How Stories in Memory Perpetuate the Continued Influence of False Information

People often encounter information that they subsequently learn is false. Past research has shown that people sometimes continue to use this misinformation in their reasoning, even if they remember that the information is false, which researchers refer to as the continued influence effect. The current work shows that the continued influence effect depends on the stories people have in memory: corrected misinformation was found to have a stronger effect on people's beliefs than information that was topically related to the story if it helped to provide a causal explanation of a story they had read previously. We argue this effect occurs because information that can fill a causal “gap” in a story enhances comprehension of the story event, which allows people to build a complete (if inaccurate) event model that they prefer over an accurate but incomplete event model. This effect is less likely to occur for stories in memory that end in a negative way, presumably because people are more motivated to accurately understand negative outcome events.     

The Role of Decision-Making Models and Reflection in Navigating Ethical Dilemmas

The ACA Code of Ethics (American Counseling Association [ACA], 2014) mandates counselor competency in using ethical decision-making models (EDMs) and mandates that counselors use an EDM when confronted with making ethical decisions. Additionally, researchers have highlighted the importance of counselors continually evaluating and reflecting when faced with ethical dilemmas and working through a decision-making model. Using a phenomenological research design, the authors examined how counselors address ethical dilemmas, including how reflection and EDMs are incorporated into the decision-making process. Emergent themes from data analysis include (a) incomplete following of EDMs, (b) varied dilemmas, (c) power, and (d) ethics training.     

Psychology and African-American women: Findings from applied research

The current applied research on African-American women is characterized by a limited focus on primarily poor and single mothers and comparative studies with white women. The resulting research ignores the diversity within African-American women generalizing from noncomparative or restrictive samples to all African-American women and provides narrowly focused and/or inappropriate research models in which to study the development of these women across the life span. This review focuses on some of this research as it relates to social context factors in African-American women's lives: social supports and parenting, work and self-esteem, gender role attitudes and marital life, and mental health. An examination of methodological problems and suggestions for future research is discussed.     

The Influence of the BDNF Val66Met Polymorphism on Mechanisms of Semantic Priming: Analyses with Drift-Diffusion Models of Masked and Unmasked Priming

Automatic and strategic processes in semantic priming can be investigated with masked and unmasked priming tasks. Unmasked priming is thought to enable strategic processes due to the conscious processing of primes, while masked priming exclusively depends on automatic processes due to the invisibility of the prime. Besides task properties, interindividual differences may alter priming effects. In a recent study, masked and unmasked priming based on mean response time (RT) and error rate (ER) differed as a function of the BDNF Val66Met polymorphism (Sanwald et al., 2020). The BDNF Val66Met polymorphism is related to the integrity of several cognitive executive functions and might thus influence the magnitude of priming. In the present study, we reanalyzed this data with drift-diffusion models. Drift-diffusion models conjointly analyze single trial RT and ER data and serve as a framework to elucidate cognitive processes underlying priming. Masked and unmasked priming effects were observed for the drift rates ν, presumably reflecting semantic preactivation. Priming effects on nondecision time t0 were especially pronounced in unmasked priming, suggesting additional conscious processes to be involved in the t0 modulation. Priming effects on the decision thresholds a may reflect a speed-accuracy tradeoff. Considering the BDNF Val66Met polymorphism, we found lowered drift rates and decision thresholds for Met allele carriers, possibly reflecting a superficial processing style in Met allele carriers. The present study shows that differences in cognitive tasks between genetic groups can be elucidated using drift-diffusion modeling.     

对以类别为基础的归纳推理的几种理论模型的评价

当前关于以类别为基础的归纳推理理论模型大致可以分为两种：强调相似性作用的归纳推理的理论模型和强调知识作用的归纳推理的理论模型。前者能较好的解释人们在知识贫乏领域的归纳推理现象,而后者则能够较好的解释人们在知识丰富领域的归纳推理现象。     

Graphical regression models for polytomous variables

When modeling the relationship between two nominal categorical variables, it is often desirable to include covariates to understand how individuals differ in their response behavior. Typically, however, not all the relevant covariates are available, with the result that the measured variables cannot fully account for the associations between the nominal variables. Under the assumption that the observed and unobserved variables follow a homogeneous conditional Gaussian distribution, this paper proposesRC(M) regression models to decompose the residual associations between the polytomous variables. Based on Goodman's (1979, 1985)RC(M) association model, a distinctive feature ofRC(M) regression models is that they facilitate the joint estimation of effects due to manifest and omitted (continuous) variables without requiring numerical integration. TheRC(M) regression models are illustrated using data from the High School and Beyond study (Tatsuoka & Lohnes, 1988). This article was accepted for publication, when Willem J. Heiser was the Editor ofPsychometrika. This research was supported by grants from the National Science Foundation (#SBR96-17510 and #SBR94-09531) and the Bureau of Educational Research at the University of Illinois. We thank Jee-Seon Kim for comments and computational assistance.     

On an Intuitionistic Modal Logic

In this paper we consider an intuitionistic variant of the modal logic S4 (which we call IS4). The novelty of this paper is that we place particular importance on the natural deduction formulation of IS4— our formulation has several important metatheoretic properties. In addition, we study models of IS4— not in the framework of Kirpke semantics, but in the more general framework of category theory. This allows not only a more abstract definition of a whole class of models but also a means of modelling proofs as well as provability.     

Duality and Canonical Extensions of Bounded Distributive Lattices with Operators,and Applications to the Semantics of Non-Classical Logics I

The main goal of this paper is to explain the link between the algebraic and the Kripke-style models for certain classes of propositional logics. We start by presenting a Priestley-type duality for distributive lattices endowed with a general class of well-behaved operators. We then show that finitely-generated varieties of distributive lattices with operators are closed under canonical embedding algebras. The results are used in the second part of the paper to construct topological and non-topological Kripke-style models for logics that are sound and complete with respect to varieties of distributive lattices with operators in the above-mentioned classes. This revised version was published online in June 2006 with corrections to the Cover Date.     

Effect size, power, and sample size determination for structured means modeling and mimic approaches to between-groups hypothesis testing of means on a single latent construct

While effect size estimates, post hoc power estimates, and a priori sample size determination are becoming a routine part of univariate analyses involving measured variables (e.g., ANOVA), such measures and methods have not been articulated for analyses involving latent means. The current article presents standardized effect size measures for latent mean differences inferred from both structured means modeling and MIMIC approaches to hypothesis testing about differences among means on a single latent construct. These measures are then related to post hoc power analysis, a priori sample size determination, and a relevant measure of construct reliability.I wish to convey my appreciation to the reviewers and Associate Editor, whose suggestions extended and strengthened the article's content immensely, and to Ralph Mueller of The George Washington University for enhancing the clarity of its presentation.     

The Four Causes of Behavior

Comprehension of a phenomenon involves identifying its origin, structure, substrate, and function, and representing these factors in some formal system. Aristotle provided a clear specification of these kinds of explanation, which he called efficient causes (triggers), formal causes (models), material causes (substrates or mechanisms), and final causes (functions). In this article, Aristotle's framework is applied to conditioning and the computation-versus-association debate. The critical empirical issue is early versus late reduction of information to disposition. Automata theory provides a grammar for models of conditioning and information processing in which that constraint can be represented.