George Lakoff

A natural language is a unified and integrated system, and the serious study of one part of the system inevitably involves one in the study of many other parts, if not the system as a whole. For this reason, the study of small, isolated fragments of a language—however necessary, valuable, and difficult this may be—will often make us think that we understand more than we really do. The fact is that you can’t really study one phenomenon adequately without studying a great many other related phenomena, and the way they fit together in terms of the linguistic system as a whole. This is the sort of thing a linguist learns very early in his career. Experience in descriptive linguistics, even at an elementary level will force a linguist to come to grips with a wide range of complex data in some language, perhaps even English, and the truism soon emerges. But, due to the vagaries of our educational institutions, few philosophers or logicians receive training in linguistic description. Consequently much of the discussion of natural language in the philosophical and logical literature is based on a very small sampling of data which is skewed in nontrivial ways. True, one has to start somewhere, and a great deal has been learned by ordinary language philosophers who have looked at only a handful of relatively simple examples and by logicians who have studied what by natural language standards are only miniscule fragments. But now that philosophers and logicians are turning to more detailed studies of natural language phenomena, it is perhaps the right time to suggest that philosophical and logical training be expanded to include the study of natural languages as entire systems. I don’t mean to suggest, for example, that logicians should stop their systematic study of small fragments, but rather that a knowledge of the kinds of phenomena outside of those fragments can enrich the study of fragments and give one a more realistic picture of what one does and does not know about natural language.

Evidence is presented to show that the role of a generative grammar of a natural language is not merely to generate the grammatical sentences of that language, but also to relate them to their logical forms. The notion of logical form is to be made sense of in terms a natural logic, a logical for natural language, whose goals are to express all concepts capable of being expressed in natural language, to characterize all the valid inferences that can be made in natural language, and to mesh with adequate linguistic descriptions of all natural languages. The latter requirement imposes empirical linguistic constraints on natural logic. A number of examples are discussed.

“Language and Emotion” (2016) showed a number of nonobvious ways in which the nature of emotion can be studied via the way that emotions are expressed, mostly unconsciously, in language. The results given there have come mostly from cognitive linguistics, structured neural computation, and embodied cognition taken together. The references given, survey those results and their empirical basis. The commentators have each made contributions to our ultimate understanding of emotion, each from a different field with a different set of assumptions and research techniques. But they are not up on contemporary embodied cognitive linguistics. As a result, most missed points made in the article. The good news is that each got a chance to introduce their own research to our readership.

Concepts are the elementary units of reason and linguistic meaning. They are conventional and relatively stable. As such, they must somehow be the result of neural activity in the brain. The questions are: Where? and How? A common philosophical position is that all concepts—even concepts about action and perception—are symbolic and abstract, and therefore must be implemented outside the brain’s sensory-motor system. We will argue against this position using (1) neuroscientific evidence; (2) results from neural computation; and (3) results about the nature of concepts from cognitive linguistics. We will propose that the sensory-motor system has the right kind of structure to characterise both sensory-motor and more abstract concepts. Central to this picture are the neural theory of language and the theory of cogs, according to which, brain structures in the sensory-motor regions are exploited to characterise the so-called “abstract” concepts that constitute the meanings of grammatical constructions and general inference patterns.