Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource in the world. Our procedure allowed subjects to provide multiple responses for each cue, which permits us to measure weak associations. We evaluate the utility of the dataset in several different contexts, including lexical decision and semantic categorization. We also show that measures based on a mechanism of spreading activation derived from this new resource are highly predictive of direct judgments of similarity. Finally, a comparison with existing English word association sets further highlights systematic improvements provided through these new norms.

One of the central functions of categorization is to support reasoning. Having categorized some entity as a bird, one may predict with reasonable confidence that it builds a nest, sings, and can fly, though none of these inferences is certain. In addition, between-category relations may guide reasoning. For example, from the knowledge that robins have some enzyme in their blood, one is likely to be more confident that sparrows also have this enzyme than that raccoons have this enzyme. The basis for this confidence may be that robins are more similar to sparrows than to raccoons or that robins and sparrows share a lower rank superordinate category (birds) than do robins and raccoons (vertebrates).Recently, researchers have developed specific models for category-based reasoning and generated a range of distinctive reasoning phenomena (see Heit, 2000, for a review). These phenomena are quite robust when American college students are the research participants, but at least some of them do not generalize well to other populations. To address these limitations, we will offer not so much a specific model but rather a framework theory organized around the principle of relevance. This theory is more abstract than many of its predecessors, and one might imagine a number of implementations consistent with the relevance framework. Nonetheless, we will see that the relevance theory has testable implications.The rest of the paper is organized as follows. First, we briefly review two of the most influential models for induction: the Osherson, Smith, Wilkie, López, and Shafir (1990) category-based induction model, and Sloman's (1993) feature-based induction model. Next, we turn to the question of the generality of reasoning phenomena and describe two, more abstract, approaches that may be able to address the question of generality. Then we offer a theory at an intermediate level of abstraction, the "relevance theory," and describe some tests of its implications. Finally, we summarize and argue that there are benefits from approaching induction from a number of levels of analysis.The similarity-coverage model (SCM A framework theory, organized around the principle of relevance,is proposed for category-basedreasoning. 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 strategiesin 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. THEORETICAL AND REVIEW ARTICLES

If different languages map words onto referents in different ways, bilinguals must either (a) learn and maintain separate mappings for their two languages or (b) merge them and not be fully native-like in either. We replicated and extended past findings of cross-linguistic differences in word-to-referent mappings for common household objects using Belgian monolingual speakers of Dutch and French. We then examined word-to-referent mappings in Dutch-French bilinguals by comparing the way they named in their two languages. We found that the French and Dutch bilingual naming patterns converged on a common naming pattern, with only minor deviations. Through the mutual influence of the two languages, the category boundaries in each language move towards one another and hence diverge from the boundaries used by the native speakers of either language. Implications for the organization of the bilingual lexicon are discussed.

A number of properties of word associations, generated in a continuous task, were investigated First, we investigated the correspondence of word class in association cues and responses. Nouns were the modal word class response, regardless of the word class of the cue, indicating a dominant paradigmatic response style. Next, the word association data were used to build an associative network to investigate the centrality of nodes. The study of node centrality showed that central nodes in the network tended to be highly frequent and acquired early. Small-world properties of the association network were investigated and compared with a large English association network (Steyvers & Tenenbaum, 2005). Networks based on a multiple association procedure showed small-world properties despite being denser than networks based on a discrete task. Finally, a semantic taxonomy was used to investigate the composition of semantic types in association responses. The majority of responses were thematically related situation responses and entity responses referring to parts, shape, or color. Since the association task required multiple responses per cue, the interaction between generation position and semantic role could be investigated and discussed in the framework of recent theories of natural concept representations (Barsalou, Santos, Simmons, & Wilson, in press).

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 multipleresponse 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 singleresponse 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.Keywords Word associations . Semantic network . Lexical decision . Semantic relatedness . Lexical centrality Associative knowledge is a central component in many accounts of recall, recognition, and semantic representations in word processing. There are multiple ways to tap into this knowledge, but word associations are considered to be the most direct route for gaining insight into our semantic knowledge (Nelson, McEvoy, & Schreiber, 2004;Mollin, 2009) and human thought in general (Deese, 1965). The type of information produced by word associations is capable of expressing any kind of semantic relationship between words. Because of this flexibility, networks are considered the natural representation of word associations, where nodes correspond to lexicalized concepts and links indicate semantic or lexical relationships between two nodes. These networks correspond to an idealized localist representation of our mental lexical network. The properties derived from such a network have been instrumental in three different research traditions, which will be described below. These traditions have focused on (1) direct association strength, (2) second-order strength and distributional similarity, and (3) network topology and centrality measures.The first tradition has used word associations to calculate a measure of associative strength and was inspired by a behaviorist view of language in terms of stimulus-response patterns. This notion of associative strength plays an important role in studies that have focused on inhibition and facilitation in list learning (e.g., Roediger & Neely, 1982), studies on episodic memory (e.g., Nelson et al., 2004), and studies that have tried to distinguish semantic and associative priming (for a recent over...