Do we think with language, or is it just a communication device used for expression of completed thoughts? What is a difference between language and cognition? Chomsky (1995) suggested that these two abilities are separate and independent. Cognitive linguistics emphasizes a single mechanism for both (Croft and Cruse, 2004). Evolutionary linguistics considers the process of transferring language from one generation to the next one (Cangelosi and Parisi, 2002; Christiansen and Kirby, 2003; Hurford, 2008). This process is a “bottleneck” that forms the language. Brighton et al. (2005) demonstrated emergence of compositional language due to this bottleneck. Still, none of these approaches resulted in a computational theory explaining how humans acquire language and cognition. Here I discuss a computational model overcoming previous difficulties and based on a hypothesis that language and cognition are two separate and closely integrated abilities. I identify their functions and discuss why human thinking ability requires both language and cognition.
Among fundamental mechanisms of cognition are mental representations, memories of objects and events (Perlovsky, 2001, 2006a). The surrounding world is understood by matching mental representations to patterns in sensor signals. However, mathematical modeling of this process since the 1950s met with difficulties. The first difficulty is related to a need to consider combinations of sensor signals, objects, and events. The number of combinations is very large and even a limited number of signals or objects form a very large number of combinations, exceeding all interactions of all elementary particles in a lifetime of the Universe (Perlovsky, 1998). This is known as combinatorial complexity, CC. This difficulty in modeling the mind has been overcome by dynamic logic (Perlovsky, 2001, 2006a,b, 2007a; Perlovsky et al., 2011). Whereas classical logic considers static statements such as “this is a chair,” dynamic logic models processes from vague to crisp representations. These processes do not need to consider combinations, an initial vague state of a “chair” matches any object in the field of view, and at the end of the process it matches the chair actually present, without CC.
The second difficulty is similar still even more complex. It is related to the fact that “events” and “situations” in the world do not necessarily exist “ready for cognition.” There are many combinations of percepts and objects, a near infinity, events and situations important for understanding and learning have to be separated from those that are just random collections of meaningless percepts or random objects (Perlovsky and Ilin, 2012). Events and situations recognized by non-human animals are very limited compared to human abilities to differentiate events in the world. Human cognitive abilities acquire their power due to language. Language is “easier” to learn than cognitive representations. Language representations: words, phrases exist in the surrounding language “ready made,” created during millennia of cultural evolution. Therefore, language could be learned without much real-life experience; only interactions with language speakers are required. Every child learns language early in life before acquiring full cognitive understanding of events and their cognitive meanings. Thus, language is learned early in life with only limited cognitive understanding of the world (Perlovsky, 2009a, 2012c). Cognitive representations of situations and abstract concepts initially exist in vague states. Throughout the rest of life, language guides acquisition of cognitive representations from experience. Vague cognitive representations become more crisp and concrete. Thinking involves both language and cognition, and as we discuss later thinking about abstract ideas usually involves language more than cognition, not too different from thinking by children.