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.

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 two experiments, we examined whether word age-of-acquisition (AoA) is a reliable predictor of processing times in semantic tasks. In the ®rst task, participants were asked to say the ®rst associate that came to mind when they saw a stimulus word; the second task involved a semantic categorisation between words with a de®nable meaning and ®rst names. In both tasks, there were signi®cantly faster responses to earlier-acquired than to later-acquired words. On the basis of these results, we argue that age-of-acquisition eects do not originate solely from the speech output system, but from the semantic system as well. Ó

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...

a b s t r a c t a r t i c l e i n f oWord processing studies increasingly make use of regression analyses based on large numbers of stimuli (the socalled megastudy approach) rather than experimental designs based on small factorial designs. This requires the availability of word features for many words. Following similar studies in English, we present and validate ratings of age of acquisition and concreteness for 30,000 Dutch words. These include nearly all lemmas language researchers are likely to be interested in. The ratings are freely available for research purposes.

We investigate how the mental lexicon changes over the lifespan using free association data from over 8,000 individuals, ranging from 10 to 84 years of age, with more than 400 cue words per age group. Using network analysis, with words as nodes and edges defined by the strength of shared associations, we find that associative networks evolve in a nonlinear (Ushaped) fashion over the lifespan. During early life, the network converges and becomes increasingly structured, with reductions in average path length, entropy, clustering coefficient, and small world index. Into late life, the pattern reverses but shows clear differences from early life. The pattern is independent of the increasing number of word types produced per cue across the lifespan, consistent with a network encoding an increasing number of relations between words as individuals age. Lifetime variability is dominantly driven by associative change in the least well-connected words.

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, including the role of environmental exposure and several cognitive mechanisms associated with learning, representation, and retrieval of information. We review the current status of research in this field and outline a framework that promises to assess the contribution of both ecological and psychological aspects to the aging lexicon. Cognitive Aging and the Mental Lexicon There is consensus in the cognitive sciences that human development extends well beyond childhood and adolescence, and there has been remarkable empirical progress in the field of cognitive aging in past decades [1]. Nevertheless, the role of environmental and cognitive factors in age-related changes in the structure and processing of lexical and semantic representations (see Glossary) is still under debate. For example, age-related memory decline is commonly attributed to a decline in cognitive abilities [2,3], yet some researchers have proposed that massive exposure to language over the course of one's life leads to knowledge gains that may contribute to, if not fully account for, age-related memory deficits [4-6]. We argue that to resolve such debates we require an interdisciplinary approach that captures how information exposure across adulthood may change the way that we acquire, represent, and recall information. We summarize recent developments in the field (Figure 1, Table 1) and propose a conceptual framework (Figure 2, Key Figure) and associated research agenda that argues for combining ecological analyses, formal modeling, and large-scale empirical studies to shed light on the contents, structure, and neural basis of the aging mental lexicon in both health and disease. Mental Lexicon: Aging and Cognitive Performance The mental lexicon can be thought of as a repository of lexical and conceptual representations, composed of organized networks of semantic, phonological, orthographic, morphological, and other types of information [7]. The cognitive sciences have provided considerable knowledge about the computational (Box 1; [8-11]) and neural basis (Box 2; [12,13]) of lexical and semantic cognition, and there has been considerable interest in how such aspects of cognition change across adulthood and aging [14,15]. Past work on the aging lexicon emphasized the amount of information acquired across the life span (e.g., vocabulary gains across adulthood; [15]); however, new evaluations using graphbased approaches suggest that both quantity and structural aspects of representations differ between individuals [16] and change across the life span [17-19]. Such insights were gathered, for example, from a large-scale analysis of free association data from thousands of individuals [17], ranging from 10 to ...

How verbal and nonverbal visuoperceptual input connects to semantic knowledge is a core question in visual and cognitive neuroscience, with significant clinical ramifications. In an event-related functional magnetic resonance imaging (fMRI) experiment we determined how cosine similarity between fMRI response patterns to concrete words and pictures reflects semantic clustering and semantic distances between the represented entities within a single category. Semantic clustering and semantic distances between 24 animate entities were derived from a concept-feature matrix based on feature generation by Ͼ1000 subjects. In the main fMRI study, 19 human subjects performed a property verification task with written words and pictures and a low-level control task. The univariate contrast between the semantic and the control task yielded extensive bilateral occipitotemporal activation from posterior cingulate to anteromedial temporal cortex. Entities belonging to a same semantic cluster elicited more similar fMRI activity patterns in left occipitotemporal cortex. When words and pictures were analyzed separately, the effect reached significance only for words. The semantic similarity effect for words was localized to left perirhinal cortex. According to a representational similarity analysis of left perirhinal responses, semantic distances between entities correlated inversely with cosine similarities between fMRI response patterns to written words. An independent replication study in 16 novel subjects confirmed these novel findings. Semantic similarity is reflected by similarity of functional topography at a fine-grained level in left perirhinal cortex. The word specificity excludes perceptually driven confounds as an explanation and is likely to be task dependent.