### Hans H. Diebner's Research

## Cognitive Systems and Brain Dynamics

My interest in brain dynamics and cognitive systems is closely connected to the "operational hermeneutics" item. The affinity to hermeneutics has its roots in my postdoctoral time in Medical Biometry dealing with immuno-epidemiological topics and - most important for the hermeneutics problem - with diagnostics. The latter is a decision making problem that uses subjective probabilities known as "Bayesean statistics". The "Bayesians" form a statistics school of thoughts as opponents to the "Frequentists". The latter can with a grain of salt be compared with the "Structuralists" in philosophy who basically rely only on the text in hand when interpreting it whereas "Hermeneuticians" take context and*a priori*knowledge into account. The same holds for diagnostics where even terms like "anamnesis" are used that stem from a hermeneutics discourse. Because of the close vicinity of "priors and prejudices" the Bayesians say: "We are not the better humans, but the better statisticians".

A considerable number of cognitive scientists believe in a "Bayesean homunculus" iteratively acting in our brain to muddling through the deciding processes we are continuously exposed. This explaines, why in recent developments of artificial cognitive systems a "Bayesian learning" is used to enhance neural networks. As a consequence, the question arises whether hermeneutics is algorithmizable is this way.

From socio-biological studies Robert Axelrod provided a hint for the limitation of the Bayesean approach to cognition. He organized a tournament of algorithms which pairwise had to compete and to prove themselves in a fundamental decision making process called "prisoner's dilemma". Hereby, the decisions have to be made under "bounded rationality", that is with restricted knowledge on the situation. The efficacy of the subsequent decision depends upon the subsequent decision of the antagonistic algorithm and vice versa. In other words, the efficacy stands and falls with a good estimation of what the other algorithm will do. One of the remarkable outcomes was that in a first round the Bayesean algorithm did a pretty good job. However, in a second round after the creators of the algorithms could analyze the first round, a tricky new algorithm estimated the outcome of the other algorithms including the Bayesean to find the most efficient counter strategy. It is exactly this creation of new hypotheses that definitely cannot be captured even by so called self-modifying algorithms so far. The mathematical modelling of human behavior is always in retrospect. It is ergodic. Time can be transformed away. In a way this modelling deals with equipment not with processes. "Dasein" has an edge on its description.

Though the limitations of artificial intelligence are clear, cognitive modelling has a huge potential for technical applications and to gain insight into brain dynamics. A couple of my papers are devoted to this research.

See also the summary on Ichiro Tsuda's approach to brain theory via chaotic itinerancy. Tsuda was among the first who expressed the close relationship of brain research and hermeneutics.

An artistic approach to brain dynamics: Liquid Perceptron.