PurposePolicies aiming to control the COVID-19 pandemic framed health guidelines as prosocial behaviors. This research aims to explore whether contextual cues reminding of the COVID-19 pandemic can activate prosocial goals unrelated to the pandemic. It is hypothesized that COVID-19 reminders, such as mask-wearing images, will increase prosocial behavioral intentions.Design/methodology/approachFive studies (N = 956) test the hypotheses. Study 1 tests whether consumers chronically concerned with the pandemic show higher prosocial intentions. Studies 2-5 test if COVID-19-related media cues increase prosocial intentions when compared with control conditions.FindingsConsumers chronically concerned or exposed to pandemic-related cues showed higher prosocial behavior intentions, were willing to donate more money and showed a higher preference to consume in smaller businesses. This tendency persisted after health policies ceased and was not explained by concerns with the pandemic or mortality salience, suggesting it may result from simple semantic associations between the COVID-19 pandemic and prosocial goals.Research limitations/implicationsSubtle contextual cues can be used to promote prosocial behaviors benefiting from previous associations between health policies and prosocial goals. Future research should further explore the mechanism underlying the reported effect and explore other associations between prosocial behaviors and contextual information.Practical implicationsPublic health policies may be used for social marketing strategies and programs promoting prosocial behavior.Social implicationsProsocial intentions may be primed by contextual reminders of crises that are strongly associated to a need to act in a prosocial way, such as the COVID-19 pandemic.Originality/valueThis research provides new insights into the consequences of health policy programs focused on the promotion of prosocial behaviors. It also highlights how contextual cues associated with COVID-19 can prime socially responsible behaviors in different domains.

Background:Adverse childhood experiences (ACEs) are associated with increased risk for psychopathology and reduced psychosocial functioning across the life course. However, digital selective prevention targeting individuals with a history of ACEs remains scarce. Impairments in emotion regulation and social information processing were confirmed as mechanisms linking ACEs with mental health and potential targets for interventions. This randomised controlled trial evaluated the uptake, efficacy, and differential component effects of the guided FACE app, a theory-based preventive intervention for emerging adults with self-reported ACEs that targets these mechanisms. Methods:Emerging adults (18-24 years) reporting at least one ACE category in a population-based cohort were invited to participate. Of 1514 eligible individuals, 167 (11%) enrolled and were randomised to immediate access to the FACE app or care as-usual with minimal intervention (CAU-MI). The 10-week intervention comprised two transdiagnostic components delivered in a cross-over design: Self- and Emotion Regulation (SER) and Social Skills and Social Information Processing (SSIP). Primary outcomes were resilience and well-being. Secondary outcomes included self-efficacy for managing emotions, social problem-solving, fear of negative evaluation, social avoidance, and self-esteem. Ecological momentary assessments captured real-life affect, burden, resilience, and social distress. Linear mixed models were applied under intention-to-treat and per-protocol principles. Results:Linear mixed models showed significant improvements over time (intervention and CAU-MI) in resilience, self-esteem, self-efficacy for managing emotions, adaptive problem-solving, momentary positive affect. Group × time interaction effects in favour of the app compared to CAU-MI emerged for some more proximal mechanism-related outcomes: self-efficacy for managing emotions, fear of negative evaluation and negative problem orientation but not for resilience and well-being, which were already at normative levels at baseline. Effects were largely maintained at follow-up. Improvements were stronger and more consistent following the SER compared to the SSIP component. Average time of app use was three hours. Conclusions:The FACE app did not improve the primary distal outcomes resilience and well-being beyond the CAU-MI condition. Nevertheless, the FACE app demonstrated modest but consistent improvements in proximal mechanisms associated with ACE-related vulnerability. Findings support the relevance of targeting emotion regulation and social-cognitive processes in digital selective prevention. However, limited uptake highlights challenges in active recruitment of unselected high risk populations and underscores the importance of integrating preventive apps into stepped-care or blended care models. Trial registration:ClinicalTrials.gov NCT05824182.

Adverse Childhood Experiences (ACEs) are robust predictors of negative mental health outcomes and psycho-social difficulties, yet the psychological mechanisms linking ACEs to later psychopathology remain only partially understood. Drawing on a three-wave longitudinal study of Swiss emerging adults (N = 1934), we conducted longitudinal mediation analyses to examine emotional processing (emotional reactivity, perseverative thinking) and social information processing (threat interpretation bias, rejection sensitivity) as pathways from ACEs to psychopathology. Factor analyses identified three distinct ACE domains: family maltreatment, peer victimization, and sexual abuse. By modelling these domains simultaneously, we accounted for their frequent co-occurrence and isolated their unique contributions. Family maltreatment and peer victimization independently were associated with heightened psychopathology and difficulties with emotional processing and social information processing at Wave 1. Furthermore, both adversity domains also predicted persistent elevations in these domains over time, even after controlling for baseline levels and sociodemographic variables. Longitudinal mediation analyses revealed that family maltreatment and peer victimization both predicted psychopathology via perseverative thinking, threat interpretation bias, and emotional reactivity. Sexual abuse, in contrast, showed weaker or delayed associations with psychopathology and operated primarily through threat interpretation bias. Rejection sensitivity, while associated at the bivariate level, did not mediate longitudinal effects. Findings support and extend McLaughlin's Model of Mechanisms Linking Childhood Trauma to Psychopathology by identifying distinct mediational pathways from specific ACEs to psychopathology. These distinct pathways underscore the relevance of personalized and mechanism-based treatment planning based on the ACEs experienced.

When predicting someone's performance, people expect that short runs of consistent successful outcomes will continue-the hot-hand. This tendency has been shown in contexts where athletes show a local performance streak, but no other information about their performance is provided. In real-life settings, performance predictions often use global-performance records like success-rate probabilities, although judgements often neglect such statistical information. Aimed at understanding psychological momentums, in a classical sports domain the present work explores how global-performance information (success rates) about an athlete impacts intentionality judgements and moderate predictions of success after a streak. Four studies show that (1) although participants tend to predict the continuation of streaks of success, they are less likely to predict that successful streaks will continue when success rates are low (vs. high or unknown); (2) sensitiveness to local performance's consistency affects perceived ability for high-success rate athletes and perceived effort for low success-rate athletes; (3) the mediation model describing that intentionality attributions mediate the effect of global success-rate information on performance predictions fits the data. Theoretical and practical implications are discussed.

To support older mourners after the loss of their partner, LEAVES, an online self-help service that delivers the LIVIA spousal bereavement intervention, was developed. It integrates an embodied conversational agent and an initial risk assessment. Based on an iterative, human-centered, and stakeholder inclusive approach, interviews with older mourners and focus groups with stakeholders were conducted to understand their perspective on grief and on using LEAVES. Subsequently, the resulting technology and service model were evaluated by means of interviews, focus groups, and an online survey. While digital literacy remains a challenge, LEAVES shows promise of being supportive to the targeted end-users.

Background Artificial intelligence (AI) tools hold much promise for mental health care by increasing the scalability and accessibility of care. However, current development and evaluation practices of AI tools limit their meaningfulness for health care contexts and therefore also the practical usefulness of such tools for professionals and clients alike. Objective The aim of this study is to demonstrate the evaluation of an AI monitoring tool that detects the need for more intensive care in a web-based grief intervention for older mourners who have lost their spouse, with the goal of moving toward meaningful evaluation of AI tools in e-mental health. Method We leveraged the insights from three evaluation approaches: (1) the F1-score evaluated the tool’s capacity to classify user monitoring parameters as either in need of more intensive support or recommendable to continue using the web-based grief intervention as is; (2) we used linear regression to assess the predictive value of users’ monitoring parameters for clinical changes in grief, depression, and loneliness over the course of a 10-week intervention; and (3) we collected qualitative experience data from e-coaches (N=4) who incorporated the monitoring in their weekly email guidance during the 10-week intervention. Results Based on n=174 binary recommendation decisions, the F1-score of the monitoring tool was 0.91. Due to minimal change in depression and loneliness scores after the 10-week intervention, only 1 linear regression was conducted. The difference score in grief before and after the intervention was included as a dependent variable. Participants’ (N=21) mean score on the self-report monitoring and the estimated slope of individually fitted growth curves and its standard error (ie, participants’ response pattern to the monitoring questions) were used as predictors. Only the mean monitoring score exhibited predictive value for the observed change in grief (R2=1.19, SE 0.33; t16=3.58, P=.002). The e-coaches appreciated the monitoring tool as an opportunity to confirm their initial impression about intervention participants, personalize their email guidance, and detect when participants’ mental health deteriorated during the intervention. Conclusions The monitoring tool evaluated in this paper identified a need for more intensive support reasonably well in a nonclinical sample of older mourners, had some predictive value for the change in grief symptoms during a 10-week intervention, and was appreciated as an additional source of mental health information by e-coaches who supported mourners during the intervention. Each evaluation approach in this paper came with its own set of limitations, including (1) skewed class distributions in prediction tasks based on real-life health data and (2) choosing meaningful statistical analyses based on clinical trial designs that are not targeted at evaluating AI tools. However, combining multiple evaluation methods facilitates drawing meaningful conclusions about the clinical value of AI monitoring tools for their intended mental health context.

Artificial intelligence (AI) tools hold much promise for mental healthcare by increasing the scalability and accessibility of care. However, current development and evaluation practices of AI tools in mental healthcare limit the meaningfulness of their evaluation for healthcare contexts and thereby, the practical usefulness of such tools for professionals and clients alike. To move towards meaningful evaluation of AI tools in eMental health, this article demonstrates the evaluation of an AI monitoring tool that detects the need for more intensive care in an online grief intervention for older mourners. We take a three-fold evaluation approach (1) using the F1-metric to evaluate the tool’s capacity to classify user monitoring parameters, including affect, as (a) in need of more intensive support, or (b) recommendable to continue using the online grief intervention as is; (2) using linear regression to assess the predictive value of users’ monitoring parameters for clinical changes in grief, depression, and loneliness over the course of a 10-week intervention. Finally, (3) we collect qualitative experience data from eCoaches (N=4) who incorporated the monitoring in their weekly e-mail guidance during the 10-week intervention. (1) Based on N=174 binary recommendation decisions, the F1-score of the monitoring tool was 0.91. (2) Due to minimal variation in depression and loneliness scores after the 10-week intervention compared to before the intervention, only one linear regression was conducted with the difference score in grief before and after the intervention as dependent variable and participants’ mean score on the monitoring assessment tool, the estimate and slope of growth curves fitted to each participant’s response pattern to the monitoring assessment tool as predictors. Only the mean score exhibited predictive value for the observed change in grief (R2 =1.19, SE 0.33, t(df) = 3.58(16), P=.002). (3) The eCoaches appreciated the monitoring tool as a) an opportunity to confirm their impression about the participant based on a clinical interview prior to the intervention, b) a source for personalizing their e-mail guidance and c) an opportunity to detect when participants’ mental health deteriorated during the intervention. Each evaluation approach used in this article comes with its own set of limitations and challenges, including (a) skewed class distributions in prediction tasks based on real-life mental health data and (b) choosing meaningful statistical analyses based on clinical trial designs not targeted at evaluating AI tools. However, using multiple evaluation methods provides a good basis for drawing clinically meaningful conclusions and recommendations for improving the clinical value of any specific AI monitoring tool for its intended clinical context.

ABSTRACTIn organizational contexts, managers often have to judge and predict others' performance. Previous research has consistently shown that when predicting someone's performance, people expect that a local sequence of successful outcomes will continue—the hot‐hand. The present work proposes that hot‐hand predictions occur when local streaks are dispositionally attributed to the agents' intentionality and explores how the inclusion of global performance success rates may guide intentionality inferences and moderate predictions of success after a streak. Three studies, using within‐ and between‐subjects' designs, manipulate agent's global success rate and show that after a local streak, intentionality attributions and predictions of success are lower when success rates are low (vs. high or unknown); intentionality attributions mediate the effect of success rate on predictions; hot‐hand predictions are lower for low success rate agents (vs. high or unknown) as they are not perceived as more responsible for streaky than for alternated performances.

Tobacco consumption is a leading cause of preventable death and, despite decades of research, smoking cessation is still a challenge. The number of smokers who attempt to quit smoking every year is high, but only 2-3% remain abstinent after 12 months. Smokers wanting to quit should have the help of healthcare professionals. However, only 1 in 20 who attempts to quit is supervised by a professional. Mobile phone technology has the potential to provide personalized smoking cessation support. Motivational messages and behavioral-changes methods used usually in face-to-face smoking cessation consultations can be modified for delivery via mobile phones. The content can be customized to be patient-centered and tailored for the age, gender and education group of the quitter. This paper presents a platform to support smoking cessation composed by a mobile application to be used by users trying to quit smoking, and a web application to be used by researchers to analyze data regarding the app users’ tobacco cessation process. The app follows the transtheoretical model by Prochaska and DiClemente, allowing users to define and be aware of their wishes (what motivates them to stop smoking), barriers that hinder the quitting process and implementation plans and strategies to help them overcoming obstacles.

Purpose To ensure a sustainable and safe implementation of e-mental health interventions for mourning older adults, we need to know how these interventions are used and whether the target group accepts them. Consequently, this research investigates the user experience of an e-mental health intervention supporting mourning older adults, called LEAVES. Methods We conducted a crossover pilot study in the Netherlands among older adults who lost their spouse: one group started with using the e-mental health intervention, the other group started with a waiting period and then used the e-mental health intervention. For both groups, a follow-up period was scheduled after using the intervention. Participants completed several questionnaires during the study and were invited to join a focus group session. Results We started with a total of 96 older adults: 45 in the intervention group and 51 in the waitlist group. The mean age of the total group was 67.9 (SD = 8.5) years old and the group consisted of slightly more females (52.1%). Participants used LEAVES on average around five times during the ten week period. The participants had positive attitudes towards their experience with LEAVES, and on an individual level we noticed that for the majority LEAVES was helpful in decreasing their grief symptoms, depression or loneliness. Conclusions Implementing an e-mental health intervention for supporting older adults while processing the loss of their spouse is promising. In our paper, we propose several recommendations for future e-mental health interventions which are important to take into account to ensure their sustainable implementation.

The prevailing understanding of work as paid work is reflected in political efforts to achieve gender equality, which include emphasising that women, like men, should increasingly pursue paid work. This exploratory research aims to question whether this idea to align female with male life patterns is conducive to gender equality and to promote new insights based on mothers' experiences. Our analysis is based on guided interviews with eight Swiss mothers in part-time employment who have at least one child aged three or older, and a working partner in the same household. The interviews show that these mothers do not share the expectation that all mothers should take on the main responsibility for domestic and care work, nor the expectation that all women should be doing full-time paid work. They would like to see greater acceptance and appreciation of different forms of work. This research concludes that gender justice can be understood as a freedom of choice that includes both the right to be doing paid work and the right to have time for domestic and care work-for men and women. Gender equality efforts do not have to be restricted to one form of work, but can leave room for different types of work and the appreciation of them.

Objective Effective internet interventions often combine online self-help with regular professional guidance. In the absence of regularly scheduled contact with a professional, the internet intervention should refer users to professional human care if their condition deteriorates. The current article presents a monitoring module to recommend proactively seeking offline support in an eMental health service to aid older mourners. Method The module consists of two components: a user profile that collects relevant information about the user from the application, enabling the second component, a fuzzy cognitive map (FCM) decision-making algorithm that detects risk situations and to recommend the user to seek offline support, whenever advisable. In this article, we show how we configured the FCM with the help of eight clinical psychologists and we investigate the utility of the resulting decision tool using four fictitious scenarios. Results The current FCM algorithm succeeds in detecting unambiguous risk situations, as well as unambiguously safe situations, but it has more difficulty classifying borderline cases correctly. Based on recommendations from the participants and an analysis of the algorithm's erroneous classifications, we propose how the current FCM algorithm can be further improved. Conclusion The configuration of FCMs does not necessarily demand large amounts of privacy-sensitive data and their decisions are scrutable. Thus, they hold great potential for automatic decision-making algorithms in mental eHealth. Nevertheless, we conclude that there is a need for clear guidelines and best practices for developing FCMs, specifically for eMental health.

OBJECTIVE:This study aims at the linguistic and cultural adaptation of the Early ARthritis for Psoriatic Patients (EARP) questionnaire into European Portuguese, for psoriatic patients attending dermatology medical examination.METHODS:Firstly, we performed a process of translation and back-translation of the English version of the EARP Questionnaire to European Portuguese, with interim and final harmonization. The resulting Portuguese version was approved by the EARP original author. Secondly, individual interviews were conducted to complete the linguistic and cultural adaptation of the initial translated Portuguese version, with the think-aloud and probe methods. At this stage, we conducted eight interviews, four with rheumatology and dermatology doctors (experts), and four with patients with psoriasis and psoriatic arthritis. Finally, the version resulting from the adaptation process was back-translated from Portuguese to English.RESULTS:Our results showed that EARP Questionnaire's items are easy to understand and do not raise comprehension concerns in experts or patients. Our findings suggested that items demanding health literacy from patients and that do not include a precise cue to signal the inflammatory nature of the joint pain may lead to confusion while answering, potentially leading to the patient's need for assistance.CONCLUSION:The Portuguese version of the EARP Questionnaire demonstrated adequate comprehension properties. Our findings support the use of this measure in clinical practice and future research, however, a validation study with Portuguese patients is needed.

Online shopping is often motivated by the opportunity to save resources due to its high convenience and accessibility. We thus propose that online-shopping contexts can prime low effort processing, which increase heuristic decisions, when compared with offline contexts. Four experimental studies test this hypothesis. Study 1 shows that consumers expect to spend fewer resources in online than in offline shopping decisions. Studies 2 to 4 show that priming an online (vs. offline) shopping context increases reliance on heuristic cues in probability judgments (Study 2) and in product choices (Studies 3 and 4). Results further show that systematic processing of relevant nonheuristic information is reduced after priming online-shopping contexts and suggest that resource-saving expectations associated to online-shopping mediate attitudes toward systematic-options. This research brings novel and important contributions by investigating the role of online shopping contexts on the activation of resource-saving expectations and on the use heuristic cues in consumer decisions. Limits and implications are discussed.

Background The death of a partner is a critical life event in later life, which requires grief work as well as the development of a new perspective for the future. Cognitive behavioral web-based self-help interventions for coping with prolonged grief have established their efficacy in decreasing symptoms of grief, depression, and loneliness. However, no study has tested the efficacy for reducing grief after losses occurring less than 6 months ago and the role of self-tailoring of the content. Objective This study aims to evaluate the clinical efficacy and acceptance of a web-based self-help intervention to support the grief process of older adults who have lost their partner. It will compare the outcomes, adherence, and working alliance in a standardized format with those in a self-tailored delivery format and investigate the effects of age, time since loss, and severity of grief at baseline as predictors. Focus groups to understand user experience and a cost-effectiveness analysis will complement the study. Methods The study includes 3 different randomized control trials. The trial in Switzerland comprises a waitlist control group and 2 active arms consisting of 2 delivery formats, standardized and self-tailored. In the Netherlands and in Portugal, the trials follow a 2-arm design that will be, respectively, complemented with focus groups on technology acceptance and cost-effectiveness analysis. The main target group will consist of adults aged >60 years from the general population in Switzerland (n≥85), the Netherlands (n≥40), and Portugal (n≥80) who lost their partner and seek help for coping with grief symptoms, psychological distress, and adaptation problems in daily life. The trials will test the intervention’s clinical efficacy for reducing grief (primary outcome) and depression symptoms and loneliness (secondary outcomes) after the intervention. Measurements will take place at baseline (week 0), after the intervention (week 10), and at follow-up (week 20). Results The trials started in March 2022 and are expected to end in December 2022 or when the needed sample size is achieved. The first results are expected by January 2023. Conclusions The trials will provide insights into the efficacy and acceptance of a web-based self-help intervention among older adults who have recently lost a partner. Results will extend the knowledge on the role of self-tailoring, working alliance, and satisfaction in the effects of the intervention. Finally, the study will suggest adaptations to improve the acceptance of web-based self-help interventions for older mourners and explore the cost-effectiveness of this intervention. Limitations include a self-selective sample and the lack of cross-cultural comparisons. Trial Registration Switzerland: ClinicalTrials.gov NCT05280041; https://clinicaltrials.gov/ct2/show/NCT05280041; Portugal: ClinicalTrials.gov NCT05156346; https://clinicaltrials.gov/ct2/show/NCT05156346 International Registered Report Identifier (IRRID) PRR1-10.2196/37827

Background: In response to rapid global spread of the newly emerged coronavirus disease 2019 (COVID-19), universities transitioned to online learning and telework to decrease risks of inter-person contact. To help administrators respond to the COVID-19 pandemic and better understand its impacts, we surveyed SARS-CoV-2 seroprevalence among NOVA University employees and assessed community mental health. Methods: Data were collected from voluntary participants at six NOVA University locations, in the Lisbon metropolitan area, from June 15–30, 2020. All subjects provided written informed consent. Of 1,627 recruited participants (mean age 42.0 ± 12.3 years), 1,624 were tested. Prior to blood collection, participants completed a questionnaire that assessed: COVID-19 symptoms during the previous 14 days, chronic non-communicable diseases, chronic medication, anxiety, and depression symptoms. SARS-CoV-2 serology tests were then performed, and results communicated approximately 4 days after blood draw. Participants with positive serology tests were contacted to assess COVID-19 symptoms since February. Results: Estimated prevalence of SARS-CoV-2 IgG antibodies was 3.1% ( n = 50), of which 43.5% reported symptoms in the previous 4 months. The Medical School had the highest seroprevalence (6.2%). Participants reported having at least one chronic disease (63.7%), depression-like symptoms (2.1%), and anxiety symptoms (8.1%). Rates of depression and anxiety symptoms were significantly higher in women, with sleep hours and occasional alcohol consumption negatively associated with depression. Male gender, older age, and sleep hours negatively associated with anxiety symptoms. School of employment and presence of comorbidities positively associated with anxiety. Conclusion: By measuring seroprevalence of SARS-CoV-2 antibodies among NOVA employees and assessing subjects' mental health, we aim to help administrators at European public universities in urban areas, such as Lisbon, Portugal, better understand the needs of their communities. This study resulted in implementation of a stricter contingency plan in the Medical School, while other schools continued to follow Government mitigation guidelines. These findings may also guide the development of tailored strategies to ensure physical and mental health of the academic community during this pandemic crisis. We conclude that, together with COVID-19 contingency plans, psychological support services and facilities to help people effectively face pandemic-associated challenges and minimise anxiety and depression should be implemented.

The project Prevention of Occupational Disorders in Public Administrations based on Artificial Intelligence (PrevOccupAI) aims to identify and characterize profiles of work-related disorders (WRD) and daily working activities profiles. WRD have major impacts on the well-being and quality of life of individuals, as on productivity and absenteeism. Thus, to increase individuals’ quality of life and productivity, a tool focusing on human attention is being developed, integrating the insights of workers of AT (Autoridade Tributária) and the literature on attention and time management. By inputting Human-Computer Interaction (HCI) and work-related variables into an Artificial Intelligence (AI) layer, a dashboard system will provide workers’ information on causes of loss of focus they may not be aware of. Additionally, another layer will provide Recommendations, such as mindfulness-based tips, to assist in the management of feelings, emotions or work-related concerns, possibly increasing awareness and focus on the present. This manuscript presents the preliminary design of this tool.

According to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition, maladaptive behavior stemming from a psychological disorder should not be attributed to personality. Attribution of behavioral symptoms to personality may undermine treatment-seeking and therapy outcomes and increase the stigmatization of the mentally ill. Although people adjust dispositional inferences given contextual alternative causes, we propose that beliefs in the stability and controllability of mental illness could lead to confounded representations of personality and psychological disorders. In six studies we tested whether people adjust dispositional inferences given a psychological disorder as they do give a physical impairment. Participants made trait ratings from short behavioral descriptions and corresponding contextual accounts. When the putative cause for the behavior was a psychological disorder, people did not reduce the trait inference to the extent they did when the cause was a physical impairment, except when the psychological disorder was presented as controllable/unstable. This suggests a conflation of psychological disorders with personality.

People's intuitive predictions under uncertainty may rely on the representativeness or on the availability heuristics (Tversky & Kahneman, 1974). However, the distinction between these two heuristics has never been clear, and both have been proposed to underlie the same judgment tasks. For instance, when judging what outcome is likely to be next in a coin flip after a streak, representativeness leads to predicting an alternation in the outcome, ending the streak (gambler's fallacy), whereas availability leads to predicting the streak's continuation. We propose that availability (direct use of accessibility) is computed earlier than representativeness (comparing to an abstract representation of the expected outcome). In five studies, we pit one heuristic against the other in binary prediction tasks, both in coin flip and athletes performance contexts. We find that, although the streak outcome is cognitively more available, judgments are usually based on representativeness, leading more often to a prediction of an alternation after a streak. However, under time-pressure conditions, representativeness processes are constrained and participants are more prone to base their predictions on the most salient and cognitively available outcomes.

In the present article, we investigate how a person's power affects the way we infer traits from their behavior. In Experiment 1, our results suggest that, when faced with behavioral descriptions about others, participants infer both positive and negative traits about powerless actors, whereas for powerful and control (power irrelevant) actors, only positive but no negative traits are inferred, an effect we call the benevolence bias. In the second experiment, (a) we replicate this effect, (b) we show that it does not depend on the specific traits used in Experiment 1, and (c) we show that it is also detected when an implicit measure of inferences is used. Experiment 3 further shows that this effect generalizes to a more generic power manipulation. Theoretical explanations for these findings are discussed.