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Notable progress has been made recently on computational models of semantics using vector representations for word meaning (Mikolov, Chen, Corrado, & Dean, 2013 ; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013 ). As representations of meaning, recent models presumably hone in on plausible organizational principles for meaning. We performed an analysis on the organization of the skip-gram model’s semantic space. Consistent with human performance (Osgood, Suci, & Tannenbaum, 1957 ), the skip-gram model primarily relies on affective distinctions to organize meaning. We showed that the skip-gram model accounts for unique variance in behavioral measures of lexical access above and beyond that accounted for by affective and lexical measures. We also raised the possibility that word frequency predicts behavioral measures of lexical access due to the fact that word use is organized by semantics. Deconstruction of the semantic representations in semantic models has the potential to reveal organizing principles of human semantics.

Sound symbolism refers to associations between language sounds (i.e., phonemes) and perceptual and/or semantic features. One example is the maluma/takete effect: an association between certain phonemes (e.g., /m/, /u/) and roundness, and others (e.g., /k/, /ɪ/) and spikiness. While this association has been demonstrated in laboratory tasks with nonword stimuli, its presence in existing spoken language is unknown. Here we examined whether the maluma/takete effect is attested in English, across a broad sample of words. Best–worst judgments from 171 university students were used to quantify the shape of 1,757 objects, from spiky to round. We then examined whether the presence of certain phonemes in words predicted the shape of the objects to which they refer. We found evidence that phonemes associated with roundness are more common in words referring to round objects, and phonemes associated with spikiness are more common in words referring to spiky objects. This represents an instance of iconicity, and thus nonarbitrariness, in human language.

The process of connected text reading has received very little attention in contemporary cognitive psychology. This lack of attention is in parts due to a research tradition that emphasizes the role of basic lexical constituents, which can be studied in isolated words or sentences. However, this lack of attention is in parts also due to the lack of statistical analysis techniques, which accommodate interdependent time series. In this study, we investigate text reading performance with traditional and nonlinear analysis techniques and show how outcomes from multiple analyses can used to create a more detailed picture of the process of text reading. Specifically, we investigate reading performance of groups of literate adult readers that differ in reading fluency during a self-paced text reading task. Our results indicate that classical metrics of reading (such as word frequency) do not capture text reading very well, and that classical measures of reading fluency (such as average reading time) distinguish relatively poorly between participant groups. Nonlinear analyses of distribution tails and reading time fluctuations provide more fine-grained information about the reading process and reading fluency.

Continuous bag of words (CBOW) and skip-gram are two recently developed models of lexical semantics (Mikolov, Chen, Corrado, & Dean, Advances in Neural Information Processing Systems, 26, 3111–3119, 2013 ). Each has been demonstrated to perform markedly better at capturing human judgments about semantic relatedness than competing models (e.g., latent semantic analysis; Landauer & Dumais, Psychological Review , 104 (2), 1997 211; hyperspace analogue to language; Lund & Burgess, Behavior Research Methods, Instruments, & Computers , 28 (2), 203–208, 1996 ). The new models were largely developed to address practical problems of meaning representation in natural language processing. Consequently, very little attention has been paid to the psychological implications of the performance of these models. We describe the relationship between the learning algorithms employed by these models and Anderson’s rational theory of memory (J. R. Anderson & Milson, Psychological Review , 96 (4), 703, 1989 ) and argue that CBOW is learning word meanings according to Anderson’s concept of needs probability. We also demonstrate that CBOW can account for nearly all of the variation in lexical access measures typically attributable to word frequency and contextual diversity—two measures that are conceptually related to needs probability. These results suggest two conclusions: One, CBOW is a psychologically plausible model of lexical semantics. Two, word frequency and contextual diversity do not capture learning effects but rather memory retrieval effects.

A vector-based model of discriminative learning is presented. It is demonstrated to learn association strengths identical to the Rescorla–Wagner model under certain parameter settings (Rescorla & Wagner, 1972 , Classical Conditioning II: Current Research and Theory , 2 , 64–99). For other parameter settings, it approximates the association strengths learned by the Rescorla–Wagner model. I argue that the Rescorla–Wagner model has conceptual details that exclude it as an algorithmically plausible model of learning. The vector learning model, however, does not suffer from the same conceptual issues. Finally, we demonstrate that the vector learning model provides insight into how animals might learn the semantics of stimuli rather than just their associations. Results for simulations of language processing experiments are reported.

Although studies of humour are as old as the Western academic tradition, most theories are too vague to allow for modelling and prediction of humour judgments. Previous work in modelling humour judgments has succeeded by focusing on the world's worst jokes: the slight humour of single nonwords (Westbury, Shaoul, Moroschan, & Ramscar, 2016) and single words (Westbury & Hollis, 2019). Here that work is extended to the world's third-worst jokes, adjective-noun pairs such as dancing dildo, flabby goldfish, and pompous snack. Participants used best-worst scaling to rate the humour of random word pairs. Those judgments were modelled using both linear regression and genetic programming, which is not constrained by assumptions of linearity. The linear regression models were as successful as the nonlinear models at predicting humour judgments, accounting for 27% of the variance in a 540-item validation set. Predictors associated only with the noun and with the relationship between the adjective and noun accounted for much more variance (over 14% each) than predictors associated only with the adjective (6.3%). Greater cosine distance of the adjective word2vec vector from the vectors of the shared neighbors of the noun and adjective is associated with higher humour ratings, whereas the opposite relationship is true for the noun. This captures a form of incongruity not seen in single items, by which neighbours of the adjective become unexpectedly relevant only when the noun brings them into focus. Bien que les études sur l'humour remontent à aussi loin que la tradition universitaire occidentale, la plupart des théories sont trop vagues pour permettre de modéliser et de prédire les jugements relatifs à l'humour. Les travaux antérieurs visant à modéliser les jugements relatifs à l'humour ont connu du succès en centrant leur attention sur les pires blagues au monde : l'humour (discret) des non-mots (Westbury, Shaoul, Moroschan et Ramscar, 2016) et des mots uniques (Westbury et Hollis, 2019). Ici, ces travaux sont étoffés pour inclure les troisièmes pires blagues au monde, soit les paires nom-adjectif, par exemple : dildo dansant, poisson rouge flasque, grignotine hypocrite. Les participants devaient utiliser une échelle allant de la meilleure blague à la pire blague pour évaluer la qualité humoristique de paires de mots aléatoires. Ces jugements étaient ensuite modélisés en utilisant les méthodes de la régression linéaire et de la programmation génétique, laquelle n'est pas limitée par les hypothèses de la linéarité. Les modèles de régression linéaire réussissaient aussi bien que les modèles non linéaires pour prédire les jugements relatifs à l'humour, et représentaient 27 % de l'écart dans un ensemble de validation de 540 éléments. Les indicateurs associés seulement au nom et à la relation entre le nom et son adjectif expliquaient une bien plus grande portion de l'écart (plus de 14 % pour chacun de ces indicateurs) que les indicateurs associés seulement à l'adjectif. Une plus grande distance cosinusoïdale du vecteur adjectif word2vec par rapport aux vecteurs des voisins partagés du nom et de l'adjectif est associée à des évaluations plus favorables de la qualité humoristique, tandis que la relation opposée est vraie pour le nom. Cela englobe une forme d'incongruité que l'on ne constate pas pour les éléments simples, selon laquelle les voisins des adjectifs deviennent soudainement pertinents, mais seulement lorsque le nom les fait ressortir. Public Significance Statement Humour is a complex phenomenon. The scientific study of humour has mainly depended on simple experiments that manipulate a single element to assess its importance in the experience of humour. In this study we take a different approach, by building and testing statistical models that use many elements to predict human humour judgments of adjective-noun word pairs such as \"stupid sausage.\"

In recent years large datasets of lexical processing times have been released for several languages, including English, French, Spanish, and Dutch. Such datasets have enabled us to study, compare, and model the global effects of many psycholinguistic measures such as word frequency, orthographic neighborhood (ON) size, and word length. We have compiled and publicly released a frequency and ON dictionary of 64,546 words and 1800 plausible NWs from a language that has been relatively little studied by psycholinguists: Persian. We have also collected visual lexical decision reaction times for 1800 Persian words and nonwords. Persian offers an interesting psycholinguistic environment for several reasons, including that it has few long words and has resultantly dense orthographic neighborhoods. These characteristics provide us with an opportunity to contrast how these factors affect lexical access by comparing them to several other languages. The results suggest that sensitivity to word length and orthographic neighbourhood may reflect the statistical structure of a particular language, rather than being a universal element of lexical processing. The dictionary and LDRT data are available from https://osf.io/tb4m6/.

The use of taboo words represents one of the most common and arguably universal linguistic behaviors, fulfilling a wide range of psychological and social functions. However, in the scientific literature, taboo language is poorly characterized, and how it is realized in different languages and populations remains largely unexplored. Here we provide a database of taboo words, collected from different linguistic communities (Study 1, N = 1046), along with their speaker-centered semantic characterization (Study 2, N = 455 for each of six rating dimensions), covering 13 languages and 17 countries from all five permanently inhabited continents. Our results show that, in all languages, taboo words are mainly characterized by extremely low valence and high arousal, and very low written frequency. However, a significant amount of cross-country variability in words’ tabooness and offensiveness proves the importance of community-specific sociocultural knowledge in the study of taboo language.

In this article, we describe the Naturalistic University of Alberta Nonlinear Correlation Explorer (NUANCE), a computer program for data exploration and analysis. NUANCE is specialized for finding nonlinear relations between any number of predictors and a dependent value to be predicted. It searches the space of possible relations between the predictors and the dependent value by using natural selection to evolve equations that maximize the correlation between their output and the dependent value. In this article, we introduce the program, describe how to use it, and provide illustrative examples. NUANCE is written in Java, which runs on most computer platforms. We have contributed NUANCE to the archival Web site of the Psychonomic Society (www.psychonomic.org/archive), from which it may be freely downloaded.

steels & belpaeme (s&b) describe the role of genetic evolution in linguistic category sharing among a population of agents. we consider their methodology and conclude that, although it is plausible that genetic evolution is sufficient for such tasks, there is a bias in the presented work for such a conclusion to be reached. we suggest ways to eliminate this bias and make the model more convincingly relevant to the cognitive sciences.