Historical evolution of concrete and abstract language revisited

Evolution of the Moral Lexicon

Morality is central to social well-being and cognition, and moral lexicon is a key device for human communication of moral concepts and experiences. How was the moral lexicon formed? We explore this open question and hypothesize that words evolved to take on abstract moral meanings from concrete and grounded experiences. We test this hypothesis by analyzing semantic change and formation of over 800 words from the English Moral Foundations Dictionary and the Historical Thesaurus of English over the past hundreds of years. Across historical text corpora and dictionaries, we discover concrete-to-abstract shifts as words acquire moral meaning, in contrast with the broad observation that words become more concrete over time. Furthermore, we find that compound moral words tend to be derived from a concrete-to-abstract shift from their constituents, and this derivational property is more prominent in moral words compared to alternative compound words when word frequency is controlled for. We suggest that evolution of the moral lexicon depends on systematic metaphorical mappings from concrete domains to the moral domain. Our results provide large-scale evidence for the role of metaphor in shaping the historical development of the English moral lexicon.

How cognitive selection affects language change

Like biological species, words in language must compete to survive. Previously, it has been shown that language changes in response to cognitive constraints and over time becomes more learnable. Here, we use two complementary research paradigms to demonstrate how the survival of existing word forms can be predicted by psycholinguistic properties that impact language production. In the first study, we analyzed the survival of words in the context of interpersonal communication. We analyzed data from a large-scale serial-reproduction experiment in which stories were passed down along a transmission chain over multiple participants. The results show that words that are acquired earlier in life, more concrete, more arousing, and more emotional are more likely to survive retellings. We reason that the same trend might scale up to language evolution over multiple generations of natural language users. If that is the case, the same set of psycholinguistic properties should also account for the change of word frequency in natural language corpora over historical time. That is what we found in two large historical-language corpora (Study 2): Early acquisition, concreteness, and high arousal all predict increasing word frequency over the past 200 y. However, the two studies diverge with respect to the impact of word valence and word length, which we take up in the discussion. By bridging micro-level behavioral preferences and macro-level language patterns, our investigation sheds light on the cognitive mechanisms underlying word competition.

Concreteness ratings for 62,000 English multiword expressions

Concreteness describes the degree to which a word's meaning is understood through perception and action. Many studies use the Brysbaert et al. (2014) concreteness ratings to investigate language processing and text analysis. However, these ratings are limited to English single words and a few two-word expressions. Increasingly, attention is focused on the importance of multiword expressions, given their centrality in everyday language use and language acquisition. We present concreteness ratings for 62,889 multiword expressions and examine their relationship to the existing concreteness ratings for single words and two-word expressions. These new ratings represent the first big dataset of multiword expressions, and will be useful for researchers interested in language acquisition and language processing, as well as natural language processing and text analysis.

Historical Psychology

A growing body of evidence suggests that many aspects of psychology have evolved culturally over historical time. A combination of approaches, including experimental data collected over the past 75 years, cross-cultural comparisons, and studies of immigrants, points to systematic changes in psychological domains as diverse as conformity, attention, emotion, morality, and olfaction. However, these approaches can go back in time only for a few decades and typically fail to provide continuous measures of cultural change, posing a challenge for testing deeper historical psychological processes. To tackle this challenge most directly, computational methods emerging from natural language processing can be adapted to extract psychological information from large-scale historical corpora. Here, we first review the benefits of psychology as a historical science and then present three useful classes of text-analytic techniques for historical psychological inquiry: dictionary-based methods, distributed-representational methods, and human-annotation-based methods. These represent an excellent suite of methodologies that can be used to examine the record of “dead minds.” Finally, we discuss the importance of going beyond English-centric text analysis in historical psychology to foster a more generalizable and inclusive science of human behavior. We propose that historical psychology should incorporate and further develop a variety of text-analytic approaches to reliably quantify the historical processes that gave rise to contemporary social, political, and psychological phenomena.

Olfactory abstraction: a communicative and metacognitive account

The usual puzzle raised about olfaction is that of a deficit of abstraction: smells, by contrast notably with colours, do not easily lend themselves to abstract categories and labels. Some studies have argued that the puzzle is culturally restricted and that abstraction is more common outside urban Western societies. Here, I argue that the puzzle is misconstrued and should be reversed: given that odours are constantly changing and that their commonalities are difficult for humans to identify, what is surprising is not that abstract terms are rare, but that they should be used at all for olfaction. Given the nature of the olfactory environment and our cognitive equipment, concrete labels referring to sources seem most adaptive. To explain the use and presence of abstract terms, we need to examine their social and communicative benefits. Here these benefits are spelt out as securing a higher agreement among individuals varying in their olfactory experiences as well as the labels they use, as well as feeling a heightened sense of confidence in one's naming capacities. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Diachronic semantic change in language is constrained by how people use and learn language

While it has long been understood that the human mind evolved to learn language, recent studies have begun to ask the inverted question: How has language evolved under the cognitive constraints of its users and become more learnable over time? In this paper, we explored how the semantic change of English words is shaped by the way humans acquire and process language. In Study 1, we quantified the extent of semantic change over the past 200 years and found that meaning change is more likely for words that are acquired later in life and are more difficult to process. We argue that it is human cognition that constrains the semantic evolution of words, rather than the other way around, because historical meanings of words were not easily accessible to people living today, and therefore could not have directly influenced how they learn and process language. In Study 2, we went further to show that semantic change, while bringing the benefit of meeting communicative needs, is cognitively costly for those who were born early enough to experience the change: Semantic change between 1970 and 2000 hindered processing speeds among middle-aged adults (ages 45–55) but not in younger adults (ages <25) in a semantic decision task. This hampering effect may have, in turn, curbed the rate of semantic change so that language does not change too fast for the human mind to catch up. Taken together, our research demonstrates that semantic change is shaped by processing and acquisition patterns across generations of language users.

Extrapolation of Human Estimates of the Concreteness/ Abstractness of Words by Neural Networks of Various Architectures

In a great deal of theoretical and applied cognitive and neurophysiological research, it is essential to have more vocabularies with concreteness/abstractness ratings. Since creating such dictionaries by interviewing informants is labor-intensive, considerable effort has been made to machine-extrapolate human rankings. The purpose of the article is to study the possibility of the fast construction of high-quality machine dictionaries. In this paper, state-of-the-art deep learning neural networks are involved for the first time to solve this problem. For the English language, the BERT model has achieved a record result for the quality of a machine-generated dictionary. It is known that the use of multilingual models makes it possible to transfer ratings from one language to another. However, this approach is understudied so far and the results achieved so far are rather weak. Microsoft’s Multilingual-MiniLM-L12-H384 model also obtained the best result to date in transferring ratings from one language to another. Thus, the article demonstrates the advantages of transformer-type neural networks in this task. Their use will allow the generation of good-quality dictionaries in low-resource languages. Additionally, we study the dependence of the result on the amount of initial data and the number of languages in the multilingual case. The possibilities of transferring into a certain language from one language and from several languages together are compared. The influence of the volume of training and test data has been studied. It has been found that an increase in the amount of training data in a multilingual case does not improve the result.

Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary

Concrete/abstract words are used in a growing number of psychological and neurophysiological research. For a few languages, large dictionaries have been created manually. This is a very time-consuming and costly process. To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. The research question that arises is how small such samples should be to do a good enough extrapolation. In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.

Assessing Lexical Psychological Properties in Second Language Production: A Dynamic Semantic Similarity Approach

Previous studies of the lexical psycholinguistic properties (LPPs) in second language (L2) production have assessed the degree of an LPP dimension of an L2 corpus by computing the mean ratings of unique content words in the corpus for that dimension, without considering the possibility that learners at different proficiency levels may perceive the degree of that dimension of the same words differently. This study extended a dynamic semantic similarity algorithm to estimate the degree of five different LPP dimensions of several sub-corpora of the Education First-Cambridge Open Language Database representing L2 English learners at different proficiency levels. Our findings provide initial evidence for the validity of the algorithm for assessing the LPPs in L2 production and contribute useful insights into between-proficiency relationships and cross-proficiency differences in the LPPs in L2 production as well as the relationships among different LPP dimensions.

Language patterns of outgroup prejudice

Although explicit verbal expression of prejudice and stereotypes may have become less common due to the recent rise of social norms against prejudice, prejudice in language still persists in more subtle forms. It remains unclear whether and how language patterns predict variance in prejudice across a large number of minority groups. Informed by construal level theory, intergroup-contact theory, and linguistic expectancy bias, we leverage a natural language corpus of 1.8 million newspaper articles to investigate patterns of language referencing 60 U.S. minority groups. We found that perception of social distance among immigrant groups is reflected in language production: Groups perceived as socially distant (vs. close) are also more likely to be mentioned in abstract (vs. concrete) language. Concreteness was also strongly positively correlated with sentiment, a phenomenon that was unique to language concerning minority groups, suggesting a strong tendency for more socially distant groups to be represented with more negative language. We also provide a qualitative exploration of the content of outgroup prejudice by applying Latent Dirichlet Allocation to language referencing minority groups in the context of immigration. We identified 15 immigrant-related topics (e.g., politics, arts, crime, illegal workers, museums, food) and the strength of their association and relationship with perceived sentiment for each minority group. This research demonstrates how perceived social distance and language concreteness are related and correlate with outgroup negativity, provides a practical and ecologically valid method for investigating perceptions of minority groups in language, and helps elaborate the connection between theoretical positions from social psychology with recent studies from computer science on prejudice embedded in natural language.

The evolutionary pattern of language in scientific writings: A case study of Philosophical Transactions of Royal Society (1665–1869)

Scientific writings, as one essential part of human culture, have evolved over centuries into their current form. Knowing how scientific writings evolved is particularly helpful in understanding how trends in scientific culture developed. It also allows us to better understand how scientific culture was interwoven with human culture generally. The availability of massive digitized texts and the progress in computational technologies today provide us with a convenient and credible way to discern the evolutionary patterns in scientific writings by examining the diachronic linguistic changes. The linguistic changes in scientific writings reflect the genre shifts that took place with historical changes in science and scientific writings. This study investigates a general evolutionary linguistic pattern in scientific writings. It does so by merging two credible computational methods: relative entropy; word-embedding concreteness and imageability. It thus creates a novel quantitative methodology and applies this to the examination of diachronic changes in the Philosophical Transactions of Royal Society (PTRS, 1665–1869). The data from two computational approaches can be well mapped to support the argument that this journal followed the evolutionary trend of increasing professionalization and specialization. But it also shows that language use in this journal was greatly influenced by historical events and other socio-cultural factors. This study, as a “culturomic” approach, demonstrates that the linguistic evolutionary patterns in scientific discourse have been interrupted by external factors even though this scientific discourse would likely have cumulatively developed into a professional and specialized genre. The approaches proposed by this study can make a great contribution to full-text analysis in scientometrics.

Ranking concrete and abstract words using Google Books Ngram data

Creation of dictionaries of abstract and concrete words is a well-known task. Such dictionaries are important in several applications of text analysis and computational linguistics. Usually, the process of assembling of concreteness scores for words begins with a lot of manual work. However, the process can be automated significantly using information from large corpora. In this paper we combine two datasets: a dictionary with concreteness scores of 40,000 English words and the GoogleBooks Ngram dataset, in order to test the following hypothesis: in text concrete words tend to occur with more concrete words, than with abstract words (and inverse: abstract words tend to occur with more abstract words, than with concrete words). Using the hypothesis, we proposed a method for automatic evaluation concreteness scores of words using a small amount of initial markup.