The poetry of psychological distance: Bidirectional associations between stimulus speed and its psychological distance and construal level.

Based on the cognitive-ecological approach and on logical-functional principles, in 12 studies (11 preregistered), we examine the novel hypotheses that psychological distance and construal level (CL) are associated in people's minds with stimulus speed: the psychologically distant/abstract is slow, and the psychologically close/concrete is fast. The findings support our expectations. Study Set I examined the association between psychological distance and speed. Findings show that psychological distance is implicitly and explicitly associated with speed (Study 1), that psychological distance is seen as compatible with slow and proximity with fast (Study 2), that stimulus psychological distance affects its perceived speed (Study 3), and that stimulus speed affects its psychological distance (Study 4). Study Set II examined the association between construal level and speed. Findings show that construal level is explicitly associated with speed (Study 5), that abstract is seen as compatible with slow and concrete with fast (Study 6), that natural language word distribution structures reflect an association between abstractness and speed (Study 7), that construal level affects speed (Study 8), and that speed affects stimulus construal level (Study 9). Study Set III examined implications for communication and person perception. Findings suggest that slow-paced (vs. fast-paced) speech is associated with larger perceived spatial and social distance between speaker and audience and larger audiences (Studies 10a, 10b) and that people infer an expansive (contractive) regulatory scope from slow-paced (fast-paced) spoken messages (Study 11). We elaborate on possible mechanisms and their theoretical and practical implications in domains including decision making and urban design. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

When liars are considered honest.

This article introduces a theoretical model of truth and honesty from a psychological perspective. We examine its application in political discourse and discuss empirical findings distinguishing between conceptions of honesty and their influence on public perception, misinformation dissemination, and the integrity of democracy.

The persuasive effects of political microtargeting in the age of generative artificial intelligence.

The increasing availability of microtargeted advertising and the accessibility of generative artificial intelligence (AI) tools, such as ChatGPT, have raised concerns about the potential misuse of large language models in scaling microtargeting efforts for political purposes. Recent technological advancements, involving generative AI and personality inference from consumed text, can potentially create a highly scalable "manipulation machine" that targets individuals based on their unique vulnerabilities without requiring human input. This paper presents four studies examining the effectiveness of this putative "manipulation machine." The results demonstrate that personalized political ads tailored to individuals' personalities are more effective than nonpersonalized ads (studies 1a and 1b). Additionally, we showcase the feasibility of automatically generating and validating these personalized ads on a large scale (studies 2a and 2b). These findings highlight the potential risks of utilizing AI and microtargeting to craft political messages that resonate with individuals based on their personality traits. This should be an area of concern to ethicists and policy makers.

From alternative conceptions of honesty to alternative facts in communications by US politicians.

The spread of online misinformation on social media is increasingly perceived as a problem for societal cohesion and democracy. The role of political leaders in this process has attracted less research attention, even though politicians who 'speak their mind' are perceived by segments of the public as authentic and honest even if their statements are unsupported by evidence. By analysing communications by members of the US Congress on Twitter between 2011 and 2022, we show that politicians' conception of honesty has undergone a distinct shift, with authentic belief speaking that may be decoupled from evidence becoming more prominent and more differentiated from explicitly evidence-based fact speaking. We show that for Republicans-but not Democrats-an increase in belief speaking of 10% is associated with a decrease of 12.8 points of quality (NewsGuard scoring system) in the sources shared in a tweet. In contrast, an increase in fact-speaking language is associated with an increase in quality of sources for both parties. Our study is observational and cannot support causal inferences. However, our results are consistent with the hypothesis that the current dissemination of misinformation in political discourse is linked to an alternative understanding of truth and honesty that emphasizes invocation of subjective belief at the expense of reliance on evidence.

Online reading habits can reveal personality traits: towards detecting psychological microtargeting.

Building on big data from Reddit, we generated two computational text models: (i) Predicting the personality of users from the text they have written and (ii) predicting the personality of users based on the text they have consumed. The second model is novel and without precedent in the literature. We recruited active Reddit users (N=1,105) of fiction-writing communities. The participants completed a Big Five personality questionnaire and consented for their Reddit activity to be scraped and used to create a machine learning model. We trained an natural language processing model [Bidirectional Encoder Representations from Transformers (BERT)], predicting personality from produced text (average performance: r=0.33). We then applied this model to a new set of Reddit users (N=10,050), predicted their personality based on their produced text, and trained a second BERT model to predict their predicted-personality scores based on consumed text (average performance: r=0.13). By doing so, we provide the first glimpse into the linguistic markers of personality-congruent consumed content.

Social media sharing of low-quality news sources by political elites.

Increased sharing of untrustworthy information on social media platforms is one of the main challenges of our modern information society. Because information disseminated by political elites is known to shape citizen and media discourse, it is particularly important to examine the quality of information shared by politicians. Here, we show that from 2016 onward, members of the Republican Party in the US Congress have been increasingly sharing links to untrustworthy sources. The proportion of untrustworthy information posted by Republicans versus Democrats is diverging at an accelerating rate, and this divergence has worsened since President Biden was elected. This divergence between parties seems to be unique to the United States as it cannot be observed in other western democracies such as Germany and the United Kingdom, where left-right disparities are smaller and have remained largely constant.

Troll and divide: the language of online polarization.

The affective animosity between the political left and right has grown steadily in many countries over the past few years, posing a threat to democratic practices and public health. There is a rising concern over the role that "bad actors" or trolls may play in the polarization of online networks. In this research, we examined the processes by which trolls may sow intergroup conflict through polarized rhetoric. We developed a dictionary to assess online polarization by measuring language associated with communications that display partisan bias in their diffusion. We validated the polarized language dictionary in 4 different contexts and across multiple time periods. The polarization dictionary made out-of-set predictions, generalized to both new political contexts (#BlackLivesMatter) and a different social media platform (Reddit), and predicted partisan differences in public opinion polls about COVID-19. Then we analyzed tweets from a known Russian troll source (N = 383,510) and found that their use of polarized language has increased over time. We also compared troll tweets from 3 countries (N = 79,833) and found that they all utilize more polarized language than regular Americans (N = 1,507,300) and trolls have increased their use of polarized rhetoric over time. We also find that polarized language is associated with greater engagement, but this association only holds for politically engaged users (both trolls and regular users). This research clarifies how trolls leverage polarized language and provides an open-source, simple tool for exploration of polarized communications on social media.

To which world regions does the valence-dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .

Differential effects of abstract and concrete processing on the reactivity of basic and self-conscious emotions.

The present research examines the influence of different processing modes (abstract vs. concrete) on the intensity of negative basic emotions (anger, fear) and self-conscious emotions (guilt, shame). We suggest that the cognitive appraisals underlying self-conscious emotions are relatively more abstract and less concrete than the appraisals underlying basic emotions. Consequently, we predicted that abstract processing would increase the intensity of self-conscious emotions and decrease the intensity of basic emotions, whereas concrete processing would increase the intensity of basic emotions and decrease the intensity of self-conscious emotions. We tested this prediction in four experiments. In Experiments 1 and 2, concrete processing led to more intense anger than abstract processing, and abstract processing led to more intense guilt than concrete processing. In Experiment 3a, concrete processing increased the intensity of fear, and in Experiment 3b, concrete processing decreased the intensity of shame. Our findings highlight the importance of considering the emotion's underlying appraisals when reflecting on one's emotional experience.

Political depression? A big-data, multimethod investigation of Americans' emotional response to the Trump presidency.

Previous studies suggested that the 2016 presidential elections gave rise to pathological levels of election-related distress in liberal Americans; however, it has also been suggested that the public discourse and the professional discourse have increasingly overgeneralized concepts of trauma and psychopathology. In light of this, in the current research, we utilized an array of big data measures and asked whether a political loss in a participatory democracy can indeed lead to psychopathology. We observed that liberals report being more depressed when asked directly about the effects of the election; however, more indirect measures show a short-lived or nonexistent effect. We examined self-report measures of clinical depression with and without a reference to the election (Studies 1A & 1B), analyzed Twitter discourse and measured users' levels of depression using a machine-learning-based model (Study 2), conducted time-series analysis of depression-related search behavior on Google (Study 3), examined the proportion of antidepressants consumption in Medicaid data (Study 4), and analyzed daily surveys of hundreds of thousands of Americans (Study 5), and saw that at the aggregate level, empirical data reject the accounts of "Trump Depression." We discuss possible interpretations of the discrepancies between the direct and indirect measures. The current investigation demonstrates how big-data sources can provide an unprecedented view of the psychological consequences of political events and sheds light on the complex relationship between the political and the personal spheres. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Updating visual working memory in the change detection paradigm.

An updating version of a visual change detection paradigm was used to investigate the behavioral outcomes and event-related potential (ERP) correlates of visual working memory updating. In each trial, participants were either presented with a memory array followed by a test probe, or with two successive memory arrays. Participants were instructed to update their working memory with the information in the second array. The second array differed from the first one in all, some, or none of the items. When a subset of the items was updated, the probe could appear in the location of a repeated item or of an updated item. Two experiments are reported, using set-sizes of six and two items, respectively. Both experiments show a benefit for probing a repeated item compared to an updated item. This result is consistent with an item-specific updating process. Experiment 2 also revealed two distinct updating-related ERP components, observed in both contralateral and ipsilateral visual hemifields. Frontal electrodes were sensitive to the number of changed items in the array. This ERP component was interpreted as reflecting the modification of information in working memory. Lateral-posterior electrodes only showed a difference between a full repetition of the array and updating, regardless of the number of updated items. This component was interpreted as reflecting attention to task-relevant information rather than the updating process per se. The finding of item-specific updating supports discrete-item architecture models of working memory.