107 research outputs found
Retrieval-induced forgetting as motivated cognition
Recalling information from a particular category can reduce one's memory capability for related, non-retrieved information. This is known as the retrieval-induced forgetting effect (RIF; Anderson et al., 1994). The present paper reviews studies that show that the RIF effect is motivated. More specifically, we describe research showing that the need for closure (NFC; the motivation to attain epistemic certainty; Kruglanski and Webster, 1996) generally enhances the RIF, because this prevents uncertainty and confusion from the intrusion of unwanted memories during selective-retrieval. However, when the content of the to-be-forgotten information serves the retriever's goals, NFC reduces RIF. Overall, the present findings are consistent with the view that motivation can affect the magnitude of RIF effects which, in turn, can serve as a mechanism for reaching preferred conclusion
The Epistemic Bases of Changes of Opinion and Choices: The Joint Effects of the Need for Cognitive Closure, Ascribed Epistemic Authority and Quality of Advice
This research investigates the epistemic underpinnings of changes of opinion and choices. Based on the Lay Epistemic Theory (Kruglanski et al., 2009) and consistent with relevant theories of persuasion (e.g., Chaiken, Liberman, & Eagly, 1989; Kruglanski, & Thompson, 1999; Petty & Cacioppo, 1986), we hypothesized that individuals with a high (vs. low) need for cognitive closure would be more influenced by the high (vs. low) level of the epistemic authority of an advisor, and would be less influenced by the quality of the provided advice. These hypotheses were supported in two experimental studies (Total N=352) within two different domains of decision-making (a legal case in Study 1 and consumer behavior in Study 2). The theoretical and practical implications of the results are discussed
"Letting myself go forward past wrongs": How regulatory modes affect self-forgiveness
The present research addresses the question of whether regulatory-mode orientations affect self-forgiveness. We expected that people with a strong locomotion orientation would be more inclined to self-forgiveness because of their tendencies toward movement and change, which focus them on the future, whereas people with a strong assessment orientation would refrain from self-forgiveness due to their evaluative tendencies which focus them on the past. These hypotheses were supported by the results in four studies that tested the relation between regulatory modes and self-forgiveness by measuring (Studies 1, 3 and 4) and manipulating (Study 2) regulatory-mode-orientations. Finally, in Study 4 we examined more closely our hypothesis that the relation between self-forgiveness and regulatory modes is mediated by past and future temporal foci. The implications of the results for regulatory mode theory are also discussed
Concern with COVID-19 pandemic threat and attitudes towards immigrants: The mediating effect of the desire for tightness
Tightening social norms is thought to be adaptive for dealing with collective threat yet it may have negative consequences for increasing prejudice. The present research investigated the role of desire for cultural tightness, triggered by the COVID-19 pandemic, in increasing negative attitudes towards immigrants. We used participant-level data from 41 countries (N = 55,015) collected as part of the PsyCorona project, a crossnational longitudinal study on responses to COVID-19. Our predictions were tested through multilevel and SEM models, treating participants as nested within countries. Results showed that people’s concern with COVID19 threat was related to greater desire for tightness which, in turn, was linked to more negative attitudes towards immigrants. These findings were followed up with a longitudinal model (N = 2,349) which also showed that people’s heightened concern with COVID-19 in an earlier stage of the pandemic was associated with an increase in their desire for tightness and negative attitudes towards immigrants later in time. Our findings offer insight into the trade-offs that tightening social norms under collective threat has for human groups
The Precarity of Progress: Implications of a Shifting Gendered Division of Labor for Relationships and Well-Being as a Function of Country-Level Gender Equality
The onset of the COVID-19 pandemic saw a shift toward a more traditional division of labor–one where women took greater responsibility for household tasks and childcare than men. We tested whether this regressive shift was more acutely perceived and experienced by women in countries with greater gender equality. Cross-cultural longitudinal survey data for women and men (N = 10,238) was collected weekly during the first few months of the pandemic. Multilevel modelling analyses, based on seven waves of data collection, indicated that a regressive shift was broadly perceived but not uniformly felt. Women and men alike perceived a shift toward a more traditional division of household labor during the first few weeks of the pandemic. However, this perception only undermined women’s satisfaction with their personal relationships and subjective mental health if they lived in countries with higher levels of economic gender equality. Among women in countries with lower levels of economic gender equality, the perceived shift predicted higher relationship satisfaction and mental health. There were no such effects among men. Taken together, our results suggest that subjective perceptions of disempowerment, and the gender role norms that underpin them, should be considered when examining the gendered impact of global crisis
Trust in government regarding COVID-19 and its associations with preventive health behaviour and prosocial behaviour during the pandemic: a cross-sectional and longitudinal study
Background. The effective implementation of government policies and measures for controlling the coronavirus disease 2019 (COVID-19) pandemic requires compliance from the public. This study aimed to examine cross-sectional and longitudinal associations of trust ingovernment regarding COVID-19 control with the adoption of recommended health behaviours and prosocial behaviours, and potential determinants of trust in government duringthe pandemic.Methods. This study analysed data from the PsyCorona Survey, an international project onCOVID-19 that included 23 733 participants from 23 countries (representative in age andgender distributions by country) at baseline survey and 7785 participants who also completedfollow-up surveys. Specification curve analysis was used to examine concurrent associationsbetween trust in government and self-reported behaviours. We further used structural equation model to explore potential determinants of trust in government. Multilevel linear regressions were used to examine associations between baseline trust and longitudinal behavioural changes.Results. Higher trust in government regarding COVID-19 control was significantly associatedwith higher adoption of health behaviours (handwashing, avoiding crowded space, self-quarantine) and prosocial behaviours in specification curve analyses (median standardised β =0.173 and 0.229, p < 0.001). Government perceived as well organised, disseminating clear messages and knowledge on COVID-19, and perceived fairness were positively associated withtrust in government (standardised β = 0.358, 0.230, 0.056, and 0.249, p < 0.01). Higher trustat baseline survey was significantly associated with lower rate of decline in health behavioursover time ( p for interaction = 0.001).Conclusions. These results highlighted the importance of trust in government in the control of Covid-19
.Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic
Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individuallevel
injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant
‘We are all in the same boat’ : how societal discontent affects intention to help during the COVID-19 pandemic
The coronavirus disease 2019 (COVID-19) pandemic has caused a global health crisis. Consequently, many countries have adopted restrictive measures that caused a substantial change in society. Within this framework, it is reasonable to suppose that a sentiment of societal discontent, defined as generalized concern about the precarious state of society, has arisen. Literature shows that collectively experienced situations can motivate people to help each other. Since societal discontent is conceptualized as a collective phenomenon, we argue that it could influence intention to help others, particularly those who suffer from coronavirus. Thus, in the present study, we aimed (a) to explore the relationship between societal discontent and intention to help at the individual level and (b) to investigate a possible moderating effect of societal discontent at the country level on this relationship. To fulfil our purposes, we used data collected in 42 countries (N = 61,734) from the PsyCorona Survey, a cross-national longitudinal study. Results of multilevel analysis showed that, when societal discontent is experienced by the entire community, individuals dissatisfied with society are more prone to help others. Testing the model with longitudinal data (N = 3,817) confirmed our results. Implications for those findings are discussed in relation to crisis management. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement
Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic
Before vaccines for COVID-19 became available, a set of infection prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection prevention behavior in 56,072 participants across 28 countries, administered in March-May 2020. The machine- learning model predicted 52% of the variance in infection prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual- level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically-derived predictors were relatively unimportant
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