Dyadic brain - a biological model for deliberative inference

The human brain is arguably the most complex information processing system. It operates by acquiring data from the environment, recognizing patterns of events' occurrence, anticipating their re-occurrence and in turn generating appropriate behavioral responses. Through the lenses of the free-energy principle any self-organizing system that is at equilibrium with its environment must minimize its free energy either by manipulating the environmental sensory input or by manipulating its internal states thus altering the recognition density of the outside stimuli. However, several sets of challenges interfere with the human brain's ability to learn and adapt in such a theoretically optimal fashion. These may include, and are not limited to, functional inconsistencies related to attention and memory processes, the functions of "fast" and "slow" thinking and responding, and the ability of emotional states to generate unintended behavioral outcomes that are less adaptive or inappropriate. This paper will review literature on the subject of how ideal learning viewed from the free-energy principle perspective may be affected by the above mentioned limitations and will suggest a model of information processing that may have developed as a way of overcoming these challenges. This neurobiological model stipulates that a neuronal network is formed in response to environmental input and is paralleled by at least one and possibly multiple networks that activate intrinsically and represent "virtual responses" to a situation that demands a behavioral response. This model accounts for how the brain generates a multiplicity of potential behavioral responses and may "choose" the one that seems most appropriate and also explains the uncanny ability of humans to socialize and collaborate. Implications for understanding humans' ability to learn from others, deliberate on opposing constructs and access and utilize information outside of individual minds are also discussed.