 Task-Specific Response Strategy Selection on the Basis of Recent Training ExperienceJacqueline M Fulvio, C Shawn Green and Paul R Schrater PLOS Computational Biology, 2014, vol. 10, issue 1, 1-16 Abstract: The goal of training is to produce learning for a range of activities that are typically more general than the training task itself. Despite a century of research, predicting the scope of learning from the content of training has proven extremely difficult, with the same task producing narrowly focused learning strategies in some cases and broadly scoped learning strategies in others. Here we test the hypothesis that human subjects will prefer a decision strategy that maximizes performance and reduces uncertainty given the demands of the training task and that the strategy chosen will then predict the extent to which learning is transferable. To test this hypothesis, we trained subjects on a moving dot extrapolation task that makes distinct predictions for two types of learning strategy: a narrow model-free strategy that learns an input-output mapping for training stimuli, and a general model-based strategy that utilizes humans' default predictive model for a class of trajectories. When the number of distinct training trajectories is low, we predict better performance for the mapping strategy, but as the number increases, a predictive model is increasingly favored. Consonant with predictions, subject extrapolations for test trajectories were consistent with using a mapping strategy when trained on a small number of training trajectories and a predictive model when trained on a larger number. The general framework developed here can thus be useful both in interpreting previous patterns of task-specific versus task-general learning, as well as in building future training paradigms with certain desired outcomes.Author Summary: Predicting what humans will learn from a training task, in particular, whether learning will generalize beyond the specifics of the given experience, is of both significant practical and theoretical interest. However, a principled understanding of the relationship between training conditions and learning generalization remains elusive. In this paper, we develop a computational framework for predicting which of two basic decision-making strategies will be utilized by human subjects - 1) simple stimulus-response mappings or 2) predictive models. Through simulation, we show that the nature of the training experience determines which of these categories leads to better in-task performance; repetitive training on a small set of examples favors simple stimulus-response mappings, whereas training on a large set of examples favors predictive strategies. We then show that humans trained under these various conditions do indeed utilize the predicted strategy. Finally, we show that the strategies that are utilized during training predict generalization of learning. Those who learn simple mappings fail to generalize their new skills, in contrast to those who use default predictive strategies. The framework developed here is useful both in interpreting previous patterns of learning, as well as in building training paradigms with given desired outcomes. Date: 2014 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1003425 DOI: 10.1371/journal.pcbi.1003425 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.