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

Valence without meaning: Investigating form and semantic components in pseudowords valence

Valence is a dominant semantic dimension, and it is fundamentally linked to basic approach-avoidance behavior within a broad range of contexts. Previous studies have shown that it is possible to approximate the valence of existing words based on several surface-level and semantic components of the stimuli. Parallelly, recent studies have shown that even completely novel and (apparently) meaningless stimuli, like pseudowords, can be informative of meaning based on the information that they carry at the subword level. Here, we aimed to further extend this evidence by investigating whether humans can reliably assign valence to pseudowords and, additionally, to identify the factors explaining such valence judgments. In Experiment 1, we trained several models to predict valence judgments for existing words from their combined form and meaning information. Then, in Experiment 2 and Experiment 3, we extended the results by predicting participants' valence judgments for pseudowords, using a set of models indexing different (possible) sources of valence and selected the best performing model in a completely data-driven procedure. Results showed that the model including basic surface-level (i.e., letters composing the pseudoword) and orthographic neighbors information performed best, thus tracing back pseudoword valence to these components. These findings support perspectives on the nonarbitrariness of language and provide insights regarding how humans process the valence of novel stimuli.

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.

Specificity ratings for Italian data

Abstraction enables us to categorize experience, learn new information, and form judgments. Language arguably plays a crucial role in abstraction, providing us with words that vary in specificity (e.g., highly generic: tool vs. highly specific: muffler). Yet, human-generated ratings of word specificity are virtually absent. We hereby present a dataset of specificity ratings collected from Italian native speakers on a set of around 1K Italian words, using the Best-Worst Scaling method. Through a series of correlation studies, we show that human-generated specificity ratings have low correlation coefficients with specificity metrics extracted automatically from WordNet, suggesting that WordNet does not reflect the hierarchical relations of category inclusion present in the speakers' minds. Moreover, our ratings show low correlations with concreteness ratings, suggesting that the variables Specificity and Concreteness capture two separate aspects involved in abstraction and that specificity may need to be controlled for when investigating conceptual concreteness. Finally, through a series of regression studies we show that specificity explains a unique amount of variance in decision latencies (lexical decision task), suggesting that this variable has theoretical value. The results are discussed in relation to the concept and investigation of abstraction.

Shared mental representations underlie metaphorical sound concepts

Communication between sound and music experts is based on the shared understanding of a metaphorical vocabulary derived from other sensory modalities. Yet, the impact of sound expertise on the mental representation of these sound concepts remains blurry. To address this issue, we investigated the acoustic portraits of four metaphorical sound concepts (brightness, warmth, roundness, and roughness) in three groups of participants (sound engineers, conductors, and non-experts). Participants (N = 24) rated a corpus of orchestral instrument sounds (N = 520) using Best-Worst Scaling. With this data-driven method, we sorted the sound corpus for each concept and population. We compared the population ratings and ran machine learning algorithms to unveil the acoustic portraits of each concept. Overall, the results revealed that sound engineers were the most consistent. We found that roughness is widely shared while brightness is expertise dependent. The frequent use of brightness by expert populations suggests that its meaning got specified through sound expertise. As for roundness and warmth, it seems that the importance of pitch and noise in their acoustic definition is the key to distinguishing them. These results provide crucial information on the mental representations of a metaphorical vocabulary of sound and whether it is shared or refined by sound expertise.

Governance of forest resource use in western Nepal: Current state and community preferences

Improved governance of natural resource use is critical to the sustainability and maintenance of environmental quality. In western Nepal, unsustainable resource extraction is seen by the local community as a major threat to forest sustainability. While most respondents to a survey of 243 households inside and outside a protected area (PA) thought the laws for managing resource use were adequate and appropriate, a far smaller proportion thought they were achieving their objectives. Disenchantment with the existing governance regime was strongest outside the PA, probably because there was greater investment in community engagement within the PA. The most likely reason for this failure is the deeply embedded corruption within the forest governance system. Devolution of power to local communities by increasing governance participation is one of the most likely means of containing corruption. It was therefore not surprising that governance participation was rated as the most important governance principle by respondents in a best-worst scaling experiment. Respondents also regarded effectiveness, accountability and transparency as important governance principle to improve management of forest resource extraction from red panda habitat.

Best-worst scaling, an alternative method to assess perceptual sound qualities

When designing sound evaluation experiments, researchers rely on listening test methods, such as rating scales (RS). This work aims to investigate the suitability of best-worst scaling (BWS) for the perceptual evaluation of sound qualities. To do so, 20 participants rated the "brightness" of a corpus of instrumental sounds (N = 100) with RS and BWS methods. The results show that BWS procedure is the fastest and that RS and BWS are equivalent in terms of performance. Interestingly, participants preferred BWS over RS. Therefore, BWS is an alternative method that reliably measures perceptual sound qualities and could be used in many-sounds paradigm.

Beyond Likert ratings: Improving the robustness of developmental research measurement using best-worst scaling

Some of the ‘best practice’ approaches to ensuring reproducibility of research can be difficult to implement in the developmental and clinical domains, where sample sizes and session lengths are constrained by the practicalities of recruitment and testing. For this reason, an important area of improvement to target is the reliability of measurement. Here we demonstrate that best–worst scaling (BWS) provides a superior alternative to Likert ratings for measuring children’s subjective impressions. Seventy-three children aged 5–6 years rated the trustworthiness of faces using either Likert ratings or BWS over two sessions. Individual children’s ratings in the BWS condition were significantly more consistent from session 1 to session 2 than those in the Likert condition, a finding we also replicate with a large adult sample (N = 72). BWS also produced more reliable ratings at the group level than Likert ratings in the child sample. These findings indicate that BWS is a developmentally appropriate response format that can deliver substantial improvements in reliability of measurement, which can increase our confidence in the robustness of findings with children.

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.

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.

Best-worst scaling improves measurement of first impressions

A common goal in psychological research is the measurement of subjective impressions, such as first impressions of faces. These impressions are commonly measured using Likert ratings. Although these ratings are simple to administer, they are associated with response issues that can limit reliability. Here we examine best-worst scaling (BWS), a forced-choice method, as a potential alternative to Likert ratings for measuring participants' facial first impressions. We find that at the group level, BWS scores correlated almost perfectly with Likert scores, indicating that the two methods measure the same impressions. However, at the individual participant level BWS outperforms Likert ratings, both in terms of ability to predict preferences in a third task, and in terms of test-retest reliability. These benefits highlight the power of BWS, particularly for use in individual differences research.