Trivialisation Argument Against Dynamical Hypothesis

Computational Theory of Mind states that physical systems feature mental properties due to the fact that they realise formal computations. Searle and Putnam have developed a trivialization argument against this position. They claimed that realization of the algorithmic procedure is not an intrinsic property of the system and may be arbitrarily attributed to any physical system. Dynamical Hypothesis describes mental processes as dynamic evolution of the system and adopts formalism of dynamical systems as a formal model of cognitive functions. We will analyse the trivialization argument and demonstrate that similar reasoning can be applied to Dynamical Hypothesis. Since dynamical systems are formal, mathematical objects, any explanation of mental phenomena in the dynamical terms faces the same objection as computational models. Trivialization of the algorithmic account of mind relies on the ability to show a labelling function that interprets physical states as computational states of the arbitrary formal system. We will show that similar function can be produced for the dynamical systems. Putnam’s argument was based on the labelling function that ex-post facto maps subsequent states of the physical system onto formal states in a single sequence of the run of the abstract algorithm. Although, the evolution of the dynamical system is not given by the sequence of states but by the continuous phase-state trajectory we still can perform ex-post facto mapping operation. We analyse the difference between the systems that compute an algorithm and systems that satisfy differential equations of motion. Possibly, non-trivial case of satisfying the equation can be made for primitive physical magnitudes. Since variables used in the dynamical models of cognition are not based on the physically primitive magnitudes, the relation between physical states of the system and abstract variables of the model remains virtually as arbitrary as relation between physical systems and computational algorithms