Physical Symbol Systems and the Language of Thought

The Physical Symbol System Hypothesis

Imagine your brain as a sophisticated computer processing symbols. Not metaphorically, but literally—manipulating physical structures that represent the world around you. This is the essence of the physical symbol system hypothesis, an influential theory proposed by cognitive scientists Allen Newell and Herbert Simon as a fundamental framework for understanding intelligence.

Much like how biologists rely on the cell doctrine or geologists on plate tectonics, cognitive scientists have used this hypothesis as their north star. It’s a starting point that frames how we think about thinking itself. Newell and Simon’s hypothesis states that “a physical symbol system has the necessary and sufficient means for general intelligent action.” In other words, if you want intelligence, you need a system that can process symbols—and if you have a system that can process symbols properly, you’ll get intelligence.

But what exactly constitutes a physical symbol system? According to Newell and Simon, such a system needs:

1. Symbols that can be physically instantiated
2. Symbol structures composed of these basic symbols
3. Processes for manipulating these symbols and structures
4. The ability to interpret and produce new symbols

Consider a simple example: the problem of getting foxes, chickens, and grain across a river in a boat that can only carry one item at a time (without leaving foxes alone with chickens or chickens alone with grain). To solve this puzzle, you need to mentally represent the objects and their relationships, consider possible moves, and evaluate potential outcomes. According to the physical symbol system hypothesis, your brain accomplishes this by physically manipulating symbols that represent foxes, chickens, boats, and so on.

At its core, this hypothesis is about taking information, encoding it into symbols, and transforming those symbols according to specific rules. That spreadsheet calculation you just ran? That’s a simple example of symbol manipulation. Your morning deliberation about whether to have coffee or tea? According to this hypothesis, that too is symbol manipulation—just happening in the wetware of your brain rather than silicon.

The Language of Thought Hypothesis

Philosopher and cognitive scientist Jerry Fodor took the physical symbol system hypothesis further with his language of thought hypothesis. His proposal is both elegant and radical: we think in sentences—not English or Mandarin sentences, but sentences in a special mental language he sometimes called “Mentalese.”

What makes this mental language special? Unlike natural languages with their ambiguities and inconsistencies, the language of thought is supposed to be precise and logical—more like the formal languages used in mathematics and logic than the messy languages we speak. This language doesn’t need to be learned; Fodor argues it’s innate, built into our cognitive architecture.

Fodor’s argument begins with an observation about how we explain human behavior. We routinely explain and predict what people do by attributing beliefs and desires to them. When I say you rushed into the water because you believed someone was drowning and wanted to save them, I’m describing internal mental states that caused your behavior. Fodor claims this “belief-desire psychology” works so well because it’s actually true—we really do have beliefs and desires that cause our actions.

But this raises a puzzle: how can mental states cause physical actions? How can the content or meaning of your beliefs affect anything physical? If I believe “the door is open” and desire “to close the door,” how does the meaning of these thoughts actually move my muscles?

This is what philosophers call the problem of “causation by content,” and it’s at the heart of Fodor’s language of thought hypothesis.

Syntax, Semantics, and the Computer Model

Fodor’s solution draws on an analogy with computers. When you type “2+3” into a calculator, the machine doesn’t understand addition or numbers. It simply follows rules for manipulating symbols. Yet somehow, it consistently produces “5” as an output.

This works because computers are designed to manipulate symbols based on their “formal properties” (their shape or syntax) in ways that respect their “semantic properties” (their meaning). The computer doesn’t know what “2” means, but it’s programmed to manipulate the symbol “2” according to rules that consistently produce the right results.

Fodor argues that our brains work in a similar way. Sentences in the language of thought are physical symbol structures that can be viewed either syntactically (in terms of their physical form) or semantically (in terms of what they represent). The brain processes these structures according to their syntax, while remaining blind to their semantics. Yet because the system is properly designed, these syntactic manipulations respect semantic relationships.

Consider a more detailed example:

1. We start with two basic symbols in our language of thought: “Ga” (meaning “Georgina is tall”) and “Fa” (meaning “Georgina has red hair”).
2. Our brain contains rules for transforming these symbols. One rule might be: if you have two symbols “S” and “T,” you can form a new symbol “(S & T).”
3. Applying this rule to our initial symbols gives us “(Ga & Fa)” (meaning “Georgina is tall and has red hair”).
4. Another rule might be: if you have a symbol containing a name, you can replace the name with a variable “x” and add “∃x” (meaning “there exists an x such that…”) at the beginning.
5. Applying this to “(Ga & Fa)” gives us “∃x (Gx & Fx)” (meaning “There exists someone who is tall and has red hair”).

According to Fodor, your brain processes these as physical symbol structures, applying transformation rules without directly “seeing” their meaning. Yet the transformations reliably preserve truth, allowing you to draw valid conclusions about the world. This is possible because the language of thought is a formal system where syntax tracks semantics, much like in formal logic.

Fodor likens this to how in formal logic, syntactic deducibility (having a formal proof) corresponds to semantic entailment (truth preservation). His key insight is that this correspondence allows purely physical systems to implement what appears to be reasoning about meanings.

The Chinese Room Argument

But can symbol manipulation alone really produce genuine understanding and intelligence? Philosopher John Searle famously challenged this idea with his “Chinese Room” thought experiment.

Imagine yourself locked in a room with nothing but an enormous instruction manual written in English. Through one window, you receive papers with Chinese symbols. You consult your manual, which tells you which Chinese symbols to send back through another window based solely on the shapes of the symbols you received. Despite knowing no Chinese whatsoever, you could theoretically provide appropriate responses to any Chinese question.

To outside observers, the room appears to understand Chinese perfectly—it passes what Alan Turing proposed as the “Turing Test” for machine intelligence. If you ask a question in Chinese, you get an appropriate answer in Chinese. Yet you, inside the room, understand nothing about the meaning of these symbols. You’re just following rules for matching patterns.

Searle’s argument cuts to the heart of computational theories of mind: if you don’t understand Chinese despite implementing the program, how could a computer understand anything by implementing essentially the same program? The Chinese Room seems to satisfy the conditions of the physical symbol system hypothesis—it manipulates symbols according to rules—yet it lacks understanding. Therefore, Searle concludes, the physical symbol system hypothesis must be wrong.

Searle’s challenge is particularly pointed because it grants the physical symbol system hypothesis everything it seems to want. The Chinese Room manipulates symbols perfectly—it gives all the right outputs for the inputs it receives. It’s just that this manipulation doesn’t seem to produce understanding. The person in the room is just “pushing symbols around” without comprehending what they mean.

Responses to the Chinese Room

Defenders of computational approaches have offered various responses to Searle’s challenge:

The systems reply argues that while you as the person in the room don’t understand Chinese, the system as a whole (you plus the instruction manual plus the room) does understand. Understanding emerges at the system level, not the component level. This is analogous to how neurons individually don’t understand language, yet the brain as a whole does. The individual components of a system need not possess the properties of the system itself.

Searle counters this by suggesting we internalize the entire system—imagine memorizing the entire instruction manual so that it’s all in your head. You’re now the entire system, yet you still don’t understand Chinese. You’re just following memorized rules for symbol manipulation.

The robot reply suggests that understanding requires embodiment and causal connections to the world. A Chinese Room embedded in a robot that can interact with the world—seeing Chinese characters, handling Chinese objects, speaking with Chinese speakers—would genuinely understand Chinese through these grounded interactions.

Searle remains unconvinced. He argues that adding sensors and motors doesn’t bridge the fundamental gap between syntax and semantics. A robot could be programmed to stop when it “sees” the Chinese character for “stop,” but this wouldn’t mean it understands what “stop” means any more than a trained pigeon understands a stop sign.

Some philosophers and cognitive scientists have proposed other responses. The biological reply suggests that perhaps understanding requires specific biological processes that non-biological systems cannot replicate. The learning reply argues that a system that developed its symbol-processing capacities through learning (rather than being hand-programmed) might genuinely understand.

The Symbol-Grounding Problem

This debate leads us to what cognitive scientists call the symbol-grounding problem: How do symbols become meaningful in the first place? How does the symbol “cat” connect to actual cats in the world?

In a computer program, symbols get their meaning from the programmers who create them. The binary code “01100011 01100001 01110100” represents “cat” because programmers decided it should. But if our minds are symbol systems, where do our symbols get their meaning? There’s no programmer assigning meanings to our mental symbols.

One approach, associated with philosophers like Fred Dretske and Ruth Millikan, suggests that meaning comes from causal connections between symbols and the world. Your mental symbol for “cat” means cat because it’s reliably caused by encounters with cats. Another approach, championed by philosopher Wilfrid Sellars, argues that symbols get their meaning from their role in a network of inferential relationships with other symbols.

The symbol-grounding problem isn’t unique to artificial systems—it’s equally mysterious how our own thoughts connect to reality. When you think about cats, what exactly makes that thought about cats rather than dogs or airplanes? This problem of “intentionality” or “aboutness” has puzzled philosophers for centuries.

Implications for Artificial Intelligence

The debate between Searle and proponents of the physical symbol system hypothesis has profound implications for artificial intelligence. If Searle is right, then no amount of symbol manipulation, no matter how sophisticated, will ever produce genuine understanding or consciousness. AI systems might simulate intelligence, but they would be what Searle calls “weak AI”—useful tools that mimic intelligence without possessing it.

On the other hand, if the physical symbol system hypothesis is correct, then there is no in-principle barrier to creating “strong AI”—artificial systems that genuinely understand and are conscious in the same way humans are. The challenge would be merely technical: figuring out the right symbols and rules for manipulating them.

Recent advances in AI, particularly in deep learning and neural networks, have complicated this picture. Modern AI systems like large language models don’t explicitly manipulate symbols according to explicit rules. Instead, they learn statistical patterns from vast amounts of data. Some researchers argue this approach might sidestep Searle’s objections by grounding symbols in patterns of use rather than explicit rules.

Beyond Symbol Systems?

The physical symbol system hypothesis and the language of thought hypothesis remain influential in cognitive science, but they’re not the only approaches. Alternative frameworks include:

Connectionism: This approach models cognition as emerging from networks of simple processing units (artificial neurons) rather than explicit symbol manipulation. Connectionists argue that intelligence emerges from the patterns of activation across these networks, not from rule-based symbol processing.

Embodied Cognition: This view holds that cognition is fundamentally shaped by the body’s interactions with the environment. Rather than abstract symbol manipulation happening in a disembodied mind, thinking is grounded in sensorimotor experiences.

Predictive Processing: This newer framework suggests the brain is fundamentally a prediction machine, constantly generating and updating predictions about sensory inputs. Intelligence emerges from the process of minimizing prediction errors rather than manipulating symbols.

These alternative approaches don’t necessarily contradict the insights of the physical symbol system hypothesis entirely. They might be seen as offering different levels of explanation or as complementary perspectives on the complex phenomenon of intelligence.

The debate continues, and with it, our understanding of what it means to think, to understand, and to be intelligent grows deeper and more nuanced. Perhaps the most fascinating aspect of cognitive science is that in studying minds, we are studying ourselves—turning the very tools we’re investigating back upon the investigators. Whether computational approaches can solve the fundamental challenges posed by Searle and others remains an open question at the frontier of cognitive science and philosophy of mind.