jose F. Quesada. 2006

461 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Developmental and Educational Psychology, Natural Language Processing

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Citations in the corpus

Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change (2016, background): “Words gain senses over time as they semantically drift (Br´eal, 1897; Wilkins, 1993; Hopper and Trau-gott, 2003), and polysemous words 1 occur in more diverse contexts, affecting lexical access speed (Adelman et al., 2006) and rates of L2 learning (Crossley et al., 2010).”
Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English (2009, background): “26 Although frequency of occurrence intuitively seems to be the most appealing definition of word frequency, Adelman et al. (2006) have suggested that contextual diversity may be a better measure, a finding upheld in our analyses.”
Subtlex-UK: A New and Improved Word Frequency Database for British English (2014, background): “Adelman, Brown, and Quesada (2006; see also Adelman & Brown, 2008; Perea, Soares, & Comesaña, 2013; Yap, Tan, Pexman, & Hargreaves, 2011) argued that not so much the frequency of occurrence of a word matters, but the number of contexts in which the word appears.”
Frequency in lexical processing (2016, methodology): “Adelman et al. (2006) based their initial analysis on a subset of the TASA corpus, which contains short excerpts from texts appearing on the curriculum of high-school students, reflecting the different subjects in which this population is educated.”
The Word Frequency Effect in Word Processing: An Updated Review (2018, background): “This idea was brought to the forefront by Adelman, Brown, and Quesada (2006), who argued that contextual diversity was a better predictor of word processing efficiency than word frequency.”
Semantic diversity: A measure of semantic ambiguity based on variability in the contextual usage of words (2012, methodology): “Contextual diversity: Following Adelman et al. (2006), this was computed for each word by calculating the total number of contexts in the BNC that contained the word (from a possible total of 87,375) and log-transforming this value.”
Demythologizing the word frequency effect: A discriminative learning perspective (2010, background): “Adelman et al., and McDonald and Shillcock argue that the variety of contexts in which a word occurs is the crucial component of the word frequency effect in lexical decision. A principal components analysis of 17 lexical predictors revealed that most of the variance in lexical space is carried by a”
SUBTLEX-CH: Chinese Word and Character Frequencies Based on Film Subtitles (2010, result): “found that subtitle frequencies not only explained more of the variance in naming times and lexical decision times than the other measures, but in addition they observed that a corpus of 16–30 million words was enough to have good frequency estimates. They also found that a measure based on contextu”
The effect of word predictability on reading time is logarithmic (2013, background): “…age at which the word was first learned (Morrison & Ellis, 1995; Morrison et al., 2003), or differences in the ease of memory retrieval as determined by the number of previous exposures to the word, modulated by recency (Morton, 1969) or the diversity of previous contexts (Adelman et al., 2006).”