The Psychology of Motivated versus Rational Impression Updating

Better social reversal learning is associated with a more social approach across time

Flexibly updating behaviors towards others is crucial for adaptive social functioning. Previous studies have found that difficulties in flexibly updating behaviors are associated with social anxiety (SA). However, it is unclear whether such difficulties relate to actual social behaviors. The current study investigated the relationships between negative-to-positive social reversal learning, social approach behavior, and SA across time. Participants (MTurk, Time 1 = 275, Time 2 = 126, 16 weeks later) completed a performance-based social reversal-learning task. In the initial phase, participants learned that interactions with certain individuals are associated with negative outcomes, whereas interactions with other individuals are associated with positive outcomes. In the reversal phase, these associations were reversed, requiring participants to update their behaviors. The relationships between the performance in the task, SA severity, and social approach behavior reported by participants were assessed cross-sectionally and longitudinally. We found that negative-to-positive updating was negatively associated with SA severity. Furthermore, negative-to-positive updating was positively correlated with social approach behavior, both cross-sectionally and prospectively. Hence, individuals with better negative-to-positive updating at Time 1 reported significantly more social approach behaviors across time. The results support the role of negative-to-positive updating as a mechanism associated with SA and social approach, advancing and refining interpersonal and cognitive theories of SA.

Motivation and prediction-driven processing of social memoranda

Social semantic memory guides many aspects of behavior. Individuals rely on acquired and inferred knowledge about personal characteristics and group membership to predict the behavior and character of social targets. These predictions then determine the expectations from, the behavior in, and the interpretations of social interactions. According to predictive processing accounts, mnemonic and attentional mechanisms should enhance the processing of prediction-violating events. However, empirical findings suggest that prediction-consistent social events are often better remembered. This mini-review integrates recent evidence from social and non-social memory research to highlight the role of motivation in explaining these discrepancies. A particular emphasis is given to the continuous nature of prediction-(in)consistency, the epistemic tendency of perceivers to maintain or update their knowledge, and the dynamic influences of motivation on multiple steps in prediction-driven social memory. The suggested framework provides a coherent outlook of existing work and offers promising future directions to better understand the ebb and flow of social memoranda.

Motivated Cognition in Cooperation

Successful cooperation is tightly linked to individuals' beliefs about their interaction partners, the decision setting, and existing norms, perceptions, and values. This article reviews and integrates findings from judgment and decision-making, social and cognitive psychology, political science, and economics, developing a systematic overview of the mechanisms underlying motivated cognition in cooperation. We elaborate on how theories and concepts related to motivated cognition developed in various disciplines define the concept and describe its functionality. We explain why beliefs play such an essential role in cooperation, how they can be distorted, and how this fosters or harms cooperation. We also highlight how individual differences and situational factors change the propensity to engage in motivated cognition. In the form of a construct map, we provide a visualization of the theoretical and empirical knowledge structure regarding the role of motivated cognition, including its many interdependencies, feedback loops, and moderating influences. We conclude with a brief suggestion for a future research agenda based on this compiled evidence.

A latent profile analysis of resilience and their relation to differences in sleep quality in patients with lung cancer

PURPOSE

Sleep problems are a significant issue in patients with lung cancer, and resilience is a closely related factor. However, few studies have identified subgroups of resilience and their relationship with sleep quality. This study aimed to investigate whether there are different profiles of resilience in patients with lung cancer, to determine the sociodemographic characteristics of each subgroup, and to determine the relationship between resilience and sleep quality in different subgroups.

METHODS

A total of 303 patients with lung cancer from four tertiary hospitals in China completed the General Sociodemographic sheet, the Connor-Davidson Resilience Scale, and the Pittsburgh Sleep Quality Index. Latent profile analysis was applied to explore the latent profiles of resilience. Multivariate logistic regression was used to analyze the sociodemographic variables in each profile, and ANOVA was used to explore the relationships between resilience profiles and sleep quality.

RESULTS

The following three latent profiles were identified: the "high-resilience group" (30.2%), the "moderate-resilience group" (46.0%), and the "low-resilience group" (23.8%). Gender, place of residence, and average monthly household income significantly influenced the distribution of resilience in patients with lung cancer.

CONCLUSION

The resilience patterns of patients with lung cancer varied. It is suggested that health care providers screen out various types of patients with multiple levels of resilience and pay more attention to female, rural, and poor patients. Additionally, individual differences in resilience may provide an actionable means for addressing sleep problems.

Computational Social Psychology

Social psychologists attempt to explain how we interact by appealing to basic principles of how we think. To make good on this ambition, they are increasingly relying on an interconnected set of formal tools that model inference, attribution, value-guided decision making, and multi-agent interactions. By reviewing progress in each of these areas and highlighting the connections between them, we can better appreciate the structure of social thought and behavior, while also coming to understand when, why, and how formal tools can be useful for social psychologists.

Motivated Categories: Social Structures Shape the Construction of Social Categories Through Attentional Mechanisms

PUBLIC ABSTRACT

Social categories like race and gender often give rise to stereotypes and prejudice, and a great deal of research has focused on how motivations influence these biased beliefs. Here, we focus on potential biases in how these categories are even formed in the first place, suggesting that motivations can influence the very categories people use to group others. We propose that motivations to share schemas with other people and to gain resources shape people's attention to dimensions like race, gender, and age in different contexts. Specifically, people will pay attention to dimensions to the degree that the conclusions produced from using those dimensions align with their motivations. Overall, we suggest that simply examining the downstream effects of social categorization like stereotyping and prejudice is not enough, and that research should look earlier in the process at how and when we form the categories on which those stereotypes are based.

One strike and you're a lout: Cherished values increase the stringency of moral character attributions

Moral dilemmas are inescapable in daily life, and people must often choose between two desirable character traits, like being a diligent employee or being a devoted parent. These moral dilemmas arise because people hold competing moral values that sometimes conflict. Furthermore, people differ in which values they prioritize, so we do not always approve of how others resolve moral dilemmas. How are we to think of people who sacrifice one of our most cherished moral values for a value that we consider less important? The "Good True Self Hypothesis" predicts that we will reliably project our most strongly held moral values onto others, even after these people lapse. In other words, people who highly value generosity should consistently expect others to be generous, even after they act frugally in a particular instance. However, reasoning from an error-management perspective instead suggests the "Moral Stringency Hypothesis," which predicts that we should be especially prone to discredit the moral character of people who deviate from our most deeply cherished moral ideals, given the potential costs of affiliating with people who do not reliably adhere to our core moral values. In other words, people who most highly value generosity should be quickest to stop considering others to be generous if they act frugally in a particular instance. Across two studies conducted on Prolific (N = 966), we found consistent evidence that people weight moral lapses more heavily when rating others' membership in highly cherished moral categories, supporting the Moral Stringency Hypothesis. In Study 2, we examined a possible mechanism underlying this phenomenon. Although perceptions of hypocrisy played a role in moral updating, personal moral values and subsequent judgments of a person's potential as a good cooperative partner provided the clearest explanation for changes in moral character attributions. Overall, the robust tendency toward moral stringency carries significant practical and theoretical implications.

Structure learning principles of stereotype change

Why, when, and how do stereotypes change? This paper develops a computational account based on the principles of structure learning: stereotypes are governed by probabilistic beliefs about the assignment of individuals to groups. Two aspects of this account are particularly important. First, groups are flexibly constructed based on the distribution of traits across individuals; groups are not fixed, nor are they assumed to map on to categories we have to provide to the model. This allows the model to explain the phenomena of group discovery and subtyping, whereby deviant individuals are segregated from a group, thus protecting the group's stereotype. Second, groups are hierarchically structured, such that groups can be nested. This allows the model to explain the phenomenon of subgrouping, whereby a collection of deviant individuals is organized into a refinement of the superordinate group. The structure learning account also sheds light on several factors that determine stereotype change, including perceived group variability, individual typicality, cognitive load, and sample size.

Focusing on the positive or the negative: Self-construal moderates negativity bias in impression updating

Negativity bias refers to the phenomenon whereby people put more weight on negative information. Although evolutionarily favorable for survival, negative bias in impression processing is detrimental to relationships and cooperation. To explore whether the motivation to maintain relationships, indicated by self-construal, mitigates negativity bias, two studies were conducted. In study 1, participants interacted with three agents (worsened, improved, baseline) in a modified social learning task and evaluated the moral level of these agents. Results showed that positivity bias appeared among interdependent individuals, with larger updating for the improved agent than for the worsened agent. Moreover, interdependent individuals exhibited less immediate decreases toward the worsened agent and steeper increases toward the improved agent than did independent individuals. To validate the results of study 1, we used a narrative description paradigm in study 2. Participants read the behavior descriptions of agents and rated them on morality. The negativity bias was significantly mitigated among individuals with high interdependence, though it did not reverse. These results indicate that interdependent individuals focus more on positive information when others change, yielding a more positive pattern in impression updating. This flexible interpersonal coping strategy can bring advantages to social interaction and cooperation.

How unexpected events are processed in theory of mind regions: A conceptual replication

Recent research in social neuroscience has postulated that Theory of Mind (ToM) regions play a role in processing social prediction error (PE: the difference between what was expected and what was observed). Here, we tested whether PE signal depends on the type of prior information people use to make predictions - an agent's prior mental states (e.g., beliefs, desires, preferences) or an agent's prior behavior - as well as the type of information that confirms or violates such predictions. That is, does prior information about mental states (versus behavior) afford stronger predictions about an agent's subsequent mental states or behaviors? Additionally, when information about an agent's prior mental states or behavior is available, is PE signal strongest when information about an agent's subsequent mental state (vs behavior) is revealed? In line with prior research, results suggest that DMPFC, LTPJ, and RTPJ are recruited more for unexpected than expected outcomes. However, PE signal does not seem to discriminate on the basis of prior or outcome information type. These findings suggest that ToM regions may flexibly incorporate any available information to make predictions about, monitor, and perhaps explain, inconsistencies in social agents.

A belief systems analysis of fraud beliefs following the 2020 US election

Beliefs that the US 2020 Presidential election was fraudulent are prevalent despite substantial contradictory evidence. Why are such beliefs often resistant to counter-evidence? Is this resistance rational, and thus subject to evidence-based arguments, or fundamentally irrational? Here we surveyed 1,642 Americans during the 2020 vote count, testing fraud belief updates given hypothetical election outcomes. Participants' fraud beliefs increased when their preferred candidate lost and decreased when he won, and both effects scaled with partisan preferences, demonstrating partisan asymmetry (desirability effects). A Bayesian model of rational updating of a system of beliefs-beliefs in the true vote winner, fraud prevalence and beneficiary of fraud-accurately accounted for this partisan asymmetry, outperforming alternative models of irrational, motivated updating and models lacking the full belief system. Partisan asymmetries may not reflect motivated reasoning, but rather rational attributions over multiple potential causes of evidence. Changing such beliefs may require targeting multiple key beliefs simultaneously rather than direct debunking attempts.

Followers forever: Prior commitment predicts post-scandal support of a social media celebrity