The Chinese room reacts just to syntax, or shape of symbols (is purely syntactic). But brains are full of structure. In the room, Chinese symbols sit scattered in "piles" on the floor or are moved around in "batches" or "bunches", or are stored jumbled up in "baskets" with no structural connections between the symbols.

The things computers process are called "symbols". Computers can build structure between symbols and react to, or follow, it, and often do. Virtual connections between memory locations can be established using pointers, and algorithms can follow the connections using the methods of direct memory addressing and indirection.

This structural, or relational, ability of the computer program can be mirrored in the Chinese room by adding to the room's ontology a new object type: string. Instances of string in the room can then connect tokenised Chinese symbols. Every piece of string has the same characteristics including length. They are the embodiment of structure, are relational elements of structure.

In the room, if the connections established between symbols are a causal consequent of temporal contiguity at the sensory surface resulting in contiguous sensory symbols exiting the sensor then entering the room, the connections between the sensory symbols record as internal structure the external instances of temporal contiguity at the sensory surface. Is such an internal structure an element of semantic content?

In the computer, if the internal memory structures built with pointers are trees, a program can walk the trees and emit as output copies of the leaves (symbols), without reacting to (identifying) the shapes of the symbols. The program merely copies and emits whatever it arrives at that has no children. The program contains no conditionals indexed on symbol shape.

Suppose Searle is blindfolded then walks a tree by following the string with his hands. When he arrives at a leaf (a card inscribed with a Chinese ideogram which card has no downward strings attached) he emits the card then continues on his tactile tree walk. Since the rules he is following do not instruct a reaction to the shape of any Chinese symbol (and hence do not contain an example or description of any Chinese symbol shape), does this mean the program in the rule book is non-syntactic with respect to Chinese symbols, and Searle manipulates the symbols non-syntactically?

In 2014, Searle says (his emphasis): "...a digital computer is a syntactical machine. It manipulates symbols and does nothing else" ("What Your computer Can't Know", in The New York Review of Books, October 9, 2014, section 2, para 7). String is not symbols. Is his careful avoidance of structure his fundamental mistake?

  • In "Mind, Brains, and Programs" Searle is given "instructions" and "rules" so he can give back answers. He doesn't specify what these are so that they are general. In the end he doesn't understand Chinese and so the program he is imitating does not understand Chinese either. I don't think he is avoiding anything. If he did understand Chinese then a strong AI mind-body dualism would be justified and mind could be separated from the body. However, he did not understand Chinese. Commented Dec 27, 2017 at 0:29
  • 3
    Strings of what? Strings are strings of symbols. They have no more semantic content than individual symbols do. The string "cat" has no more semantic content than the individual strings 'c', 'a', and 't'. It's the humans who assign meaning to that string. And for that matter, isn't an individual symbol just a string of length one? I don't follow the point you are trying to make. Symbols or strings of symbols are the same thing. Blindfolded walking of a data structure is nothing more than syntactic processing of symbols.
    – user4894
    Commented Dec 27, 2017 at 1:24
  • @user4894. With text, sure, the relationship between the symbols, eg c,a,t (of temporal contiguity (TC) as they pass through a surface or spatial contiguity when stored), has no semantic properties. But for sensory symbols, the fact that one follows another into the computer mirrors (not denotes, not means) TC at the sensory surface between what caused the sensor to create the symbols. It's the same relation: TC in the environment, TC between sensory symbols. It's the same thing on the inside as on the outside. Isn't this a semantic element? (That might even be a component of representations)
    – Roddus
    Commented Dec 28, 2017 at 1:26
  • @Frank Hubeny To me, the program doesn't understand the Chinese answers because all it contains is conditionals about the shapes of Chinese symbols. If Searle understood Chinese merely by virtue of identifying the Chinese symbol shapes, I don't quite see how this might imply dualism. The mind would still be a resident of the physical, not spiritual, plane. The mind (the program) could be separated from the body (the computer), but the program would still be a physical object. Do I understand you comment properly?
    – Roddus
    Commented Dec 28, 2017 at 1:40
  • @Roddus I'm not sure what you mean by "sensory symbols." If you mean the sequence of symbols generated by a sensor connected to the outside, how does the computer know anything about that? If a cpu sees a stream of bits, a human may know that those are the output of a physical sensor, but the computer has no such knowledge. It's just another bitstring to be manipulated according to rules. That's a perfect example of a human supplying the semantics. The humans know that the bitstring represents a temperature in the real world. The cpu only sees the bitstring and has no idea what it means.
    – user4894
    Commented Dec 28, 2017 at 2:16

2 Answers 2


I am aware that this response is slightly off topic, however I hope it still helps.

I think viewing Searls Chines room as "Intuition Pump", a concept introduced by Daniel Denett, is a usefull approach. Where thought experiments are entities that give us better or worse intuition of a certain phenomena. By slightly changing parts of the thought experiment in question one sees if, it is a good intuition pump or not. By analyzing if the changed thought experiment sustains the same intuition.

My conclusion is that the CRA is dependending strongly on it's intial form to create the demanded intuition. Meaning adding new entities like you suggest f.e. "strings" shows the limitied validity of the CRA for the analog phenomena it tries to describe.

I disagree with your statement that there are:

no structural connections between the symbols in the CRA.

Since the ordering them, guided by the rulebook, creates a structure that contains meaning for the reciever. The key point seems rather to be an unawareness/unintrestedness of/in the structure by the person in the room. This creates the clear cut between syntax and semantics. This clear cut also is caused by the rulebook containing 2 languages, which are superimposed by someone who isn't the person in the room that just understands one and shuffles expressions of the other language around.

This unintrestedness poses the question, that given the temporal structure of the sensory input, does the person in the CRA have the desire to derive the semantic property? Seemingly not he just does his work.

Note that the part where you discuss software you seem to distance yourself from what Searle seems to mean since you are arguing about the structures used in the rulebook to transmit the desired semantic properties. Not the CRA itself.

To me it seems as if the CRA would mainly focus on the analogy of a single CPU core. So demanding an intresed for the mechanism flipping bits seems problematic.

Due to the mentioned above intuition pump your approach seems appropriate but inappropriate aswell. Appropriate since you restructure the intial CRA to make it give better intuitions for possibly more complex computers. However the intial CRA still holds for sympler systems like normal calculators.

Others have chosen simular approaches f.e. trying to identify the overall system as relevant, laying more importance on the structure of the rulebook(software). I myself tryed this by reformulating the CRA to appear more like a nervcell and adding it with other modiefied CRA's together to get a 3D brain like structure.

My conclusion is that the CRA illustrates the wrong level of analysis for complex systems. Therefore I view your approach as inappropriat since the choosing the CRA as model seems unnecessary to general questions you seem to express. Like how does a semantic in a system arise. Or what exactly is semantics, how does complexity affect semantics ect.

  • Thanks. Yep, the input questions and output answers comprise temporally contiguous symbols as the strings enter/leave the room (hence the symbols as they enter/exit are terms of instances of the relation of temporal contiguity). But once inside and before they leave, and the spares in the "boxes" or "baskets" , don't seem to be parts of any structure, and are just thought of as individual tokens. There are no tree structures, for example, but brains are full of these (and the room is supposed to be a computer trying to be a brain - and programs can easily create tree structures).
    – Roddus
    Commented Dec 28, 2017 at 1:57
  • Cont... What exactly is semantics is a huge problem, of course. Linguists and philosophers are fairly clear about it - as per the concepts of linguistics and philosophy of language, but not of computer science. One idea I wanted to discuss is that a semantics is a forest of trees in which syntax (symbols or equivalents) alone is insufficient for semantics but when the structural element of tree connections ("arcs") are added, the two types of component, symbols and connections, combine to yield semantic properties. Though there's quite a lot of resistance to such a simple idea.
    – Roddus
    Commented Dec 28, 2017 at 2:19
  • @Roddus Why do you focus your view on tree structures. Is it only because "brains are full of them"? Why not lists, arrays, ect.? Isn't it percievable that the rulebook instanciates a program that creates a working of the person in the room that resembels a tree structure. Doesn't this just increase the speed with wich output is generated due to more efficent structuring of action and tokens rather then ascribe semantic properties on this level of analysis? Even if one is sympathetic to your idea it's unclear why the mechanism adding the components together should be aware of this semantics.
    – CaZaNOx
    Commented Dec 28, 2017 at 8:23
  • Trees seem interesting for several reasons, but linked lists, etc. could be built too. Presumably the rule book could instruct Searle to create tree structures, if there were a way to relate symbols (and created nodes) together. When you say the mechanism adding the components do you mean the program and/or the CPU is the mechanism? I wasn't thinking that these might have semantic properties, but rather that the built structure itself is the semantics. Awareness is a high-level feature. I was looking more at the low level problem of making inner representations of external objects.
    – Roddus
    Commented Dec 28, 2017 at 9:01
  • @Roddus. By calling awareness a "high-level feature," are you suggesting that it's something that could just happen all by itself without a programmer intending having any idea as to how to bring it about?
    – user3017
    Commented Dec 28, 2017 at 15:27

The only way that adding structure could make Searle’s Chinese Room Argument (CRA) semantic is if one could imagine Searle understanding Chinese by going through the programmatic process with this additional structure, whatever it is, included. Searle does not specify what a program might be asking him to do. It may be so advanced it is beyond our imagination today. It may be highly successful and convince everyone it understands Chinese. Even with all this, Searle claims, and I would agree, he would not understand Chinese after imitating the process. So, I conclude that “adding structure” does not help. Searle has already implicitly added it.

Consider the final question: “Is his [Searle’s] careful avoidance of structure his fundamental mistake?” I don’t think Searle is making any mistake with the CRA. However, he may be making a mistake with his physicalism, but that is independent of the CRA. An idealist or a traditional mind-body dualist could use the CRA to get the same two results Searle does in his “Minds, Brains, and Programs”, namely, that machines cannot understand and the machine and its programs do not explain our human ability to understand. There may be many ways to explain our ability to understand besides Searle's preferred “certain brain processes”, but AI programs are not one of them.

  • 2
    It may be so advanced it is beyond our imagination today. -- Actually we have a working implementation of the Chinese room in Google Translate. Does anyone think Google Translate understands Chinese?
    – user4894
    Commented Dec 27, 2017 at 3:10
  • @user4894 Since we know there is a program underlying Google Translate we would not be tempted to anthropomorphize it. The same could be said for when our ebook reader opens up a file. Do we think this software reads with understanding what is in the book it opens for us to actually read? Or consider a physical book. Do we think the physical book understands the text it is presenting to us when we read it? Commented Dec 27, 2017 at 15:32
  • "* Even with all this [added programmatic complexity and structure], Searle claims, and I would agree, he would not understand Chinese after imitating the process*". I haven't seen him talk about structures in the room. He seems to focus solely on the extrinsic meaning and intrinsic syntax of symbols. He uses the term "data base" and "database" (no space) but he is talking about "baskets" and "boxes" and there is no structure of symbols inside these (and in fact they are not databases). Just as he talks of "bunches" and "batches", there is no structure.
    – Roddus
    Commented Dec 28, 2017 at 2:34
  • Cont... I think structure plus syntax can explain semantics but syntax alone can't. Which is an interesting point. If computer programs can create structure (which they can) then the Chinese room fails to take into account all the things a computer can do. And the Chinese room argument also fails to take into account all the things a computer can do. My argument is that structure is not syntax. If true, then Searle's premiss "computers are purely syntactic devices" is false and the CRA is unsound. So a good question seems to be: Is structure syntax?
    – Roddus
    Commented Dec 28, 2017 at 2:41
  • @Roddus "Is structure syntax?" suggests the "systems reply" that Searle addresses in "Minds, Brains, and Programs". To eliminate any influence from outside the individual (which is what I assume you mean by "structure"), he lets the individual internalize all the elements of the system and even moves the person outside. Regardless whether structure is syntax or not, Searle takes it into account. A question he asks is how would strong AI distinguish the mental from the non-mental should one accept strong AI? For example, does a thermostat understand temperature? Commented Dec 28, 2017 at 15:29

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