Model of Making Decisions during an Information Search Task

This paper presents a cognitive computational model of the way people read a paragraph with the task of quickly deciding whether it is related or not to a given goal. In particular, the model attempts to predict the time at which participants would decide to stop reading the paragraph because they have enough information to make their decision. Our model makes predictions at the level of words that are likely to be xated before the paragraph is abandoned. Human semantic judgments are mimicked by computing the semantic similarities between sets of words using Latent Semantic Analysis. A two-variable linear threshold is proposed to account for that decision, based on the rank of the xation and the semantic similarity between the paragraph and the goal. Model performance is compared to eyetracking data of 19 participants.