Distributed semantic representations for modeling human judgment

People make judgments about thousands of different objects and concepts on a day-to-day basis; however, capturing the knowledge that subserves these judgments has been difficult. Recent advances in computational linguistics are filling this gap, as the statistics of language use yield rich, distributed semantic representations for natural objects and concepts. These representations have been shown to predict semantic and linguistic judgments, such as judgments of meaning and relatedness, and more recently, high-level judgments, including probability judgment and forecasting, stereotyping and various types of social judgment, consumer choice, and perceptions of risk. Distributed semantic representations are now a key component of computational models that represent knowledge, make evaluations and attributions, and give responses, in a human-like manner.