The parametric regressors modeling familiarity, autobiographical salience, and affective valence were constructed based on the responses to the questions following each song excerpt.
In this model, familiarity, unlike recollection, is assumed to reflect a signal-detection process, so familiarity is measured with d′.
The results of the multivariate GLM models for familiarity with evolutionary terms and acceptance of evolution are displayed in Tables 3 and 4, respectively.
When the same models for familiarity with evolutionary terms and acceptance of evolution were run recoding religious affiliation into "non-denominational Christians" and "all others", the basic results (not shown) are similar.
Data warehouse modeling is a complex task, which involves knowledge of business processes of the domain of discourse, understanding the structural and behavioral system's conceptual model, and familiarity with data warehouse technologies.
Finally, we are modelling recognition using a paradigm that some believe taps mostly 'familiarity' based, as opposed to context-dependent 'recollection' based recognition memory (Aggleton and Brown, 1999).
In the familiarity task, we modeled the ideal observer as comparing directly the probabilities that the subjective distribution assigned to the two faces and choosing the one with the higher probability.
In addition, a sixth contrast modeled the sum of the familiarity, autobiographical salience, and valence effects (FAV), that is, the contrast weighted the beta-coefficients for each of the parametric regressors equally.
We utilized Dormant's (2011) CACAO (Change, Adopters, Change Agent, and Organization) model because of our familiarity with the model and access to local experts in using this model (see also Shadle et al., 2017).
Second, we modelled the users' profiles based on their familiarity with ontologies.
