Cognitive Processing Mechanisms in Multi-attribute Decision Making: Theoretical Evolution, Process Evidence, and Future Directions
Zheng Jiehui1, Chen Fadong2, Zhu Zhi3
1.Alibaba Business School, Hangzhou Normal University, Hangzhou 311121, China 2.School of Management, Zhejiang University, Hangzhou 310058, China 3.School of Management, Xi’an Jiaotong University, Xi’an 710049, China
Abstract：The central question in multi-attribute decision making concerns not only what individuals ultimately choose, but also how different attributes are represented, compared, and integrated as preferences. Traditional as-if models use utility functions to describe attribute integration and predict choice outcomes. Yet distinct psychological mechanisms may generate the same behavioral result, making competing explanations difficult to distinguish from final choices alone. Combining bibliometric and theoretical analyses, this review examines the theoretical evolution, process evidence, application value, and research prospects for the cognitive mechanisms of multi-attribute decision making. Social and intertemporal decision making are used as representative domains for examining two foundational attribute conflicts: self-benefit versus other-benefit, and benefit magnitude versus temporal delay.Following the PRISMA framework, this review searches the Web of Science Core Collection database and conducts citation tracing, yielding a final sample of 236 publications. The bibliometric analysis suggests that the field has evolved through three broad stages: value representation and neural mechanisms, process measurement and contextual applications, and computational modeling and mechanistic refinement. Correspondingly, the field has progressed from asking how attributes are encoded as subjective values to examining how attention allocation, attribute-processing onset (attribute latency), and evidence accumulation jointly shape preferences.Theoretically, utility models provide a parsimonious account of attribute integration and choice outcomes. However, because they are evaluated primarily on the basis of final choices, they have difficulty distinguishing between different cognitive processes that may produce the same choice outcome. Dual-process theories explain choice differences through interactions between intuitive and deliberative processing, yet do not precisely specify when attributes enter processing or how they are compared and weighted. Evidence accumulation models treat choices and response times as joint products of dynamic accumulation and incorporate starting-point bias, attribute weights, attribute-processing onset (attribute latency), and decision thresholds into a testable computational framework. Specifically, the drift diffusion model (DDM) jointly generates choices and response times; the starting-time DDM (stDDM) further incorporates attribute latencies; and attentional DDMs (aDDM/maaDDM) embed trial-level fixation data into the accumulation process. Available evidence further suggests that option-wise and attribute-wise processing are not mutually exclusive; decision makers may switch between these paths depending on task structure and cognitive resources.Response times, mouse trajectories, eye movements, and neural activity provide information about processing duration, behavioral dynamics, attention allocation, and neural representation, respectively. When combined with choice outcomes, experimental manipulations, and computational models, these measures offer incremental evidence for comparing competing models and identifying attribute weights, attribute-processing onset, and decision thresholds. Research on social and intertemporal decisions indicates that attributes do not necessarily enter processing simultaneously or receive equal weight. Differences in attribute weighting, attention, and processing onset alter evidence accumulation and final choice.The main contribution of this review lies in organizing the literature around attribute processing. It connects utility models, dual-process theories, and evidence accumulation models with process data, and compares social and intertemporal decisions to show how attribute weights, attention allocation, and attribute processing onset jointly shape preference formation. By integrating theoretical developments with empirical process evidence, this review provides an integrative framework for understanding and testing the dynamic cognitive mechanisms of multi-attribute decision making. In addition, the review compares different types of process data in terms of their typical measures, strengths, and interpretive boundaries, thereby clarifying the conditions under which each type of evidence is informative. It also has implications for information presentation, attentional guidance, differentiated intervention, and the prediction of preferences and behavior from process data, and it further discusses cross-domain tests, high-dimensional modeling, multimodal data integration, human-AI collaboration, and research grounded in Chinese contexts.
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