Selling just preservation

Treves et al. argue for better representation of voiceless groups in current policy decisions. We agree with the argument but believe it will be challenging to convince enough people of its importance to change policy — especially those political groups who are not predisposed to agreeing with these kinds of arguments. We draw on the social psychology literature to recommend three principles for increasing the persuasiveness of the argument to the public: pre-suasion, framing, and tailoring for the audience. We apply these principles to make concrete recommendations for framing the argument to persuade the American political right

Detecting The Corruption Of Online Questionnaires By Artificial Intelligence

Online questionnaires that use crowd-sourcing platforms to recruit participants have become commonplace, due to their ease of use and low costs. Artificial Intelligence (AI) based Large Language Models (LLM) have made it easy for bad actors to automatically fill in online forms, including generating meaningful text for open-ended tasks. These technological advances threaten the data quality for studies that use online questionnaires. This study tested if text generated by an AI for the purpose of an online study can be detected by both humans and automatic AI detection systems. While humans were able to correctly identify authorship of text above chance level (76 percent accuracy), their performance was still below what would be required to ensure satisfactory data quality. Researchers currently have to rely on the disinterest of bad actors to successfully use open-ended responses as a useful tool for ensuring data quality. Automatic AI detection systems are currently completely unusable. If AIs become too prevalent in submitting responses then the costs associated with detecting fraudulent submissions will outweigh the benefits of online questionnaires. Individual attention checks will no longer be a sufficient tool to ensure good data quality. This problem can only be systematically addressed by crowd-sourcing platforms. They cannot rely on automatic AI detection systems and it is unclear how they can ensure data quality for their paying clients

Better than Us: The Role of Implicit Self-Theories in Determining Perceived Threat Responses in HRI

Robots that are capable of outperforming human beings on mental and physical tasks provoke perceptions of threat. In this article we propose that implicit self-theory (core beliefs about the malleability of self-attributes, such as intelligence) is a determinant of whether one person experiences threat perception to a greater degree than another. We test for this possibility in a novel experiment in which participants watched a video of an apparently autonomous intelligent robot defeating human quiz players in a general knowledge game. Following the video, participants received either social comparison feedback, improvement-oriented feedback, or no feedback, and were then given the opportunity to play against the robot. We show that those who adopt a malleable self-theory (incremental theorists) are more likely to play against a robot after imagining losing to it, as well as exhibit more favorable responses and less identity threats than entity theorists (those adopting a fixed self-theory). Moreover, entity theorists (vs. incremental theorists) perceive autonomous intelligent robots to be significantly more threatening (both in terms of realistic and identity threats). These findings offer novel theoretical and practical implications, in addition to enriching the HRI literature by demonstrating that implicit self-theory is, in fact, an influential variable underpinning perceived threat

Detecting the corruption of online questionnaires by artificial intelligence

Online questionnaires that use crowdsourcing platforms to recruit participants have become commonplace, due to their ease of use and low costs. Artificial intelligence (AI)-based large language models (LLMs) have made it easy for bad actors to automatically fill in online forms, including generating meaningful text for open-ended tasks. These technological advances threaten the data quality for studies that use online questionnaires. This study tested whether text generated by an AI for the purpose of an online study can be detected by both humans and automatic AI detection systems. While humans were able to correctly identify the authorship of such text above chance level (76% accuracy), their performance was still below what would be required to ensure satisfactory data quality. Researchers currently have to rely on a lack of interest among bad actors to successfully use open-ended responses as a useful tool for ensuring data quality. Automatic AI detection systems are currently completely unusable. If AI submissions of responses become too prevalent, then the costs associated with detecting fraudulent submissions will outweigh the benefits of online questionnaires. Individual attention checks will no longer be a sufficient tool to ensure good data quality. This problem can only be systematically addressed by crowdsourcing platforms. They cannot rely on automatic AI detection systems and it is unclear how they can ensure data quality for their paying clients

Psicología social y moral de COVID-19 en 69 países

La pandemia de COVID-19 ha afectado a todos los ámbitos de la vida humana, incluido el tejido económico y social de las sociedades. Una de las estrategias centrales para gestionar la salud pública a lo largo de la pandemia ha sido el envío de mensajes persuasivos y el cambio de comportamiento colectivo. Para ayudar a los estudiosos a comprender mejor la psicología social y moral que subyace al comportamiento en materia de salud pública, presentamos un conjunto de datos compuesto por 51.404 individuos de 69 países. Este conjunto de datos se recopiló para el proyecto de la Colaboración Internacional en Psicología Social y Moral de COVID-19 (ICSMP COVID-19). Esta encuesta de ciencias sociales invitó a participantes de todo el mundo a completar una serie de medidas morales y psicológicas y actitudes de salud pública sobre COVID-19 durante una fase temprana de la pandemia de COVID-19 (entre abril y junio de 2020). La encuesta incluía siete grandes categorías de preguntas: Creencias sobre COVID-19 y conductas de cumplimiento; identidad y actitudes sociales; ideología; salud y bienestar; creencias morales y motivación; rasgos de personalidad; y variables demográficas. Presentamos los datos brutos y depurados, junto con todos los materiales de la encuesta, las visualizaciones de los datos y las evaluaciones psicométricas de las variables clave.The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables

National identity predicts public health support during a global pandemic

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.publishedVersio

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Peer reviewe

National identity predicts public health support during a global pandemic (vol 13, 517, 2022) : National identity predicts public health support during a global pandemic (Nature Communications, (2022), 13, 1, (517), 10.1038/s41467-021-27668-9)

Publisher Copyright: © The Author(s) 2022.In this article the author name ‘Agustin Ibanez’ was incorrectly written as ‘Augustin Ibanez’. The original article has been corrected.Peer reviewe

National identity predicts public health support during a global pandemic.

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics