Bounded optimality of time investments in rats, mice, and humans

Abstract

Time is our scarcest resource. Allocating time optimally presents a universal challenge for all organisms because the future benefits of time investments are uncertain. We developed a normative framework for assessing bounded optimality in time allocation, emphasizing the accuracy of future predictions, independent of subjective costs and benefits. In a common decision task across humans, rats, and mice, we varied uncertainty by titrating ambiguous sensory evidence and measured the time each subject was willing to invest post-decision. We observed that all species and subjects invested more time when they were more likely to be correct, which reflected a statistical confidence of uncertain evidence. Time allocation strategy approached the lower bound of optimality, indicating an accurate decision-by-decision assessment of confidence in the likelihood that waiting will pay off - independent of the subjective payoff values and time costs. We demonstrate that an elementary algorithm based on a drift-diffusion process algorithm can implement this optimal time investment strategy. These results illuminate the computational mechanisms governing rational time investment, showing that humans, rats, and mice can maximize payoffs via confidence-guided time allocation. HighlightsO_LIComputational and behavioral framework to assess bounded optimality of investments. C_LIO_LIHumans, rats, and mice invest more time to obtain more likely payoffs, in proportion to statistical confidence. C_LIO_LITime investment was close to optimal model predictions, reflecting bounded optimality of investments under uncertainty. C_LIO_LIBounded-optimal time investment may be an evolutionary ancient adaptive behavioral strategy. C_LI