The language of thought hypothesis (LOTH) is the hypothesis that mental representation has a linguistic structure, or in other words, that thought takes place within a mental language. The hypothesis is sometimes expressed as the claim that thoughts are sentences in the head.
It is one of a cluster of other hypotheses that together offer a theory of the nature of thought and thinking. The other hypotheses in the cluster include the causal-syntactic theory of mental processes (CSMP), and the representational theory of mind (RTM).
The former is the hypothesis that mental processes are causal processes defined over the syntax of mental representations. The latter is the hypothesis that propositional attitudes are relations between subjects and mental representations.
Taken together these theses purport to explain how rational thought and behavior can be produced by a physical object, such as the human brain. In short, the explanation is that the brain is a computer and that thinking is a computational process. The cluster therefore is referred to often (and aptly) as the computational theory of mind (CTM).
LOTH was first introduced by Jerry Fodor in his 1975 book The Language of Thought, and further elaborated and defended in a series of works by Fodor and several collaborators. Fodor’s original argument for LOTH rested on the claim that (at the time) the only plausible psychological models presupposed linguistically structured mental representations.
Subsequent arguments for LOTH are inferences to the best explanation.
They appeal to supposed features of human cognition such as productivity, systematicity, and inferential coherence, arguing that these features are best explained if LOTH is true. Important objections to LOTH have come from those who believe that the mind is best modeled by connectionist networks, and by those who believe that (at least some) mental representation takes place in other formats, such as maps and images.
This article has three main sections. The first explains LOTH, as well as CSMP, RTM, and the importance of conjoining all three to arrive at the resulting CTM. The second describes the major arguments in favor of LOTH. The third describes some important problems for LOTH and objections to it.
1. The Language of Thought Hypothesis a. Combinatorial Syntax and Compositional Semantics
LOTH is the claim that mental representation has a linguistic structure. A representational system has a linguistic structure if it employs both a combinatorial syntax and a compositional semantics (see Fodor and Pylyshyn 1988 for this account of linguistic structuring).
A representational system possesses a combinatorial syntax if,
i) it employs two sorts of representation: atomic representations and compound representations, and
(ii) the constituents of compound representations are either compound or atomic.
A representational system possesses a compositional semantics if,
(iii) the semantic content of a representation is a function of the semantic content of its syntactic constituents, the overall structure of the representation, and the arrangement of the constituents within the overall structure.
Formal languages are good examples of languages possessing both combinatorial syntax and compositional semantics. For example, sentential logic (propositional logic) employs symbols to represent simple declarative sentences (usually the capital letters ‘A’, ‘B’, ‘C’…) and symbols for logical connectives (usually ‘·’ for ‘and’, ‘v’ for ‘or’, ‘→’ for ‘if… then…,’ and so on).
Thus, ‘A’ might be an atomic representation of the sentence ‘Gail is tall’, ‘B’ an atomic representation of the sentence ‘Alan is bald’, and ‘C’ an atomic representation of the sentence ‘Amanda is funny’. In that case, ‘(A · B) v C’ would be a compound representation of the sentence ‘Either Gail is tall and Alan is bald, or Amanda is funny’.
The components of this compound representation are the compound representation ‘(A · B)’ and the atomic representation ‘C’. In short, sentential logic employs both atomic and compound representations, and the components of its compound representations are themselves either atomic or compound. Thus, it possesses a combinatorial syntax.
Moreover, the semantic content of a representation within sentential logic (generally taken to be a truth-value—either TRUE or FALSE) is a function of the content of the syntactic constituents, together with overall structure and arrangement of the representation.
For instance, the truth-value of a representation with the form ‘A → B’ is TRUE just in case the truth-value of ‘A’ is FALSE or the truth-value of ‘B’ is TRUE. Alter the arrangement of the parts (B → A) or the overall structure (A · B) or the components (A → C) and the truth-value of the whole may change as well. Therefore it also possesses a compositional semantics.
LOTH amounts to the idea that mental representation has both a combinatorial syntax and a compositional semantics. It is the idea that thoughts occur in a formal mental language (termed the “language of thought” or often “mentalese”).
A common way of casting it is as the claim that thoughts are literally sentences in the head. This way of explaining the thesis can be both helpful and misleading.
First, it is important to note that sentences can be implemented in a multitude of different kinds of media, and they can be written in a natural language or encoded in some symbolic language. For example, they may be written on paper, etched in stone, or encoded in the various positions of a series of electrical switches. They may be written in English, French, first-order logic, or Morse code.
LOTH claims that at a high level of abstraction, the brain can be accurately described as encoding the sentences of a formal language.
Second, it is equally important to note that the symbolic language LOTH posits is not equivalent to any particular spoken language but is the common linguistic structure in all human thought. Part of Fodor’s (1975) original argument for LOTH was that learning a spoken language requires already possessing an internal mental language, the latter being common to all members of the species.
Third, the posited language is not appropriately thought of as being introspectively accessible to a thinking subject. In other words, while thinkers may have access to much of what goes on while they are thinking (for example the images, words and so on that may be visible “in the mind’s eye”), the language of thought is not “visible” as such.
Rather, it is best thought of as the representations that are being tokened in and processed by the brain, during and “beneath” all that is accessible to the thinker. (However, that they are not introspectively accessible is not to be taken to indicate that they are not causally efficacious in the production of behavior. On the contrary, they must be, if the theory is to explain the production of rational behavior.)Casting LOTH as the idea of sentences in the head can be useful, if understood appropriately: as sentences of a species-wide formal language, encoded in the operations of the brain, which are not accessible to the thinker.
One commonality that holds among the last three objections discussed is that they can all reasonably be described as claiming that at least some mental representation is analog, while LOTH describes mental representation as digital. The distinction is usually understood in terms of continuity and discreteness. Digital representations are discrete (as words and sentences).
Analog representations are continuous, or possess continuously variable properties such as distances between parts of an image or map, or activation values of the nodes in a network.
However, the distinction between analog and digital representation has been understood in a number of ways. David Lewis (1971) says that “analog representation of numbers is representation of numbers by physical magnitudes that are either primitive or almost primitive,” (325) and that “digital representation of numbers [is] representation of numbers by differentiated multi-digital magnitudes” (327).
Fred Dretske (1981) says that “a signal… carries the information that s is F in digital form if and only if the signal carries no additional information about s, no information that is not already nested in s’s being F. If the signal does carry additional information about s, information that is not nested in s’s being F, then… the signal carries this information in analog form (137).
And James Blachowitz (1997) says that “the function of analog representation is to map or model what it represents (83). See also Von Neumann 1958, Goodman 1968, Trenholme 1994, Haugeland 1998, and Katz 2008.
The analog/digital distinction may be drawn in reference to different kinds of things: computers, representations, processes, machines, and so forth. Haugeland (1998) argues that, although all digital representations share some important features, there may be no set of features uniquely characterizing analog representation.
If that is the case, then the idea that images, maps, and networks are analog should not be taken to indicate that they share some important set of features other than being non-digital. Moreover, because it remains a possibility that thought is best modeled by a connectionist network implementing a system that processes linguistically structured representations, and because it remains a possibility that some thinking takes place in images, some in maps, some
in linguistically structured representations, and some in yet other forms of representation, it would be misleading to suggest that the question whether the mind is best modeled by an analog or digital machine has a singular answer.
RTM, LOTH, and CSMP was inspired on one hand by the development of modern logic, and in particular by the formalization of logical inference (that is, the development of rules of inference that are sensitive to syntax but that respect semantic constraints).
On the other hand, it was inspired by Alan Turing’s work showing that formal procedures can be mechanized, and thus, implemented as causal processes in physical machines. These two developments led to the creation of the modern digital computer, and Turing (1950) argued that if the conversational behavior (via teletype) of such a machine was indistinguishable from that of a human being, then that machine would be a thinking machine.
The combination of RTM, LOTH, and CSMP is in a sense the converse of this latter claim. It is the idea that the mind is a computer, and that thinking is a computational process. Hence the combination of these theses has come to be known as the Computational Theory of Mind (CTM).
The importance of CTM is twofold. First, the idea that thinking is a computational process involving linguistically structured representations is of fundamental importance to cognitive science. It is among the origins of work in artificial intelligence, and though there has since been much debate about whether the digital computer is the best model for the brain (see below) many researchers still presume linguistic representation to be a central component of thought.
Second, CTM offers an account of how a physical object (in particular, the brain) can produce rational thought and behavior. The answer is that it can do so by implementing rational processes as causal processes. This answer provides a response to what some philosophers—most famously Descartes, have believed: that explaining human rationality demands positing a form of existence beyond the physical.
That is, it is a response to dualism (See Descartes 1637/1985, 139-40, and see Rey 1997 for discussion of CTM as being a solution to “Descartes’ challenge”). It therefore stands as a major development in the philosophy of mind.
Explaining rationality in purely physical terms is one task for a naturalized theory of mind. Explaining intentionality (the meaning or “aboutness” of mental representations) in purely physical terms is a related, though separate, task for a naturalized theory of mind. Famously, Brentano (1874/1995) worried that intentionality cannot be explained in physical terms, as Descartes believed rationality could not be explained in physical terms (see Rey 1997 for CTM being a solution to “Brentano’s challenge”).
Still, CTM lends itself to a physicalist account of intentionality. There are two general strategies here. Internalist accounts explain meaning without making mention of any objects or features external to the subject. For example, conceptual role theories (see for instance Loar 1981) explain the meaning of a mental representation in terms of the relations it bears to other representations in the system.
Externalist accounts explicitly tie the meaning of mental representations to the environment of the thinker. For example, causal theories (see for instance Dretske 1981) explain meaning in terms of causal regularities between environmental features and mental representations.
Fodor (1987) has argued for an “asymmetric dependency theory,” which is a kind of causal theory of meaning, intended specifically to deal with the disjunction problem that plagues causal theories. The problem arises for causal theories of meaning because of the seemingly obvious fact that some mental representations are caused by objects they do not represent. For example, on a dark evening, someone might easily mistake a cow for a horse; in other words, a cow might cause the tokening of a mental representation that means horse.
But if, as causal theories have it, the meaning of a representation is determined by the object or objects that cause it, then the meaning of such a representation is not horse, but rather horse or cow (since the type of representation is sometimes caused by horses and sometimes caused by cows).
Fodor’s solution is to suggest that such a representation means horse and not horse or cow, because the fact that it may sometimes be caused by cows is dependent on the fact that it is usually caused by horses. That is, if the representation was not caused by horses, then it would not sometimes be caused by cows. But this dependence is asymmetric:
if the representation was not ever caused by cows, it would nevertheless still be caused by horses.
CTM, and LOTH in particular, need not be wedded to Fodor’s account. As all of the above examples explain meaning in physical terms, the coupling of a successful CTM with a successful version of any of them would yield an entirely physical account of two of the most important general features of the mind: rationality and intentionality.
