How Stories Become Decisions: Narrative Processing as a Neurocognitive Framework for Understanding Decision-Making

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Abstract

Most economic models of decision-making assume that individuals maintain comprehensive mental representations of possible world states and compute expected utilities across these representations. The cognitive demands of such exhaustive state-space evaluation appear difficult to reconcile with known constraints on working memory, attention, and neural computation, motivating alternative accounts of how humans navigate complex choice environments. This review examines the hypothesis that neural systems supporting narrative processing contribute substantially to real-world decision-making. We synthesize evidence from three converging lines of research: hierarchical temporal processing in the cortex, the integrative functions of the default mode network, and inter-brain synchronization during naturalistic communication. We acknowledge the ongoing debates about the interpretation of these findings, but we argue that the neural architecture supporting narrative comprehension and generation offers cognitive efficiency advantages relevant to decision-making, including dimensionality reduction through causal structuring, integration of emotional and contextual information, and facilitation of social coordination through shared mental models. Rather than claiming that narrative processing constitutes the exclusive mechanism for decision-making, we propose that it represents one important component of a broader cognitive toolkit that also includes heuristic strategies, model-based planning, and habitual responses. We examine how this perspective relates to phenomena in cognitive economics, including context-dependent preferences, framing effects, and the propagation of economic beliefs through populations. By integrating findings from cognitive neuroscience with decision science, we aim to contribute toward a more biologically informed understanding of human choice behavior and identify key questions for future empirical investigation.

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