Neural evidence that humans reuse strategies to solve new tasks

@article{hall2025neural,
    doi = {10.1371/journal.pbio.3003174},
    author = {Hall-McMaster, Sam AND Tomov, Momchil S. AND Gershman, Samuel J. AND Schuck, Nicolas W.},
    journal = {PLOS Biology},
    publisher = {Public Library of Science},
    title = {Neural evidence that humans reuse strategies to solve new tasks},
    year = {2025},
    month = {06},
    volume = {23},
    url = {https://doi.org/10.1371/journal.pbio.3003174},
    pages = {1-31},
    abstract = {Generalization from past experience is an important feature of intelligent systems. When faced with a new task, one efficient computational approach is to evaluate solutions to earlier tasks as candidates for reuse. Consistent with this idea, we found that human participants (n = 38) learned optimal solutions to a set of training tasks and generalized them to novel test tasks in a reward-selective manner. This behavior was consistent with a computational process based on the successor representation known as successor features and generalized policy improvement (SF&GPI). Neither model-free perseveration or model-based control using a complete model of the environment could explain choice behavior. Decoding from functional magnetic resonance imaging data revealed that solutions from the SF&GPI algorithm were activated on test tasks in visual and prefrontal cortex. This activation had a functional connection to behavior in that stronger activation of SF&GPI solutions in visual areas was associated with increased behavioral reuse. These findings point to a possible neural implementation of an adaptive algorithm for generalization across tasks.},
    number = {6},
}