The Mental Lexicon - Volume 13, Issue 3, 2018

The frequency and distribution of forms within a lexeme’s paradigm affect how quickly forms are accessed (e.g., Kostić, 1991; Milin, Filipović Đurđević, & Moscoso del Prado Martín, 2009; Moscoso del Prado Martı́n, Kostić, & Baayen, 2004). The distribution of forms across paradigms, in contrast, has received little experimental attention. Theoretical studies investigate the distribution of forms across paradigms because forms vary in how predictive they are of other (unknown) forms. Such investigations have uncovered typological tendencies (e.g., Ackerman & Malouf, 2013; Stump & Finkel, 2013) and contribute to explanations of language-specific phenomena (e.g., Sims, 2015; Parker & Sims, To appear). The intersection of these research approaches raises questions about how the distribution of forms within and across paradigms affects lexical access and representation. Based on forms of Russian nouns representing two morphosyntactic property sets and lexemes from three inflection classes, it is shown that speakers are sensitive to differences in form and morphosyntactic property set in a visual lexical decision task. In a priming task, nominative forms prime locative forms better than vice versa regardless of suffix, despite differences between the same forms in the lexical decision task. These results suggest that speakers make generalizations about forms across classes, including at the level of word forms and morphosyntactic property sets.

Idiom studies typically consider variables such as familiarity, decomposability and literal plausibility, and the contributions of these to how figurative phrases are processed are well established. In this study we consider the effect of a previously untested variable: semantic richness. Semantic richness refers broadly to the range of semantic information denoted by a lexical item, and reflects features such as imageability, number of senses, semantic neighbourhood, etc. This has generally been restricted to single words and sometimes to metaphors, so here we investigate how some aspects of this measure – specifically those reflecting perceptual characteristics – contribute to the processing of idiomatic expressions. Results show that aspects of semantic richness affect idiom processing in different ways, with some (emotional valence) contributing to faster processing of figuratively related words, and others (those that highlight physical and literal aspects of the idiom) showing an inhibitory effect. We also show that for some of the dimensions of semantic richness considered here, there is a significant correlation between a measure constructed from the ratings of component words, and one gathered from ratings for the phrase as a whole, suggesting a straightforward way to operationalise semantic richness at a multiword level.

An oft-cited shortcoming of Interactive Activation as a psychological model of word reading is that it lacks the ability to simultaneously represent words of different lengths.

We present an implementation of the Interactive Activation model, which we call Metameric, that can simulate words of different lengths, and show that there is nothing inherent to Interactive Activation which prevents it from simultaneously representing multiple word lengths. We provide an in-depth analysis of which specific factors need to be present, and show that the inclusion of three specific adjustments, all of which have been published in various models before, lead to an Interactive Activation model which is fully capable of representing words of different lengths. Finally, we show that our implementation is fully capable of representing all words between 2 and 11 letters in length from the English Lexicon Project (31, 416 words) in a single model. Our implementation is completely open source, heavily optimized, and includes both command line and graphical user interfaces, but is also agnostic to specific input data or problems. It can therefore be used to simulate a myriad of other models, e.g., models of spoken word recognition. The implementation can be accessed at www.github.com/clips/metameric.

To date, normative psycholinguistics research has mainly focused on establishing norms for producing databases for concrete words using standardized pictures, while abstract words have been subject to much less attention. Understandably, the fact that the first can be represented visually helps in formulating picture-naming tasks to elicit verbal identification for pictures representing nouns and verbs, which greatly contributes to language experiments in both theoretical and clinical studies. The present study argues for the equal importance of studies that aim to develop databases for abstract words, as language use is not restricted to picturable/concrete concepts. We provide norms for a set of 165 abstract nouns, 56 abstract verbs and 109 abstract adjectives, collected from healthy speakers of Arabic. Using rating tasks, norms for imageability, age of acquisition, and familiarity are established. Linguistic factors such as syllable length and phoneme length are also accounted for. We also include orthographic frequency values (extracted from AraLex; Boudelaa and Marslen-Wilson, 2010). The norms for the processing of abstract words collected in the current study present a valuable resource for researchers and clinicians working with speakers of Arabic. To the best of our knowledge, this is the first dataset of abstract words for the Arabic language.

The contributions of semantic processing have come under increasing attention in recent years (Yap, Pexman, Wellsby, Hargreaves, & Huff, 2012), and variables that measure the semantic content of words are a requirement of this increased experimental attention. The density and size of semantic neighborhoods derived from computational models have been shown to predict reaction times across a range of psycholinguistic tasks (e.g., Danguecan & Buchanan, 2016), and the distance between two words in semantic space has been shown to predict priming (Kenett, Levi, Anaki & Faust, 2017). The data to support the construction of stimulus sets that use these variables are complicated to obtain. The app that we describe here makes these measures of semantics available for 100,000 English words.