Adaptive planning depth in human problem-solving

Area of Science:

• Cognitive Science
• Decision Making
• Human Problem Solving

Background:

• Human problem-solving capabilities are extensive but adaptive strategies remain incompletely understood.
• Challenging planning problems require strategic approaches to cognitive resource management.

Purpose of the Study:

• To investigate the adaptive strategies humans employ in planning tasks of varying complexity.
• To compare human performance against computational planning models.

Main Methods:

• Designed problem-solving tasks with planning requirements across different depths (1-8 subgoals).
• Systematically compared participant performance with established planning models.
• Analyzed the relationship between task demands and selected planning depth.

Main Results:

• Participants adaptively selected initial plan depths, matching them to the minimum required for each problem.
• A tendency to choose the minimum necessary planning depth was observed, rather than a fixed depth for all tasks.
• Human performance aligned with models exhibiting bounded rationality.

Conclusions:

• Human problem-solving demonstrates bounded rationality, adapting cognitive resource investment to task-specific demands.
• The selection of planning depth is a flexible and adaptive cognitive process.
• Findings contribute to understanding how cognitive systems manage limited resources efficiently.