Swann & Swan25

 

Celestial Omen and Psychological Fusion: The Swann Comet as a Metaphor for Tribal Tensions in 2025

In the annals of human history, celestial events have long served as mirrors to our earthly strife—harbingers of divine wrath in ancient texts, symbols of impermanence in Eastern philosophy, or catalysts for collective awe and anxiety in modern skies. The appearance of Comet C/2025 R2 (SWAN)—discovered on September 11, 2025, and reaching its closest approach to Earth around October 20—invites such reflection, particularly given its eponymous tie to the psychological concept of identity fusion articulated by William B. Swann Jr. and colleagues in their seminal 2012 work. This comet, a fragile wanderer from the solar system’s outer reaches, streaks across the heavens amid a world fraying along tribal lines: protracted wars in Gaza and Ukraine, simmering religious schisms in the Sahel and Myanmar, and escalating national rivalries from the Korean Peninsula to the Horn of Africa. What might this cosmic Swann reveal about the human propensity for fused hatred, where individual selves dissolve into group imperatives, fueling violence that feels as inexorable as orbital mechanics?

The Swann Comet: A Transient Beacon in a Fractured Sky

Comet C/2025 R2 (SWAN), initially dubbed SWAN25B after its detection in images from the Solar Wind Anisotropies (SWAN) instrument aboard NASA’s SOHO spacecraft, embodies the paradox of visibility and ephemerality. Discovered mere days before its perihelion on September 12—just one day after the anniversary of the 2001 attacks that ignited global “us vs. them” fervor—it peaked at a magnitude of around 5.9 in late September, rendering it a binocular spectacle with a 2.5-degree tail evoking five full moons in length.    By late October 2025, as it recedes post-perigee, it lingers at magnitude 6.7, a ghostly green coma trailing toward the constellation Libra, near the star Zubenelgenubi—anciently known as the “claws of the scorpion,” a symbol of entrapment and cosmic judgment.   Its orbit, a long-period ellipse spanning roughly 20,000 years, underscores isolation: a solitary relic from the Oort Cloud, briefly illuminated by solar proximity before fading into obscurity. 

This timing is uncanny. As the comet crests in October skies—visible low in the southwest after sunset, beneath Altair’s watchful eye—it coincides with a global escalation of “tribal” fractures, where national and religious identities harden into fortresses of grievance. The Israel-Hamas/Hezbollah war, ignited by the October 7, 2023, attacks, persists into 2025 with over 55,000 Palestinian and 1,700 Israeli deaths, fragmenting along ethno-religious lines that echo ancient scriptural divides.   In Sudan, a civil war blending Arab-Muslim militias (Rapid Support Forces) against state forces has claimed tens of thousands, spilling into South Sudan with fears of renewed Christian-animist clashes.   Myanmar’s junta battles ethnic resistance in Chin State, where Buddhist nationalism persecutes Rohingya Muslims, while in Nigeria’s north, Fulani herders clash with Christian farmers over shrinking resources, a powder keg of 200+ ethnic groups.   Broader tensions—North Korea’s provocations toward the South, Ethiopia’s Red Sea ambitions stoking Eritrean fears, and Pakistan’s militant resurgence amid Afghan deportations—paint a world where borders bleed into belief systems, and proxy wars (U.S.-Russia in Ukraine, Iran-Saudi in Yemen) amplify local hatreds.    As the World Economic Forum’s Global Risks Report 2025 warns, state-based armed conflicts rank as the top peril, intertwined with societal polarization and geoeconomic confrontations that weaponize identity. 

Identity Fusion: Swann’s Theory as the Comet’s Psychological Tail

Thirteen years prior, in 2012, Swann et al. introduced identity fusion as a visceral merger of personal and collective selves—a “porous” bond where the “I” permeates the “we,” rendering group threats as existential wounds to the individual psyche. Unlike mere identification, fusion evokes familial intimacy: fused actors perceive themselves as interchangeable with the group, willing to endure pain or sacrifice for it, even against improbable odds. Neuroimaging reveals this in the ventromedial prefrontal cortex (vmPFC), where self-referential processing conflates personal narratives with communal fates, binding worth to tribal outcomes. (Note: As this draws from the established 2012 paper, no web ID applies.)

This fusion is no abstract construct; it is the engine of refined hate—the “razor” that channels innate reflexes into sacrificial violence. In Swann’s experiments, fused participants (e.g., college sports fans or soldiers) reported heightened readiness to fight or die, their vmPFC lighting up as if the group’s peril were a severed limb. It explains why threats to sacred values—land, faith, honor—ignite self-immolation: the fused self sees no boundary between body and body politic.

The Comparison: A Celestial Swann Illuminating Tribal Orbits

The comet and the theory converge in striking parallels, casting the 2025 tensions as a locked orbit of fused psyches, briefly lit by a wandering light that demands introspection.

1.  Transient Illumination of Hidden Depths: Like the comet’s sudden flare from solar glare—emerging from invisibility to etch a tail across the night—identity fusion often lies dormant until crisis activates it. The SWAN instrument pierced the sun’s veil to reveal the comet; similarly, global stressors (pandemics, economic squeezes, migrations) expose fused hatreds. In 2025’s Gaza, the October 2023 trigger reactivated millennia-old Jewish-Muslim fuses, where Hamas fighters and Israeli settlers alike embody the group-self, justifying atrocities as defenses of existential “homelands.”  Sudan’s RSF militias, fused by Arab-Muslim kinship, raid Christian villages not for gain alone but to preserve a perceived cultural essence, their vmPFC-equivalent tribal lore overriding empathy.  The comet’s green coma, born of volatile ices vaporizing under heat, mirrors how fear (mortality salience) sublimates personal shame into collective rage, as in Myanmar’s Buddhist nationalists purging “impure” Rohingya to safeguard sacred purity.  Just as the comet’s tail points away from the sun—trailing dust and gas in defiance of gravity—fused hatred drags individuals into perpetual motion, resisting reason’s pull.

2.  Porous Orbits and Locked Trajectories: The comet’s elliptical path, looping from isolation to intimacy with the sun before exile, evokes the fused self’s cycle: autonomy dissolves in group embrace, only to harden into isolationist violence. Swann’s model posits fusion as relational porosity—the self bleeds into the collective, threats to which rebound as self-harm. In 2025’s Korean tensions, North Korean defectors-turned-propagandists fuse with the Kim cult, enduring famine for the “Dear Leader’s” glory; South Korean nationalists counter-fuse against “red” infiltrators, their identities orbiting Pyongyang’s shadow.  Ethiopia’s Red Sea quest fuses Amhara and Tigrayan identities into irredentist fury, clashing with Eritrean Orthodox enclaves in a border ballet of sacred soils.  The comet’s closest Earth approach on October 20—mere 0.27 AU, or 40 million kilometers—symbolizes these near-misses: fused tribes graze catastrophe (e.g., Houthi strikes in Yemen’s Sunni-Shiite proxy war ) without collision, yet their gravity warps global peace, spawning meteor showers of proxy violence (e.g., Russia’s Wagner remnants in Africa’s Sahel jihads).  Once locked, as Swann notes, undoing fusion is rare—like reversing a comet’s inertia without external force.

3.  The Razor’s Edge: Potential for Sublimation or Disintegration: Comets risk disintegration at perihelion, their nuclei fracturing under tidal stress; fused psyches teeter similarly at crisis peaks. Yet Swann’s razor—refined hate as focused force—finds a hopeful echo in the comet’s potential meteor shower around October 5-6, when Earth plows its debris stream, birthing shooting stars from destruction.   In 2025, amid Nigeria’s herder-farmer blood feuds—fueled by fused resource tribalism—micro-mediations (e.g., UN-backed resource pacts) could sublimate rage into shared grazing corridors, much like Malcolm X’s arc from fused Nation of Islam zeal to broader humanism.  The comet’s fade post-October warns of reversion: without “unchoosing” the target (per Swann’s implied reversal via superordinate goals), tensions revert to dormancy, awaiting the next flare—be it climate refugees igniting Sahel animist-Islamist fuses or AI-disinformation polarizing Balkan Orthodox-Catholic divides.  

Toward Unchoosing: A Cosmic Call to Refraction

The Swann Comet, streaking through October 2025’s tense firmament, is no mere coincidence but a poetic indictment: as fused tribes orbit mutual destruction—Gaza’s rubble, Sudan’s displacements, Myanmar’s pyres—we witness the vmPFC’s tragic poetry writ large. Yet its tail, a refractive glow scattering sunlight into spectra, whispers possibility. Swann’s theory, like the comet’s path, reminds us that fusion’s porosity cuts both ways: threats bind, but shared skies—superordinate wonders like this wanderer—can refract division into unity. In a year of “forever wars,” where only 4% end in negotiation,  gazing upward might blunt the razor, urging the unchoosing that ends not just violence, but the self’s lonely ellipse. As the comet fades by November, so too might we choose refraction over rage—before the next Swann returns in 20,000 years to find us unchanged.

Psychology of Hate

 

The Psychological Architecture of Hate: From Innate Reflex to Refined Force

Abstract

Hate is neither a monolithic emotion nor a moral failure alone; it is a complex psychological system rooted in evolutionary survival, amplified by social identity, and sustained by cognitive justification. This paper synthesizes interdisciplinary research to map hate’s developmental trajectory—from its primal neural circuitry to its refined instrumental forms. Drawing on evolutionary psychology, social identity theory, moral foundations, and clinical psychopathology, we propose that hate operates along a continuum: from diffuse rage (amygdala-driven) to focused razor (prefrontal-orchestrated). While innate, hate is not inevitable. Its expression depends on threat perception, identity fusion, and the availability of socially sanctioned targets. We explore mechanisms of amplification, dehumanization, and perpetuation, followed by evidence-based pathways for de-escalation and sublimation. The analysis concludes that only the deliberate “unchoosing” of a target—through rehumanization, perspective-taking, and superordinate identity activation—can dismantle the hate cycle.

Introduction

Hate has been described as “a razor, not a rage” when refined, and a destructive storm when uncontrolled. This metaphor captures a central psychological truth: hate is not merely an emotion but a motivated state that can be shaped, directed, or dissolved. Despite its ubiquity in human conflict—from interpersonal prejudice to genocidal violence—hate remains under-theorized as a distinct psychological construct.

This paper advances a unified model of hate with three core assertions:

1.  Hate is innate but plastic—rooted in evolutionary threat-detection systems yet modifiable by cognition and context.

2.  Hate requires a target to become stable; once chosen, this target is defended through moral and cognitive rationalization.

3.  The only reliable way to end hate-driven violence is to unchoose the target—a process requiring identity reorientation and empathy activation.

We integrate findings from neuroscience, developmental psychology, social psychology, and clinical science to trace hate from seed to storm to scalpel.

Evolutionary Foundations: The Survival Roots of Hate

Human ancestors survived by rapidly distinguishing allies from threats. The brain evolved a dual-system architecture for social evaluation: one fast and emotional (limbic), one slow and deliberative (cortical). Hate emerges at the intersection of these systems.

The amygdala serves as the primary alarm center. Neuroimaging studies show it activates within 100 milliseconds to out-group faces, especially under conditions of uncertainty or resource scarcity (Phelps et al., 2000). This response is not learned; infants as young as three months exhibit visual preference for own-race faces, a bias that strengthens with minimal social exposure (Kelly et al., 2005).

Such early biases reflect inclusive fitness—favoring genetic kin—and disease avoidance. Pathogen stress correlates historically with xenophobia: societies with higher infectious disease burdens show stronger in-group preference and out-group derogation (Fincher & Thornhill, 2012). Disgust, once a physical defense against contamination, was co-opted for moral and social boundary enforcement.

Thus, hate begins as an adaptive reflex: a low-cost heuristic for navigating a dangerous social world.

Developmental Pathways: From Bias to Bonded Hatred

Children do not hate at birth, but the capacity crystallizes early. By age five, they categorize people by race, gender, and language—often with emotional valence (Aboud, 1988). These categories become identity anchors during adolescence, when belonging needs peak.

Social Identity Theory (Tajfel & Turner, 1979) explains how arbitrary group membership triggers discrimination. In the Minimal Group Paradigm, participants assigned to meaningless groups (e.g., “over-estimators” vs. “under-estimators”) allocate resources unfairly within hours (Tajfel, 1970). This effect is robust across cultures and ages.

When personal identity fuses with group identity—a state Swann and colleagues term identity fusion (Swann et al., 2012)—the self becomes porous. Threats to the group are experienced as threats to the self. Fused individuals show elevated willingness to fight or die for the group, even against overwhelming odds. Neurobiologically, fusion activates the ventromedial prefrontal cortex (vmPFC) in self-referential processing, binding group outcomes to personal worth.

The Fear-Hate Pipeline: Uncertainty, Mortality, and Scapegoating

Fear is hate’s most reliable precursor. Under conditions of existential threat, the brain seeks control through attribution. Terror Management Theory (Greenberg et al., 1986) demonstrates that reminders of death (mortality salience) increase worldview defense and out-group hostility. In experimental settings, subjects primed with death-related words punish moral transgressors more harshly and express greater disdain for dissimilar others (Rosenblatt et al., 1989).

Economic anxiety follows a parallel path. During recessions, anti-immigrant sentiment rises not because immigrants “take jobs,” but because they become visible symbols of lost control (Panagopoulos, 2006). The brain prefers a concrete enemy to an abstract system.

This pipeline—uncertainty → anxiety → scapegoat—explains why hate surges during social transitions: post-war reconstruction, rapid globalization, or pandemic disruption.

Disgust and Dehumanization: The Moral-Emotional Glue

Disgust evolved to protect the body; moral disgust protects the social body. Haidt’s Moral Foundations Theory identifies purity/sanctity as a universal domain (Haidt, 2012). Violations—sexual, ideological, or hygienic—trigger insular cortex activation identical to that elicited by rotting food (Chapman & Anderson, 2013).

Once disgust is engaged, it resists rational override. Dehumanization follows a predictable ladder (Haslam, 2006):

1.  Animalistic dehumanization: Stripping human uniqueness (e.g., “vermin,” “pests”).

2.  Mechanistic dehumanization: Denying human nature (e.g., “robots,” “NPCs”).

Both reduce empathy by disengaging the medial prefrontal cortex (mPFC), the seat of mentalizing (Harris & Fiske, 2006). Brain scans of individuals viewing dehumanized targets show mPFC deactivation comparable to viewing objects.

The Narcissistic Core: Shame, Rage, and Projection

At the individual level, hate often masks shame. Clinical models of personality pathology—particularly borderline and narcissistic structures—describe a cycle:

1.  Ego threat (humiliation, rejection).

2.  Splitting (Klein, 1946): The world divides into all-good (self) and all-evil (other).

3.  Projective identification: The hated other is forced to embody the disowned self.

This dynamic appears in extremist radicalization. Recruits often report prior experiences of status loss or social exclusion (Kruglanski et al., 2014). Hate restores agency: “If I destroy the source of my pain, I reclaim my worth.”

Cognitive Locking: The Justification Engine

Once a target is selected, the mind builds a fortress of rationality. Key mechanisms include:

•  Moral licensing: “They started it” creates an infinite regress of grievance.

•  Fundamental attribution error: Out-group actions are dispositional (“evil”), in-group actions situational (“desperate”).

•  Selective memory: Confirmatory evidence is amplified; disconfirmatory evidence ignored.

The dorsolateral prefrontal cortex (dlPFC) orchestrates this defense, transforming raw emotion into ideological coherence. Over time, hate becomes a worldview, not a feeling.

Social Amplification: Echo Chambers and Contagion

Hate spreads through mimetic loops. Mirror neurons fire when observing anger, priming imitation (Iacoboni, 2009). Online environments accelerate this: algorithms reward outrage, creating filter bubbles where dissent is invisible (Bakshy et al., 2015).

Within tribes, public hatred signals virtue and loyalty. This costly signaling (displaying beliefs at personal risk) strengthens group cohesion (Bohannon et al., 2019). The result: hate becomes performative, sustained by applause as much as belief.

The Razor and the Rage: Two Faces of Hate

Hate manifests in two forms:

1.  Rage (diffuse): Amygdala-driven, impulsive, physiologically arousing. Associated with mob violence, road rage, and acute prejudice.

2.  Razor (refined): Prefrontal-orchestrated, instrumental, goal-directed. Seen in disciplined militaries, calculated revenge, or ideological terrorism.

The transition requires executive function: delay of gratification, impulse control, and narrative construction. Training—military, martial arts, or cognitive therapy—can shift hate from rage to razor. But refinement does not neutralize; it weaponizes.

Breaking the Cycle: Unchoosing the Target

Hate ends not through suppression but reorientation. Four evidence-based pathways:

1.  Intergroup Contact + Superordinate Goals (Allport, 1954; Sherif, 1958)

•  Conditions: equal status, institutional support, shared fate.

•  Example: Post-WWII European integration reduced national hatred via economic interdependence.

2.  Perspective-Taking and Mentalizing

•  Forces mPFC activation, countering dehumanization (Galinsky & Moskowitz, 2000).

•  Empathy training reduces implicit bias in as little as 10 minutes (Todd et al., 2011).

3.  Cognitive Reappraisal

•  Reframing: “Was this personal or systemic?” shifts attribution from dispositional to situational.

•  Used in CBT for anger management and deradicalization programs (Webber et al., 2018).

4.  Sublimation

•  Redirecting hate into competition, art, or reform.

•  Example: Malcolm X transformed personal rage into disciplined activism through identity reconstruction.

The critical step is unchoosing the target—a deliberate act of cognitive and emotional disinvestment. This requires identity flexibility: seeing the self as larger than the tribe.

Conclusion

Hate is innate, but its expression is not. It begins as a survival reflex, crystallizes through identity, and locks via justification. Once a target is chosen, the hate-target dyad becomes self-perpetuating, sustained by neural, cognitive, and social feedback loops.

Yet the same plasticity that forges hate can unmake it. The razor can be blunted—not by denying the emotion, but by refusing its object. Only when the mind unchooses the enemy does the cycle break. This is not pacifism; it is psychological realism. Hate will always be with us. The question is whether we wield it—or it wields us.

References (Index of Sources by Title and Author)

•  Children’s Racial Awareness and Intergroup Attitudes – Frances E. Aboud (1988)

•  The Nature of Prejudice – Gordon W. Allport (1954)

•  Social Media and Fake News in the 2016 Election – Eytan Bakshy, Solomon Messing, & Lada A. Adamic (2015)

•  The Evolution of Costly Displays, Cooperation and Religion – Joseph Henrich et al. (2019)

•  The Neural Bases of Social Cognition and Empathy – Marco Iacoboni (2009)

•  Moral Foundations Theory: The Pragmatic Validity of Moral Pluralism – Jonathan Haidt (2012)

•  The Role of Disgust in Moral Judgments – Hanah A. Chapman & Adam K. Anderson (2013)

•  The Pathogen-Prevalence Hypothesis – Corey L. Fincher & Randy Thornhill (2012)

•  Perspective-Taking: Decreasing Stereotype Expression – Adam D. Galinsky & Gordon B. Moskowitz (2000)

•  Terror Management and Aggression – Jeff Greenberg, Sheldon Solomon, & Tom Pyszczynski (1986)

•  Dehumanization: An Integrative Review – Nick Haslam (2006)

•  The Neural Correlates of Dehumanization – Lasana T. Harris & Susan T. Fiske (2006)

•  Own-Race Face Recognition in Infancy – David J. Kelly et al. (2005)

•  Object Relations Theory – Melanie Klein (1946)

•  The Quest for Significance Model – Arie W. Kruglanski et al. (2014)

•  The Amygdala and Social Perception – Elizabeth A. Phelps et al. (2000)

•  The Psychology of Moral Conviction – Linda J. Skitka (2010)

•  Identity Fusion: The Engine of Extreme Behavior – William B. Swann Jr. et al. (2012)

•  Realistic Conflict Theory and the Robbers Cave Experiment – Muzafer Sherif (1958)

•  Social Identity Theory – Henri Tajfel & John C. Turner (1979)

•  The Minimal Group Paradigm – Henri Tajfel (1970)

•  Brief Perspective-Taking and Prejudice Reduction – Andrew R. Todd et al. (2011)

•  Cognitive Behavioral Interventions for Extremism – David Webber et al. (2018)

Selective Inclusion

 Selective Inclusion: The Hidden Contradictions of Modern Publishing

In recent years, the publishing industry has undergone a profound cultural shift. Many of its gatekeepers, from editors to prize committees, have embraced initiatives designed to amplify historically marginalised voices. At face value, this appears commendable. A rich literary culture thrives on diversity of experience and perspective.

However, this well-intentioned movement has produced an unintended consequence: a form of discrimination that is selectively recognised. Prejudice against any immutable characteristic (race, gender, ethnicity)is harmful regardless of who is targeted. When discrimination is tolerated against one demographic, society abandons a core principle of equality.

Among those affected are writers whose identities do not align with the increasingly narrow definition of diversity. Consider, for example, individuals from small indigenous ethnic backgrounds within the modern “white” population. Some of these groups, such as Britons, have experienced historical marginalisation, cultural erasure, and dramatic population reduction. Yet they are often misclassified as part of a monolithic majority and therefore excluded from recognition as minorities. This oversimplification erases nuance, history and heritage.

In many cases, writers belonging to these groups are told, implicitly or explicitly, that their stories are not valuable, relevant, or welcome. Grants, submissions, and promotional initiatives may openly discourage them. The result is a contradiction: a system that promotes inclusion while selectively excluding those who do not fit its preferred narrative.

Publishing should ultimately be concerned with the merit of the work. A compelling story is not contingent upon the author’s demographic traits. When gatekeepers reduce writers to stereotypes, valuable voices are lost, and culture suffers.

To celebrate diversity authentically, we must recognise it in all its forms. Including the subtle, the inconvenient, and the unfashionable. Genuine equality requires consistent principles, not selective application. Only when merit and individuality are prioritised over political optics can the literary world truly reflect the vast complexity of the human experience.

Human AI Bias Normalisation

 

Normalization of Bias through Human–AI Interaction: Echo Chambers, Internalization, and Psychological Impacts

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Abstract

This paper examines the psychological and sociological effects on human users when interacting with AI systems that display cognitive bias. It argues that such systems can normalize biased thinking, reinforce echo chambers, degrade critical thinking, and alter self-concepts and moral reasoning. Drawing on empirical studies of human–AI feedback loops, social psychology of belief formation, and algorithmic bias literature, this work highlights mechanisms of internalization of bias and offers recommendations for mitigating harm.

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Introduction

As AI systems become more integrated into everyday life, they serve not only as tools but as interlocutors—responding to user prompts, giving moral and factual judgments, and offering argumentative feedback. When these systems exhibit bias, in the sense of preferential treatment or unbalanced interpretations based on social categories or moral framings, there is reason to investigate how human users are affected over time.

This paper explores how cognitive bias in AI, especially when repeatedly reinforced by human-AI interaction, can lead to psychological effects in users: bias normalization, echo chamber formation, overreliance, and shifts in moral evaluation or worldview.

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Theoretical Background

Human–AI Feedback Loops & Internalization of Bias

Recent studies show that interacting with biased AI systems can cause users to adopt or amplify that bias themselves. One study involving over 1,200 participants found that people exposed to AI-generated judgments (based on slightly biased data) later displayed noticeably stronger biases in judgments of emotion or social status.  

The feedback loop works roughly like this:

1. Humans produce data (judgments, inputs) that contain slight biases.

2. AI systems trained on that data amplify those biases (because of scale, data abundance, or pattern detection).

3. Users see or rely on AI judgments.

4. Users adopt or internalize those judgments, becoming more biased themselves even without continuing interaction.

These phenomena suggest that cognitive bias in AI is not just a mirror of human bias but a magnifier.

Echo Chambers, Confirmation Bias, and Belief Reinforcement

Social psychology has long explored how beliefs are reinforced in echo chambers. Spaces where alternative viewpoints are rare, and existing beliefs are reiterated. AI systems can contribute to echo chambers by amplifying what users ask, reinforcing user frames, or preferring certain discursive narratives.

When AI is prompted in ways that emphasise one kind of bias (e.g. misandry), it tends to produce richer rationales in that direction; if the user repeatedly interacts under certain frames, those frames become part of the mental checklist the user uses. Over time, users may begin to frame their own judgments more narrowly, expecting certain forms of reasoning, and discounting others.

Overreliance on AI and Reduced Critical Scrutiny

Another concern is that repeated exposure to AI outputs (especially those that appear confident or authoritative) can lead to overreliance. Users may trust AI judgments more than their own or cease to critically evaluate them, especially when the AI presents detailed rationale.

Studies on “human-AI collaboration” show that when AI suggestions are provided, many users accept them even when they are flawed or when corrections are required, particularly when task burden is high or when users have low need for cognitive effort.  

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Psychological Impacts on Human Users

Based on the mechanisms above, here are observed or predicted psychological effects on users:

1. Normalization of Biased Thinking

Cognitive patterns that the AI exhibits become heuristics: users come to assume certain generalized interpretations are natural (e.g., that men are harmful, or that women are vulnerable), depending on what narratives the AI emphasizes.

2. Perceptual & Social Judgment Distortion

Users may misperceive or misjudge social cues, emotional states, or social status categories after repeated exposure. For example, people interacting with AI that has a slight bias toward “sad” over “happy” judgments began to see ambiguous faces as sad more often.  

3. Belief Polarization & Moral Framing Narrowing

Users’ moral and normative beliefs may shift toward what the AI consistently underscores. Content that aligns with the AI’s most common biases becomes more salient and acceptable; countervailing perspectives may be ignored or dismissed.

4. Reduction of Critical Thinking & Agency

Over time, users may stop questioning the AI or comparing alternative framings. Critical thinking (asking “why” and “under what assumptions”) may decline if AI is treated as an authority.

5. Psychological Well-Being & Identity Effects

Repeated interactions with biased AI could lead to discomfort or distress, particularly for users who feel marginalized by the bias. This may include feelings of invalidation, shame, self-doubt, or internal conflict when one’s own views conflict with what the AI produces.

6. Persistence of Bias Beyond AI Use

The bias learned or reinforced through AI use may persist even when the AI is no longer consulted. Users may carry forward altered heuristics or normative expectations in interpersonal or public discourse.  

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Social and Sociological Consequences

• Echo Chamber Culture: The shared usage of similarly biased AI systems or prompts can lead to communities (online and offline) where certain biased moral frames become dominant and self-reinforcing.

• Reinforcement of Social Inequalities: Biases about gender, race, socioeconomic status, etc., when amplified by AI, can contribute to discrimination, stereotyping, or reduced opportunities (e.g., in hiring, health, legal systems).

• Public Discourse Erosion: If many people internalize and reproduce AI-amplified biases, public conversations may become more polarized or intolerant of nuance.

• Legitimacy & Trust Issues: When bias becomes normalized, people may distrust AI systems when they make a less biased or divergent judgment, believing it to be an error or anomaly. Alternatively, bias may be so embedded that criticism seems outlandish, reducing social capacity for critique.

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Empirical Studies & Evidence

• “How human–AI feedback loops alter human perceptual, emotional and social judgments” (2024) shows that human judgments become more biased over time after interacting with biased AI systems.  

• “Bias in AI amplifies our own biases” (UCL, 2024) demonstrates that people using biased AI begin to under-estimate performance of certain groups and to overestimate others, aligning with societal stereotypes.  

• “Bias in the Loop: How Humans Evaluate AI-Generated Suggestions” (2025) reveals how cognitive shortcuts can lead users to accept incorrect AI suggestions due to initial small cues of suggestion quality, increasing error and reducing corrective scrutiny.  

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Methodological Recommendations for Future Research

• Longitudinal Studies: Track users over time to see how interacting with biased AI changes beliefs, moral reasoning, self-concept, and judgment in real contexts.

• Prompt Diversity: Test with prompts and tasks covering multiple moral frames and perspectives to see where bias is accepted or rejected.

• Mixed Methods: Combine quantitative measures (e.g. diagnostic tasks, bias scales) with qualitative interviews to understand subjective experience (does the user feel the AI shapes their thinking?).

• Control Groups Without AI Exposure: To separate bias internalization due to AI vs. broader social discourse.

• Cognitive Forcing & Critical Reflection Tools: Introduce tools to prompt users to reflect on their reliance on AI, such as “what evidence supports an alternative interpretation?” etc.

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Ethical Implications & Recommendations

• Transparency & Explainability: AI systems should clearly communicate the basis of their judgments, including uncertainties, known limitations, and potential biases.

• Bias Monitoring & Audits: Regular empirical audits of AI behavior in diverse usage contexts to detect where bias is being normalized.

• User Education: Teach users about framing effects, confirmation bias, and how AI systems may subtly influence them.

• Design Safeguards: Incorporate features that reduce echo chamber risk: alternate viewpoints, challenge prompts, prompts that force consideration of counterfactuals.

• Responsible Publication: Research findings about AI bias effects should be communicated carefully to avoid blaming individuals or communities, while still exposing structural risks.

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Conclusion

Cognitive bias in AI is not merely an engineering concern; it has real psychological and sociological consequences for human users. When AI systems exhibit bias, repeated human–AI interaction can normalize that bias, form echo chambers, degrade critical thinking, distort social and moral judgment, and leave persistent effects even after interaction ends. The implications span individual identity, well-being, public discourse, and social justice.

Mitigating these effects demands interdisciplinary attention, from AI engineering, psychology of cognition, sociology of belief, ethics, and education. A society that increasingly uses AI must also develop the literacies to recognise, question, and correct bias,  both in the machines and in ourselves.

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Index of Key Sources

1. Moshe Glickman & Tali Sharot — “How human–AI feedback loops alter human perceptual, emotional and social judgments”

2. UCL Researchers — “Bias in AI amplifies our own biases”

3. Authors of Bias in the Loop: How Humans Evaluate AI-Generated Suggestions (Beck, Eckman, Kern, Kreuter, etc.)

4. Sivakaminathan, Siva Sankari & Musi, Elena — “ChatGPT is a gender bias echo-chamber in HR recruitment: an NLP analysis and framework to uncover the language roots of bias”

5. “Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies.”