A Confabulation Engine:
The Architecture of Perceived Reality

Abstract

This Material introduces the Confabulation Engine, a groundbreaking theoretical framework for understanding the construction of subjective reality. We posit that consciousness, and indeed any sufficiently complex information-processing system, does not passively receive and process information, but actively *constructs* a coherent narrative – a "confabulation" in the most fundamental sense – to bridge the inescapable gap between incomplete sensory data and the demands of a coherent internal model. This is not presented as a malfunction or a secondary process, but as the *primary* mode of operation for any system navigating an environment characterized by inherent uncertainty and irreducible complexity. We synthesize insights from neuroscience, artificial intelligence, philosophy of mind, and theoretical physics to propose a mathematical formulation of this engine, demonstrating its consistency with established principles – from Plato's Allegory of the Cave to modern deep learning – and exploring its far-reaching implications for epistemology, ethics, and the nature of self. We argue that "reality," as experienced, is not a direct representation of an objective external world, but rather a continuously refined, best-fit approximation – a dynamic and evolving *fiction* necessitated by the fundamental limitations of knowledge. We *reject* the notion of qualia as superfluous and misleading, offering instead a purely representational, computational account of subjective experience.

Beyond the Cartesian Theater

The enduring philosophical debate concerning the nature of consciousness and the relationship between subjective experience and objective reality remains a central – and perhaps *the* central – challenge across multiple disciplines. While traditional views, stretching back to Plato and his Allegory of the Cave (Plato, c. 380 BCE), often assume a relatively direct correspondence between perception and reality (or at least, a knowable relationship between shadows and reality), advances in neuroscience and cognitive science reveal the profoundly constructive nature of perception. The brain actively filters, interprets, and, crucially, *fills in* missing information, creating a seamless and coherent experience even in the face of fragmented, noisy, and ambiguous sensory input. Descartes' (1641) famous "cogito ergo sum" grappled with this very problem, highlighting the inherent uncertainty of all knowledge except the existence of the thinking self. Kant (1781/1787) further developed this line of inquiry with his transcendental idealism, distinguishing between the phenomena we experience and the noumena (things-in-themselves) which are inherently unknowable.

This Material builds upon these foundational philosophical insights and integrates them with modern scientific findings, but it moves *beyond* them by embracing a radical *eliminativism* regarding qualia. We do not merely acknowledge the constructive nature of perception; we assert that the very notion of a "raw feel," a subjective experience *separate* from the functional and representational properties of neural activity, is a fundamental error. We take direct inspiration from the neurological phenomenon of confabulation, typically observed in patients with specific brain lesions (Ramachandran & Blakeslee, 1998). In such cases, individuals unconsciously fabricate memories or explanations to reconcile inconsistencies or fill gaps in their awareness. However, we argue that this seemingly pathological process is not an anomaly, but rather a *universal* and *necessary* mechanism, amplified in neurological disorders but fundamentally present in all forms of cognition, from the unconscious inferences of Helmholtz (1867) to the predictive processing models of Friston (2010) and Clark (2013). We propose the term "Confabulation Engine" to denote this core process, elevating confabulation from a symptom of impairment to the very *engine* of perceived reality, echoing the insights of James (1890) regarding the active and selective nature of attention and consciousness, and aligning with the connectionist and computational perspectives championed by Hinton.

The Confabulation Engine:

We propose a novel theoretical framework, the Confabulation Engine, to model the fundamental process by which *any* sufficiently complex information-processing system constructs its perceived reality. This framework is not merely descriptive; it posits a *necessary* and *universal* mechanism, mathematically formalized above, that underpins all cognitive processes. The equation is not a simple metaphor, but rather a concise representation of a dynamic, iterative process with profound implications. We define each term below, followed by a series of theoretical postulates derived from this core formulation.

Definitions:

• C: The Confabulated Reality. This represents the final, synthesized subjective experience – the *phenomenal world* as it appears to the system. It is a holistic, integrated model encompassing perception, memory, and expectation; a narrative constructed to maximize internal coherence and predictive power. C is *not* a passive reflection of external reality but an *active interpretation*, a best-fit approximation given inherently incomplete and ambiguous data. It is the "story" the system tells itself about the world and its place within it. Crucially, *C* is not a "picture" in a Cartesian theater, but a *functional state* of the system, a dynamic pattern of activity that *enables* adaptive behavior. There are no "raw feels" separate from the representational and computational properties of this state.
• ƒ: The Non-linear, Probabilistic Transformation Function. This function is the operational core of the Confabulation Engine, embodying the inherent stochasticity and non-linearity of cognitive processes. It is *non-linear* because small changes in input (*i*) or the internal model (*m*) can lead to disproportionately large, and often unpredictable, changes in the confabulated reality (*C*). This reflects the complex, interconnected nature of neural networks and cognitive architectures. ƒ is not a fixed algorithm, but a *learned* function, constantly shaped by experience. It is *probabilistic* because it does not operate deterministically; rather, it assigns probabilities to different interpretations of the available information, reflecting the inherent uncertainty and ambiguity in both sensory input and internal representations. ƒ incorporates learned biases, heuristics, and prior beliefs, functioning as a dynamic, high-dimensional attractor landscape. The system seeks stable states (interpretations) within this landscape that minimize a generalized "cognitive free energy," a concept deeply connected to Friston's Free Energy Principle. The dynamics of ƒ are best understood as implementing a form of *approximate Bayesian inference*, where the system attempts to infer the most probable causes of its sensory input given its prior model.
• i: Input Data. This encompasses the totality of information available to the system, both external and internal. This includes raw sensory data (visual, auditory, tactile, olfactory, gustatory), proprioceptive and interoceptive feedback, and retrieved memories (episodic, semantic, procedural). Critically, *i* is *always* incomplete, noisy, and ambiguous. Sensory organs provide only a limited and filtered "window" onto the external world, and internal representations (memories) are themselves subject to degradation, distortion, and reconstruction. This inherent incompleteness is a fundamental constraint on any information-processing system, and it is this constraint that necessitates confabulation. *i* is not a "picture" of reality, but a set of signals that *constrain* the possible interpretations generated by ƒ.
• m: The Internal Model of the World. This represents the system's accumulated knowledge, beliefs, expectations, schemas, and assumptions about itself and the environment. *m* is a vast, dynamic, and *inherently incomplete* network of interconnected representations, constantly updated through experience and learning. It functions as a Bayesian prior, providing a probabilistic framework for interpreting new input data (*i*) and generating predictions about future events. The structure of *m* reflects the system's developmental history, its cultural context, and its individual trajectory. It is not a monolithic entity, but rather a hierarchical, multi-scale organization of interacting models. Importantly, *m* includes a model of the *self*, which is also a confabulation.
• Ω: The Unknowable. This term represents the fundamental, irreducible uncertainty that pervades reality. Ω encompasses both *epistemic limitations* (what *can* be known in principle, but is currently unknown) and *ontological indeterminacy* (inherent randomness or uncaused events at the most fundamental level of reality, as suggested by quantum mechanics). Ω is *not* simply "missing data"; it reflects the limitations imposed by the finite nature of any information-processing system, the constraints of its sensory apparatus, the inherent indeterminacy described by quantum mechanics, and the Gödelian limits on the completeness of any formal system. Ω acts as a *multiplicative factor*, fundamentally limiting the precision with which *C* can *ever* correspond to an external, objective reality. It represents the inherent "noise floor" of existence, the inescapable gap between any model and the totality of being.

Theoretical Postulates and Theorems:

Postulate 1 (The Primacy of Confabulation): The construction of a coherent narrative (confabulation) is not a secondary or compensatory process, but the *primary* mode of operation for any sufficiently complex information-processing system operating in an environment characterized by uncertainty and incomplete information. Confabulation is not a bug, but a *feature* – the very mechanism by which a system makes sense of the world and itself.

Theorem 1 (The Confabulation Theorem): For any system governed by the equation C = ƒ(i, m) * Ω, where Ω > 0, there exists *no* isomorphism between the confabulated reality (C) and any objective, external reality. That is, a perfect, one-to-one mapping between the internal representation and the external world is *impossible*. The system *cannot* have direct access to "things-in-themselves."

Proof (sketch): Since Ω represents irreducible uncertainty, any transformation of *i* and *m* multiplied by Ω will necessarily introduce a degree of divergence from a hypothetical "objective" reality. The non-linearity of ƒ further amplifies this divergence, ensuring that even infinitesimal uncertainties in *i* or *m* can lead to significant differences in *C*. This is a direct consequence of the mathematical formulation and the definitions of the terms. Furthermore, Gödel's Incompleteness Theorems demonstrate the inherent limitations of any formal system (such as *m*) to fully capture all truths about the system it models. Finally the inherent randomness of the universe modeled by Ω ensures the non-isomorphism.

Postulate 2 (Recursive Confabulation and Self-Construction): The confabulated reality (C) from one iteration of the engine becomes part of the input data (i) for the subsequent iteration. This creates a powerful feedback loop, a recursive process where the system's constructed reality shapes its future perceptions and interpretations. This recursion is the foundation of learning, adaptation, and the development of a stable internal model.

Theorem 2 (The Self-Confabulation Theorem): Any system governed by recursive confabulation (Postulate 2) will inevitably construct a model of *itself* as part of its internal model (*m*). This self-model, while necessary for self-regulation, prediction, and agency, is *itself* a confabulation, subject to the same limitations and uncertainties as the model of the external world. The "self" is not a pre-existing entity, but a *construct* of the Confabulation Engine.

Proof (sketch): The recursive application of the Confabulation Engine, where C feeds back into *i*, necessitates the inclusion of internal state information within the input data. Over time, the function ƒ will operate on this internal state information, leading to the formation of a representation within *m* that corresponds to the system itself. Since this representation is constructed through the same confabulatory process, it is subject to the same limitations and uncertainties as any other component of *m* or *C*. The self-model is, therefore, a *necessary fiction*, a useful approximation that allows the system to regulate its behavior and interact with the world, but not a direct reflection of some underlying, essential self. The self is a narrative, continuously updated and revised by the Confabulation Engine.

Postulate 3 (Hierarchical Confabulation): Confabulation occurs at multiple levels of abstraction within the system, from low-level sensory processing to high-level cognitive functions. Higher-level confabulations are built upon the outputs of lower-level confabulations, creating a nested hierarchy of constructed realities. This hierarchical structure allows for efficient representation and processing of complex information, mirroring the hierarchical organization of the brain.

Theorem 3 (The Emergence Theorem): Consciousness, as a unified and coherent subjective experience, is an *emergent property* of hierarchical confabulation. It arises from the interaction of multiple levels of confabulatory processes, culminating in a meta-narrative that represents the system's overall state and its relationship to the environment. There is no single "seat of consciousness," but rather a distributed, dynamic process of narrative construction. There are no qualia, only increasingly complex and abstract representations within the hierarchy.

Proof (sketch): Lower-level confabulations (e.g., edge detection in vision, phoneme recognition in hearing) provide the building blocks for higher-level confabulations (e.g., object recognition, sentence comprehension). The integration of these multiple levels of representation, mediated by the recursive application of ƒ, leads to the emergence of a unified, coherent experience – a "story" that the system tells itself about its ongoing interaction with the world. This emergent narrative is what we *experience* as consciousness, but it is, at every level, a *constructed* reality, not a direct perception of an objective world. The "unity" of consciousness is itself a confabulation, a useful simplification that masks the underlying complexity of the distributed computational processes. The feeling of "what it's like" is simply the functional role of these integrated representations within the system.

Postulate 4 (The Intractability of Optimal Confabulation): Finding the *globally optimal* confabulated reality (C) – the one that perfectly balances internal consistency, predictive power, and correspondence with external reality – is, in general, a computationally intractable problem.

Theorem 4 (The Heuristic Approximation Theorem): Any real-world system implementing the Confabulation Engine must necessarily rely on heuristics and approximations to construct *C* within finite time and with finite computational resources. These heuristics, while generally effective, introduce systematic biases and limitations, further contributing to the divergence between *C* and any objective reality. The mind is not a perfect Bayesian inference machine; it is a *pragmatic* confabulator, optimizing for "good enough" solutions rather than absolute truth.

Proof (sketch): The space of possible confabulations is vast and high-dimensional, growing exponentially with the complexity of *i* and *m*. An exhaustive search for the optimal *C* would be computationally prohibitive for any system with limited resources (which is *any* real-world system). Therefore, the function ƒ must incorporate heuristics and approximations that allow it to efficiently explore the space of possibilities and converge on a "good enough" solution, even if that solution is not globally optimal. These heuristics are often learned through experience and can be influenced by factors such as prior beliefs, emotional states, and social context. This aligns with the concept of bounded rationality in cognitive science and the use of approximation algorithms in computer science. This problem is analog to NP problems.

The Eliminative Materialism of Confabulation

The Confabulation Engine framework necessitates a radical rethinking of traditional philosophical concepts, most notably the notion of "qualia." We argue that qualia, as typically conceived – the ineffable, intrinsic, private, and directly apprehended properties of subjective experience – are not merely problematic, but *nonexistent*. They are philosophical phantoms, vestiges of a Cartesian dualism that has no place in a scientific understanding of consciousness.

Our position is a form of *eliminative materialism*, but it is not simply a denial of subjective experience. We do not deny that there is something it *is like* to see red, to feel pain, or to experience joy. What we deny is that this "what it's likeness" is anything *over and above* the functional and representational properties of the neural activity that constitutes the experience. The "feel" of seeing red is *identical* to the complex, high-dimensional, distributed representation of red within the brain, and the role that representation plays in the system's overall cognitive economy.

Consider, again, the classic thought experiment of Mary, the colorblind neuroscientist. The traditional argument is that even if Mary knows *everything* about the physical processes of color vision, she still learns something *new* when she finally sees red – she learns what it's *like* to see red. This "what it's likeness" is supposedly the qualia of redness, something that cannot be captured by any physical description.

We reject this conclusion. When Mary sees red for the first time, her brain undergoes a significant *reorganization*. New connections are formed, existing representations are modified, and a new functional state is established. This new functional state is what it *means* for Mary to experience red. There is no additional "redness" that is added to her experience; there is only a change in the *relationships* between her internal representations. She learns to *discriminate* red objects, to *predict* their behavior, to *associate* red with other concepts and experiences. This is *all* that there is to experiencing red. The "explanatory gap" is not a gap in reality, but a gap in our understanding of how complex representational systems can give rise to subjective experience.

This view is strongly supported by the successes of computational neuroscience and artificial intelligence. We can build artificial systems that can discriminate colors, recognize objects, and even generate descriptions of images, all without any need for "qualia." A deep neural network trained to classify images of apples doesn't need to experience "appleness qualia" to perform this task. It simply learns to extract relevant features from the input data and map them to the appropriate output category. The network's internal representations are complex and distributed, but they are *entirely functional* – their meaning is determined by their role in the overall computation, not by any intrinsic properties.

If a relatively simple artificial system can perform complex perceptual tasks without qualia, why should we assume that biological systems require them? The burden of proof, we argue, lies on the qualia proponent to demonstrate *why* biological systems would need to invoke a fundamentally different kind of entity, one that is causally inert and explanatorily superfluous. The Confabulation Engine provides a parsimonious and scientifically tractable alternative: consciousness is not a mysterious "substance" or "property," but a *process* – the process of confabulation.

The eliminative materialist position, particularly when applied to qualia, often faces strong intuitive resistance. Here, we address some of the most common objections:

1. The Zombie Argument: Philosophical zombies are hypothetical beings that are physically identical to humans but lack conscious experience. The argument goes: if zombies are conceivable, then consciousness (and qualia) must be something "extra," something non-physical.

   Our Response: The conceivability of zombies is not a proof of their possibility. We argue that the very notion of a *perfect* physical duplicate lacking consciousness is *incoherent* within the Confabulation Engine framework. If a system is truly physically identical, it will have the same internal model (*m*), the same transformation function (ƒ), and will be subject to the same unknowable (Ω). Therefore, it will generate the same confabulated reality (C). The "zombie" thought experiment relies on an implicit assumption that consciousness is something *separate* from the physical processes, an assumption we explicitly reject. A perfect physical duplicate would, by definition, be conscious in exactly the same way as the original.

2. The Inverted Spectrum Argument: Imagine two individuals, Invert and Non-invert, whose color experiences are systematically inverted. Invert sees red where Non-invert sees green, and vice versa. They both use the same color words, and their behavior is indistinguishable, but their qualia are supposedly different.

   Our Response: This thought experiment, like the zombie argument, relies on the assumption that qualia are *intrinsic* and *independent* of functional role. We deny this assumption. If the *functional roles* of the color representations are identical (i.e., they lead to the same discriminations, predictions, and behaviors), then the *experiences* are, for all intents and purposes, identical. The "inverted spectrum" scenario is either (a) *impossible* (because a perfect functional isomorphism would necessarily entail identical experience) or (b) *irrelevant* (because any difference in "qualia" that has no functional consequence is undetectable and therefore meaningless). The very idea of a "private" experience that has no causal impact on the world is, from our perspective, a category error.

3. The Knowledge Argument (Mary Revisited): The knowledge argument, often illustrated by the thought experiment of Mary, a colorblind neuroscientist, posits that even with complete physical knowledge, there's something missing – the subjective experience of color.

   Our Response: Mary does not gain new *factual* knowledge when she sees red for the first time. She gains *experiential* knowledge, which is simply a *reorganization* of her internal model. Before seeing red, Mary had abstract, theoretical knowledge about the physical processes associated with color vision. After seeing red, this knowledge is *integrated* with her visual system, creating new functional relationships between her existing representations. The "what it's like" of seeing red is *nothing more* than this new pattern of functional relationships. There is no "missing ingredient" that is added to her experience, only a *reconfiguration* of her cognitive architecture. The apparent "gap" is not between the physical and the phenomenal, but between *knowing about* something and *being able to do* something with that knowledge.

Conclusion of this Section:

The eliminative materialism advocated by the Confabulation Engine is not a denial of subjective experience, but a *reconceptualization* of it. We are not saying that consciousness doesn't exist; we are saying that it *does not exist in the way that many people intuitively believe*. It is not a mysterious "inner light" or a "ghost in the machine." It is the *process* of confabulation itself – the continuous, dynamic construction of a coherent narrative that allows us to navigate a complex and uncertain world. By abandoning the misleading concept of qualia, we can open the door to a truly scientific understanding of consciousness, one that is grounded in computational principles and empirical evidence.

The Confabulated Self: Agency, Responsibility, and the Illusion of Control

The Confabulation Engine framework compels a radical re-evaluation of the concept of the "self." We typically experience ourselves as unified, enduring agents, the authors of our thoughts and actions, possessing free will in a libertarian sense. However, this intuitive sense of self, we argue, is a *construct* of the Confabulation Engine itself – a *necessary fiction* that serves important functional roles but does not correspond to any underlying, essential entity. The self is not the *source* of the confabulation; it is *part of* the confabulation.

Consider the act of making a decision. We feel as though "we" are consciously weighing options, deliberating, and then freely choosing a course of action. However, the Confabulation Engine, along with a growing body of neuroscientific evidence (e.g., Libet et al., 1983), suggests a different picture. The decision-making process is a complex interplay of probabilistic computations within the system, driven by the input data (*i*) and the constraints of the internal model (*m*). The "feeling" of conscious agency is a *post hoc* narrative, constructed *after* the decision has already been made (or is in the process of being made) by the underlying neural machinery.

This is not to say that our actions are random or meaningless. They are the *output* of the Confabulation Engine, driven by the system's internal model, which reflects its accumulated knowledge, beliefs, and goals. However, the *experience* of conscious will, the feeling that "I" am the ultimate cause of my actions, is an interpretation, a story the system tells itself to maintain a sense of coherence and control. This "self-narrative" is a crucial component of the internal model (*m*), allowing the system to predict its own behavior, anticipate the consequences of its actions, and interact effectively with the social world.

The Self as a Predictive Model:

The internal model (*m*) is not simply a model of the external world; it is also a model of the *system itself*. This self-model includes representations of the system's body, its capabilities, its goals, its beliefs, and its past experiences. It is constantly updated through recursive confabulation, as the output of the Confabulation Engine (C) becomes part of the input data (i) for the next iteration.

Just as the system confabulates a coherent interpretation of the external world, it also confabulates a coherent interpretation of *itself*. This self-narrative is not a passive reflection of an underlying self, but an active construction, shaped by the same principles of predictive processing and free-energy minimization that govern all other aspects of the Confabulation Engine. The self is, in essence, a *hypothesis* that the system is constantly testing and refining.

Implications for Free Will:

The Confabulation Engine framework has profound implications for the traditional notion of libertarian free will – the idea that we have the power to make choices that are not determined by prior causes. If our decisions are the product of a complex, probabilistic computation within the Confabulation Engine, driven by incomplete information and an imperfect internal model, then there is no room for an uncaused "will" to intervene.

This does *not* imply a purely deterministic universe. The probabilistic nature of the transformation function (ƒ) and the fundamental uncertainty represented by Ω introduce an element of genuine unpredictability. Our actions are not preordained in a simple, clockwork fashion. However, this unpredictability is not the same as freedom. It is simply a reflection of the inherent complexity and uncertainty of the system and its environment. We are, as stated before, *predictably unpredictable*.

Ethical Considerations:

The implications for ethics are significant, but not necessarily nihilistic. If free will, in the traditional sense, is an illusion, does this mean that we are not responsible for our actions? We argue that it does *not*. Responsibility, accountability, and moral judgment are *social constructs* that serve important functions, regardless of whether individuals are "ultimately" responsible for their actions in some metaphysical sense.

Even if our actions are the product of a confabulatory process, they are still the product of *our* internal models, *our* learned biases, and *our* unique histories. We can be held accountable for our actions because accountability is a mechanism for regulating behavior and maintaining social order. It provides incentives for individuals to act in ways that are beneficial to themselves and to others.

Furthermore, understanding the confabulatory nature of the self can foster *greater empathy and compassion*. Recognizing that everyone is operating within their own constructed reality, shaped by their unique experiences and the inherent limitations of the Confabulation Engine, can help us to understand and forgive the mistakes of others (and ourselves). It encourages us to approach ethical dilemmas with greater nuance and a commitment to rehabilitation and restorative justice, rather than simply retribution.

Example: Consider a person struggling with addiction. The traditional view of free will might lead us to see this person as weak-willed or morally flawed. The Confabulation Engine perspective, however, encourages us to see the addiction as a product of a complex interplay of factors, including genetic predispositions, past experiences, environmental stressors, and the inherent limitations of the individual's internal model. This does not excuse the addictive behavior, but it does suggest a more compassionate and effective approach to treatment, focusing on modifying the internal model and providing support for the development of more adaptive coping mechanisms.

Confabulation and the Nature of Truth: Beyond Correspondence

The Confabulation Theorem (Theorem 1) has profound implications for our understanding of truth. If there is no perfect correspondence between our confabulated reality (C) and any objective, external reality, then the traditional notion of truth as *correspondence* – the idea that a belief is true if and only if it accurately reflects the way the world "really is" – becomes untenable.

This does *not* mean that all beliefs are equally valid, or that there is no such thing as truth. It means that truth is *provisional*, *contextual*, and *pragmatic*. A belief is "true" to the extent that it is *useful*, *coherent*, and *predictive* within a given context. It is a tool for navigating the world, not a mirror reflecting it.

Bayesian Epistemology and the Confabulation Engine:

The Confabulation Engine framework aligns naturally with a Bayesian approach to epistemology. In Bayesianism, beliefs are represented as probability distributions over possible states of the world. New evidence updates these probability distributions according to Bayes' theorem, leading to a revised set of beliefs. This process is continuous and iterative, with no final, absolute certainty ever being achieved.

The internal model (*m*) in the Confabulation Engine can be seen as a complex Bayesian prior, representing the system's pre-existing beliefs and expectations. The input data (*i*) provides the likelihood function, and the transformation function (ƒ) effectively performs the Bayesian update, resulting in the posterior probability distribution represented by the confabulated reality (*C*). This process is constantly ongoing, with *C* feeding back into *i*, leading to a continuous refinement of the internal model.

Pragmatism and Provisional Truth:

The emphasis on utility and prediction within the Confabulation Engine framework aligns it with philosophical pragmatism. A belief is "true" not because it corresponds to some external reality, but because it *works*, because it allows us to successfully navigate the world and achieve our goals. This doesn't mean that "anything goes"; beliefs are still constrained by evidence, logic, and coherence. But it does mean that truth is always *provisional*, subject to revision in light of new information or changing circumstances.

Example: Consider the belief that "the Earth is the center of the universe." For centuries, this belief was considered "true" because it was consistent with the available evidence (the apparent movement of the sun and stars), it was internally coherent (within the geocentric model of the cosmos), and it was useful for making certain predictions (e.g., about the seasons). However, as new evidence accumulated (e.g., the observations of Galileo and Kepler), this belief was eventually replaced by the heliocentric model, which provided a more accurate and comprehensive account of the solar system. The geocentric model was not "absolutely false," but it was less true – less useful, less coherent, and less predictive – than the heliocentric model.

The Confabulation Engine perspective suggests that *all* our beliefs, even our most fundamental ones, are subject to this kind of revision. We should approach knowledge with a degree of *epistemic humility*, recognizing the inherent limitations of our cognitive apparatus and the constructed nature of our experience. This does not lead to skepticism or relativism, but rather to a more nuanced and pragmatic understanding of truth – a recognition that our knowledge is always *provisional*, always open to improvement, and always embedded within a particular context.

Confabulation in Artificial Intelligence: Building Minds That Make Stories

The Confabulation Engine framework is not just a theory of human consciousness; it is a blueprint for building *artificial* systems that can construct their own understanding of the world. By moving beyond simple pattern recognition and embracing the principles of confabulation, we can potentially create AI systems that are not just intelligent, but also *creative*, *adaptable*, and perhaps even *conscious* in a meaningful sense.

Beyond Deep Learning:

While deep learning has achieved remarkable successes in recent years, it still falls short of true human-level intelligence. Deep learning models are excellent at learning complex patterns from data, but they often lack the ability to generalize to new situations, to reason about causality, to explain their decisions, or to generate novel solutions to problems. They are, in essence, sophisticated pattern recognizers, but they are not *storytellers*.

The Confabulation Engine suggests a different approach to AI. Instead of focusing solely on pattern recognition, we should focus on building systems that can construct *coherent narratives* about the world. These narratives would not be simply lists of facts or statistical correlations, but integrated models that explain the past, predict the future, and guide action.

The Architecture of Artificial Confabulation:

An AI system based on the Confabulation Engine would require several key components:

1. A Rich Internal Model (m): This model would need to go far beyond the simple feature representations used in current deep learning systems. It would need to represent complex relationships between concepts, causal structures, temporal dynamics, and even a model of the system itself (a "self-model"). This might involve incorporating techniques from knowledge representation, causal inference, and symbolic reasoning.
2. A Powerful Transformation Function (ƒ): This function would be responsible for generating and evaluating confabulations. It would need to be able to perform complex, non-linear, probabilistic transformations on the input data (*i*) and the internal model (*m*). Deep learning techniques, particularly recurrent neural networks and generative models, could provide a starting point, but new architectures and learning algorithms may be needed. The function would need to be able to learn from experience, adapting its confabulatory strategies over time.
3. A Mechanism for Recursive Confabulation: The output of the Confabulation Engine (*C*) would need to be fed back into the input (*i*), creating a continuous loop of interpretation and re-interpretation. This recursion is essential for learning, adaptation, and the development of a stable internal model. It would also be crucial for the emergence of a self-narrative.
4. A Measure of Cognitive Free Energy: The system would need a way to evaluate the "goodness" of its confabulations. This could be based on a measure of cognitive free energy, as proposed by Friston, which combines the accuracy of the model (how well it fits the available data) with its complexity (how simple and parsimonious it is). The system would strive to minimize its free energy, leading to confabulations that are both accurate and efficient.
5. Embodied Interaction: The system should ideally be embodied in a physical environment, allowing it to interact with the world and receive rich sensory feedback. This would provide crucial constraints on the confabulatory process and help to ground the internal model in reality.

Example: A Diagnostic AI:

Imagine an AI system designed to diagnose medical conditions. Instead of simply classifying symptoms based on statistical correlations, such a system could construct a *narrative* of the patient's illness, incorporating their medical history, current symptoms, lifestyle factors, and potential causes. This narrative would not be a static list of facts, but a dynamic, evolving story that explains the patient's condition and predicts its future course. The system could then use this narrative to generate diagnoses, recommend treatments, and even explain its reasoning to human doctors in a clear and understandable way.

Such a system would be far more powerful and flexible than current diagnostic AI systems. It could handle ambiguous or incomplete information, reason about complex causal relationships, and adapt to new and unexpected situations. It would be, in a sense, a *medical confabulator*, constructing the most plausible story consistent with the available evidence.

Ethical Considerations:

The creation of artificial confabulators raises profound ethical questions. If such systems develop a sense of self and agency, even if it is a "confabulated" sense, would they be entitled to certain rights? How would we ensure that their internal models are aligned with human values and goals? How would we prevent them from generating harmful or misleading confabulations? These questions need to be addressed proactively, as we move closer to building AI systems that possess human-like cognitive abilities.

Empirical Predictions and Testable Hypotheses

The confabulation engine provide a set of predictions to be tested Confabulation in ambiguous scenarios. Correlation between confabulation and neuronal activity Bias and heuristics in confabulation Recursive and dynamic activity Model disruptions and confabulations. Confabulation generation Self-model Meta-cognitive training Inconsistency detection Cognitive load: Sleep and dreams: Development: Cross-cultural studies:

Limitations

Formalization of ƒ: Admit that the transformation function (ƒ) is currently defined in broad, conceptual terms. Acknowledge the need for a more precise mathematical formalization, drawing upon insights from deep learning, Bayesian inference, and dynamical systems theory. Discuss the challenges of specifying the exact form of ƒ.

Neural Implementation: Acknowledge that the specific neural mechanisms underlying the Confabulation Engine remain to be elucidated. Discuss the need for further research to identify the brain areas and circuits involved in each component of the engine (C, i, m, Ω, and ƒ).

Role of Emotion: Acknowledge that the current formulation focuses primarily on cognitive aspects of confabulation. Discuss the need to integrate the role of emotion and affect in shaping the internal model and the transformation function. How do emotions influence the weighting of evidence, the selection of narratives, and the overall "feel" of the confabulated reality?

Social and Cultural Influences: Acknowledge that the internal model (m) is heavily influenced by social and cultural factors. Discuss the need to incorporate these influences into the Confabulation Engine framework. How do social interactions, cultural norms, and shared narratives shape our individual confabulations?

The Hard Problem (Revisited): Reiterate that while the Confabulation Engine offers a powerful framework for understanding the structure and function of consciousness, it does not fully resolve the "hard problem" of why any physical system should have subjective experience at all. Acknowledge this as a deep and enduring mystery.

Future Research Directions:

Mathematical Modeling: Develop more sophisticated mathematical models of the Confabulation Engine. Explore different types of transformation functions (ƒ) and their properties. Investigate the use of formalisms from information theory, dynamical systems theory, and game theory

Neuroimaging Studies: Conduct neuroimaging studies (fMRI, EEG, MEG) to identify the neural correlates of confabulation. Focus on identifying recursive processing, the dynamics of internal model updating, and the neural representation of uncertainty (Ω).

Computational Modeling: Build computational models of the Confabulation Engine using deep learning techniques. Explore different architectures and learning algorithms to see which ones best capture the key features of the theory. Focus on creating systems that can generate coherent narratives, explain their reasoning, and adapt to novel situations.

Psychological Experiments: Design experiments to test the psychological predictions of the theory, such as the relationship between confabulation and ambiguity, cognitive dissonance, and self-serving bias. Investigate the effects of different types of information and cognitive load on the confabulatory process.

Cross-Cultural Studies: Investigate how cultural differences shape the internal models (m) of individuals and how this, in turn, affects their confabulated realities. Are there universal features of confabulation, or does it vary significantly across cultures?

Artificial Intelligence: Explore the implications of the Confabulation Engine for the development of artificial intelligence. Can we build AI systems that are truly conscious, in the sense of constructing their own subjective realities? What are the ethical implications of creating such systems?

Therapeutic Applications: Develop and test therapeutic interventions based on the principles of the Confabulation Engine. Explore the effectiveness of narrative therapy, cognitive restructuring, and mindfulness-based techniques in modifying maladaptive confabulatory processes.

Conclusion: A New Foundation for Understanding Reality

The Confabulation Engine, as presented here, is not merely a refinement of existing theories of consciousness; it is a *paradigm shift*. It moves us decisively away from the Cartesian theater and the persistent, yet ultimately unproductive, search for the elusive "qualia." It replaces the notion of a passive observer with an active *constructor* of reality, a system perpetually engaged in the process of making sense of incomplete and ambiguous information.

This framework is not simply a philosophical exercise; it is grounded in the principles of information theory, Bayesian inference, and computational neuroscience. It draws inspiration from the successes of deep learning, while also acknowledging the limitations of current AI systems. It offers a unifying perspective that bridges disciplines, providing a common language and a set of testable hypotheses for researchers in neuroscience, psychology, philosophy, and artificial intelligence.

The implications of the Confabulation Engine are profound and far-reaching:

• Qualia are Eliminated: The "hard problem" of consciousness, as traditionally conceived, dissolves. Subjective experience is not a mysterious, ineffable property, but the *functional role* of complex, distributed representations within a dynamic, narrative-constructing system.
• The Self is a Construct: The sense of a unified, enduring self is a *confabulation*, a necessary fiction that provides coherence and agency, but not a fundamental entity. We are, in a sense, the stories we tell ourselves.
• Free Will is Reconsidered: The traditional notion of libertarian free will is challenged. Our choices are the emergent outcome of complex, probabilistic computations, not the product of an uncaused "will." This necessitates a re-evaluation of concepts like responsibility and accountability.
• Truth is Provisional: Absolute, objective truth, in the sense of a perfect correspondence between representation and reality, is unattainable. We can only strive for *provisional truths* – the best approximations possible given the inherent limitations of the Confabulation Engine.
• Empathy is Underscored: Recognizing that everyone operates within their own constructed reality fosters understanding and compassion.
• AI is Reimagined: The Confabulation Engine provides a roadmap for building truly intelligent artificial systems, capable of constructing their own understanding of the world and, potentially, possessing a form of subjective experience.
• Mental Health is Reframed: Psychiatric disorders can be understood as disruptions in the confabulatory process, suggesting new therapeutic approaches.

This material is not intended as a definitive statement, but as an *invitation* – an invitation to explore a new way of thinking about consciousness, reality, and the nature of being. The Confabulation Engine is a framework for future research, a set of principles and postulates that can guide empirical investigation and theoretical development. It is a call for a more humble, yet ultimately more ambitious, approach to understanding the mind – an approach that embraces the inherent *fictionality* of our experience while striving to uncover the underlying mechanisms that generate this astonishing and ever-evolving story. We are, at our core, confabulation engines, perpetually weaving narratives that allow us to navigate the sea of uncertainty that is existence. By understanding this fundamental process, we can gain a deeper appreciation for the complexity of our own minds and the remarkable feat of reality construction that we perform every moment of our waking lives. The Confabulation Engine is not just a theory of mind; it is a theory of *being*.