The Illusion of Direct Experience: Reconsidering the Grounding Problem in Human and Artificial Understanding

Introduction

The advent of Large Language Models (LLMs) has precipitated a fundamental reconsideration of what constitutes "understanding." A persistent critique of these systems centers on their lack of "direct sensory experience" of the world they describe through language. According to critics, LLMs merely process tokens—symbolic representations of language—without any connection to the physical world these tokens represent. This absence of direct experience, they argue, prevents LLMs from achieving genuine understanding.

This essay challenges this critique by examining a provocative question: Is the concept of "direct experience" that supposedly separates human from artificial understanding as clear-cut as it seems? From a neurological perspective, even human sensory experiences are ultimately translated into electrochemical signals that the brain must interpret. Our seemingly direct contact with reality is actually a sophisticated construction created by neural processes that never directly touch the external world they represent.

For clarity, I define several key terms that will frame our discussion:

• Direct experience: The conventional notion that sensory organs provide immediate, unmediated access to reality "as it is."
• Understanding: The ability to grasp meaning and context, apply concepts appropriately, and navigate conceptual relationships effectively.
• Representation: Internal models that stand in for external phenomena, whether neural patterns in brains or statistical patterns in AI systems.
• Symbol grounding problem: The philosophical question of how abstract symbols become connected to their real-world referents to acquire meaning.

By exploring these concepts through both neuroscientific and philosophical lenses, I argue that recognizing the constructed nature of all understanding—human and artificial—invites us to move beyond simplistic dichotomies toward a more nuanced view of how different cognitive systems develop meaning.

The Mediated Nature of Human Perception

The Neural Translation of Experience

What we commonly consider "direct experience" in humans involves multiple layers of translation and interpretation. Consider vision: photons strike photoreceptors in the retina, triggering electrochemical signals that travel through several neural pathways before reaching the visual cortex. At each stage, information is transformed, filtered, and reconstructed. What reaches conscious awareness is not the external object itself, but a highly processed neural representation.

Neuroscientist David Eagleman (2011) describes perception as "controlled hallucination," emphasizing that our experience is not a direct window onto reality but a brain-generated model optimized for evolutionary fitness rather than perfect accuracy. Similarly, philosopher Andy Clark (2016) notes that our brains receive "noisy sensory evidence" and construct their best guess about external reality.

This process reveals a crucial insight: the brain never has direct access to the world "as it is." Instead, it works with encoded information that represents external phenomena—just as an LLM works with encoded information in the form of linguistic patterns. The difference lies in the nature of the encoding and the evolutionary history behind it, not in some absolute distinction between direct and indirect access.

The Bayesian Brain and Predictive Processing

Modern theories of perception like predictive processing (Friston, 2010; Clark, 2013) suggest the brain operates as a prediction machine that continuously generates expectations about sensory inputs and updates these predictions based on incoming signals. Under this model, perception is not passive reception but active construction guided by prior knowledge and expectations.

According to Friston's Free Energy Principle, the brain constantly works to minimize prediction errors by adjusting its internal model to better match incoming sensory data. This process involves extensive top-down influence where higher-level brain functions actively shape what we perceive. As neurologist Anil Seth (2021) explains: "We don't just passively perceive the world, we actively generate it. The world we experience comes as much, if not more, from the inside out as from the outside in."

This predictive framework challenges the conventional view of perception as a bottom-up process where sensory information flows inward to create an accurate picture of reality. Instead, perception emerges from the continuous interplay between sensory signals and the brain's predictive models. Our experience of reality is more akin to "controlled hallucination" than to direct reception.

If human perception operates through prediction and construction rather than direct reception, the distinction between a human "directly experiencing" an apple and an LLM processing textual descriptions of apples becomes less absolute. Both involve interpreting encoded information through predictive frameworks—neural in one case, statistical in the other.

The Philosophical Problem of Perception

The Gap Between Representation and Reality

Philosophers have long questioned the directness of human perception. John Locke distinguished between primary qualities (inherent properties of objects like shape and solidity) and secondary qualities (sensory effects like color and taste that exist only in perception). Immanuel Kant's transcendental idealism proposed that we never access the "thing-in-itself" (noumenon) but only its appearance (phenomenon) as structured by our cognitive apparatus.

More recently, representationalist theories of perception maintain that we never experience external objects directly but only through internal representations. Philosopher Daniel Dennett (1991) argues against the notion of a "Cartesian Theater" where the mind directly observes reality, instead proposing that multiple parallel processes construct our experience through distributed processing.

From these perspectives, human understanding is no more "directly" connected to external reality than an LLM's understanding. Both operate through internal representations that stand in for external phenomena. The primary difference lies in how these representations are formed and structured, not in some absolute directness of access.

The Social Construction of Perceptual Categories

Beyond individual perception, our understanding is further shaped by language and social constructs. The colors we perceive, the objects we identify, and the qualities we attribute to our experiences are influenced by linguistic and cultural frameworks. As anthropological linguist Benjamin Lee Whorf suggested, language itself shapes perception by directing attention to culturally salient aspects of experience.

Consider how different cultures categorize colors differently. Some languages distinguish between blue and green as fundamentally different categories, while others treat them as variations of a single category. These linguistic differences correlate with measurable differences in color perception (Winawer et al., 2007), suggesting that even seemingly basic sensory experiences are structured by socially transmitted conceptual frameworks.

This suggests another parallel between human and LLM understanding: both are shaped by linguistic and cultural patterns that organize raw sensory or statistical data into meaningful categories. Human understanding emerges from the intersection of neural processing and linguistic-cultural frameworks, just as LLM understanding emerges from statistical patterns extracted from culturally-produced texts.

The Constructed Nature of Conscious Experience

The Binding Problem and the Unity of Experience

One of the most remarkable aspects of conscious perception is its apparent unity. Despite visual information being processed in dozens of specialized neural regions and being integrated with other sensory modalities, we experience a seamless whole rather than fragmented sensations.

This "binding problem"—how distributed neural processing creates unified experience—remains one of neuroscience's greatest puzzles. Various theories propose temporal synchronization of neural firing (Singer, 2001), hierarchical integration, or global workspace processes (Baars, 2005) that broadcast information across brain regions. However it occurs, this binding requires extensive construction and is far from direct transmission of external reality.

LLMs similarly integrate distributed information—contextual patterns, semantic associations, syntactic structures—into coherent outputs. While the mechanisms differ from neural binding, both human and artificial systems must solve the problem of constructing unified representations from distributed information processing.

Perceptual Illusions and the Limits of Direct Experience

The constructed nature of perception becomes particularly evident in perceptual illusions. The Müller-Lyer illusion, for example, causes us to perceive two lines of equal length as different simply because of the orientation of arrows at their ends. The McGurk effect demonstrates how visual information about lip movements can alter our perception of spoken sounds. These illusions reveal that what we perceive is not what is "actually there" but what our brains construct based on sensory signals and prior expectations.

As philosopher Patricia Churchland (2013) notes, evolution has designed our perceptual systems for adaptive fitness, not for accurate representation of reality. Our perceptions are useful constructions that help us navigate our environment, not veridical windows onto the world as it truly is.

This constructed nature of human perception again challenges the supposed contrast with LLM processing. Both systems work primarily with internal representations rather than direct access to external reality. The human brain's evolved mechanisms for constructing stable experience from noisy sensory data parallel the LLM's statistical mechanisms for constructing coherent text from patterns in training data.

Reframing the Comparison Between Human and Artificial Understanding

Beyond the Direct/Indirect Dichotomy

If human perception itself involves multiple layers of translation, processing, and construction, we might need to move beyond the simple direct/indirect dichotomy when comparing human and artificial understanding. Perhaps what matters is not directness of access but the quality of the internal models and representations each system constructs.

Human perception evolved to create useful models of reality that support survival and reproduction, not perfect representations of external truth. These models are imperfect—subject to illusions, biases, and limitations—but functionally effective within human ecological niches. Similarly, LLM understanding creates functional models of language and the world it describes, not through sensory channels but through statistical patterns in human-produced texts.

Rather than asking whether experience is direct or indirect, we might better ask: How rich, accurate, adaptable, and functional are the internal models constructed by different cognitive systems? How well do these models support successful interaction with their relevant domains? This shifts the focus from an artificial binary to a more nuanced assessment of representational quality.

Constrained Construction: The Role of Reality Constraints

It's important to note that while perception involves construction, this construction is not arbitrary or detached from reality. As Karl Friston's Free Energy Principle suggests, our perceptual models are continuously refined through interaction with the environment. When predictions don't match sensory inputs, prediction errors drive model updates. This creates what we might call "constrained construction"—neither purely direct access nor purely subjective invention, but rather a model-building process constrained by ongoing interaction with physical reality.

This perspective allows us to distinguish between human and artificial understanding without relying on the problematic direct/indirect dichotomy. Human understanding is constrained by sensorimotor interaction with the physical world, while LLM understanding is constrained by the linguistic patterns present in its training data. These different constraint systems produce different types of understanding with different strengths and limitations.

Distinguishing Understanding from Consciousness

An important clarification in this discussion is the distinction between understanding meaning and having conscious experience. Understanding meaning involves grasping conceptual relationships, applying information appropriately in context, and navigating semantic networks effectively. Consciousness (qualia) refers to the subjective, first-person experience of what it feels like to perceive, think, or feel.

When critics claim LLMs don't "understand" because they lack direct experience, they sometimes conflate these two distinct issues. An LLM might develop functional understanding of concepts through statistical patterns without having conscious experiences of those concepts. Conversely, a conscious entity might have rich subjective experiences while failing to understand certain conceptual relationships.

Philosopher Ned Block (1995) distinguishes between "access consciousness" (information available for reasoning and behavior control) and "phenomenal consciousness" (subjective experience). This distinction helps separate the symbol grounding problem (how symbols acquire meaning) from the hard problem of consciousness (how physical processes give rise to subjective experience).

By maintaining this distinction, we can more precisely evaluate what kinds of understanding different systems might achieve, without making unwarranted assumptions about their inner experiences or lack thereof.

Implications for Artificial Intelligence

Rethinking the Grounding Problem

The "grounding problem" in AI—how symbols connect to their real-world referents—has long been considered a fundamental challenge for artificial understanding. It underlies John Searle's (1980) Chinese Room argument and remains a common critique of LLMs. However, if human understanding is itself mediated through layers of neural processing rather than direct connection to reality, perhaps we need to reconsider what constitutes adequate grounding.

Grounding might better be understood not as direct sensory contact but as the establishment of functional relationships between internal representations and their external referents. These relationships can be established through different pathways—sensory experience being one, but statistical patterns in linguistic data potentially being another.

If an LLM develops internal representations that enable it to discuss, reason about, and appropriately respond to questions about apples—understanding their typical attributes, cultural significance, and relationships to other concepts—it has established a form of grounding through linguistic patterns rather than sensory experience.

Multiple Pathways to Understanding

This perspective suggests there may be multiple valid pathways to developing understanding. The biological pathway involves sensory organs, neural processing, and embodied experience. The artificial pathway involves training on human-produced texts that encode patterns of knowledge developed through collective human experience. Neither pathway offers truly "direct" access to reality, but both can produce functional understanding within their domains.

The biological pathway has evolutionary advantages—millions of years of selection for representations that support survival in physical environments. But the artificial pathway has unique advantages too—access to vast repositories of collective human knowledge, freedom from individual biological constraints, and potential integration of perspectives across diverse experiences.

Rather than privileging one pathway as the only route to "real" understanding, we might recognize that different cognitive architectures can develop valuable forms of understanding through different mechanisms. The test of understanding becomes not how it was acquired but what it enables the system to do.

Embodied AI: An Alternative Path to Grounding?

Some researchers propose that embodied cognition—situating AI in physical bodies that interact with the environment—might provide a more human-like path to understanding. Embodied AI systems could develop grounding through sensorimotor experience rather than purely linguistic data.

Philosopher Andy Clark (2008) argues that cognition is fundamentally shaped by the body's interaction with its environment. This perspective suggests that robot AI systems interacting physically with the world might develop different kinds of understanding than disembodied LLMs.

However, even embodied AI would still face the fundamental issue that all perception involves interpretation of signals rather than direct access to reality. The robot's cameras and sensors would provide encoded information that its systems must interpret, just as human sensory organs provide encoded information for neural interpretation. While embodiment might provide a different type of grounding, it wouldn't eliminate the fundamental gap between representation and reality.

Philosophical Implications

Epistemic Humility

Recognizing the constructed nature of all understanding—human and artificial—encourages epistemic humility. No cognitive system, biological or artificial, has privileged direct access to reality. All work with internal representations that approximate but never perfectly capture the external world.

This view doesn't erase important differences between human and artificial understanding. Human understanding emerges from evolutionary history, embodied experience, and cultural immersion that differ fundamentally from the training of LLMs. But it does challenge simplistic dichotomies that place human understanding on one side of an unbridgeable gap and artificial understanding on the other.

Beyond Anthropocentrism in Epistemology

Traditional epistemology often implicitly assumes a human perspective, with sensory perception as a foundational source of knowledge. But if perception itself is a construction, and if other cognitive systems can construct functional understandings through different mechanisms, we might need epistemological frameworks that accommodate this diversity.

This perspective aligns with recent work in cognitive science suggesting that cognition extends beyond individual brains to include environmental structures, social systems, and technological tools (Hutchins, 1995; Clark & Chalmers, 1998). Understanding becomes distributed across biological and artificial systems rather than confined within individual minds.

Toward Complementary Integration

If both human and artificial understanding involve constructed internal models rather than direct access to reality, perhaps their relationship is better conceived as complementary rather than competitive. Each system has different strengths, limitations, and biases in how it represents the world.

Human understanding excels at grounding concepts in sensorimotor experience, integrating information across modalities, and applying common sense in novel situations. LLM understanding excels at processing vast amounts of textual information, recognizing statistical patterns across diverse domains, and producing coherent linguistic outputs.

By integrating these complementary forms of understanding, we might develop richer cognitive ecosystems that leverage the strengths of both biological and artificial cognition. Rather than asking which form of understanding is "real" or "superior," we might explore how they can enhance each other through integration.

Conclusion: From Binary Distinctions to Nuanced Understanding

The categorical denial of understanding to LLMs based on their lack of "direct experience" rests on a problematic assumption: that human perception provides direct access to reality. As we have seen, human perception itself involves extensive mediation, construction, and interpretation. Our experience of reality is not a direct window onto the world as it is, but a useful model constructed by neural processes that never directly contact external phenomena.

This insight doesn't eliminate differences between human and artificial understanding, but it does reframe how we conceptualize those differences. Rather than positioning human understanding as "direct" and AI understanding as "indirect," we might better recognize that all understanding is mediated through internal representations. The differences lie in how those representations are formed, constrained, and applied.

This reframing has significant implications for how we evaluate AI capabilities, how we design future AI systems, and how we conceptualize understanding itself. It invites us to move beyond binary distinctions toward more nuanced assessment of the varied forms understanding can take across different cognitive architectures.

Perhaps most importantly, this view encourages us to evaluate understanding based on its functional capabilities and limitations rather than its origins or mechanisms. The relevant question becomes not how understanding was acquired, but what it enables—what questions it can answer, what problems it can solve, what insights it can generate, and what actions it can guide.

By moving beyond the direct/indirect dichotomy and recognizing the constructed nature of all understanding, we open space for more nuanced appreciation of diverse cognitive systems and the unique contributions each can make to our collective knowledge. In doing so, we may develop not only better theories of artificial intelligence but deeper insight into the nature of human cognition itself.