Ramón Casares

Ramón Casares

To investigate the evolution of syntax, we need to ascertain the evolutionary rôle of syntax and, before that, the very nature of syntax. Here, we will assume that syntax is computing. And then, since we are computationally Turing complete, we meet an evolutionary anomaly, the anomaly of sytax: we are syntactically too competent for syntax. Assuming that problem solving is computing, and realizing that the evolutionary advantage of Turing completeness is full problem solving and not syntactic proficiency, we explain the anomaly of syntax by postulating that syntax and problem solving co-evolved in humans towards Turing completeness. Examining the requirements that full problem solving impose on language, we find firstly that semantics is not sufficient and that syntax is necessary to represent problems. Our final conclusion is that full problem solving requires a functional semantics on an infinite tree-structured syntax. Besides these results, the introduction of Turing completeness and problem solving to explain the evolution of syntax should help us to fit the evolution of language within the evolution of cognition, giving us some new clues to understand the elusive relation between language and thinking.

Ramón Casares

The Turing machine, as it was presented by Turing himself, models the calculations done by a person. This means that we can compute whatever any Turing machine can compute, and therefore we are Turing complete. The question addressed here is why, Why are we Turing complete? Being Turing complete also means that somehow our brain implements the function that a universal Turing machine implements. The point is that evolution achieved Turing completeness, and then the explanation should be evolutionary, but our explanation is mathematical. The trick is to introduce a mathematical theory of problems, under the basic assumption that solving more problems provides more survival opportunities. So we build a problem theory by fusing set and computing theories. Then we construct a series of resolvers, where each resolver is defined by its computing capacity, that exhibits the following property: all problems solved by a resolver are also solved by the next resolver in the series if certain condition is satisfied. The last of the conditions is to be Turing complete. This series defines a resolvers hierarchy that could be seen as a framework for the evolution of cognition. Then the answer to our question would be: to solve most problems. By the way, the problem theory defines adaptation, perception, and learning, and it shows that there are just three ways to resolve any problem: routine, trial, and analogy. And, most importantly, this theory demonstrates how problems can be used to found mathematics and computing on biology.