Scaling prediction errors to reward variability benefits error-driven learning in humans.
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In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease "adapters'" accuracy in predicting the means of reward distributions.However, exaggerated scaling beyond the standard deviation resulted in impaired performance.Thus efficient adaptation makes learning more robust to changing variability.
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Affiliation: Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridge, United Kingdom k.diederen@gmail.com.
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Mentions: Thus far, it is unclear whether scaling of prediction errors relative to the variability of reward distributions results in improved performance, as predicted by learning models (Preuschoff and Bossaerts 2007). Increases in computational demands during prediction error scaling may, for instance, impede optimal deceleration of learning rates, resulting in suboptimal performance. In addition, although scaling of prediction errors relative to the variability in reward benefits performance, scaling with the standard deviation (SD) limits the power of the learning rate to update predictions. For instance, when a prediction error of 15 is divided by an SD of 15, the prediction can only be adjusted with 1 point (see Fig. 2F). |
View Article: PubMed Central - PubMed
Affiliation: Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridge, United Kingdom k.diederen@gmail.com.