Coherence and coreference

Coherence in conversations and in texts can be partially characterized by a set of coherence relations, motivated ultimately by the speaker's or writer's need to be understood. In this paper, formal definitions are given for several coherence relations, based on the operations of an inference system; that is, the relations between successive portions of a discourse are characterized in terms of the inferences that can be drawn from each. In analyzing a discourse, it is frequently the case that we would recognize it as coherent, in that it would satisfy the formal definition of some coherence relation, if only we could assume certain noun phrases to be coreferential. In such cases, we will simply assume the identity of the entities referred to, in what might be called a “petty conversational implicature,” thereby solving the coherence and coreference problems simultaneously. Three examples of different kinds of reference problems are presented. In each, it is shown how the coherence of the discourse can be recognized, and how the reference problems are solved, almost as a by-product, by means of these petty conversational implicatures.     

Candidate Vulnerability and Exposure to Counterattitudinal Information: Evidence From Two U.S. Presidential Elections

Politically motivated selective exposure has traditionally been understood through the lens of long‐standing attitudes and beliefs, but the role of environment in shaping information exposure practices merits further consideration. Citizens might respond to the political environment in their information‐seeking behavior for numerous reasons. Citizens who believe their position is politically vulnerable have specific cognitive and affective needs that may make them uniquely attuned to counterattitudinal information. In the context of a presidential election, this means that as the defeat of a supported candidate appears more likely, attention to counterattitudinal content will increase. Data collected in the 2008 and 2012 U.S. Presidential elections support this prediction, although this relationship was observed primarily among supporters of the Republican candidate in both elections.     

Decoding the language of the heart: Developing a physiology of inclusion

Constructs such as homeostasis and fight/flight have supported a scientific approach to physiology that has yielded a vast database of obvious heuristic value. Yet in spite of its value, these constructs have tended to create a mind-set that unwittingly supports what this article has labeled a “physiology of exclusion.” Reinforced by the philosophy of René Descartes, this perspective has led investigators to focus on isolated or separate animal organisms that are reflexively wired for self-preservation. It has created a mind-set in which both research investigators and the public at large tend to view the human body as either in a steady state of vigilance, maximally prepared for fight/flight, or in a state of quiescence. Assumptions of the solitary body, and solitary man wired to react for “self” preservation, has made it difficult to incorporate a growing body of evidence that indicates that social support and loving relationships are conducive to good health. It also has made it difficult for investigators to fully understand why human loneliness is a major cause of premature death. This article delineates these trends and offers a new construct, one that suggests that a “physiology of inclusion” be added to the prevailing view of a “physiology of exclusion.” Recent cardiovascular research is cited to help underscore the potential heuristic value of this new physiological construct.     

Sensitivity and responsivity measures for discrimination learning

The present report considers alternative measures of sensitivity and response bias for the discrimination learning paradigm. The classical signal detection measures, d′ and β, were compared with their nonparametric equivalents, A′ and B″, with theoretical measures derived from threshold theory and with empirical measures derived from the ROC graph. Differential rabbit eyelid conditioning data from three experiments were analyzed with these measures, and the results of these analyses were used along with other information to determine which measures of sensitivity and response bias are most useful for the analysis of discrimination learning data.