We address this issue by having γ G depend on the number of observed types of features in the group: where type(G, t) is the number of different kinds of features seen in that group (e.g., DOG and CAT are two different feature types from the same group) up through time t. In this way, the P u t of a feature that occurs in a group with more observed feature types is higher than the P u t in a group with fewer observed types.
both the type frequency of features in G and their token frequency of co-occurrence with word w j will influence -the first positively and the second negatively -how readily w j can refer to objects with previously unseen features from G. We model X&T's behavioural experiments with our computational word learner as extended above.