Taboo language across the globe: A multi-lab study.

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.

Why are human animacy judgments continuous rather than categorical? A computational modeling approach.

Introduction: The concept of animacy is often taken as a basic natural concept, in part I because most cases seem unambiguous. Most entities either are or are not animate. However, human animacy judgments do not reflect this binary classification. They suggest that there are borderline cases, such as virus, amoeba, fly, and imaginary beings (giant, dragon, god). Moreover, human roles (professor, mother, girlfriend) are consistently recognized as animate by far less than 100% of human judges. Method: In this paper, I use computational modeling to identify features associated with human animacy judgments, modeling human animacy and living/non-living judgments using both bottom-up predictors (the principal components from a word embedding model) and top-down predictors (cosine distances from the names of animate categories). Results: The results suggest that human animacy judgments may be relying on information obtained from imperfect estimates of category membership that are reflected in the word embedding models. Models using cosine distance from category names mirror human judgments in distinguishing strongly between humans (estimated lower animacy by the measure) and other animals (estimated higher animacy by the measure). Discussion: These results are consistent with a family resemblance approach to the apparently categorical concept of animacy.

The Persian Lexicon Project: minimized orthographic neighbourhood effects in a dense language.

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/ .

Is theology more of a field than a father is a king? Modelling semantic relatedness in processing literal and metaphorical statements.

One major question in the study of metaphors historically is: Are different mechanisms involved in the comprehension of figurative statements versus literal statements? Many studies have addressed this question from a variety of perspectives, with mixed results. Following Harati, Westbury, and Kiaee (Behavior Research Methods, 53, 2214-2225, 2021), we use a computational (word embedding) model of semantics to approach the question in a way that allows for the quantification of the semantic relationship between the two keywords in literal and metaphorical "x is a y" statements. We first demonstrate that almost all literal statements (95.2% of 582 statements we considered) have very high relatedness values. We then show that literality decisions are slower for literal statements with low relatedness and metaphorical statements with high relatedness. We find a similar but smaller effect attributable to the cosine of the vectors representing the two keywords. The fact that the same measurable characteristics allow us to predict which metaphors or literal sentences will have the slowest literality decision times suggests that the same processes underlie the comprehension of both literal and metaphorical statements.

English Speakers' Implicit Gender Concepts Influence Their Processing of French Grammatical Gender: Evidence for Semantically Mediated Cross-Linguistic Influence.

Second language (L2) learners often show influence from their first language (L1) in all domains of language. This cross-linguistic influence could, in some cases, be mediated by semantics. The purpose of the present study was to test whether implicit English gender connotations affect L1 English speakers' judgments of the L2 French gender of objects. We hypothesized that gender estimates derived from word embedding models that measure similarity of word contexts in English would affect accuracy and response time on grammatical gender (GG) decision in L2 French. L2 French learners were asked to identify the GG of French words estimated to be either congruent or incongruent with the implicit gender in English. The results showed that they were more accurate with words that were congruent with English gender connotations than words that were incongruent, suggesting that English gender connotations can influence grammatical judgments in French. Response times showed the same pattern. The results are consistent with semantics-mediated cross-linguistic influence.

Evaluating the predication model of metaphor comprehension: Using word2vec to model best/worst quality judgments of 622 novel metaphors.

In this paper our goal is to undertake a systematic assessment of the first, most widely known, and simplest computational model of metaphor comprehension, the predication model developed by Kintsch (Cognitive Science, 25(2), 173-202, 2000). 622 metaphors of the form "x is a y" were selected from a much larger set generated randomly. The metaphors were judged for quality using best/worst judgments, which asks judges to pick the best and worst metaphor from among four presented metaphors. The metaphors and their judgments have been publicly released. We modeled the judgments by extending Kintsch's predication model (2000) by systematically walking through the parameter space of that model. Our model successfully differentiated metaphors rated as good (> 1.5z) from metaphors rated as bad (< -1.5z; Cohen's d = 0.72) and was able to successfully classify good metaphors with an accuracy of 82.9%. However, it achieved a true negative rate below chance at 36.3% and had a resultantly low kappa of 0.037. The model could not distinguish unselected random metaphors from those selected by humans as having metaphorical potential. In a follow-up study we showed that the model's quality estimates reliably predict metaphor decision times, with better metaphors being judged more quickly than worse metaphors.

Sound symbolism shapes the English language: The maluma/takete effect in English nouns.

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.

A pompous snack: On the unreasonable complexity of the world's third-worst jokes.

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. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Prenominal adjective order is such a fat big deal because adjectives are ordered by likely need.

When multiple adjectives precede a noun in English, they are often ordered in a way that is implicitly understood by all fluent speakers of the language. Adjective order might be described as a big fat deal, but to describe it as a fat big deal betrays a lack of knowledge of English. Sweet (A New English Grammar: Part II, 1898/1955) proposed two related semantic principles to explain the phenomenon: definiteness of denotation (adjectives that denote a property that is most independent of the modified noun must be placed furthest from that noun) and closeness of adjective/noun in meaning (adjectives that denote properties essential to or inherent in the modified noun are placed closer to the noun). These observational descriptions of the phenomenon have received experimental support (Martin, Journal of Verbal Learning and Verbal Behavior, 8(6), 697-704, 1969). However, the issue of why Sweet's rules are true has not yet been solved. I propose, operationalize, test, and find strong support for a simple theory: that prenominal adjective order reflects likely need, the a priori probability that a particular adjective will be needed.

Wriggly, squiffy, lummox, and boobs: What makes some words funny?

Theories of humor tend to be post hoc descriptions, suffering from insufficient operationalization and a subsequent inability to make predictions about what will be found humorous and to what extent. Here we build on the Engelthaler & Hills' (2017) humor rating norms for 4,997 words, by analyzing the semantic, phonological, orthographic, and frequency factors that play a role in the judgments. We were able to predict the original humor rating norms and ratings for previously unrated words with greater reliability than the split half reliability in the original norms, as estimated from splitting those norms along gender or age lines. Our findings are consistent with several theories of humor, while suggesting that those theories are too narrow. In particular, they are consistent with incongruity theory, which suggests that experienced humor is proportional to the degree to which expectations are violated. We demonstrate that words are judged funnier if they are less common and have an improbable orthographic or phonological structure. We also describe and quantify the semantic attributes of words that are judged funny and show that they are partly compatible with the superiority theory of humor, which focuses on humor as scorn. Several other specific semantic attributes are also associated with humor. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Conceptualizing syntactic categories as semantic categories: Unifying part-of-speech identification and semantics using co-occurrence vector averaging.

Co-occurrence models have been of considerable interest to psychologists because they are built on very simple functionality. This is particularly clear in the case of prediction models, such as the continuous skip-gram model introduced in Mikolov, Chen, Corrado, and Dean (2013), because these models depend on functionality closely related to the simple Rescorla-Wagner model of discriminant learning in nonhuman animals (Rescorla & Wagner, 1972), which has a rich history within psychology as a model of many animal learning processes. We replicate and extend earlier work showing that it is possible to extract accurate information about syntactic category and morphological family membership directly from patterns of word co-occurrence, and provide evidence from four experiments showing that this information predicts human reaction times and accuracy for class membership decisions.

Simple Co-Occurrence Statistics Reproducibly Predict Association Ratings.

What determines human ratings of association? We planned this paper as a test for association strength (AS) that is derived from the log likelihood that two words co-occur significantly more often together in sentences than is expected from their single word frequencies. We also investigated the moderately correlated interactions of word frequency, emotional valence, arousal, and imageability of both words (r's ≤ .3). In three studies, linear mixed effects models revealed that AS and valence reproducibly account for variance in the human ratings. To understand further correlated predictors, we conducted a hierarchical cluster analysis and examined the predictors of four clusters in competitive analyses: Only AS and word2vec skip-gram cosine distances reproducibly accounted for variance in all three studies. The other predictors of the first cluster (number of common associates, (positive) point-wise mutual information, and word2vec CBOW cosine) did not reproducibly explain further variance. The same was true for the second cluster (word frequency and arousal); the third cluster (emotional valence and imageability); and the fourth cluster (consisting of joint frequency only). Finally, we discuss emotional valence as an important dimension of semantic space. Our results suggest that a simple definition of syntagmatic word contiguity (AS) and a paradigmatic measure of semantic similarity (skip-gram cosine) provide the most general performance-independent explanation of association ratings.

When is best-worst best? A comparison of best-worst scaling, numeric estimation, and rating scales for collection of semantic norms.

Large-scale semantic norms have become both prevalent and influential in recent psycholinguistic research. However, little attention has been directed towards understanding the methodological best practices of such norm collection efforts. We compared the quality of semantic norms obtained through rating scales, numeric estimation, and a less commonly used judgment format called best-worst scaling. We found that best-worst scaling usually produces norms with higher predictive validities than other response formats, and does so requiring less data to be collected overall. We also found evidence that the various response formats may be producing qualitatively, rather than just quantitatively, different data. This raises the issue of potential response format bias, which has not been addressed by previous efforts to collect semantic norms, likely because of previous reliance on a single type of response format for a single type of semantic judgment. We have made available software for creating best-worst stimuli and scoring best-worst data. We also made available new norms for age of acquisition, valence, arousal, and concreteness collected using best-worst scaling. These norms include entries for 1,040 words, of which 1,034 are also contained in the ANEW norms (Bradley & Lang, Affective norms for English words (ANEW): Instruction manual and affective ratings (pp. 1-45). Technical report C-1, the center for research in psychophysiology, University of Florida, 1999).

Non-Arbitrariness in Mapping Word Form to Meaning: Cross-Linguistic Formal Markers of Word Concreteness.

Arbitrary symbolism is a linguistic doctrine that predicts an orthogonal relationship between word forms and their corresponding meanings. Recent corpora analyses have demonstrated violations of arbitrary symbolism with respect to concreteness, a variable characterizing the sensorimotor salience of a word. In addition to qualitative semantic differences, abstract and concrete words are also marked by distinct morphophonological structures such as length and morphological complexity. Native English speakers show sensitivity to these markers in tasks such as auditory word recognition and naming. One unanswered question is whether this violation of arbitrariness reflects an idiosyncratic property of the English lexicon or whether word concreteness is a marked phenomenon across other natural languages. We isolated concrete and abstract English nouns (N = 400), and translated each into Russian, Arabic, Dutch, Mandarin, Hindi, Korean, Hebrew, and American Sign Language. We conducted offline acoustic analyses of abstract and concrete word length discrepancies across languages. In a separate experiment, native English speakers (N = 56) with no prior knowledge of these foreign languages judged concreteness of these nouns (e.g., Can you see, hear, feel, or touch this? Yes/No). Each naïve participant heard pre-recorded words presented in randomized blocks of three foreign languages following a brief listening exposure to a narrative sample from each respective language. Concrete and abstract words differed by length across five of eight languages, and prediction accuracy exceeded chance for four of eight languages. These results suggest that word concreteness is a marked phenomenon across several of the world's most widely spoken languages. We interpret these findings as supportive of an adaptive cognitive heuristic that allows listeners to exploit non-arbitrary mappings of word form to word meaning.

Extrapolating human judgments from skip-gram vector representations of word meaning.

There is a growing body of research in psychology that attempts to extrapolate human lexical judgments from computational models of semantics. This research can be used to help develop comprehensive norm sets for experimental research, it has applications to large-scale statistical modelling of lexical access and has broad value within natural language processing and sentiment analysis. However, the value of extrapolated human judgments has recently been questioned within psychological research. Of primary concern is the fact that extrapolated judgments may not share the same pattern of statistical relationship with lexical and semantic variables as do actual human judgments; often the error component in extrapolated judgments is not psychologically inert, making such judgments problematic to use for psychological research. We present a new methodology for extrapolating human judgments that partially addresses prior concerns of validity. We use this methodology to extrapolate human judgments of valence, arousal, dominance, and concreteness for 78,286 words. We also provide resources for users to extrapolate these human judgments for three million English words and short phrases. Applications for large sets of extrapolated human judgments are demonstrated and discussed.

Imaging Imageability: Behavioral Effects and Neural Correlates of Its Interaction with Affect and Context.

The construct of imageability refers to the extent to which a word evokes a tangible sensation. Previous research (Westbury et al., 2013) suggests that the behavioral effects attributed to a word's imageability can be largely or wholly explained by two objective constructs, contextual density and estimated affect. Here, we extend these previous findings in two ways. First, we show that closely matched stimuli on the three measures of contextual density, estimated affect, and human-judged imageability show a three-way interaction in explaining variance in LD RTs, but that imagebility accounts for no additional variance after contextual density and estimated affect are entered first. Secondly, we demonstrate that the loci and functional connectivity (via graphical models) of the brain regions implicated in processing the three variables during that task are largely over-lapping and similar. These two lines of evidence support the conclusion that the effect usually attributed to human-judged imageability is largely or entirely due to the effects of other correlated measures that are directly computable.

The principals of meaning: Extracting semantic dimensions from co-occurrence models of semantics.

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.

Examining Critical Thinking Skills in Family Medicine Residents.

BACKGROUND AND OBJECTIVES: Our objective was to determine the relationship between critical thinking skills and objective measures of academic success in a family medicine residency program. METHODS: This prospective observational cohort study was set in a large Canadian family medicine residency program. Intervention was the California Critical Thinking Skills Test (CCTST), administered at three points in residency: upon entry, at mid-point, and at graduation. Results from the CCTST, Canadian Residency Matching Service file, and interview scores were compared to other measures of academic performance (Medical Colleges Admission Test [MCAT] and College of Family Physicians of Canada [CCFP] certification examination results). RESULTS: For participants (n=60), significant positive correlations were found between critical thinking skills and performance on tests of knowledge. For the MCAT, CCTST scores correlated positively with full scores (n=24, r=0.57) as well as with each section score (verbal reasoning: r=0.59; physical sciences: r=0.64; biological sciences: r=0.54). For CCFP examination, CCTST correlated reliably with both sections (n=49, orals: r=0.34; short answer: r=0.47). Additionally, CCTST was a better predictor of performance on the CCFP exam than was the interview score at selection into the residency program (Fisher's r-to-z test, z=2.25). CONCLUSIONS: Success on a critical thinking skills exam was found to predict success on family medicine certification examinations. Given that critical thinking skills appear to be stable throughout residency training, including an assessment of critical thinking in the selection process may help identify applicants more likely to be successful on final certification exam.

Avoid violence, rioting, and outrage; approach celebration, delight, and strength: Using large text corpora to compute valence, arousal, and the basic emotions.

Ever since Aristotle discussed the issue in Book II of his Rhetoric, humans have attempted to identify a set of "basic emotion labels". In this paper we propose an algorithmic method for evaluating sets of basic emotion labels that relies upon computed co-occurrence distances between words in a 12.7-billion-word corpus of unselected text from USENET discussion groups. Our method uses the relationship between human arousal and valence ratings collected for a large list of words, and the co-occurrence similarity between each word and emotion labels. We assess how well the words in each of 12 emotion label sets-proposed by various researchers over the past 118 years-predict the arousal and valence ratings on a test and validation dataset, each consisting of over 5970 items. We also assess how well these emotion labels predict lexical decision residuals (LDRTs), after co-varying out the effects attributable to basic lexical predictors. We then demonstrate a generalization of our method to determine the most predictive "basic" emotion labels from among all of the putative models of basic emotion that we considered. As well as contributing empirical data towards the development of a more rigorous definition of basic emotions, our method makes it possible to derive principled computational estimates of emotionality-specifically, of arousal and valence-for all words in the language.

Performance impact of stop lists and morphological decomposition on word-word corpus-based semantic space models.

Corpus-based semantic space models, which primarily rely on lexical co-occurrence statistics, have proven effective in modeling and predicting human behavior in a number of experimental paradigms that explore semantic memory representation. The most widely studied extant models, however, are strongly influenced by orthographic word frequency (e.g., Shaoul & Westbury, Behavior Research Methods, 38, 190-195, 2006). This has the implication that high-frequency closed-class words can potentially bias co-occurrence statistics. Because these closed-class words are purported to carry primarily syntactic, rather than semantic, information, the performance of corpus-based semantic space models may be improved by excluding closed-class words (using stop lists) from co-occurrence statistics, while retaining their syntactic information through other means (e.g., part-of-speech tagging and/or affixes from inflected word forms). Additionally, very little work has been done to explore the effect of employing morphological decomposition on the inflected forms of words in corpora prior to compiling co-occurrence statistics, despite (controversial) evidence that humans perform early morphological decomposition in semantic processing. In this study, we explored the impact of these factors on corpus-based semantic space models. From this study, morphological decomposition appears to significantly improve performance in word-word co-occurrence semantic space models, providing some support for the claim that sublexical information-specifically, word morphology-plays a role in lexical semantic processing. An overall decrease in performance was observed in models employing stop lists (e.g., excluding closed-class words). Furthermore, we found some evidence that weakens the claim that closed-class words supply primarily syntactic information in word-word co-occurrence semantic space models.