The aim of this thesis is to study the effect that linguistic context exerts on the activation and processing of word meaning over time. Previous studies have demonstrated that a biasing context makes it possible to predict upcoming words. The context causes the pre-activation of expected words and facilitates their processing when they are encountered. The interaction of context and word meaning can be described in terms of feature overlap: as the context unfolds, the semantic features of the processed words are activated and words that match those features are pre-activated and thus processed more quickly when encountered. The aim of the experiments in this thesis is to test a key prediction of this account, viz., that the facilitation effect is additive and occurs together with the unfolding context. Our first contribution is to analyse the effect of an increasing amount of biasing context on the pre-activation of the meaning of a critical word. In a self-paced reading study, we investigate the amount of biasing information required to boost word processing: at least two biasing words are required to significantly reduce the time to read the critical word. In a complementary visual world experiment we study the effect of context as it unfolds over time. We identify a ceiling effect after the first biasing word: when the expected word has been pre-activated, an increasing amount of context does not produce any additional significant facilitation effect. Our second contribution is to model the activation effect observed in the previous experiments using a bag-of-words distributional semantic model. The similarity scores generated by the model significantly correlate with the association scores produced by humans. When we use point-wise multiplication to combine contextual word vectors, the model provides a computational implementation of feature overlap theory, successfully predicting reading times. Our third contribution is to analyse the effect of context on semantically similar words. In another visual world experiment, we show that words that are semantically similar generate similar eye-movements towards a related object depicted on the screen. A coherent context pre-activates the critical word and therefore increases the expectations towards it. This experiment also tested the cognitive validity of a distributional model of semantics by using this model to generate the critical words for the experimental materials used.