Distributional semantic models (DSM) are widely used in psycholinguistic research to automatically assess the degree of semantic relatedness between words. Model estimates strongly correlate with human similarity judgements and offer a tool to successfully predict a wide range of language-related phenomena. In the present study, we compare the state-of-art model with pointwise mutual information (PMI), a measure of local association between words based on their surface cooccurrence. In particular, we test how the two indexes perform on a dataset of sematic priming data, showing how PMI outperforms DSM in the fit to the behavioral data. According to our result, what has been traditionally thought of as semantic effects may mostly rely on local associations based on word co-occurrence.
