Preamble

Influencing Factors and Mechanisms of Health Motivated Reasoning

(Department of Social Psychology, School of Sociology, Nankai University)

Introduction

In the field of health communication and preventive medicine, a common paradox persists: individuals often ignore or discount high-quality scientific evidence that contradicts their existing lifestyle choices or health beliefs. This phenomenon is largely driven by health motivated reasoning, a cognitive process where individuals process health-related information in a biased manner to reach a desired conclusion, typically one that minimizes perceived personal risk or justifies current behaviors. Understanding the factors that trigger and sustain motivated reasoning is critical for developing effective health interventions.

1. Individual-Level Factors

Individual differences play a foundational role in how health information is processed. Research indicates that several psychological traits and states significantly influence the propensity for motivated reasoning.

• Prior Beliefs and Identity: Individuals are highly motivated to protect their self-identity and existing belief systems. When health information (e.g., the risks of red meat consumption or the necessity of vaccinations) threatens a core aspect of an individual’s identity or social group affiliation, they are more likely to engage in defensive processing to discredit the source or the methodology of the information.
• Self-Affirmation: Conversely, self-affirmation theory suggests that when an individual's sense of self-worth is bolstered in an unrelated domain, their need to engage in motivated reasoning decreases. This allows them to process threatening health information more objectively, as their global self-integrity is no longer contingent on denying the specific health risk.
• Affective States: Emotions such as fear, anxiety, and guilt serve as powerful catalysts. While moderate fear can motivate protective action, excessive fear often triggers defensive motivated reasoning as a coping mechanism to reduce psychological discomfort.

2. Cognitive and Information-Processing Factors

The way health information is presented and the cognitive resources available to the individual also dictate the extent of biased reasoning.

• Cognitive Dissonance: At the heart of motivated reasoning is the need to resolve cognitive dissonance. When new health data ($\mathcal{D}$) conflicts with a deeply held behavior ($B$), the resulting psychological tension ($\Delta \Psi$) drives the individual to either change the behavior or, more commonly, "reason away" the data.
• Information Complexity and Ambiguity: Highly technical or ambiguous health information provides more "maneuvering room" for motivated reasoning. Individuals can selectively focus on specific uncertainties within a study—

摘要

Health motivated reasoning is a psychological process in which individuals selectively process information to reinforce or maintain their existing health beliefs and behaviors. This phenomenon primarily involves the classification patterns of health motivations. Research in this area categorizes health motivated reasoning based on several key characteristics, including the nature of the health motivation itself, individual psychological profiles, specific reasoning goals, and the lifecycle of the information being processed.

This form of reasoning is closely related to three categories of factors: first, internal factors such as health beliefs and cognitive traits; second, informational factors including information conflict and message framing; and third, external factors such as social identity and cultural norms. Bayesian models provide a framework for understanding how motivation influences the processing of health information, specifically regarding how individuals adjust their beliefs when presented with new health evidence. Future research should focus on constructing a comprehensive model of the factors influencing health motivated reasoning, utilizing cognitive neuroscience methods to explore its underlying mechanisms more deeply, and further optimizing health intervention strategies.

1 引言

Motivated reasoning refers to a biased psychological process in which individuals defend their pre-existing beliefs. Psychological research on motivated reasoning has largely focused on highly politicized and adversarial concepts and behaviors, such as partisan disputes and social conflicts \cite{Filindra & Harbridge, Hutmacher et al.}.

Compared to these high-conflict areas, research on more mundane, everyday issues remains relatively scarce \cite{Mehlhaff et al.}. This paper focuses on motivated reasoning among the general public regarding health issues—hereafter referred to as "health motivated reasoning." This is a specific type of motivated reasoning defined as the psychological process of selecting information to reinforce or maintain one's own health beliefs and behaviors \cite{Savolainen, 2022; Sylvester, 2021}. Although public health events can trigger positional shifts and disagreements between different groups \cite{Graham & Singh, 2024; Moon et al.}, health issues are generally universal topics of daily life that occur with high frequency and involve a broad population. When health information concerns an individual's core interests or identity, motivated reasoning tends to be risk-oriented and affect-driven \cite{Peng, 2022; Simonovic et al., 2023}.

Health motivated reasoning can lead to biased information processing, yet it also serves certain adaptive functions. On one hand, it may cause individuals to resist health advice and selectively accept scientific evidence, thereby negatively impacting critical health behaviors such as vaccination, disease prevention, and medical compliance \cite{Appel & Sanatkar, 2024; Courbage & Peter, 2021}. On the other hand, health motivated reasoning can play a positive role in certain contexts \cite{Green et al., 2020}, helping individuals maintain positive health beliefs that drive the achievement of health goals, such as adhering to exercise routines. Although research has identified this "double-edged sword" effect, there is still a lack of systematic reviews on the subject. Consequently, discussions regarding its specific theoretical foundations, influencing factors, internal mechanisms, and practical applications remain limited.

Understanding the core components of health motivated reasoning not only helps clarify how individuals form and maintain health beliefs but also provides important theoretical support for optimizing health communication strategies and enhancing the effectiveness of health interventions. This is of great significance for navigating the increasingly complex health communication environment and improving public health literacy.

This paper focuses on motivated reasoning within the health domain, deeply exploring the factors that influence its manifestation. We begin by defining the concept of health motivated reasoning and summarizing the personal and informational factors identified in previous research. We then analyze the mechanisms underlying health motivated reasoning through both traditional perspectives and Bayesian model perspectives. Finally, we examine how it influences information processing and its application in key health domains, while proposing directions for future research.

2 健康

Health motivated reasoning is essentially a cognitive processing mechanism that represents the application of "motivated reasoning" within the health domain. It refers to the tendency of individuals, driven by personal health goals or emotions in health-related contexts, to accept or interpret information that aligns with their health motivations while dismissing or devaluing contradictory information. This results in systematic biases during information selection, interpretation, and memory \cite{Dickinson & Kakoschke, 2021; Meppelink et al., 2019}. Similar to general motivated reasoning \cite{Braman & Nelson, 2007; Epley & Gilovich, 2016; Taber & Lodge, 2006}, this process is not entirely rational or aimed at seeking objective facts; rather, it serves specific psychological goals. However, health motivated reasoning is characterized by a stronger risk orientation compared to general motivated reasoning. This risk orientation implies that health motivated reasoning is often accompanied by high-risk behavioral consequences.

These processes directly impact an individual's quality of life and broader public health levels, making individuals more likely to exhibit "benefit-seeking and harm-avoiding" motivational biases when processing information \cite{Kalke et al., 2021; Simonovic et al., 2023}. For instance, although patients receiving weight loss advice may perceive negative evaluations regarding their weight, they may still accept the corresponding health recommendations because they are more concerned with the potential health benefits of improving their dietary habits \cite{Standen et al., 2025}. This underscores the unique sensitivity of health motivated reasoning toward risk information.

Health motivated reasoning is fundamentally emotion-driven and revolves around core issues such as mortality. The emotional drivers of health motivated reasoning, such as fear and anxiety, play a critical role \cite{2022; Zhang & Zhou, 2019}. When facing major illnesses, individuals may excessively deny or ignore disease risks due to fear, relying on optimistic self-consolation to alleviate actual experiences of anxiety. As the foundational driving force of health motivated reasoning, the classification of health motivations determines an individual's cognitive and reactive patterns toward health information. Given that health motivations are diverse and may influence the processing of specific health topics either independently or interactively, accurately classifying these motivations is a vital prerequisite for understanding the logic behind individual health information processing. Throughout the development of motivational classification, different theoretical backgrounds and research objectives have collectively enriched and refined the field. Although many existing motivational classification theories were not originally proposed specifically for health behaviors, their extensive application in health research provides significant support for a deeper understanding of individual information processing. The following section introduces five common motivational classification models in chronological order of representative research, elaborated alongside their specific applications within health contexts:

[TABLE:1]
- Schutz (1962): Traceability motives, experience, or future orientation.
- Deci & Ryan (1985): Self-Determination Theory; intrinsic motivation, extrinsic motivation, and amotivation (the motivation continuum).
- Kunda (1990): Cognitive Dissonance Theory; accuracy motivation and directional motivation.
- Higgins (1997): Regulatory Focus Theory; promotion orientation and prevention orientation.
- General Classification: Positive or negative motivations.

结果

Higgins (1997) explored the generation, promotion, and dissemination of motivation. Within the framework of Information Lifecycle Theory, Wilson & Maceviciute (2022) noted that early research often classified motivations based on the temporal orientation of individual behavior, distinguishing between "because-of" motives (retrospective) and "in-order-to" motives (prospective; Schutz, 1962). This research indicates that retrospective motives originate from an individual's past.

These motives are rooted in psychological states and personal experiences, pointing toward past conditions and environments—such as attempting to quit smoking because of a long-term cough. In contrast, prospective motives are linked to future goals, such as developing a fitness plan to reduce future cancer risks. In the context of health motivated reasoning, retrospective motives lead individuals to process information based on past health experiences, tending to retain explanatory patterns consistent with existing cognitions. Prospective motives guide individuals toward future health expectations, aligning with anticipated outcomes and manifesting as goal-oriented reasoning. This distinction reveals how motivation influences reasoning paths across temporal dimensions, helping to explain how individuals mobilize experience and expectations in health contexts. As research deepened, Self-Determination Theory (SDT) further refined types of motivation to explain differences in individual behavior. Ryan & Deci distinguished between intrinsic motivation, extrinsic motivation, and amotivation along a motivational continuum (Deci & Ryan, 2000). Intrinsic motivation refers to performing an activity for inherent interest or satisfaction, while extrinsic motivation refers to acting for external rewards or pressures. Amotivation reflects a state lacking clear behavioral intent (Ryan & Deci, 2000). These motivations shape how individuals process health and exercise information (Converse et al., 2019). Individuals with high intrinsic motivation, characterized by greater autonomy, tend to accept information that supports self-worth and internal satisfaction, showing a selective processing bias that makes them more likely to adhere to exercise (Camp et al., 2024). Those dominated by extrinsic motivation are driven by social evaluation and prefer information that enhances their social image; they may exhibit identified reasoning biases, such as compulsive exercise to gain rewards or avoid punishment (Staples et al., 2022). Amotivated individuals, lacking clear drive, invest little in information processing and typically lack the intention to exercise (Ednie & Stibor, 2017). The development of SDT shifted motivation research from a temporal orientation toward internal needs, providing a theoretical basis for the diverse biases found in motivated reasoning. Cognitive Dissonance Theory further refined these classifications from an information-processing perspective.

Individuals process information in biased ways to maintain internal cognitive consistency (Festinger, 1957). Kunda (1990) provided a framework to understand this theory through two lenses: accuracy motivation and directional motivation. Accuracy motivation is the drive to reach the most correct and objective conclusion possible, whereas directional motivation is the psychological force driving behavior toward a specific goal or object. Accuracy motivation prompts individuals to engage in verification-oriented processing aimed at reducing judgment bias and improving reasoning objectivity. Conversely, directional motivation drives individuals to prioritize information that supports established goals or positions; for instance, under the influence of social motivation, individuals tend to share health information that aligns with group identity (Rathje et al., 2023). This division emphasizes the importance of goal-setting in the reasoning process, providing a key perspective for exploring motivational bias in health information processing. Regulatory Focus Theory offers another perspective, suggesting that individual motivation may also stem from different self-regulatory systems: promotion focus and prevention focus (Higgins, 1997). The fundamental difference between these two is sensitivity to outcomes: promotion focus is sensitive to positive outcomes.

Prevention focus, on the other hand, is sensitive to negative outcomes (Scholer & Higgins, 2012). Regulatory focus also significantly influences information processing styles. Those with a promotion focus are more likely to attend to information that brings positive change, such as the benefits of nutritional interventions, while those with a prevention focus are more sensitive to potential threats and prioritize warning content regarding disease prevention (Melbye & Hansen, 2015; Kumar et al., 2021; Tudoran et al., 2012). Both orientations can lead to selective reasoning, though they bias different types of information. Regulatory Focus Theory reveals how different motivational systems influence information selection and the direction of reasoning, providing an important supplement to our understanding of information preferences in health motivated reasoning.

Based on Information Lifecycle Theory (Cai et al., 2022), subsequent research has refined the types of motivation in health information processing across temporal dimensions (Wilson & Maceviciute, 2022). This perspective divides information into different stages—from generation and reception to dissemination—and explores the dominant motivations at each stage.

During the information generation stage, intrinsic motivation may drive individuals to create health content that aligns with their own beliefs (Stehr et al., 2021). In the reception and dissemination stages, accuracy motivation prompts individuals to make decisions only after verifying the authenticity of the information (Ecker et al., 2023). Differences in motivation at various stages can cause information selection and the direction of reasoning to evolve over time.

The lifecycle perspective emphasizes that the role of motivation in information processing is dynamic. Understanding this dynamism helps in designing more precise health communication and intervention strategies. The various motivation types categorized by temporal sequence are not mutually exclusive but rather form a complementary explanatory framework. Health motivation may be a collection of characteristics from different motivational types; for example, the motivation to share health information for social purposes reflects directional motivation, possesses characteristics of extrinsic motivation, and also carries attributes of prospective motivation.

Compared to a single classification method, integrating multiple perspectives reveals that different motivation types determine an individual's "starting point" and "pathway" during information processing. Motivation not only triggers behavior but also guides individuals toward specific conclusions during information selection, processing, and interpretation. By influencing judgments of information authenticity and importance, it serves as the critical psychological foundation for health motivated reasoning.

3 健康动机性推理的影响因素

The classification of motivations provides a fundamental perspective for understanding health-related motivated reasoning; however, in actual reasoning processes, an individual's motivational characteristics are jointly regulated by numerous factors.

Based on previous empirical research (Dong et al., 2020; Klinenberg & Sherman, 2021), the reasoning process for health information often involves direct interactions between individual characteristics and information attributes, while also being potentially influenced by specific cultural contexts. Previous research categorizes the factors influencing health motivated reasoning into three major classes: individual factors, informational factors, and cultural factors. Each shapes motivated reasoning in its own unique way as a dynamic source of psychology and behavior. Individual factors primarily include health beliefs, cognitive traits, and discrete emotions. Informational factors emphasize the nature and presentation of the information itself, while cultural factors provide a broader contextual perspective involving social identity and cultural norms.

Within the Bayesian reasoning process, individuals tend to accept information that aligns with their own health beliefs. Individuals with different personality traits and cognitive styles show varying susceptibility to reasoning biases. Negative emotions with moderate arousal are more conducive to effective information encoding and benign health motivated reasoning. Information conflict increases cognitive discomfort, leading individuals to selectively process information. Different information frameworks mobilize distinct emotional and motivational response pathways. Individual reasoning tendencies are influenced by identity; information consistent with group identity is more easily accepted. Cultural norms constrain an individual's reasoning style, thereby influencing health decisions. Among these, the mechanism of information conflict—partially adapted from Kahan (2016a)—suggests that health beliefs are a critical antecedent of motivated reasoning. Research indicates that health beliefs determine not only how individuals evaluate health information but also the reasoning tendencies they exhibit during information processing (Masarwa et al., 2023; Stekelenburg et al., 2020). If corrective information regarding vaccinations or food safety contradicts an individual's prior health beliefs, people are more likely to reject or ignore such information rather than accept it rationally. Langford (2018) found that beliefs about the causes of hypertension affect the acceptance of related information: those who believe health behaviors are the primary cause are more likely to accept lifestyle recommendations, while those who attribute it to genetic factors tend to maintain their original habits.

Existing health beliefs may influence the way individuals filter and interpret information through motivated reasoning, thereby shaping their health decisions.

The Health Belief Model (HBM) provides a classic theoretical framework for predicting and explaining health behaviors. Based on a rational decision-making framework, this model assumes that individuals form decisions by systematically evaluating health threats—such as perceived susceptibility and severity—and behavioral evaluations—such as perceived benefits, barriers, and self-efficacy (Rosenstock, 1974; Wang et al., 2022). However, research on health motivated reasoning shows that actual decision-making processes often deviate from this rational assumption (Dibbets et al., 2021). When processing health information, individuals may employ reasoning strategies such as exaggerating behavioral barriers or underestimating disease risks to maintain existing behaviors (Kuhfeldt et al., 2024; Mercadante & Law, 2021). These irrational biases challenge the rational premises of the HBM. For instance, a smoker might acknowledge the "perceived severity" of smoking's harms on one hand, while using motivated reasoning—such as "my genes are healthier"—to deny personal susceptibility on the other, thus continuing the smoking behavior.

The HBM and health motivated reasoning are based on rational and irrational assumptions, respectively, yet there is potential space for integration. Parwati’s motivational interviewing enhances health adoption rates by increasing self-efficacy and reducing perceived barriers.

Future research should clarify under what circumstances health decisions follow rational HBM predictions and when they are susceptible to health motivated reasoning, as well as the role of their interaction in behavior change. Furthermore, research should explore how to leverage HBM components, such as perceived benefits, to mitigate irrational biases. Cognitive traits mainly encompass personality characteristics and cognitive styles. Individuals show significant differences in their susceptibility to health motivated reasoning. People with high delusional tendencies tend to maintain emotionally favorable but inaccurate information to avoid the risk of rejection (Rigoli et al., 2019). Conversely, individuals with high obsessive-compulsive traits repeatedly check health information; this excessive self-monitoring reduces the interference of emotional motivation in information processing, thereby aiding the rationalization of reasoning (Bensi et al., 2010). People with high conscientiousness and agreeableness are more inclined to accept mainstream and authoritative health advice, reducing reasoning biases stemming from personal stances (Yao, 2022). Individuals with strong narcissistic tendencies, due to their self-centered cognitive mode, are more likely to produce biased interpretations in health contexts and become potential spreaders of misinformation (Haupt et al., 2024). In summary, populations with higher narcissism are more susceptible to health motivated reasoning, while those with high obsessive-compulsive traits, conscientiousness, and agreeableness are relatively less affected, with their information processing tending toward rationality.

Actively open-minded thinking and analytical thinking also play key roles in an individual's motivated reasoning. Actively open-minded thinking can alleviate cognitive biases brought about by motivated reasoning and encourage the acceptance of scientific consensus (Stenhouse et al., 2018).

Southworth (2021) found that by strengthening the motivation to pursue truth, individuals are able to accept perspectives that challenge their existing beliefs, thereby exhibiting lower tendencies toward reasoning bias.

Individual differences in reasoning are primarily driven by analytical thinking ability rather than external situational factors like time pressure (Strömbäck et al., 2021; Viator et al., 2020). In a data interpretation study evaluating the effectiveness of skin creams for treating rashes, individuals with higher analytical thinking abilities were able to integrate evidence more accurately compared to those with lower abilities, making their interpretation of data less likely to be biased toward supporting prior views (Kahan et al., 2017). However, subsequent replication experiments (Maguire et al., 2022; Persson et al., 2021) failed to confirm a significant correlation between analytical thinking ability and motivated reasoning. These results suggest that an individual's prior beliefs may play a greater role in the reasoning process.

This reflects that health motivated reasoning may be influenced by multiple factors. Personality traits and cognitive styles modulate an individual's health motivated reasoning in different ways. Discrete emotions of different valence and arousal levels also influence patterns of health motivated reasoning. Discrete emotions are basic emotions that are independent of each other and have specific characteristics, such as joy, anger, and fear (Boyer et al., 2024). Discrete emotions of different valence have different effects: negative discrete emotions can intensify an individual's focus on threatening information, prompting them to overestimate risks and ignore mitigating evidence, thereby exacerbating motivated reasoning biases (Chen et al., 2021; Na et al., 2018). In health-related issues, strong negative emotional reactions make individuals more likely to accept and spread health rumors consistent with their emotional state during information processing (Dong et al., 2020). Conversely, positive emotions may reduce defensive processing and enhance acceptance of counter-attitudinal information, thereby reducing bias in reasoning (Suhay & Erisen, 2018). For example, research has found that compared to negative emotions, positive emotions not only prompt individuals to form more positive attitudes and behavioral intentions but also indirectly improve their evaluation of various health information (Jin & Oh, 2022). Thus, negative emotions reinforce motivated reasoning tendencies, while positive emotions help promote rational information processing. The impact of emotional arousal on motivated reasoning is reflected in its regulation of individual attention, which in turn affects information processing. One study found that health public service advertisements with strong emotional arousal can improve the perceived usefulness of health information, thereby enhancing its dissemination (Lang & Yegiyan, 2008). Another study on obesity prevention further indicated that while high-arousal emotional experiences can attract more attention, they may lead to difficulties in information encoding. Negative emotions with moderate arousal are more conducive to the effective encoding and benign dissemination of information, promoting rational analysis while attracting attention (Bailey et al., 2018). Moderate emotional arousal can enhance information acceptance while mitigating biases in health motivated reasoning, driving a more rational decision-making process. Future research could explore the interaction between the valence and arousal of discrete emotions on health motivated reasoning.

Information conflict—specifically, conflicting health information—triggers motivated reasoning. Research in this area has formed a relatively independent field. Conflicting health information refers to contradictory or inconsistent conclusions or recommendations from different sources or studies regarding the same health topic or issue (Wang et al., 2022).

This type of information is distinct from the more widely discussed health misinformation (also translated as false or inaccurate health information). It cannot be simply distinguished as true or false through logic; its veracity is often in a vague, pending state, or its truthfulness is highly context-dependent and difficult to generalize.

For example, research results on the health effects of coffee are inconsistent: some suggest moderate consumption protects cardiovascular health and enhances cognitive function, while others warn it may increase the risk of osteoporosis and anxiety (Haigh & Birch; Ihekweazu; Nieber). Similar information exists regarding whether the general population needs vitamin supplements or whether red meat consumption poses a cancer risk. When faced with conflicting health information where truth is hard to discern, an individual's motivated reasoning is often activated, leading to selective information processing.

The conflict and uncertainty of health information can influence motivated reasoning, causing individuals to selectively accept or reject information to alleviate cognitive discomfort (Chang, 2015). When exposed to contradictory health research reports, motivated reasoning may manifest as a bias toward information supporting one's existing position or as a rationalization of uncertain information (Zimbres et al., 2022). For instance, individuals may tend to accept studies that support their own dietary habits while questioning contrary evidence.

Carpenter (2016) categorized conflicting health information into thematic conflict, source quantity conflict, evidence heterogeneity, and temporal inconsistency; all of these factors can induce motivated reasoning. For example, when conflicts arise between health experts or between heterogeneous sources (such as experts versus social media bloggers), individuals are more likely to selectively believe one side based on identity (Ahn & Kahlor, 2023). Overall, conflicting health information prompts individuals to process information through motivated reasoning by increasing perceived uncertainty and triggering cognitive dissonance.

In motivated reasoning, different types of information framing influence how individuals process and react to information by altering its presentation. Existing research has explored the different effects of gain vs. loss framing and thematic vs. episodic framing on individual reasoning. Under gain and loss framing, information can emphasize the positive consequences of health behaviors (e.g., "moderate weight loss will make you feel pleasant") or the negative consequences of unhealthy behaviors (e.g., "excessive weight gain can cause negative emotions").

This guides the individual's motivated reasoning (t Riet et al., 2016). When smokers receive smoking cessation messages in a gain frame, it is more likely to stimulate their growth needs and promotion orientation, leading to receptive reasoning and enhancing their adoption of health advice. Conversely, a loss frame activates security needs and prevention orientation, inducing defensive motivated reasoning that causes individuals to focus on the threatening parts of the message, resulting in resistance or denial. Gain framing is more helpful in reducing motivational avoidance and increasing smoking cessation intentions, an effect also observed in cancer information communication (Jiang et al., 2022).

These types of framing do more than just change the presentation of information; more importantly, they activate different motivational orientations in individuals, thereby determining whether they adopt receptive or defensive reasoning (Cohen et al., 2007).

This further influences the acceptance of health information (Gallagher & Updegraff, 2012). Thematic and episodic framing are also informational factors leading to health motivated reasoning. Thematic framing presents information in a macro, neutral way, reducing the motivation to maintain existing beliefs based on emotional bias. Episodic framing, by highlighting vivid narratives of individuals rather than groups, triggers emotional responses and enhances the acceptance of information consistent with prior beliefs (Boyer et al., 2024). Media outlets tend to use thematic framing when reporting on health issues, which may weaken individual focus on specific responsibilities and lower motivational engagement. In contrast, episodic narratives are more likely to trigger emotional resonance and motivational responses, prompting individuals to interpret information in a self-centered manner (Dorfman et al., 2005).

According to Higgins and Ju et al. (2016), episodic narratives tend to reinforce belief defense and biased processing, while thematic reporting may weaken the tendency toward motivated reasoning. In summary, information framing profoundly influences individual choices and preferences in motivated reasoning by adjusting the presentation and expression of information to mobilize different emotional and motivational response paths. This also provides theoretical support for the design of information interventions.

Social identity plays a key role in motivated reasoning, and its influence is significantly affected by the individual's cultural background, particularly identity or group affiliation. Currently, the "identity-defensive cognition" hypothesis has been developed to explain the role of social identity in motivated reasoning. Based on evolutionary psychology and utility maximization theory, this hypothesis posits that social identity satisfies basic needs for belonging and security (Bayes & Druckman, 2021). The benefits and utility of selectively accepting information consistent with the group's position far outweigh the costs of rejecting it (Boyer et al., 2024). These phenomena reflect that individuals are influenced by group identity during information processing, maintaining the group's positive image and their own social identity by selectively integrating information favorable to the group's stance. For example, research on Traditional Chinese Medicine (TCM) shows that an individual's level of identification with TCM significantly influences their evaluative tendencies.

Individuals with a high level of identification with TCM tend to give higher evaluations to positive cues about it, while those with lower identification tend to emphasize or amplify negative cues. Regarding research topics, current health-related studies are mostly concentrated in Western countries, focusing on motivated reasoning in the formation of political attitudes toward health issues. Ideological identity leads to asymmetric motivated reasoning patterns and exacerbates polarization between partisan groups (Pennycook et al., 2021).

Research by Young et al. (2022) and Gadarian (2021) shows that in Western cultural contexts, liberal individuals are more inclined than conservatives to take health precautions and support epidemic prevention policies. Furthermore, liberals perform better on the accuracy of COVID-19 knowledge, while conservatives rely more on ideological media for health information (Sylvester, 2021). Further research reveals that the intensity of motivated reasoning is driven not only by political identity but also by an individual's status within the group (Boyer et al., 2022). Members of high-status groups are more motivated by political identity and tend to selectively accept information that supports their group's interests, reinforcing their existing positions and social status. These findings highlight the important role of political identity in health motivated reasoning, suggesting that identity not only affects the acceptance of health information but may also exacerbate cognitive divergence between groups. Cultural norms profoundly influence the way individuals engage in health motivated reasoning.

A large body of research shows that although gender differences in drinking behavior exist across different cultural norms, the degree and form of these differences vary by culture, further influencing the patterns of motivated reasoning in drinking behavior (Holmila et al., 2010).

According to Temmen & Crockett (2020) and Davis & Schlauch (2021), social norms and gender role expectations regarding drinking in different cultures may lead men and women to exhibit different motivational preferences in drinking decisions. For example, in some cultures, men are expected to drink more frequently and exhibit stronger motivated reasoning in drinking decisions to rationalize their behavior (Fugitt et al., 2017). Among adolescents, parenting styles within a cultural context also influence motivated reasoning regarding unhealthy drinking. For instance,

the warmth and rationality shared by authoritative and permissive parenting styles in certain cultures may serve as protective factors, effectively reducing

the risk of adolescents developing motivations for drinking (Garcia et al., 2020). Cultural norms also influence epidemic prevention behaviors, such as motivated reasoning in mask-wearing decisions.

Driven by social norms, group pressure, and group security needs, individuals in collectivist cultures are more likely to selectively accept information supporting mask-wearing and ignore opposing views to meet social expectations (Badillo-Goicoechea et al., 2021). In individualistic cultures, individuals tend to accept information emphasizing personal freedom and may even rationalize the choice not to wear a mask (Lu et al., 2021). In regions with a collectivist atmosphere within individualistic countries, individuals may still exhibit motivated reasoning due to cultural norms,

thereby accepting the legitimacy of mask-wearing. This indicates that cultural norms influence health decisions by shaping an individual's motivated reasoning. All the factors summarized above play a role in an individual's motivated reasoning. Health beliefs, cognitive traits, and discrete emotions of different valence or arousal influence the reasoning process in unique ways. Conflicting information, gain framing, and thematic framing are more likely to trigger motivated reasoning in the public. Differentiated social identities and cultural norms exert varying degrees of constraint on motivated reasoning. It should be noted that current research mostly explores the impact of single factors on health motivated reasoning; systematic empirical testing of specific interactions between these factors is still lacking and awaits further exploration in future studies.

4 健康动机性推理的

Although the preceding sections have explored the driving foundations and boundary conditions of health motivated reasoning from the perspectives of motivational types and regulatory factors, its psychological essence remains to be further revealed. Only by elucidating the specific implementation pathways of this type of reasoning during information processing can we comprehensively grasp its internal operational mechanisms and formative logic.

Current research can integrate traditional perspectives with Bayesian modeling to further explore and explain the underlying mechanisms of health-related motivated reasoning. Early studies have confirmed that motivation influences reasoning by relying on biased cognitive processes—specifically, strategies for acquiring, constructing, and evaluating beliefs \cite{Braman & Nelson, 2007; Epley & Gilovich, 2016}. This perspective suggests that the mechanism of motivated reasoning stems primarily from cognitive biases \cite{Taber & Lodge, 2006}. Specifically, individuals tend to select and confirm information that aligns with their existing views while avoiding counter-attitudinal information or evidence that challenges their self-concept \cite{Dibbets et al., 2021; Dickinson & Kakoschke, 2021; Meppelink et al., 2019}.

While traditional mechanisms can explain why individuals exhibit selective belief patterns, relying solely on a cognitive bias perspective has certain limitations. In particular, it lacks a dynamic characterization of the information-updating process, failing to clarify under what specific conditions an individual might update their beliefs versus when they might stubbornly adhere to them.

Recent theoretical attempts have employed Bayesian models to explain health-related motivated reasoning, conceptualizing it as a non-optimal probabilistic updating process \cite{Damgaard & James, 2024; Priniski et al., 2022}. This modeling approach not only provides a novel theoretical framework for understanding motivated reasoning in health contexts but also helps elucidate why individuals systematically deviate from optimal decision-making in specific situations.

According to the Bayesian model, when individuals receive new information, they update their prior beliefs based on the likelihood of the new evidence to form a posterior belief. As evidence continues to accumulate, the posterior distribution will eventually converge to the true parameter value (convergence to the truth; \cite{Tappin et al., 2020}). Within this framework, individuals primarily evaluate and integrate information based on its veracity and the strength of the evidence provided.

...authenticity and reliability to update their beliefs (Rigoli et al., 2021; Wojtowicz & DeDeo, 2020). In the context of motivated health reasoning, individuals' processing of health information systematically deviates from the standard Bayesian updating process. This phenomenon is characterized by a "triple deviation" from rational Bayesian inference.

Masarwa et al. (2023) suggest that individuals, such as smokers, may tend to assign a lower prior probability to the proposition that "smoking causes cancer" as a psychological mechanism to avoid the cognitive dissonance associated with accepting such information. This deviation occurs because the emotional value an individual attaches to a specific hypothesis—such as the pleasure derived from smoking—interferes with their ability to objectively integrate statistical information.

Evidence evaluation is subject to selective distortion based on individual preferences \cite{Yang et al., 2023}. For instance, individuals who are vaccine-hesitant may tend to overestimate the causal link between post-vaccination discomfort and the vaccine itself—a phenomenon known as likelihood ratio inflation. Conversely, they may underestimate the impact of snack calories through likelihood ratio compression. By selectively amplifying or weakening relevant evidence, the updating of posterior beliefs becomes directional \cite{Piksa et al., 2023}. Under the influence of motivated reasoning, individuals perform an emotional "correction" on the credibility of new evidence. Motivated reasoning is characterized by a bias toward prior beliefs, selectivity in processing new evidence, and directionality in posterior belief updates.

Bayesian models have been employed to explain the process of health-related motivated reasoning \cite{Priniski et al., 2022}. In one landmark study, researchers introduced participants to a salivary enzyme test and informed them that the enzyme was associated with either positive or negative health outcomes. The results demonstrated that when participants believed the enzyme was linked to negative health outcomes, they were significantly more likely to question the reliability of the test. In contrast, they exhibited much less skepticism when the enzyme was associated with positive health outcomes \cite{Ditto et al., 1998}. This highlights an asymmetry in how participants process positive versus negative evidence due to the role of motivated reasoning.

Motivation leads individuals to assign different weights to different types of evidence, thereby influencing the magnitude of belief adjustment. Similarly, Nyhan (2014) found that providing corrective information regarding the measles, mumps, and rubella (MMR) vaccine failed to change the minds of the most skeptical individuals; instead, it often reinforced their erroneous beliefs. This suggests that motivation not only affects the weighting of evidence but can also cause belief updates to deviate from the Bayesian optimal mode. Consequently, individuals may cling to their original beliefs or even strengthen their prior convictions when faced with contradictory evidence. Other related studies have also examined motivated reasoning within a Bayesian framework, exploring how people maintain their original beliefs when evidence conflicts with their personal stances \cite{Kahan, 2016a, 2016b}. By assigning higher weights to information consistent with their position and weakening the impact of opposing information, individuals' posterior beliefs ultimately deviate from rational expectations.

It should be noted that research situating motivated reasoning within a Bayesian framework has primarily focused on theoretical development and conceptual expansion. To date, specific computational modeling remains incomplete, and there is a notable lack of empirical evidence to support these frameworks. Future research could build upon these foundations to conduct more in-depth explorations in these areas.

The underlying mechanisms of health-motivated reasoning can be summarized through the dual lenses of individual cognition and Bayesian modeling. Specifically, the cognitive perspective focuses on the behavioral characteristics and manifestations of motivated reasoning, typically adopting a static viewpoint. In contrast, the Bayesian perspective emphasizes the modeling and explanation of the underlying processes, focusing on the dynamic adjustment of reasoning over time. These two approaches are complementary: the former establishes the theoretical foundation for motivated reasoning, while the latter provides a quantitative framework to describe how beliefs are adjusted under the influence of subjective motivations. Together, they offer a comprehensive understanding of health-motivated reasoning.

Research into reasoning provides more precise analytical tools and methodological frameworks.

5 研究

As theoretical perspectives continue to deepen and research content gradually enriches, there remains an urgent need to explore relevant issues. Based on this, future research can focus on three directions. First, an interactive model of factors influencing health motivated reasoning can be constructed to explore the interactions between individual traits, information characteristics, and cultural backgrounds. Second, the application of Bayesian models in health motivated reasoning should be deepened to investigate the impact of different types of health motivations on Bayesian inference and to identify new neural correlates of motivation. Finally, the role of health motivated reasoning in health intervention strategies should be examined to develop precision health intervention programs tailored to different motivation types and the challenges posed by the era of network intelligence. The details are as follows.

Models of factors influencing health motivated reasoning have revealed various determinants; however, there is still a lack of systematic theoretical integration regarding how factors at different levels interact and under what conditions they play a key role.

Future research should attempt to construct a more integrated and interactive theoretical model to systematically explore the interaction mechanisms between individual traits, information characteristics, and cultural backgrounds.

The interaction effect between the individual level and the information level on health motivated reasoning cannot be ignored. Personality traits such as neuroticism and individual cognitive styles—such as field independence and field dependence—may moderate how individuals process health information with different characteristics (e.g., emotional vs. neutral) \cite{Haarmann et al., 2025; Mohammed et al., 2022; Pröllochs et al., 2021}. When health risk information carries a strong emotional tone, individuals with high neuroticism and field dependence may be more likely to exhibit motivated reasoning responses, such as selective reception, defensive processing, and emotion-driven cognitive distortions regarding information consistent with their existing beliefs. In contrast, individuals with low neuroticism and field independence are generally not significantly disturbed by emotional fluctuations and tend to evaluate the credibility of information based on rational analysis when faced with information of various characteristics.

The interaction between individual traits and information characteristics jointly shapes the intensity and direction of motivated reasoning. Cultural background also plays an important role in this interaction process.

In collectivist cultures, there is a greater emphasis on group identity; therefore, when faced with highly inflammatory group-related health information, emotional drive and cognitive bias are stronger, leading to a tendency to accept information consistent with the group. In contrast, in individualistic cultures, individuals rely more on rational evaluation; even if information is inflammatory, the emotional drive remains relatively stable, and judgments are made more based on the credibility of the information.

Future research could manipulate the inflammability and cultural matching of health information \cite{Dryhurst et al., 2020; Zhang, 2021; Nestor & Woodhull, 2024; Flanja, 2020} to examine their interactions with individual factors such as health beliefs, personality traits, and emotional susceptibility. This would allow for a deeper exploration of how cultural backgrounds influence these interaction processes and the pathways of motivated reasoning.

Verifying this multi-driven mechanism in a cross-cultural context will not only help in understanding individual and group health behaviors across different cultural settings but also provide a theoretical basis for formulating more culturally adaptive health communication strategies and intervention programs.

Deepening the application of Bayesian models in health motivated reasoning provides a highly potential theoretical framework for understanding individual cognitive processes. However, its explanatory power still needs to be tested through more rigorous experimental designs.

Research should examine the effects of health motivation on Bayesian inference. Different types of health motivations may shape how individuals process health information by influencing the strength of prior beliefs, the allocation of evidence weights, and the update rate of posterior beliefs \cite{Thaler, 2024}. Studies have found that individuals with "approach" motivations, such as the desire to maintain health, may hold stronger prior beliefs; consequently, they tend to maintain original beliefs more than incorporating new information. Conversely, individuals with "avoidance" motivations, such as the desire to avoid disease, may have higher sensitivity to risks and negative information, making them more likely to consider information that contradicts their beliefs and thus more inclined to adjust their beliefs \cite{Tudoran et al., 2012}. Future experiments could design different scenarios to manipulate the magnitude of belief updating for the same health information, thereby accurately measuring the role of different types of health motivations in Bayesian inference.

Future research could further introduce cognitive neuroscience methods to compensate for the limitations of traditional behavioral research in exploring the psychological mechanisms of health motivated reasoning. For example, using functional magnetic resonance imaging (fMRI), researchers can reveal the biased processing characteristics exhibited by individuals when receiving threatening health information at the neural level, thereby expanding the understanding of health motivated reasoning.

Existing research indicates that motivated reasoning involves the coordinated activity of multiple brain regions, particularly neural networks related to motivation-driven processing, emotional processing, cognitive control, and belief updating, such as the prefrontal cortex, cingulate gyrus, striatum, and amygdala \cite{Ji et al., 2021; Lois et al., 2024; Prado et al., 2020}. Combining Bayesian modeling, future research can further explore the roles of specific brain regions in belief updating. For instance, task-based fMRI can be used to observe neural responses when receiving health information, extracting activation levels in the prefrontal cortex as parameters for posterior belief adjustment to quantify the impact of health motivation on the Bayesian inference process. Furthermore, neuroimaging methods can reveal functional connectivity patterns between different brain regions, providing evidence for network interactions in motivated reasoning. Future research could apply dynamic causal modeling or functional connectivity analysis \cite{Lyu et al., 2021} to examine the interactive relationship between motivation-regulating brain regions and cognitive control regions (such as the prefrontal cortex).

If it is found that individuals with strong health motivation show increased connectivity in relevant brain regions when receiving unfavorable health information, it may indicate that they mobilize more resources at the neural level to maintain existing beliefs. This would provide support for understanding the neural implementation pathways of belief-update bias in health motivated reasoning.

Cognitive neuroscience methods can not only verify the neural mechanisms of the motivated reasoning process but also provide a biological basis for intervention strategies, such as by modulating the activity of specific brain regions.

Improving the acceptance and processing of health information can enhance the effectiveness of health communication. Regarding the role of health motivated reasoning in intervention strategies, individuals often focus on information consistent with their own motivations during health decision-making. This characteristic can be utilized to optimize health intervention strategies by tailoring them to different types of motivation. For example, for those driven by intrinsic motivation \cite{Alqahtani et al., 2023}, interactive health education projects or knowledge competitions can be designed to enhance their proactive acquisition of health information. For those driven by extrinsic motivation \cite{van Velsen et al., 2019}, social norm interventions (e.g., informing them that most people have adopted healthy behaviors) or reward mechanisms (e.g., points exchange for health products) can be used to improve intervention effects.

Emotion-driven intervention strategies also deserve attention. Public health interventions can promote public health behaviors by stimulating specific emotions \cite{Martino et al., 2024; McNeil & Purdon, 2022; Zhang & Liao, 2021}. Moderate fear appeals help increase health risk perception \cite{Ho et al., 2021; Sun et al., 2021}. Future research can further explore the long-term effects of motivated reasoning on health behavior.

To reduce the bias of health motivated reasoning, future health intervention research could explore cognitive restructuring strategies \cite{de Mooij et al., 2023} to help individuals re-evaluate the reliability of health information. Additionally, increasing health information transparency and cultivating critical thinking skills can also help mitigate the adverse effects of motivated reasoning.

The arrival of the network intelligence era also brings new challenges to health intervention strategies, with "information cocoons" and collective public opinion being the most prominent issues. The former exacerbates an individual's adherence to their own health concepts and leads to stronger rejection of heterogeneous views \cite{Yang et al., 2024}, while the latter reinforces internal biases through group pressure, thereby inhibiting the acceptance of out-group perspectives \cite{Bavel et al., 2020}. Future research could explore health information optimization strategies within information cocoon environments, such as by moderately introducing heterogeneous viewpoints during the recommendation process to achieve de-biased information delivery, thereby reducing the interference of information polarization on health decision-making.

Social network experiments can also be combined to analyze health information acceptance patterns under group pressure and further explore how to reduce the adverse effects of group polarization on health information dissemination by enhancing group diversity and guiding critical thinking. Furthermore, special populations such as the elderly and chronic disease patients are more susceptible to misleading or biased information when facing online health information due to their physiological and psychological characteristics \cite{Liu et al., 2024; Miller et al., 2023; Wang et al., 2021}. Future research could develop health application interfaces better suited to the cognitive characteristics of the elderly or combine online and offline health education to improve their ability to identify health information. Contextualized health intervention strategies could also be explored, such as dynamically adjusting the expression of health information based on the emotional state of chronic disease patients to enhance information acceptability and persuasiveness. This aligns closely with the core concepts of people-oriented and precision health communication emphasized in the "Healthy China" strategy.

Health motivated reasoning has a significant impact on individual health information processing and decision-making; it is not static but is shaped and regulated by various factors in a dynamic process. This paper systematically introduces the classification of health motivations.

By examining the patterns, influencing factors, and occurrence mechanisms of health motivated reasoning from a Bayesian perspective, this paper provides a framework for a comprehensive understanding of its theoretical foundations, driving conditions, and operational mechanisms.

By considering individual factors, information characteristics, and socio-cultural backgrounds, this paper reveals the dynamic role of health motivated reasoning in health information processing. Based on the Bayesian inference model, it elucidates how motivation affects an individual's calculation of information weights, leading to varying degrees of reasoning bias. Future research should strive to integrate individual characteristics, information attributes, and socio-cultural backgrounds to reveal their interactions in shaping the health motivated reasoning process and their similarities and differences across contexts. Furthermore, cognitive neuroscience methods should be employed to verify the applicability of the Bayesian model while expanding its application in health communication and behavioral intervention.

参考文献

. (2024). The mechanisms and conditions underlying environmental materialism. Advances in Psychological Science. (2022). The influence of traditional Chinese medicine cultural identity on individual motivated reasoning. Psychology: Techniques and Applications. Z. (2013).

The Science of Insight. Posts and Telecom Press. (2020). How to Think Independently. (2024). The influence of environmental sensory cues on dietary decision-making and its underlying mechanisms. Advances in Psychological Science. (2015).

The effects of decision framing and regulatory focus on ambiguity aversion. Chinese Journal of Clinical Psychology. & Kahlor, L. A. (2023). When experts offer conflicting information: A study of perceived ambiguity, information insufficiency, trustworthiness, and risk information behaviors.

Health Communication Alqahtani Riper Mccleary Witteman & Mcgrath P. (2023). Motivation based approach for tailoring persuasive mental health applications.

Behaviour & Information Technology Appel & Sanatkar S. (2024). Systematic search and scoping review of physicians intolerance of uncertainty and medical decision making uncertainties during the COVID 19 pandemic summary of the literature and directions for future research.

Journal of Clinical Psychology in Medical Settings Badillo Goicoechea, E., Chang, T. H., Kim, E., LaRocca, S., Morris, K., Deng, X., ... & Stuart, E. A. (2021). Global trends and predictors of face mask usage during the COVID 19 pandemic.

BMC Public Health, 21 Bailey, R. L., Wang, T., & Kaiser, C. K. (2018). Clash of the primary motivations: Motivated processing of emotionally experienced content in fear appeals about obesity prevention.

Health Communication, 33 Bayes & Druckman J. N. (2021). Motivated reasoning and climate change.

Current Opinion in Behavioral Sciences Bensi Giusberti & Gambetti E. (2010). Individual differences and reasoning A study on personality traits.

British Journal of Psychology Boyer M. M. (2023). Aroused argumentation How the news exacerbates motivated reasoning. T he International Journal of Press/Politics Boyer M. M.

Aaldering & Lecheler S. (2022). Motivated reasoning in identity politics Group status as a moderator of political motivations.

Political Studies Boyer M. M. Lecheler & Aaldering L. (2024). Don hrow the frame out with the bathwater: ow episodic

news frames can prevent identity motivated reasoning Journalism & Mass Communication Quarterly Braman & Nelson T. E. (2007). Mechanism of motivated reasoning? Analogical perception in discrimination disputes.

American Journal of Political Science & Wang W. (2022). Influence of information attributes on information dissemination in public health emergencies.

Humanities & Social Sciences Communications 1−22. Vagnetti Penner Ramos Hunter Hough & Magistro D. (2024). It is not just a matter of motivation role of self control in promoting physical activity in older adults Bayesian mediation model Healthcare Carpenter D. M.

Geryk L. L. A. T. Nagler R. H. Dieckmann N. F. P. K. (2016). Conflicting health information a critical research need.

Health Expectations Chang C. (2015). Motivated processing How people perceive news covering novel or contradictory health research

findings. Science Communication , 37 (5) , 602 − 634.

& Jiang J. (2021). Dissemination refutation of rumors during the COVID 19 outbreak in China Infodemiology study.

Journal of Medical Internet Research e22427. Cohen Perozich Rajan Persky Parisi Bowie ... & Cheskin L. J. (2017). Framed interactive theory driven texting effects of message framing on health behavior change for weight loss.

Family &

Community Health , 40 (1) , 43 − 51.

Converse B. A. Juarez & Hennecke M. (2019). Self control and the reasons behind our goals.

Journal of Personality and Social Psychology Courbage & Peter R. (2021). On the effect of uncertainty on personal vaccination decisions.

Health conomics Damgaard P. R. & James O. (2024). Public service users responses to performance information Bayesian learning or motivated reasoning?

Journal of Public Administration Research and Theory Davis J. A. & Schlauch R. C. (2024). Gender differences in college drinkers A test of the precarious manhood hypothesis on drinking motivation.

Journal of Social and Clinical Psychology E. L. & Ryan R. M. (1985).

Intrinsic motivation and self determination in human behavior . Plenum Press. de Mooij, B., Fekkes, M., Miers, A. C., van den Akker, A. L., Scholte, R. H., & Overbeek, G. (2023). What works in preventing emerging social anxiety: Exposure, cognitive restructuring, or a combination?

Journal of Child and Family Studies, 32 (2), 498 Dickinson D. L. & Kakoschke N. (2021). Seeking confirmation? Biased information search and deliberation in the food domain.

Food Quality and Preference Dibbets Borger & Nederkoorn C. (2021). Filthy fruit! Confirmation bias and novel food.

Appetite Ditto P. H. Scepansky J. A. Munro G. D. Apanovitch A. M. & Lockhart L. K. (1998). Motivated sensitivity to preference inconsistent information.

Journal of Personality and Social Psychology Jiang & Liu Y. (2020). Public emotions and rumors spread during the COVID 19 epidemic in China based correlation study.

Journal of Medical Internet Research e21933. Dorfman Wallack & Woodruff K. (2005). More than a message Framing public health advocacy to change corporate practices.

Health Education & Behavior ( 3 ) Dryhurst Schneider Freeman Recchia van der Bles Spiegelhalter & van der Linden S. (2020). Risk perceptions of COVID 19 around the world.

Journal of Risk Research

Ecker U. K. Sanderson J. A. McIlhiney Rowsell J. J.

Quekett H. L. Brown G. D. & Lewandowsky (2023). Combining refutations and social norms increases belief change.

Quarterly Journal of Experimental Psychology Ednie, A., & Stibor, M. (2017). Influence and interpretation of intrinsic and extrinsic exercise motives.

Journal of Human Sport and Exercise, 12 (2), 414 Epley & Gilovich T. (2016). The mechanics of motivated reasoning.

Journal of Economic Perspectives

Festinger , L. 1957. A Theory of Cognitive Dissonance.

Stanford University Press.

Filindra & Harbridge L. (2022 ). How do partisans navigate intra group conflict? A theory of leadership

driven motivated reasoning. Political Behavior , 44 (3) , 1437 – 1458.

Fugitt J. L. L. S. & Bridges A. J. (2017). Undifferentiated gender role orientation drinking motives increased alcohol use in men and women.

Substance Use & Misuse Gadarian S. K. Goodman S. W. & Pepinsky T. B. (2021). Partisanship health behavior and policy attitudes in the early stages of the COVID pandemic.

Plos One e0249596. Gallagher K. M. & Updegraff J. A. (2012). Health message framing effects on attitudes intentions and behavior analytic review.

Annals of Behavioral Medicine Garcia O. F. Serra Zacares J. J.

Calafat & Garcia F. (2020). Alcohol use and abuse and motivations for drinking and non drinking among Spanish adolescents o we know enough when we know parenting style?

Psychology & Health Graham, M. H., & Singh, S. (2024). An outbreak of selective attribution: Partisanship and blame in the COVID pandemic.

American Political Science Review, 118 (1), 423 Green E. C.

Murphy E. M. & Gryboski K. (2020). The health belief model. he Wiley Encyclopedia Of Health Psychology Haarmann, L., Kalbe, E., Anapa, G., Kurt, D., & Seven, Ü. S. (2025). Subjective health literacy and personality in older adults: onscientiousness, neuroticism, and openness as key predictors cross sectional study International Journal of Environmental Research and Public Health, 22 (3), 392.

Haigh & Birch H. A. (2021). When ‘scientists say’coffee is good for you one day and bad for you the next generic attributions to ‘scientists’ and ‘experts’ amplify perceived conflict Collabra Psychology Haupt , M. R., Cuomo, R., Mackey, T. K., & Coulson, S. (2024). The role of narcissism and motivated reasoning on

misinformation propagation. Frontiers in Communication, 9 , 1472631.

Higgins E. T. (1997). Beyond pleasure and pain. American Psychologist Higgins J. W.

Naylor P. J. Berry O'Connor & McLean D. (2006). The health buck stops where? Thematic framing of health discourse to understand the context for CVD prevention.

Journal of Health Communication Holmila & Raitasalo K. (2005). Gender differences in drinking hy do they still exist?

Addiction L. L. K. W. H. C. Cheung A. T. & Xia W. (2021). Effectiveness of smoking cessation interventions for smokers with chronic diseases A systematic review.

Journal of Advanced Nursing Hutmacher Reichardt & Appel M. (2024). Motivated reasoning about climate change and the influence of numeracy need for cognition and the dark factor of personality Scientific Reports Ihekweazu C. (2023). Is coffee the cause or the cure? Conflicting nutrition messages in two decades of online New

York Times ’ nutrition news coverage. Health Communication , 38 (2) , 260 – 274.

Ji, X., Zhao, J., Li, H., Pizzagalli, D. A., Law, S., Lin, P., Fan, L., Zhang, P., Fang, S., Wang, X., Yao, S., & Wang, X. (2021). From motivation, decision making to action: An fMRI study on suicidal behavior in patients with major depressive disorder.

Journal of Psychiatric Research, 139 Jiang & Wang Y. (2021). The effects of message framing on online health headline selection of female users A moderation of approach/avoidance motivation.

International Journal of Medical Informatics Jin, E., & Oh, J. (2022). The role of emotion in interactivity effects: Positive emotion enhances attitudes, negative emotion helps information processing.

Behaviour & Information Technology, 41 (16), 3487 & Cheng J. (2023). Promoting mental health on social media A content analysis of organizational

tweets. Health Communication , 38 (8) , 1540 – 1549.

Kahan D. M. (2016a). The politically motivated reasoning paradigm part 1 What politically motivated reasoning is and how to measure it.

Emerging Trends in the Social and Behavioral Sciences Kahan D. M. (2016 ). The politically motivated reasoning paradigm Part 2 Unanswered questions.

Emerging Trends in the Social and Behavioral Sciences Kahan D. M.

Peters Dawson E. C. & Slovic P. (2017). Motivated numeracy and enlightened self government.

Behavioural ublic olicy Kalke Studd & Scherr C. L. (2021). The communication of uncertainty in health A scoping review.

Patient Education and Counseling Klinenberg, E., & Sherman, M. (2021). Face mask face offs: Culture and conflict in the COVID 19 pandemic.

Public Culture, 33 (3), 441 Kuhfeldt K. J. Kaiser J. L.

Morgan A. J. Ngoma Hamer D. H. Rockers P. C. Chirwa & Scott N. A. (2024). Motivation cues to action and barriers to COVID 19 vaccine uptake qualitative application of the health belief model among women in rural Zambia.

The American Journal of Tropical Medicine and Hygiene Kumar Gupta & Sood N. (2021). Prevention versus promotion focus regulatory efforts on the disease incidence and mortality of COVID A multinational diffusion study using functional data analysis. ournal of International Marketing Kunda Z. (1990). The case for motivated reasoning.

Psychological Bulletin Lang, A., & Yegiyan, N. S. (2008). Understanding the interactive effects of emotional appeal and claim strength in

health messages. Journal of Broadcasting & Electronic Media, 52 (3), 432 – 447 .

Langford A. T. Solid C. A. L. C. Rabinowitz E. P. Williams S. K. & Seixas A. A. (2018). Beliefs about the causes of hypertension and associations with pro health behaviors.

Health Psychology ... & Li L. (2024). Association between personality and

cognitive bias in adults with and without depression.

BMC Psychology , 12 (1) , 779.

Lois, G., Tsakas, E., Yuen, K., & Riedl, A. (2024). Tracking politically motivated reasoning in the brain: he role of mentalizing, value encoding, and error detection networks.

Social Cognitive and Affective Neuroscience, (1), nsae056.

Lu, J. G., Jin, P., & English, A. S. (2021). Collectivism predicts mask use during COVID Proceedings of the National Academy of Sciences, 118 (23), e2021793118.

Lyu, D., Pappas, I., Menon, D. K., & Stamatakis, E. A. (2021). A precuneal causal loop mediates external and internal information integration in the human brain.

Journal of Neuroscience, 41 (48), 9944 Maguire Persson Västfjäll & Tinghög G. (2022). COVID 19 and politically motivated reasoning.

Medical Decision Making

Martino M. L. Lemmo Donizzetti A. R. Bianchi Freda M. F. & Caso D. (2024). Emotions and narrative reappraisal strategies of users of breast cancer screening Reconstructing t he past passing through the present and predicting emotions Qualitative Health Research Masarwa, D., Niazov, Y., Ben Natan, M., & Mostovoy, D. (2023). The role of parental health beliefs in seeking an eye examination for their child.

BMC Ophthalmology, 23 (1), 269. McNeil & Purdon C. (2022). Anxiety disorders COVID 19 fear and vaccine hesitancy.

Journal of Anxiety Disorders Mehlhaff I. D. T. J. Hetherington M. J. & MacKuen M. B. (2024). Where motivated reasoning withers and looms large Fear and partisan reactions to the COVID 19 pandemic.

American Journal of Political Science Zhang & Yin L. (2020). Neural responses of in group favoritism and out group discrimination toward moral behaviors.

Neuropsychologia Melbye E. L. & Hansen H. (2015). Promotion and prevention focused feeding strategies xploring the effects on healthy and unhealthy child eating. ed Research International Meppelink C. S.

E. G. Fransen M. L. & Diviani N. (2019). I was right about vaccination Confirmation bias and health literacy in online health information seeking.

Journal of Health Communication Mercadante A. R. & Law A. V. (2021). Will they or won't they? Examining patients' vaccine intention for flu and COVID 19 using the Health Belief Model.

Research in Social & Administrative Pharmacy Miller C. A.

Schroeder M. W. Guidry J. P. Fuemmeler B. F. & Pagoto S. (2023). Older adults exposure to and posting of health related messages on Facebook by chronic health condition status.

Digital Health Mohammed S. A. Rajashekar Giri Ravindran Kakarla Ausaja Gambo Yousri Salama Haidar Ismail Tavalla Uppal & Hamid P. (2022). Does vaccination increase the risk of autism spectrum disorder?

Cureus e27921. Moon, W. K., Chung, M., & Jones Jang, S. M. (2023). How can we fight partisan biases in the COVID 19 pandemic?

AI source labels on fact checking messages reduce motivated reasoning.

Mass Communication and Society, (4), 646 hlberger Endrejat Möller Herrmann Kauffeld & Jonas E. (2022). Focus meets motivation When regulatory focus aligns with approach/avoidance motivation in creative processes.

Frontiers in Psychology Garrett R. K. & Slater M. D. (2018). Rumor acceptance during public health crises Testing the emotional

congruence hypothesis. Journal of H ealth C ommunication , 23 (8) , 791 – 799.

Nestor, P. G., & Woodhull, A. A. (2024). Neuropsychology of social cognition: ulture, display rules, and emotional

expressivity. Journal of Clinical and Experimental Neuropsychology, 46 (9), 81 – 827.

Nieber, K. (2017). The impact of coffee on health.

Planta edica, 83 (16), 1256 Nyhan, B., Reifler, J., Richey, S., & Freed, G. L. (2014). Effective messages in vaccine promotion: randomized trial.

Pediatrics, 133 (4), e835 e842. Parwati N. M. Bakta I. M.

Januraga P. P. & Wirawan I. M. A. (2021). A health belief model based motivational interviewing for medication adherence and treatment success in pulmonary tuberculosis patients International Journal f Environmental Research nd Public Health Peng, R. X. (2022). How online searches fuel health anxiety: Investigating the link between health related searches, health anxiety, and future intention.

Computers in Human Behavior, 136 , 107384.

Pennycook McPhetres & Rand D. G. (2022). Beliefs about COVID 19 in Canada the United Kingdom and the United States A novel test of political polarization and motivated reasoning.

Personality and Social Psychology Bulletin Persson Andersson Koppel Västfjäll & Tinghög G. (2021). A preregistered replication of motivated

numeracy. Cognition , 214 , 104768.

Piksa Noworyta Gundersen Kunst Morzy Piasecki & Rygula R. (2024). The impact of confirmation bias awareness on mitigating susceptibility to misinformation.

Frontiers in Public Health Prado, J., L one, J., Epinat Duclos, J., Trouche, E., & Mercier, H. (2020). The neural bases of argumentative

reasoning. Brain and Language, 208 , 104827.

Priniski, J. H., Horne, Z., & Solanki, P. (2022). A Bayesian decision theoretic framework for studying motivated

reasoning. https

Flanja D. (2020). Cross cultural differences in risk perception and risk communication A case study on the

COVID - 19 outbreak. Redefining Community In Intercultural Context , 9 (1) , 68 – 74.

Pröllochs & Feuerriegel S. (2021). Emotions explain differences in the diffusion of true vs. false social media rumors.

Scientific Reports Rathje Roozenbeek Van Bavel J. J. & Van Der Linden S. (2023). Accuracy and social motivations shape judgements of (mis) information.

Nature Human Behaviour Rigoli Martinelli & Pezzulo G. (2021). I want to believe delusion motivated reasoning Bayesian

decision theory. Cognitive Neuropsychiatry , 26 (6) , 408 – 420.

Rosenstock I. M. (1974). The health belief model and preventive health behavior.

Health Education Monographs R. M. & Deci E. L. (2017). determination theory Basic psychological needs in motivation development and wellness . Guilford Press.

R. M. & Deci E. L. (2020). Intrinsic and extrinsic motivation from a self determination theory perspective Definitions theory practices and future directions.

Contemporary Educational Psychology Savolainen R. (2022). What drives people to prefer health related misinformation? The viewpoint of motivated

reasoning. Information Research , 27 (2) , 927 .

Schutz, A. (1962). Collected papers I: The problem of social reality, part I . Martinus Nijhoff Simonovic Taber J. M.

Scherr C. L. Howell J. L. ... & Politi M. C. (2023). Uncertainty in healthcare and health decision making Five methodological and conceptual research recommendations from an interdisciplinary team.

Journal of Behavioral Medicine Southworth, J. (2021). A perspective taking theory of open mindedness : Confronting the challenge of motivated

reasoning . Educational Theory, 71 (5), 589 – 607.

Standen, E. C., Rothman, A. J., & Mann, T. (2025). Weight loss advice from a healthcare provider is motivating, but it is also stigmatizing: n experimental, scenario based approach.

Annals of Behavioral Medicine, 59 kaaf018. Staples, C., Palermo, M., & Rancourt, D. (2022). Intrinsic and extrinsic motivations as moderators of the association between exercise frequency and exercise behavior.

Eating and Weight Disorders Studies on Anorexia, Bulimia and Obesity, 27 (7), 2801 Stenhouse Myers T. A.

Vraga E. K. Kotcher J. E. Beall & Maibach E. W. (2018). The potential role of actively open minded thinking in preventing motivated reasoning about controversial science.

Journal of Environmental Psychology Stehr, P., Luetke Lanfer, H., & Rossmann, C. (2021). Beliefs and motivation regarding physical activity among older

adults in Germany: results of a qualitative study.

International Journal of ualitative tudies on ealth and being, 16 (1), 1932025.

Strömbäck Andersson Västfjäll & Tinghög G. (2024).

Motivated reasoning fast and slow. Behavioural Public Policy Suhay & Erisen C. (2018). The role of anger in the biased assimilation of political information.

Political Psychology R. T. Chang H. L. & Shaw M. J. (2021). Motivating adherence to exercise plans through a personalized mobile health app enhanced action design research approach.

JMIR mHealth and uHealth e19941. Sylvester S. M. (2021). COVID 19 and motivated reasoning The influence of knowledge on COVID related policy and health behavior.

Social Science Quarterly Taber C. S. and Lodge M. 2006. Motivated skepticism in the evaluation of political beliefs American Journal of Political Science 50 Tappin B. M.

Pennycook & Rand D. G. (2020). Bayesian or biased? Analytic thinking and political belief

updating. Cognition , 204 , 104375.

Temmen C. D. & Crockett L. J. (2020). Relations of stress and drinking motives to young adult alcohol misuse Variations by gender.

Journal of Youth and Adolescence Thaler M. (2024). The fake news effect Experimentally identifying motivated reasoning using trust in news.

American Economic Journal Microeconomics Tudoran A. A.

Scholderer & Brunsø K. (2012). Regulatory focus efficacy and outcome expectations as drivers of motivation to consume healthy food products.

Appetite van Stekelenburg Schaap Veling & Buijzen M. (2020). Correcting misperceptions The causal role of motivation in corrective science communication about vaccine and food safety.

Science Communication van Velsen Broekhuis Jansen Kosterink & Op den Akker H. (2019). Tailoring persuasive electronic health strategies for older adults on the basis of personal motivation based survey study.

Journal of Medical Internet Research e11759. t Riet A. D.

Zimet G. D. De Bruijn G. J. Van den Putte Ruiter R. A. C. (2016).

Does perceived risk influence the effects of message framing? Revisiting the link between prospect theory and message framing.

Health Psychology Review Viator R. E. N. L. Rinaldo S. B. & Marquardt B. B. (2020). The mediating effect of reflective analytic

cognitive style on rational thought. Thinking & Reasoning , 26 (3) , 381 – 413.

Gollust S. E. Rothman A. J. Vogel R. I. M. C. & Nagler R. H. (2024). Effects of exposure to conflicting health information on topic specific information sharing and seeking intentions Health

Communication , 1 – 9.

Xiong & Liu X. (2022). Chinese residents' healthy eating intentions and behaviors Based on an extended health belief model International Journal f Environmental Research nd Public Health ... & Fang H. (2021). Willingness to accept COVID 19 vaccine among the elderly and the chronic disease population in China.

Human Vaccines & Immunotherapeutics Ward, L. A., Cain, O. L., Mullally, R. A., Holliday, K. S., Wernham, A. G., Baillie, P. D., & Greenfield, S. M. (2009).

Health beliefs about bottled water: qualitative study.

BMC Public Health, 9 Wilson T. D. & Maceviciute E. (2022).

Information misbehaviour odelling the motivations for the creation

acceptance and dissemination of misinformation. Journal of Documentation Wojtowicz & DeDeo S. (2020). From probability to consilience How explanatory values implement Bayesian

reasoning. Trends in Cognitive Sciences , 24 (12) , 981 – 993.

Greenbaum Cupid & Reed M. (2023). Health appeal appraisal and memory in older adults effects of goal and valence framing.

Aging Neuropsychology and Cognition P. (2024). Decoding algorithm fatigue The role of algorithmic literacy information cocoons and algorithmic opacity.

Technology in Society V. (2022). Personality traits and covid vaccine uptake.

International Journal of Social Science and Education Research Young D. G.

Maloney E. K. Bleakley Langbaum J. (2022). I feel it in my gut epistemic motivations political beliefs and misperceptions of COVID 19 and the 2020 US presidential election.

Journal of Social and Political Psychology Zhang X. A. (2021).

Understanding the cultural orientations of fear appeal variables cross cultural comparison of pandemic risk perceptions efficacy perceptions and behaviors.

Journal of Risk Research Zhang, X., & Zhou, S. (2019). Clicking health risk messages on social media: Moderated mediation paths through perceived threat, perceived efficacy, and fear arousal.

Health Communication, 34 (11), 1359 Zhang I. Y. & Liao J. M. (2021). Incorporating emotions into clinical decision making solutions.

Healthcare Zimbres T. M. R. A. Miller L. M. S. & Zhang J. (2022). Contradictory and complex health messages experimental test of different sources of uncertainty.

Communication Research Reports Factors and mechanisms underlying health related motivated reasoning LIU X LYU Xiaokang Department of Social Psychology, School of Sociology, Nankai University, Tianjin 300350, China

Abstract

ealth related motivated reasoning refers to the psychological process by which individuals selectively process health information to maintain or reinforce their existing health beliefs and behaviors. Current research primarily addresses the classification of health mot ivation, the influencing factors of health related motivated reasoning, and its underlying mechanisms.

Health motivation can be categorized based on dimensions such as time orientation, psychological traits, reasoning goals, regulatory strategies, and stag es of the information life cycle. This reasoning process is shaped by three major types of factors: personal factors (e.g., health beliefs, cognitive traits, discrete emotions), informational characteristics (e.g., information conflict, framing effects), nd socio cultural factors (e.g., social identity, cultural norms). Employing Bayesian models to dynamically examine how motivation modulates health information processing can provide insights into the integration of new evidence and the adjustment of indiv idual beliefs. Future research should aim to construct a comprehensive model of influencing factors, incorporate cognitive neuroscience methods to elucidate underlying mechanisms, and further refine strategies for health interventions eywords motivated reasoning motivation type, health beliefs information framework Bayesian inference