In human semantic cognition, proper names (names which refer to individual entities) are harder to learn and retrieve than common nouns. This seems to be the case for machine learning algorithms too, but the linguistic and distributional reasons for this behaviour have not been investigated in depth so far. To tackle this issue, we show that the semantic distinction between proper names and common nouns is reflected in their linguistic distributions by employing an original task for distributional semantics, the Doppelg\"anger test, an extensive set of models, and a new dataset, the Novel Aficionados dataset. The results indicate that the distributional representations of different individual entities are less clearly distinguishable from each other than those of common nouns, an outcome which intriguingly mirrors human cognition.

The current study investigates the relationship between children’s curiosity and question asking ability. Generation of two types of questions was assessed: identification (yes/no questions asked to identify a target from an array) and understanding questions, asked to learn more about a topic. The latter was related to children’s curiosity, as was the ability to recognize the effectiveness of questions in solving a mystery. Training on asking identification questions was effective in improving children’s ability to ask that type of question, but did not transfer to the other task. Training on asking understanding questions was not successful. Children’s curiosity did not influence the effectiveness of the training.