A cognitive-computational framework for studying help-seeking decision-making

doi:10.3724/SP.J.1042.2026.1606

Abstract

Abstract: When faced with challenging problems, individuals need to weigh the pros and cons to decide whether and from whom to seek help in order to obtain assistance from others. Such help-seeking decision-making constitutes a crucial foundation for human cooperation and adaptation. However, fragmented and non-quantitative research perspectives and methods in previous studies have hindered the construction of a systematic knowledge framework and limited the quantitative analysis of the trade-offs and integration of core cognitive components. Consequently, the cognitive-computational and neural mechanisms underlying help-seeking decisions remain poorly understood. From an integrated and quantitative perspective, this review proposes a cognitive-computational framework for studying help-seeking decision-making.
First, this review synthesizes previous theoretical and empirical studies to address the fragmentation of existing research on help-seeking. Although previous studies have identified a variety of factors related to help seeking, these factors have often been examined separately, making it difficult to construct a systematic account of help-seeking decision-making. To address this issue, the present review summarizes three stages of the help-seeking process—need perception, interpersonal request, and reciprocal exchange—and distills three corresponding core cognitive components: perceived help-seeking benefit, anticipated social rejection cost, and anticipated reciprocity anxiety cost. On this basis, the review further proposes three key aspects for studying help-seeking decision-making: decision generation, dynamic adjustment, and the moderating role of dyadic interaction features.
Second, this review further extends and applies rational and bounded rationality decision theories to the study of help-seeking decision-making, thereby constructing a systematic set of candidate cognitive-computational model hypotheses based on the three key aspects. These model hypotheses include rational models, bounded rational heuristic models, and hybrid models combining the two. Rational models assume that individuals weigh and integrate different cognitive components, including perceived benefits and anticipated costs, in a relatively systematic manner, so that help-seeking decisions are generated and dynamically adjusted through value-based trade-offs, integration, and social learning. In contrast, bounded rational heuristic models assume that, under conditions such as uncertainty, limited information, and cognitive constraints, individuals may actively ignore part of the available information and rely on simpler decision rules or more limited processing strategies, thereby making decisions more quickly and with lower cognitive demands, and in some cases even more accurately. In addition, this review proposes hybrid models that combine rational and bounded rational heuristic strategies, thereby extending existing accounts of how help-seeking strategies may shift between these two modes and suggesting that rational and bounded rational heuristic processes are better understood not as a static dichotomy, but as dynamically interacting processes within individuals.
Third, based on the proposed cognitive-computational model hypotheses, this review outlines key scientific questions and methodological prospects for future research on the cognitive-computational and neural mechanisms underlying help-seeking decision-making. Future research needs to clarify when individuals rely on rational or bounded rational heuristic strategies, how core cognitive components are represented, weighed, and integrated to generate and adjust help-seeking decisions, and how dyadic interaction features and other individual difference factors shape these processes. Methodologically, future studies should develop interactive paradigms that can repeatedly elicit and quantitatively measure help-seeking decisions, while collecting behavioral data to test, compare, and refine rational models, bounded rational heuristic models, and hybrid models, and to combine computational modeling with neuroimaging methods such as fMRI, EEG, and MEG to reveal the cognitive-computational and neural mechanisms underlying help-seeking decision-making. In addition, future studies could incorporate relevant individual-difference indicators, thereby providing a quantitative basis for the precise identification of related problems and for targeted intervention.
Overall, this review addresses the fragmentation and non-quantitative nature of previous help-seeking research by proposing an integrated cognitive-computational framework. It summarizes three stages of the help-seeking process, distills the corresponding core cognitive components, constructs a systematic set of candidate cognitive-computational model hypotheses, and outlines key scientific questions and methodological prospects for future research. Together, these contributions provide a foundation for future research on the cognitive-computational and neural mechanisms underlying help-seeking decision-making and related domains.

Key words: help-seeking decision-making, cognitive-computational framework, decision generation, dynamic adjustment, dyadic interaction features

CLC Number:

B849: C91

LUO Haocheng, DU Wei, WEI Lianting, ZHOU Xiaolin, GAO Xiaoxue. A cognitive-computational framework for studying help-seeking decision-making[J]. Advances in Psychological Science, 2026, 34(9): 1606-1628.

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