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Adaptive planning depth in human problem-solving

Year: 2025
Type: article
Abstract: 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.
DOI: 10.1098/rsos.241161
Authors: Mattia Eluchans, Gian Luca Lancia, Antonella Maselli, Marco D’Alessandro, Jeremy Raboff Gordon, Giovanni Pezzulo
Institutions: National Academies of Sciences, Engineering, and Medicine, National Research Council, Institute of Cognitive Sciences and Technologies, Sapienza University of Rome, University of Messina, University of California, Berkeley
Language: English
Open Access status: gold
Estimated APC paid: $1,471
Awards: G2501504014, G3936970252, G434337789, G5030833526, G534784824, Human Brain Project Specific Grant Agreement 3
OpenAlex ID: W4409260552
FWCI: 3.711
Cites: 97
Cited by: 10
Related to: 10