Reading, while structured, is a non-linear process. Readers may skip some words, linger on others, or revisit earlier text. Emerging work has started exploring the incorporation of reading behaviour through eye-tracking into the training of specific language tasks. In this work, we investigate the broader question of how gaze data can shape word embeddings by using text as read by human participants and predicting gaze measures from them. To that end, we conducted an eye-tracking experiment with 76 participants reading 20 short stories in Spanish and fine-tuned Word2Vec and LSTM models on the collected data. Evaluations with representational similarity analysis and word pair similarities showed a limited, but largely consistent, gain from gaze incorporation, suggesting future work should expand linguistic diversity and use cognitively aligned evaluations to better understand its role in bridging computational and human language representations.