The generalized polymorphous concept account of graded structure in abstract categories

Verheyen, Steven; Stukken, Loes; Deyne, Simon De; Dry, Matthew J.; Storms, Gerrit

doi:10.3758/s13421-011-0083-2

Cited by 11 publications

(18 citation statements)

References 61 publications

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“…In this experiment, category for abstract words was defined within a taxonomic structure (Verheyen et al, 2011), as was done for concrete words in Experiment 1 and concrete words elsewhere (e.g., Crutch & Warrington, 2007). The significantly larger categorical effect for abstract words observed in Experiment 3 (and marginally significant in Experiment 2) suggests that the representation of abstract words relies more on category than association.…”

Section: Resultsmentioning

confidence: 99%

“…Hamilton & Coslett, 2008;Hamilton & Martin, 2010) we predicted equal associative and categorical effects for concrete and abstract concepts. We 11 tested these predictions using concrete words (Experiment 1) and abstract words represented in categories either using synonyms (to replicate Crutch and colleagues, Experiment 2) or abstract taxonomically represented categories from Verheyen et al (2011) (Experiment 3).…”

Section: Current Studymentioning

confidence: 99%

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The representation of concrete and abstract concepts: Categorical versus associative relationships.

Geng¹,

Schnur²

2015

Journal of Experimental Psychology: Learning, Memory, and Cogni

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In four word-translation experiments, we examined the different representational frameworks theory (Crutch & Warrington, 2005) that concrete words are represented primarily by category whereas abstract words are represented by association. In our experiments, Chinese-English bilingual speakers were presented with an auditory Chinese word and three or four written English words simultaneously and asked to select the English word that corresponded to the auditory word. For both abstract and concrete words, higher error rates and longer response times were observed when the English words were categorically or associatively related compared to the unrelated conditions and the magnitude of the categorical effect was bigger than the associative effect. These results challenge the different representational frameworks theory and suggest that although category and association are important for representing abstract and concrete concepts, category plays a greater role for both types of words.Keywords: Concrete and abstract words; Semantic processing; Semantic categorical and associative effects; Bilingual translation 3 Investigating the representations of concrete (e.g., mouse) and abstract words (e.g., future) has important implications for understanding how our memory of factual information and general knowledge of the world (i.e., semantic memory) is represented. A number of studies show that concrete words are more easily recognized and memorized in various cognitive tasks (e.g., lexical decision, word reading) compared to abstract words (e.g., James, 1975;Kroll & Merves, 1986; Paivio, 1971;Strain, Patterson, & Seidenberg, 1995; see Paivio, 1991, for a review). More importantly, individuals with semantic memory deficits show a selective impairment of concrete knowledge with relatively preserved abstract knowledge or vice versa (e.g., Breedin et al., 1994; Sirigu et al., 1992;Warrington, 1975;Warrington & Shallice, 1984), suggesting that concrete and abstract concepts are represented in a different way.Concepts may be represented by taxonomic categories defined by the overlap in the semantic features between concepts (e.g., ducks and pigeons are in the bird category, as they have wings, feathers and can fly), or alternatively, by the links between concepts that tend to co-occur in language or which form familiar scenes or events (by association, e.g., "The mouse ate the cheese"). Most research has focused on concrete concepts, showing that semantic category and association both play a key role in representing concrete concepts (e.g., Kalénine et al., 2009;Sachs, Weis, Krings, Huber, & Kircher, 2008;Sachs, et al., 2011;Schwartz, et al., 2011; for reviews, see Hutchison, 2003;Mahon & Caramazza, 2009;Martin, 2007), but the representations of abstract concepts are less explored. The recent different representational frameworks theory proposes that category and association are important for representing both concrete and abstract words but concrete words rely more on category whereas abstract words rely more on ...

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Section: Resultsmentioning

confidence: 99%

Section: Current Studymentioning

confidence: 99%

The representation of concrete and abstract concepts: Categorical versus associative relationships.

Geng¹,

Schnur²

2015

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…In a first study, similarity judgments for exemplars from concrete and abstract basic level categories, derived from De Deyne et al (2008) and Verheyen et al (2011) respectively, were used. The data consists of similarity judgments for all pairwise combinations of exemplars from 5 animal categories (birds, fish, insects, mammals, and reptiles), 6 artifact categories (clothing, kitchen utensils, musical instruments, tools, vehicles, and weapons) and 6 abstract categories (art forms, crimes, diseases, emotions, media, sciences, and virtues).…”

Section: Semantic Relatedness Evaluationmentioning

confidence: 99%

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

Deyne

Verheyen

Storms

2016

Understanding Complex Systems

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Abstract. 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.

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“…Given that word associations incorporate both semantic and lexical co-occurrence information, the prediction of similarity of abstract concepts should be similar to those of other concepts. To verify this, a third data set was taken from Verheyen, Stukken, De Deyne, Dry, and Storms (2012), in which the similarity of abstract concepts was rated by participants.…”

Section: Study 2: Semantic Distancementioning

confidence: 99%

Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations

2012

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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.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...

show abstract

The generalized polymorphous concept account of graded structure in abstract categories

Cited by 11 publications

References 61 publications

The representation of concrete and abstract concepts: Categorical versus associative relationships.

The representation of concrete and abstract concepts: Categorical versus associative relationships.

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

Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations

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