Rational use of cognitive resources in human planning

Abstract

Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near-optimal under some circumstances, but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.

A computational process-tracing method for measuring people’s planning strategies and how they change over time

“…Analyzing the data collected with our process-tracing paradigm suggested that people use a wide range of different planning strategies. We found that which strategy people use does not only depend on the structure of the environment (Callaway et al, 2018;Callaway et al, 2022b) but also on the participant's learning history and individual differences. Concretely, we found that people may use as many as 79 different planning strategies across different environments and different points in time.…”

“…The process-tracing data from the intermediate trials documents the participant's transition between these two very different ways of planning process-tracing paradigm for measuring individual planning operations (Section 5). Measuring planning operations can yield valuable insights into how people plan (Callaway et al, 2017;Callaway et al, 2022b). But most research questions, such as how human planning compares to planning algorithms used in artificial intelligence, are not formulated at the level of individual planning operations but instead at the level of planning strategies.…”

“…We assume that each planning strategy performs a sequence of internal information gathering operations (Callaway et al, 2022b). Concretely, we assume that each of these planning operations mentally simulates what might happen if one took a particular action in a particular situation.…”

“…Furthermore, we treat the mental simulation of each state-action pair as a separate planning operation. These assumptions make it possible to measure planning by externalizing the process of information gathering that would otherwise occur through memory recall and mental simulation (Callaway et al, 2017;Callaway et al, 2018;Callaway et al, 2022b). Building on this theory and a previous method for studying how people choose between alternatives with multiple attributes (Payne et al, 1993), we introduce a process-tracing paradigm for revealing the sequence of information gathering operations people perform during planning (see Fig.…”

Recurring Suboptimal Choices Result in Superior Decision Making

“…Recent behavioral and neuroscience ndings appear to support this general prediction (Glimcher, 2022;Ho et al, 2022;Mastrogiuseppe & Moreno-Bote, 2022). For instance, people adapt their behavior in a complex problem-solving task and use heuristic planning strategies to behave in an optimal manner when resources are constrained (Callaway et al, 2022). A reanalysis of eye-tracking data has revealed that attentional bias to a low-EV option was related to a higher reward rate-the reward that could be obtained per unit of time invested in a choice problem (Zilker, 2022).…”