Adaptive planning depth in human problem-solving
We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them is not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants and planning models, we found that when facing problems that require planning to a certain number of subgoals (from 1 to 8), participants make an adaptive use of their cognitive resources-namely, they tend to select an initial plan having the minimum required depth, rather than selecting the same depth for all problems. These results support the view of problem-solving as a bounded rational process, which adapts costly cognitive resources to task demands.
Authors
- Mattia Eluchans (ORCID: https://orcid.org/0000-0002-5151-3055)
- Gian Luca Lancia (ORCID: https://orcid.org/0000-0001-8773-1727)
- Giovanni Pezzulo (ORCID: https://orcid.org/0000-0001-6813-8282)
- Antonella Maselli (ORCID: https://orcid.org/0000-0002-0564-3174)
- Jeremy Gordon (ORCID: https://orcid.org/0000-0002-4514-5284)
- Marco D’Alessandro (ORCID: https://orcid.org/0000-0002-3596-5780)
Institutions
- University of Messina (IT)
- National Academies of Sciences, Engineering, and Medicine (US)
- Institute of Cognitive Sciences and Technologies (IT)
- Sapienza University of Rome (IT)
- University of California, Berkeley (US)
Publication Details
- Journal
- Royal Society Open Science
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1098/rsos.241161
- Citations
- 4
- Primary Topic
- AI-based Problem Solving and Planning
- Type
- article
- Field-Weighted Citation Impact
- 9.92
Funders
- H2020 European Research Council