A growing body of research in perceptual and economic decision-making has shown that the subjective value of an option is not computed in isolation, but depends critically on the range of available alternatives—a phenomenon known as contextual valuation. However, despite the ecological relevance of this effect, most existing work has focused on artificially static and explicit contexts, leaving open the question of how value representations adapt in more naturalistic environments shaped by past experiences. This project aims to fill that gap by investigating whether, and how, outcome values learned from experience are dynamically rescaled through a process of range adaptation. Our central hypothesis is that such range adaptation is not a marginal feature, but a pervasive computational strategy that optimizes information processing in learning systems. By adapting the internal value scale to the distribution of experienced outcomes, the brain reduces computational costs and enhances local efficiency. However, this comes at a cost: the resulting value representations may become distorted, impairing the ability to generalize across novel contexts. To address this trade-off, we are developing and empirically validating a comprehensive theoretical and experimental framework. Through the integration of behavioral studies, computational modeling, and cross-cultural experiments, the project seeks to provide a rigorous account of range adaptation in human learning. Ultimately, our goal is to uncover fundamental computational constraints on human cognition and contribute to a deeper understanding of the mechanisms underlying bounded rationality. Beyond its implications for cognitive science, this work may inform broader discussions on decision-making, memory, and adaptability in complex environments.
