Trait rumination and response to negative evaluative lab-induced stress: neuroendocrine,affective, and cognitive outcomes

Theoretical models of depression posit that, under stress, elevated trait rumination predicts more pronounced or prolonged negative affective and neuroendocrine responses, and that trait rumination hampers removing irrelevant negative information from working memory. We examined several gaps regarding these models in the context of lab-induced stress. Non-depressed undergraduates completed a rumination questionnaire and either a negative-evaluative Trier Social Stress Test (n?=?55) or a non-evaluative control condition (n?=?69), followed by a modified Sternberg affective working memory task assessing the extent to which irrelevant negative information can be emptied from working memory. We measured shame, negative and positive affect, and salivary cortisol four times. Multilevel growth curve models showed rumination and stress interactively predicted cortisol reactivity; however, opposite predictions, greater rumination was associated with blunted cortisol reactivity to stress. Elevated trait rumination interacted with stress to predict augmented shame reactivity. Rumination and stress did not significantly interact to predict working memory performance, but under control conditions, rumination predicted greater difficulty updating working memory. Results support a vulnerability-stress model of trait rumination with heightened shame reactivity and cortisol dysregulation rather than hyper-reactivity in non-depressed emerging adults, but we cannot provide evidence that working memory processes are critical immediately following acute stress.     

A New Task for Philosophy of Science

This paper argues that we philosophers of science have before us an important new task that we urgently need to take up. It is to convince the scientific community to adopt and implement a new philosophy of science that does better justice to the deeply problematic basic intellectual aims of science than that which we have at present. Problematic aims evolve with evolving knowledge, that part of philosophy of science concerned with aims and methods thus becoming an integral part of science itself. The outcome of putting this new philosophy into scientific practice would be a new kind of science, both more intellectually rigorous and one that does better justice to the best interests of humanity.     

Consciousness as a concrete physical phenomenon

The typical empirical approach to studying consciousness holds that we can only observe the neural correlates of experiences, not the experiences themselves. In this paper we argue, in contrast, that experiences are concrete physical phenomena that can causally interact with other phenomena, including observers. Hence, experiences can be observed and scientifically modelled. We propose that the epistemic gap between an experience and a scientific model of its neural mechanisms stems from the fact that the model is merely a theoretical construct based on observations, and distinct from the concrete phenomenon it models, namely the experience itself. In this sense, there is a gap between any natural phenomenon and its scientific model. On this approach, a neuroscientific theory of the constitutive mechanisms of an experience is literally a model of the subjective experience itself. We argue that this metatheoretical framework provides a solid basis for the empirical study of consciousness.     

Network models for social influence processes

This paper generalizes thep* class of models for social network data to predict individual-level attributes from network ties. Thep* model for social networks permits the modeling of social relationships in terms of particular local relational or network configurations. In this paper we present methods for modeling attribute measures in terms of network ties, and so constructp* models for the patterns of social influence within a network. Attribute variables are included in a directed dependence graph and the Hammersley-Clifford theorem is employed to derive probability models whose parameters can be estimated using maximum pseudo-likelihood. The models are compared to existing network effects models. They can be interpreted in terms of public or private social influence phenomena within groups. The models are illustrated by an empirical example involving a training course, with trainees' reactions to aspects of the course found to relate to those of their network partners.This research was supported by grants from the Australian Research Council. The authors would like to acknowledge the help of Stanley Wasserman, Janice Langan-Fox and Larry Hubert, and would like to thank four anonymous reviewers for their helpful comments. Earlier versions of this article were presented at the North American Conference of the Psychometric Society, Lawrence, Kansas, June, 1999, and at the Australasian Mathematical Psychology Conference, Brisbane, Australia, December 1999.     

The wording of conclusions in relational reasoning

Literature on relational reasoning mainly focuses on the performance question. It is typically argued that problem difficulty relies on the number of "mental models" compatible with the problem. However, no study has ever investigated the wording of conclusions that participants formulate. In the present work, we analyze the relational terms that people use in drawing conclusions from spatial relation problems (A is to the left of B, B is to the left C, D is in front of A, E is in front C, What is the relation between D and E?). We observed a general preference for expressing conclusions with 'left' rather than conclusions with 'right'. We also found that three factors had an influence on the wording of the conclusions: the linguistic form of premises, the question type and the presentation format. On the other hand, the number of models and premise order did not affect the wording of conclusions. Our study shows that the type of conclusion produced provides a new key to identifying the mental processes involved in spatial reasoning. Implications for the two main approaches to reasoning processes (i.e. the analogical and the propositional approaches) are discussed.     

Naïve deontics: a theory of meaning, representation, and reasoning

Deontic assertions concern what one ought to do, may do, and ought not to do. This paper proposes a theory of their meanings and of how these meanings are represented in mental models. The meanings of deontic assertions refer to sets of permissible and impermissible states. An experiment corroborated the ability of individuals to list these states. The most salient were those corresponding to the mental models of the assertions. When individuals reason, they rely on mental models, which do not make all states explicit. The theory predicts the most frequent conclusions drawn from deontic premises. It also predicts the occurrence of illusory inferences from assertions of permission, i.e., inferences that seem highly plausible but that are in fact invalid. Assertions of prohibitions, according to the theory, should reduce the illusions. Further experiments corroborated these predictions.     

Causal inferences with group based trajectory models

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This paper lays out and applies a method for using observational longitudinal data to make more confident causal inferences about the impact of such events on developmental trajectories. The method draws upon two distinct lines of research: work on the use of finite mixture modeling to analyze developmental trajectories and work on propensity scores. The essence of the method is to use the posterior probabilities of trajectory group membership from a finite mixture modeling framework, to create balance on lagged outcomes and other covariates established prior to t for the purpose of inferring the impact of first-time treatment at t on the outcome of interest. The approach is demonstrated with an analysis of the impact of gang membership on violent delinquency based on data from a large longitudinal study conducted in Montreal. The research has been supported by the National Science Foundation (NSF) (SES-99113700) and the National Institute of Mental Health (RO1 MH65611-01A2). It also made heavy use of data collected with the support from Québec’s CQRS and FCAR funding agencies, Canada’s NHRDP and SSHRC funding agencies, and the Molson Foundation. We thank Stephen Fienberg, Susan Murphy, Paul Rosenbaum, the editor, Paul De Boeck, and two anonymous reviewers for their insightful suggestions.     

Simplified Models of the Relationship Between Health and Disease

The concepts of health and disease are crucial in defining the aim and the limits of modern medicine. Accordingly it is important to understand them and their relationship. However, there appears to be a discrepancy between scholars in philosophy of medicine and health care professionals with regard to these concepts. This article investigates health care professionals’ concepts of health and disease and the relationship between them. In order to do so, four different models are described and analyzed: the ideal model, the holistic model, the medical model and the disjunctive model. The analysis reveals that each model has its pros and cons, and that health care professionals appear to apply more than one models. Furthermore, the models and the way health care professionals’ use them may be helpful for scholars in philosophy of medicine with regard to developing theories and communicating them to health care professionals.     

The repeated insertion model for rankings: Missing link between two subset choice models

Several probabilistic models for subset choice have been proposed in the literature, for example, to explain approval voting data. We show that Marley et al.'s latent scale model is subsumed by Falmagne and Regenwetter's size-independent model, in the sense that every choice probability distribution generated by the former can also be explained by the latter. Our proof relies on the construction of a probabilistic ranking model which we label the repeated insertion model. This model is a special case of Marden's orthogonal contrast model class and, in turn, includes the classical Mallows -model as a special case. We explore its basic properties as well as its relationship to Fligner and Verducci's multistage ranking model.The authors are grateful to the National Science Foundation for grants SES98-18756 to Regenwetter and Peke, and SBR97-30076 to Regenwetter. This collaborative research was carried out in the context of the conference Random Utility 2000 held at Duke University and sponsored by NSF, the Fuqua School of Business and the Center for International Business Education and Research. We thank the editor and four referees for helpful suggestions and we are grateful to Prof. J. I. Marden for providing useful information on contrast models. We thank Moon-Ho Ho for programming and running the data analyses.     

Reasoning with conditionals: a test of formal models of four theories

The four dominant theories of reasoning from conditionals are translated into formal models: The theory of mental models (Johnson-Laird, P. N., & Byrne, R. M. J. (2002). Conditionals: a theory of meaning, pragmatics, and inference. Psychological Review, 109, 646-678), the suppositional theory (Evans, J. S. B. T., & Over, D. E. (2004). If. Oxford: Oxford University Press), a dual-process variant of the model theory (Verschueren, N., Schaeken, W., & d'Ydewalle, G. (2005). A dual-process specification of causal conditional reasoning. Thinking &Reasoning, 11, 278-293), and the probabilistic theory (Oaksford, M., Chater, N., & Larkin, J. (2000). Probabilities and polarity biases in conditional inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 883-899). The first three theories are formalized as multinomial models. The models are applied to the frequencies of patterns of acceptance or rejection across the four basic inferences modus ponens, acceptance of the consequent, denial of the antecedent, and modus tollens. Model fits are assessed for two large data sets, one representing reasoning with abstract, basic conditionals, the other reflecting reasoning with pseudo-realistic causal and non-causal conditionals. The best account of the data was provided by a modified version of the mental-model theory, augmented by directionality, and by the dual-process model.