  The corpus, from which a predictive language model is trained, can be
considered the experience of a semantic system. We recorded everyday reading of
two participants for two months on a tablet, generating individual corpus
samples of 300/500K tokens. Then we trained word2vec models from individual
corpora and a 70 million-sentence newspaper corpus to obtain individual and
norm-based long-term memory structure. To test whether individual corpora can
make better predictions for a cognitive task of long-term memory retrieval, we
generated stimulus materials consisting of 134 sentences with uncorrelated
individual and norm-based word probabilities. For the subsequent eye tracking
study 1-2 months later, our regression analyses revealed that individual, but
not norm-corpus-based word probabilities can account for first-fixation
duration and first-pass gaze duration. Word length additionally affected gaze
duration and total viewing duration. The results suggest that corpora
representative for an individual's longterm memory structure can better explain
reading performance than a norm corpus, and that recently acquired information
is lexically accessed rapidly.
