Melvin J. Yap

The English Lexicon Project.

The English Lexicon Project is a multiuniversity effort to provide a standardized behavioral and descriptive data set for 40,481 words and 40,481 nonwords. It is available via the Internet at elexicon.wustl.edu. Data from 816 participants across six universities were collected in a lexical decision task (approximately 3400 responses per participant), and data from 444 participants were collected in a speeded naming task (approximately 2500 responses per participant). The present paper describes the motivation for this project, the methods used to collect the data, and the search engine that affords access to the behavioral measures and descriptive lexical statistics for these stimuli.

Visual Word Recognition of Single-Syllable Words

Speeded visual word naming and lexical decision performance are reported for 2428 words for young adults and healthy older adults. Hierarchical regression techniques were used to investigate the unique predictive variance of phonological features in the onsets, lexical variables (e.g., measures of consistency, frequency, familiarity, neighborhood size, and length), and semantic variables (e.g. imageahility and semantic connectivity). The influence of most variables was highly task dependent, with the results shedding light on recent empirical controversies in the available word recognition literature. Semantic-level variables accounted for unique variance in both speeded naming and lexical decision performance, level with the latter task producing the largest semantic-level effects. Discussion focuses on the utility of large-scale regression studies in providing a complementary approach to the standard factorial designs to investigate visual word recognition.

Moving beyond Coltheart's N: A New Measure of Orthographic Similarity

Visual word recognition studies commonly measure the orthographic similarity of words using Coltheart’s orthographic neighborhood size metric (ON). Although ON reliably predicts behavioral variability in many lexical tasks, its utility is inherently limited by its relatively restrictive definition. In the present article, we introduce a new measure of orthographic similarity generated using a standard computer science metric of string similarity (Levenshtein distance). Unlike ON, the new measure—named orthographic Levenshtein distance 20 (OLD20)—incorporates comparisons between all pairs of words in the lexicon, including words of different lengths. We demonstrate that OLD20 provides significant advantages over ON in predicting both lexical decision and pronunciation performance in three large data sets. Moreover, OLD20 interacts more strongly with word frequency and shows stronger effects of neighborhood frequency than does ON. The discussion section focuses on the implications of these results for models of visual word recognition.

Moving Beyond the Mean in Studies of Mental Chronometry The Power of Response Time Distributional Analyses

Although it is widely recognized that response time (RT) distributions are almost always positively skewed and that mathematical psychologists have developed straightforward procedures for capturing characteristics of RT distributions, researchers continue to rely primarily on mean performance, which can be misleading for such data. We review simple procedures for capturing characteristics of underlying RT distributions and show how such procedures have recently been useful to better understand effects from standard cognitive experimental paradigms and individual differences in performance. These well-studied procedures for understanding RT distributions indicate that effects in means can be produced by (a) shifts of RT distributions, (b) stretching of slow tails of RT distributions, or (c) some combination. Importantly, effects in means can actually be obscured by opposing influences on the modal and tail portions of RT distributions. Such disparate patterns demand novel theoretical interpretations.

Smart Phone, Smart Science: How the Use of Smartphones Can Revolutionize Research in Cognitive Science

Investigating human cognitive faculties such as language, attention, and memory most often relies on testing small and homogeneous groups of volunteers coming to research facilities where they are asked to participate in behavioral experiments. We show that this limitation and sampling bias can be overcome by using smartphone technology to collect data in cognitive science experiments from thousands of subjects from all over the world. This mass coordinated use of smartphones creates a novel and powerful scientific “instrument” that yields the data necessary to test universal theories of cognition. This increase in power represents a potential revolution in cognitive science.

Effects of Healthy Aging and Early Stage Dementia of the Alzheimer's Type on Components of Response Time Distributions in Three Attention Tasks

OBJECTIVE The characteristics of response time (RT) distributions beyond measures of central tendency were explored in 3 attention tasks across groups of young adults, healthy older adults, and individuals with very mild dementia of the Alzheimers type (DAT). METHOD Participants were administered computerized Stroop, Simon, and switching tasks, along with psychometric tasks that tap various cognitive abilities and a standard personality inventory (NEO-FFI). Ex-Gaussian (and Vincentile) analyses were used to capture the characteristics of the RT distributions for each participant across the 3 tasks, which afforded 3 components: mu and sigma (mean and standard deviation of the modal portion of the distribution) and tau (the positive tail of the distribution). RESULTS The results indicated that across all 3 attention tasks, healthy aging produced large changes in the central tendency mu parameter of the distribution along with some change in sigma and tau (mean etap(2) = .17, .08, and .04, respectively). In contrast, early stage DAT primarily produced an increase in the tau component (mean etap(2) = .06). tau was also correlated with the psychometric measures of episodic/semantic memory, working memory, and processing speed, and with the personality traits of neuroticism and conscientiousness. Structural equation modeling indicated a unique relation between a latent tau construct (-.90), as opposed to sigma (-.09) and mu constructs (.24), with working memory measures. CONCLUSIONS The results suggest a critical role of attentional control systems in discriminating healthy aging from early stage DAT and the utility of RT distribution analyses to better specify the nature of such change.

Visual Word Recognition: The Journey from Features to Meaning (A Travel Update)

Publisher Summary This chapter presents a discussion on the word recognition literature. Word recognition research is central to notions regarding different levels/codes of analysis in language processing, attention, and memory. The lexical unit is ideally suited for such work because words can be analyzed at many different levels—for example, features, letters, graphemes, phonemes, morphemes, and semantics. Word recognition research is also central in the development of theories of automatic and attentional processes. Part of the reason for this emphasis is the natural relation between the development of reading skills and the development of automaticity. One can see the extra impetus from education circles regarding the development of word recognition skills. Moreover, the notion that the aspect of word recognition has been automatized and is no longer under the conscious control of the reader has historically provided some of the major fuel for arguments regarding self-encapsulated linguistic processing modules. The issue of how attentional control signals might modulate processes involved in word recognition has received renewed interest recently, and hence, notions of automaticity and modularity have been reevaluated.

Individual Differences in Visual Word Recognition: Insights from the English Lexicon Project

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 among individuals who contributed to the English Lexicon Project (http://elexicon.wustl.edu), an online behavioral database containing nearly 4 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 their utilization of lexical and nonlexical information.

Additive and interactive effects on response time distributions in visual word recognition.

Across 3 different word recognition tasks, distributional analyses were used to examine the joint effects of stimulus quality and word frequency on underlying response time distributions. Consistent with the extant literature, stimulus quality and word frequency produced additive effects in lexical decision, not only in the means but also in the shape of the response time distributions, supporting an early normalization process that is separate from processes influenced by word frequency. In contrast, speeded pronunciation and semantic classification produced interactive influences of word frequency and stimulus quality, which is a fundamental prediction from interactive activation models of lexical processing. These findings suggest that stimulus normalization is specific to lexical decision and is driven by the tasks emphasis on familiarity-based information.

Is more always better? Effects of semantic richness on lexical decision, speeded pronunciation, and semantic classification

Evidence from large-scale studies (Pexman, Hargreaves, Siakaluk, Bodner, & Pope, 2008) suggests that semantic richness, a multidimensional construct reflecting the extent of variability in the information associated with a word’s meaning, facilitates visual word recognition. Specifically, recognition is better for words that (1) have more semantic neighbors, (2) possess referents with more features, and (3) are associated with more contexts. The present study extends Pexman et al. (2008) by examining how two additional measures of semantic richness, number of senses and number of associates (Pexman, Hargreaves, Edwards, Henry, & Goodyear, 2007), influence lexical decision, speeded pronunciation, and semantic classification performance, after controlling for an array of lexical and semantic variables. We found that number of features and contexts consistently facilitated word recognition but that the effects of semantic neighborhood density and number of associates were less robust. Words with more senses also elicited faster lexical decisions but less accurate semantic classifications. These findings point to how the effects of different semantic dimensions are selectively and adaptively modulated by task-specific demands.