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.

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.

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.

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 .

The role of number of items per trial in best-worst scaling experiments.

Best-worst scaling is a judgment format in which participants are presented with K items and must choose the best and worst items from that set, along some underlying latent dimension. Best-worst scaling has seen recent use in natural-language processing and psychology to collect lexical semantic norms. In such applications, four items have always been presented on each trial.

Learning about things that never happened: A critique and refinement of the Rescorla-Wagner update rule when many outcomes are possible.

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.

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.

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.

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.

Estimating the average need of semantic knowledge from distributional semantic models.

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.

Scoring best-worst data in unbalanced many-item designs, with applications to crowdsourcing semantic judgments.

Best-worst scaling is a judgment format in which participants are presented with a set of items and have to choose the superior and inferior items in the set. Best-worst scaling generates a large quantity of information per judgment because each judgment allows for inferences about the rank value of all unjudged items. This property of best-worst scaling makes it a promising judgment format for research in psychology and natural language processing concerned with estimating the semantic properties of tens of thousands of words.

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.

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.

Now you see it, now you don't: on emotion, context, and the algorithmic prediction of human imageability judgments.

Many studies have shown that behavioral measures are affected by manipulating the imageability of words. Though imageability is usually measured by human judgment, little is known about what factors underlie those judgments. We demonstrate that imageability judgments can be largely or entirely accounted for by two computable measures that have previously been associated with imageability, the size and density of a word's context and the emotional associations of the word.

Connected text reading and differences in text reading fluency in adult readers.

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.

The blue-collar brain.

Much effort has gone into elucidating control of the body by the brain, less so the role of the body in controlling the brain. This essay develops the idea that the brain does a great deal of work in the service of behavior that is controlled by the body, a blue-collar role compared to the white-collar control exercised by the body. The argument that supports a blue-collar role for the brain is also consistent with recent discoveries clarifying the white-collar role of synergies across the body's tensegrity structure, and the evidence of critical phenomena in brain and behavior.

NUANCE 3.0: using genetic programming to model variable relationships.

Previously, we introduced a new computational tool for nonlinear curve fitting and data set exploration: the Naturalistic University of Alberta Nonlinear Correlation Explorer (NUANCE) (Hollis & Westbury, 2006). We demonstrated that NUANCE was capable of providing useful descriptions of data for two toy problems. Since then, we have extended the functionality of NUANCE in a new release (NUANCE 3.

NUANCE: Naturalistic University of Alberta Nonlinear Correlation Explorer.

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.