Negative Information Research Articles

Art perception is affected by negative knowledge about famous and unknown artists.

The biographies of some celebrated artists are marked by accounts that paint a far from beautiful portrait. Does this negative-social knowledge influence the aesthetic experience of an artwork? Does an artist's fame protect their paintings from such an influence? We present two preregistered experiments examining the effect of social-emotional biographical knowledge about famous and unknown artists on the reception and perception of their paintings, using aesthetic ratings and neurocognitive measures. In Experiment 1, paintings attributed to artists characterised by negative biographical information were liked less, evoked greater feelings of arousal and were judged lower in terms of quality, than paintings by artists associated with neutral information. No modulation of artist renown was found. Experiment 2 fully replicated these behavioural results and revealed that paintings by artists associated with negative social-emotional knowledge also elicited enhanced early brain activity related to visual perception (P1) and early emotional arousal (early posterior negativity; EPN). Together, the findings suggest that negative knowledge about famous artists can shape not only explicit aesthetic evaluations, but may also penetrate the perception of the artwork itself.

Schwinger correlation of Dirac fields in accelerated frames

Abstract We study the Schwinger correlation of Dirac fields in the noninertial frames under the influences of both constant and pulsed electric fields. We use both the entanglement negativity and quantum mutual information between particle and antiparticle as the indicator of the Schwinger correlation observed by the accelerated observers. We find that the Schwinger correlation in the inertial frames is the largest. With the increase of acceleration of the observers, the Schwinger correlation becomes smaller and smaller, but does not vanish in the limit of infinite acceleration. For the given acceleration, the Schwinger correlation is a nonmonotonic function of the electric field intensity, and there is an optimal value of electric field intensity for which the Schwinger correlation is the largest. In the case of pulsed electric fields, the Schwinger correlation is also the nonmonotonic function of pulsed width, which suggests the existence of optimal pulsed width for observing Schwinger correlation.

Trust as a key object of control in cognitive wars

The article is aimed at developing a systematic understanding of trust as an object of management when waging cognitive wars. The study is based on the hypothesis that with the growing complexity of identifying the truth of information against the backdrop of exponential growth in its volume, as well as the complexity of information technology, the most adequate countermeasure may be the trust of subjects of information interaction. The presence of such a hypothesis dictates the need to consider the phenomenon of trust as an object of management, as well as existing and promising mechanisms of influence on trust. The article structures the main factors influencing the formation of social trust and systematizes the idea of the types of objects of influence when waging a hybrid war. Based on the analysis of the results of research into the phenomenon of trust and the factors influencing it, a structural diagram of information management of trust is presented, where social trust is shown as the object of management, and informational and cultural aspects of the formation of trust are selected as regulating elements. The article also identifies groups of goals, objects and groups of indicators of negative information impact on trust factors, and proposes approaches to identifying the presence of such impact. The example shows that modern information technologies are an effective tool for influencing the level of trust between people, due to the intensive impact on the cognitive sphere of a person.

The political divide: The case of expectations and preferences

The divergence of attitudes towards their ideological extremes has become an identifying feature in the United States. Little is known about its source, how large it is, whether information can attenuate it, and its causal impact on civic behavior. We design a survey experiment that identifies differences in beliefs rather than preferences as a source of division. We randomly introduce factual information about government spending and show that it corrects beliefs. We further use this variation and estimate effects on a suite of outcomes. For individuals who learn the government spends worse than they would prefer, they become 0.35 s.d. less supportive towards the government, believe the government is less efficient by 0.42 s.d.and are less willing to compromise and trust by 0.43 s.d. We do not find any changes for those who learn the government spends more in line with their preferences. This asymmetric response is consistent with a literature showing that negative information has a greater impact on attitudes and beliefs than positive information.

A Multiinterest and Social Interest-Field Framework for Financial Security

Online payment has become an influential method of transaction. While improving convenience, this method of payment also brings great financial risks. Prevalidating transactions can be effective in reducing the number of frauds. For this reason, recommendation algorithms have been introduced to measure the credibility of transactions by predicting users’ ratings of items. However, most algorithms deal with the relationships in social networks without distinction, mixing positive and negative information into the recommender system, which brings huge noise. And, they only generate a single interest representation for each user to measure the similarity between users and spread interest, ignoring the diversity of user interests. Moreover, they did not consider the propagation of different interests would be different. In this article, we propose a multiinterest and social interest-field framework (MISIF) for social recommendations in financial security, which introduces capsule networks into social recommendation and extends the traditional single-interest representation to user multiinterest embedding by dynamic routing (DR) and other methods to improve the expressiveness of user embedding. After that, we construct social interest fields to integrate social interests based on multiinterest embedding, which alleviates the noise in social networks and user data sparsity problems. Finally, we aggregate user multiinterest embedding and additional information through neural networks to obtain the final prediction scores. Experiments with three publicly available datasets show that our proposed MISIF framework outperforms the state-of-the-art social recommendation methods.

Compliance with COVID-19 preventive policies and governmental transparency: an experimental analysis

ABSTRACT This study investigates the impact of government transparency on COVID-related policy compliance among 722 Taiwanese college students. Results reveal a positive correlation between transparency, operationalized as information disclosure, and policy compliance. However, this relationship is mediated by individuals’ trust in the government, particularly when trust levels are low. The findings challenge conventional assumptions about the direct link between transparency and trust, especially in instances of negative performance information. The study underscores the significance of transparency in fostering policy compliance while emphasizing the nuanced role of trust in shaping public behavior. Implications for policy-making are discussed in light of these findings.

Forensic interviewers' difficulty with the birthday narrative

BackgroundNarrative practice increases children's productivity in forensic interviews, and one recommended topic is the child's last birthday, though interviewers have raised concerns about its productivity. Study 1 ObjectiveStudy 1 surveyed forensic interviewers' use of and attitudes about the birthday narrative. Participants and settingParticipants included 170 forensic interviewers who subscribed to a webinar promoting use of the birthday narrative (Mage = 43 years, SD = 10.2, 94 % female). ResultsOver half (55 %) of interviewers reported that they rarely/never asked about children's birthdays, and non-users were especially likely to view the birthday narrative as never/rarely productive. Although interviewers viewed memory difficulties as more likely to occur with the birthday narrative than other practice topics (the child's likes, the child's day), non-users did not view memory difficulties, reluctance, generic reports, or religious objections as especially problematic. Open-ended responses identified negative experiences with the birthday as an additional concern, and interviewers' recommended wording of the prompts suggested suboptimal questioning strategies. Study 2 ObjectiveStudy 2 assessed the use of the birthday narrative in forensic interviews. Participants and settingThe sample included 350 forensic interviews with 4- to 12-year-old children (Mage = 8.85, SD = 2.59). ResultsOnly 4 % of children failed to recall substantive information if interviewers persisted, though another 11 % failed when interviewers stopped persisting. Invitations were more effective than other question types, especially among older children. 21 % of children mentioned a negative detail during their narrative. ConclusionsInterviewers' skepticism about the birthday narrative may be due to suboptimal questioning and sensitivity to occasional failures and negative information.

A study on the influence of a single bout of moderate-intensity exercise on processing bias towards emotional information of individuals with high psychosocial stress levels

AbstractTo explore the impact of a single bout of moderate-intensity exercise on cognitive bias of individuals with high psychosocial stress levels. 42 college students with high stress levels were recruited and divided into a exercise group (N = 21) and a no-exercise group (N = 21). The psychological and behavioral characteristics were studied using word-face stroop task, memory bias task, and interpretion bias task. College students with high psychosocial stress levels have attention bias, memory bias, and Interpretion bias towards negative emotional information, and a single bout of moderate-intensity exercise can increase attention bias towards neutral and positive emotional information, and memory bias towards neutral emotional information, interpretation bias towdrds positive emotional information. A single bout of moderate-intensity exercise can increase the positive cognitive bias towards emotional information, which is beneficial for regulating psychosocial stress levels.

Healthcare Workers’ Readiness for COVID-19 Vaccination Using Health Belief Model

Objective:: Acceptance of COVID-19 vaccination of medical personnel reduces the spread of infection the research aimed to evaluate healthcare professionals' attitudes toward vaccination by the health belief model. Methods:: A quantitative, cross-sectional research design was used for the study. Results:: Most medical personnel agreed to get the COVID-19 vaccination (84%; n=384). Medical workers over 45 (90%) showed more readiness for vaccination. The main pushbacks against the vaccination included the development of a vaccine in a short period (n=56; 73.7%), fear of side effects (n=49; 64.5%), lack of reliable information about vaccination (n=35; 46.1%), negative information spread by the media (n=26, 34.2%). Recommendations by healthcare workers (63.9%), health authorities (73.1), media (70%), large number of people being vaccinated (72.6%), and provision of trustworthy information (71.3%) facilitate vaccination. Conclusion:: The high rate of COVID-19 vaccination among medical personnel shows that they have a high risk perception of infection. Among Health Belief Model components, cues to actions were most important in willingness to carry out vaccination. Recommendations of the WHO and scientists significantly influence the willingness to carry out vaccination. The regulatory responsibilities of the state and the public's trust in it play a crucial role in raising the population's awareness about the vaccine.

Chinese young consumers’ response to negative brand information of electric vehicles – do personal cultural values matter?

Purpose This study aims to investigate the responses of young Chinese consumers with different cultural characteristics to negative brand information about electric vehicles. Design/methodology/approach The current study is quantitative research with an experimental method. It shows two different levels of severity for negative publicity and asks participants to self-report through questionnaires. Findings Chinese young consumers, being collectivist and of high uncertainty avoidance, tend to search for and spread information; consumers with low power distance search and share information more under low information severity. In addition, information search positively affects brand attitude under lower severity; negative word-of-mouth intention negatively affects brand attitudes at both severity levels. Research limitations/implications The current study examines the influence of personal cultural values on information searching and negative information dissemination among young consumers, providing insights to complement previous studies. Furthermore, it explores how such exposure influences young consumers’ brand attitude and intention to purchase. Limitations include simple sample scopes and single-product stimuli. Practical implications This research highlights the importance of cultural dimensions in shaping young consumers’ responses to negative publicity. Marketers worldwide should consider cultural influence and develop specific strategies to address negative information about different products. Understanding customers’ unique characteristics and preferences can help marketers effectively tailor their approaches to counter negative publicity. Originality/value This study originally provides a supplement to prior studies on cultural dimensions and consumer behavior and provides suggestions to marketers on young Chinese consumers.

Attentional biases to signals of negative information: Reliable measurement across three anxiety domains.

Cognitive models propose that individuals with elevated vulnerability to experiencing negative emotion are characterised by biased attentional responding to negative information. Typically, methods of examining these biases have measured attention to pictures of emotional scenes, emotional faces, or rewarding or feared objects. Though these approaches have repeatedly yielded evidence of anxiety-linked biases, their measurement reliability is suggested to be poor. Recent research has shown that attentional responding to cues signalling negative information can be measured with greater reliability. However, whether such biases are associated with emotion vulnerability remains to be demonstrated. The present study conducted three experiments that recruited participants who varied in trait and state anxiety (N = 134), social anxiety (N = 122), or spider fear (N = 131) to complete an assessment of selective attention to cues signalling emotionally congruent negative information. Analyses demonstrated that anxiety and fear were associated with biased attentional responding to cues signalling negative information, and that such biases could be measured with acceptable reliability (rsplit-half = .69-.81). Implications for research on the relation between emotion and attention are discussed.

Reducing the negative impact of ultra-processed foods: Consumer response to natural claims, organic claims, and processing level disclosures

Drawing from theories related to choice architecture, nudges, and the integration of negative and positive information, this research examines the use of ultra-processing package disclosures in conjunction with favorable food processing claims (e.g., “natural,” “organic”) and nutrient content (e.g., sugar) warnings on perceived food processing level, disease risk evaluations, and purchase intentions. Four studies examine how marketers’ positive claims and disclosures interact to jointly affect consumer evaluations of these outcomes. Results show the usefulness of stop-sign disclosures for ultra-processed foods in counterbalancing the positive inferences created from both unregulated (e.g., natural) and regulated (e.g., organic) package claims on perceived food processing level, which mediates the interaction effects on perceptions of disease risk, healthfulness, and purchase intentions. Because high ultra-processed food consumption has recently been linked to increased obesity, type-2 diabetes, heart disease, and cancer, there are implications of results for CPG marketers, consumers, and the global health community, as well as enhancing the conceptual understanding of efforts to promote consumer well-being.

Performance management and political accountability: how local governments respond to performance feedback

ABSTRACT Using the results of an annual assessment of local governments’ integrity levels from 2012 to 2016, we explore how Korea’s local governments respond to performance feedback provided by the central government. First, we find clear performance improvement only among localities performing below the average of their social comparison groups. This result supports the negativity bias hypothesis, which posits that policymakers are driven more by negative than by positive information. Second, local governments’ responses to feedback increased as election dates approached. This outcome suggests that electoral incentives magnify the effectiveness of performance management. Third, negative performance information released immediately before elections is associated with the incumbents’ electoral defeat, which is consistent with the hypothesis that performance information helps citizens hold policymakers accountable during elections. Overall, the findings imply that performance management (managerial accountability) and elections (political accountability) are mutually reinforcing mechanisms and that voter myopia can partially erode this synergy.

Hierarchical Convolutional Neural Network with Knowledge Complementation for Long-Tailed Classification

Existing methods based on transfer learning leverage auxiliary information to help tail generalization and improve the performance of the tail classes. However, they cannot fully exploit the relationships between auxiliary information and tail classes and bring irrelevant knowledge to the tail classes. To solve this problem, we propose a hierarchical CNN with knowledge complementation, which regards hierarchical relationships as auxiliary information and transfers relevant knowledge to tail classes. First, we integrate semantics and clustering relationships as hierarchical knowledge into the CNN to guide feature learning. Then, we design a complementary strategy to jointly exploit the two types of knowledge, where semantic knowledge acts as a prior dependence and clustering knowledge reduces the negative information caused by excessive semantic dependence (i.e., semantic gaps). In this way, the CNN facilitates the utilization of the two complementary hierarchical relationships and transfers useful knowledge to tail data to improve long-tailed classification accuracy. Experimental results on public benchmarks show that the proposed model outperforms existing methods. In particular, our model improves accuracy by 3.46% compared with the second-best method on the long-tailed tieredImageNet dataset.

A multiple risk coupled propagation model for emergency information considering government information and government mandatory measures

In the era of new media, it is crucial to investigate the transmission paths of emergency information due to the rising risk of such information. We take the risks caused by the dissemination of various heterogeneous negative information public opinion arising from the emergencies as the research object. Starting from the coupling of multiple risks and considering the behavioral impact of different government measures, we construct a three-layer coupling network S1I1S1-S2I2S2-UAU model between different types of negative information (Info1, Info2) and government information (Info3) diffusion, and analyze the coupling mechanisms of negative information and government information. Second, the MMCA is used to analyze the dissemination process and the diffusion threshold of government information. Finally, the rationality and effectiveness of the S1I1S1-S2I2S2-UAU model are verified by numerical simulation and real case. Our research results can help establish the cognitive of information risk coupling propagation law, providing support for government departments in controlling information risk.

Motivated Belief Updating and Rationalization of Information

We study belief updating about relative performance in an ego-relevant task. Manipulating the perceived ego relevance of the task, we show that subjects substantially overweight positive information relative to negative information because they derive direct utility from holding positive beliefs. This finding provides a behavioral explanation why and how overconfidence can evolve in the presence of objective information. Moreover, we document that subjects who receive more negative information downplay the ego relevance of the task. These findings suggest that subjects use two alternative strategies to protect their ego when presented with objective information. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: The authors gratefully acknowledge financial support from the ExperimenTUM. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.02537 .

Semi-supervised nonnegative matrix factorization with label propagation and constraint propagation

Semi-supervised nonnegative matrix factorization (SNMF) improves the clustering effect of nonnegative matrix factorization (NMF) by integrating label information. However, as one of the most commonly used semi-supervised learning methods, label propagation algorithm (LP) has some limitations due to its heavy dependence on the predefined similarity matrix. To address this deficiency and effectively use limited supervisory information, a structural NMF algorithm with label propagation and constraint propagation (PNLP-SCNMF) is proposed in this paper, which simultaneously performs data representation and classification in a joint manner. Both positive label information and negative label information are introduced into the proposed method, and the factor matrix is constrained by label information to construct the tri-decomposition form. Constraint propagation algorithm (CPA) is used to correct the constructed similarity matrix, so that the low-dimensional subspace obtained by matrix decomposition can retain the geometric structure characteristics of the original sample space. In addition, an efficient iterative update optimization scheme is proposed to solve this objective function, and its convergence is proved theoretically. A large number of comparative experiments on nine real datasets show that the proposed method can consistently achieve higher clustering accuracy in image clustering tasks.

Fitting Insurance Claim Reserves with Two-Way ANOVA and Intuitionistic Fuzzy Regression

A highly relevant topic in the actuarial literature is so-called “claim reserving” or “loss reserving”, which involves estimating reserves to be provisioned for pending claims, as they can be deferred over various periods. This explains the proliferation of methods that aim to estimate these reserves and their variability. Regression methods are widely used in this setting. If we model error terms as random variables, the variability of provisions can consequently be modelled stochastically. The use of fuzzy regression methods also allows modelling uncertainty for reserve values using tools from the theory of fuzzy subsets. This study follows this second approach and proposes projecting claim reserves using a generalization of fuzzy numbers (FNs), so-called intuitionistic fuzzy numbers (IFNs), through the use of intuitionistic fuzzy regression. While FNs allow epistemic uncertainty to be considered in variable estimation, IFNs add bipolarity to the analysis by incorporating both positive and negative information regarding actuarial variables. Our analysis is grounded in the ANOVA two-way framework, which is adapted to the use of intuitionistic regression. Similarly, we compare our results with those obtained using deterministic and stochastic chain-ladder methods and those obtained using two-way statistical ANOVA.

Bridging the Gap Between Legitimacy and Neutralisation Strategies in Sustainability Reporting

Amid global sustainability challenges, such as climate change, companies operating in different industries face increasing demands for accountability and transparency. To meet these demands, sustainability reporting (SR) has become a mainstream business practice in communicating companies’ positive and negative sustainability impacts. This short paper aims to increase our understanding of how companies may release negative information while seeking to maintain their legitimacy. For this purpose, the paper bridges the gap between legitimacy strategies, impression management and neutralisation techniques by reviewing their associated literature. The nature of neutralisation techniques and how organisations use them in the SR context is underexplored by scholars. Finally, the paper presents prospective discussions of an ongoing study that examines negative sustainability disclosure in the tourism transportation context, namely the airlines.

Biodiversity management and stock price crash risk

AbstractThis study explores the link between corporate biodiversity management and the risk of sudden declines in future stock prices, an area largely overlooked in empirical research despite the societal significance of biodiversity loss. We posit that robust corporate biodiversity efforts mitigate the suppression of negative information, consequently reducing the risk of abrupt stock price declines. Leveraging a global dataset and innovative biodiversity management metrics from Moody's ESG Solutions (former Vigero Eiris), our multivariate regression analysis demonstrates that companies with stronger biodiversity structures and actions exhibit lower stock price crash risk. In an additional analysis, we focus on environmental inspections as a possible conduit for releasing negative information on biodiversity management. Using a subsample of North American firms that were inspected by the U.S. Environmental Protection Agency (EPA), we find that firms which experience an inspection record an increase in their stock price crash risk.