Using deep neural networks as a guide for modeling human planning
Abstract When developing models in cognitive science, researchers typically start with their own intuitions about human behavior in a given task and then build in mechanisms that explain additional aspects of the data. This refinement step is often hindered by how difficult it is to distinguish the...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46850-1 |
| _version_ | 1797453275140718592 |
|---|---|
| author | Ionatan Kuperwajs Heiko H. Schütt Wei Ji Ma |
| author_facet | Ionatan Kuperwajs Heiko H. Schütt Wei Ji Ma |
| author_sort | Ionatan Kuperwajs |
| collection | DOAJ |
| description | Abstract When developing models in cognitive science, researchers typically start with their own intuitions about human behavior in a given task and then build in mechanisms that explain additional aspects of the data. This refinement step is often hindered by how difficult it is to distinguish the unpredictable randomness of people’s decisions from meaningful deviations between those decisions and the model. One solution for this problem is to compare the model against deep neural networks trained on behavioral data, which can detect almost any pattern given sufficient data. Here, we apply this method to the domain of planning with a heuristic search model for human play in 4-in-a-row, a combinatorial game where participants think multiple steps into the future. Using a data set consisting of 10,874,547 games, we train deep neural networks to predict human moves and find that they accurately do so while capturing meaningful patterns in the data. Thus, deviations between the model and the best network allow us to identify opportunities for model improvement despite starting with a model that has undergone substantial testing in previous work. Based on this analysis, we add three extensions to the model that range from a simple opening bias to specific adjustments regarding endgame planning. Overall, our work demonstrates the advantages of model comparison with a high-performance deep neural network as well as the feasibility of scaling cognitive models to massive data sets for systematically investigating the processes underlying human sequential decision-making. |
| first_indexed | 2024-03-09T15:20:29Z |
| format | Article |
| id | doaj.art-6189f488a63d4f1d9a62f3832bdfa739 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:20:29Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-6189f488a63d4f1d9a62f3832bdfa7392023-11-26T12:50:47ZengNature PortfolioScientific Reports2045-23222023-11-0113111110.1038/s41598-023-46850-1Using deep neural networks as a guide for modeling human planningIonatan Kuperwajs0Heiko H. Schütt1Wei Ji Ma2Center for Neural Science, New York UniversityCenter for Neural Science, New York UniversityCenter for Neural Science, New York UniversityAbstract When developing models in cognitive science, researchers typically start with their own intuitions about human behavior in a given task and then build in mechanisms that explain additional aspects of the data. This refinement step is often hindered by how difficult it is to distinguish the unpredictable randomness of people’s decisions from meaningful deviations between those decisions and the model. One solution for this problem is to compare the model against deep neural networks trained on behavioral data, which can detect almost any pattern given sufficient data. Here, we apply this method to the domain of planning with a heuristic search model for human play in 4-in-a-row, a combinatorial game where participants think multiple steps into the future. Using a data set consisting of 10,874,547 games, we train deep neural networks to predict human moves and find that they accurately do so while capturing meaningful patterns in the data. Thus, deviations between the model and the best network allow us to identify opportunities for model improvement despite starting with a model that has undergone substantial testing in previous work. Based on this analysis, we add three extensions to the model that range from a simple opening bias to specific adjustments regarding endgame planning. Overall, our work demonstrates the advantages of model comparison with a high-performance deep neural network as well as the feasibility of scaling cognitive models to massive data sets for systematically investigating the processes underlying human sequential decision-making.https://doi.org/10.1038/s41598-023-46850-1 |
| spellingShingle | Ionatan Kuperwajs Heiko H. Schütt Wei Ji Ma Using deep neural networks as a guide for modeling human planning Scientific Reports |
| title | Using deep neural networks as a guide for modeling human planning |
| title_full | Using deep neural networks as a guide for modeling human planning |
| title_fullStr | Using deep neural networks as a guide for modeling human planning |
| title_full_unstemmed | Using deep neural networks as a guide for modeling human planning |
| title_short | Using deep neural networks as a guide for modeling human planning |
| title_sort | using deep neural networks as a guide for modeling human planning |
| url | https://doi.org/10.1038/s41598-023-46850-1 |
| work_keys_str_mv | AT ionatankuperwajs usingdeepneuralnetworksasaguideformodelinghumanplanning AT heikohschutt usingdeepneuralnetworksasaguideformodelinghumanplanning AT weijima usingdeepneuralnetworksasaguideformodelinghumanplanning |