How are inflectional affixes organized in the mental lexicon?: evidence from the investigation of agreement errors in agrammatic aphasics

Recent psycholinguistic studies have provided evidence that regularly inflected words are decomposed into stems and affixes, both of which have their own representations in the mental lexicon. Specific models of the lexical organization of inflectional affixes have, however, only rarely been investigated in psycho- or neurolinguistic work. We test two recently proposed theoretical models: a representation of affixes (i) in default inheritance trees (Corbett and Fraser, 1993) and (ii) in underspecified paradigms (Wunderlich, 1996). Based on an analysis of agreement errors in elicited speech-production data of German agrammatic aphasics, we argue that affixes are organized with respect to the morphosyntactic features they encode. Specifically, our data indicate that inflectional affixes are best captured within an underspecified paradigm.     

Interpretive biases for ambiguous information in body dysmorphic disorder

Anxiety-disordered patients and individuals with high trait anxiety tend to interpret ambiguous information as threatening. The purpose of this study was to investigate whether interpretive biases would also occur in body dysmorphic disorder (BDD), which is characterized by a preoccupation with imagined defects in one's appearance. We tested whether BDD participants, compared with obsessive-compulsive disorder participants and healthy controls, would choose threatening interpretations for ambiguous body-related, ambiguous social, and general scenarios. As we hypothesized, BDD participants exhibited a negative interpretive bias for body-related scenarios and for social scenarios, whereas the other groups did not. Moreover, both clinical groups exhibited a negative interpretive bias for general scenarios.     

Vom Verhältnis zwischen Psychosomatik und Verhaltensmedizin

Ohne Zusammenfassung     

The rationality of informal argumentation: a Bayesian approach to reasoning fallacies

Classical informal reasoning "fallacies," for example, begging the question or arguing from ignorance, while ubiquitous in everyday argumentation, have been subject to little systematic investigation in cognitive psychology. In this article it is argued that these "fallacies" provide a rich taxonomy of argument forms that can be differentially strong, dependent on their content. A Bayesian theory of content-dependent argument strength is presented. Possible psychological mechanisms are identified. Experiments are presented investigating whether people's judgments of the strength of 3 fallacies--the argumentum ad ignorantiam, the circular argument or petitio principii, and the slippery slope argument--are affected by the factors a Bayesian account predicts. This research suggests that Bayesian accounts of reasoning can be extended to the more general human activity of argumentation.     

D-optimal design for the Rasch counts model with multiple binary predictors

In this paper we derive optimal designs for the Rasch Poisson counts model and its extended version of the (generalized) negative binomial counts model incorporating several binary predictors for the difficulty parameter. To efficiently estimate the regression coefficients of the predictors, locally D-optimal designs are developed. After an introduction to the Rasch Poisson counts model and its extension, we will specify these models as particular generalized linear models. Based on this embedding, optimal designs for both models including several binary explanatory variables will be presented. Therefore, we will derive conditions on the effect sizes for certain designs to be locally D-optimal. Finally, it is pointed out that the results derived for the Rasch Poisson models can be applied for more general Poisson regression models which should receive more attention in future psychological research.     

A probabilistic model of semantic plausibility in sentence processing

Experimental research shows that human sentence processing uses information from different levels of linguistic analysis, for example, lexical and syntactic preferences as well as semantic plausibility. Existing computational models of human sentence processing, however, have focused primarily on lexico-syntactic factors. Those models that do account for semantic plausibility effects lack a general model of human plausibility intuitions at the sentence level. Within a probabilistic framework, we propose a wide-coverage model that both assigns thematic roles to verb–argument pairs and determines a preferred interpretation by evaluating the plausibility of the resulting ( verb , role , argument ) triples. The model is trained on a corpus of role-annotated language data. We also present a transparent integration of the semantic model with an incremental probabilistic parser. We demonstrate that both the semantic plausibility model and the combined syntax/semantics model predict judgment and reading time data from the experimental literature.     

Estimating the probability of negative events

How well we are attuned to the statistics of our environment is a fundamental question in understanding human behaviour. It seems particularly important to be able to provide accurate assessments of the probability with which negative events occur so as to guide rational choice of preventative actions. One question that arises here is whether or not our probability estimates for negative events are systematically biased by their severity. In a minimal experimental context involving an unambiguous, objective representation of probability, we found that participants judged a controllable event as more likely to occur when its utility was extremely negative than when it was more neutral. A decision-theoretic explanation based on loss function asymmetries is advanced which supports the claim that probability estimates are not intrinsically biased by utilities.