Feedback systems have often been considered as relevant for an understanding of the brain. But obviously most biological systems have a much more complicated structure. Surprisingly the functional and structural necessities determining the route from simple feedback systems to complex goal-orientated systems like the brain have never been investigated. Therefore this paper studies systematically the possibilities for adding functional elements like sensors, memory, effectors etc. to a feedback system. Then it investigates which combinations of functional elements are necessary to get to working goal-orientated systems. Based on that necessities and options of data organization and the resulting cognitive possibilities are analysed. This abstract analysis delivers design rules for complex-technical and biological-goal-orientated systems and basic epistemological facts how additional functional elements can add to their cognitive abilities. Finally, a 'design for a simple brain' illustrates the application of these design rules.

Purpose – The purpose of this paper is to show how individual acts of goal-setting of two persons or systems A and B determine, which modes of coexistence become possible in an interaction of the two. Design/methodology/approach – Some person or system A can approach another person or system B with an inclination to realize one of four goal-setting processes: first, A sets goals for B; second, A sets no own goals; third, A pursues own goals alone; and fourth, A and B develop mutual goals. And an interaction of A and B can lead to just four modes of coexistence: first, conflict – A and B fight; second, hierarchy – A submits to B; third, independence in niches – A and B do not interact; and fourth, cooperation – A and B work together. Findings – Placing the inclinations of A and B to realize one of the four goal-setting processes in a 4×4 matrix leads to the interaction matrix. It shows that individual goal-setting processes predetermine and limit the available modes of coexistence, i.e. cause certain patterns of interactions. Practical implications – The interaction matrix can be applied to all interactions between persons, groups and social units generally. Originality/value – The paper introduces a theoretical framework covering all options of goal-orientated behavior. It explains the interrelation between individual goal-setting of persons and systems and the resulting behavioral options in interactions. It is applicable to all behavioral sciences.

Purpose -The purpose of this paper is to analyze how sequence learning can build on pattern-recognition systems and how it can contribute to the behavioral options of goal-oriented systems. Design/methodology/approach -A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning, that can recognize patterns, register patterns occurring repeatedly and connect these to sequences. Based on that it is analyzed how goal-oriented systems can use information about reoccurring sequences. Findings -A subsystem for sequence learning basically requires pattern recognition and it needs a structure for the directed connection of single standards for pattern matching to standards for sequences, given that it can learn both new patterns and new sequences. Such a subsystem for sequence learning may recognize a certain pattern and with that the end of a certain sequence. So it may deliver more than one output signal at a point in time, and therefore needs additionally a subsystem for directing attention. Practical implications -The paper analyses the principles of an "associative" way of connecting standards for pattern matching to standards for sequences. Also it shows the cybernetic necessity of an attention directing system that has to decide how to deal with the multiple outputs of a subsystem for sequence learning, i.e. to decide to act either towards a pattern or a whole sequence. Originality/value -The paper investigates basic mechanisms of sequence learning and its contribution to goal-oriented behavior. Also, it lays the base for an analysis of attention directing systems and anticipatory systems.

Purpose -The purpose of the paper is to analyze cybernetic necessities of output-side attention directing systems, i.e. how systems can decide to act towards one of various inputs. Design/methodology/approach -Complex pattern recognition and sequence learning systems may recognize more than one pattern and deliver more than one output at a point in time. Therefore, they require an output-side attention directing system to decide to act towards just one pattern. The necessary cybernetic structures of such systems are analyzed using a functional approach. Findings -An output-side attention directing system has to evaluate the effect of current observations (patterns, sequences, etc.) on highest level goal-values (in a living system these are existential goal-values like a body temperature or energy supply). Measure of this effect is the degree of goal-approximation towards these goal-values. This measure can either be preprogrammed for some patterns or sequences, or has to be determined in trial and error processes for new patterns or sequences learned by the system. Practical implications -The paper shows the cybernetic necessities of the development of the "know how" of sequence learning systems in time, starting with default behavior, via learning new patterns and sequences, and trial and error to develop goal-orientated actions towards them, until finally the achieved results enable experience based directing of attention. Originality/value -The paper shows basic cybernetic structures and functions for output-side attention directing systems required for all complex pattern recognition and sequence learning systems.

Purpose -The purpose of this paper is to analyze how elementary anticipation, understood as anticipation of the repetition of one known pattern, can emerge out of sequence learning and how it can contribute to the behavioral options of goal-oriented systems. Design/methodology/approach -A functional approach is used to develop the necessary cybernetic structures of a subsystem for sequence learning that can additionally provide standards of anticipated patterns for future pattern matching. Based on that it is analyzed, how a goal-oriented system can use the information about the actual occurrence of an anticipated pattern. Findings -A subsystem for elementary anticipation of single patterns builds on sequence learning and requires additionally a structure: first, to unequivocally identify the beginning of known sequences just from their first patterns; and second, to decide to use a latter pattern of such a sequence as standard for an anticipated pattern. Deciding to actually use such a pattern for anticipation requires an additional subsystem to switch between the feedback pattern recognition mode and feedforward. Then the occurrence of such an anticipated pattern allows immediate recognition and action.Practical implications -The paper shows a necessary evolution of cybernetic structures from pattern recognition via sequence learning to anticipation; and it shows, too, a necessary evolution in the cognitive development of individual systems. In the simple anticipatory structures analyzed here, only known patterns, that are part of a known sequence, can become anticipated patterns. Originality/value -The paper places elementary anticipation of single patterns in an evolutionary development based on pattern recognition and sequence learning. It provides the base to analyze more complex forms of anticipation.