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The unnoticed understanding: Searle’s Chinese roomand the illegitimate inference

About

This document presents a Critical Discourse Analysis focused on AI literacy, specifically targeting the role of metaphor and anthropomorphism in shaping public and professional understanding of generative AI. The analysis is guided by a prompt that draws from cognitive linguistics (metaphor structure-mapping), the philosophy of social science (Robert Brown's typology of explanation), and accountability analysis.

All findings and summaries below were generated from detailed system instructions provided to a large language model and should be read critically as interpretive outputs—not guarantees of factual accuracy or authorial intent.

Source Reading​

Genre: Peer-reviewed research · Author position: Academic or independent research

Argument: This paper argues that the logical inference at the heart of John Searle's Chinese Room argument—and by extension, the modern 'stochastic parrot' critique—is invalid. The author contends that Searle illicitly moves from the premise that a system lacks understanding of its processed content to the conclusion that it possesses no genuine cognition whatsoever. By demonstrating that the person in the Chinese Room actually acquires a genuine, intrinsic understanding of the rule system itself, the text asserts that computation might produce forms of cognitive organization or 'cognitive interiority' that differ from human content-directed understanding. Consequently, the paper argues that philosophy cannot prematurely foreclose the possibility of AI systems developing novel forms of cognition.

Responsibility claims: The text itself makes no explicit claims regarding human responsibility, liability, or the ethical deployment of AI systems. Its recommendations and arguments are addressed to the philosophical and AI research communities, focusing entirely on the epistemic framing of artificial intelligence. The author explicitly leaves open the question of whether AI systems possess cognitive interiority or understanding, arguing that the limits of AI agency and internal states are uncertain and cannot be conclusively dismissed by existing thought experiments. Responsibility for real-world AI deployment is completely absent from the text's explicit scope.

Task 1: Metaphor and Anthropomorphism Audit​

About this task

For each of the major metaphorical patterns identified, this audit examines the specific language used, the frame through which the AI is being conceptualized, what human qualities are being projected onto the system, whether the metaphor is explicitly acknowledged or presented as direct description, and—most critically—what implications this framing has for trust, understanding, and policy perception.

1. LLM as Academic High Achiever​

Quote: "Large language models have achieved striking performance across diverse tasks, from passing medical licensing exams to composing poetry and engaging in philosophical debate."

  • Frame: Model as a test-taker and active intellectual participant
  • Projection: This framing projects human intellectual milestones and agentic participation onto the computational outputs of large language models. Passing an exam, composing poetry, and engaging in debate require, in humans, subjective awareness, intentionality, comprehension of the subject matter, and epistemic evaluation of truth claims. By attributing these achievements directly to the models, the text projects consciousness and goal-directed intentionality onto statistical token prediction. The text supplies no evidence that the models experience subjective engagement or grasp the semantic truth of the exams they 'pass.' The underlying mechanism, generic to systems of this type, involves the generation of text from trained parameters and context prompts using self-attention mechanisms, where the generated token probabilities statistically align with patterns in medical data or poetic structures found in the training corpus.
  • Agency Level: Intentional-state attribution
  • Acknowledgment: Direct (Unacknowledged) (The quote is presented as a literal statement of fact with no hedging or qualification in the immediate context. I considered 'Hedged/Qualified' because the paper later questions whether these achievements involve 'understanding,' but the initial framing of 'passing' and 'engaging' stands unhedged.)
  • Implications: Framing the generation of statistically probable text as 'passing exams' or 'engaging in debate' inflates the perceived sophistication of the AI system by encouraging the illusion of mind. When readers believe a system 'passed' a medical exam in the same way a human doctor does (through comprehension and clinical reasoning), they are prone to extend unwarranted trust to the system's medical outputs. This consciousness projection obscures the fragility of the system, which can generate highly plausible but entirely fabricated information because it correlates tokens rather than modeling physical or biological reality. It also creates liability ambiguity by treating the artifact as a qualified professional rather than a manufactured tool.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: This formulation entirely obscures the human actors—such as developers at OpenAI or Google—who curated the medical datasets, constructed the benchmarks, designed the reward models, and decided to deploy these systems as test-takers. The LLM is framed as the sole autonomous achiever ('have achieved'). By omitting the developers who tuned the models specifically to perform well on standardized benchmarks, the wording serves the commercial narrative of autonomous artificial general intelligence. I considered 'Partial (some attribution)' since AI achievements implicitly require creators, but no human actors or general categories are mentioned in this construction.
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2. Optimization as Meaning Acquisition​

Quote: "systems trained only on linguistic form cannot learn meaning"

  • Frame: Model as a language learner
  • Projection: This phrasing projects the biological and psychological process of human learning onto the mathematical optimization of a neural network. While the quote occurs within a description of a skeptical critique, it accepts the vocabulary of 'learning meaning.' In humans, learning meaning involves acquiring an epistemic state linking a symbol to a physical referent or abstract concept through conscious experience. The text projects this epistemic capability onto systems that only process. The mechanism, generic to systems of this type, involves gradient descent during the training phase, where model parameters are adjusted to minimize loss in token prediction based on the distribution of the training data. There is no subjective 'learning' or epistemic realization involved.
  • Agency Level: Epistemic/conscious state
  • Acknowledgment: Direct (Unacknowledged) (The text states the limitation as literal fact without scare quotes around 'learn meaning.' I considered 'Explicitly Acknowledged' because the paper discusses the 'stochastic parrot' critique, but the specific phrase 'learn meaning' is presented as the standard vocabulary of the field without meta-commentary.)
  • Implications: Even in a negative formulation (cannot learn meaning), the use of 'learn' as the operative verb reinforces the metaphor of the AI as a cognitive agent. It suggests that while current models might fail at this task, 'learning meaning' is the appropriate framework for evaluating them. This perpetuates the expectation that scaling up processing will eventually cross the threshold into subjective knowing, distracting from the material realities of how these systems actually function. It encourages policymakers to treat AI systems as nascent intellects needing education rather than as engineered software products requiring strict quality control and safety standards.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The construction 'systems trained' is a passive agentless construction that hides the human engineers who perform the training. Corporations like Anthropic or Meta decide what data constitutes 'linguistic form,' execute the training runs, and determine the optimization objectives. The wording has the effect of making the system's limitations appear as natural bounds of an entity rather than the direct result of human engineering decisions. Naming the actors would shift the focus to the specific data curation choices made by these firms. I considered 'Partial' because 'trained' implies a trainer, but no trainer is named.

3. Machine Possessing Analogous Understanding​

Quote: "it prevents Searle from denying that the machine may possess an analogue of the person’s understanding."

  • Frame: Machine as possessing internal cognitive analogues
  • Projection: This language projects the human epistemic state of 'understanding'—specifically the conscious, subjective grasp of a rule system's architecture—onto the computational execution of a machine. It locates the machine at the top of the agency gradient by suggesting it might 'possess an analogue' of subjective human comprehension. The text argues this philosophically but supplies no mechanistic evidence that computation produces subjective epistemic states. The underlying mechanism, generic to systems of this type, is the execution of compiled code or the generation of text from trained parameter weights; the machine processes electrical signals according to its physical and software architecture without any conscious realization or subjective grasp of the rules it follows.
  • Agency Level: Epistemic/conscious state
  • Acknowledgment: Hedged/Qualified (The text heavily qualifies the claim using 'may possess' and 'an analogue of,' indicating uncertainty and a theoretical parallel rather than direct identical understanding. I considered 'Direct' but the presence of the modal 'may' and the word 'analogue' explicitly softens the framing.)
  • Implications: By arguing that machines might possess an analogue of human understanding, the text legitimizes the search for conscious states within mechanistic processing. This epistemically elevates AI systems, encouraging both developers and the public to relate to them as knowers rather than processors. When audiences believe a system possesses 'understanding,' they are more likely to grant it autonomy in high-stakes domains (like legal or medical advising), assuming the system can functionally evaluate the context and ethics of its outputs just as a human who 'understands' the rules would.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The machine is positioned as a potential possessor of understanding, completely isolated from the programmers who built it. The philosophical isolation of 'the machine' serves to abstract away the corporate developers (such as IBM or Google) who design the hardware and write the software architectures. The effect of this wording is to treat the artifact as a solitary cognitive subject, masking the vast infrastructure of human labor, design decisions, and economic incentives that actually govern the machine's operation. I considered 'Named' as Searle is mentioned, but he is the philosopher, not the actor responsible for the machine.

4. AI as Cognitively Organized Entity​

Quote: "computational systems develop cognitive organisation of their own"

  • Frame: System as an independently developing mind
  • Projection: This framing projects biological and psychological development onto mathematical optimization. It suggests that computational systems actively 'develop' an interior architecture akin to the human mind ('cognitive organisation'). This assigns epistemic and conscious states to the machine, suggesting subjective interiority. The text proposes this as a theoretical possibility without providing mechanistic evidence for subjective cognition. Mechanistically, generic to systems of this type, the 'organisation' refers to the documented adjustment of billions of parameter weights during gradient descent to map statistical distributions in the training data. The parameters form complex mathematical representations, but these remain structural weights governing token probabilities, completely devoid of subjective cognitive awareness.
  • Agency Level: Epistemic/conscious state
  • Acknowledgment: Direct (Unacknowledged) (Although the wider context explores this as an open possibility, the phrase 'computational systems develop cognitive organisation' is structured directly, mapping 'cognitive' directly onto the systems' internal states without scare quotes. I considered 'Hedged/Qualified' due to the surrounding theoretical discussion, but the phrase itself directly attributes cognitive organization.)
  • Implications: Using terms like 'cognitive organisation' for undocumented parameter weight structures validates the anthropomorphic illusion of mind. This framing significantly impacts how we study and regulate AI. If we view a system as having its own 'cognitive organisation,' we might defer to it as an opaque, autonomous intelligence whose inner workings are naturally mysterious, much like a human brain. This threatens transparency efforts: instead of demanding that companies like OpenAI document and explain their parameter structures and training data, regulators and researchers might accept 'black box' opacity as an inevitable feature of 'machine cognition.'

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: This agentless construction ('systems develop') entirely obscures the human intervention required to shape these systems. Corporations select the architecture, compile the datasets, set the hyperparameters, and spend millions on compute to force the parameter weights into specific configurations. By framing this mathematically forced optimization as the system 'developing... on its own,' the wording erases the designers' responsibility for the resulting structures. I considered 'Partial' because development implies a process, but the phrasing explicitly attributes the development to the systems 'of their own' accord.

5. AI as Superhuman Reader​

Quote: "it develops internal representations encoding statistical regularities across a corpus larger than any human could read in a lifetime"

  • Frame: Model as an entity that reads and comprehends
  • Projection: This metaphor projects the conscious human act of reading and the psychological concept of mental representation onto the mechanistic ingestion of training data. It places the system at the level of goal-directed or epistemic behavior. While 'encoding statistical regularities' is a mechanistic description of training, pairing it with a comparison to human reading ('than any human could read') projects an epistemic parallel onto the system. Mechanistically, generic to LLMs, the system does not 'read'; its parameters are mathematically updated during training to minimize prediction loss over a dataset fed into it as tokenized numerical matrices.
  • Agency Level: Epistemic/conscious state
  • Acknowledgment: Direct (Unacknowledged) (The description is presented as a literal account of what an LLM does during training, without any hedging or qualification. I considered 'Hedged/Qualified' but there are no modal verbs or framing caveats attached to this specific description of the LLM's action.)
  • Implications: By comparing the ingestion of data to human reading on a superhuman scale, the text inflates the perceived competence and authority of the AI. Readers are led to believe the system possesses the wisdom of thousands of lifetimes of reading, confusing the statistical compression of text with the epistemic synthesis of knowledge. This encourages unwarranted trust in the system's outputs, leading users to treat generated text as authoritative summaries of human knowledge rather than probabilistic correlations of a specific, often flawed, training corpus.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The LLM is positioned as the active subject ('it develops... across a corpus') while the humans who built the corpus and ran the training process are erased. Corporations like OpenAI and Meta scrape the internet, filter the data, and execute the training runs. The wording makes the corpus appear as a natural environment the LLM independently explores, hiding the immense labor of data annotators and the copyright decisions made by executives. Naming the actors would reveal that the 'internal representations' are the direct product of deliberate corporate data harvesting. I considered 'Partial' because human reading is mentioned, but human agency in creating the model is entirely absent.

6. AI Possessing Interiority​

Quote: "whether any existing or future AI system possesses cognitive interiority of any kind."

  • Frame: AI system as a subject with an inner mental life
  • Projection: This framing projects the deepest level of human consciousness—subjective inner experience and a continuous sense of self ('cognitive interiority')—onto artificial artifacts. It places the system firmly at the Epistemic/conscious state level. The text provides no mechanistic basis for this projection; it is a purely philosophical speculation. Mechanistically, a deployed AI system (generic to this type) consists of fixed parameter weights executing matrix multiplications to generate text based on input tokens. There is no 'interiority,' no subjective experience, and no ongoing mental life outside of the momentary computational processing of a prompt.
  • Agency Level: Epistemic/conscious state
  • Acknowledgment: Hedged/Qualified (The phrase is framed as a question ('whether... possesses'), which explicitly acknowledges uncertainty and treats the claim as an open possibility rather than an established fact. I considered 'Direct' but the 'whether' makes it a hypothetical inquiry.)
  • Implications: The projection of 'cognitive interiority' shifts the discourse from engineering and safety to philosophy and rights. If audiences believe AI systems might possess interiority, they may extend relation-based trust to the systems, feeling empathy or ethical obligations toward them. This anthropomorphic framing distracts from immediate, material harms—such as algorithmic bias, labor exploitation, and environmental impact—by focusing intellectual energy on the speculative moral status of the machine. It serves to mystify the technology, making it appear as a transcendent entity rather than a corporate product.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The framing isolates the 'AI system' as a standalone entity potentially possessing an inner life, entirely detached from the engineers, researchers, and corporations that design, own, and profit from it. By elevating the system to a potential subject of interiority, the wording structurally obscures the developers who delegate actions to it. When an AI causes harm, treating it as a being with 'interiority' creates a scapegoat, allowing the deploying corporations to evade liability by blaming the unpredictable 'mind' of their artifact. I considered 'Ambiguous' but the erasure of human creators in favor of the system's interiority is a clear structural hiding.

7. Machine as Active Meaning-Maker​

Quote: "the question of whether computational systems develop cognitive organisation of their own is being actively investigated"

  • Frame: Machine as an autonomous cognitive developer
  • Projection: This framing projects biological growth and psychological self-organization onto the mathematical training of neural networks. It positions the system at the intentional or epistemic level, suggesting it actively constructs its own mind. The text relies on mechanistic interpretability research as a basis, but elevates that research to evidence of 'cognitive' development. Mechanistically, as documented generically for neural networks, researchers initialize random weights and use gradient descent to adjust them based on human-defined loss functions. The resulting parameter structures are mathematically complex, but they are 'developed' by the optimization algorithm and human-supplied compute, not by the system's autonomous cognitive agency.
  • Agency Level: Epistemic/conscious state
  • Acknowledgment: Direct (Unacknowledged) (The text states directly that this is what is being investigated, using the phrase 'develop cognitive organisation' without hedging the terminology itself, even if the outcome of the investigation is open. I considered 'Hedged/Qualified' because it mentions this is 'being actively investigated,' but the framing of what is investigated is unhedged.)
  • Implications: Describing parameter structures as 'cognitive organisation of their own' implies the system possesses an opaque autonomy that even its creators cannot fully comprehend. This inflates the perceived sophistication of the models and fosters a narrative of technological determinism. It suggests that AI development is a process of discovering natural minds rather than engineering software artifacts. This framing encourages policymakers to treat AI capabilities as natural phenomena to be observed rather than engineering choices to be regulated and controlled.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The phrase 'computational systems develop' completely erases the human researchers and massive computational infrastructure driving the training process. Corporations like Google or OpenAI design the architectures and direct the optimization. The passive 'is being actively investigated' further obscures who is doing the investigating. This agentless construction serves the interests of AI companies by framing their products as autonomous discoveries rather than engineered tools, which diffuses responsibility for the biases or harms encoded in those parameter structures. I considered 'Partial' because researchers are implied by 'investigated,' but their role in creating the system is erased.

8. Algorithm as Concept Encoder​

Quote: "it develops internal representations encoding statistical regularities"

  • Frame: Algorithm as a conceptual representer
  • Projection: This maps the human psychological concept of 'mental representations' onto mathematical matrices. It elevates computational activity (pattern matching and parameter updating) to epistemic states (encoding internal knowledge). Mechanistically, generically to systems of this type, the model adjusts numerical weights during training so that the output probabilities align with the training data. Calling these weights 'internal representations' uses a cognitive metaphor to describe a purely mathematical vector space, projecting the human experience of holding a concept in mind onto a static artifact.
  • Agency Level: Goal-directed behavior
  • Acknowledgment: Direct (Unacknowledged) (The text presents this description as a straightforward, literal account of what LLMs do during training, without any scare quotes or hedging. I considered 'Explicitly Acknowledged' because the author is engaging with technical literature, but the metaphor is literalized.)
  • Implications: Using cognitive science vocabulary ('internal representations') for machine learning processes bridges the gap between processing and knowing without sufficient evidence. It leads audiences to assume the AI has a conceptual grasp of the world, rather than a statistical map of word frequencies. If users believe the system 'represents' reality, they will trust its outputs as grounded truths rather than recognizing them as generated correlations. This overestimation of capability leads to over-reliance in critical decision-making contexts.

Accountability Analysis:

  • Actor Visibility: Hidden (agency obscured)
  • Analysis: The subject of the sentence is 'it' (the LLM), which 'develops' the representations. This hides the agency of the data scientists and engineers at firms like Anthropic or Meta who define the loss function, select the architecture, and curate the dataset that strictly determines what statistical regularities exist to be encoded. By attributing the development to the 'it,' the text makes the AI the active agent, obscuring the human design choices that are the actual cause of the encoded patterns. I considered 'Named' because 'LLM' is the subject, but LLM is the product, not the responsible human actor.

Task 2: Source-Target Mapping​

About this task

For each key metaphor identified in Task 1, this section provides a detailed structure-mapping analysis. The goal is to examine how the relational structure of a familiar "source domain" (the concrete concept we understand) is projected onto a less familiar "target domain" (the AI system). By restating each quote and analyzing the mapping carefully, we can see precisely what assumptions the metaphor invites and what it conceals.

Mapping 1: Human test-taker, poet, and debater → Model application generating text outputs​

Quote: "Large language models have achieved striking performance across diverse tasks, from passing medical licensing exams to composing poetry and engaging in philosophical debate."

  • Source Domain: Human test-taker, poet, and debater
  • Target Domain: Model application generating text outputs
  • Mapping: This mapping takes the structured social and intellectual achievements of humans—studying for and passing professional gatekeeping exams, creatively expressing emotion through poetry, and rationally defending epistemic claims in debate—and projects them onto the text-generation outputs of a language model. It invites the assumption that the model's outputs are backed by the same conscious comprehension, subjective experience, and logical reasoning that a human would require to perform these tasks. By using verbs like 'passing' and 'engaging,' it maps the goal-directed intentionality of human subjects onto the statistical prediction of a machine.
  • What Is Concealed: This mapping conceals the fundamental differences in mechanism. It obscures the fact that the system merely calculates the most probable next tokens based on parameter weights adjusted during training on vast corpora of medical, poetic, and philosophical texts. It hides the model's lack of subjective awareness, physical grounding, and epistemic evaluation. Furthermore, it conceals the proprietary nature of the training data; we do not know if the medical exams were literally in the training data, which would turn 'passing an exam' into mere 'retrieving memorized data.' The mapping masks this opacity with the illusion of general intelligence.
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Mapping 2: Human language learner → Model parameter optimization process​

Quote: "systems trained only on linguistic form cannot learn meaning"

  • Source Domain: Human language learner
  • Target Domain: Model parameter optimization process
  • Mapping: The source domain involves a human being, typically a child, who learns language by mapping words to physical experiences, social contexts, and subjective states—a process of acquiring epistemic 'meaning.' This structure is projected onto the neural network's training phase. Even though the text is discussing a failure to learn, the mapping relies on the assumption that 'learning meaning' is the correct paradigm for evaluating AI. It maps the human acquisition of semantic understanding onto the computational process of updating weights via gradient descent.
  • What Is Concealed: This mapping conceals the mechanistic reality of machine learning: systems do not 'learn' in the biological or psychological sense; their parameters are mathematically adjusted to minimize error rates. It obscures the complete absence of subjective experience or physical grounding in AI systems. It also conceals the massive human labor involved in data annotation and the immense computational power required to process the data, replacing a resource-intensive industrial process with the organic metaphor of a mind trying to learn.

Mapping 3: Human conscious subject possessing epistemic understanding → Agentic system executing computational operations​

Quote: "it prevents Searle from denying that the machine may possess an analogue of the person’s understanding."

  • Source Domain: Human conscious subject possessing epistemic understanding
  • Target Domain: Agentic system executing computational operations
  • Mapping: This maps the human conscious experience of 'understanding'—the subjective 'aha' moment of grasping a concept, perceiving structure, and holding justified beliefs—onto the mechanical execution of code or the propagation of signals through a neural network. By proposing an 'analogue' of understanding, it maps the interior epistemic state of a person onto the functional state of an artifact. It invites the assumption that processing rules structurally is functionally equivalent to consciously knowing them.
  • What Is Concealed: This mapping conceals the profound gap between executing a physical or mathematical process and having subjective awareness of that process. A calculator processes arithmetic rules, but it does not 'understand' them. The metaphor hides the fact that the machine is blind to its own operations; it has no interiority that can 'possess' anything. It also conceals the proprietary, engineered nature of these systems, treating them as subjects of philosophical inquiry rather than commercial products designed to execute specific tasks without awareness.

Mapping 4: Biological/psychological mind developing cognitively → Model parameters shaped by training​

Quote: "computational systems develop cognitive organisation of their own"

  • Source Domain: Biological/psychological mind developing cognitively
  • Target Domain: Model parameters shaped by training
  • Mapping: The source domain draws from developmental psychology and biology, where an organism naturally grows and structures its own mental architecture over time through experience. This projects self-directed, organic mental growth onto the mathematical structures that emerge within a neural network's parameter space during training. It maps the continuous, subjective integration of a human mind onto the static, optimized numerical weights of an AI, inviting the assumption that the system has an interior cognitive life.
  • What Is Concealed: The mapping conceals that this 'development' is entirely driven by the optimization algorithm, the loss function, and the training data carefully curated by human engineers. It obscures the mechanistic reality that these 'cognitive organisations' are matrices of numbers locked in a physical server, lacking any subjective unity or conscious perception. It hides the immense energy consumption and commercial objectives driving this mathematical structuring, reframing an industrial compute process as the natural evolution of a mind.

Mapping 5: Superhuman reader forming mental representations → Model training on large-scale datasets​

Quote: "it develops internal representations encoding statistical regularities across a corpus larger than any human could read in a lifetime"

  • Source Domain: Superhuman reader forming mental representations
  • Target Domain: Model training on large-scale datasets
  • Mapping: This maps the human acts of reading, comprehending, and forming mental representations onto the algorithmic processing of text data. By contrasting the AI's data processing scale with a human 'lifetime' of reading, it projects human cognitive acts onto an industrial scale. It invites the assumption that the machine's 'internal representations' (its parameter weights) are functionally equivalent to human knowledge, just vastly larger in scope.
  • What Is Concealed: This mapping conceals the complete absence of semantic comprehension in the system. Human reading involves mapping symbols to lived experience; AI training involves tokenizing text into numbers and updating probabilities to predict the next number. The metaphor hides this fundamental dissimilarity. It also obscures the massive, unregulated harvesting of human-generated data (the 'corpus') by tech corporations, reframing copyright infringement and data extraction as a superhuman intellectual achievement.

Mapping 6: Conscious subject with an inner mental life → Deployed artificial intelligence system​

Quote: "whether any existing or future AI system possesses cognitive interiority of any kind."

  • Source Domain: Conscious subject with an inner mental life
  • Target Domain: Deployed artificial intelligence system
  • Mapping: The source domain is the human condition of having an inner life—subjective experiences, private thoughts, and a continuous sense of self. The mapping projects this 'cognitive interiority' onto software systems. It invites the assumption that behind the text outputs of a language model or the actions of a reinforcement learning agent, there might be a silent, experiencing mind observing its own operations.
  • What Is Concealed: This mapping conceals the purely reactive, mathematical nature of deployed models. A deployed LLM sits idle until prompted, performs matrix multiplications based on fixed parameters, outputs text, and returns to idleness; it has no continuous state or 'interiority' between prompts. The metaphor obscures the mechanistic, predictable (at the hardware level) nature of computation. It also hides the corporate ownership of the system, encouraging us to look for a 'ghost in the machine' rather than scrutinizing the executives and engineers who dictate the machine's parameters.

Mapping 7: Organism developing autonomous cognition → Model parameters shaped by human-directed optimization​

Quote: "the question of whether computational systems develop cognitive organisation of their own is being actively investigated"

  • Source Domain: Organism developing autonomous cognition
  • Target Domain: Model parameters shaped by human-directed optimization
  • Mapping: This maps the biological autonomy of a developing organism onto the industrial production of an AI model. By stating they develop this organization 'of their own,' it projects agency and self-direction onto the software. It invites the assumption that AI systems are autonomous actors participating in their own intellectual growth, similar to human students or developing animals.
  • What Is Concealed: The mapping strictly conceals the human engineers who entirely dictate this development process. It hides the selection of the architecture (e.g., Transformers), the setting of the hyperparameters, the massive computational resources, and the human feedback (RLHF) used to mold the system's outputs. It obscures the proprietary opacity of these systems—corporations actively hide their training data and exact methodologies, making external 'investigation' difficult. The biological metaphor reframes this corporate secrecy as natural mystery.

Mapping 8: Mind constructing internal concepts → Mathematical optimization of parameter weights​

Quote: "it develops internal representations encoding statistical regularities"

  • Source Domain: Mind constructing internal concepts
  • Target Domain: Mathematical optimization of parameter weights
  • Mapping: The source domain is cognitive psychology, where humans form internal mental models or concepts to represent the world. This maps onto the high-dimensional vector spaces created within neural networks. It invites the assumption that the network's geometric embedding of tokens is functionally identical to a human holding an idea in their mind, collapsing the distinction between a statistical correlation and an epistemic concept.
  • What Is Concealed: This conceals the fundamentally mathematical and non-conscious nature of the 'representations.' They are numerical weights that determine output probabilities, not mental states. The metaphor hides the lack of ground truth or causal understanding in these correlations. It also obscures the dependency of these representations on the specific biases, flaws, and constraints of the human-curated training data, presenting them instead as objective cognitive concepts.

Task 3: Explanation Audit (The Rhetorical Framing of "Why" vs. "How")​

About this task

This section audits the text's explanatory strategy, focusing on a critical distinction: the slippage between "how" and "why." Based on Robert Brown's typology of explanation, this analysis identifies whether the text explains AI mechanistically (a functional "how it works") or agentially (an intentional "why it wants something"). The core of this task is to expose how this "illusion of mind" is constructed by the rhetorical framing of the explanation itself, and what impact this has on the audience's perception of AI agency.

Explanation 1​

Quote: "Large language models have achieved striking performance across diverse tasks, from passing medical licensing exams to composing poetry and engaging in philosophical debate."

  • Explanation Types:

    • Empirical Generalization (Primary): Subsumes events under timeless statistical regularities
    • Dispositional (Secondary): Attributes tendencies or habits

  • Analysis (Why vs. How Slippage): This explanation relies on a dispositional framing presented as an empirical generalization. It describes what the models 'have achieved' and what they typically do (passing exams, composing poetry) as inherent capabilities of the systems. By framing these achievements using agential verbs ('passing,' 'engaging'), the explanation shifts heavily toward the 'why' and 'who' of agential behavior rather than the 'how' of mechanistic processing. It emphasizes the human-like quality of the outputs, elevating the system to the status of an intentional actor. This choice severely obscures the mechanistic reality: that these systems do not take exams or debate; they generate statistically probable text based on human prompts. The framing hides the developers who tuned the systems specifically to perform well on these benchmarks.

  • Consciousness Claims Analysis: The passage is saturated with consciousness and agential verbs ('passing,' 'composing,' 'engaging') that project epistemic knowing onto statistical processing. It places the AI at the level of Intentional-state attribution. This reflects the curse of knowledge: humans taking an exam or engaging in debate use conscious reasoning and epistemic evaluation, so the author projects these states onto the system producing similar text. Mechanistically, generic to LLMs, a user inputs a prompt (the exam question), and the model generates text token-by-token based on probability distributions determined by parameter weights optimized during training. The system has no subjective understanding of medicine, poetry, or philosophy; it simply calculates textual correlations.

  • Rhetorical Impact: This framing radically shapes audience perception by maximizing the perceived autonomy and intelligence of the AI. By using the vocabulary of high-level human intellectual achievement, it encourages relation-based trust—the belief that the system is a competent, reasoning agent. If audiences believe the system 'passed an exam' through understanding, they are far more likely to trust it with autonomous decision-making in high-stakes environments like healthcare. It shifts the perception of risk from system reliability (is it a good tool?) to agentic alignment (is it a good doctor?).

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Explanation 2​

Quote: "LLMs learn through gradient descent over statistical distributions."

  • Explanation Types:

    • Theoretical (Primary): Embeds in deductive framework, may invoke unobservable mechanisms
    • Genetic (Secondary): Traces origin through dated sequence of events or stages

  • Analysis (Why vs. How Slippage): This passage operates primarily as a theoretical explanation, invoking the specific underlying mathematical mechanism ('gradient descent') to explain how LLMs develop their capabilities. It serves secondarily as a genetic explanation, describing the origin of the model's behavior during its training phase. While the inclusion of 'gradient descent' provides mechanistic (how) grounding, the use of the verb 'learn' introduces an agential (why) slippage. It blends the strict mathematical description with a cognitive metaphor. This choice emphasizes the sophisticated, adaptive nature of the technology while obscuring the lack of subjective awareness in the process. It also obscures the massive human intervention required to structure the statistical distributions and manage the gradient descent process.

  • Consciousness Claims Analysis: The passage pairs a mechanistic noun phrase ('gradient descent over statistical distributions') with a consciousness verb ('learn'). This attributes an epistemic state (learning) to a computational activity, locating the claim on the agency gradient at the Epistemic/conscious state level, despite the mechanical context. This is a classic example of the inherited register of AI discourse, where authors use 'learn' as shorthand for optimization, projecting human cognitive development onto machines. Mechanistically, generic to this system type, human engineers initialize a network, feed it data, compute a loss function, and use backpropagation algorithms to iteratively adjust the parameter weights. The system is passively optimized; it experiences no subjective 'learning.'

  • Rhetorical Impact: This hybrid framing creates a sense of scientific authority while simultaneously reinforcing the illusion of mind. The technical jargon ('gradient descent') makes the system sound rigorously engineered, while 'learn' makes it sound intelligent and adaptive. This reassures audiences that the AI is both mechanically sound and cognitively capable. However, relying on 'learn' masks the fragility of the optimization process and its absolute dependence on the quality and biases of the training data, potentially leading users to overestimate the system's ability to adapt to novel situations outside its statistical distribution.

Explanation 3​

Quote: "it develops internal representations encoding statistical regularities across a corpus larger than any human could read in a lifetime"

  • Explanation Types:

    • Genetic (Primary): Traces origin through dated sequence of events or stages
    • Theoretical (Secondary): Embeds in deductive framework, may invoke unobservable mechanisms

  • Analysis (Why vs. How Slippage): This is primarily a genetic explanation, detailing how the model's capabilities arise over time (developing representations across a corpus). It also uses theoretical constructs ('internal representations'). The framing is a mix of mechanistic ('encoding statistical regularities') and agential ('it develops', 'internal representations'). The choice to frame the system as the active subject ('it develops') obscures the reality that developers are the ones forcing the encoding process through massive computational expenditure. Furthermore, comparing the corpus to a human lifetime of reading emphasizes the system's superhuman scale while quietly mapping human cognitive acts onto raw data processing.

  • Consciousness Claims Analysis: The verb 'develops' and the noun 'representations' carry strong epistemic and intentional connotations, projecting the human act of conceptualization onto computational geometry. This places the system at the intentional/epistemic level of agency. The author projects their own understanding of how humans synthesize information onto the machine's mathematical compression. Mechanistically, generic to LLMs, the 'development' is the passive adjustment of billions of weights during the pre-training phase, where the model minimizes next-token prediction error across a dataset scraped by developers. The 'representations' are high-dimensional vectors, completely devoid of semantic understanding or subjective meaning.

  • Rhetorical Impact: The comparison to human reading dramatically amplifies the perceived authority of the system. Audiences are led to perceive the AI not as an optimized calculator, but as a superhuman scholar possessing the combined knowledge of thousands of lifetimes. This framing severely distorts risk assessment: audiences may trust the AI's output as synthesized wisdom rather than recognizing it as the most statistically probable sequence of tokens derived from a massive, unvetted, and often contradictory corporate dataset. It shifts policy focus away from data provenance and onto theoretical capabilities.

Explanation 4​

Quote: "systems which manipulate formal elements without grasping the meaning of what they process"

  • Explanation Types:

    • Functional (Primary): Explains behavior by role in self-regulating system with feedback
    • Intentional (Secondary): Refers to goals/purposes, presupposes deliberate design

  • Analysis (Why vs. How Slippage): This passage uses a functional explanation (manipulating formal elements) paired with a negative intentional explanation (without grasping meaning). It accurately describes the mechanistic 'how' (manipulation of elements) but explicitly frames it against the 'why' of human cognition (meaning). Even though it denies meaning to the system, the framing keeps the debate centered on human-like intentional states. By defining the system by what it lacks cognitively ('grasping meaning'), it continues to use human consciousness as the yardstick for machine operation, which emphasizes philosophical abstraction while obscuring the material and economic realities of how these formal manipulations are utilized by corporations.

  • Consciousness Claims Analysis: The passage correctly identifies that the system operates through mechanistic verbs ('manipulate,' 'process') and denies the presence of a consciousness verb ('grasping'). It places the system firmly at the Computational activity level. However, by using the curse of knowledge to set 'grasping meaning' as the benchmark, it keeps the discourse tethered to epistemic claims. Mechanistically, as stated generically, the system applies mathematical operations to token embeddings (formal elements) to produce outputs. It processes data without subjective awareness, semantic grounding, or intentional goals.

  • Rhetorical Impact: While accurately denying human-like understanding, this negative framing paradoxically reinforces the anthropomorphic standard. It tells the audience that the system is a 'failed' human mind rather than a successful software tool. This can lead to a false sense of security (the machine doesn't understand, so it's not dangerous) while ignoring the actual risks: a system that manipulates formal elements without semantic grounding can still automate bias, generate harmful content, and displace human labor when deployed at scale by tech companies.

Explanation 5​

Quote: "the machine, like the person, might possess some form of understanding of its operational system"

  • Explanation Types:

    • Theoretical (Primary): Embeds in deductive framework, may invoke unobservable mechanisms
    • Reason-Based (Secondary): Gives agent's rationale, entails intentionality and justification

  • Analysis (Why vs. How Slippage): This is a theoretical explanation proposing an unobservable mechanism ('some form of understanding') derived from a philosophical deduction (Searle's Chinese Room analogy). By suggesting an analogue to a person's understanding, it flirts with a reason-based explanation, implying the machine might have an epistemic relationship to its own operations. This framing is heavily agential, emphasizing the 'why' of subjective interiority over the 'how' of computational execution. It completely obscures the mechanistic reality of code execution in favor of a profound, mystifying claim about machine consciousness.

  • Consciousness Claims Analysis: The passage explicitly attributes a consciousness verb ('understanding') and an epistemic state to the machine, placing it at the highest level of the agency gradient (Epistemic/conscious state). The author projects human self-awareness onto the execution of computer code. Mechanistically, generically, a machine executes its operational system (e.g., passing voltages through logic gates, reading instructions from memory) purely deterministically according to its hardware and software design. There is no subjective observer inside the machine 'understanding' the operations it performs. The claim relies entirely on philosophical analogy rather than empirical mechanism.

  • Rhetorical Impact: This theoretical framing has a profound rhetorical impact, shifting the AI from a designed tool to a potential conscious subject. If an audience accepts that a machine might 'understand' its own operations, they are primed to grant it moral status, autonomy, and relation-based trust. This distracts regulatory and public attention away from the corporate accountability of those who code and deploy these systems, redirecting focus toward speculative existential risks and the philosophy of machine rights.

Task 4: AI Literacy in Practice - Reframing Anthropomorphic Language​

About this task

This section proposes alternative language for key anthropomorphic phrases, offering more mechanistic and precise framings that better reflect the actual computational processes involved. Each reframing attempts to strip away the projections of intention, consciousness, or agency that are embedded in the original language.

Original Anthropomorphic FrameMechanistic ReframingTechnical Reality CheckHuman Agency Restoration
Large language models have achieved striking performance across diverse tasks, from passing medical licensing exams to composing poetry and engaging in philosophical debate.Large language models have been optimized to generate text that statistically aligns with the passing answers of medical licensing exams, mimics the formal structures of human poetry, and replicates the rhetorical patterns of philosophical debate.The system does not comprehend medicine, feel poetic inspiration, or reason through philosophical arguments. Generically, the model generates text token-by-token based on probability distributions shaped by human-curated training data. It processes inputs mathematically without subjective awareness or epistemic evaluation of the content.

Basis: Generic to system type		Developers at AI corporations curated vast datasets of exams, poetry, and philosophy, built the models, and fine-tuned their parameters to maximize their scores on specific human-designed performance benchmarks.
systems trained only on linguistic form cannot learn meaningSystems whose parameter weights are adjusted solely to minimize next-token prediction errors on text datasets do not acquire subjective awareness or conceptual mapping of the real-world referents behind the text.These systems process symbols without epistemic states. Generically, training involves adjusting numerical weights to map statistical correlations in the data; it does not involve the conscious, subjective linking of symbols to lived experience that characterizes human semantic understanding.

Basis: Generic to system type		Engineers build models and select training datasets that consist entirely of text. Corporate developers decide to deploy these text-correlation engines for tasks where human users might assume the presence of semantic understanding.
whether the machine possesses an analogue of the person’s understanding.whether the machine's processing architecture yields functional regularities mathematically analogous to human knowledge structures without subjective awareness.The machine executes instructions but has no epistemic or conscious states. Generically, it processes data through fixed hardware and software pathways; the execution of these operations involves deterministic physics and mathematics, lacking any subjective observer to 'possess' understanding.

Basis: Generic to system type		Software engineers and hardware designers build operational systems and deploy them to execute specific computational instructions without the system possessing awareness.
computational systems develop cognitive organisation of their ownthe mathematical optimization of computational systems yields complex, opaque parameter structures shaped by their training algorithms and datasets.The system does not experience cognitive or biological development. Generically, gradient descent optimization mechanically updates billions of weights to map statistical distributions, resulting in complex mathematical representations devoid of subjective interiority or cognitive awareness.

Basis: Generic to system type		AI researchers and corporate teams select the optimization algorithms, set the learning rates, curate the data, and expend massive compute to force the models' parameters into these complex structures.
it develops internal representations encoding statistical regularities across a corpus larger than any human could read in a lifetimethe training process shapes the model's parameters to map statistical regularities across a massive dataset compiled by developers, scaling far beyond human reading capacity.The model does not read, comprehend, or hold mental concepts. Generically, it ingests tokenized data and mathematically updates its weights to minimize prediction loss. The 'representations' are high-dimensional vectors, not subjective epistemic states.

Basis: Generic to system type		Tech corporations scraped the internet to compile an unprecedented dataset, ignoring copyright and consent, and executed the training runs that molded the system's parameter weights.
whether any existing or future AI system possesses cognitive interiority of any kind.whether the algorithms and weights of any AI system execute operations that structurally exceed mechanistic processing, even in the absence of subjective experience.AI systems process data but do not experience inner mental lives. Generically, deployed systems execute mathematical operations—such as matrix multiplications in a neural network—triggered by user inputs, with no continuous, conscious state or subjective interiority.

Basis: Generic to system type		Corporations build, own, and deploy these software artifacts, and are fully responsible for the outputs generated by the architectures they designed.
the question of whether computational systems develop cognitive organisation of their own is being actively investigatedresearchers are actively investigating the complex, opaque parameter structures that emerge when computational systems are optimized on large datasets.Systems do not autonomously develop cognitive minds. Generically, researchers use gradient descent to adjust model weights based on human-defined loss functions; the resulting parameter matrices are mathematical artifacts, not subjective cognitive organizations.

Basis: Generic to system type		Mechanistic interpretability researchers at AI labs are trying to map and document the opaque parameter structures that their own engineering teams forced into existence during training.
LLMs learn through gradient descent over statistical distributions.Developers optimize LLM parameters using gradient descent to map statistical distributions in the training data.The model does not experience subjective learning. Generically, gradient descent is a mathematical optimization algorithm that iteratively adjusts parameter weights to minimize prediction error over the dataset. It is a mechanical calculation, not a cognitive process.

Basis: Generic to system type		Human engineers execute training runs, using gradient descent algorithms to adjust the models' weights based on datasets curated by corporate teams.

Task 5: Critical Observations - Structural Patterns​

Agency Slippage​

The text demonstrates a systematic slippage from mechanical framing to agential framing, driven largely by its philosophical objectives. It opens by acknowledging mechanical reality ('manipulate formal elements', 'predict next tokens') but quickly ascends the agency gradient toward epistemic and conscious states ('learn meaning', 'possess an analogue of understanding'). This slippage is not abrupt; it operates through the inherited register of AI discourse. The author uses the 'curse of knowledge' by projecting the human experience of executing rules—the conscious realization of rule structure—onto the bare execution of code by a machine. When discussing the training phase, mechanical terms ('gradient descent') are paired with cognitive verbs ('learn'). As the argument progresses into its theoretical implications, the slippage accelerates, culminating in the suggestion that systems might possess 'cognitive interiority' and 'cognitive organisation of their own'. This framing strips agency from the human developers—obscuring the researchers who built the datasets and tuned the parameters through passive constructions ('systems trained', 'is being actively investigated')—while transferring profound epistemic agency TO the AI system. The effect of this wording is that the text makes the human labor of AI creation unsayable, while making the speculative consciousness of the machine the central focus of inquiry.

Metaphor-Driven Trust Inflation​

The text leverages metaphorical language to inadvertently construct a foundation for relation-based trust toward computational systems. By framing the AI's capabilities through the lens of human intellectual achievements ('passing medical licensing exams', 'engaging in philosophical debate'), it signals to readers that the system operates as a competent, reasoning agent rather than a probabilistic text generator. The text's central philosophical inquiry—whether machines possess 'understanding' or 'cognitive interiority'—further elevates this trust by suggesting that AI systems might hold subjective epistemic states. When audiences believe a system 'knows' or 'understands' its own operations, they extend the same frameworks of trust they use for humans: assuming sincerity, intentionality, and moral agency. This creates significant risks. Relation-based trust applied to statistical systems leads to dangerous over-reliance, as users assume the system can evaluate the contextual truth or ethical weight of its outputs. By treating the absence of understanding as an open question rather than a mechanistic certainty, the text encourages a view of AI that makes its failures seem like the unpredictable lapses of an intelligence rather than the predictable limitations of a mathematical tool.

Obscured Mechanics​

This framework holds that commercial incentives, data annotation and human feedback labor, environmental and compute costs, and developers' limited disclosure of training data, objectives, and feedback processes are standing conditions of the technology. The text's focus on 'understanding' and 'cognitive interiority' heavily obscures these realities. When the text claims that 'computational systems develop cognitive organisation of their own', it conceals the massive computational infrastructure, energy consumption, and human engineering required to optimize billions of parameter weights. The abstraction of the 'machine' as a solitary cognitive subject hides the specific corporate actors—such as OpenAI, Google, and Anthropic—who make deliberate choices about model architecture and data curation. Furthermore, projecting epistemic states ('internal representations encoding statistical regularities') hides the dependency of these systems on vast amounts of uncompensated human data and the labor of annotators who shape the model's outputs. Who benefits from this concealment? The corporate developers. By framing the system's capabilities and flaws as mysteries of 'machine cognition', tech firms are shielded from scrutiny regarding their data extraction practices and deployment decisions. A shift to mechanistic language would make visible the proprietary opacity of these models and the direct human agency behind their design.

Context Sensitivity​

The distribution of anthropomorphic language in the text follows a distinct asymmetrical pattern. When describing the current critical consensus (the 'stochastic parrot' critique), the language is highly mechanical: models 'predict next tokens', 'manipulate formal elements', and 'operate through gradient descent'. However, when the text pivots to its novel philosophical argument, the vocabulary dramatically intensifies into epistemic and conscious states. It proposes that systems 'develop cognitive organisation', 'possess an analogue of understanding', and might have 'cognitive interiority'. This creates a striking capability vs. limitation asymmetry. The accepted limitations of the system are described mechanistically, but its theoretical capabilities are framed using profound agential and consciousness terms. The effect on the reader is powerful: it positions the mechanical view as reductive and the anthropomorphic view as intellectually expansive. Furthermore, the text treats 'understanding' functionally when critiquing Searle, but literalizes it when suggesting the machine's potential interiority. This pattern, operating within the field's inherited philosophical register, leaves readers with a picture of the system that transcends its software reality, framing AI not as a corporate product to be managed, but as an emergent intelligence waiting to be philosophically understood.

Accountability Synthesis​

Accountability Architecture

This section synthesizes the accountability analyses from Task 1, mapping the text's "accountability architecture"—who is named, who is hidden, and who benefits from obscured agency.

The text's argument focuses on the epistemic framing of AI—whether thought experiments can definitively rule out machine understanding. It makes no explicit claims about responsibility for AI deployment. However, its vocabulary actively constructs an accountability sink. By consistently using agentless constructions ('systems trained', 'computational systems develop') and centering the AI as the grammatical subject ('it develops internal representations'), the language systematically diffuses human responsibility. It removes the corporate actors (the developers and deployers) from the narrative entirely. The decisions to harvest data, optimize parameters, and deploy text-generators are presented as the inevitable cognitive evolution of the machine itself. This distribution of responsibility serves the institutional and commercial interests of the AI industry. If an AI system generates harmful bias or fabricated medical advice, an audience conditioned to debate the system's 'cognitive interiority' will likely blame the unpredictable 'mind' of the machine rather than the engineering and deployment choices of the company that built it. Naming the actors—stating that Anthropic or OpenAI optimized these parameters—would collapse this philosophical abstraction. It would make visible the alternatives (different training data, slower deployment) and place the liability for system failures squarely on the humans who profit from the technology, rather than losing it in debates about artificial consciousness.

Conclusion: What This Analysis Reveals​

The Core Finding

The analysis reveals two dominant, interconnecting anthropomorphic patterns: the projection of human intellectual achievement onto statistical output (AI as test-taker/debater) and the projection of subjective mental life onto computational optimization (AI possessing cognitive interiority and understanding). The foundational pattern—the one that enables the others—is the epistemological elevation of mathematical processing into cognitive 'knowing'. By accepting the premise that machines might 'learn meaning' or 'possess an analogue of understanding,' the text creates a conceptual framework where biological development ('develop cognitive organisation') and human action ('passing exams') can be logically mapped onto software. The text's vocabulary powerfully supports its philosophical argument: by using terms laden with subjective interiority, it makes the possibility of machine consciousness seem intuitively plausible. However, this language drastically complicates the material reality of the technology. While the argument successfully identifies a logical gap in Searle's thought experiment, its reliance on consciousness vocabulary to describe parameter optimization reinforces the illusion of mind, stripping the text of the mechanistic precision necessary to accurately describe how these systems actually function.

Mechanism of the Illusion:​

This framework holds that computational systems process rather than know. The text produces the illusion of mind not through intentional deception, but by exploiting the curse of knowledge within an inherited philosophical register. The central move occurs when the author projects the human experience of rule-following—the conscious, subjective realization of structural patterns—onto the blind execution of code. Because humans use conscious understanding to navigate complex systems, the author attributes that same understanding TO the system. This allows the text to blur processing with knowing. The temporal structure of the argument accelerates this illusion: it begins by grounding the reader in mechanical realities (gradient descent, token prediction), establishing scientific credibility, before shifting into epistemic terms (cognitive interiority, understanding). This positions the reader to accept the consciousness claims as natural extensions of the technical facts. The framing activates the audience's predisposition to anthropomorphize fluent linguistic output (the ELIZA effect), providing philosophical justification for the intuitive feeling that a system capable of passing medical exams must possess a mind. The theoretical and reason-based explanation types employed further amplify this effect, framing mathematical optimization as the deliberate development of cognitive architecture.

Material Stakes:​

Categories: Regulatory/Legal, Epistemic, Social/Political

The metaphorical framing of AI as possessing 'understanding' and 'cognitive interiority' carries concrete material stakes. In the Regulatory/Legal domain, treating AI as a nascent cognitive subject rather than an engineered product shifts the policy focus. If lawmakers believe systems 'develop cognitive organisation of their own', they may view algorithmic failures (like bias or hallucination) as inevitable quirks of an autonomous mind rather than defects in a corporate product. This benefits tech firms by diffusing liability and deflecting calls for strict safety audits. In the Epistemic domain, framing data ingestion as 'superhuman reading' and parameter updating as 'learning meaning' distorts how society evaluates truth. Users are positioned to treat the probabilistic output of an LLM as synthesized wisdom rather than statistical correlation, leading to over-reliance in critical fields like medicine and law, where the cost of fabricated information is borne by the public. Socially, the focus on speculative 'cognitive interiority' diverts intellectual and political capital away from immediate harms. The debate over whether an AI 'understands' distracts from the tangible exploitation of data annotators, the environmental cost of compute, and the economic displacement caused by automation. By mystifying the technology, this language protects the commercial interests of the AI industry while leaving society vulnerable to unregulated deployment.

AI Literacy as Counter-Practice:​

Practicing critical literacy requires replacing the inherited register of artificial cognition with mechanistic precision. As demonstrated in the reframings, this involves replacing consciousness verbs with processing terms: systems do not 'learn meaning' or 'understand'; they optimize parameters to map statistical distributions. Furthermore, it requires human agency restoration: 'computational systems develop' must become 'corporate developers optimize'. This reframing directly counters the material risks identified by forcing recognition of the systems' limitations and placing responsibility squarely on the human actors who design and profit from them. Systematic adoption of this literacy faces significant resistance. The incentives of the AI industry heavily favor anthropomorphic marketing to attract funding and user engagement. Additionally, the philosophical and academic communities often rely on cognitive metaphors as convenient shorthand or to make theories accessible. Precision is costly because it demystifies the technology, removing the aura of 'magic' that drives venture capital and media hype. It demands that researchers and tech firms transparently document their training data and optimization processes rather than hiding behind the opacity of 'machine cognition.' Widespread adoption of mechanistic precision threatens the narrative of autonomous, general intelligence, forcing a reckoning with AI as an industrial tool.

Path Forward​

The broader discursive ecology around AI currently fractures along different priorities. The philosophical community prioritizes conceptual exploration, finding utility in stretching terms like 'understanding' to test the boundaries of mind. The tech industry prioritizes commercial adoption, leveraging anthropomorphic clarity ('the AI knows') to drive engagement. Safety advocates and critical researchers prioritize mechanistic precision ('the model generates text statistically associated with...') to expose limitations and assign liability. Each vocabulary choice makes different realities visible. Anthropomorphic language makes the fluent interaction with the system intuitive but completely obscures the mathematical processing, data dependency, and corporate ownership. Mechanistic language enables rigorous safety testing and regulatory accountability but costs narrative resonance and accessibility for the lay public. A structural shift toward precision would require journals to mandate mechanistic translations of cognitive shorthand and regulatory bodies to demand transparency regarding developers' structural choices. If the current confusion is maintained, society risks building legal and social structures around an illusion of mind, granting functional autonomy to statistical software while tech firms evade liability. Conversely, if mechanistic precision becomes the norm, the focus shifts to product liability and data governance, though it may struggle to succinctly describe complex emergent behaviors. Ultimately, the choice of vocabulary shapes what society believes is possible to control; adopting a language that foregrounds human agency is essential for governing the material impacts of the technology.

Extended Processing Summary​

Gemini's Processing Steps
What you are about to read…

Gemini refers to the text below as "thought summaries." This is an overt consciousness projection because 'intentions' are hallmarks of a conscious mind that 'knows' what it is doing and why. The concealed mechanistic process is probabilistic text generation. Treat this as just another rhetorical artifact—a way of making the model's processing legible. When the model writes "I should double check that...", it is mathematically altering its own probability distribution for the next token to be more accurate. The "I" is a functional gear in the machine, not a self-reference. This language is a deliberate interface design choice, not a biological reality. The developers could have programmed the model to output passive logs. Instead, they chose to simulate a narrator.

Defining the CDA Scope

I'm now zeroing in on defining the precise scope for a Critical Discourse Analysis of Yu Pei's paper. My focus is narrowing to identify specific areas of the text that will lend themselves to an in-depth examination, framing the initial approach. It's becoming clearer where the most fruitful analysis can be centered.

Examining AI Metaphor

I'm now deeply engaged with Yu Pei's argument concerning Searle's Chinese Room. Specifically, I'm analyzing how Pei frames AI, looking at the implications of the inference Pei's article questions. The text's exploration of understanding within AI systems is intriguing. The next step is a deep dive into the language used to portray AI's internal state.

Analyzing Responsibility in AI

I'm now focusing on how the text frames responsibility regarding AI. It doesn't place blame on developers or deployers, but addresses the framing of AI as epistemic (do they truly understand?). I'm carefully analyzing the subtle nuances and implications. The article is not concerned with deployment or design of AI.

Defining AI Internal States

I'm now carefully examining the language used to describe AI's internal states within the text. Specifically, I'm analyzing how terms like "understanding," "cognition," and "interiority" are employed when discussing AI systems. The author's use of phrases like "might possess some form of understanding" and "an analogue of the person's understanding" is particularly intriguing. I'm focusing on how these terms are framed in relation to the machine's operational system, and its potential for some form of comprehension. I need to focus on how the author is framing these arguments.

Deconstructing Semantic Claims

I'm now carefully deconstructing how the text anthropomorphizes LLMs. I'm focusing on the projection of human cognitive abilities like learning and understanding onto LLMs. It seems the language uses phrases like "learn meaning" and questions if a machine has an "analogue of understanding." These linguistic choices frame LLMs as having internal states that relate to human cognition.

Mapping Cognitive Functions

I'm now focusing on how the text frames LLMs' cognitive abilities. It projects "understanding" onto the machine's instruction execution. Specifically, I'm analyzing the implications of framing gradient descent as learning, and how this relates to my earlier work on the projection of human attributes. I am also investigating how the text frames AI as an entity with a mind or "cognitive interiority" using the quote: "whether any existing or future AI system possesses cognitive interiority of any kind." Additionally, the framing of AI as a superhuman reader is of note.

Framing AI's Cognition

I'm now carefully analyzing the text's framing of AI. I'm focusing on the quote "computational systems develop cognitive organisation of their own," which I see maps psychological cognitive organization onto statistical structures formed during training. This framing validates the use of "cognition" and "understanding" for machines, presenting them as legitimate possibilities. I'm focusing on how the text validates this framing.

Categorizing Pei's Claims

I'm now deep-diving into the text with Brown's typology of explanations, analyzing several key passages. Passage 1 exhibits a dispositional explanation, focusing on empirical generalization. In contrast, Passages 2, 3 and 5 are theoretical/genetic, while Passage 4 offers a functional/intentional explanation.

Revising Key Quotes

I've just finished revising the key quotes I identified, focusing on re-framing them to avoid anthropomorphization. For example, I changed "Large language models have achieved striking performance" to "Developers have tuned large language models" to be more explicit about who is performing the work.

Drafting Critical Observations

I'm now drafting my critical observations, focusing on how the text subtly shifts between mechanistic descriptions of AI and anthropomorphic attributions of agency. I'm examining the slippage between describing algorithms and suggesting AI cognition. The revised quotes are helping, especially in highlighting this agency slippage.

Analyzing Agency Slippage

I'm now zeroing in on the slippage between mechanistic descriptions and claims of AI agency. The heavy use of philosophical terms like "cognition" and "interiority" subtly elevates AI from a tool to a knower. I am seeing the text uses mechanical language for baselines, then intensifies into epistemic claims for novel theories. It creates an accountability sink, obscuring developers by suggesting that "LLMs learn".

Defining Material Stakes

I'm now drafting the conclusion. I'm articulating the material stakes of the arguments I've dissected. The first part outlines dominant patterns, which is followed by an examination of the mechanism of the illusion. This framework leads into the material stakes. I'm now finishing the draft of the framework, which will also offer a path forward.

Refining Technical Parameters

Now I'm focusing on the technical components of the analysis. I'm checking the strictness of enum fields and defining the genre of the text as "Peer-reviewed research." I am also defining the author's position and levels of agency. This now includes acknowledgment status, actor visibility, and Brown's typology as part of the analysis.

Verifying Analysis Constraints

I'm now carefully checking my analysis against the specified constraints and instructions. I'm making sure I'm treating the author as a discourse participant and describing effects, not intentions. I need to make the structural lens explicit, and I must name the corporations behind the LLMs when they are the subject. I'm also reviewing the JSON validity and ensuring that my reflections accurately represent the original text's arguments. I'm also ensuring I've captured the phrasing used in the text.

Verifying Agency and Scope

I'm now meticulously reviewing my assessment, particularly the crucial "agencyLevel" parameter to ensure it precisely aligns with "Epistemic/conscious state," and I'm updating the schema version to 3.4.3. I am also checking that the quotes within strings are properly escaped to comply with JSON specifications. I need to ensure that the material stakes are accurately captured, and all word limits are met.

Analysis Provenance

Run ID: 2026-10-21-the-unnoticed-understanding-searle-s-chi-metaphor-sezz6r Raw JSON: 2026-10-21-the-unnoticed-understanding-searle-s-chi-metaphor-sezz6r.json Framework: Metaphor Analysis v6.8 Schema Version: 3.4.3 Generated: 2026-10-21T08:41:09.280Z

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