Abstract
One of the most striking features of human cognition is the ability to plan. Two aspects of human planning stand out—its efficiency and flexibility. Efficiency is especially impressive because plans must often be made in complex environments, and yet people successfully plan solutions to many everyday problems despite having limited cognitive resources1,2,3. Standard accounts in psychology, economics and artificial intelligence have suggested that human planning succeeds because people have a complete representation of a task and then use heuristics to plan future actions in that representation4,5,6,7,8,9,10,11. However, this approach generally assumes that task representations are fixed. Here we propose that task representations can be controlled and that such control provides opportunities to quickly simplify problems and more easily reason about them. We propose a computational account of this simplification process and, in a series of preregistered behavioural experiments, show that it is subject to online cognitive control12,13,14 and that people optimally balance the complexity of a task representation and its utility for planning and acting. These results demonstrate how strategically perceiving and conceiving problems facilitates the effective use of limited cognitive resources.
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Using deep neural networks as a guide for modeling human planning
Scientific Reports Open Access 20 November 2023
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Data availability
Data for the current study are available through the Open Science Foundation repository https://doi.org/10.17605/OSF.IO/ZPQ69.
Code availability
Code for this study is available through the Open Science Foundation repository https://doi.org/10.17605/OSF.IO/ZPQ69, which links to a GitHub repository and contains an archived version of the repository. The value-guided construal model and alternative models were implemented in Python (v.3.7.4) using the msdm (v.0.6) library, numpy (v.1.19.2) and scipy (v.1.5.2). Experiments were implemented using psiTurk (v.3.2.0) and jsPsych (v.6.0.1). Hierarchical generalized linear regressions were implemented using rpy2 (v.3.3.6), lme4 (v.1.1.21) and R (v.3.6.1).
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Acknowledgements
We thank J. Hamrick, L. Gularte, C. Sayalı, Q. Zhang, R. Dubey and W. Thompson for feedback on this work. This work was funded by NSF grant 1545126, John Templeton Foundation grant 61454 and AFOSR grant FA 9550-18-1-0077.
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All of the authors contributed to conceptualizing the project and editing the manuscript. M.K.H., D.A., M.L.L. and T.L.G. developed the value-guided construal model. M.K.H. implemented the value-guided construal model. M.K.H. and C.G.C. implemented the heuristic search models and msdm library. M.K.H., J.D.C. and T.L.G. designed the experiments. M.K.H. implemented the experiments, analysed the results and drafted the manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Experimental measures on mazes 0 to 5.
Average responses associated with each obstacle in mazes 0 to 5 in the initial experiment (awareness judgement), the up-front planning experiment (awareness judgement), and the process-tracing experiment (whether an obstacle was hovered over and, if so, the duration of hovering in log milliseconds). Obstacle colours are normalized by the minimum and maximum values for each measure/maze, except for awareness judgements, which are scaled from 0 to 1.
Extended Data Fig. 2 Experimental measures on mazes 6 to 11.
Average responses associated with each obstacle in mazes 6 to 11 in the initial experiment (awareness judgement), the up-front planning experiment (awareness judgement), and the process-tracing experiment (whether an obstacle was hovered over and, if so, the duration of hovering in log milliseconds). Obstacle colours are normalized by the minimum and maximum values for each measure/maze, except for awareness judgements, which are scaled from 0 to 1.
Extended Data Fig. 3 Experimental measures on mazes 12 to 15.
Average responses associated with each obstacle in mazes 12 to 15 in the critical mazes experiment (recall accuracy, recall confidence, and awareness judgement) and the process-tracing experiment (whether an obstacle was hovered over and, if so, the duration of hovering in log milliseconds). Obstacle colours are scaled to range from 0.5 to 1.0 for accuracy, 0 to 1 for hovering, confidence, and awareness judgements, and the minimum to maximum values across obstacles in a maze for hovering duration in log milliseconds.
Extended Data Fig. 4 Additional Experimental Details.
a, Items from critical mazes experiment. Blue obstacles are the location of obstacles during the navigation part of the trial. Orange obstacles with corresponding number are copies that were shown during location recall probes. During recall probes, participants only saw an obstacle paired with its copy. b, Example trial from process-tracing experiment. Participants could never see all of the obstacles at once, but, before navigating, could use their mouse to reveal obstacles. We analyzed whether value-guided construal predicted which obstacles people tended to hover over and, if so, the duration of hovering.
Extended Data Fig. 5 Model predictions on mazes 0 through 7.
Shown are the predictions for six of the eleven predictors we tested: fixed parameter value-guided construal modification obstacle probability (VGC, our model); trajectory-based heuristic search obstacle hit score (Traj HS); graph-based heuristic search obstacle hit score (Graph HS); distance to optimal bottleneck (Bottleneck); successor representation overlap score (SR Overlap); and distance to optimal paths (Opt Dist) (see Methods, Model Implementations). Mazes 0 to 7 were all in the initial set of mazes. Darker obstacles correspond to greater predicted attention according to the model. Obstacle colours normalized by the minimum and maximum values for each model/maze.
Extended Data Fig. 6 Model predictions on mazes 8 through 15.
Shown are the predictions for six of the eleven predictors we tested (see Methods, Model Implementations). Mazes 8 to 11 were part of the initial set of mazes, while mazes 12 to 15 constituted the set of critical mazes. Darker obstacles correspond to greater predicted attention according to the model. Obstacle colours normalized by the minimum and maximum values for each model/maze.
Extended Data Fig. 7 Summaries of candidate models and data from planning experiments.
Each row corresponds to a measurement of attention to obstacles from a planning experiment: Awareness judgements from the initial memory experiment, the up-front planning experiment, and the critical mazes experiment; recall accuracy and confidence from the critical mazes experiment; and the binary hovering measure and hovering duration measure (in log milliseconds) from the two process-tracing experiments. Each column corresponds to candidate processes that could predict attention to obstacles: fixed parameter value-guided construal modification obstacle probability (VGC, our model), trajectory-based heuristic search hit score (Traj HS), graph-based heuristic search hit score (Graph HS), distance to bottleneck states (Bottleneck), successor-representation overlap (SR Overlap), expected distance to optimal paths (Opt Dist), distance to the goal location (Goal Dist), distance to the start location (Start Dist), distance to the invariant black walls (Wall Dist), and distance to the centre of the maze (Centre Dist). Note that for distance-based predictors, the x-axis is flipped. For each predictor, we quartile-binned the predictions across obstacles, and for each bin we plot (bright red lines) the mean and standard deviation of the predictor and mean by-obstacle response (overlapping bins were collapsed into a single bin). Black circles correspond to the mean response and prediction for each obstacle in each maze. Dashed dark red lines are simple linear regressions on the black circles, with R2 values shown in the lower right of each plot. Across the nine measures, value-guided construal tracks attention to obstacles, while other candidate processes are less consistently associated with obstacle attention (data are based on n = 84215 observations taken from 825 independent participants).
Extended Data Fig. 8 Sufficiency of individual and pairs of mechanisms for explaining attention to obstacles when planning.
To assess the individual and pairwise sufficiency of each predictor for explaining responses in the planning experiments, we fit hierarchical generalized linear models (HGLMs) that included pairs of predictors as fixed effects. Each lower-triangle plot corresponds to one of the experimental measures, where pairs of predictors included in a HGLM as fixed-effects are indicated on the x- and y-axes. Values are the ΔAIC for each model relative to the best fitting model associated with an experimental measure (lower values indicate better fit). Values along the diagonals correspond to models fit with a single predictor. According to this criterion, across all experimental measures, value-guided construal is in the first or second best single-predictor HGLM, and is always in the best two-predictor HGLM.
Supplementary information
Supplementary Information
Supplementary Analyses, Supplementary Discussion, with details of the construal optimization algorithms, Supplementary Methods, Supplementary Tables 1 and 2, Supplementary Figs. 1–12 and Supplementary References.
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Ho, M.K., Abel, D., Correa, C.G. et al. People construct simplified mental representations to plan. Nature 606, 129–136 (2022). https://doi.org/10.1038/s41586-022-04743-9
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DOI: https://doi.org/10.1038/s41586-022-04743-9
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