Puritanism needs purity, and moral psychology needs pluralism.

This account of puritanical morality is useful and innovative, but makes two errors. First, it mischaracterizes the purity foundation as being unrelated to cooperation. Second, it makes the leap from cooperation (broadly construed) to a monist account of moral cognition (as harm or fairness). We show how this leap is both conceptually incoherent and inconsistent with empirical evidence about self-control moralization.

Sex differences in moral judgements across 67 countries.

Most of the empirical research on sex differences and cultural variations in morality has relied on within-culture analyses or small-scale cross-cultural data. To further broaden the scientific understanding of sex differences in morality, the current research relies on two international samples to provide the first large-scale examination of sex differences in moral judgements nested within cultures. Using a sample from 67 countries (Study 1; n = 336 691), we found culturally variable sex differences in moral judgements, as conceptualized by Moral Foundations Theory. Women consistently scored higher than men on Care, Fairness, and Purity. By contrast, sex differences in Loyalty and Authority were negligible and highly variable across cultures. Country-level sex differences in moral judgements were also examined in relation to cultural, socioeconomic, and gender-equality indicators revealing that sex differences in moral judgements are larger in individualist, Western, and gender-equal societies. In Study 2 (19 countries; n = 11 969), these results were largely replicated using Bayesian multi-level modelling in a distinct sample. The findings were robust when incorporating cultural non-independence of countries into the models. Specifically, women consistently showed higher concerns for Care, Fairness, and Purity in their moral judgements than did men. Sex differences in moral judgements were larger in individualist and gender-equal societies with more flexible social norms. We discuss the implications of these findings for the ongoing debate about the origin of sex differences and cultural variations in moral judgements as well as theoretical and pragmatic implications for moral and evolutionary psychology.

Body Maps of Moral Concerns.

It has been proposed that somatosensory reaction to varied social circumstances results in feelings (i.e., conscious emotional experiences). Here, we present two preregistered studies in which we examined the topographical maps of somatosensory reactions associated with violations of different moral concerns. Specifically, participants in Study 1 (N = 596) were randomly assigned to respond to scenarios involving various moral violations and were asked to draw key aspects of their subjective somatosensory experience on two 48,954-pixel silhouettes. Our results show that body patterns corresponding to different moral violations are felt in different regions of the body depending on whether individuals are classified as liberals or conservatives. We also investigated how individual differences in moral concerns relate to body maps of moral violations. Finally, we used natural-language processing to predict activation in body parts on the basis of the semantic representation of textual stimuli. We replicated these findings in a nationally representative sample in Study 2 (N = 300). Overall, our findings shed light on the complex relationships between moral processes and somatosensory experiences.

Do you need a roadmap or can someone give you directions: When school-focused possible identities change so do academic trajectories.

INTRODUCTION: Despite the assumed importance of school-focused possible identities for academic motivation and outcomes, interventions rarely assess the effect of intervention on possible identities. This may be due to difficulty coding open-ended text at scale but leaves open a number of questions: 1) how do school-focused possible identities change over the course of the school year, 2) whether these changes are associated with changes in school outcomes, and 3) whether a machine coding approach is viable. METHODS: In Study 1 (n = 247 Chicago 8th-graders) we assess fall-to-spring change in school-focused possible identities. We test whether change in school-focused possible identities predicts 8th-grade academic outcomes. We include robustness checks. Then we examine school context effects. In Study 2 (n = 1006 Chicago 8th-graders) we address the problem of coding at scale, using a separate data set to train a machine-learning algorithm. RESULTS: On average, school-focused possible identities decline over the school year. But nearly a third of students have increasing school-focused possible identity scores. Increase is associated with improved grades. School context influences whether linked strategies matter. Our machine-learning algorithm accurately classifies school-focused possible identities in our original sample and this school-focused classification reliably predicts academic trajectories. CONCLUSIONS: Change in school-focused possible identities is normative over the course of the school year, interventions should take this into account. On average, students have fewer school-focused possible identities by spring. This decline is associated with declining academic trajectories. However, when school-focused possible identities increase, so do grades. Whether strategies matter is context dependent.

Counterfactual curiosity in preschool children.

We investigated whether young children are curious about what could have been ("counterfactual curiosity"). In two experiments, children aged 4 and 5 years (N = 32 in Experiment 1, N = 24 in Experiment 2) played a matching game in which they turned over cards in the hope that they matched a picture. After choosing a card, children could use "x-ray glasses" to uncover unchosen cards. In Experiment 1, most children spontaneously used the glasses to peek at past alternatives, even when the outcome could no longer be altered. In Experiment 2, children concentrated their information search on alternatives that were within their control. In both experiments, children showed greater interest in counterfactual outcomes when the card they chose turned out not to match the picture. The findings suggest that young children are curious not only about what is but also about what could have been. Curiosity about alternative outcomes seems to precede counterfactual reasoning.

Linguistic positivity in historical texts reflects dynamic environmental and psychological factors.

People use more positive words than negative words. Referred to as "linguistic positivity bias" (LPB), this effect has been found across cultures and languages, prompting the conclusion that it is a panhuman tendency. However, although multiple competing explanations of LPB have been proposed, there is still no consensus on what mechanism(s) generate LPB or even on whether it is driven primarily by universal cognitive features or by environmental factors. In this work we propose that LPB has remained unresolved because previous research has neglected an essential dimension of language: time. In four studies conducted with two independent, time-stamped text corpora (Google books Ngrams and the New York Times), we found that LPB in American English has decreased during the last two centuries. We also observed dynamic fluctuations in LPB that were predicted by changes in objective environment, i.e., war and economic hardships, and by changes in national subjective happiness. In addition to providing evidence that LPB is a dynamic phenomenon, these results suggest that cognitive mechanisms alone cannot account for the observed dynamic fluctuations in LPB. At the least, LPB likely arises from multiple interacting mechanisms involving subjective, objective, and societal factors. In addition to having theoretical significance, our results demonstrate the value of newly available data sources in addressing long-standing scientific questions.

Measuring the importance of context when modeling language comprehension.

It is widely accepted that language requires context in order to function as communication between speakers and listeners. As listeners, we make use of background knowledge - about the speaker, about entities and concepts, about previous utterances - in order to infer the speaker's intended meaning. But even if there is consensus that these sources of information are a necessary component of linguistic communication, it is another matter entirely to provide a thorough, quantitative accounting for context's interaction with language. When does context matter? What kinds of context matter in which kinds of domains? The empirical investigation of these questions is inhibited by a number of factors: the challenge of quantifying language, the boundless combinations of domains and types of context to be measured, and the challenge of selecting and applying a given construct to natural language data. In response to these factors, we introduce and demonstrate a methodological framework for testing the importance of contextual information in inferring speaker intentions from text. We apply Long Short-term Memory (LSTM) networks, a standard for representing language in its natural, sequential state, and conduct a set of experiments for predicting the persuasive intentions of speakers in political debates using different combinations of text and background information about the speaker. We show, in our modeling and discussion, that the proposed framework is suitable for empirically evaluating the manner and magnitude of context's relevance for any number of domains and constructs.

Processing Narratives Concerning Protected Values: A Cross-Cultural Investigation of Neural Correlates.

Narratives are an important component of culture and play a central role in transmitting social values. Little is known, however, about how the brain of a listener/reader processes narratives. A receiver's response to narration is influenced by the narrator's framing and appeal to values. Narratives that appeal to "protected values," including core personal, national, or religious values, may be particularly effective at influencing receivers. Protected values resist compromise and are tied with identity, affective value, moral decision-making, and other aspects of social cognition. Here, we investigated the neural mechanisms underlying reactions to protected values in narratives. During fMRI scanning, we presented 78 American, Chinese, and Iranian participants with real-life stories distilled from a corpus of over 20 million weblogs. Reading these stories engaged the posterior medial, medial prefrontal, and temporo-parietal cortices. When participants believed that the protagonist was appealing to a protected value, signal in these regions was increased compared with when no protected value was perceived, possibly reflecting the intensive and iterative search required to process this material. The effect strength also varied across groups, potentially reflecting cultural differences in the degree of concern for protected values.

Conversation level syntax similarity metric.

The syntax and semantics of human language can illuminate many individual psychological differences and important dimensions of social interaction. Accordingly, psychological and psycholinguistic research has begun incorporating sophisticated representations of semantic content to better understand the connection between word choice and psychological processes. In this work we introduce ConversAtion level Syntax SImilarity Metric (CASSIM), a novel method for calculating conversation-level syntax similarity. CASSIM estimates the syntax similarity between conversations by automatically generating syntactical representations of the sentences in conversation, estimating the structural differences between them, and calculating an optimized estimate of the conversation-level syntax similarity. After introducing and explaining this method, we report results from two method validation experiments (Study 1) and conduct a series of analyses with CASSIM to investigate syntax accommodation in social media discourse (Study 2). We run the same experiments using two well-known existing syntactic metrics, LSM and Coh-Metrix, and compare their results to CASSIM. Overall, our results indicate that CASSIM is able to reliably measure syntax similarity and to provide robust evidence of syntax accommodation within social media discourse.

Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis : Distributed dictionary representation.

Theory-driven text analysis has made extensive use of psychological concept dictionaries, leading to a wide range of important results. These dictionaries have generally been applied through word count methods which have proven to be both simple and effective. In this paper, we introduce Distributed Dictionary Representations (DDR), a method that applies psychological dictionaries using semantic similarity rather than word counts. This allows for the measurement of the similarity between dictionaries and spans of text ranging from complete documents to individual words. We show how DDR enables dictionary authors to place greater emphasis on construct validity without sacrificing linguistic coverage. We further demonstrate the benefits of DDR on two real-world tasks and finally conduct an extensive study of the interaction between dictionary size and task performance. These studies allow us to examine how DDR and word count methods complement one another as tools for applying concept dictionaries and where each is best applied. Finally, we provide references to tools and resources to make this method both available and accessible to a broad psychological audience.

Decoding the neural representation of story meanings across languages.

Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin, and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading. Our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network. These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages. Hum Brain Mapp 38:6096-6106, 2017. © 2017 Wiley Periodicals, Inc.

TACIT: An open-source text analysis, crawling, and interpretation tool.

As human activity and interaction increasingly take place online, the digital residues of these activities provide a valuable window into a range of psychological and social processes. A great deal of progress has been made toward utilizing these opportunities; however, the complexity of managing and analyzing the quantities of data currently available has limited both the types of analysis used and the number of researchers able to make use of these data. Although fields such as computer science have developed a range of techniques and methods for handling these difficulties, making use of those tools has often required specialized knowledge and programming experience. The Text Analysis, Crawling, and Interpretation Tool (TACIT) is designed to bridge this gap by providing an intuitive tool and interface for making use of state-of-the-art methods in text analysis and large-scale data management. Furthermore, TACIT is implemented as an open, extensible, plugin-driven architecture, which will allow other researchers to extend and expand these capabilities as new methods become available.

Perils and opportunities in using large language models in psychological research.

The emergence of large language models (LLMs) has sparked considerable interest in their potential application in psychological research, mainly as a model of the human psyche or as a general text-analysis tool. However, the trend of using LLMs without sufficient attention to their limitations and risks, which we rhetorically refer to as "GPTology", can be detrimental given the easy access to models such as ChatGPT. Beyond existing general guidelines, we investigate the current limitations, ethical implications, and potential of LLMs specifically for psychological research, and show their concrete impact in various empirical studies. Our results highlight the importance of recognizing global psychological diversity, cautioning against treating LLMs (especially in zero-shot settings) as universal solutions for text analysis, and developing transparent, open methods to address LLMs' opaque nature for reliable, reproducible, and robust inference from AI-generated data. Acknowledging LLMs' utility for task automation, such as text annotation, or to expand our understanding of human psychology, we argue for diversifying human samples and expanding psychology's methodological toolbox to promote an inclusive, generalizable science, countering homogenization, and over-reliance on LLMs.

The Sins of the Parents Are to Be Laid Upon the Children: Biased Humans, Biased Data, Biased Models.

Technological innovations have become a key driver of societal advancements. Nowhere is this more evident than in the field of machine learning (ML), which has developed algorithmic models that shape our decisions, behaviors, and outcomes. These tools have widespread use, in part, because they can synthesize massive amounts of data to make seemingly objective recommendations. Yet, in the past few years, the ML community has been drawing attention to the need for caution when interpreting and using these models. This is because these models are created by humans, from data generated by humans, whose psychology allows for various biases that impact how the models are developed, trained, tested, and interpreted. As psychologists, we thus face a fork in the road: Down the first path, we can continue to use these models without examining and addressing these critical flaws and rely on computer scientists to try to mitigate them. Down the second path, we can turn our expertise in bias toward this growing field, collaborating with computer scientists to reduce the models' deleterious outcomes. This article serves to light the way down the second path by identifying how extant psychological research can help examine and curtail bias in ML models.

Difference of cardiac rehabilitation in the morning or evening on indexes of left ventricular and N-terminal pro-brain natriuretic peptide: a randomized controlled trial.

The present study aimed to evaluate the effects a cardiac rehabilitation program (CRP) performed in the morning or evening on left ventricular (LV) filling indices and the level of N-terminal fragment of pro-brain natriuretic peptide (NT-proBNP) in patients undergoing percutaneous coronary angioplasty during the COVID-19 pandemic. Methods: This was a randomized controlled single-blinded clinical trial. Ninety-six patients (mean age: 50.2 ± 8.1 years, 36 women and 44 men) with percutaneous coronary angioplasty were divided into two groups of intervention and control. In each group, the CRP was performed in either morning or evening. The CRP included walking and performing push-ups and sit-ups for 8 weeks. The participants of the control groups received routine care. The functional indices of LV, including LV ejection fraction, systolic function, and diastolic function (i.e. the transmitral flow), the E/e' to left atrium peak strain ratio (as an estimation for LA stiffness), and NT-proBNP level were measured in all participants before starting and at the end of the CRP. Results: In the intervention group, the individuals performing the CRP in the evening had significantly higher E-wave (0.76±0.02 vs. 0.75±0.03; P=0.008), ejection fraction (52.5±5.64 vs. 55.5±3.59; P=0.011), and diastolic function velocity (E/A ratio, 1.03±0.06 vs. 1.05±0.03; P=0.014) and significantly lower A-wave (0.72±0.02 vs. 0.71±0.01; P=0.041), E/e' ratio (6.74±0.29 vs. 6.51±0.38; P=0.038), and NT-proBNP level (2007.9±214.24 vs. 1933.9±253.13; P=0.045) compared with those performing the program in the morning. Conclusions: A supervised CRP performed in the evening compared with morning was more effective in improving LV functional indices. Therefore, such home-based interventions are recommended to be performed in the evening during the COVID-19 pandemic.

Morality beyond the WEIRD: How the nomological network of morality varies across cultures.

Moral foundations theory has been a generative framework in moral psychology in the last 2 decades. Here, we revisit the theory and develop a new measurement tool, the Moral Foundations Questionnaire-2 (MFQ-2), based on data from 25 populations. We demonstrate empirically that equality and proportionality are distinct moral foundations while retaining the other four existing foundations of care, loyalty, authority, and purity. Three studies were conducted to develop the MFQ-2 and to examine how the nomological network of moral foundations varies across 25 populations. Study 1 (N = 3,360, five populations) specified a refined top-down approach for measurement of moral foundations. Study 2 (N = 3,902, 19 populations) used a variety of methods (e.g., factor analysis, exploratory structural equations model, network psychometrics, alignment measurement equivalence) to provide evidence that the MFQ-2 fares well in terms of reliability and validity across cultural contexts. We also examined population-level, religious, ideological, and gender differences using the new measure. Study 3 (N = 1,410, three populations) provided evidence for convergent validity of the MFQ-2 scores, expanded the nomological network of the six moral foundations, and demonstrated the improved predictive power of the measure compared with the original MFQ. Importantly, our results showed how the nomological network of moral foundations varied across cultural contexts: consistent with a pluralistic view of morality, different foundations were influential in the network of moral foundations depending on cultural context. These studies sharpen the theoretical and methodological resolution of moral foundations theory and provide the field of moral psychology a more accurate instrument for investigating the many ways that moral conflicts and divisions are shaping the modern world. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The (moral) language of hate.

Humans use language toward hateful ends, inciting violence and genocide, intimidating and denigrating others based on their identity. Despite efforts to better address the language of hate in the public sphere, the psychological processes involved in hateful language remain unclear. In this work, we hypothesize that morality and hate are concomitant in language. In a series of studies, we find evidence in support of this hypothesis using language from a diverse array of contexts, including the use of hateful language in propaganda to inspire genocide (Study 1), hateful slurs as they occur in large text corpora across a multitude of languages (Study 2), and hate speech on social-media platforms (Study 3). In post hoc analyses focusing on particular moral concerns, we found that the type of moral content invoked through hate speech varied by context, with Purity language prominent in hateful propaganda and online hate speech and Loyalty language invoked in hateful slurs across languages. Our findings provide a new psychological lens for understanding hateful language and points to further research into the intersection of morality and hate, with practical implications for mitigating hateful rhetoric online.

The paucity of morality in everyday talk.

Given its centrality in scholarly and popular discourse, morality should be expected to figure prominently in everyday talk. We test this expectation by examining the frequency of moral content in three contexts, using three methods: (a) Participants' subjective frequency estimates (N = 581); (b) Human content analysis of unobtrusively recorded in-person interactions (N = 542 participants; n = 50,961 observations); and (c) Computational content analysis of Facebook posts (N = 3822 participants; n = 111,886 observations). In their self-reports, participants estimated that 21.5% of their interactions touched on morality (Study 1), but objectively, only 4.7% of recorded conversational samples (Study 2) and 2.2% of Facebook posts (Study 3) contained moral content. Collectively, these findings suggest that morality may be far less prominent in everyday life than scholarly and popular discourse, and laypeople, presume.

Moral values predict county-level COVID-19 vaccination rates in the United States.

Despite the widespread availability of COVID-19 vaccines, the United States has a depressed rate of vaccination relative to similar countries. Understanding the psychology of vaccine refusal, particularly the possible sources of variation in vaccine resistance across U.S. subpopulations, can aid in designing effective intervention strategies to increase vaccination across different regions. Here, we demonstrate that county-level moral values (i.e., Care, Fairness, Loyalty, Authority, and Purity) are associated with COVID-19 vaccination rates across 3,106 counties in the contiguous United States. Specifically, in line with our hypothesis, we find that fewer people are vaccinated in counties whose residents prioritize moral concerns about bodily and spiritual purity. Further, we find that stronger endorsements of concerns about Fairness and Loyalty to the group predict higher vaccination rates. These associations are robust after adjusting for structural barriers to vaccination, the demographic makeup of the counties, and their residents' political voting behavior. Our findings have implications for health communication, intervention strategies based on targeted messaging, and our fundamental understanding of the moral psychology of vaccination hesitancy and behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).