 Another area where models play a central role in measurement is psychology. The measurement of most psychological attributes, such as intelligence, anxiety and depression, does not rely on homomorphic mappings of the sort espoused by the Representational Theory of Measurement (Wilson 2013: 3766). Instead, psychometric theory relies predominantly on the development of abstract models that are meant to predict subjects’ performance in certain tasks. These models are constructed from substantive and statistical assumptions about the psychological attribute being measured and its relation to each measurement task. For example, Item Response Theory, a popular approach to psychological measurement, employs a variety of models to evaluate the reliability and validity of questionnaires. Consider a questionnaire that is meant to assess English language comprehension (the “ability”), by presenting subjects with a series of yes/no questions (the “items”). One of the simplest models used to calibrate such questionnaires is the Rasch model (Rasch 1960). This model supposes a straightforward algebraic relation—known as the “log of the odds”—between the probability that a subject will answer a given item correctly, the difficulty of that particular item, and the subject’s ability. New questionnaires are calibrated by testing the fit between their indications and the predictions of the Rasch model and assigning difficulty levels to each item accordingly. The model is then used in conjunction with the questionnaire to infer levels of English language comprehension (outcomes) from raw questionnaire scores (indications) (Wilson 2013; Mari and Wilson 2014).