Solving the problem of perception with narrative and symbology

The problem of perception

The problem of perception is a fundamental issue in psychology and cognitive science that has intrigued scholars and researchers for centuries. It centers around the question of how we perceive and make sense of the world through our sensory experiences. This problem delves into the complexities of how sensory information is processed, interpreted, and transformed into our conscious perception of the world around us.

One of the key aspects of the problem of perception is the concept of perceptual processing. This refers to the intricate cognitive processes that occur between the reception of sensory input and our conscious awareness of it. These processes involve various stages, such as sensory encoding, feature extraction, pattern recognition, and ultimately, the integration of sensory information into a coherent perceptual experience (Goldstein, 2020).

Furthermore, the problem of perception also encompasses the debate over the role of top-down and bottom-up processing. Top-down processing involves the influence of our prior knowledge, expectations, and cognitive biases on how we perceive sensory information. In contrast, bottom-up processing emphasizes the importance of raw sensory data in shaping our perception (Palmer, 1999).

This problem has led to various theories and models in psychology and cognitive science, including Gibson’s ecological approach to perception, which emphasizes the importance of information in the environment (Gibson, 1979), and Marr’s computational theory of vision, which focuses on the algorithms and computations underlying visual perception (Marr, 1982).

The challenge of combinatorial explosion

The task of understanding perception is further complicated by the phenomenon known as combinatorial explosion. This concept addresses the vast number of possible combinations and interpretations that can arise from the sensory information received by our senses. Indeed, it has led to a long debate among cognitive scientists over whether or not our problem solving capabilities can be described as computational at all and, if so, to what degree. For example, in a standard game of chess, there are more possible operations (4.2×10⁸⁸) than there are particles in the universe, let alone neurons or neuronal connections in the brain (Vervaeke, 2017). 

Combinatorial explosion becomes particularly evident when we consider the richness and complexity of sensory input. For instance, in visual perception, our eyes receive an immense amount of information about colors, shapes, textures, and spatial relationships. Likewise, in auditory perception, we process a multitude of auditory cues, such as pitch, tone, loudness, and temporal patterns. When all these sensory inputs converge, the number of potential combinations and interpretations becomes overwhelming (Marr, 1982).

This phenomenon is further exacerbated by the fact that our perceptual system must not only process the individual features of sensory information but also integrate them into coherent and meaningful percepts. This integration process involves identifying objects, scenes, and events in the environment, which adds another layer of complexity to the problem of perception.

To address the challenge of combinatorial explosion, our minds employ various strategies and mechanisms. One such mechanism is selective attention, which allows us to focus on specific aspects of sensory input while filtering out irrelevant information. This helps reduce the cognitive load and narrow down the potential combinations that need to be considered (Treisman & Gelade, 1980).

Additionally, our perceptual system relies on prior knowledge, expectations, and contextual information to guide the interpretation of sensory input. This top-down processing helps constrain the range of possible interpretations and makes our perception more efficient (Bar, 2007).

Relevance realization and perception

Relevance realization, as described by cognitive scientist and philosopher John Vervaeke, offers a valuable framework for understanding how the human cognitive system addresses the issue of combinatorial explosion in the context of perception. 

Vervaeke proposes that relevance realization is a central cognitive process that allows us to identify and prioritize information that is meaningful and pertinent to our current context and goals (Vervaeke & Hutto, 2016). In the context of perception, this process plays a crucial role in managing the overwhelming amount of sensory data we encounter. It does so via the following functions:

• Selective Attention: Relevance realization guides our attention to specific aspects of sensory input that are most relevant to our current tasks or objectives. For example, when reading a book, we focus on the text and disregard background noises. This selective attention reduces the number of possible combinations of sensory information that we need to process at any given moment (Desimone & Duncan, 1995).
• Integration of Context: Relevance realization takes into account the broader context in which sensory information is perceived. Our prior knowledge, expectations, and the context in which we find ourselves all influence what we consider relevant. By incorporating contextual cues, we can quickly eliminate many irrelevant interpretations and reduce the cognitive load associated with combinatorial explosion (Bar, 2007).
• Goal-Oriented Processing: Our cognitive system continually evaluates sensory input in light of our goals and objectives. For example, when searching for a particular object in a cluttered environment, we actively seek out information relevant to the target and suppress irrelevant details. This goal-driven processing streamlines perception and reduces the need to consider all possible combinations (Vervaeke, 2021).

The old view of perception vs. where the data now points

The traditional model of perception, often associated with other seemingly common sense (at least under Western physicalism) views in psychology and philosophy, posits that we perceive objects first and then infer their meaning or significance. In contrast, newer frameworks of perception, rooted in ideas like predictive processing, affordances, and the Gibsonian ecological view, suggest that we first perceive meaning and then infer the objects or entities that are relevant to that meaning. Let’s explore these two models in more detail.

Passive Model of Perception (Perceive Objects and Infer Meaning):

In the traditional model of perception, the focus is on the idea that sensory information is processed in a bottom-up manner, with our senses gathering data about the world, which is then analyzed and interpreted by higher-level cognitive processes (Gregory, 1970). According to this model:

• Sensory Input: Our sensory organs detect and transmit raw sensory data (e.g., light waves, sound waves) to the brain.
• Perceptual Processing: The brain processes these sensory inputs to extract features and characteristics of objects in the environment, such as their shape, color, size, and location.
• Object Identification: Once the sensory data is processed, we identify and recognize specific objects or entities in the environment based on stored templates or representations in our memory.
• Meaning Inference: After identifying objects, we subsequently infer their meaning or significance based on our prior knowledge, beliefs, and experiences.

This model suggests that perception begins with the analysis of sensory input and culminates in the attribution of meaning to the perceived objects.

Active model of Perception (Perceive Meaning and Infer Objects):

The newer model of perception, influenced by ideas from ecological psychology (Gibson, 1979), predictive processing (Friston, 2010), and affordance theory (Gibson, 1979; Chemero, 2003), challenges the sequential nature of the old model (a nature that fit well with the old naive realist, Newtonian worldview). Instead, it proposes that perception begins with the extraction of meaning or affordances from the environment, and object recognition follows. Here’s how this model works:

• Affordance Detection: Rather than merely processing sensory data, our perceptual system is attuned to detect affordances, which are opportunities for action or interaction in the environment. For example, we perceive a chair not as a static object with certain visual features but as something we can sit on.
  • Affordances are reciprocal and dialogical between a given agent and their arena. For example, a chair is afforded the property of being a place to sit only by agents to whom the chair can reciprocally afford the property of being “sitters”. A snake would not perceive a chair with the same affordances, then, as would a human, and thus the perceptual models of the world would differ between the snake and the human (though they would still be unique up to an isomorphism with the structure of the reality that both the snake and the human share). 
  • Perception is, thus, an active and participatory process, in which our perspectival knowledge of the world determines the world as we experience it. This is in contrast to the traditional model, which frames perception as a passive process. 
• Predictive Processing: We generate predictions about the sensory input we are likely to encounter based on our expectations and the affordances we detect. These predictions guide our perception and help us prioritize relevant sensory information (Clark, 2013).
• Object Identification: After detecting affordances and generating predictions, we recognize objects or entities in the environment that are relevant to the perceived affordances. This recognition is context-dependent and influenced by the meaning or action potential of the object (Gibson, 1979).

This model highlights that perception is an active, dynamic process that starts with the detection of affordances and is heavily influenced by our expectations and goals. It suggests that our perception is not just about identifying objects but understanding their potential significance in our interactions with the environment.

In other words, we don’t perceive objects and infer meaning. Rather, we perceive meaning and infer objects. 

Perception as a narrative built from a hierarchy of patterns

This view can be conceptualized as a hierarchy of patterns and mental categories that form a series of narratives about entropy (uncertainty) reduction. That is, solving problems–the central structure of any great story. Specifically:

• Hierarchy of Patterns and Mental Categories: In this framework, perception involves the recognition of patterns and mental categories at multiple levels of abstraction. These patterns and categories encompass a wide range of sensory information, from low-level features like color and shape to high-level concepts like objects, actions, and intentions (Bar, 2007). This hierarchy reflects our capacity to simultaneously process information at different levels of granularity.
• Narratives: Rather than perceiving isolated objects or features, our perceptual system constructs narratives or stories about the environment and our role in it. These narratives are composed of interconnected patterns and categories that give meaning to what we perceive (Vervaeke & Hutto, 2016). For example, when you enter a kitchen, your perceptual system constructs a narrative about preparing a meal, which guides your attention and actions.
• Problem-Solving: At its core, perception is a problem-solving process. The narratives formed through perception are like problem-solving scripts or scenarios. They guide our interactions with the environment by highlighting relevant affordances and predicting the sensory information we should encounter (Clark, 2013). These problem-solving narratives help us navigate the world efficiently and adapt to changing contexts, taking us from our inciting incident (the initial state), through rising conflict, to an insight, through falling action, and then to a resolution and ending, in which the problem is either solved or unsolved.
  • In this way, the literature on narratives and symbolism can be applied to the problem of perception. 
• Contextual Adaptation: The narratives in this framework are highly context-dependent. They adapt to the specific situation, taking into account our goals, intentions, and expectations. For instance, the narrative of “preparing a meal” in the kitchen will differ depending on whether you are looking for a snack or cooking a family dinner.
• Predictive Nature: Predictive processing is a key element of this framework. The narratives we construct generate predictions about sensory input, allowing us to anticipate what we will perceive next. These predictions serve as a feedback loop, guiding our attention and shaping our perception. As we’ll explore in more depth below, our predictive modeling is meant to find the path of least entropy/uncertainty (Friston, 2010).
• Meaningful Action: Ultimately, the narratives formed through perception guide us toward meaningful actions. Whether it’s reaching for a cup of coffee or avoiding an obstacle while driving, our perception is intimately tied to our capacity to act in the world (Chemero, 2003).

Once again, this view highlights the active, adaptive, and dynamic nature of perception, emphasizing the role of affordances, context, and prediction in shaping our perceptual experiences. This perspective challenges the traditional view of perception as a passive process of object identification, and instead portrays it as a fundamental aspect of our problem-solving and sense-making abilities.

Affordances are stories about pathways to goals

The concept of narratives within this framework of perception can be extended to include the idea that affordances entail pathways to goals, which can be seen as stories with narrative structures. Within these narratives, we can identify tools that facilitate progress toward a goal and obstacles that hinder advancement, introducing elements of tension and resolution akin to storytelling. This perspective emphasizes the dynamic and goal-oriented nature of perception and its close alignment with the structure of narratives. 

As previously explained, affordances refer to the opportunities for action or interaction that the environment offers to an organism, and vice versa, in reciprocal fashion (Gibson, 1979). These affordances are often tied to specific goals or intentions. For instance, a chair affords sitting, and a doorknob affords turning and opening the door, but only to agents that afford “sit-ability” and “turnability”. 

Within the narratives of affordances, tools and obstacles emerge as key elements. Tools are objects or features in the environment that bring us closer to our goals. For example, a fork is a tool when the goal is eating a meal. In contrast, obstacles are elements that hinder progress or introduce challenges. A locked door is an obstacle when the goal is to exit a room. Tools and obstacles introduce tension and complexity into the narrative. The pathway from perceiving an affordance to achieving a goal has a narrative structure with elements such as a beginning (perception of affordance), middle (engagement with tools and obstacles), and an end (goal attainment). This narrative structure reflects our active engagement with the environment, where we navigate through a sequence of events to achieve our objectives (Bruner, 1990).

As in traditional narratives, the presence of obstacles within the pathway introduces tension. Overcoming these obstacles and successfully reaching the goal provides a sense of resolution and accomplishment (Vervaeke & Hutto, 2016). The specific narrative that unfolds depends on the context and the individual’s goals. Different narratives can emerge from the same affordance based on the tools and obstacles present and the unique goals of the individual (Chemero, 2003). For example, a football player can only be a football player on a football field, and a football field can only be a football field if a football player is present. As such, the agent-arena relationship is at the heart of this dynamic. 

Predictive processing, a key aspect of this framework, involves generating expectations and predictions about sensory input (Clark, 2013). These predictions play a role in shaping the narrative as we anticipate the outcome of our actions and adjust our behavior accordingly.

Entropy, uncertainty, and free energy: the physics of perception

Richard Feynman’s “Path Integral: Formulation of Quantum Dynamics” provides a fascinating connection to the concepts of reducing entropy and uncertainty, which can be seen as isomorphic to the narratives entailed in perception within the framework of affordances. 

In Feynman’s path integral formulation, particles are considered to take all possible paths from an initial state to a final state, and each path contributes with a probability amplitude. The summation of these probability amplitudes yields the probability distribution for the particle’s trajectory. This approach acknowledges the fundamental uncertainty in quantum mechanics, where particles exhibit wave-particle duality and do not follow a single deterministic path (Feynman, 1948).

Similarly, in the framework of perceptual narratives, individuals explore various pathways (i.e., interactions with the environment) to achieve their goals. These pathways are imbued with tools (facilitating progress) and obstacles (introducing uncertainty and tension). By navigating these narratives, individuals reduce uncertainty and achieve their objectives (Vervaeke & Hutto, 2016).

There is an isomorphism between Feynman’s “sum over paths” and the narratives of perception. Just as particles explore multiple paths to reach their final states, individuals perceive affordances and engage in multiple interactions with their environment to attain their goals. Each interaction is akin to a “path” in the narrative. The probabilistic nature of particle trajectories and the dynamic, context-dependent nature of perceptual narratives both reflect the inherent uncertainty and complexity of their respective domains.

In both quantum mechanics and perception, the reduction of entropy (uncertainty) occurs through interaction and observation. In quantum mechanics, particles only have defined physical existence and properties once measured. Only then is the uncertainty about a particle’s position or momentum reduced. In perceptual narratives, interaction with the environment, guided by the detection of affordances and the overcoming of obstacles, reduces the uncertainty regarding the outcome of actions (Clark, 2013).

Just as different initial and final states in quantum mechanics yield different probabilities for particle trajectories, variations in goals and environmental contexts lead to different probabilistic narratives in perception. Different narratives emerge based on the affordances detected, tools used, and obstacles encountered.

In addition to quantum mechanics, this framework of perception also converges with thermodynamics. Karl Friston’s work on the Free Energy Principle (FEP) has profound implications for our understanding of perception and its role in reducing entropy and increasing informational certainty. It provides a unifying framework in neuroscience and cognitive science that posits that biological systems have evolved to minimize free energy or surprise (Friston, 2010). In the context of perception, free energy represents the difference between the sensory input we receive and our internal predictions or models of what that input should be.

According to the FEP, perception can be viewed as a process of making inferences and predictions about the sensory input we encounter. We continuously generate hypotheses or predictions about the world based on prior knowledge and sensory evidence.

Entropy in this context refers to the level of uncertainty or surprise associated with sensory input. When sensory input matches our predictions, entropy is low, and there is no surprise. However, when there is a mismatch between predictions and input, entropy increases, signaling a prediction error or surprise. The primary goal, according to the FEP, is to minimize free energy or surprise. To achieve this, we continually update our internal models or generative models based on incoming sensory information. When predictions align with sensory input, free energy is minimized, and we achieve a state of informational certainty.

Once again, perception is not a passive process but an active one. We actively select actions and gather sensory data that are most likely to minimize free energy. This process, known as active inference, involves both perception and action, as a given agent interacts with the environment to test and refine its predictions (Friston et al., 2017).

The FEP suggests that we perceive the world in a way that minimizes surprise or uncertainty. Our predictions about what we should perceive are continuously updated based on sensory input, and our perceptual experiences are shaped by these predictions. When our perceptual experiences match our predictions, we achieve a state of reduced entropy and increased informational certainty. Friston’s work highlights the adaptive function of perception. By reducing entropy and increasing informational certainty, our perceptual system helps us navigate the world effectively, make sense of complex environments, and respond to changes and challenges.

The Interface Theory of Perception and the Fitness-Beats-Truth Theorem

Donald D. Hoffman’s Interface Theory of Perception proposes that our perceptual experiences are not direct representations of an objective external reality but rather interfaces that evolved for fitness (Hoffman & Singh, 2015). According to this theory:

• Fitness-Driven Perception: Evolution has shaped our perceptual systems to prioritize fitness (survival and reproduction) over truth. Our perceptions are adapted to help us survive and reproduce, not to accurately represent the world.
• Perceived Reality: The perceptual interface provides us with a simplified and user-friendly version of reality that is tailored to our goals and needs. It may omit or distort information that is irrelevant to our fitness goals, though an isomorphism between the interface and reality, in and of itself, necessarily holds. 
• Multimodal Interfaces: Different organisms may have different perceptual interfaces based on their ecological niches and fitness requirements. These interfaces would explain and converge the concept of affordances, to which a reciprocal relationship between agent and arena is central. 
• Structural Integrity: To perceive reality as it is, in and of itself, we would need to mirror in our internal state what exists in our external state. However, our external state, as we’ve already discussed, is highly entropic and combinatorially explosive. As such, if we were to mirror it within our internal state, we would literally dissolve into entropic soup, for we would be unable to maintain our structural integrity. To combat this, the interface of perception acts as a Markov blanket, separating out internal and external states (Kirchhoff et al, 2018). It provides us with a simpler, symbolic, and isomorphic representation of reality, in and of itself, so that we can work with that reality without falling prey to the endless tide of entropy. 

Similar to a video game’s virtual world, the interface serves as a language that allows us to read incoming information, cognize it, and then act back upon that world through the same interface, and thus in the same language. 

As stated above, perception serves as the “read-write” functionality inherent to conscious agents. 

Additionally, the Fitness-Beats-Truth Theorem, proposed by Hoffman and Singh, states that in the competition between organisms with true perceptions and organisms with fitness-driven perceptions, the latter will always outcompete the former. This theorem suggests that organisms with perceptions optimized for fitness have an evolutionary advantage (Hoffman & Singh, 2012).

• Selective Advantage: Organisms with perceptions that prioritize fitness are more likely to survive and reproduce successfully because their perceptions are tailored to the practical demands of their environment. In other words, their perceptual narratives better reduce entropy (uncertainty) than those of organisms who perceive the truth of reality. 
• Implications for Perception: This theorem implies that our perceptual systems have evolved not necessarily to provide us with an accurate representation of objective reality but to support our biological fitness. As a result, our perceptions may prioritize certain aspects of the environment over others, emphasizing what is relevant to our survival and well-being.

Perception is the language that carries affordance narratives

Of course, in order for a narrative to convey meaning, it requires a linguistic medium. It is in this sense that perception must, itself, be a language. 

The idea that perception can be considered linguistic, with a syntax isomorphic to the patterns of meaning in reality, is a concept rooted in cognitive science and philosophy of mind. It suggests that our perception of the world is akin to reading a language, where objects act as physical symbols that instantiate meaning, much like letters written on a page.

In this view, the physical world is full of objects that serve as symbols (and, indeed, is itself one unitary symbol containing the entire multiplicity of apparently distinct symbols). These objects are not mere sensory data but, as we’ve seen, are imbued with meaning. When we perceive an object, we are not simply registering sensory features; we are interpreting a symbol that represents something in our cognitive framework (Barsalou, 1999). That symbol, like a letter or a word on a page, can be analyzed as having any number of markings (properties) that make it up, but the markings (properties) must be interpreted within a cognitive and linguistic structure in order to be realized. 

Just as a language has a syntax governing the arrangement of words and symbols to convey meaning, perception must have its own syntax. This syntax dictates how sensory features are combined and organized to form meaningful percepts. It is this structure and ruleset that make reality intelligible to us, allowing us to perceive affordances and construct the narratives necessary for accessing reality via perception. Without it, we would not be able to apprehend reality at all. Indeed, reality must be intelligible, and so the structure of reality and the syntax of our perception must be isomorphic (Santos, 2023). 

Similar to how linguistic rules determine how words are combined to form meaningful sentences, perceptual processes involve the association of sensory features based on cognitive rules and expectations (Hoffman & Singh, 2015). These rules help us realize objects and scenes in our environment and understand their meaning. Moreover, just as language comprehension relies on context and prior knowledge, perception is highly context-dependent. Our expectations, beliefs, and cultural background influence how we interpret sensory input and assign meaning to objects (Gibson, 1979).

Much like language is multimodal (e.g., spoken, written, signed), perception also involves multiple sensory modalities (e.g., vision, audition, touch). These modalities work together to construct a coherent and meaningful perceptual experience (Calvert et al., 2004). Perception, like language comprehension, is not a passive process. It is dynamic and active, involving constant interaction with the environment. Just as we actively construct meaning from words and sentences, we actively construct our perceptual experiences by exploring and interacting with the world (Clark, 2013).

In essence, perception is a language that allows us to “read” reality via incoming information, and then “write” back onto reality via our actions. 

In this way, the Interface Theory of Perception and the Fitness-Beats-Truth Theorem suggest that our perceptual language is adapted to serve our evolutionary goals rather than to provide an objective or truthful representation of the world, though a structural isomorphism must exist between the truth of reality, in and of itself, and our perceptual linguistic syntax. In essence, perception can be considered an evolved reality “self-simulation” (Santos, 2023).

This has profound implications for fields such as ontology and epistemology. After all, since we can only ever engage with the world through our perceptual experiences of it, any “thing” that is unintelligible to our perception might as well not exist. The structure/syntax of our perception, therefore, necessarily tells us something about the nature of reality. This can be explored in depth in the paper, “What The Hard Problem Of Consciousness Misses: The Case For A Coherent Reality Theory”.

Ruminations on the implications for the study of ethics

While ethics is not the primary field of study relevant to this piece, the framework of perception that has been outlined herein has potential implications for our understanding of ourselves, each other, and entire societies. The following are meant to be ruminations and speculative thinking, and are not to be taken as part of the argument proper that has been laid out up to this point.

As we’ve covered so far, perception is about reducing uncertainty. Therefore, here’s a hypothesis: by reducing entropy or uncertainty, perception plays a key role in fostering ethical interactions, shared identities, and social order in the following ways:

Perception, Uncertainty, and Ethical Paths

• Reducing Uncertainty: Perception can be seen as a process of reducing uncertainty or entropy. When individuals engage in ethical interactions, they often seek to reduce uncertainty about the intentions, actions, and behaviors of others.
• Trust and Predictability: Ethical behavior often involves trust, predictability, and a shared understanding of norms and values. When individuals can predict each other’s actions and behavior with confidence, it reduces uncertainty and contributes to ethical relationships.

Shared Identity and Love

• Shared Identity: Shared identity, which can be broadly construed as a form of social cohesion, plays a significant role in ethical interactions. When individuals or groups share common goals, values, and narratives, it fosters a sense of unity and belonging.
• Love as a Metaphor: Love, in this context, can be seen as a metaphor for a strong sense of connection and empathy between individuals or groups. When there is a shared identity and mutual understanding, it can lead to feelings of affinity and cooperation that are often associated with the concept of love.
  • In Vervaeke’s framework, love is not merely an emotion or feeling but a cognitive and existential process. It involves a profound form of knowing and understanding the other person, encompassing their thoughts, feelings, desires, and experiences (Vervaeke & Hutto, 2016). This perspective emphasizes that love is not superficial but involves a deep, empathetic, and reciprocal connection where individuals seek to understand each other’s inner worlds and perspectives (Hutto, 2017). Vervaeke’s view suggests that love is not a passive emotion but an active, ongoing process of knowing and growing together with the other person. It involves continuously deepening one’s understanding of the other’s evolving self. In other words, love is a kind of knowing that, in its purest form, entails an identity relationship.

Social Order and Shared Narrative

• Shared Narrative: Social order emerges when multiple individuals or groups come together under a common narrative or set of shared beliefs. This shared narrative provides a framework for understanding the world and guides ethical behavior within a society. These society-level narratives are macrocosms of the affordance narratives of individuals with other individuals within that social order.
• Alignment of Perceptions: Ethical behavior is facilitated when individuals within a society have aligned perceptions of reality. Shared narratives help align perceptions and expectations, reducing conflicts and promoting cooperation (Durkheim, 1893).

Ethical Relationships and Predictability

• Predictability and Reduced Entropy: Ethical relationships often involve predictability, where individuals can anticipate each other’s actions and responses. This predictability reduces the entropy or uncertainty in interactions. After all, we’re part of each other’s environments. 
• Reciprocity and Cooperation: Ethical behavior is also characterized by reciprocity and cooperation. When individuals trust that their actions will be reciprocated in kind, it fosters ethical relationships and social order (Axelrod, 1984).

Social Contract Theory

• Relevance to Ethics: The hypothesis aligns with ideas in social contract theory, where individuals come together to form a society based on shared norms and values. This social contract helps reduce uncertainty and promote ethical behavior within the society (Hobbes, Locke, Rousseau).

Challenges and Considerations

• Diversity of Perspectives: In diverse societies, different groups may have contrasting narratives and identities. Managing these differences while maintaining social order and ethical behavior can be a challenge.
• Ethical Dilemmas: Ethical dilemmas often arise when individuals or groups have conflicting goals or values. Resolving these dilemmas requires careful consideration of shared identity and ethical principles.
• Maintaining plurality in the hierarchy of identities. The challenge for any society, then, is to find the balance between a pluralistic multiplicity of perspectives, identities, and ideas with (and within) an overarching identity that supervenes on that plurality. In essence, freedom must be maintained via the promotion of plurality, as in a democratic system. Simultaneously, that plurality must give to and take from a higher identity, which distributes over the plurality, thereby providing a common order that reduces entropy between individuals, communities, and societies, themselves.
  • If done correctly, a society can avoid the dangers and atrocities of movements such as the fascism and Marxism of the previous century. If done poorly, then either the higher order will subjugate the plurality of identities on which it supervenes (authoritarian nationalism), or the plurality of identities will not cohere into a single order and society will be too entropic to maintain its structural integrity (radical identity politics and anarchy). 

When individuals or groups align their perceptions of reality, form shared identities, and engage in ethical interactions characterized by predictability and reciprocity, it can contribute to a sense of unity and cooperation, akin to love, within a society.

Conclusion

The irony of this framework of perception, wherein we perceive meaning and then infer objects, is that it leads us back to an ancient worldview that values symbology, metaphor, and narrative over literalism. For instance, an ancient audience perceiving an object would not just ask materialistic questions such as, “How does it work?” and “What material is it made of?”, but also deeper, complementary questions such as, “What does it mean?” and “What truth does it embody?” (Pageau, 2018). 

Such a worldview prioritizes meaning over any specific material properties, just as this model of perception does. This perspective resonates with the idea that symbolism and narrative have played significant roles in human cognition and understanding throughout history, far more fundamentally than our older paradigms assumed. It is that isomorphic through-line of intelligible meaning across the structure of reality, the syntax and symbols of our perception, the structure of our cognition, and the syntaxes of our natural and formal languages, that makes reality intelligible. 

For instance, we perceive a “tree” because that is the proper symbol to convey what a “tree” means and what truth it embodies, as it is relevant to our fitness and survival. The concrete, physical symbol instantiates meaning, while the meaning reciprocally imbues the symbol with intelligibility. This dynamic will sound familiar to anyone with a knowledge of ancient myths in which some version of Heaven (meaning) and Earth (matter) form a union, with us as a combination of the two and localized in between. Conscious agents perceive each other and our own bodies as material forms but, of course, we are also conscious entities with conceptual faculties. As such, we act as mediators who facilitate that unity of meaning and matter. That same dynamic also parallels the way in which words and sentences (material) are instantiations of language and syntax (meaning). 

Throughout this extensive discussion, we’ve ventured into the intricate landscape of perception, cognition, consciousness, and their profound intersections with philosophy, neuroscience, and psychology. Our exploration has uncovered several key insights that offer a deeper understanding of how we make sense of the world around us.

At the heart of this framework is the realization that perception is a dynamic and active process. It’s not simply a passive detection of external reality; rather, it involves the active perception of meaning. We engage in the continuous generation of hypotheses and predictions, actively shaping our understanding of the world. The concepts of narratives and affordances have emerged as a powerful lens through which to view our interactions with the environment. Narratives, akin to stories, reveal the dynamic, goal-oriented nature of our engagement with the world, while affordances represent the pathways leading to our objectives.

In conclusion, we have revealed the intricate and multifaceted nature of perception and its far-reaching implications. We have witnessed how it transcends disciplinary boundaries, with cognitive science, philosophy, neuroscience, and psychology converging to illuminate the depths of consciousness and meaning-making. The exploration of perception encourages us to reconsider how we perceive the world and our place within it, offering insights into the complex processes that mold our comprehension of reality.

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