How do instructions, examples, and testing shape task representations?

Aba Szollosi, Vladimir Grigoras, Tadeg Quillien, Christopher G Lucas, Neil R Bramley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

People need to generate and test hypotheses in order to create accurate representations of their environments. But how do they know which hypotheses to consider when there are often infinitely many possibilities? Here we explore the idea that evolutionary mental representation generation and selection processes – responsible for the generation of both local (i.e., within a task) and global (i.e., about a task) representations – enable people to address this problem. We investigated this through an active learning experiment, where participants’ task was to discover a hidden rule determining the behavior of a simple physical system. Specifically, we aimed to manipulate factors that constrain this process, particularly through experimental instructions and feedback. We found that providing more opportunities for participants to recognize when their initial task conceptualization was wrong and adjust it helped them create more accurate representations about the task, which in turn led to better accuracy within the task.
Original languageEnglish
Title of host publicationProceedings of the 45th Annual Meeting of the Cognitive Science Society
EditorsMicah Goldwater, Florencia Anggoro, Brett Hayes, Desmond Ong
PublisherThe Cognitive Science Society
Pages3032-3038
Number of pages7
Volume45
Publication statusPublished - 29 Jul 2023
Event45th Annual Meeting of the Cognitive Science Society: Cognition in Context - Sydney, Australia
Duration: 26 Jul 202329 Jul 2023
https://cognitivesciencesociety.org/cogsci-2023/

Publication series

NameProceedings of the Cognitive Science Society
PublisherThe Cognitive Science Society
ISSN (Electronic)1069-7977

Conference

Conference45th Annual Meeting of the Cognitive Science Society
Abbreviated titleCogSci2023
Country/TerritoryAustralia
CitySydney
Period26/07/2329/07/23
Internet address

Keywords / Materials (for Non-textual outputs)

  • active learning
  • constructive models
  • hypothesis generation and selection

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