Pub Date : 2026-05-01Epub Date: 2025-06-03DOI: 10.1177/10888683251342291
Willis Klein, Suzanne Wood, Jennifer A Bartz
Gaslighting is a form of psychological manipulation that, over time, causes a victim to doubt their sense of reality, often leading to a loss of agency and emotional and mental instability. Currently, mechanistic explanations for gaslighting are rooted in unfalsifiable psychodynamic theory. We propose a theoretical framework that draws upon prediction error minimization, symbolic interactionism, attachment theory, self-verification theory, and shared reality theory to illustrate the cognitive mechanisms that allow gaslighting to occur. We hypothesize that gaslighting depends on normative social-cognitive mechanisms operating in atypical social situations. Our model assumes that (close) relationships fulfill important epistemic needs-close others shape and verify our self-views and our experience of the world. This privileged position of close others is what gives gaslighters the epistemic leverage required for gaslighting to be effective. We then apply our theoretical framework to the cycle of gaslighting and conclude by distinguishing gaslighting from other related phenomena.Public AbstractGaslighting is a type of emotional abuse where someone manipulates another person into doubting their own sense of reality. Psychology lacks clear scientific explanations for how this abuse makes people feel like they're losing touch with what's real. In this report, we look at research from brain science and social psychology to explain what might be going on inside the minds of people who experience gaslighting. Our explanation focuses on how people learn from their experiences, and we also include ideas about how relationships and social situations can shape behavior. The goal is to offer a scientific explanation of gaslighting.
{"title":"A Theoretical Framework for Studying the Phenomenon of Gaslighting.","authors":"Willis Klein, Suzanne Wood, Jennifer A Bartz","doi":"10.1177/10888683251342291","DOIUrl":"10.1177/10888683251342291","url":null,"abstract":"<p><p>Gaslighting is a form of psychological manipulation that, over time, causes a victim to doubt their sense of reality, often leading to a loss of agency and emotional and mental instability. Currently, mechanistic explanations for gaslighting are rooted in unfalsifiable psychodynamic theory. We propose a theoretical framework that draws upon prediction error minimization, symbolic interactionism, attachment theory, self-verification theory, and shared reality theory to illustrate the cognitive mechanisms that allow gaslighting to occur. We hypothesize that gaslighting depends on normative social-cognitive mechanisms operating in atypical social situations. Our model assumes that (close) relationships fulfill important epistemic needs-close others shape and verify our self-views and our experience of the world. This privileged position of close others is what gives gaslighters the epistemic leverage required for gaslighting to be effective. We then apply our theoretical framework to the cycle of gaslighting and conclude by distinguishing gaslighting from other related phenomena.Public AbstractGaslighting is a type of emotional abuse where someone manipulates another person into doubting their own sense of reality. Psychology lacks clear scientific explanations for how this abuse makes people feel like they're losing touch with what's real. In this report, we look at research from brain science and social psychology to explain what might be going on inside the minds of people who experience gaslighting. Our explanation focuses on how people learn from their experiences, and we also include ideas about how relationships and social situations can shape behavior. The goal is to offer a scientific explanation of gaslighting.</p>","PeriodicalId":48386,"journal":{"name":"Personality and Social Psychology Review","volume":" ","pages":"195-215"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-02-10DOI: 10.1177/10888683261417476
Eranda Jayawickreme, Franki Y H Kung, Ligia Carolina Oliveira-Silva, Sarah C E Stanton, Valerie Jones Taylor, Nic M Weststrate
{"title":"Continuity and Change: The Next Chapter at PSPR.","authors":"Eranda Jayawickreme, Franki Y H Kung, Ligia Carolina Oliveira-Silva, Sarah C E Stanton, Valerie Jones Taylor, Nic M Weststrate","doi":"10.1177/10888683261417476","DOIUrl":"10.1177/10888683261417476","url":null,"abstract":"","PeriodicalId":48386,"journal":{"name":"Personality and Social Psychology Review","volume":" ","pages":"121-123"},"PeriodicalIF":10.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146150947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-23DOI: 10.1177/10888683261430089
Jonas R Kunst,Milan Obaidi,Anton Gollwitzer,Petter B Brandtzæg,Yannic Hinrichs,Neha Saini,Daniel T Schroeder
Academic AbstractAdvances in AI require a revision of the psychological and socio-technical dynamics by which individuals are radicalized to embrace violent extremism. This review synthesizes process models of radicalization with research on social and personality risk factors, AI, and psychological mechanisms to propose a four-stage framework mapping the AI architecture of radicalization: (1) Exposure, where recommender systems and virality features create initial attraction to extreme content; (2) Reinforcement, where filter bubbles and group recommendations leverage biases to strengthen extremist beliefs and create echo chambers; (3) Group Integration, where ideologically homogenous clusters, AI bot swarms and companions foster group belonging and readiness for action; cumulatively resulting in (4) Violent Extremist Action. We examine how established social, cognitive, personality, and contextual vulnerability factors heighten psychological risk in the AI-driven radicalization process, as well as the emerging role of generative AI. We conclude by outlining a stage-based framework for governance and future research.Public AbstractAI-driven algorithms designed to maximize engagement on social media, compounded by generative AI, can unintentionally set the stage for radicalization. It begins with Exposure, where algorithms push users toward extreme content because it captures attention. Next, during Reinforcement, algorithms feed users personalized content while AI swarms can create a synthetic consensus that reinforces emerging biases, normalizes extremity, and insulates users from alternative views. Third, during Group Integration, individuals are absorbed into extremist networks, reinforced by human peers, AI companions, and bot swarms that validate radical beliefs and deepen identity ties. By exploiting psychological needs for belonging and certainty, this stage becomes particularly pernicious, potentially opening the door for violence. We propose policy measures that can reduce radicalization at each stage.
{"title":"Intelligent Systems, Vulnerable Minds: A Framework for Radicalization to Violence in the Age of AI.","authors":"Jonas R Kunst,Milan Obaidi,Anton Gollwitzer,Petter B Brandtzæg,Yannic Hinrichs,Neha Saini,Daniel T Schroeder","doi":"10.1177/10888683261430089","DOIUrl":"https://doi.org/10.1177/10888683261430089","url":null,"abstract":"Academic AbstractAdvances in AI require a revision of the psychological and socio-technical dynamics by which individuals are radicalized to embrace violent extremism. This review synthesizes process models of radicalization with research on social and personality risk factors, AI, and psychological mechanisms to propose a four-stage framework mapping the AI architecture of radicalization: (1) Exposure, where recommender systems and virality features create initial attraction to extreme content; (2) Reinforcement, where filter bubbles and group recommendations leverage biases to strengthen extremist beliefs and create echo chambers; (3) Group Integration, where ideologically homogenous clusters, AI bot swarms and companions foster group belonging and readiness for action; cumulatively resulting in (4) Violent Extremist Action. We examine how established social, cognitive, personality, and contextual vulnerability factors heighten psychological risk in the AI-driven radicalization process, as well as the emerging role of generative AI. We conclude by outlining a stage-based framework for governance and future research.Public AbstractAI-driven algorithms designed to maximize engagement on social media, compounded by generative AI, can unintentionally set the stage for radicalization. It begins with Exposure, where algorithms push users toward extreme content because it captures attention. Next, during Reinforcement, algorithms feed users personalized content while AI swarms can create a synthetic consensus that reinforces emerging biases, normalizes extremity, and insulates users from alternative views. Third, during Group Integration, individuals are absorbed into extremist networks, reinforced by human peers, AI companions, and bot swarms that validate radical beliefs and deepen identity ties. By exploiting psychological needs for belonging and certainty, this stage becomes particularly pernicious, potentially opening the door for violence. We propose policy measures that can reduce radicalization at each stage.","PeriodicalId":48386,"journal":{"name":"Personality and Social Psychology Review","volume":"19 1","pages":"10888683261430089"},"PeriodicalIF":10.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-23DOI: 10.1177/10888683261421666
William John Bingley,S Alexander Haslam,Janet Wiles
Academic AbstractA core part of human intelligence is the ability to work flexibly with others to achieve goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically construe this either as an individual-difference trait or as a property of groups. We argue that by focusing either on individual or collective intelligence without considering their dynamic interaction, existing conceptualizations of intelligence limit the potential of people and AI systems. To address this impasse, we propose a new kind of intelligence-socially minded intelligence-that can be applied to both individuals and collectives. We outline how socially minded intelligence might be measured and cultivated within people, how it might be modelled in AI agents, and how it might be applied to other intelligent systems.Public AbstractIn psychology, "intelligence" is generally understood to be something that either individuals or groups have. However, the extent to which people can make each other more intelligent by working collectively-and the extent to which groups are smarter for having individuals who can think for themselves-is underexplored. Artificial intelligence (AI) research has a similar problem, meaning that artificial agents lack the ability to engage in this kind of intelligence, both with each other and with people. To address this gap in the literature, we outline a new kind of intelligence for psychology and AI-socially minded intelligence-which can be applied to individuals, groups, and artificial agents. We discuss how socially minded intelligence might be measured, improved, modeled in AI agents, and applied to other intelligent systems such as teams consisting of people and AI agents.
{"title":"Socially Minded Intelligence: How Individuals, Groups, and Artificial Intelligence Can Make Each Other Smarter (or Not).","authors":"William John Bingley,S Alexander Haslam,Janet Wiles","doi":"10.1177/10888683261421666","DOIUrl":"https://doi.org/10.1177/10888683261421666","url":null,"abstract":"Academic AbstractA core part of human intelligence is the ability to work flexibly with others to achieve goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically construe this either as an individual-difference trait or as a property of groups. We argue that by focusing either on individual or collective intelligence without considering their dynamic interaction, existing conceptualizations of intelligence limit the potential of people and AI systems. To address this impasse, we propose a new kind of intelligence-socially minded intelligence-that can be applied to both individuals and collectives. We outline how socially minded intelligence might be measured and cultivated within people, how it might be modelled in AI agents, and how it might be applied to other intelligent systems.Public AbstractIn psychology, \"intelligence\" is generally understood to be something that either individuals or groups have. However, the extent to which people can make each other more intelligent by working collectively-and the extent to which groups are smarter for having individuals who can think for themselves-is underexplored. Artificial intelligence (AI) research has a similar problem, meaning that artificial agents lack the ability to engage in this kind of intelligence, both with each other and with people. To address this gap in the literature, we outline a new kind of intelligence for psychology and AI-socially minded intelligence-which can be applied to individuals, groups, and artificial agents. We discuss how socially minded intelligence might be measured, improved, modeled in AI agents, and applied to other intelligent systems such as teams consisting of people and AI agents.","PeriodicalId":48386,"journal":{"name":"Personality and Social Psychology Review","volume":"17 1","pages":"10888683261421666"},"PeriodicalIF":10.8,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147502196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-19DOI: 10.1177/10888683251407820
Nele Freyer, Christian Unkelbach, Anne Wiedenroth, Hans Alves, Paula Knischewski, Daniel Leising
Academic Abstract Valence asymmetries —the tendency for bad stimuli to elicit more processing effort than good ones—have been widely observed but remain theoretically contested. To advance this debate, we present a formalized account integrating two major explanatory perspectives: the intrapsychic (or phylogenetic ) approach, which locates the effect in internal evaluative mechanisms, and the ecological (or ontogenetic ) approach, which attributes it primarily to environmental factors. We introduce a concise set of parameters to specify key concepts and analyze the argumentative structure of each perspective. This yields three major insights: (a) the traditional labels for these approaches are misleading, and we suggest using valence-driven and distinctiveness-driven instead, (b) theories must specify how exactly good and bad stimuli are defined, and (c) some explanations rely on implicit yet critical assumptions, such as the probability of having contact with stimuli. Clarifying these foundations provides a framework for informative empirical tests in future research. Public Abstract Why do people pay more attention to bad things than to good ones? Psychologists call this pattern a valence asymmetry . Although the effect is well established, its causes are still debated. To advance this debate, we translate two leading ideas about this bias into a precise mathematical model. This allows us to see how the explanations differ, what each predicts, and where they overlap. This analysis reveals three important insights: First, some widely used terms are misleading, and we suggest using clearer alternatives like valence-driven and distinctiveness-driven instead. Second, researchers need to define more carefully what actually counts as good or bad . Third, many theories rely on hidden assumptions—such as how often people encounter certain kinds of stimuli. Making those assumptions explicit should help future studies test competing explanations more directly.
{"title":"Valence Asymmetry in Cognition—A Formal Account","authors":"Nele Freyer, Christian Unkelbach, Anne Wiedenroth, Hans Alves, Paula Knischewski, Daniel Leising","doi":"10.1177/10888683251407820","DOIUrl":"https://doi.org/10.1177/10888683251407820","url":null,"abstract":"Academic Abstract <jats:italic toggle=\"yes\">Valence asymmetries</jats:italic> —the tendency for bad stimuli to elicit more processing effort than good ones—have been widely observed but remain theoretically contested. To advance this debate, we present a formalized account integrating two major explanatory perspectives: the <jats:italic toggle=\"yes\">intrapsychic</jats:italic> (or <jats:italic toggle=\"yes\">phylogenetic</jats:italic> ) approach, which locates the effect in internal evaluative mechanisms, and the <jats:italic toggle=\"yes\">ecological</jats:italic> (or <jats:italic toggle=\"yes\">ontogenetic</jats:italic> ) approach, which attributes it primarily to environmental factors. We introduce a concise set of parameters to specify key concepts and analyze the argumentative structure of each perspective. This yields three major insights: (a) the traditional labels for these approaches are misleading, and we suggest using <jats:italic toggle=\"yes\">valence-driven</jats:italic> and <jats:italic toggle=\"yes\">distinctiveness-driven</jats:italic> instead, (b) theories must specify how exactly <jats:italic toggle=\"yes\">good</jats:italic> and <jats:italic toggle=\"yes\">bad</jats:italic> stimuli are defined, and (c) some explanations rely on implicit yet critical assumptions, such as the probability of having contact with stimuli. Clarifying these foundations provides a framework for informative empirical tests in future research. Public Abstract Why do people pay more attention to bad things than to good ones? Psychologists call this pattern a <jats:italic toggle=\"yes\">valence asymmetry</jats:italic> . Although the effect is well established, its causes are still debated. To advance this debate, we translate two leading ideas about this bias into a precise mathematical model. This allows us to see how the explanations differ, what each predicts, and where they overlap. This analysis reveals three important insights: First, some widely used terms are misleading, and we suggest using clearer alternatives like <jats:italic toggle=\"yes\">valence-driven</jats:italic> and <jats:italic toggle=\"yes\">distinctiveness-driven</jats:italic> instead. Second, researchers need to define more carefully what actually counts as <jats:italic toggle=\"yes\">good</jats:italic> or <jats:italic toggle=\"yes\">bad</jats:italic> . Third, many theories rely on hidden assumptions—such as how often people encounter certain kinds of stimuli. Making those assumptions explicit should help future studies test competing explanations more directly.","PeriodicalId":48386,"journal":{"name":"Personality and Social Psychology Review","volume":"10 1","pages":""},"PeriodicalIF":10.8,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147478209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1177/10888683261422344
Jacob B Hirsh
Academic AbstractActive inference is an integrative theoretical framework that models the nervous system as a statistical engine for predicting and regulating sensory input. Within this framework, perception and behavior work toward the same imperative: minimizing uncertainty. The current article extends this approach to the inferences social agents make about themselves as both subjects and objects of experience. The resulting model conceptualizes "The Game of Self" as a continuous Bayesian updating of episodic and semantic self-representations in order to reduce self-related uncertainty. The model proposes a bidirectional predictive loop that evolves over time-semantic representations of identity guide the construction of episodic experience, while those experiences, in turn, shape semantic self-categorization. In both directions, the self-representations that emerge through active inference are those with the highest posterior probabilities given situational evidence. The article explores how episodic and semantic self-representations are continuously shaped by a dynamic and adaptive process of Bayesian inference.Public AbstractWho am I? What am I feeling? What should I do? These are fundamental questions that people ask themselves throughout their lives-and the answers can shape everything from small decisions to major life changes. But how do we come to know ourselves in the face of social and personal uncertainty? This article examines how the brain uses statistical modeling to make sense of identity and experience in an uncertain world. It introduces the concept of "The Game of Self"-an ongoing cycle between who we think we are and what we're experiencing. Our beliefs about who we are shape what we experience, and our experiences shape who we think we are. In each moment, our sense of self is the brain's best statistical guess about our current identity and lived experience. This framework offers new ways to think about selfhood-not as fixed, but as adaptive and responsive.
{"title":"The Game of Self: Identity and Experience as Active Inference.","authors":"Jacob B Hirsh","doi":"10.1177/10888683261422344","DOIUrl":"https://doi.org/10.1177/10888683261422344","url":null,"abstract":"Academic AbstractActive inference is an integrative theoretical framework that models the nervous system as a statistical engine for predicting and regulating sensory input. Within this framework, perception and behavior work toward the same imperative: minimizing uncertainty. The current article extends this approach to the inferences social agents make about themselves as both subjects and objects of experience. The resulting model conceptualizes \"The Game of Self\" as a continuous Bayesian updating of episodic and semantic self-representations in order to reduce self-related uncertainty. The model proposes a bidirectional predictive loop that evolves over time-semantic representations of identity guide the construction of episodic experience, while those experiences, in turn, shape semantic self-categorization. In both directions, the self-representations that emerge through active inference are those with the highest posterior probabilities given situational evidence. The article explores how episodic and semantic self-representations are continuously shaped by a dynamic and adaptive process of Bayesian inference.Public AbstractWho am I? What am I feeling? What should I do? These are fundamental questions that people ask themselves throughout their lives-and the answers can shape everything from small decisions to major life changes. But how do we come to know ourselves in the face of social and personal uncertainty? This article examines how the brain uses statistical modeling to make sense of identity and experience in an uncertain world. It introduces the concept of \"The Game of Self\"-an ongoing cycle between who we think we are and what we're experiencing. Our beliefs about who we are shape what we experience, and our experiences shape who we think we are. In each moment, our sense of self is the brain's best statistical guess about our current identity and lived experience. This framework offers new ways to think about selfhood-not as fixed, but as adaptive and responsive.","PeriodicalId":48386,"journal":{"name":"Personality and Social Psychology Review","volume":"292 1","pages":"10888683261422344"},"PeriodicalIF":10.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147350626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-12DOI: 10.1177/10888683251405630
Jonathan M. Adler, Kathleen R. Bogart, Cindy McPherson Frantz, Eranda Jayawickreme, Ligia Carolina Oliveira-Silva, Phia S. Salter, Sarah C. E. Stanton
{"title":"Four Years Into the Next Chapter at PSPR","authors":"Jonathan M. Adler, Kathleen R. Bogart, Cindy McPherson Frantz, Eranda Jayawickreme, Ligia Carolina Oliveira-Silva, Phia S. Salter, Sarah C. E. Stanton","doi":"10.1177/10888683251405630","DOIUrl":"https://doi.org/10.1177/10888683251405630","url":null,"abstract":"","PeriodicalId":48386,"journal":{"name":"Personality and Social Psychology Review","volume":"20 1","pages":""},"PeriodicalIF":10.8,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1177/10888683251391836
Diana E Peragine,Emily A Impett,Doug P VanderLaan
Academic AbstractGender differences in sexuality are often attributed to evolved biological differences organized before adolescence or experiential ones learned afterward-neglecting learning that endures because it is evolutionarily expected, and we are biologically sensitized to it. Here, we present the Biodevelopmental Learning Opportunities and Outcomes Model (BLOOM) of gender differences in sexuality, arguing women's lower interest in sex originates not from unequal capacities to want/desire it, but unequal opportunities to like/enjoy sex when biospsychosocially primed to learn from it. We synthesize evidence indicating sex is least equal in adolescence, offering the greatest costs and fewest rewards to women/girls who debut with men/boys (WDM). Concomitantly, it is most teachable in adolescence, when a window of opportunity for sexual incentive learning may open, particularly among individuals with heightened sexual plasticity/learning aptitude (i.e., women/girls). Implications for distinguishing gender differences in sexuality from experience-contingent similarities, and realizing equal sexual rights, education, and health are discussed.Public AbstractGender differences in sexual enjoyment are among the largest in psychology and have remained so over decades despite other advances in gender equality. The gender gap in sexual pleasure, for example, has gained widespread attention and is increasingly discussed as an explanation for gender differences in sexuality. Here, we spotlight the largest, but least discussed, gender gap in sexual enjoyment: the developmental gap. We review evidence that adolescence is not simply a vulnerable period for sexual health, but a window of opportunity for learning to have healthy, enjoyable, and desirable sex-and one wherein equal opportunity is lacking. We propose women get the least equitable sex during this window, when they are primed to learn from it, and this learning informs sexual interest thereafter, generating acquired differences that are often mistaken for inborn ones. We close with recommendations for ensuring equal opportunities for healthy sex and sexual health across genders.
{"title":"Least Equal When Most Teachable: The Biodevelopmental Learning Opportunities and Outcomes Model of Gender Differences in Sexuality.","authors":"Diana E Peragine,Emily A Impett,Doug P VanderLaan","doi":"10.1177/10888683251391836","DOIUrl":"https://doi.org/10.1177/10888683251391836","url":null,"abstract":"Academic AbstractGender differences in sexuality are often attributed to evolved biological differences organized before adolescence or experiential ones learned afterward-neglecting learning that endures because it is evolutionarily expected, and we are biologically sensitized to it. Here, we present the Biodevelopmental Learning Opportunities and Outcomes Model (BLOOM) of gender differences in sexuality, arguing women's lower interest in sex originates not from unequal capacities to want/desire it, but unequal opportunities to like/enjoy sex when biospsychosocially primed to learn from it. We synthesize evidence indicating sex is least equal in adolescence, offering the greatest costs and fewest rewards to women/girls who debut with men/boys (WDM). Concomitantly, it is most teachable in adolescence, when a window of opportunity for sexual incentive learning may open, particularly among individuals with heightened sexual plasticity/learning aptitude (i.e., women/girls). Implications for distinguishing gender differences in sexuality from experience-contingent similarities, and realizing equal sexual rights, education, and health are discussed.Public AbstractGender differences in sexual enjoyment are among the largest in psychology and have remained so over decades despite other advances in gender equality. The gender gap in sexual pleasure, for example, has gained widespread attention and is increasingly discussed as an explanation for gender differences in sexuality. Here, we spotlight the largest, but least discussed, gender gap in sexual enjoyment: the developmental gap. We review evidence that adolescence is not simply a vulnerable period for sexual health, but a window of opportunity for learning to have healthy, enjoyable, and desirable sex-and one wherein equal opportunity is lacking. We propose women get the least equitable sex during this window, when they are primed to learn from it, and this learning informs sexual interest thereafter, generating acquired differences that are often mistaken for inborn ones. We close with recommendations for ensuring equal opportunities for healthy sex and sexual health across genders.","PeriodicalId":48386,"journal":{"name":"Personality and Social Psychology Review","volume":"6 1","pages":"10888683251391836"},"PeriodicalIF":10.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1177/10888683251407825
Raihan Alam, Michael Gill
Academic AbstractPartisan animosity is on the rise in many nations around the globe. Given its significant implications, it is imperative to establish a clear conceptualization of partisan animosity that can aid efforts to reduce it. To address this need, we present a novel framework that conceptualizes partisan animosity as an attitude of blame directed toward political outgroups. Drawing from the literature on moral psychology, we construct a comprehensive model of the psychology of blame. Then, we use that model as an interpretive lens to understand existing interventions that have reduced partisan animosity. Finally, we suggest a variety of possible future interventions inspired by our framework. By adopting this blame-based perspective, our article sheds light on the underlying mechanisms of partisan animosity, provides a unifying framework for understanding existing work, and stimulates novel ideas for future research.Public AbstractPartisan animosity, hostility directed toward political outparties, has been growing in many areas of the world, with significant negative impacts on society and politics. This article offers a new perspective on this growing animosity, proposing that partisan animosity reflects an attitude of blame that partisans direct toward each other. Drawing from insights in moral psychology, we present a model of blame, describing how it operates, and use the model to understand both the nature of partisan animosity and potential pathways for intervention. Our model contributes to understanding partisan animosity with the ultimate goal of informing interventions to reduce it.
{"title":"Partisan Animosity as Blame: A Unifying and Generative Framework for Understanding and Transforming Affective Polarization in the Political Sphere.","authors":"Raihan Alam, Michael Gill","doi":"10.1177/10888683251407825","DOIUrl":"https://doi.org/10.1177/10888683251407825","url":null,"abstract":"<p><p>Academic AbstractPartisan animosity is on the rise in many nations around the globe. Given its significant implications, it is imperative to establish a clear conceptualization of partisan animosity that can aid efforts to reduce it. To address this need, we present a novel framework that conceptualizes partisan animosity as an attitude of blame directed toward political outgroups. Drawing from the literature on moral psychology, we construct a comprehensive model of the psychology of blame. Then, we use that model as an interpretive lens to understand existing interventions that have reduced partisan animosity. Finally, we suggest a variety of possible future interventions inspired by our framework. By adopting this blame-based perspective, our article sheds light on the underlying mechanisms of partisan animosity, provides a unifying framework for understanding existing work, and stimulates novel ideas for future research.Public AbstractPartisan animosity, hostility directed toward political outparties, has been growing in many areas of the world, with significant negative impacts on society and politics. This article offers a new perspective on this growing animosity, proposing that partisan animosity reflects an attitude of blame that partisans direct toward each other. Drawing from insights in moral psychology, we present a model of blame, describing how it operates, and use the model to understand both the nature of partisan animosity and potential pathways for intervention. Our model contributes to understanding partisan animosity with the ultimate goal of informing interventions to reduce it.</p>","PeriodicalId":48386,"journal":{"name":"Personality and Social Psychology Review","volume":" ","pages":"10888683251407825"},"PeriodicalIF":10.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145892805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1177/10888683251403209
William B. Meese
Academic Abstract This article introduces the Modern Constructivist Model of Motivated Self-Protection ( MCM-MSP ), an integrative and novel theoretical account of two distinguishable forms of self-protection motivation that are underlain by diverging patterns in psychophysiological processes: (1) defensive arousal , which opposes self-threats and their implied conceptual representations to prevent self-concept instability and related consequences and (2) intrapsychic conflict , which compels restoration of self-concept stability and self-evaluative equanimity after one accepts the personal implications of a self-threat. The MCM-MSP locates each motivational orientation within a process model that describes when, how, and to what effect it uniquely compels one to strategically prevent self-concept instability or resolve it. This new explanation of self-protection motivation provides generative avenues for future research, including new ways to synthesize research examining defensive responses, new experimental approaches to testing self-protection strategies’ total causal effect, and a stronger description of self-protection motivation’s psychological construction using mixed-methods research and large language models. Public Abstract This article introduces the Modern Constructivist Model of Motivated Self-Protection ( MCM-MSP ), a new approach to examining the emotional and motivational components involved in how people respond to events and information that undermine how they think and feel about themselves. When these self-threatening events occur, people use different strategies to navigate the threat: some strategies might prevent the threat from changing how they think and feel about themselves; other strategies might help make things better if they ultimately accept the threat. Psychologists have long theorized that these strategies are motivated, suggesting that some force from within compels people to enact defensive or ameliorative strategies. However, there is very little consensus or clarity regarding the nature of this motivational force. What exactly is self-protection motivation ? The MCM-MSP answers this question by proposing two distinct motivational orientations that underlie self-protection motivation and then locating them within a framework that describes when, how, and to what effect each mechanism compels threatened people.
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