Informational Entropy-Based Value Formation: A New Paradigm for a Deeper Understanding of Value

Short Communication Evaluation Review 2025, Vol. 0(0) 1–25 © The Author(s) 2025 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0193841X251396210 journals.sagepub.com/home/erx Informational Entropy-Based Value Formation: A New Paradigm for a Deeper Understanding of Value Quan-Hoang Vuong 1,2 , Viet-Phuong La 1,3 , and Minh-Hoang Nguyen 1  Abstract The major global challenges of our time—such as climate change, environmental degradation, rising inequality, and the emergence of disruptive technologies—demand interdisciplinary research to generate effective solutions. A clear understanding of value is essential for guiding socio-cultural and economic transitions to address these issues. Despite numerous attempts to define value, existing approaches remain inconsistent across disciplines and lack a comprehensive framework. This paper introduces a novel perspective on value through the lens of granular interaction thinking theory, proposing an informational entropy-based notion of value. Grounded on quantum mechanics, Shannon’s information theory, and the mindsponge theory, this framework integrates both subjective and objective considerations and is 1 Centre for Interdisciplinary Social Research, Phenikaa University, Hanoi, Vietnam 2 University College, Korea University, Seoul, South Korea 3 A.I. for Social Data Lab (AISDL), Hanoi, Vietnam Corresponding Authors: Minh-Hoang Nguyen, Centre for Interdisciplinary Social Research, Phenikaa University, Yen Nghia Ward, Ha Dong District, Hanoi 100803, Vietnam. Email: [email protected] Quan-Hoang Vuong, University College, Korea University, Seoul, South Korea. Email: [email protected] highly compatible with interdisciplinary research. The informational entropybased notion of value effectively bridges diverse concepts of value, including use and exchange value in economics, personal values in psychology, and cultural, moral, ethical, and esthetic values in society. By offering a unifying perspective, granular interaction thinking theory provides a valuable framework for translating insights from quantum mechanics into socio-cultural, economic, and psychological contexts, enriching theoretical discourse and enhancing analytical effectiveness. Keywords value, interdisciplinary research, entropy, information-processing perspective, social sciences, humanities, Granular Interaction Thinking Theory, GITT, nature-human nexus “Nightingale Elder steps forward to confront Kingfisher: ‒Sir Kingfisher, you told me earlier that the Nightingales have the right argument. How can we now lose? Without hesitation, the Kingfisher replies sternly: ‒True. The Nightingales have the right argument. But, the Magpies have multiple larger-scale right arguments. Understand?” —In “Righteous Judge-bird”;Wild Wise Weird (2024) A Need for a New Paradigm of Understanding Values The world is facing major interconnected crises that threaten societal stability and the planet’s future. Climate change and environmental degradation are among the most urgent challenges, pushing the Earth beyond its safe planetary boundaries. Crossing these thresholds can trigger irreversible cascading effects, posing severe risks to humanity’s survival and development (Armstrong McKay et al., 2022; Richardson et al., 2023). At the same time, widening global inequality is fueling social discontent, driving political polarization and radicalization (Franc & Pavlovi´ c, 2023;Qureshi, 2023). Adding to these pressures is the rapid advancement of technologies, such as artificial intelligence, where controlling its power has become increasingly critical. Unchecked AI development presents multiple risks, including socio-economic disruptions, widespread misinformation and manipulation, increasing concentration of power, and even existential threats to humanity (Marr, 2023;Turchin & Denkenberger, 2020). In such scenarios, 2Evaluation Review 0(0) interdisciplinary knowledge has become critical to help solve these global problems effectively (Ledford, 2015). Addressing interconnected global challenges requires an interdisciplinary understanding of value to guide and drive socio-cultural and economic worldviews and transitions effectively. Values are fundamental in shaping how people think, make decisions, act, and share cultural norms and beliefs, as well as in influencing the foundations and functions of social and economic structures. However, current definitions of value remain inconsistent, even within the same discipline, let alone across different fields of study. This lack of coherence presents a significant barrier to developing holistic frameworks for sustainable and equitable solutions. For instance, the question of “what is value”has long been a subject of debate in economics, where value is central to understanding socio-economic systems and individual economic decisions (Corsi et al., 2024). Various schools of economic thought offer different perspectives on value, including classical economics (i.e., cost of production), neoclassical economics (i.e., marginal utility and relative scarcity), evolutionary economics (i.e., procedural uncertainty and dynamic analysis), bioeconomics (i.e., lowentropy matter scarcity and enjoyment of life), and econophysics (i.e., Information Theory of Intrinsic Value, embodied information, and negentropy), among others (Corsi et al., 2024). Among these approaches, neoclassical economics dominates mainstream economic thought, defining value primarily through marginal utility and relative scarcity (King & McLure, 2014). This perspective considers value an entirely contextual and subjective construct, detached from any objective physical basis. According to neoclassical economics, value is fluid and constantly changing, shaped by individuals’subjective interpretations based on their preference structures at a given moment. This shifts the focus of value from objective, production-based (supply-side) interpretations to subjective, exchange-based (demand-side) ones. As a result, the fair price determined at the supply-demand equilibrium is often used as a proxy for value in economic analysis (Corsi et al., 2024). Environmental economists adopt this neoclassical understanding of value in their efforts to address climate change and environmental degradation by internalizing externalities into market mechanisms (Wiesmeth, 2012). However, this approach has inherent limitations due to the subjectivity of valuation processes, the risks of creating and reinforcing delusions in decision-making and policy-making, and the decoupling of climate change from biodiversity loss (Vuong et al., 2025). These shortcomings prevent it from effectively addressing the climate and environmental crises, highlighting the need for a more comprehensive framework for understanding value. Vuong et al. 3 Therefore, this paper aims to introduce a fresh perspective on understanding value through the information-processing lens of granular interaction thinking theory (GITT) (Vuong & Nguyen, 2024a): the informational entropybased notion of value. This theoretical framework is conceptualized based on the worldview of quantum mechanics (Hertog, 2023;Rovelli, 2018), information theory (Shannon, 1948), and the mindsponge theory (Vuong, 2023), offering a more comprehensive and interdisciplinary approach to understanding value. There are several reasons that we think GITT is a valuable paradigm for understanding value in an era of multiple global crises and rising interdisciplinary science. First of all, GITT is interdisciplinary in nature. The theory originates from the mindsponge mechanism, an information-processing sociocultural framework that later evolved into the mindsponge theory, integrating evidence from the life sciences and neuroscience (De Lange et al., 2018; Fletcher & Frith, 2009;Seth & Friston, 2016;Vuong, 2023). This extension is motivated by the recognition that without accounting for the basic building blocks of nature and humanity, it is difficult to construct reliable theories, frameworks, or bodies of knowledge capable of addressing today’s interdisciplinary challenges—particularly the mismatch between the economic system and environmental sustainability. As the mindsponge theory extends into the domains of environmental and physical issues, its evolution accelerates due to its high compatibility with the worldview of quantum physics. At its core, the theory posits that the world is composed of informational “particles,”interactions, and processes of information exchange. Within this framework, individuals, organizations, humanity, and even living organisms are understood as information-processing systems that strive to maximize perceived benefits and minimize perceived costs (including energy expenditure) in pursuit of the fundamental objective of sustaining existence and fostering development. As external environments change, these systems adjust accordingly in order to adapt and evolve. This worldview bears significant similarities to the principles of quantum physics. Building on these similarities, mindsponge theory has been further developed into GITT, which integrates insights from quantum mechanics and Shannon’s information theory to articulate a more comprehensive worldview—one capable of recognizing the objective values of nature that mainstream economic notions of value often fail to capture. The plausibility of applying core principles from quantum physics and Shannon’s information theory to cognitive and social phenomena rests on the fact that the physical laws governing electrons, atoms, molecules, and energy underpin the structure and functions of cells, the basic units of human life (Ernberg et al., 2022). Schr¨ odinger’sreflections on life as an open system that 4Evaluation Review 0(0) sustains low entropy through the continuous intake of energy and order from its environment further reinforce this rationale (Schr¨ odinger, 1944). In this light, GITT employs Pólya (1954)’s mathematical reasoning methods not only to validate the mindsponge theory but also to employ fundamental mechanisms and principles from quantum physics and information theory to refine concepts, elucidate processes, and enhance the reliability of the theory. This theory has been successfully applied to explain various value-related challenges in social sciences and humanities, including the gender gap and filtering system in knowledge production (Elouaourti et al., 2025;Vuong & Nguyen, 2024b), the weaponization of climate change (Vuong et al., 2023), climate change denialism (La et al., 2024), social interactions and mental health (Li et al., 2025;Wang et al., 2025), and the conceptualization of Nature Quotient (Vuong & Nguyen, 2025). Second, GITT is conceptualized based on the worldview and features of quantum mechanics, allowing it to integrate both objectivity and subjectivity into the notion of value. This perspective is aligned with the econophysics approach (Rodr´ ıguez & C´ aceres-Hern´ andez, 2018;Stanley & Mantegna, 2000), which applies statistical mechanics and nonlinear dynamics to study economic systems, particularly financial markets. However, while econophysics seeks universal patterns but lacks integration with socio-cultural and psychological knowledge, GITT incorporates situational complexity and human cognition dynamics, making it more adaptable for interdisciplinary research on values. Through this new perspective on value, we hope to help social scientists, including economists, view values as dynamic properties that evolve under the right conditions. It also helps equip them to more effectively address emerging phenomena beyond existing value frameworks, such as environmental crises, artificial intelligence (AI), and interdisciplinary information. The paper is organized into four main sections. The first section highlights the importance of a comprehensive, interdisciplinary understanding of value in addressing global challenges and outlines the paper’s objective. The second section introduces a new paradigm of value through the informationprocessing lens of GITT: the informational entropy-based notion of value. The third section applies this paradigm to integrate and expand existing perspectives on economic, cultural, humanistic, and ethical values. Finally, the fourth section discusses the potential applications and future directions of this paradigm in social sciences and humanities research. Granular Interaction Thinking Theory The granular interaction thinking theory, grounded in the principles of quantum mechanics, posits that macroscopic reality emerges from interactions Vuong et al. 5 between quanta at the microscopic level. Quantum physics, as the study of matter and energy in their most fundamental forms, reveals the properties and behaviors of the elementary building blocks of nature. It suggests that humans inhabit a granular universe composed of finite quanta of fields—a condition that also applies to Earth, its ecosystems, and all species, including humans (Rovelli, 2016, 2018;Susskind & Friedman, 2014). A quantum of a field (whether described as a grain or particle) is a discrete unit of energy associated with that field—for example, photons as the quanta of the electromagnetic field (light) and electrons as the quanta of the electron field. Through interactions at the microscopic scale, these quanta generate the macroscopic reality that we perceive in the observable world, that is, the length scale at which objects and phenomena are large enough to be seen by the naked eye without magnification. In physics, information—defined by Shannon as a set of possible alternatives—is a fundamental concept. Each quantum carries its information, meaning that any physical system composed of multiple quanta inherently possesses a corresponding set of information or possible alternatives (Rovelli, 2018). Since the universe functions as a network of interacting physical systems, it also operates as a network of reciprocal information exchange. This aligns with John Wheeler’s“it from bit”concept, which suggests that all physical entities originate from information (Wheeler, 2018). Within the GITT paradigm, there are two primary spectrums: the mind and the environment. The mind functions as an information collection-cum-processor, while the environment serves as a broader information-processing system—such as the Earth system or a social system—that encompasses the mind. The human mind continuously interacts with its external environment, adapting and restructuring itself to sustain its existence. Only systems that effectively manage this interaction survive, grow, and reproduce. In other words, successfully adapting to a dynamic environment requires efficiently managing information—acquiring, storing, transmitting, and processing it. This principle aligns with Charles Darwin’s theory of evolution (Darwin & Wallace, 1858;Darwin, 1859). As quanta, atoms, molecules, and energy are fundamental to the structure and functions of cells, the building blocks that constitute nature and humans (Ernberg et al., 2022;Schr¨ odinger, 1944), the mental processes within the human mind can also be viewed as information processes (Vuong, 2023). Thus, they logically exhibit three main features of the quantum world (Rovelli, 2018): granularity, relationality, and indeterminacy. Granularity This feature implies that information (including energy) in a physical system, such as the human mind, is finite. As the number of information units (or 6Evaluation Review 0(0) “grains of information”) increases, the entropy (uncertainty or missing information) in the human mind also increases. The level of informational entropy within the mind can be calculated using the following formula of Shannon (1948): HðXÞ¼X n i¼1 PðxiÞlog2PðxiÞ HðXÞis the informational entropy of a random variable Xwith possible outcomes fx1,x2,…,xngand corresponding probabilities fPðx1Þ,Pðx2Þ,…, PðxnÞg.PðxiÞis the probability of the outcome xi. Each probability PðxiÞ represents how likely each outcome xiis to occur. In this context, the variable Xcan be interpreted as an individual’s mind in the current state, with inumber of information units. Each information unit has its PðxiÞprobability to be stored and processed within the mind. According to this formula, when the number of information units increases without clear differentiation and prioritization of their importance, informational entropy will rise rapidly, reaching a maximum when all information is equally important, precisely when PðxiÞ¼1 n. In other words, individuals face the highest risk of information loss if they fail to establish a priority system. The more information units are stored and processed within the mind, the more likely they are to be lost or forgotten. Thus, for optimizing finite information within the mind for survival, growth, and reproduction with finite energy, individuals need to evaluate, distinguish, compare, combine, and assign different probabilities of being stored and used to information based on its relevance to survival, growth, and reproduction. Information deemed more essential is assigned a higher probability or energy to maintain and has a greater influence capability over other information within the system. Speaking differently, it is more valuable and can serve as a benchmark to shape future cognitive and decision-making processes (Vuong, 2023). In essence, a person’s values represent information (or possible alternatives) within the mind deemed critical for sustaining their existence, growth, and reproduction. Relationality This feature suggests that world events are always interactions, and all variable aspects of an object exist only in relation to other objects. This means that when information exists within the same system, the internal states and outcomes of that system are determined by the interactions among the information within it. As these interactions occur, the information can influence Vuong et al. 7 one another. Here, “influence”refers to the ability to induce changes in the state of interacting information. A scattering event—where information undergoes deflection, redirection, or a change in its internal state due to interactions with other information—is one of the most common types of interactions in a quantum system (Derezinski & G´ erard, 2013). Such scattering events provide crucial insights into the fundamental properties of the information involved, as they reveal how information is processed, transmitted, and transformed between its initial and final states. Thus, values can be understood as emerging from the interactions between numerous units of information that constitute the mind—including experiences, perceptions, beliefs, biological traits, emotions, and worldviews— along with newly absorbed information from environmental, socio-cultural, and economic contexts. While values themselves are information, they carry a higher probability of being retained within the mind (or assigned higher energy to maintain), distinguishing them from ordinary information. In this context, the assignment of higher “energy”to more important information should be understood in psychological, cognitive, and social terms as the allocation of limited mental and social resources to process and maintain particular informational quanta. In practice, this “energy”corresponds to constructs well-established in psychology, neuroscience, and the social sciences, including but not limited to attention allocation (Anderson, 2013;Jeong et al., 2023;Treisman, 1964), neural activation (Brosch et al., 2012;Le Houcq Corbi & Soutschek, 2024), emotional responses (Brown & Brown, 2006; Verma et al., 2025), and social reinforcement mechanisms (Chen, 2012; Elsayed, 2024;Green & Sergeeva, 2019). The scattering interactions are often analyzed using Feynman diagrams (Feynman, 1949), which visually represent particle exchanges at the quantum level. Thus, we adapted the Feynman diagram and combined it with the mindsponge theory’s diagram to illustrate primary types of interactions that happen between information and values (see Figure 1). In the new diagram, we call QuðiÞ informational quantum, representing information iand QuðvalÞvalue quantum, representing value val. Interactions of informational quanta within the mind and newly absorbed from the environment can be classified into three main types: ·Type 1: the interaction between QuðiÞabsorbed from the environment and QuðiÞwithin the mind. During this interaction, information units are evaluated, distinguished, compared, and combined to generate insights, or synthetic information units, that are beneficial for the mind. Such information units are assigned a greater probability of being stored and used as benchmarks for later mental processes. Meanwhile, informational quanta that are deemed irrelevant or costly for the mind are 8Evaluation Review 0(0) Figure 1. Primary types of interactions between informational quanta, adapted from the visual logic of Feynman diagrams (Feynman, 1949) and mindsponge theory (Vuong, 2023). The diagram represents a mind as a system of conceptual space: the innermost mindset (pink), surrounded by a buffer zone (yellow), all within a perceivable range (outer dashed circle). The straight arrows represent informational quanta, with QuðiÞdenoting an informational quantum and QuðvalÞas a value quantum. The wavy lines illustrate their interaction, symbolizing the exchange of an intermediate quantum of information and energy. Vuong et al. 9 and creates order within the social system, facilitating interactions with lower energy costs. This leads to shared cultural values within a collective, community, or nation. These shared values also include moral and ethical values and esthetic values, which are expressed and disseminated through cultural products, such as arts, dance, literature, fashion, and music, to manifest, shape, and reinforce the society’s values, identity, and structure (Coote & Shelton, 1992;Haidt, 2012;Nguyen, 2024;Vuong et al., 2020). Beyond human interactions, individuals also engage with man-made products (e.g., technology), societal systems (e.g., economic, legal, and political), and the natural world. In stable conditions, these systems and products are relatively fixed, which shapes and reinforces cultural values and consensus. However, changes in the environment (e.g., climate change and biodiversity loss), technological breakthroughs (e.g., artificial intelligence), and social transitions (e.g., economic reforms and wars) create pressures that force individuals to adapt. This leads to the transformation or creation of new cultural values for individuals, groups, organizations, or nations. The transformation or creation of these new values is not deterministic but probabilistic, influenced by objective conditions. Implications and Potential for Future Social Sciences and Humanities Research The informational entropy-based notion of value, derived from granular interaction thinking theory, demonstrates a strong capacity to explain, integrate, and bridge various existing value concepts. By embracing a dynamic and holistic perspective, this approach can unify different types of value—such as use and exchange value in economic activities, personal value in psychological processes, and cultural, moral, ethical, and esthetic values within society—under a common information-processing standard. This convergence not only enhances understanding of value systems across disciplines but also fosters interdisciplinary research. The new concept of value offers a more cohesive and flexible framework for analyzing complex human and societal phenomena. One key area it addresses is the transition of value systems, such as incorporating environmental values into economic structures and embedding them within cultural norms (Nguyen & Jones, 2022). It also provides a foundation for exploring cultural evolution, explaining why certain values that were once widely accepted may no longer be relevant while others endure (Cohen, 2022; Richerson & Christiansen, 2024). Furthermore, the information entropy-based concept of value paves the way for new research directions that evaluate values and make predictions, accounting for uncertainties arising from 16 Evaluation Review 0(0) dynamic interactions at multiple levels, ranging from individuals and small groups to nations, humanity, and the Earth’s ecosystem. From a practical standpoint, insights from these research directions can guide the development of tailored governance, education, and communication strategies that shape and update societal value systems, ensuring they stay aligned with evolving realities. By integrating dynamic interactions and information processing, these strategies may promote more adaptive and resilient social frameworks, strengthening collective decision-making and supporting cultural evolution in response to contemporary challenges. As GITT is a comprehensive framework encompassing diverse concepts to provide a big-picture explanation, it requires testing through multiple hypotheses across varied conditions and contexts to establish its validity. Such testing can be conducted using multiple methods, including Bayesian Mindsponge Framework (BMF) analytics (Vuong et al., 2022). Because GITT is developed from mindsponge theory, the empirical testing methods of BMF can be fully employed to generate testable hypotheses from GITT, similar to those derived from mindsponge theory. Since Bayesian inference treats all properties—including unknown parameters and uncertainties— probabilistically, it enables researchers to examine how the probability of the occurrence of an informational quantum QuðiaÞ, or the probability of the perceived importance of a value quantum, Qu vala ðÞ, varies under different conditions of the mind (the presence of QuðibÞor QuðvalbÞwithin the mind) and its surrounding environment (e.g., the existence of QuðicÞin the external socio-cultural, economic, or environmental setting of the individual). From the probabilistic perspective of Bayesian inference, the PðxiÞin Shannon’s entropy formula is inherently expressed through the variation and shifts in the probabilities of informational and value quanta. Information units with higher probabilities are regarded as QuðvalÞ,reflecting the relative nature of values across different individuals’personal conditions and environmental contexts. An informational quantum QuðiÞin GITT and BMF is best understood as a discrete unit of information that can enter, be processed by, or be filtered out of the mind’s information system. In a cognitive context, QuðiÞwithin the mind may manifest as an idea, memory, belief, perception, or sensory cue— essentially any discrete piece of information that the mind encounters and evaluates. Beyond the mind, QuðiÞexists in the surrounding environment as external informational units, which can take multiple forms, including messages circulating in media, signals in social interactions, institutional rules, or environmental cues such as the sight of polluted water, the sound of traffic, or scientific data in a report. The granularity of QuðiÞis contextdependent and determined by the level of abstraction relevant to the research question. For example, in experimental designs, an informational quantum Vuong et al. 17 might be operationalized as a single message, claim, or stimulus, whereas in more abstract modeling, it could represent a broader construct, such as a belief schema. Thus, granularity is shaped by both theoretical framing and empirical resolution: finer granularity yields more detailed modeling of cognitive processing, while coarser granularity facilitates analysis of larger informational patterns and systemic interactions. The external forms of QuðicÞin the surrounding environment can be collected through multiple methods, including—but not limited to—survey techniques, observational and behavioral tracking, social network analysis, media and discourse analysis, and remote sensing. Meanwhile, to capture QuðibÞor QuðvalbÞwithin the mind, researchers may employ self-report methods (e.g., surveys, interviews, free listing, and cognitive mapping), observational and behavioral proxies (e.g., think-aloud protocols and behavioral experiments), implicit and projective techniques (e.g., implicit association tests and sentence-completion or storytelling tasks), and digital or computational methods (e.g., ecological momentary assessment, text mining, and linguistic analysis). Additionally, BMF is well-suited to handling hierarchical structures, as highlighted by GITT. Specifically, multilevel models in the Bayesian framework assign probability distributions to varying regression parameters, while MCMC techniques address the computational complexity of multilevel modeling and generate posterior distributions (Gelman & Hill, 2007; Wagenmakers et al., 2018). Such Bayesian multilevel modeling is particularly appropriate for estimating variations of information across groups or levels— for example, differences in informational content across conceptual areas (environment, perceivable range, buffer zone, and mindset), across individuals with varying constituents of QuðiaÞand QuðvalaÞ, or across socio-cultural contexts shaping the associations between internal QuðiÞ,QuðvalÞand external QuðicÞ. Moreover, the updating feature of Bayesian inference allows examination of the temporal dynamics of QuðiaÞand QuðvalaÞas they interact with shifting internal states and changing QuðicÞin the surrounding environment. In general, it seems to us that psychological and socio-economic phenomena are deeply interconnected, necessitating interdisciplinary research. However, as the research scope expands, so does informational entropy, increasing uncertainty (Vuong & Nguyen, 2024b). To mitigate entropy (or information loss) and maintain coherence in understanding psychological and socio-economic issues, linking the social sciences and humanities with quantum physics is a good way to ensure we are not following the wrong track (Rovelli, 2018). By “wrong track,”we refer to an understanding that conflicts with the fundamental mechanisms and principles governing the world. 18 Evaluation Review 0(0) In other words, this connection ensures that understanding of psychological and socio-economic phenomena remains consistent with the fundamental mechanisms governing the physical world. In this way, advancements in our understanding of the physical world can significantly contribute to enhancing theory-building in the social sciences and economics. Beyond the core features of quantum mechanics already employed to introduce the new notion of value, additional quantum phenomena—such as superposition, wave–particle duality, entanglement, the Heisenberg uncertainty principle, quantum tunneling, decoherence, and Bose–Einstein condensation—also hold considerable potential for advancing social science research. The granular interaction thinking theory provides a structured framework for translating such advances in quantum theory into insights for the social sciences. By enriching the conceptual vocabulary and methodological toolkit, GITT can enhance the study and explanation of socio-cultural, economic, and psychological phenomena without reducing them to purely physical processes. Nevertheless, it is essential to clarify that we do not rely on physics as direct empirical evidence for explaining economic or social issues. Instead, we draw on physics as a source of conceptualization through mathematical reasoning—namely, generalization, specialization, and analogy. This approach seeks to use mechanisms and principles at the most fundamental level to clarify and refine concepts, processes, and explanatory principles at the cognitive and social levels. In this sense, our use of the quantum analogy is epistemological rather than ontological: it expands the grammar and supports theory-building in social and psychological theory, rather than asserting physical equivalence. At the same time, we acknowledge the risk of over-simplification and reductionism if the unique properties of quantum systems are too literally translated onto social and psychological phenomena. To avoid misinterpretation, it is important to clarify that the analogy serves a conceptual purpose and should not be taken as deterministic. ORCID iD Minh-Hoang Nguyen https://orcid.org/0000-0002-7520-3844 Author Contributions Conceptualization: Q.-H.V. and M.-H.N. Formal analysis: M.-H.N. Investigation: M.- H.N. Resources: M.-H.N. and V.-P.L. Writing—original draft preparation: M.-H.N. Writing—review and editing: M.-H.N. and Q.-H.V. Supervision: Q.-H.V. Project administration: Q.-H.V. All authors have read and agreed to the published version of the manuscript. Vuong et al. 19 Funding The authors received no financial support for the research, authorship, and/or publication of this article. Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. References Anderson, B. A. (2013). A value-driven mechanism of attentional selection. Journal of Vision,13(3), 7. https://doi.org/10.1167/13.3.7 Armstrong McKay, D. I., Staal, A., Abrams, J. F., Winkelmann, R., Sakschewski, B., Loriani, S., Lenton, T. M., Cornell, S. 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