Steven Verheyen

Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts

Features are at the core of many empirical and modeling endeavors in the study of semantic concepts. This article is concerned with the delineation of features that are important in natural language concepts and the use of these features in the study of semantic concept representation. The results of a feature generation task in which the exemplars and labels of 15 semantic categories served as cues are described. The importance of the generated features was assessed by tallying the frequency with which they were generated and by obtaining judgments of their relevance. The generated attributes also featured in extensive exemplar by feature applicability matrices covering the 15 different categories, as well as two large semantic domains (that of animals and artifacts). For all exemplars of the 15 semantic categories, typicality ratings, goodness ratings, goodness rank order, generation frequency, exemplar associative strength, category associative strength, estimated age of acquisition, word frequency, familiarity ratings, imageability ratings, and pairwise similarity ratings are described as well. By making these data easily available to other researchers in the field, we hope to provide ample opportunities for continued investigations into the nature of semantic concept representation. These data may be downloaded from the Psychonomic Society’s Archive of Norms, Stimuli, and Data, www.psychonomic.org/archive.

Determining the dimensionality in spatial representations of semantic concepts

When multidimensional scaling solutions are used to study semantic concepts, the dimensionality of the optimal configuration has to be determined. Several strategies have been proposed to choose the appropriate dimensionality. In the present paper, the traditional dimensionality choice criteria were evaluated and compared to a method based on the prediction of an external criterion. Two studies were conducted in which typicality of an exemplar within a semantic concept was predicted from its distance to the concept centroid. In contrast to the low-dimensional solutions selected by the traditional methods, predictions of an external criterion improved with additional dimensions up till dimensionalities that were much higher than what is common in the literature. This suggests that traditional methods underestimate the richness of semantic concepts as revealed in spatial representations derived from similarity measures.

A crossed random effects diffusion model for speeded semantic categorization decisions.

Choice reaction times (RTs) are often used as a proxy measure of typicality in semantic categorization studies. However, other item properties have been linked to choice RTs as well. We apply a tailored process model of choice RT to a speeded semantic categorization task in order to deconfound different sources of variability in RT. Our model is based on a diffusion model of choice RT, extended to include crossed random effects (of items and participants). This model retains the interesting process interpretation of the diffusion models parameters, but it can be applied to choice RTs even in the case where there are few or no repeated measurements of each participant-item combination. Different aspects of the response process are then linked to different types of item properties. A typicality measure turns out to predict the rate of information uptake, while a lexicographic measure predicts the stimulus encoding time. Accessibility measures cannot reliably predict any component of the decision process.

The role of corpus size and syntax in deriving lexico-semantic representations for a wide range of concepts

One of the most significant recent advances in the study of semantic processing is the advent of models based on text and other corpora. In this study, we address what impact both the quantitative and qualitative properties of corpora have on mental representations derived from them. More precisely, we evaluate models with different linguistic and mental constraints on their ability to predict semantic relatedness between items from a vast range of domains and categories. We find that a model based on syntactic dependency relations captures significantly less of the variability for all kinds of words, regardless of the semantic relation between them or their abstractness. The largest difference was found for concrete nouns, which are commonly used to assess semantic processing. For both models we find that limited amounts of data suffice in order to obtain reliable predictions. Together, these findings suggest new constraints for the construction of mental models from corpora, both in terms of the corpus size and in terms of the linguistic properties that contribute to mental representations.

Uncovering Contrast Categories in Categorization With a Probabilistic Threshold Model

A contrast category effect on categorization occurs when the decision to apply a category term to an entity not only involves a comparison between the entity and the target category but is also influenced by a comparison of the entity with 1 or more alternative categories from the same domain as the target. Establishing a contrast category effect on categorization in natural language categories has proven to be laborious, especially when the categories concerned are natural kinds situated at the superordinate level of abstraction. We conducted 3 studies with these categories to look for an influence on categorization of both similarity to the target category and similarity to a contrast category. The results are analyzed with a probabilistic threshold model that assumes categorization decisions arise from the placement of threshold criteria by individual categorizers along a single scale that holds the experimental stimuli. The stimulis positions along the scale are shown to be influenced by similarity to both target and contrast. These findings suggest that the prevalence of contrast category effects on categorization might have been underestimated. Additional analyses demonstrate how the proposed model can be employed in future studies to systematically investigate the origins of contrast category effects on categorization.

Structure and Organization of the Mental Lexicon: A Network Approach Derived from Syntactic Dependency Relations and Word Associations

Semantic networks are often used to represent the meaning of a word in the mental lexicon. To construct a large-scale network for this lexicon, text corpora provide a convenient and rich resource. In this chapter the network properties of a text-based approach are evaluated and compared with a more direct way of assessing the mental content of the lexicon through word associations. This comparison indicates that both approaches highlight different properties specific to linguistic and mental representations. Both types of network are qualitatively different in terms of their global network structure and the content of the network communities. Moreover, behavioral data from relatedness judgments show that language networks do not capture these judgments as well as mental networks.

A mixture approach to vagueness and ambiguity

When asked to indicate which items from a set of candidates belong to a particular natural language category inter-individual differences occur: Individuals disagree which items should be considered category members. The premise of this paper is that these inter-individual differences in semantic categorization reflect both ambiguity and vagueness. Categorization differences are said to be due to ambiguity when individuals employ different criteria for categorization. For instance, individuals may disagree whether hiking or darts is the better example of sports because they emphasize respectively whether an activity is strenuous and whether rules apply. Categorization differences are said to be due to vagueness when individuals employ different cut-offs for separating members from non-members. For instance, the decision to include hiking in the sports category or not, may hinge on how strenuous different individuals require sports to be. This claim is supported by the application of a mixture model to categorization data for eight natural language categories. The mixture model can identify latent groups of categorizers who regard different items likely category members (i.e., ambiguity) with categorizers within each of the groups differing in their propensity to provide membership responses (i.e., vagueness). The identified subgroups are shown to emphasize different sets of category attributes when making their categorization decisions.

Raising Argument Strength Using Negative Evidence: A Constraint on Models of Induction

Both intuitively, and according to similarity-based theories of induction, relevant evidence raises argument strength when it is positive and lowers it when it is negative. In three experiments, we tested the hypothesis that argument strength can actually increase when negative evidence is introduced. Two kinds of argument were compared through forced choice or sequential evaluation: single positive arguments (e.g., “Shostakovich’s music causes alpha waves in the brain; therefore, Bach’s music causes alpha waves in the brain”) and double mixed arguments (e.g., “Shostakovich’s music causes alpha waves in the brain, X’s music DOES NOT; therefore, Bach’s music causes alpha waves in the brain”). Negative evidence in the second premise lowered credence when it applied to an item X from the same subcategory (e.g., Haydn) and raised it when it applied to a different subcategory (e.g., AC/DC). The results constitute a new constraint on models of induction.

On domain differences in categorization and context variety

Membership in many natural categories is considered all-or-none, while membership in most artifact categories is found to be graded. We introduce an alternative for the prevailing view that this domain difference in categorization results from representational differences. The context variety account posits that an item’s gradedness reflects the variety of contexts it appears in. Items that feature in a variety of contexts are assumed to be more likely to elicit a graded categorization response, since the suggested target category only provides one of many solutions to the question of the item’s identity. We review earlier work that suggested a domain difference in context variety, with artifactual items appearing in a greater variety of contexts than natural ones. The context variety domain difference is established in two separate experiments but is shown not to explain the domain difference in categorization. A selection of artifactual and natural items, for which the domain difference in context variety is reversed, is presented for categorization in a third experiment. This selection, too, fails to provide evidence for the context variety account of categorization differences. The domain difference in categorization is shown to be robust against this manipulation. Context variety appears to have no bearing on categorization, so the context variety account is not a sustainable alternative to accounts that posit representational differences between natural and artifact categories.

Caveats for the spatial arrangement method: Comment on Hout, Goldinger, and Ferguson (2013).

The gold standard among proximity data collection methods for multidimensional scaling is the (dis)similarity rating of pairwise presented stimuli. A drawback of the pairwise method is its lengthy duration, which may cause participants to change their strategy over time, become fatigued, or disengage altogether. Hout, Goldinger, and Ferguson (2013) recently made a case for the Spatial Arrangement Method (SpAM) as an alternative to the pairwise method, arguing that it is faster and more engaging. SpAM invites participants to directly arrange stimuli on a computer screen such that the interstimuli distances are proportional to psychological proximity. Based on a reanalysis of the Hout et al. (2013), data we identify three caveats for SpAM. An investigation of the distributional characteristics of the SpAM proximity data reveals that the spatial nature of SpAM imposes structure on the data, invoking a bias against featural representations. Individual-differences scaling of the SpAM proximity data reveals that the two-dimensional nature of SpAM allows individuals to only communicate two dimensions of variation among stimuli properly, invoking a bias against high-dimensional scaling representations. Monte Carlo simulations indicate that in order to obtain reliable estimates of the group average, SpAM requires more individuals to be tested. We conclude with an overview of considerations that can inform the choice between SpAM and the pairwise method and offer suggestions on how to overcome their respective limitations.