COMPLEX STRUCTURES AND SEMANTICS IN FREE WORD ASSOCIATION (original) (raw)
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The mental lexicon contains the knowledge about words acquired over a lifetime. A central question is how this knowledge is structured and changes over time. Here we propose to represent this lexicon as a network consisting of nodes that correspond to words and links reflecting associative relations between two nodes, based on free association data. A network view of the mental lexicon is inherent to many cognitive theories, but the predictions of a working model strongly depend on a realistic scale, covering most words used in daily communication. Combining a large network with recent methods from network science allows us to answer questions about its organization at different scales simultaneously, such as: How efficient and robust is lexical knowledge represented considering the global network architecture? What are the organization principles of words in the mental lexicon (i.e. thematic versus taxonomic)? How does the local connectivity with neighboring words explain why certain words are processed more efficiently than others? Networks built from word associations are specifically suited to address prominent psychological phenomena such as developmental shifts, individual differences in creativity, or clinical states like schizophrenia. While these phenomena can be studied using these networks, various future challenges and ways in which this proposal complements other perspectives are also discussed.
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Free-word association has been used as a vehicle to understand the organization of human thoughts. The original studies relied mainly on qualitative assertions, yielding the widely intuitive notion that trajectories of word associations are structured, yet considerably more random than organized linguistic text. Here we set to determine a precise characterization of this space, generating a large number of word association trajectories in a web implemented game. We embedded the trajectories in the graph of word co- ...
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Free association is a task that requires a subject to express the first word to come to their mind when presented with a certain cue. It is a task which can be used to expose the basic mechanisms by which humans connect memories. In this work we have made use of a publicly available database of free associations to model the exploration of the averaged network of associations using a statistical and the ACT-R model. We performed, in addition, an online experiment asking participants to navigate the averaged network using their individual preferences for word associations. We have investigated the statistics of word repetitions in this guided association task. We find that the considered models mimic some of the statistical properties, viz the probability of word repetitions, the distance between repetitions and the distribution of association chain lengths, of the experiment, with the ACT-R model showing a particularly good fit to the experimental data for the more intricate properties as, for instance, the ratio of repetitions per length of association chains.
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The ability to associate words is an important cognitive skill. In this study we investigate different methods for representing word associations in the brain, using the Remote Associates Test (RAT) as a task. We explore representations derived from free association norms and statistical n-gram data. Although n-gram representations yield better performance on the test, a closer match with the human performance is obtained with representations derived from free associations. We propose that word association strengths derived from free associations play an important role in the process of RAT solving. Furthermore, we show that this model can be implemented in spiking neurons, and estimate the number of biologically realistic neurons that would suffice for an accurate representation.
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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.
A Computational Model to Disentangle Semantic Information Embedded in Word Association Norms
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Two well-known databases of semantic relationships between pairs of words used in psycholinguistics, feature-based and association-based, are studied as complex networks. We propose an algorithm to disentangle feature based relationships from free association semantic networks. The algorithm uses the rich topology of the free association semantic network to produce a new set of relationships between words similar to those observed in feature production norms.
Using complex networks to understand the mental lexicon
Network science is an emerging discipline drawing from sociology, computer science, physics and a number of other fields to examine complex systems in economical, biological, social, and technological domains. To examine these complex systems, nodes are used to represent individual entities, and links are used to represent relationships between entities, forming a web-like structure, or network, of the entire system. The structure that emerges in these complex networks influences the dynamics of that system. We provide a short review of how this mathematical approach has been used to examine the structure found in the phonological lexicon, and of how subsequent psycholinguistic investigations demonstrate that several of the structural characteristics of the phonological network influence various language-related processes, including word retrieval during the recognition and production of spoken words, recovery from instances of failed lexical retrieval, and the acquisition of word-forms. This approach allows researchers to examine the lexicon at the micro-, meso-, and macro-levels, holding much promise for increasing our understanding of language-related processes and representations.
Analyzing and modeling free word associations
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Human free association (FA) norms are believed to reflect the strength of links between words in the lexicon of an average speaker. Large-scale FA norms are commonly used as a data source both in psycholinguistics and in computational modeling. However, few studies aim to analyze FA norms themselves, and it is not known what are the most important factors that guide speakers’ lexical choices in the FA task. Here, we first provide a statistical analysis of a large-scale data set of English FA norms. Second, we argue that such analysis can inform existing computational models of semantic memory, and present a case study with the topic model to support this claim. Based on our analysis, we provide the topic model with dictionary-based knowledge about word synonymy/antonymy, and demonstrate that the resulting model predicts human FA responses better than the topic model without this information.
Neural Networks, 2012
Understanding cognition has been a central focus for psychologists, neuroscientists and philosophers for thousands of years, but many of its most fundamental processes remain very poorly understood. Chief among these is the process of thought itself: the spontaneous emergence of specific ideas within the stream of consciousness. It is widely accepted that ideas, both familiar and novel, arise from the combination of existing concepts. From this perspective, thought is an emergent attribute of memory, arising from the intrinsic dynamics of the neural substrate in which information is embedded. An important issue in any understanding of this process is the relationship between the emergence of conceptual combinations and the dynamics of the underlying neural networks. Virtually all theories of ideation hypothesize that ideas arise during the thought process through association, each one triggering the next through some type of linkage, e.g., structural analogy, semantic similarity, polysemy, etc. In particular, it has been suggested that the creativity of ideation in individuals reflects the qualitative structure of conceptual associations in their minds. Interestingly, psycholinguistic studies have shown that semantic networks across many languages have a particular type of structure with small-world, scale free connectivity. So far, however, these related insights have not been brought together, in part because there has been no explicitly neural model for the dynamics of spontaneous thought. Recently, we have developed such a model. Though simplistic and abstract, this model attempts to capture the most basic aspects of the process hypothesized by theoretical models within a neurodynamical framework. It represents semantic memory as a recurrent semantic neural network with itinerant dynamics. Conceptual combinations arise through this dynamics as co-active groups of neural units, and either dissolve quickly or persist for a time as emergent metastable attractors and are recognized consciously as ideas. The work presented in this paper describes this model in detail, and uses it to systematically study the relationship between the structure of conceptual associations in the neural substrate and the ideas arising from this system's dynamics. In particular, we consider how the small-world and scale-free characteristics influence the effectiveness of the thought process under several metrics, and show that networks with both attributes indeed provide significant advantages in generating unique conceptual combinations.