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Uncertainty is a ubiquitous property of both physical and mental realms. Goal-directed actions that take place under these conditions thus probabilistically predict their consequences. Traditional decision-making research has shown that particularly humans are non-normative decision-makers under uncertainty. On the other hand, considering the cognitive system as an output of evolutionary history, it is not unlikely that it models the uncertainties that partly determine the consequences of its actions. It is also natural to assume that the same system uses these models of uncertainty originating from multiple stochastic processes along with its metric representation of the consequences in planning its actions. Indeed, more recent research has shown closer to optimal performance in decision-making tasks in which the uncertainty was experienced and/or originated from the sensori-motor system. In this research, we investigated this very process in the context of temporal decision-making in both human and mice subjects. We further used this experimental context to answer the essential questions regarding the functional architecture of mind. This questioning specifically targeted the degree of representational and computational power needed to account for decision-making under uncertainty. In order to answer this question, we conducted computer simulations providing different degrees of representational substitution/power and compared their outputs to the empirical data. We conclude that both human and mice are optimal decision-makers under uncertainty that originates from extrinsic and intrinsic (mental) stochastic processes and observed performance can be better explained by information-processing rather than associative frameworks of mind.
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