Information about the affective meanings of words is used by researchers working on emotions and moods, word recognition and memory, and text-based sentiment analysis. Three components of emotions are traditionally distinguished: valence (the pleasantness of a stimulus), arousal (the intensity of emotion provoked by a stimulus), and dominance (the degree of control exerted by a stimulus). Thus far, nearly all research has been based on the ANEW norms collected by Bradley and Lang (1999) for 1,034 words. We extended that database to nearly 14,000 English lemmas, providing researchers with a much richer source of information, including gender, age, and educational differences in emotion norms. As an example of the new possibilities, we included stimuli from nearly all of the category norms (e.g., types of diseases, occupations, and taboo words) collected by Van Overschelde, Rawson, and Dunlosky (Journal of Memory and Language 50:289-335, 2004), making it possible to include affect in studies of semantic memory.

CCo on nc cr re et te en ne es ss s r ra at ti in ng gs s f fo or r 4 40 0 t th ho ou us sa an nd d g ge en ne er ra al ll ly y k kn no ow wn n E En ng gl li is sh h w wo or rd d l le em mm ma as s AbstractConcreteness ratings are presented for 37,058 English words and 2,896 two-word expressions (such as "zebra crossing" and "zoom in"), obtained from over four thousand participants by means of a norming study using internet crowdsourcing for data collection. Although the instructions stressed that the assessment of word concreteness would be based on experiences involving all senses and motor responses, a comparison with the existing concreteness norms indicates that participants, as before, largely focused on visual and haptic experiences. The reported dataset is a subset of a comprehensive list of English lemmas and contains all lemmas known by at least 85% of the raters. It can be used in future research as a reference list of generally known English lemmas. Concreteness ratings for 40 thousand English word lemmasConcreteness evaluates the degree to which the concept denoted by a word refers to a perceptible entity. The variable came to the foreground in Paivio's dual-coding theory (Paivio, 1971(Paivio, , 2013. According to this theory, concrete words are easier to remember than abstract words, because they activate perceptual memory codes in addition to verbal codes.Schwanenflugel, Harnishfeger, and Stowe (1988) presented an alternative context availability theory, according to which concrete words are easier to process because they are related to strongly supporting memory contexts, whereas abstract words are not, as can be demonstrated by asking people how easy it is to think of a context in which the word can be used.

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.

Emotion influences most aspects of cognition and behavior, but emotional factors are conspicuously absent from current models of word recognition. The influence of emotion on word recognition has mostly been reported in prior studies on the automatic vigilance for negative stimuli, but the precise nature of this relationship is unclear. Various models of automatic vigilance have claimed that the effect of valence on response times is categorical, an inverted-U, or interactive with arousal. The present study used a sample of 12,658 words, and included many lexical and semantic control factors, to determine the precise nature of the effects of arousal and valence on word recognition. Converging empirical patterns observed in word-level and trial-level data from lexical decision and naming indicate that valence and arousal exert independent monotonic effects: Negative words are recognized more slowly than positive words, and arousing words are recognized more slowly than calming words. Valence explained about 2% of the variance in word recognition latencies, whereas the effect of arousal was smaller. Valence and arousal do not interact, but both interact with word frequency, such that valence and arousal exert larger effects among low-frequency words than among high-frequency words. These results necessitate a new model of affective word processing whereby the degree of negativity monotonically and independently predicts the speed of responding. This research also demonstrates that incorporating emotional factors, especially valence, improves the performance of models of word recognition.

This study is a large-scale exploration of the influence that individual reading skills exert on eye-movement behavior in sentence reading. Seventy one non-college-bound 16–24 year-old speakers of English completed a battery of 18 verbal and cognitive skill assessments, and read a series of sentences as their eye movements were monitored. Statistical analyses were performed to establish what tests of reading abilities were predictive of eye-movement patterns across this population and how strong the effects were. We found that individual scores in rapid automatized naming and word identification tests (i) were the only participant variables with reliable predictivity throughout the time-course of reading; (ii) elicited effects that superceded in magnitude the effects of established predictors like word length or frequency; and (iii) strongly modulated the influence of word length and frequency on fixation times. We discuss implications of our findings for testing reading ability, as well as for research of eye-movements in reading.