@article {4832,
	title = {What Is the Model in Model-Based Planning?},
	journal = {Cognitive Science},
	volume = {45},
	year = {2021},
	month = {01/2021},
	abstract = {<p>Flexibility is one of the hallmarks of human problem-solving. In everyday life, people adapt to changes in common tasks with little to no additional training. Much of the existing work on flexibility in human problem-solving has focused on how people adapt to tasks in new domains by drawing on solutions from previously learned domains. In real-world tasks, however, humans must generalize across a wide range of within-domain variation. In this work we argue that representational abstraction plays an important role in such within-domain generalization. We then explore the nature of this representational abstraction in realistically complex tasks like video games by demonstrating how the same model-based planning framework produces distinct generalization behaviors under different classes of task representation. Finally, we compare the behavior of agents with these task representations to humans in a series of novel grid-based video game tasks. Our results provide evidence for the claim that within-domain flexibility in humans derives from task representations composed of propositional rules written in terms of objects and relational categories.</p>
},
	issn = {0364-0213},
	doi = {10.1111/cogs.v45.110.1111/cogs.12928},
	url = {https://onlinelibrary.wiley.com/toc/15516709/45/1},
	author = {Pouncy, Thomas and Tsividis, Pedro and Samuel J Gershman}
}
