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But when a subgoal must be resolved first, irrelevant future information should not interfere with the processing of more proximal, subgoal-relevant information. We explore the idea that decision making in both situations relies on the flexible modulation of the degree to which different pieces of information under consideration are weighted, rather than explicitly decomposing a problem into smaller parts and solving each part independently. We asked participants to find the shortest goal-reaching paths in mazes and modeled their initial path choices as a noisy, weighted information integration process. In a base task where choosing the optimal initial path required weighting starting-point and goal-proximal factors equally, participants did take both constraints into account, with participants who made more accurate choices tending to exhibit more balanced weighting. The base task was then embedded as an initial subtask in a larger maze, where the same two factors constrained the optimal path to a subgoal, and the final goal position was irrelevant to the initial path choice. In this more complex task, participants\u2019 choices reflected predominant consideration of the subgoal-relevant constraints, but also some influence of the initially-irrelevant final goal. More accurate participants placed much less weight on the optimality-irrelevant goal and again tended to weight the two initially-relevant constraints more equally. These findings suggest that humans may rely on a graded, task-sensitive weighting of multiple constraints to generate approximately optimal decision outcomes in both hierarchical and non-hierarchical goal-directed tasks.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009553","type":"journal-article","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T13:54:37Z","timestamp":1655387677000},"page":"e1009553","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A weighted constraint satisfaction approach to human goal-directed decision making"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3974-6814","authenticated-orcid":true,"given":"Yuxuan","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8217-405X","authenticated-orcid":true,"given":"James L.","family":"McClelland","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"pcbi.1009553.ref001","doi-asserted-by":"crossref","first-page":"10618","DOI":"10.1073\/pnas.1710913114","article-title":"Continuous track paths reveal additive evidence integration in multistep decision making","volume":"114","author":"CB Calderon","year":"2017","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"pcbi.1009553.ref002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pbio.1002588","article-title":"The neural representation of prospective choice during spatial planning and decisions","volume":"15","author":"R Kaplan","year":"2017","journal-title":"PLoS Biology"},{"key":"pcbi.1009553.ref003","doi-asserted-by":"crossref","first-page":"11708","DOI":"10.1073\/pnas.1505483112","article-title":"Evidence integration in model-based tree search","volume":"112","author":"A Solway","year":"2015","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"pcbi.1009553.ref004","unstructured":"Wickelgren WA. 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