The neocortex comprises anatomically discrete yet interconnected areas that are symmetrically located across the two hemispheres. Determining the logic of these macrocircuits is necessary for understanding high level brain function. Here in mice, we have mapped the areal and laminar organization of the ipsi- and contralateral cortical projection onto the primary visual, somatosensory, and motor cortices. We find that although the ipsilateral hemisphere is the primary source of cortical input, there is substantial contralateral symmetry regarding the relative contribution and areal identity of input. Laminar analysis of these input areas show that excitatory Layer 6 corticocortical cells (L6 CCs) are a major projection pathway within and between the two hemispheres. Analysis of the relative contribution of inputs from supra- (feedforward) and infragranular (feedback) layers reveals that contra-hemispheric projections reflect a dominant feedback organization compared to their ipsi-cortical counterpart. The magnitude of the interhemispheric difference in hierarchy was largest for sensory and motor projection areas compared to frontal, medial, or lateral brain areas due to a proportional increase in input from L6 neurons. L6 CCs therefore not only mediate long-range cortical communication but also reflect its inherent feedback organization.

Reward-rate maximization is a prominent normative principle in behavioral ecology, neuroscience, economics, and AI. Here, we identify, compare, and analyze equations to maximize reward rate when assessing whether to initiate a pursuit. In deriving expressions for the value of a pursuit, we show that time’s cost consists of both apportionment and opportunity cost. Reformulating value as a discounting function, we show precisely how a reward-rate-optimal agent’s discounting function (1) combines hyperbolic and linear components reflecting apportionment and opportunity costs, and (2) is dependent not only on the considered pursuit’s properties but also on time spent and rewards obtained outside the pursuit. This analysis reveals how purported signs of suboptimal behavior (hyperbolic discounting, and the Delay, Magnitude, and Sign effects) are in fact consistent with reward-rate maximization. To better account for observed decision-making errors in humans and animals, we then analyze the impact of misestimating reward-rate-maximizing parameters and find that suboptimal decisions likely stem from errors in assessing time’s apportionment—specifically, underweighting time spent outside versus inside a pursuit—which we term the ‘Malapportionment Hypothesis’. This understanding of the true pattern of temporal decision-making errors is essential to deducing the learning algorithms and representational architectures actually used by humans and animals.