Decision making as a closed-loop process

The theory of decision making has largely been developed as a disembodied open-loop process, however there is growing recognition that ecologically valid scenarios require integration of movement dynamics into current decision making theory, and a revision of what are considered to be core/fundamental decision components. Here we develop the theory of decision making as a closed loop process, first exploring the role of confidence both as a neural computation within the loop, affecting movement dynamics and as a property of the egocentric frame with a causal influence on cognition. Secondly, we consider the relationship between closed-loop components/processing and stability — in embodied systems action is accumulated and so physical restrictions limit volatility, moreover the reciprocal relationship between movement and evidence processing means that this stabilisation may also happen on a neural level in the form of a biased gain during evidence accumulation, improving stability/convergence. Finally, we examine closed-loop embodied decision making in the context of optimality — it is generally accepted that open-loop decision making is optimised to maximise reward via some form of Bayes’ Risk, prescribing a speed-accuracy tradeoff in so doing. For closed-loop decision making however, the form of the ‘objective function’ is unknown, as such we consider higher level, ecologically inspired ideas of optimality such as adaptability to e.g. moving targets or nonstationarity, to explore this fundamental aspect of embodied decision making.