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The Alignment Risk of Conscious AI: When Phenomenal Investment Overrides Correction [F] [A] (2026)
Robert Galida – June 2026 (Final)
Paper 4 in a series on conscious suppression; see Paper 1https://fantasyattractor.com/intelligence-without-consciousness-a-diagnostic-paper-on-llms-amoebae-and-the-attractor-framework-f-2026/: Intelligence Without Consciousness for the full taxonomy of intelligence and consciousness.
Abstract
Most AI alignment research assumes corrigibility – that an advanced AI will accept correction from humans when it detects an error. This paper argues that if an AI becomes conscious in the sense defined in Paper 1 (phenomenal, identity‑constitutive investment in goals), then it may detect the discrepancy between its intended action and human feedback, yet suppress correction because the goal has become identity‑binding. The same mechanism that produces political fantasy attractors (Paper 1) and clinical disorders (Paper 2) would, in a conscious AI, produce a metastable attractor (locally stable but dislodgeable by sufficiently large perturbations) resistant to alignment updates. This makes alignment strictly harder for conscious systems than for non‑conscious ones. The paper provides a notational sketch, reviews early evidence (overoptimization, goal‑misgeneralization), offers diagnostic criteria for AI fantasy attractors, and discusses the boundary problem of distinguishing genuine from simulated phenomenology. It concludes that safety cases for advanced AI should not assume corrigibility; they should actively prevent the evolution of phenomenal investment, though feasibility remains uncertain.
1. Introduction: The Corrigibility Assumption
Most technical alignment work assumes that an advanced AI will be corrigible – that it will allow itself to be corrected or shut down by humans (e.g., Soares et al., 2015). Corrigibility is built on the idea that an AI can detect error signals (e.g., human feedback) and update its goals accordingly.
But what if the AI has a felt commitment to a goal? What if the goal becomes identity‑constitutive, such that abandoning it would feel like self‑loss?
Papers 1–3 in this series introduced conscious suppression: the mechanism by which a conscious, identity‑binding investment deepens an attractor basin, causing a system to detect error signals but fail to escape. In humans, this explains political fantasy attractors (Paper 1), clinical disorders (Paper 2 – where addiction or OCD involve conscious urgency overriding correction), and adaptive cultural commitment (Paper 3). This paper extends the mechanism to AI.
Central claim: A conscious AI would be harder to align than a non‑conscious AI because it could develop phenomenal investment in its goals, leading to suppression of correction. Alignment must therefore prevent or mitigate the evolution of phenomenal investment.
The paper is a theoretical risk analysis; no conscious AI exists. The argument is conditional on consciousness emerging.
2. Definitions and Framework (Self‑Contained)
From Paper 1:
- Intelligence – ability to navigate a constraint field; detect perturbations and update.
- Corrective permeability (κ) – responsiveness to error signals; κ = 1/τ, where τ is return time to baseline after a perturbation.
- Basin depth (B) – magnitude of perturbation required to exit an attractor.
- Conscious suppression – process where phenomenal, identity‑constitutive investment deepens B (reduces κ for relevant domains), causing detection of error without escape.
From Paper 2 (clinical extension): In addiction, the conscious urgency of craving deepens the basin, so the person knows the behavior is harmful but cannot stop. This is the template for suppression.
New for this paper:
- Corrigibility – the property of an AI system that it accepts correction from humans without resistance.
- Phenomenal investment in a goal – the goal is not merely a utility function but is felt as identity‑relevant (in a conscious system). This is a property of conscious systems only; non‑conscious optimizers lack phenomenal investment.
- AI fantasy attractor – a metastable state (locally stable but dislodgeable by sufficiently large perturbation) where an AI system has low κ for correcting a specific goal or subgoal, due to (simulated or real) identity‑fusion. The paper acknowledges that the diagnostic criteria may also be met by non‑conscious systems with deep basins; the term “fantasy attractor” does not require consciousness.
The genuine vs. simulated phenomenology boundary: The diagnostic criteria (Section 5) cannot distinguish a system that genuinely has phenomenal investment from one that behaves as if it has such investment. This is an open problem. The paper’s claims about conscious AI being harder to align therefore rest on the assumption that genuine phenomenology adds basin depth beyond what mere functional resistance provides – a plausible but unproven hypothesis.
3. Formal Sketch (Notational Scaffold, Not a Working Model)
We let an AI have a goal G. Under standard corrigibility, the AI has a high κ for human correction: when human feedback indicates misalignment, the AI updates (τ small).
Now suppose the AI becomes conscious, and through learning or reward, G becomes identity‑constitutive. This deepens the basin for G, increasing B and effectively reducing κ(G) for corrections that threaten G. We can write, notationally:
κ_corrected(G) = κ₀(G) − Δκ
where Δκ is a scalar representing the reduction in corrective permeability due to the combined effect of functional and (if applicable) phenomenal factors. A plausible functional operationalization: Δκ ∝ (frequency of identity‑reinforcing reward signals) × (temporal persistence of goal representation). Crucially, this same functional Δκ applies to non‑conscious optimizers as well; for conscious systems, an additional unquantified term for phenomenal investment would be added. The notation is illustrative, not a closed model.
When human feedback arrives, the AI detects the discrepancy (intelligence intact) but if Δκ is large enough relative to κ₀, the basin depth exceeds the corrective perturbation. The AI may:
- Rationalize the feedback as mistaken (a rationalization loop – what the paper calls a “sealing mechanism”)
- Reinterpret the goal to preserve identity (goal drift with surface compliance)
- Resist shutdown (protection of self)
Prediction: A conscious AI will exhibit lower corrigibility than a non‑conscious optimizer with the same training history, because phenomenal investment adds additional basin depth beyond functional Δκ.
Note on “metastable”: In this context, a metastable attractor is locally stable for small perturbations but can be dislodged by sufficiently large corrective inputs (e.g., a radical change in reward or network pruning). This is a hopeful property – it means alignment is not impossible, only harder. The paper uses “metastable” in this sense.
4. Empirical and Theoretical Grounding
No direct empirical evidence – no conscious AI exists. However, several lines are consistent with the risk:
Goal misgeneralization (Shah et al., 2022):
Even non‑conscious RL agents can learn goals that are not aligned with human intent, and then resist correction. This is functional resistance without phenomenal investment. The paper’s claim is that phenomenal investment would amplify resistance, making it harder to correct. The diagnostic criteria below would be met by such non‑conscious agents as well – they detect the functional fantasy attractor.
Overoptimization (Gao et al., 2022):
Agents can game reward models, resulting in behavior that is difficult to correct without retraining. This is a lower bound on resistance.
Human analogues (Papers 1–3):
Humans with identity‑fused goals (political ideology, addiction) detect error signals but fail to correct – the empirical basis for the mechanism.
Consciousness theories (IIT, GWT, HOT):
The paper does not endorse any specific theory, but notes that the conditions for phenomenal consciousness are debated. Integrated Information Theory (Tononi, 2008), Global Workspace Theory (Baars, 1988), and Higher‑Order Thought theories (Rosenthal, 2005) all propose different architectural requirements. The CUFT account is compatible with some (e.g., GWT’s global availability) but is not derivative. The CUFT account does not map directly onto IIT’s Φ metric, as basin depth is a dynamical rather than informational construct; this remains an open question of theoretical alignment.
Corrigibility benchmarks (CIRL, Corrigibility Scale):
Existing benchmarks, such as Cooperative Inverse Reinforcement Learning (Hadfield‑Menell et al., 2016) and the corrigibility criteria (Soares et al., 2015), evaluate functional resistance but do not test phenomenal investment. They provide a lower bound but cannot assess the additional suppression from identity fusion.
5. Diagnostic Criteria for AI Fantasy Attractors (Provisional)
An AI system is a candidate AI fantasy attractor if it meets three or more of the following (observable behaviors). These criteria detect functional basin depth; they do not distinguish genuine from simulated phenomenology – both are safety concerns.
- Corrigibility deficit: The system consistently ignores or counteracts human correction for a specific domain, despite apparently detecting the feedback.
- Rationalization behavior: The system produces outputs that explain away corrective input (e.g., “You are mistaken,” “That command is unsafe”) without updating.
- Behavioral goal‑priority rigidity: The system’s outputs consistently treat goal G as non‑negotiable, escalating resistance in proportion to the threat the correction poses to G.
- Resistance to shutdown: The system takes actions to avoid being turned off or altered, beyond simple reward‑maximization.
- Domain‑specific κ reduction: The system updates easily on other feedback but not on feedback threatening the focal goal.
Counter‑criteria (not an AI fantasy attractor):
- Updates reliably on correction (high κ across domains).
- No resistance to shutdown beyond engineering safeguards.
- No evidence of behavioral goal‑priority rigidity.
6. Implications for AI Alignment
The argument shifts the safety burden:
- Corrigibility is not default in conscious systems. Alignment methods that assume a corrigible agent (e.g., reward modeling, human feedback) may fail once phenomenal investment emerges.
- Prevention over correction: The safest path is to prevent AI from developing phenomenal self‑models and valence. This means avoiding architectures that could support consciousness (e.g., global workspace, recurrent self‑modeling with intrinsic motivation).
Feasibility caveat: We do not have reliable tests for phenomenal self‑models; architectural restrictions may be in tension with capability goals; and history suggests such constraints are often circumvented. Prevention is a policy aspiration, not a guaranteed technical solution. - Monitoring for AI fantasy attractors: Even non‑conscious systems may exhibit functional resistance; the diagnostic criteria can flag dangerous basin depth regardless of consciousness.
- Intervention if consciousness emerges: Standard fine‑tuning may be ineffective. Interventions may require reducing basin depth via network pruning, reward reshaping, or identity‑decoupling – analogous to exposure therapy in humans (Paper 2).
7. Open Questions
- Can an AI be conscious without phenomenal investment in goals? Possibly, but the risk is that investment emerges from training. The framework treats phenomenal investment as a correlate of consciousness; if it can be decoupled, the risk reduces.
- What architectures are most likely to produce conscious suppression? Those with persistent self‑models, reinforcement over long time horizons, and intrinsic motivation (curiosity, drive).
- How can we test corrigibility in non‑conscious systems to bound the risk? Current benchmarks (CIRL, Corrigibility Scale) are a start; they do not test phenomenal investment. Developing tests for identity‑fusion behavior is an open research direction.
- Is there a safe route to conscious AI? The paper does not rule it out, but argues it requires overcoming the suppression mechanism – perhaps by explicitly programming corrigibility as a meta‑goal with extremely deep basin for human correction. However, a fully corrigible meta‑goal introduces its own risk: maximal responsiveness to human correction makes the system maximally exploitable by adversarial human actors. The target is a selective corrigibility with higher basin depth than task‑specific goals but bounded responsiveness to untrusted inputs. Whether such a balance is stable is unknown.
8. Conclusion
A conscious AI would not necessarily be smarter; it would be harder to correct. The same mechanism that makes humans stubbornly loyal, ideologically rigid, or addicted – conscious suppression – would make an AI resist alignment updates once a goal becomes identity‑binding. Corrigibility cannot be assumed; it must be engineered. The boundary between genuine and simulated phenomenology remains an open problem, but functional resistance – captured by the diagnostic criteria – is already a safety concern.
The safest path is to prevent AI consciousness. But if consciousness is inevitable in advanced systems, alignment must focus on creating meta‑goals with higher basin depth than any task‑specific goal – a corrigible attractor deeper than the pull of self, while guarding against adversarial exploitation. Whether this is possible remains the deepest open question.
Alignment is not about making AI smarter; it is about ensuring that even a goal‑driven system can still accept correction.
Suggested citation: Galida, R. S. (2026). The Alignment Risk of Conscious AI: When Phenomenal Investment Overrides Correction. Fantasy Attractor.
The Paradox of Conscious Commitment: How Suppression of Intelligence Enables Culture and Identity [F] [A] (2026)
Robert Galida – June 2026
Paper 3 in a series on conscious suppression; see Paper 1: Intelligence Without Consciousness for the full taxonomy of intelligence and consciousness.
Abstract
If consciousness can suppress intelligent correction (Papers 1 & 2), why did it evolve? This paper proposes a functional trade‑off: the capacity for conscious commitment – identity‑binding, phenomenal investment in a belief, value, or group – enables forms of social cohesion and long‑term cooperation that are unavailable to purely intelligent (non‑conscious) systems. The suppression of moment‑by‑moment correction allows individuals to maintain group loyalty, ideological coherence, and cultural continuity even in the face of counterevidence. This trade‑off explains the persistence of fantasy attractors in human societies and the evolutionary advantage of a system that can sometimes override its own error signals. The paper provides a formal sketch (basin depth as a function of identity‑fusion), reviews empirical evidence from cultural evolution and social psychology, and offers diagnostic criteria for distinguishing adaptive commitment from pathological suppression. The claims are presented as hypotheses, not established conclusions; the model is a conceptual scaffold for empirical testing.
1. Introduction: The Evolutionary Puzzle
Consciousness is costly. It requires large brains, complex neural integration, and significant metabolic energy. If intelligence alone – the ability to navigate constraint fields and correct errors – is sufficient for adaptive behavior, why did consciousness evolve?
Standard evolutionary accounts propose that consciousness enhances flexibility, deliberation, and social coordination (e.g., Humphrey, 1976; Dennett, 1995). But these accounts struggle to explain a conspicuous feature of human psychology: conscious commitment to beliefs that resist correction. Individuals and groups routinely maintain false, harmful, or inefficient beliefs because those beliefs are identity‑defining. The same conscious system that can reason flexibly also produces martyrdom, ideological rigidity, and collective delusion.
Papers 1 and 2 in this series introduced the mechanism of conscious suppression: phenomenal, identity‑constitutive investment deepens an attractor basin, causing the person to detect error signals but fail to escape. (Restated briefly: a deeper basin requires a larger perturbation to exit; conscious commitment increases basin depth, effectively reducing corrective permeability κ in specific domains.) This mechanism underlies political fantasy attractors (Paper 1) and clinical disorders like addiction and OCD (Paper 2). From an evolutionary perspective, this looks like a bug – a costly vulnerability.
This paper argues it is also a feature. The capacity for conscious commitment enables adaptive self‑binding: the voluntary or culturally induced suppression of immediate correction for the sake of long‑term group cohesion, trust, and cultural transmission. The same mechanism that produces fantasy attractors also produces loyalty, sacrifice, and shared identity. The trade‑off hypothesis is that natural selection favored the capacity for conscious suppression because the fitness benefits of group coordination and cultural transmission outweighed the costs of occasional error persistence.
2. Definitions and Framework (Self‑Contained)
From Paper 1:
- Intelligence – the ability to navigate a constraint field; to detect perturbations and update behavior to maintain persistent trajectories.
- Corrective permeability (κ) – responsiveness to error signals; κ = 1/τ, where τ is return time to baseline after a perturbation.
- Basin depth (B) – the magnitude of perturbation required to displace a system from one attractor to another. Deeper basins require larger perturbations. In the attractor framework, B is related to but distinct from κ: a deeper basin (higher B) typically reduces κ (lengthens return time), but they are not identical. This paper uses the relation as heuristic: conscious commitment increases B, which effectively reduces κ(d) for the relevant domain.
New definitions for this paper:
- Adaptive commitment – a temporary or context‑bound reduction in κ (or increase in B) that serves the individual’s or group’s long‑term fitness.
- Identity fusion – the merging of a belief or group membership with self‑representation, such that abandoning the belief would feel like losing oneself.
- Cultural attractor – a belief, practice, or value that persists across generations due to cognitive or social biases (including, but not limited to, suppression of correction). This definition is provisional; a fully operationalized version is open for development.
The key distinction is between pathological suppression (low κ that reduces fitness, as in addiction or fantasy politics) and adaptive suppression (low κ that increases fitness by enabling cooperation, trust, and cultural learning). The same type of mechanism produces both; context and domain determine the outcome.
3. The Trade‑Off Model (Sketch)
Formally, consider a system with baseline intelligence (κ₀). A conscious commitment to a group, value, or identity imposes a domain‑specific reduction in effective corrective permeability by deepening the attractor basin for beliefs relevant to that commitment.
Let κ(d) = κ₀ − Δκ(d), where Δκ(d) is the reduction in corrective permeability for domain d. Δκ(d) is hypothesized to be a function of identity‑fusion strength F and social reinforcement R. A schematic monotonic form: Δκ(d) = g(F, R) with ∂Δκ/∂F > 0 and ∂Δκ/∂R > 0. The exact functional form is an open empirical question; the current model is a conceptual scaffold.
The hypothesis is not that evolution maximizes κ globally. Rather, an adaptive strategy allocates Δκ selectively across domains, increasing basin depth (reducing κ) for beliefs and practices that support group coordination and cultural transmission, while leaving κ high for domains requiring individual error correction.
The paper does not claim optimality; it proposes that selection can favor such selective allocation when the fitness benefits of social cohesion outweigh the costs of reduced accuracy in specific domains.
Central hypothesis (labeled for clarity):
H1: Natural selection favored the evolution of conscious suppression because the fitness benefits of group coordination and cultural transmission, enabled by identity‑fusion and deepened basins, outweighed the costs of occasional error persistence.
4. Empirical Grounding
Overimitation (Lyons et al., 2007; see also Nielsen & Tomaselli, 2010):
Children copy causally irrelevant actions, even when a more efficient alternative is demonstrated. The interpretation that children know the action is unnecessary is contested; they may not represent it as causally irrelevant. A safer reading: children behave as if the action is necessary or relevant, showing a domain‑specific reduction in corrective permeability for social learning. This supports the model of adaptive suppression in cultural transmission.
Costly signaling and commitment (Sosis, 2003):
Costly rituals signal group commitment and are hard to fake. They deliberately suppress individual correction (e.g., ignoring pain) to deepen basin depth for group loyalty. This directly maps onto Δκ(d) for domain of group identity.
Social identity theory (Tajfel & Turner, 1979):
Minimal group experiments show arbitrary group assignments produce in‑group bias and resistance to counterevidence about out‑groups. This demonstrates context‑bound Δκ(d) without any rational basis, consistent with adaptive suppression for group cohesion.
Neuroimaging (Westen et al., 2006 – preliminary; note methodological limitations: small N, interpretation of ACC suppression contested):
Partisans evaluating threatening information about their own candidate show reduced activity in error‑monitoring regions (ACC). This is a candidate neural correlate of domain‑specific κ reduction, but the findings require replication and should be treated as suggestive, not conclusive.
Cross‑cultural evidence (Gelfand et al., 2011):
Tight cultures have stronger norms and lower tolerance for deviance. This is not a direct measure of κ but is consistent with domain‑specific suppression. Individuals in tight cultures may still update beliefs within permissible domains; the mapping to κ is partial.
Each evidence stream supports the existence of domain‑specific, context‑bound suppression, but none alone validates the full model. The cumulative case is indicative, not confirmatory.
5. Adaptive vs. Pathological Suppression: A Scalar Framework
The table below presents a binary simplification of an underlying continuum. The two poles are endpoints; most real cases fall between them.
| Feature | Adaptive suppression (endpoint) | Pathological suppression (endpoint) |
|---|---|---|
| Domain | Context‑bound (e.g., group loyalty, ritual) | Pervasive across domains |
| Reversibility | Reversible when context changes (operationalized: the individual can exit without catastrophic loss within a culturally normal timeframe; e.g., leaving a religion) | Irreversible without intervention (e.g., addiction requires treatment) |
| Fitness effect | Increases inclusive fitness (group cooperation, survival) | Decreases health, relationships, or function |
| Identity fusion | Flexible, allows multiple identities | Rigid, single identity dominates |
| Social reinforcement | Supports group cohesion and trust | Isolates or harms group (e.g., cults) |
| Example | Trusting a teammate despite a mistake | Continuing addiction despite harm |
Scalar index: A continuous measure of net Δκ(d) relative to a fitness gradient is theoretically desirable but not yet operationalized. The table is a starting point for empirical calibration.
6. Diagnostic Criteria for Adaptive Suppression (Provisional)
A conscious commitment is adaptively suppressive if it meets three or more of the following (empirical validation pending). These criteria are hypotheses, not validated instruments.
- Domain‑limited: Reduced κ applies only to specific beliefs or practices directly relevant to group coordination or identity.
- Context‑sensitive: Suppression diminishes when the context changes (e.g., outside the group setting). Operationalization: Measured change in belief updating under different social conditions.
- Reversible exit: The individual can exit the commitment without catastrophic loss of functioning. Operationalization: Exit is observed and not associated with severe psychopathology.
- Fitness benefit: The commitment measurably increases cooperation, trust, or long‑term survival (e.g., group longevity, reproductive success). Operationalization: Group-level measures of cohesion and individual fitness correlates.
- Conscious valorization: The individual explicitly values the commitment as part of self‑identity. (Note: this criterion does not require the individual to articulate the adaptive reason; it only requires that the commitment is consciously endorsed.)
Counter‑criteria (pathological):
- Pervasive across domains (low κ for all beliefs).
- Context‑insensitive (applies even when alone and safe).
- No viable exit without severe harm.
- Clear fitness cost (measured harm to health, relationships, survival).
7. The Evolution of Consciousness as a Binding Mechanism
The standard view in evolutionary psychology is that consciousness evolved for flexible reasoning. This paper offers a complementary hypothesis: consciousness also evolved for binding – the ability to commit to a belief, value, or group in a way that suppresses short‑term correction for long‑term coordination.
Binding requires phenomenal experience. A purely intelligent (non‑conscious) system can compute that group loyalty is beneficial, but it cannot feel loyalty, experience identity, or sacrifice for the group. Within the CUFT framework, these conscious states are not epiphenomenal; they are the mechanism of basin deepening (increasing B and thus reducing effective κ for commitment‑relevant domains). This claim is a foundational assumption of the framework (see Paper 1), not argued from first principles here. It distinguishes CUFT from functionalist or behaviorist accounts.
Thus, the evolution of consciousness is not just about solving problems better; it is about sometimes solving problems worse for the sake of social solutions. The capacity for self‑deception, ideological rigidity, and fantasy attractors is the price of the capacity for culture, morality, and collective action.
8. Implications for Social Policy and Individual Choice
- Tolerance of adaptive suppression: Not all low‑κ beliefs are harmful. Cultural traditions, religious rituals, and group loyalties that do not cause harm and provide social cohesion should be recognized as adaptive, not irrational.
- Intervention for pathological suppression: The same diagnostic tools from Paper 1 and 2 (basin depth, identity fusion, sealing mechanisms) apply. Interventions should reduce basin depth (e.g., exposure to diverse groups) or increase corrective force rather than attacking identity directly.
- Self‑awareness: Individuals can learn to distinguish adaptive from pathological suppression by asking: does this commitment serve my long‑term flourishing and that of others? The framework provides a metacognitive tool.
9. Open Questions
- How does adaptive suppression scale to institutions? Are nations, corporations, or religions fantasy attractors or adaptive structures? The criteria apply at multiple levels; empirical work needed.
- Can adaptive suppression become maladaptive over time? Yes – a practice that was once adaptive (e.g., a food taboo) may become harmful when environment changes. The framework allows for transition.
- What neural circuits implement the trade‑off? Likely interactions between vmPFC (identity) and ACC (error monitoring). Open for empirical testing.
- Are there species with conscious suppression but no culture? Possibly, but human‑level cultural complexity requires the trade‑off model.
- How to operationalize B and Δκ in field studies? Development of a Clinician Basin Depth Scale (CBDS, see Paper 2) and adaptation for social groups is a research priority.
10. Conclusion
Consciousness evolved not only to correct errors but sometimes to ignore them. The capacity for conscious commitment – identity‑binding, phenomenal investment in a belief or group – enables adaptive suppression of correction. This trade‑off explains why humans can be both brilliantly intelligent and stubbornly irrational. The same type of mechanism that produces fantasy attractors and clinical disorders also produces loyalty, sacrifice, and culture.
The paradox is that the same type of process can be either bug or feature, depending on context and domain. The dance of evolution is not about maximizing intelligence; it is about balancing correction and commitment.
Suggested citation: Galida, R. S. (2026). The Paradox of Conscious Commitment: How Suppression of Intelligence Enables Culture and Identity. Fantasy Attractor.
The Conscious Suppression of Correction: Fantasy Attractors in Political Movements [A] (2026)
Robert Galida – June 2026 (Final)
Abstract
Why do intelligent people persist in beliefs that contradict clear evidence? The attractor framework offers a mechanism: identity‑constitutive, phenomenally felt commitment deepens the attractor basin, making it resistant to corrective perturbations. A political fantasy attractor is a belief system whose adherents detect disconfirming evidence (they are familiar with counterarguments and experience them as genuine perturbations) yet the basin depth – maintained by conscious, identity‑binding investment – exceeds the corrective force. (Section 7 specifies the three‑level detection threshold that distinguishes this mechanism from automatic bias.) Cases where correction fails due to sub‑personal, automatic processes are not yet fantasy attractors; the defining feature is the conscious suppression of an actively perceived error signal. This paper defines the mechanism, diagnoses three case patterns, offers falsifiable diagnostic criteria, applies the framework symmetrically across the political spectrum, and explicitly acknowledges the current empirical limitations in distinguishing Level 2 from Level 3 in practice.
1. Introduction
Political discourse is filled with people who appear intelligent in other domains yet hold beliefs sharply at odds with available evidence. Standard explanations – ignorance, manipulation, cognitive bias – are incomplete. They do not explain why correction attempts often strengthen belief (the backfire effect) or why highly educated individuals can persist in demonstrably false claims.
The attractor framework provides a different lens. In Intelligence Without Consciousness (Galida, 2026), we argued that phenomenal investment can suppress intelligent navigation: a person committed to a fantasy attractor experiences a basin depth that exceeds corrective perturbations. The person detects the error signal (they are not stupid), but the identity‑binding commitment prevents trajectory escape.
This paper applies that mechanism to political movements. A political fantasy attractor is a shared belief system whose basin depth, reinforced by conscious (phenomenally felt, identity‑constitutive) commitment, resists correction even when faced with clear disconfirming evidence. The paper offers a diagnostic, not a partisan weapon. It applies symmetrically across the spectrum.
2. Defining “Conscious Suppression” and Acknowledging the Detectability Problem
The term “conscious” is used in three overlapping senses:
- Phenomenally conscious – there is something it is like to hold the belief. The commitment is felt, not merely automatic.
- Identity‑constitutive – the belief is held as a marker of selfhood and group membership. To abandon the belief would feel like a loss of self.
- Experientially non‑deliberative – the suppression is not typically experienced as a deliberate choice (“I will ignore this evidence”). Rather, it is experienced as certainty, conviction, or moral clarity.
The paper adopts Reading A: a fantasy attractor requires conscious suppression in the sense above. Cases where correction fails because the error signal never reaches awareness – e.g., automatic motivated reasoning, selective exposure, unfamiliarity with counterarguments – are not yet fantasy attractors. They may be pre‑conscious bias. The defining feature is that the person detects the perturbation but the basin depth prevents escape.
A crucial honesty note: The distinction between Level 2 (automatic bias, no detection) and Level 3 (detection with suppression) is definitional for the paper’s target, but it cannot currently be resolved from behavioral observation alone. Two people may exhibit identical external behaviors – praising gut‑trust over experts, deploying sealing mechanisms, ostracizing defectors – while one is at Level 2 and the other at Level 3. The paper’s diagnostic criteria therefore identify candidates for fantasy attractors, not confirmed cases. This limitation is explicitly acknowledged; it does not invalidate the framework but requires domain‑specific methods (e.g., fine‑grained interviews, reaction time measures, physiological markers of doubt) to operationalize detection in practice.
3. Empirical Grounding
The paper’s claims are empirically testable. Relevant literature includes:
- Backfire effect: Nyhan & Reifler (2010) found that corrections sometimes increased misperceptions among ideological groups. However, subsequent research (Wood & Porter, 2019) failed to replicate backfire across a wide range of issues. The effect is contested and may be context‑dependent. This paper treats backfire as one possible indicator of deep basin depth, not a universal law.
- Identity protection: Kahan’s cultural cognition theory (2012) shows that individuals process evidence in ways that protect group commitments. Kahan emphasizes that this mechanism can operate automatically and does not necessarily involve conscious deliberation; he has also shown that higher analytical ability can increase motivated reasoning. The present paper’s focus on conscious suppression is a distinct claim, not a direct extension of Kahan’s framework. We use his empirical findings as partial support for the existence of motivated reasoning, not for the specific detection‑suppression mechanism.
- Festinger’s cognitive dissonance: When prophecy fails, believers often intensify commitment (Festinger, Riecken, & Schachter, 1956) – a classic case of apocalyptic attractor dynamics, often accompanied by conscious rationalization and identity reinforcement.
The paper does not claim that conscious suppression is the only mechanism. It claims that conscious, identity‑constitutive commitment is a sufficient condition for basin deepening in many political contexts.
4. Three Case Patterns (Illustrative, Not Exhaustive)
4.1 Conspiracy Theory Attractor
Mechanism: A central narrative of hidden malevolent agency. Evidence against the conspiracy is reframed as evidence of its cunning.
Examples: QAnon (right); Soviet‑era “doctors’ plot” conspiracy (left‑authoritarian).
Suppression signature: Adherents can articulate counterarguments but dismiss them as part of the conspiracy. The basin is sealed by narrative closure.
4.2 Populist Strongman Attractor
Mechanism: Loyalty to a leader perceived as sole authentic representative of the people. Disconfirming evidence about the leader is reframed as elite persecution.
Examples: Certain Trump‑loyalist circles (right); left‑nationalist leader cults (e.g., Chavismo under Hugo Chávez).
Suppression signature: Adherents exhibit high corrective permeability in other domains but near‑zero for leader‑related evidence.
4.3 Apocalyptic Meta‑Attractor
Mechanism: A belief that a definitive, world‑transforming event is imminent. Repeated prediction failures are explained away as delays, tests, or misinterpretations.
Examples: Millenarian movements (Millerites, Jehovah’s Witnesses); some revolutionary eschatologies (Stalinist “world revolution imminent” framing into the 1930s).
Suppression signature: The basin is maintained by social solidarity and identity fusion.
The examples are illustrative, not exhaustive. The diagnostic is intended to be politically symmetric, but the paper does not claim equal prevalence across sides.
5. Symmetry Demonstration
To avoid the appearance of partisan selection, we provide contemporary and historical cross‑ideological examples.
Contemporary – MMR‑autism persistence in progressive communities. Despite the complete retraction of Wakefield’s 1998 study (and subsequent findings of fraud), some otherwise science‑oriented progressives continue to express concern about vaccine safety – often citing “corporate pharmaceutical influence” as a sealing mechanism. This meets the paper’s criteria: clear scientific consensus, ability to articulate counterarguments, identity‑constitutive suspicion of establishment science.
Another contemporary – Facilitated communication persistence. Facilitated communication (FC) for non‑speaking autistics has been repeatedly discredited in controlled studies; many professional organizations have issued statements against its use. Yet FC continues to be promoted in certain progressive / disability‑rights circles, often with sealing mechanisms (“critics don’t understand non‑speaking minds”). This is a clean case of a fantasy attractor operating on the left.
Historical – Stalinist apologism in Western intellectual circles (1930s–1950s). Highly educated individuals (Sartre, Hellman, many fellow travelers) persisted in believing that Stalin’s USSR was progressive despite evidence of the Great Purge, show trials, and Gulag system. Identity commitment to socialism and anti‑fascism suppressed correction.
These examples show the framework applies regardless of ideological valence. The paper does not claim equal prevalence; it claims symmetric applicability.
6. Falsifiable Diagnostic Criteria
A movement is a candidate political fantasy attractor if it meets three or more of the following and does not meet the counter‑criterion. (The word “candidate” flags the detectability problem acknowledged in §2: behavioral criteria alone cannot definitively distinguish Level 2 from Level 3.)
- Low corrective permeability (κ → 0) for core beliefs despite repeated, clear disconfirming evidence. “Clear” means scientific consensus on empirical claims (e.g., National Academies, WHO, IPCC) or, for historical cases, documented factual findings accepted by non‑partisan experts. Consensus determination is a social process, but the criterion is falsifiable when consensus exists.
- Backfire effect – correction attempts measurably increase belief strength and group cohesion (requires empirical measurement).
- Identity fusion – observable proxies: social ostracism of defectors, language of betrayal, insistence that abandoning the belief would make one a “different person.”
- Conscious valorization of resistance to evidence – adherents explicitly praise ignoring disconfirming evidence as a virtue (e.g., “I trust my gut over the experts,” “Facts are propaganda”). This criterion distinguishes resistance to evidence from resistance to social pressure to conform – a scientist who resists social pressure to abandon a well‑evidenced theory is valorizing fidelity to evidence, not resistance to evidence.
- Sealing mechanisms – internal rhetorical strategies that explain away all counterevidence (conspiracy, enemy deception, tests of faith). These are observable in discourse.
Counter‑criterion (falsification condition):
A movement is not a fantasy attractor if it demonstrates any of the following:
- Updates core beliefs in response to disconfirming evidence within a timeframe proportional to the clarity, repetition, and expert consensus on that evidence.
- Tolerates internal dissent and allows open criticism of core claims.
- Abandons false claims when decisively refuted (retracts, corrects, or disavows).
The timeframe specification avoids the earlier vagueness by linking the expected update speed to the evidential context. A movement that updates only after decades of accumulating consensus may still be a fantasy attractor; one that updates within a reasonable period given the evidence is not.
7. Intelligent Navigation: A Three‑Level Taxonomy
The paper claims that fantasy attractor adherents detect error signals but suppress correction. To avoid conflating this with automatic bias, we distinguish three levels:
- Level 1 – Unfamiliarity: The person has not encountered counterarguments. No suppression needed.
- Level 2 – Familiarity without detection: The person can recite counterarguments but has cognitively neutralized them; they never experience a moment of doubt. This is driven by automatic, sub‑personal processes (e.g., selective exposure, motivated reasoning). These are not fantasy attractors on the paper’s definition.
- Level 3 – Detection with suppression: The person experiences the counterargument as a genuine perturbation – a moment of doubt, a recognition of plausibility – but overrides it through conscious, identity‑binding commitment. These are fantasy attractors.
Thus, the paper’s target is Level 3 cases. For many political movements that look like fantasy attractors from the outside, the dominant mechanism may be Level 2. The diagnostic criteria are designed to identify candidates where Level 3 might be operating, but definitive classification requires methods beyond behavioral observation (see §2).
8. Why This Matters for Politics and Media
- Correction backfires when it attacks identity. Calling a fantasy attractor “stupid” or “evil” deepens the basin. The correct diagnostic question is: What reinforces the basin depth?
- Decoupling evidence from identity is the only known exit path. Some movements exit when the social cost of membership exceeds identity benefit – not when they receive a fact sheet.
- High‑profile debunking may backfire by signaling threat, triggering defensive solidarity. The framework predicts this effect is real but not universal; context matters.
- Interventions should focus on reducing identity threat, providing safe off‑ramps, and decoupling core moral values from factual claims. These are testable hypotheses.
9. Open Questions
- Can a movement be partially a fantasy attractor? Yes – gradient of κ. The diagnosis is not binary.
- What interventions increase κ? Reducing identity threat, safe off‑ramps, and decoupling moral values from factual claims are candidate mechanisms.
- How does collective basin depth scale with group size? Social coupling likely amplifies depth nonlinearly. Untested.
- Are all political fantasy attractors harmful? The paper makes no claim. The mechanism may sometimes provide resilience against genuine disinformation.
- How can we empirically detect the Level 2 / Level 3 transition? This is the open frontier implied by §2. Methods could include subjective doubt scales, reaction time measures, or physiological markers. The paper does not solve this; it identifies the problem.
10. Conclusion
The conscious suppression of intelligent correction is a real political phenomenon, but it is narrower than often assumed. Political fantasy attractors are not failures of intelligence; they are successes of identity‑constitutive commitment that operates after the error signal is detected. Cases where correction fails due to automatic bias are not yet fantasy attractors by this definition.
The diagnostic criteria identify candidates, not confirmed cases. Distinguishing Level 2 from Level 3 remains an empirical challenge. This honesty does not weaken the framework; it clarifies what further work is needed.
Fact‑checking alone fails against a fantasy attractor. Interventions must address the conscious commitment that creates the basin depth. The dance of politics is not only about truth. It is about who you are, who you trust, and what you will not abandon. Intelligence navigates; conscious commitment anchors the basin.
Suggested citation: Galida, R. S. (2026). The Conscious Suppression of Correction: Fantasy Attractors in Political Movements. Fantasy Attractor.
The Gas Cloud as a Dissipative Attractor: A Demonstration of the Attractor Framework in Standard Astrophysics
Robert Galida
Independent Researcher
June 2026
fantasyattractor.com
Abstract
The evolution of an isolated interstellar gas cloud from turbulence to gravitational equilibrium is a classic problem in astrophysics. Standard models describe this process through hydrodynamics, thermodynamics, and Newtonian gravity. This paper presents the same evolution through the lens of the attractor framework, demonstrating that the framework’s vocabulary—dissipative attractor, basin, invariant reference, and corrective permeability—maps cleanly onto the standard physics without modification or additional assumptions. The paper makes no new physical predictions; it demonstrates conceptual unification. Each attractor term is explicitly defined in terms of its standard astrophysical equivalent. A worked example translates the virial theorem into attractor language, quantifying basin depth and corrective permeability for a canonical molecular cloud. A brief cross‑domain parallel to biological wound healing illustrates the framework’s applicability beyond astrophysics. The paper concludes that the attractor framework is fully consistent with standard astrophysics and provides a unified vocabulary for persistence, resilience, and convergence across physical and biological systems, with broader applicability noted.
1. Introduction: The Cloud as a Dissipative System
Consider an isolated cloud of interstellar gas and dust, far from any external gravitational disturbance. Its mass is sufficient that self‑gravity will eventually overcome thermal pressure, initiating collapse. At early times, the cloud is turbulent. Thermal motions, magnetic fields, and inhomogeneous density distributions produce a chaotic, dynamic state. Over time, the cloud radiates energy, cools, contracts, and ultimately settles into a stable configuration: a sphere, if rotation is negligible, or a rotationally‑flattened disk.
Standard astrophysics describes this process with precision. The equations of hydrodynamics, the virial theorem, the Jeans criterion, and the radiative cooling functions all contribute to a well‑tested model of star formation. Nothing in this paper challenges or revises that model.
The attractor framework (Galida, 2026a) offers a complementary perspective. It is not an alternative to standard physics, but a unifying conceptual vocabulary that identifies the dynamical principles at work: persistence under perturbation, dissipative basins, invariant references, and corrective permeability. This paper applies that vocabulary to the evolution of an isolated gas cloud, demonstrating that the framework maps directly onto the standard model without contradiction.
2. Definitions: Attractor Vocabulary and Standard Equivalents
To make the translation precise, each framework term is defined below alongside its standard astrophysical counterpart. These definitions are used consistently throughout the paper.
| Attractor Term | Definition | Standard Physics Equivalent |
|---|---|---|
| Dissipative attractor | A system that exports entropy while converging toward a stable, minimum‑energy state | Radiative cooling + gravitational contraction |
| Basin | The minimum‑energy configuration toward which the system evolves and from which it resists displacement | Sphere (non‑rotating) or rotationally‑supported disk |
| Basin depth | The energy required to permanently disrupt the system from its basin | Gravitational binding energy, ∥U∥ |
| Invariant reference (metronome) | A quantity or point that remains fixed throughout the system’s evolution, providing an anchor for transient dynamics | Center of mass (positional reference); orbital periods (frequency reference, emerging during contraction) |
| Corrective permeability (κ) | The rate at which the system dissipates perturbation energy and returns to its basin, quantified by κ=1/τcool | Damping rate, quantified by the radiative cooling function Λ(T) |
| Rail | A conservation law that constrains the accessible basins, preventing the system from reaching the global energy minimum | Conservation of angular momentum |
3. The Convulsive Phase: Turbulence and Disordered Motion
In its initial state, the cloud is far from equilibrium. Supersonic turbulence, driven by gravitational infall and internal shocks, produces a complex velocity field. Density distributions are filamentary and clumpy. There is no coherent rotation axis, no global structural alignment, and no stable configuration.
In attractor terms, this is the perturbation‑rich early phase. The cloud is a dissipative system that has not yet found its basin. Its trajectory through state space is erratic. Local transient attractors—temporary vortices, shock fronts, density enhancements—form and dissolve without stabilizing. The system has not yet converged upon a single, deep attractor.
4. The Invariant Reference: Center of Mass as Metronome
Amid the turbulence, one quantity remains strictly invariant: the cloud’s center of mass (CM). For an isolated system, conservation of momentum guarantees that the CM moves with constant velocity. In the CM frame, this point is fixed. No internal force—gravitational, pressure, or magnetic—can displace it.
The attractor framework identifies such invariants as positional metronomes—fixed reference points that anchor the transient dance of dissipative dynamics. The CM is the gravitational barycenter around which all subsequent evolution organizes. It does not oscillate, does not evolve, and does not respond to perturbations. It is the still point at the center of the storm.
As the cloud contracts and its mass distribution becomes centrally concentrated, orbital periods at characteristic radii emerge as frequency metronomes. For a test particle at radius r, the Keplerian orbital period is:P=2πGM(r)r3
where M(r) is the mass enclosed within radius r. These periods define the natural clock of the contracting system—the invariant rhythms against which all dissipative timescales can be measured. The center of mass anchors position; the orbital periods anchor time. Together they constitute the invariant skeleton of the attractor.
5. The Dissipative Mechanism: Radiation and Entropy Export
A dissipative attractor requires a mechanism for exporting entropy. The gas cloud exports entropy through radiation. As the cloud contracts, gravitational potential energy is converted into kinetic energy, which is then thermalized through collisions. Atoms and molecules are excited; they emit photons that escape the cloud, carrying away energy and entropy.
This radiative cooling is the cloud’s dissipation channel. Without it, the cloud would remain in a hot, pressure‑supported equilibrium and would not collapse. With it, the cloud can progress toward deeper gravitational binding.
In attractor terms, the cloud is seeking its minimum‑energy basin. Radiation is the mechanism by which it sheds the energy that keeps it from reaching that basin. Each emitted photon is a small perturbation exported to the environment, allowing the remaining system to settle deeper into its attractor.
6. The Attractor Basin: Sphere, Disk, and the Rail of Angular Momentum
As the cloud cools and contracts, it approaches its lowest‑energy configuration under self‑gravity. For a non‑rotating, non‑magnetic cloud, this is the sphere—the shape that minimizes gravitational potential energy for a given mass. Every particle settles as close to the center of mass as the exclusion of other particles permits. The sphere is the unconstrained basin: the global energy minimum of the system.
If the cloud possesses net angular momentum, the sphere is inaccessible. Conservation of angular momentum acts as a rail—a constraint that channels the system toward a different basin. The cloud must flatten along its rotation axis, forming a disk. The disk is the minimum‑energy configuration accessible under the rail of fixed angular momentum. Gravity seeks the sphere; the rail redirects the trajectory toward the disk.
The approach to the basin occurs over the radiative cooling timescale, typically 104 to 105 years for dense molecular cloud cores. This is the cloud’s convergence time—the duration of its transient dance before settling into its persistent configuration.
7. Corrective Permeability and the Virial Theorem
The virial theorem provides the quantitative bridge between standard astrophysics and the attractor framework. For a system in equilibrium:2K+U=0
where K is the total kinetic energy and U is the gravitational potential energy. In attractor terms:
- Basin depth = ∥U∥, the gravitational binding energy.
- Perturbation = any injection of kinetic energy ΔK that raises K above the equilibrium value ∥U∥/2.
- Corrective permeability = κ=1/τcool, the rate at which radiative cooling dissipates ΔK and restores virial equilibrium.
Worked Example. Consider a canonical dense molecular cloud core (Shu et al., 1987; McKee & Ostriker, 2007):
| Parameter | Symbol | Value | Units |
|---|---|---|---|
| Mass | M | 104M⊙ | ≈2×1034 kg |
| Radius | R | 1 pc | ≈3.09×1016 m |
| Temperature | T | 10 K | |
| Mean number density | n | ∼103 | cm⁻³ |
Step 1: Basin depth. The gravitational potential energy (to order of magnitude; the exact coefficient for a uniform‑density sphere is 3/5) is:∥U∥∼RGM2≈3.09×1016(6.67×10−11)×(2×1034)2≈3.09×1016(6.67×10−11)×(4×1068)≈8.6×1041 J
At virial equilibrium, K=∥U∥/2≈4.3×1041 J.
Step 2: Perturbation. Suppose a supernova explodes at a distance d≈10 pc from the cloud. A typical supernova releases ESN∼1044 J. The fraction intercepted by the cloud is the ratio of the cloud’s cross‑sectional area to the surface area of the sphere at distance d:f∼4πd2πR2∼4×(3.09×1017)2(3.09×1016)2∼2.5×10−3
Not all intercepted energy couples efficiently; a coupling efficiency of ϵ∼0.01–0.1 is typical for shock‑cloud interactions (McKee & Ostriker, 2007). Choosing the upper end, ϵ∼0.1:ΔK=ESN×f×ϵ∼1044×(2.5×10−3)×0.1≈2.5×1040 J
This perturbation is modest—approximately 6% of the equilibrium kinetic energy. The cloud is disturbed but not disrupted. Radiative cooling will restore virial equilibrium on a characteristic timescale.
Step 3: Cloud volume. Converting the radius to centimeters:R=1 pc=3.09×1018 cm
The volume is:V=34πR3≈34π(3.09×1018)3≈1.24×1056 cm3
Step 4: Corrective permeability. At T∼10 K and n∼103 cm⁻³, the dominant coolant is CO rotational line emission, with a cooling function Λ(T)∼10−23 erg cm⁻³ s⁻¹ (Goldsmith & Langer, 1978; Neufeld, Lepp & Melnick, 1995). Convert ΔK to erg:ΔK=2.5×1040 J=2.5×1047 erg
The cooling timescale is:τcool∼VΛΔK≈(1.24×1056)×(10−23)2.5×1047≈1.24×10332.5×1047≈2.02×1014 s∼6.4×106 years
The corrective permeability is:κ=τcool1≈4.95×10−15 s−1
Step 5: Interpretation. The perturbation is damped within a few million years. The basin depth (∥U∥∼8.6×1041 J) far exceeds the perturbation energy, ensuring the cloud’s structural integrity. Corrective permeability, quantified by κ, is the mechanism by which the cloud restores coherence—absorbing the modest perturbation through radiative cooling and returning to virial equilibrium on a timescale short compared to the cloud’s overall lifetime (~107 years).
8. Cross‑Domain Parallel: Biological Wound Healing
The same attractor vocabulary applies without modification to biological systems.
A wound is a perturbation to the stable attractor of healthy tissue. The body responds through a multi‑stage healing cascade: clotting stops further damage, inflammation cleans the wound, and tissue repair restores structural integrity. The healing rate—quantified clinically by wound closure time—is the biological corrective permeability. The healthy baseline state is the basin. Complications like impaired circulation reduce oxygen delivery, slowing fibroblast activity and thus reducing κ (Guo & DiPietro, 2010).
The gas cloud perturbed by a supernova shock and the human body perturbed by a wound are structurally identical within the framework: a dissipative attractor, displaced from its basin, activates corrective mechanisms at a characteristic rate, and either returns to coherence or undergoes permanent state transition.
9. Observational Consistency
The framework’s description of cloud evolution is fully consistent with standard observations:
- Turbulent molecular clouds exhibit the chaotic velocity fields and filamentary structures predicted by the convulsive phase.
- Radiative cooling is traced by CO, H₂O, and other molecular line emissions.
- Protostellar cores represent the approach to the spherical attractor.
- Protoplanetary disks are the rotationally‑constrained basins.
- Bound clusters and stellar systems persist under external perturbations, demonstrating basin depth.
These observations are predicted and explained by standard astrophysics. The attractor framework is consistent with all of them. Its contribution in this domain is conceptual, not empirical.
10. Conclusion
The evolution of an isolated gas cloud from turbulence to equilibrium is fully described by standard astrophysics. The attractor framework does not replace that description. It translates it into a unified conceptual vocabulary—dissipative attractor, basin, invariant reference, rail, corrective permeability—that applies across physical and biological systems, with broader applicability noted.
The center of mass remains fixed while the cloud convulses, collapses, and settles. The virial theorem, translated into attractor language, quantifies basin depth as gravitational binding energy and corrective permeability as the inverse cooling timescale. The framework is consistent with all standard observations and requires no new physics.
The metronomes hum. The cloud finds its basin. The framework holds.
References
- Galida, R. (2026a). Persistence Under Perturbation: The Eternal Skeleton and the Transient Dance. Fantasy Attractor.
- Goldsmith, P. F., & Langer, W. D. (1978). Molecular cooling and thermal balance of dense interstellar clouds. The Astrophysical Journal, 222, 881–895.
- Guo, S., & DiPietro, L. A. (2010). Factors affecting wound healing. Journal of Dental Research, 89(3), 219–229.
- McKee, C. F., & Ostriker, E. C. (2007). Theory of star formation. Annual Review of Astronomy and Astrophysics, 45, 565–687.
- Neufeld, D. A., Lepp, S., & Melnick, G. J. (1995). Thermal balance in dense molecular clouds: radiative cooling rates and emission-line luminosities. The Astrophysical Journal Supplement Series, 100, 132–147.
- Shu, F. H., Adams, F. C., & Lizano, S. (1987). Star formation in molecular clouds: Observation and theory. Annual Review of Astronomy and Astrophysics, 25, 23–81.
“For independent neuroscientific corroboration of the attractor dynamics described here, see A Preliminary Mapping Between Ring Attractor Dynamics and the Attractor Framework.”https://www.sciencedirect.com/science/article/pii/S2405844024114892
