We present age-of-acquisition (AoA) ratings for 30,121 English content words (nouns, verbs, and adjectives). For data collection, this megastudy used the Web-based crowdsourcing technology offered by the Amazon Mechanical Turk. Our data indicate that the ratings collected in this way are as valid and reliable as those collected in laboratory conditions (the correlation between our ratings and those collected in the lab from U.S. students reached .93 for a subsample of 2,500 monosyllabic words). We also show that our AoA ratings explain a substantial percentage of the variance in the lexical-decision data of the English Lexicon Project, over and above the effects of log frequency, word length, and similarity to other words. This is true not only for the lemmas used in our rating study, but also for their inflected forms. We further discuss the relationships of AoA with other predictors of word recognition and illustrate the utility of AoA ratings for research on vocabulary growth. Keywords Word recognition . Age of acquisition . Ratings . Amazon Mechanical TurkResearchers using words as stimulus materials typically control or manipulate their stimuli on a number of variables.The four that are most commonly used are word frequency, word length, similarity to other words, and word onset. In this article, we will argue that age of acquisition (AoA) should be part of this list, and we provide ratings for a substantial number of words in order to do so. First, however, we will discuss the evidence in favor of the big four.

Ratings of age of acquisition (AoA), imageability, and familiarity were collected for 1,526 words. The methodology made use of a modular approach, in which the full sample of words was divided into five separate blocks. Within each block, each word was rated on each of the three variables by 20 participants (undergraduate students from the University of Bristol). Analyses comparing these ratings to existing norm databases demonstrated that this methodology resulted in high reliability (assessed by Cronbach's ) and validity. The ratings were also transformed to be compatible with the Gilhooly and Logie (1980) norms. This transformation resulted in a set of norms for 3,394 words, which is by far the largest database of ratings for AoA, imageability, and familiarity to date. The resulting database should be useful for researchers interested in manipulating or controlling these factors in word recognition, neuropsychological, or memory studies. These norms can be downloaded from language.psy.bris .ac.uk/bristol_norms.html.

Most current models of research on emotion recognize valence (how pleasant a stimulus is) and arousal (the level of activation or intensity that a stimulus elicits) as important components in the classification of affective experiences (Barrett, 1998; Kuppens, Tuerlinckx, Russell, & Barrett, 2012). Here we present a set of norms for valence and arousal for a very large set of Spanish words, including items from a variety of frequencies, semantic categories, and parts of speech, including a subset of conjugated verbs. In this regard, we found that there were significant but very small differences between the ratings for conjugations of the same verb, validating the practice of applying the ratings for infinitives to all derived forms of the verb. Our norms show a high degree of reliability and are strongly correlated with those of Redondo, Fraga, Padron, and Comesana's (2007) Spanish version of the influential Affective Norms for English Words (Bradley & Lang, 1999), as well as those from Warriner, Kuperman, and Brysbaert (2013), the largest available set of emotional norms for English words. Additionally, we included measures of word prevalence-that is, the percentage of participants that knew a particular word-for each variable (Keuleers, Stevens, Mandera, & Brysbaert, 2015). Our large set of norms in Spanish not only will facilitate the creation of stimuli and the analysis of texts in that language, but also will be useful for cross-language comparisons and research on emotional aspects of bilingualism. The norms can be downloaded and available as a supplementary materials to this article

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The name-picture verification task is widely used in spoken production studies to control for nonlexical differences between picture sets. In this task a word is presented first and followed, after a pause, by a picture. Participants must then make a speeded decision on whether both word and picture refer to the same object. Using regression analyses, we systematically explored the characteristics of this task by assessing the independent contribution of a series of factors that have been found relevant for picture naming in previous studies. We found that, for "match" responses, both visual and conceptual factors played a role, but lexical variables were not significant contributors. No clear pattern emerged from the analysis of "no-match" responses. We interpret these results as validating the use of "match" latencies as control variables in studies or spoken production using picture naming. Norms for match and no-match responses for 396 line drawings taken from Cycowicz, Friedman, Rothstein, and Snodgrass (1997) can be downloaded at: http://language.psy.bris.ac.uk/name-picture_verification.html.

The discrete emotion theory proposes that affective experiences can be reduced to a limited set of universal "basic" emotions, most commonly identified as happiness, sadness, anger, fear, and disgust. Here we present norms for 10,491 Spanish words for those five discrete emotions collected from a total of 2,010 native speakers, making it the largest set of norms for discrete emotions in any language to date. When used in conjunction with the norms from Hinojosa, Martínez-García et al. (Behavior Research Methods, 48, 272-284, 2016) and Ferré, Guasch, Martínez-García, Fraga, & Hinojosa (Behavior Research Methods, 49, 1082-1094, 2017), researchers now have access to ratings of discrete emotions for 13,633 Spanish words. Our norms show a high degree of inter-rater reliability and correlate highly with those from Ferré et al. (2017). Our exploration of the relationship between the five discrete emotions and relevant lexical and emotional variables confirmed findings of previous studies conducted with smaller datasets. The availability of such large set of norms will greatly facilitate the study of emotion, language and related fields. The norms are available as supplementary materials to this article.

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