The missing link between faces and names: Evidence from Alzheimer's disease patients

Retrieval of proper names is a cause of concern and complaint among elderly adults and it is an early symptom of patients suffering from neurodegenerative diseases such as Alzheimer's disease (AD). While it is well established that AD patients have deficits of proper name retrieval, the nature of such impairment is not yet fully understood. Specifically, it is unknown whether this deficit is due to a degradation of the links between faces and proper names, or due to deficits in intentionally accessing and retrieving proper names from faces. Here, we aim to investigate the integrity of the links between famous faces and proper names in AD while minimizing the impact of the explicit retrieval. We compare the performances of AD patients and elderly controls in a face-name priming task. We assess the integrity of the link between faces and names at two different levels: identity level - the name and face belong to the same person; and semantic level - the name and face belong to the same category (e.g., politicians). Our results reveal that AD patients compared with controls show intact semantic priming but reduced priming for person identity. This suggests that the deficits in intentionally retrieving proper names in AD are the result of a partial disruption of the network at the identity level, i.e., the links between known faces and proper names.     

Monotone Quantifiers Emerge via Iterated Learning

Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.