Directed forgetting (DF) assists in preventing outdated information from interfering with cognitive processing. Previous studies pointed that self-referential items alleviated DF effects due to the elaboration of encoding processes. However, the retrieval mechanism of this phenomenon remains unknown. Based on the dual-process framework of recognition, the retrieval of self-referential informati...

The default-mode network (DMN) of the brain shows highly coherent intrinsic activity in healthy subjects and is implicated in self-referential processing important for social cognitive functioning. Schizophrenia patients show abnormal resting-state connectivity within the DMN and this aberrant connectivity is thought to contribute to difficulties in self-referential and introspective processing...

Referential integrity, which guarantees that named resources can be accessed when referenced, is an important property for reliability and security. In distributed systems, however, the attempt to provide referential integrity can itself lead to security vulnerabilities that are not currently well understood. This paper identifies three kinds of referential security vulnerabilities related to t...

We report a study of referential choice in discourse production, understood as the choice between various types of referential devices, such as pronouns and full noun phrases. Our goal is to predict referential choice, and to explore to what extent such prediction is possible. Our approach to referential choice includes a cognitively informed theoretical component, corpus analysis, machine lear...

Algorithms for generating referring expressions typically assume that an object in a scenary can be identified through a set of commonly agreed properties. This is a strong assumption, since in reality properties of objects may be perceived differently among people, due to a number of factors including vagueness, knowledge discrepancies, and limited perception capabilities. Taking these discrep...

In this paper, we present a machine learning system for identifying non-referential it. Types of non-referential it are examined to determine relevant linguistic patterns. The patterns are incorporated as features in a machine learning system which performs a binary classification of it as referential or non-referential in a POS-tagged corpus. The selection of relevant, generalized patterns lea...