Gert Storms

A relevance theory of induction

A framework theory, organized around the principle of relevance, is proposed for category-based reasoning. According to the relevance principle, people assume that premises are informative with respect to conclusions. This idea leads to the prediction that people will use causal scenarios and property reinforcement strategies in inductive reasoning. These predictions are contrasted with both existing models and normative logic. Judgments of argument strength were gathered in three different countries, and the results showed the importance of both causal scenarios and property reinforcement in categorybased inferences. The relation between the relevance framework and existing models of category-based inductive reasoning is discussed in the light of these findings.

Expressed emotion: A review of assessment instruments

Expressed emotion (EE) refers to the quality of the emotional climate between a relative and a family member with a serious psychiatric disorder. Well-established, it has proven to be a reliable predictor of the relapse rate of psychiatric patients. In this article, the Camberwell Family Interview (CFI), the standard instrument, and 11 alternative EE measures will be presented and discussed with regard to their psychometric properties. It is concluded that the CFI remains the best instrument for assessing the quality of the relationship.

Better explanations of lexical and semantic cognition using networks derived from continued rather than single word associations

In this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three responses in a multiple-response free association task. The goal of this study was (1) to create a semantic network that covers a large part of the human lexicon, (2) to investigate the implications of a multiple-response procedure by deriving a weighted directed network, and (3) to show how measures of centrality and relatedness derived from this network predict both lexical access in a lexical decision task and semantic relatedness in similarity judgment tasks. First, our results show that the multiple-response procedure results in a more heterogeneous set of responses, which lead to better predictions of lexical access and semantic relatedness than do single-response procedures. Second, the directed nature of the network leads to a decomposition of centrality that primarily depends on the number of incoming links or in-degree of each node, rather than its set size or number of outgoing links. Both studies indicate that adequate representation formats and sufficiently rich data derived from word associations represent a valuable type of information in both lexical and semantic processing.

Expressed emotion in staff-patient relationships: the professionals' and residents' perspectives

Background: Expressed emotion (EE) is a well-established, important predictor of the relapse rate of patients suffering from schizophrenia and other severe psychiatric disorders. EE measures the quality of the social interaction between a patient and his most important (in)formal caregiver. The aim of this study was to investigate the quality of the relationship in the staff-patient dyad as measured by the concept of EE. Methods: EE was assessed using the Camberwell Family Interview (CFI, professionals) and the Perceived Criticism Scale (PCS, residents and professionals form) in a sample of 56 professional caregivers and their residents in nine sheltered living facilities in Flanders. Results: Depending on the instrument, high EE was found to exist in one out of six (CFI) or one out of three (PCS) relationships. There was a significant positive correlation between the resident PCS and the critical comment scale of the CFI. Conclusions: The results of this study support the hypothesis that high levels of EE exist in some staff-resident relationships, which are mainly manifest as frequent critical comments and the presence of hostility. Emotional overinvolvement appears to be exceptional. Compared with the PCS, the CFI provides the most information about the quality of the relationship.

Feature integration in natural language concepts.

Two experiments measured the joint influence of three key sets of semantic features on the frequency with which artifacts (Experiment 1) or plants and creatures (Experiment 2) were categorized in familiar categories. For artifacts, current function outweighed both originally intended function and current appearance. For biological kinds, appearance and behavior, an inner biological function, and appearance and behavior of offspring all had similarly strong effects on categorization. The data were analyzed to determine whether an independent cue model or an interactive model best accounted for how the effects of the three feature sets combined. Feature integration was found to be additive for artifacts but interactive for biological kinds. In keeping with this, membership in contrasting artifact categories tended to be superadditive, indicating overlapping categories, whereas for biological kinds, it was subadditive, indicating conceptual gaps between categories. It is argued that the results underline a key domain difference between artifact and biological concepts.

Predicting conjunction typicalities by component typicalities.

In two studies, we investigated to what extent typicalities in conjunctive concepts phrased as relative clauses—such aspets that are also birds—can be predicted from simple functions of constituent typicalities and from extensions of such functions. In a first study, analyses of a large aggregated data set, based on seven different experiments, showed that a calibrated minimum rule model and some extensions of this model accounted for a very large part of the variance in the conjunction typicalities. The same models can also account for the so-called guppy effect. A psychological explanation is presented, which states that typicalities in contrast categories, likepets that are not birds andbirds that are not pets, further improve the prediction of conjunction typicalities. This hypothesis is tested in a second study.

Features of graded category structure: Generalizing the family resemblance and polymorphous concept models

Many real-world categories contain graded structure: certain category members are rated as more typical or representative of the category than others. Research has shown that this graded structure can be well predicted by the degree of commonality across the feature sets of category members. We demonstrate that two prominent feature-based models of graded structure, the family resemblance (Rosch & Mervis, 1975) and polymorphous concept models (Hampton, 1979), can be generalized via the contrast model (Tversky, 1977) to include both common and distinctive feature information, and apply the models to the prediction of typicality in 11 semantic categories. The results indicate that both types of feature information play a role in the prediction of typicality, with common features weighted more heavily for within-category predictions, and distinctive features weighted more heavily for contrast-category predictions. The same pattern of results was found in additional analyses employing rated goodness and exemplar generation frequency. It is suggested that these findings provide insight into the processes underlying category formation and representation.

Similar, But Not the Same: A Comparison of the Utility of Directly-rated and Feature-based Similarity Measures for Generating Spatial Models of Conceptual Data

Spatial models are employed to represent conceptual data in a wide range of fields within psychological research. In order to generate spatial models, it is necessary to first obtain empirical similarity data. A number of methods are available for collecting these data, but little effort has been made to compare their relative utility. In this article, we compare directly rated and five feature-based similarity data types in regard to their ability to be adequately represented by a spatial model (representational goodness of fit), and the ability of the representations to predict three external empirical variables (predictive validity). The results indicate that the representational goodness of fit of the feature-based similarities is noticeably superior to the directly rated similarities, and that the predictions of representations derived from common feature similarity data are substantially more likely than the predictions of all of the alternative representations. It is suggested that these findings are highly relevant to researchers employing spatial models to represent conceptual data, given that direct pairwise ratings have generally been considered the “gold standard” means of obtaining empirical similarities.

Linear separability in superordinate natural language concepts.

Two experiments are reported in which linear separability was investigated in superordinate natural language concept pairs (e.g.,toiletry-sewing gear). Representations of the exemplars of semantically related concept pairs were derived in two to five dimensions using multidimensional scaling (MDS) of similarities based on possession of the concept features. Next, category membership, obtained from an exemplar generation study (in Experiment 1) and from a forced-choice classification task (in Experiment 2) was predicted from the coordinates of the MDS representation using log linear analysis. The results showed that allnatural kind concept pairs were perfectly linearly separable, whereasartifact concept pairs showed several violations. Clear linear separability of natural language concept pairs is in line with independent cue models. The violations in the artifact pairs, however, yield clear evidence against the independent cue models.

The instantiation principle re-evaluated

Three experiments are presented in which different aspects concerning Heit and Barsalous (1996) instantiation principle were investigated. Mean typicalities of subordinate categories within superordinates were predicted very accurately for all investigated concepts. Multiple instantiations were shown to yield somewhat better predictions than single instantiation. The instantiation principle also successfully predicted mean typicalities on a different level (i.e., in lower-level concepts). An alternative account of Heit and Barsalous findings was also proven wrong. Finally, correspondence between empirically obtained and predicted standard deviations is argued to be dubious, because of several possible sources of bias in the observed and predicted values.