Flexible and fast: Linguistic shortcut affects both shallow and deep conceptual processing

Previous research has shown that people use linguistic distributional information during conceptual processing, and that it is especially useful for shallow tasks and rapid responding. Using two conceptual combination tasks, we showed that this linguistic shortcut extends to the processing of novel stimuli, is used in both successful and unsuccessful conceptual processing, and is evident in both shallow and deep conceptual tasks. Specifically, as predicted by the ECCo theory of conceptual combination, people use the linguistic shortcut as a “quick-and-dirty” guide to whether the concepts are likely to combine into a coherent conceptual representation, in both shallow sensibility judgment and deep interpretation generation tasks. Linguistic distributional frequency predicts both the likelihood and the time course of rejecting a novel word compound as nonsensical or uninterpretable. However, it predicts the time course of successful processing only in shallow sensibility judgment, because the deeper conceptual process of interpretation generation does not allow the linguistic shortcut to suffice. Furthermore, the effects of linguistic distributional frequency are independent of any effects of conventional word frequency. We discuss the utility of the linguistic shortcut as a cognitive triage mechanism that can optimize processing in a limited-resource conceptual system.     

Prototypicality in sentence production

Three cued-recall experiments examined the effect of category typicality on the ordering of words in sentence production. Past research has found that typical items tend to be mentioned before atypical items in a phrase—a pattern usually associated with lexical variables (like word frequency), and yet typicality is a conceptual variable. Experiment 1 revealed that an appropriate conceptual framework was necessary to yield the typicality effect. Experiment 2 tested ad hoc categories that do not have prior representations in long-term memory and yielded no typicality effect. Experiment 3 used carefully matched sentences in which two category members appeared in the same or in different phrases. Typicality affected word order only when the two words appeared in the same phrase. These results are consistent with an account in which typicality has its origin in conceptual structure, which leads to differences in lexical accessibility in appropriate contexts.     

Proximity attitudes toward objects and people: reference to a category and a self-representation?

The relative strength of similarity to self and category typicality as predictors of proximity attitudes (social distance) toward people of varying race and objects associated with people of varying race was investigated. Similarity to self and category typicality were significant predictors of proximity attitudes toward both objects and people, but similarity to self was the significantly stronger predictor. The predictive utility of similarity to self was greater for object judgments than person judgments, but category typicality was a better predictor of person judgments than object judgments. Although the results provide evidence of ingroup favoritism in proximity attitudes toward people, the ingroup bias did not extend to objects associated with people. Category typicality was positively related to attitudes, even for distanced groups. The role of predictability of the target in determining proximity attitudes is discussed.     

What some effects might not be: The time to verify membership in 'well-defined' categories

Armstrong, Gleitman, and Gleitman (1983) reported shorter categorization times for members of well-defined categories judged more typical. They concluded that these effects could not originate in a graded, similarity-based category representation and consequently that the typicality effects obtained with natural categories might not be indicative of such a structure either. In this article, we re-examine this conclusion, focusing first on the performance obtained with well-defined categories of different sizes. Only the larger categories used showed variations in typicality ratings and produced typicality effects on categorization times. However,multiple regression analyses showed the effects on categorization times to be better explained by a measure of associative strength, called category dominance. The range of various predictor variables was equated in a follow-up experiment involving large, natural, and well-defined categories. Results obtained with well-defined categories showed pronounced dominance effects when typicality was controlled, but no reliable typicality effect when category dominance and instance familiarity were controlled. Results were opposite for natural categories. By showing that well-defined categories fail to produce unbiased typicality effects, our results bring added support to the hypothesis that the effects obtained with natural categories originate in a graded, similarity-based category structure.     

Developmental effects of typicality and superordinate property dominance on sentence verification

Second graders (mean age, 8 years 3 months), fourth graders (mean age, 10 years 4 months), and adults verified telegraphic sentences with typical or atypical subject nouns and high or low dominant property predicates. The hypothesis tested was that the similarity in the attribute structures of category members to their superordinate prototype should be related to degree of typicality. Adult reaction time and error data supported the prototype model of semantic category structure. Second and fourth graders showed comparable property knowledge to adults, but evidenced different organizational patterns than predicted by the adult model. The results suggest that with development children learn to simultaneously use many attribute dimensions and to abstract the family resemblance structure and relative importance of category properties.     

Representing the meanings of object and action words: the featural and unitary semantic space hypothesis

This paper presents the Featural and Unitary Semantic Space (FUSS) hypothesis of the meanings of object and action words. The hypothesis, implemented in a statistical model, is based on the following assumptions: First, it is assumed that the meanings of words are grounded in conceptual featural representations, some of which are organized according to modality. Second, it is assumed that conceptual featural representations are bound into lexico-semantic representations that provide an interface between conceptual knowledge and other linguistic information (syntax and phonology). Finally, the FUSS model employs the same principles and tools for objects and actions, modeling both domains in a single semantic space. We assess the plausibility of the model by showing that it can capture generalizations presented in the literature, in particular those related to category-related deficits, and show that it can predict semantic effects in behavioral experiments for object and action words better than other models such as Latent Semantic Analysis (Landauer & Dumais, 1997) and similarity metrics derived from Wordnet (Miller & Fellbaum, 1991).     

Visual and Affective Multimodal Models of Word Meaning in Language and Mind

One of the main limitations of natural language-based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic-level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both abstract and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed.     

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.     

Ideal is typical.

A well-established finding in research on concepts and categories is that some members are rated as better or more typical examples than others. It is generally thought that typicality reflects centrality, that is, that typical examples are those that are similar to many other members of the category. This interpretation of typicality is based on studies in which participants had little knowledge about the relevant categories. In the present study, experienced fishermen were asked to give goodness-of-example ratings to familiar freshwater fish. These fishermen were of two cultural groups with somewhat different goals and ideals. Typicality was well predicted by fishes' desirability and poorly predicted by their centrality. Further, the two cultural groups differed in their typicality ratings in ways that corresponded to their different goals and ideals. For knowledgeable reasoners typicality in natural taxonomic categories appears based on ideals rather than on centrality.     

Categorical target templates: Typical category members are found and identified quickly during word-cued search

What information do people use to guide search when they lack precise details about the appearance of their target? In this study, we employed categorical (word-cued) search and eye tracking, to examine how category typicality influences search performance. We found that typical category members were fixated and identified more quickly than atypical categories. This finding held when the participant was cued at the superordinate level (finding “clothing” among non-clothing items) or the basic level (finding a “shirt” among other clothing items). This suggests that categorical target templates may be constructed by piecing together features from the most typical category member(s).     

Typicality and group variability as dual moderators of category-based inferences

We propose and test two alternative hypotheses bearing on the dual roles of group variability and typicality when people form impressions of single category members. The latitude of acceptance hypothesis suggests that a wider range of individual group members are likely to be seen as good-fitting members (i.e., typical) if the group is heterogeneous, thereby increasing the extent to which stereotypical attitudes are used as a basis for responding to these persons. In contrast, the typicality-functionality hypothesis suggests that typicality plays different roles depending on group variability. This view suggests that typicality plays the “gatekeeper function” as postulated by Fiske and Neuberg (1990) when the group is homogeneous, but not when it is heterogeneous. Across two studies, stronger support was found for the typicality-functionality hypothesis. Implications for the extant literature on category-based processing are discussed.     

Representation at different levels in a conceptual hierarchy

The present study examines the influence of hierarchical level on category representation. Three computational models of representation – an exemplar model, a prototype model and an ideal representation model – were evaluated in their ability to account for the typicality gradient of categories at two hierarchical levels in the conceptual domain of clothes. The domain contains 20 subordinate categories (e.g., trousers, stockings and underwear) and an encompassing superordinate category (CLOTHES). The models were evaluated both in terms of their ability to fit the empirical data and their generalizability through marginal likelihood. The hierarchical level was found to clearly influence the type of representation: For concepts at the subordinate level, exemplar representations were supported. At the superordinate level, however, an ideal representation was overwhelmingly preferred over exemplar and prototype representations. This finding contributes to the increasingly dominant view that the human conceptual apparatus adopts both exemplar representations and more abstract representations, contradicting unitary approaches to categorization.     

Developmental trends in children’s typicality judgments

Category typicality norms from 12 natural language categories are presented for kindergarten, third-grade, sixth-grade, and college students. Subjects first selected examples of familiar word concepts and rated them on a 3-point scale in terms of category typicality. Age differences in the percentage of items included as category members were found primarily for the less typical items, with inclusion rates varying as a function of both age and typicality level. The absolute level of typicality judgments increased with age, although correlations between the children’s and college students’ ratings were generally significant for all three children’s groups, with average correlations increasing somewhat with age. It was suggested that the rating data would be useful to developmental investigators interested in children’s processing of category information.     

Nonverbal categorization in fluent and nonfluent anomic aphasics

This study aimed to demonstrate that the naming difficulties of a particular group of aphasics, namely, fluent aphasics, are related to an underlying inability to organize feature set information. In order to test this hypothesis, the performance of fluent aphasics, nonfluent aphasics, and a nonaphasic brain-injured control group, was examined on a nonverbal categorization task, which was carefully structured in terms of instance typicality. Scores of visuoperceptual and naming tests were correlated with categorization task errors. As predicted, fluent aphasics showed a significant deficit in performance on the categorization task in comparison with other subjects. Differences in the nature of the errors the fluent aphasics made suggested that their problems were related to difficulties in abstracting the prototype for each category and in sorting category members with reference to these prototypes. For fluent aphasics, but not other subjects, a significant correlation was found between categorization task performance and naming ability.     

‘Most advanced,yet acceptable’: Typicality and novelty as joint predictors of aesthetic preference in industrial design

Typicality and novelty have often been shown to be related to aesthetic preference of human artefacts. Since a typical product is rarely new and, conversely, a novel product will not often be designated as typical, the positive effects of both features seem incompatible. In three studies it was shown that typicality (operationalized as ‘goodness of example’) and novelty are jointly and equally effective in explaining the aesthetic preference of consumer products, but that they suppress each other's effect. Direct correlations between both variables and aesthetic preference were not significant, but each relationship became highly significant when the influence of the other variable was partialed out. In Study 2, it was furthermore demonstrated that the expertise level of observers did not affect the relative contribution of novelty and typicality. It was finally shown (Study 3) that a more ‘objective’ measure of typicality, central tendency — operationalized as an exemplar's average similarity to all other members of the category — yielded the same effect of typicality on aesthetic preference. In sum, all three studies showed that people prefer novel designs as long as the novelty does not affect typicality, or, phrased differently, they prefer typicality given that this is not to the detriment of novelty. Preferred are products with an optimal combination of both aspects.     

Representing Spatial Structure Through Maps and Language: Lord of the Rings Encodes the Spatial Structure of Middle Earth

Spatial mental representations can be derived from linguistic and non‐linguistic sources of information. This study tested whether these representations could be formed from statistical linguistic frequencies of city names, and to what extent participants differed in their performance when they estimated spatial locations from language or maps. In a computational linguistic study, we demonstrated that co‐occurrences of cities in Tolkien’s Lord of the Rings trilogy and The Hobbit predicted the authentic longitude and latitude of those cities in Middle Earth. In a human study, we showed that human spatial estimates of the location of cities were very similar regardless of whether participants read Tolkien’s texts or memorized a map of Middle Earth. However, text‐based location estimates obtained from statistical linguistic frequencies better predicted the human text‐based estimates than the human map‐based estimates. These findings suggest that language encodes spatial structure of cities, and that human cognitive map representations can come from implicit statistical linguistic patterns, from explicit non‐linguistic perceptual information, or from both.     

Exploring the determinants of the graded structure of vocal emotion expressions

We examined what determines the typicality, or graded structure, of vocal emotion expressions. Separate groups of judges rated acted and spontaneous expressions of anger, fear, and joy with regard to their typicality and three main determinants of the graded structure of categories: category members' similarity to the central tendency of their category (CT); category members' frequency of instantiation, i.e., how often they are encountered as category members (FI); and category members' similarity to ideals associated with the goals served by its category, i.e., suitability to express particular emotions. Partial correlations and multiple regression analysis revealed that similarity to ideals, rather than CT or FI, explained most variance in judged typicality. Results thus suggest that vocal emotion expressions constitute ideal-based goal-derived categories, rather than taxonomic categories based on CT and FI. This could explain how prototypical expressions can be acoustically distinct and highly recognisable but occur relatively rarely in everyday speech.     

What is typical about the typicality effect in category-based induction?

Research on category-based induction has documented a consistent typicality effect: Typical exemplars promote stronger inferences about their broader category than atypical exemplars. This work has been largely confined to categories whose central tendencies are also the most typical members of the category. Does the typicality effect apply to the broad set of categories for which the ideal category member is considered most typical? In experiments with natural and artificial categories, typicality and induction-strength ratings were obtained for ideal and central-tendency exemplars. Induction strength was greatest for the central-tendency exemplars, regardless of whether the central tendency or the ideal was rated more typical. These results suggest that the so-called “typicality” effect is a special case of a more universal central-tendency effect in category-based induction.     

Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis

The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text-based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected.