The Structure of Goal Systems Predicts Human Performance

David D. Bourgin, Falk Lieder, Daniel Reichman, Nimrod Talmon, Thomas L. Griffiths

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Most psychological theories attribute people's failure to achieve their goals exclusively to insufficient motivation or lack of skill. Here, we offer a complementary explanation that emphasizes the inherent complexity of the computational problems that arise from the structure of people's goal systems. Concretely, we hypothesize that people's capacity to achieve their goals can be predicted from combinatorial parameters of the structure of the network connecting their goals to the means available to pursue them. To test this hypothesis, we expressed the relationship between goals and means as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. This allowed us to map two computational challenges that arise in goal achievement onto two classic NP-hard problems: Set Cover and Maximum Coverage. The connection between goal pursuit and NP-hard problems led us to predict that people should perform better with goal systems that are tree-like. Three behavioral experiments confirmed this prediction. Our results imply that network parameters that are instrumental to algorithm design could also be useful for understanding when and why people struggle in their goal pursuits.

Original languageEnglish
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages1660-1665
Number of pages6
ISBN (Electronic)9780991196760
StatePublished - 1 Jan 2017
Externally publishedYes
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: 26 Jul 201729 Jul 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period26/07/1729/07/17

Keywords

  • computational complexity
  • decision-making
  • goals
  • graph theory
  • rational analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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