Effects of Level of Detail on Predictive Processing

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According to the predictive processing account, the human brain tries to predict the world around it, and revises its own prediction based on the prediction errors these predictions result in. Immature models with no or very little experience to base their predictions on, will inevitable make inaccurate predictions resulting in high prediction errors. Reducing the prediction error of the predictive models is always important, but arguably even more so in the case of these immature models. One way of reducing the prediction error of a model is by reducing the level of predictive detail, meaning that the resolution of what it predicts is lowered. Because this reduces the amount of uncertainty about what it predicts, the prediction error should drop accordingly. This is of course not without its downsides, because a lower level of predictive detail will mean the amount of information that be gained from the model is reduced along with the prediction error. This thesis tried to answer two questions: “What is the difference in the size of the early prediction error between predicting at a high level of detail (control scenario) and predicting at a low level of detail (test scenario), and will switching to a higher level of detail while learning result in a lower overall prediction error?” As expected, the decrease in the prediction error of the test scenario predicting at a lower level of detail was found to be significant. The effect on the later prediction error was not found to be significant. However, the environment used in this thesis was far from complex, and more experiments in such complex environments would thus be desired.
Faculteit der Sociale Wetenschappen