Individual differences in visual word recognition: Insights from the English Lexicon Project.

Abstract

Empirical work and models of visual word recognition have traditionally focused on group-level performance. Despite the emphasis on the prototypical reader, there is clear evidence that variation in reading skill modulates word recognition performance. In the present study, we examined differences between individuals who contributed to the English Lexicon Project (http://elexicon.wustl.edu), an online behavioral database containing nearly four million word recognition (speeded pronunciation and lexical decision) trials from over 1,200 participants. We observed considerable within- and between-session reliability across distinct sets of items, in terms of overall mean response time (RT), RT distributional characteristics, diffusion model parameters (Ratcliff, Gomez, & McKoon, 2004), and sensitivity to underlying lexical dimensions. This indicates reliably detectable individual differences in word recognition performance. In addition, higher vocabulary knowledge was associated with faster, more accurate word recognition performance, attenuated sensitivity to stimuli characteristics, and more efficient accumulation of information. Finally, in contrast to suggestions in the literature, we did not find evidence that individuals were trading-off in their utilization of lexical and nonlexical information.

Individual Differences in Language Acquisition and Processing

“…Take, for example, the lexical decision task, where participants typically decide whether a letter sequence (e.g., 'house or 'vouse') that appears on a computer is a word. Means and standard deviations of lexical decision tasks exhibit excellent test/retest reliability (r = 0.87 [142]). However, these raw means reflect the combination of many distinct cognitive parameters, such as encoding, evidence accumulation, response thresholds, and response execution times [143].…”

A database of 629 English compound words: ratings of familiarity, lexeme meaning dominance, semantic transparency, age of acquisition, imageability, and sensory experience

“…The field of visual word recognition is quite productive, and recently there has been great interest in using available databases of visual lexical decision and word naming latencies, such as the English Lexicon Project (ELP: Balota et al, 2007), to examine word recognition processes (e.g., New, Ferrand, Pallier, & Brysbaert, 2006;Yap & Balota, 2009;Yap, Balota, Sibley, & Ratcliff, 2012). Although recent work has extended the examination of visual word recognition processes beyond the short monosyllabic words that were previously the main source of data (e.g., Balota, Cortese, Sergent-Marshall, Spieler, & Yap, 2004), this approach has not often been used to investigate morphological processing (however, see Baayen, Feldman, & Schreuder, 2006).…”

A learning perspective on individual differences in skilled reading: Exploring and exploiting orthographic and semantic discrimination cues.

“…It has been shown that statistical patterns of language use (frequency, family size, spelling variation probabilities, among other things) are modulated by speakersÕ experiences with language, i.e., by the speakerÕs individual exposure to language (Kuperman & Van Dyke, 2011a;2011b;Falkauskas & Kuperman, 2015). Using behavioural data, mainly from eyetracking, Kuperman et al showed that Òthe amount of support [given general] distributional patterns in natural language may factor into individualsÕ lexical representations by way of differential exposure to [specific linguistic] formsÓ (Falkauskas & Kuperman, 2015, p. 1617 The frequency by skill interaction has, however, been challenged as nothing but a confound of participantsÕ base-line recognition latencies (Butler & Hains, 1979;Faust et al, 1999;Yap et al, 2012). This is reminiscent of the mental speed hypothesis: Òsmarter people [simply] have faster brainsÓ (Jolij et al, 2006, p. 39), which has a long history in psychology (for a comprehensive overview see Jensen, 2006; see also Hick, 1952 for his Òrate of gain of informationÓ, which is known as HickÕs Law; more recent work and a revival of the original ideas can be found in Deary & Caryl, 1997;Deary, 2001;van Ravenzwaaij, Brown, & Wagenmakers, 2011;Shubert at al., 2015).…”

“…Although elementary cognitive tasks that tap into processing speed may have been used to raise methodological concerns (Yap et al, 2012, for example), they have not been specifically invoked to explain reading behaviour. In our study, we do precisely this and we refine the notion of mental speed by postulating: (a) average speed, i.e.…”