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Published online: 18 February 2020 Vocabulary size seems to be affected by multiple factors, including those that belong to the properties of the words themselves and those that relate to the characteristics of the individuals assessing the words. In this study, we present results from a crowdsourced lexical decision megastudy in which more than 150,000 native speakers from around 20 Spanish-speaking countries performed a lexical decision task to 70 target word items selected from a list of about 45,000 Spanish words. We examined how demographic characteristics such as age, education level, and multilingualism affected participants’ vocabulary size. Also, we explored how common factors related to words like frequency, length, and orthographic neighbourhood influenced the knowledge of a particular item. Results indicated important contributions of age to overall vocabulary size, with vocabulary size increasing in a logarithmic fashion with this factor. Furthermore, a contrast between monolingual and bilingual communities within Spain revealed no significant vocabulary size differences between the communities. Additionally, we replicated the standard effects of the words’ properties and their interactions, accurately accounting for the estimated knowledge of a particular word. These results highlight the value of crowdsourced approaches to uncover effects that are traditionally masked by smallsampled in-lab factorial experimental designs. This research is supported by the Basque Government through the BERC 2018-2021 program and by the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation SEV-2015-0490. This study was also partially supported by grants PGC2018-097145-B-I00, RED2018-102615-T, and RTI2018-093547-B-I00 from the Spanish State Research Agency. Work by JA was supported by “la Caixa” Foundation and the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 713673, and fellowship code LCF/BQ/IN17/116200154004. We would also like to thank the reviewers for their insightful comments and efforts towards improving this manuscript.

We present a new database of Dutch word recognition times for a total of 54 thousand words, called the Dutch Crowdsourcing Project. The data were collected with an internet vocabulary test. The database is limited to native Dutch speakers. Participants were asked to indicate which words they knew. Their response times were registered, even though the participants were not asked to respond as fast as possible. Still, the response times correlate around .7 with the response times of the Dutch Lexicon Projects for shared words. Also results of virtual experiments indicate that the new response times are a valid addition to the Dutch Lexicon Projects. This not only means that we have useful response times for some 20 thousand extra words, but we now also have data on differences in response latencies as a function of education and age. The new data correspond better to word use in the Netherlands.

Emotions play a fundamental role in language learning, use, and processing. Words denoting positivity account for a larger part of the lexicon than words denoting negativity, and they also tend to be used more frequently, a phenomenon known as positivity bias. However, language experience changes over an individual’s lifetime, making the examination of the emotion-laden lexicon an important topic not only across the life span but also across languages. Furthermore, existing theories predict a range of different age-related trajectories in processing valenced words. The present study pits all of these predictions against written productions (Facebook status updates from over 20,000 users) and behavioral data from three publicly available megastudies on different languages, namely English, Dutch, and Spanish, across adulthood. The production data demonstrated an increase in positive word types and tokens with advancing age. In terms of comprehension, the results showed a uniform and consistent effect of valence across languages and cohorts based on data from a visual word recognition task. The difference in reaction times to very positive and very negative words declined with age, with responses to positive words slowing down more strongly with age than responses to negative words. We argue that the results stem from lifelong learning and emotion regulation: Advancing age is accompanied by an increased type frequency of positive words in language production, which is mirrored as a discrimination penalty in comprehension. To our knowledge, this is the first study to simultaneously target both language production and comprehension across adulthood and in a cross-linguistic perspective. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

The major aim of the present megastudy of picture-naming norms was to address the shortcomings of the available picture data sets used in psychological and linguistic research by creating a new database of normed colour images that researchers from around the world can rely upon in their investigations. In order to do this, we employed a new form of normative study, namely a megastudy, whereby 1620 colour photographs of items spanning across 42 semantic categories were named and rated by a group of German speakers. This was done to establish the following linguistic norms: speech onset times (SOT), name agreement, accuracy, familiarity, visual complexity, valence, and arousal. The data, including over 64,000 audio files, were used to create the LinguaPix database of pictures, audio recordings, and linguistic norms, which to our knowledge, is the largest available research tool of its kind (http://linguapix.uni-mannheim.de). In this paper, we present the tool and the analysis of the major variables.

To have more information about the English words known by second language (L2) speakers, we ran a large-scale crowdsourcing vocabulary test, which yielded 17 million useful responses. It provided us with a list of 445 words known to nearly all participants. The list was compared to various existing lists of words advised to include in the first stages of English L2 teaching. The data also provided us with a ranking of 61,000 words in terms of degree and speed of word recognition in English L2 speakers, which correlated r = .85 with a similar ranking based on native English speakers. The L2 speakers in our study were relatively better at academic words (which are often cognates in their mother tongue) and words related to experiences English L2 students are likely to have. They were worse at words related to childhood and family life. Finally, a new list of 20 levels of 1,000 word families is presented, which will be of use to English L2 teachers, as the levels represent the order in which English vocabulary seems to be acquired by L2 learners across the world.

We present word prevalence data for 61,858 English words. Word prevalence refers to the number of people who know the word. The measure was obtained on the basis of an online crowdsourcing study involving over 220,000 people. Word prevalence data are useful for gauging the difficulty of words and, as such, for matching stimulus materials in experimental conditions or selecting stimulus materials for vocabulary tests. Word prevalence also predicts word processing times, over and above the effects of word frequency, word length, similarity to other words, and age of acquisition, in line with previous findings in the Dutch language.

The word frequency effect refers to the observation that high-frequency words are processed more efficiently than low-frequency words. Although the effect was first described over 80 years ago, in recent years it has been investigated in more detail. It has become clear that considerable quality differences exist between frequency estimates and that we need a new standardized frequency measure that does not mislead users. Research also points to consistent individual differences in the word frequency effect, meaning that the effect will be present at different word frequency ranges for people with different degrees of language exposure. Finally, a few ongoing developments point to the importance of semantic diversity rather than mere differences in the number of times words have been encountered and to the importance of taking into account word prevalence in addition to word frequency.

Vocabulary size seems to be affected by multiple factors, including those that belong to the properties of the words themselves and those that relate to the characteristics of the individuals assessing the words. In this study, we present results from a crowdsourced lexical decision megastudy in which more than 150,000 native speakers from around 20 Spanish-speaking countries performed a lexical decision task to 70 target word items selected from a list of about 45,000 Spanish words. We examined how demographic characteristics such as age, education level, and multilingualism affected participants' vocabulary size. Also, we explored how common factors related to words like frequency, length, and orthographic neighbourhood influenced the knowledge of a particular item. Results indicated important contributions of age to overall vocabulary size, with vocabulary size increasing in a logarithmic fashion with this factor. Furthermore, a contrast between monolingual and bilingual communities within Spain revealed no significant vocabulary size differences between the communities. Additionally, we replicated the standard effects of the words' properties and their interactions, accurately accounting for the estimated knowledge of a particular word. These results highlight the value of crowdsourced approaches to uncover effects that are traditionally masked by small-sampled in-lab factorial experimental designs.

We present a new dataset of English word recognition times for a total of 62 thousand words, called the English Crowdsourcing Project. The data were collected via an internet vocabulary test in which more than one million people participated. The present dataset is limited to native English speakers. Participants were asked to indicate which words they knew. Their response times were registered, although at no point were the participants asked to respond as quickly as possible. Still, the response times correlate around .75 with the response times of the English Lexicon Project for the shared words. Also, the results of virtual experiments indicate that the new response times are a valid addition to the English Lexicon Project. This not only means that we have useful response times for some 35 thousand extra words, but we now also have data on differences in response latencies as a function of education and age.

Recent developments in distributional semantics (Mikolov, Chen, Corrado, & Dean, 2013; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013) include a new class of prediction-based models that are trained on a text corpus and that measure semantic similarity between words. We discuss the relevance of these models for psycholinguistic theories and compare them to more traditional distributional semantic models. We compare the models’ performances on a large dataset of semantic priming (Hutchison et al., 2013) and on a number of other tasks involving semantic processing and conclude that the prediction-based models usually offer a better fit to behavioral data. Theoretically, we argue that these models bridge the gap between traditional approaches to distributional semantics and psychologically plausible learning principles. As an aid to researchers, we release semantic vectors for English and Dutch for a range of models together with a convenient interface that can be used to extract a great number of semantic similarity measures.

We monitored the progress of 40 children when they first started to acquire a second language (L2) implicitly through immersion. Employing a longitudinal design, we tested them before they had any notions of an L2 (Time 0) and after 1 school year of L2 exposure (Time 1) to determine whether cognitive abilities can predict the success of L2 learning. Task administration included measures of intelligence, cognitive control, and language skills. Initial scores on measures of inhibitory control seemed predictive of L2 Dutch vocabulary acquisition. At the same time, progress on IQ, inhibitory control, attentional shifting, and working memory were also identified as contributing factors, suggesting a more intricate relationship between cognitive abilities and L2 learning than previously assumed. Furthermore, L1 development was mainly predicted by performance on inhibitory control and working memory. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Subjective ratings for age of acquisition, concreteness, affective valence, and many other variables are an important element of psycholinguistic research. However, even for well-studied languages, ratings usually cover just a small part of the vocabulary. A possible solution involves using corpora to build a semantic similarity space and to apply machine learning techniques to extrapolate existing ratings to previously unrated words. We conduct a systematic comparison of two extrapolation techniques: k-nearest neighbours, and random forest, in combination with semantic spaces built using latent semantic analysis, topic model, a hyperspace analogue to language (HAL)-like model, and a skip-gram model. A variant of the k-nearest neighbours method used with skip-gram word vectors gives the most accurate predictions but the random forest method has an advantage of being able to easily incorporate additional predictors. We evaluate the usefulness of the methods by exploring how much of the human performance in a lexical decision task can be explained by extrapolated ratings for age of acquisition and how precisely we can assign words to discrete categories based on extrapolated ratings. We find that at least some of the extrapolation methods may introduce artefacts to the data and produce results that could lead to different conclusions that would be reached based on the human ratings. From a practical point of view, the usefulness of ratings extrapolated with the described methods may be limited.

This paper presents the first systematic examination of the monolingual and bilingual frequency effect (FE) during natural reading. We analyzed single fixations durations on content words for participants reading an entire novel. Unbalanced bilinguals and monolinguals show a similarly sized FE in their mother tongue (L1), but for bilinguals the FE is considerably larger in their second language (L2) than in their L1. The FE in both L1 and L2 reading decreased with increasing L1 proficiency, but it was not affected by L2 proficiency. Our results are consistent with an account of bilingual language processing that assumes an integrated mental lexicon with exposure as the main determiner for lexical entrenchment (Diependaele, Lemhöfer, & Brysbaert, 2013; Gollan et al., 2008). This means that no qualitative difference in language processing between monolingual, bilingual L1 or bilingual L2 is necessary to explain reading behavior. We specify this account and argue that not all groups of bilinguals necessarily have lower L1 exposure than monolinguals do and, in line with Kuperman and Van Dyke (2013), that individual vocabulary size and language exposure change the accuracy of the relative corpus word frequencies and thereby determine the size of the FE’s in the same way for all participants.

Computational psycholinguistics has a long history of investigation and modeling of morphological phenomena. Several computational models have been developed to deal with the processing and production of morphologically complex forms and with the relation between linguistic morphology and psychological word representations. Historically, most of this work has focused on modeling the production of inflected word forms, leading to the development of models based on connectionist principles and other data-driven models such as Memory-Based Language Processing (MBLP), Analogical Modeling of Language (AM), and Minimal Generalization Learning (MGL). In the context of inflectional morphology, these computational approaches have played an important role in the debate between single and dual mechanism theories of cognition. Taking a different angle, computational models based on distributional semantics have been proposed to account for several phenomena in morphological processing and composition. Finally, although several computational models of reading have been developed in psycholinguistics, none of them have satisfactorily addressed the recognition and reading aloud of morphologically complex forms.

We assess the amount of shared variance between three measures of visual word recognition latencies: eye movement latencies, lexical decision times, and naming times. After partialling out the effects of word frequency and word length, two well-documented predictors of word recognition latencies, we see that 7–44% of the variance is uniquely shared between lexical decision times and naming times, depending on the frequency range of the words used. A similar analysis of eye movement latencies shows that the percentage of variance they uniquely share either with lexical decision times or with naming times is much lower. It is 5–17% for gaze durations and lexical decision times in studies with target words presented in neutral sentences, but drops to 0.2% for corpus studies in which eye movements to all words are analysed. Correlations between gaze durations and naming latencies are lower still. These findings suggest that processing times in isolated word processing and continuous text reading are affected by specific task demands and presentation format, and that lexical decision times and naming times are not very informative in predicting eye movement latencies in text reading once the effect of word frequency and word length are taken into account. The difference between controlled experiments and natural reading suggests that reading strategies and stimulus materials may determine the degree to which the immediacy-of-processing assumption and the eye–mind assumption apply. Fixation times are more likely to exclusively reflect the lexical processing of the currently fixated word in controlled studies with unpredictable target words rather than in natural reading of sentences or texts.

The correspondence in meaning extracted from written versus spoken input remains to be fully understood neurobiologically. Here, in a total of 38 subjects, the functional anatomy of cross-modal semantic similarity for concrete words was determined based on a dual criterion: First, a voxelwise univariate analysis had to show significant activation during a semantic task (property verification) performed with written and spoken concrete words compared to the perceptually matched control condition. Second, in an independent dataset, in these clusters, the similarity in fMRI response pattern to two distinct entities, one presented as a written and the other as a spoken word, had to correlate with the similarity in meaning between these entities. The left ventral occipitotemporal transition zone and ventromedial temporal cortex, retrosplenial cortex, pars orbitalis bilaterally, and the left pars triangularis were all activated in the univariate contrast. Only the left pars triangularis showed a cross-modal semantic similarity effect. There was no effect of phonological nor orthographic similarity in this region. The cross-modal semantic similarity effect was confirmed by a secondary analysis in the cytoarchitectonically defined BA45. A semantic similarity effect was also present in the ventral occipital regions but only within the visual modality, and in the anterior superior temporal cortex only within the auditory modality. This study provides direct evidence for the coding of word meaning in BA45 and positions its contribution to semantic processing at the confluence of input-modality specific pathways that code for meaning within the respective input modalities. publisher: Elsevier articletitle: Cross-modal representation of spoken and written word meaning in left pars triangularis journaltitle: NeuroImage articlelink: http://dx.doi.org/10.1016/j.neuroimage.2017.02.032 content_type: article copyright: © 2017 Elsevier Inc. All rights reserved. ispartof: NeuroImage vol:150 pages:292-307 ispartof: location:United States status: published

Based on an analysis of the literature and a large scale crowdsourcing experiment, we estimate that an average 20 year old native speaker of American English knows 42,000 lemmas and 4,200 non-transparent multiword expressions, derived from 11,100 word families. The numbers range from 27,000 lemmas for the lowest 5% to 52,000 for the highest 5%. Between the ages of 20 and 60, the average person learns 6,000 extra lemmas or about one new lemma every two days. The knowledge of the words can be as shallow as knowing that the word exists. In addition, people learn tens of thousands of inflected forms and proper nouns (names), which account for the substantially high numbers of ‘words known’ mentioned in other publications.

People tend to slow down after they make an error. This phenomenon, generally referred to as post-error slowing, has been hypothesized to reflect perceptual distraction, time wasted on irrelevant processes, an a priori bias against the response made in error, increased variability in a priori bias, or an increase in response caution. Although the response caution interpretation has dominated the empirical literature, little research has attempted to test this interpretation in the context of a formal process model. Here, we used the drift diffusion model to isolate and identify the psychological processes responsible for post-error slowing. In a very large lexical decision data set, we found that post-error slowing was associated with an increase in response caution and-to a lesser extent-a change in response bias. In the present data set, we found no evidence that post-error slowing is caused by perceptual distraction or time wasted on irrelevant processes. These results support a response-monitoring account of post-error slowing. © 2011 The Author(s).

Allomorphy is a phenomenon that occurs in many languages. Several psycholinguistic studies have shown that allomorphy, if present, co-determines cognitive processing. In the present paper we discuss allomorphic variations of Serbian instrumental singular form of pseudo-nouns as emerging from analogical learning. We compare the predictions derived from memory-based language processing models with results from a previous experimental study with adult Serbian native speakers. Results confirm that the production of suffix allomorphs in Serbian instrumental singular masculine nouns can be accounted for by memory-based learning and simple analogical inferences. The present findings are in line with a growing body of research showing that memory-based learning models make relevant predictions about the cognitive processes involved in various linguistic phenomena.

Keuleers, Stevens, Mandera, and Brysbaert (2015) presented a new variable, word prevalence, defined as word knowledge in the population. Some words are known to more people than other. This is particularly true for low-frequency words (e.g., screenshot vs. scourage). In the present study, we examined the impact of the measure by collecting lexical decision times for 30,000 Dutch word lemmas of various lengths (the Dutch Lexicon Project 2). Word prevalence had the second highest correlation with lexical decision times (after word frequency): Words known by everyone in the population were responded to 100 ms faster than words known to only half of the population, even after controlling for word frequency, word length, age of acquisition, similarity to other words, and concreteness. Because word prevalence has rather low correlations with the existing measures (including word frequency), the unique variance it contributes to lexical decision times is higher than that of the other variables. We consider the reasons why word prevalence has an impact on word processing times and we argue that it is likely to be the most important new variable protecting researchers against experimenter bias in selecting stimulus materials.

This paper introduces and summarizes the special issue on megastudies, crowdsourcing, and large datasets in psycholinguistics. We provide a brief historical overview and show how the papers in this issue have extended the field by compiling new databases and making important theoretical contributions. In addition, we discuss several studies that use text corpora to build distributional semantic models to tackle various interesting problems in psycholinguistics. Finally, as is the case across the papers, we highlight some methodological issues that are brought forth via the analyses of such datasets.

We use the results of a large online experiment on word knowledge in Dutch to investigate variables influencing vocabulary size in a large population and to examine the effect of word prevalence-the percentage of a population knowing a word-as a measure of word occurrence. Nearly 300,000 participants were presented with about 70 word stimuli (selected from a list of 53,000 words) in an adapted lexical decision task. We identify age, education, and multilingualism as the most important factors influencing vocabulary size. The results suggest that the accumulation of vocabulary throughout life and in multiple languages mirrors the logarithmic growth of number of types with number of tokens observed in text corpora (Herdan's law). Moreover, the vocabulary that multilinguals acquire in related languages seems to increase their first language (L1) vocabulary size and outweighs the loss caused by decreased exposure to L1. In addition, we show that corpus word frequency and prevalence are complementary measures of word occurrence covering a broad range of language experiences. Prevalence is shown to be the strongest independent predictor of word processing times in the Dutch Lexicon Project, making it an important variable for psycholinguistic research.

We present SUBTLEX-PL, Polish word frequencies based on movie subtitles. In two lexical decision experiments, we compare the new measures with frequency estimates derived from another Polish text corpus that includes predominantly written materials. We show that the frequencies derived from the two corpora perform best in predicting human performance in a lexical decision task if used in a complementary way. Our results suggest that the two corpora may have unequal potential for explaining human performance for words in different frequency ranges and that corpora based on written materials severely overestimate frequencies for formal words. We discuss some of the implications of these findings for future studies comparing different frequency estimates. In addition to frequencies for word forms, SUBTLEX-PL includes measures of contextual diversity, part-of-speech-specific word frequencies, frequencies of associated lemmas, and word bigrams, providing researchers with necessary tools for conducting psycholinguistic research in Polish. The database is freely available for research purposes and may be downloaded from the authors' university Web site at http://crr.ugent.be/subtlex-pl .

We present word frequencies based on subtitles of British television programmes. We show that the SUBTLEX-UK word frequencies explain more of the variance in the lexical decision times of the British Lexicon Project than the word frequencies based on the British National Corpus and the SUBTLEX-US frequencies. In addition to the word form frequencies, we also present measures of contextual diversity part-of-speech specific word frequencies, word frequencies in children programmes, and word bigram frequencies, giving researchers of British English access to the full range of norms recently made available for other languages. Finally, we introduce a new measure of word frequency, the Zipf scale, which we hope will stop the current misunderstandings of the word frequency effect.

The extent to which a symbolic time–series (a sequence of sounds or letters) is a typical word of a language, referred to as WORDLIKENESS, has been shown to have effects in speech perception and production, reading proficiency, lexical development and lexical access, short–term and long–term verbal memory. Two quantitative models have been suggested to account for these effects: serial phonotactic probabilities (the likelihood for a given symbolic sequence to appear in the lexicon) and lexical density (the extent to which other words can be obtained from a target word by changing, deleting or inserting one or more symbols in the target). The two measures are highly correlated and thus easy to be confounded in measuring their effects in lexical tasks. In this paper, we propose a computational model of lexical organisation, based on Self–Organising Maps with Hebbian connections defined over a temporal layer (TSOMs), providing a principled algorithmic account of effects of lexical acquisition, processing and access, to further investigate these issues. In particular, we show that (morpho–)phonotactic probabilities and lexical density, though correlated in lexical organisation, can be taken to focus on different aspects of speakers’ word processing behaviour and thus provide independent cognitive contributions to our understanding of the principles of perception of typicality that govern lexical organisation., Pokazano je da stupanj do kojeg je određeni simbolički vremenski slijed (slijed zvukova ili slova) tipična riječ u jeziku, odnosno TIPIčNOST OBLIKA RIJEčI, ima učinaka u proizvodnji i percepciji govora, uspješnosti čitanja, leksičkom razvoju i pristupu leksemima te kratkotrajnoj i dugotrajnoj verbalnoj memoriji. Predložena su dva kvantitativna modela kako bi se objasnili navedeni učinci: serijalne fonotaktičke vjerojatnosti (vjerojatnost pojavljivanja određenog simboličkog slijeda u leksikonu) i leksička gustoća (mjera do koje se druge riječi mogu proizvesti zamjenom, brisanjem ili umetanjem jednog ili više simbola u ciljnu riječ). Te dvije mjere visoko koreliraju, zbog čega su teško razdvojive pri mjerenju njihovih učinaka u leksičkim zadacima. U ovom radu predlažemo računalni model leksičke organizacije koji pruža sustavan algoritamski prikaz učinaka leksičkog usvajanja, obrade i pristupa kako bi se dodatno istražila ova pitanja. Taj se model temelji na samoorganizirajućim mapama s hebijanskim vezama definiranim preko vremenske razine (engl. TSOMs). Posebice pokazujemo da se (morfo-)fonotaktičke vjerojatnosti i leksička gustoća, iako korelirani u leksičkoj organizaciji, mogu shvatiti kao načini usredotočavanja na različite aspekte govornikova ponašanja pri obradi riječi i tako pružiti nezavisne kognitivne doprinose našem razumijevanju principa percepcije i tipičnosti koji upravljaju leksičkom organizacijom.

The SUBTLEX-US corpus has been parsed with the CLAWS tagger, so that researchers have information about the possible word classes (parts‐of‐speech, or PoSs) of the entries. Five new columns have been added to the SUBTLEX-US word frequency list: the dominant (most frequent) PoS for the entry, the frequency of the dominant PoS, the frequency of the dominant PoS relative to the entry’s total frequency, all PoSs observed for the entry, and the respective frequencies of these PoSs. Because the current definition of lemma frequency does not seem to provide word recognition researchers with useful information (as illustrated by a comparison of the lemma frequencies and the word form frequencies from the Corpus of Contemporary American English), we have not provided a column with this variable. Instead, we hope that the full list of PoS frequencies will help researchers to collectively determine which combination of frequencies is the most informative.

In this Perspective Article we assess the usefulness of Google's new word frequencies for word recognition research (lexical decision and word naming). We find that, despite the massive corpus on which the Google estimates are based (131 billion words from books published in the United States alone), the Google American English frequencies explain 11% less of the variance in the lexical decision times from the English Lexicon Project (Balota et al., 2007) than the SUBTLEX-US word frequencies, based on a corpus of 51 million words from film and television subtitles. Further analyses indicate that word frequencies derived from recent books (published after 2000) are better predictors of word processing times than frequencies based on the full corpus, and that word frequencies based on fiction books predict word processing times better than word frequencies based on the full corpus. The most predictive word frequencies from Google still do not explain more of the variance in word recognition times of undergraduate students and old adults than the subtitle-based word frequencies.

We present a new database of lexical decision times for English words and nonwords, for which two groups of British participants each responded to 14,365 monosyllabic and disyllabic words and the same number of nonwords for a total duration of 16 h (divided over multiple sessions). This database, called the British Lexicon Project (BLP), fills an important gap between the Dutch Lexicon Project (DLP; Keuleers, Diependaele, & Brysbaert, Frontiers in Language Sciences. Psychology, 1, 174, 2010) and the English Lexicon Project (ELP; Balota et al., 2007), because it applies the repeated measures design of the DLP to the English language. The high correlation between the BLP and ELP data indicates that a high percentage of variance in lexical decision data sets is systematic variance, rather than noise, and that the results of megastudies are rather robust with respect to the selection and presentation of the stimuli. Because of its design, the BLP makes the same analyses possible as the DLP, offering researchers with a new interesting data set of word-processing times for mixed effects analyses and mathematical modeling. The BLP data are available at http://crr.ugent.be/blp and as Electronic Supplementary Materials. Electronic supplementary material The online version of this article (doi:10.3758/s13428-011-0118-4) contains supplementary material, which is available to authorized users.

We report performance measures for lexical decision, word naming, and progressive demasking for a large sample of monosyllabic, monomorphemic French words (N = 1,482). We compare the tasks and also examine the impact of word length, word frequency, initial phoneme, orthographic and phonological distance to neighbors, age-of-acquisition, and subjective frequency. Our results show that objective word frequency is by far the most important variable to predict reaction times in lexical decision. For word naming, it is the first phoneme. Progressive demasking was more influenced by a semantic variable (word imageability) than lexical decision, but was also affected to a much greater extent by perceptual variables (word length, first phoneme/letters). This may reduce its usefulness as a psycholinguistic word recognition task.

In three experiments we compared the performance of native English speakers to that of Spanish–English and Dutch–English bilinguals on a masked morphological priming lexical decision task. The results do not show significant differences across the three experiments. In line with recent meta-analyses, we observed a graded pattern of facilitation across stem priming with transparent suffixed primes (e.g., viewer–view), opaque suffixed or pseudo-suffixed primes (e.g., corner–corn) and form control primes (e.g., freeze–free). Priming was largest in the transparent condition, smallest in the form condition and intermediate in the opaque condition. Our data confirm the hypothesis that bilinguals largely adopt the same processing strategies as native speakers (e.g., Lemhofer et al., 2008 ), and constrain the hypothesis that bilinguals rely more heavily on whole-word processing in their second language ( Clahsen et al., 2010 , Ullman, 2004 , Ullman, 2005 ). The observed pattern of morphological priming is in line with earlier monolingual studies, further highlighting the reality of semantic transparency effects in the initial stages of word recognition.

Pseudowords play an important role in psycholinguistic experiments, either because they are required for performing tasks, such as lexical decision, or because they are the main focus of interest, such as in nonword-reading and nonce-inflection studies. We present a pseudoword generator that improves on current methods. It allows for the generation of written polysyllabic pseudowords that obey a given language's phonotactic constraints. Given a word or nonword template, the algorithm can quickly generate pseudowords that match the template in subsyllabic structure and transition frequencies without having to search through a list with all possible candidates. Currently, the program is available for Dutch, English, German, French, Spanish, Serbian, and Basque, and, with little effort, it can be expanded to other languages.

We present a new database of Dutch word frequencies based on film and television subtitles, and we validate it with a lexical decision study involving 14,000 monosyllabic and disyllabic Dutch words. The new SUBTLEX frequencies explain up to 10% more variance in accuracies and reaction times (RTs) of the lexical decision task than the existing CELEX word frequency norms, which are based largely on edited texts. As is the case for English, an accessibility measure based on contextual diversity explains more of the variance in accuracy and RT than does the raw frequency of occurrence counts. The database is freely available for research purposes and may be downloaded from the authors’ university site at http://crr.ugent.be/subtlex-nl or from http://brm psychonomic-journals.org/content/supplemental.

The French Lexicon Project involved the collection of lexical decision data for 38,840 French words and the same number of nonwords. It was directly inspired by the English Lexicon Project (Balota et al., 2007) and produced very comparable frequency and word length effects. The present article describes the methods used to collect the data, reports analyses on the word frequency and the word length effects, and describes the Excel files that make the data freely available for research purposes. The word and pseudoword data from this article may be downloaded from http://brm.psychonomic-journals.org/content/supplemental.

In recent years, psycholinguistics has seen a remarkable growth of research based on the analysis of data from large-scale studies of word recognition, in particular lexical decision and word naming. We present the data of the Dutch Lexicon Project (DLP) in which a group of 39 participants made lexical decisions to 14,000 words and the same number of nonwords. To examine whether the extensive practice precludes comparison with the traditional short experiments, we look at the differences between the first and the last sesssion, compare the results with the English Lexicon Project (ELP) and the French Lexicon Project (FLP), and examine to what extent established findings in Dutch psycholinguistics can be replicated in virtual experiments. Our results show that when good nonwords are used, practice effects are minimal in lexical decision experiments and do not invalidate the behavioral data. For instance, the word frequency curve is the same in DLP as in ELP and FLP. Also, the Dutch-English cognate effect is the same in DLP as in a previously published factorial experiment. This means that large-scale word recognition studies can make use of psychophysical and psychometrical approaches. In addition, our data represent an important collection of very long series of individual reaction times that may be of interest to researchers in other areas.

We develop the view that inflection is driven partly by non-phonological analogy and that non-phonological information is of particular importance to the inflection of non-canonical roots, which in the view of [Marcus, G. F., Brinkmann, U., Clahsen, H., Wiese, R., & Pinker, S. (1995). German inflection: the exception that proves the rule. Cognitive Psychology, 29, 189-256.] are inflected by a symbolic rule process. We used the Dutch plural to evaluate these claims. An analysis of corpus data shows that a model using non-phonological information (orthography) produces significantly fewer errors on plurals of non-canonical Dutch nouns, in particular borrowings, than a model that includes only phonological information. Moreover, we show that a double default system, as proposed by Pinker [Pinker, S. (1999). Words and rules. London: Phoenix.], does not offer an advantage over the latter model. A second study, examining the use of orthography in an online plural production task, shows that, in Dutch, the chosen pseudoword plural is significantly affected by non-phonological information. A final simulation study confirms that these results are in line with a model of inflectional morphology that explains the inflection of non-canonical roots by non-phonological analogy instead of by a default rule process.