Taking the High (or Low) Road: A Quantifier Priming Perspective on Basic Anchoring Effects

ABSTRACT

Current explanations of basic anchoring effects, defined as the influence of an arbitrary number standard on an uncertain judgment, confound numerical values with vague quantifiers. I show that the consideration of numerical anchors may bias subsequent judgments primarily through the priming of quantifiers, rather than the numbers themselves. Study 1 varied the target of a numerical comparison judgment in a between-participants design, while holding the numerical anchor value constant. This design yielded an anchoring effect consistent with a quantifier priming hypothesis. Study 2 included a direct manipulation of vague quantifiers in the traditional anchoring paradigm. Finally, Study 3 examined the notion that specific associations between quantifiers, reflecting values on separate judgmental dimensions (i.e., the price and height of a target) can affect the direction of anchoring effects. Discussion focuses on the nature of vague quantifier priming in numerically anchored judgments.     

Using finite mixture of GLMs to explore variability in children's flexibility in a task-switching paradigm

The present study illustrates the usefulness of finite mixture of generalized linear models (GLMs) to examine variability in cognitive strategies during childhood. More precisely, it addresses this variability in set-shifting situations where task-goal updating is endogenously driven. In a task-switching paradigm 5–6-year-olds had to switch between color- and shape-matching rules as a function of a predetermined, predictable task sequence. A finite mixture of GLMs was fitted to explore individual differences in performance. The statistical model revealed five response profiles, defined by accuracy and response times. These response profiles likely correspond to different cognitive strategies with varying efficiency and differential relations to working memory capacity (assessed by backward digit span). These results illustrate the heuristic value of statistical modeling to reveal the behavioral and cognitive variability in the temporal dynamics of children's cognitive functioning.     

Framing social information and generalized social phobia

Patients with generalized social phobia (GSP, N=33) and matched community controls (N=31) engaged in a social interaction that was constructed to go well, and then received feedback that framed social cues reflecting either the absence of negative outcomes or the presence of positive outcomes. Following feedback that framed positive social cues, the GSP group predicted they would experience more anxiety in a subsequent interaction than did non-phobic controls. In contrast, following feedback framing the absence of negative outcomes, the GSP group did not differ from controls in their anxiety predictions. The results demonstrated that framing paradigms and methods can be usefully applied to the study of cognitive processes in social phobia and indicated that research to examine how GSP patients process specific types of social information is needed.     

Confirmatory factor analysis of the Penn State Worry Questionnaire: Multiple factors or method effects?

The latent structure of the Penn State Worry Questionnaire (PSWQ) was evaluated with confirmatory factor analyses (CFAs) in 1200 outpatients with DSM-IV anxiety and mood disorders. Of particular interest was the comparative fit and interpretability of a two-factor solution (cf. Behaviour Research and Therapy 40 (2002) 313) vs. a one-factor model that specified method effects arising from five reverse-worded items. Consistent with prediction, the superiority of the one-factor model was demonstrated in split-sample CFA replications (ns=600). Multiple-group CFAs indicated that the measurement properties of the PSWQ were invariant in male and female patients. In addition to their direct relevance to the psychometrics of the PSWQ, the results are discussed in regard to methodological considerations for using factor analytic methods in the evaluation of psychological tests.     

Reasoning with quantifiers

In the semantics of natural language, quantification may have received more attention than any other subject, and one of the main topics in psychological studies on deductive reasoning is syllogistic inference, which is just a restricted form of reasoning with quantifiers. But thus far the semantical and psychological enterprises have remained disconnected. This paper aims to show how our understanding of syllogistic reasoning may benefit from semantical research on quantification. I present a very simple logic that pivots on the monotonicity properties of quantified statements--properties that are known to be crucial not only to quantification but to a much wider range of semantical phenomena. This logic is shown to account for the experimental evidence available in the literature as well as for the data from a new experiment with cardinal quantifiers ("at least n" and "at most n"), which cannot be explained by any other theory of syllogistic reasoning.     

Implicit theories of intelligence and IQ test performance in adolescents with Generalized Anxiety Disorder

During the past decade, several studies have reported positive effects of cognitive-behavioral therapy (CBT) in the treatment of children and adolescents with mental disorders. One of the most important CBT interventions is to teach children and adolescents to challenge negative thoughts that lead to maladjusted behaviors. Based on the implicit theories of intelligence framework, the main purpose of this study was to test whether an incremental theory manipulation could be used to affect IQ test performance in adolescents with Generalized Anxiety Disorder (GAD). Results showed that patients demonstrated enhanced IQ performance and experienced less state anxiety when they were exposed to an incremental theory of intelligence manipulation. Our findings suggest that incremental theory manipulation provides a useful cognitive strategy for addressing school-related anxiety in adolescents with mental disorders such as GAD.     

Predicate Logics on Display

The paper provides a uniform Gentzen-style proof-theoretic framework for various subsystems of classical predicate logic. In particular, predicate logics obtained by adopting van Behthem's modal perspective on first-order logic are considered. The Gentzen systems for these logics augment Belnap's display logic by introduction rules for the existential and the universal quantifier. These rules for x and x are analogous to the display introduction rules for the modal operators and and do not themselves allow the Barcan formula or its converse to be derived. En route from the minimal modal predicate logic to full first-order logic, axiomatic extensions are captured by purely structural sequent rules.     

Using generalized additive (mixed) models to analyze single case designs

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R.     

Dyadic predictors of outcome in a cognitive-behavioral program for patients with generalized anxiety disorder in committed relationships: a "spoonful of sugar" and a dose of non-hostile criticism may help

The present study tested whether pre-treatment levels of partner hostility and non-hostile criticism predicted outcome in an individual cognitive-behavioral therapy package for generalized anxiety disorder (GAD). Eighteen patients with a principal or co-principal diagnosis of GAD were randomly allocated to a treatment condition (n=8) or a delayed treatment condition (n=10). In addition, the patients and their partners were videotaped discussing the patients’ worries. These videotapes were later coded for levels of partner hostility and non-hostile criticism directed at the patients. Treatment resulted in statistically and clinically significant change at post-test. Finally, partner hostility predicted worse end-state functioning whereas partner non-hostile criticism predicted better end-state functioning.