Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts

Deyne, Simon De; Verheyen, Steven; Ameel, Eef; Vanpaemel, Wolf; Dry, Matthew J.; Voorspoels, Wouter; Storms, Gerrit

doi:10.3758/brm.40.4.1030

Cited by 107 publications

(134 citation statements)

References 104 publications

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“…1 provides a visualization of the sort of input that might underlie people's everyday knowledge of various types of animals. These representations were derived from norms of the frequencies with which participants at the University of Leuven generated features characterizing various animals (De Deyne et al, 2008;see Shafto, Kemp, Mansinghka, & Tenenbaum, 2011). Each animal in the norms is associated with a set of frequencies across more than 750 features.…”

Section: How Are Magnitudes Generated?mentioning

confidence: 99%

“…For the animals used in the simulations reported in the present paper, intercorrelations among the four dimensions were moderate, ranging from .38 (size with speed) to .60 (size with fierceness). For our first set of simulations, we identified a set of 44 animals that also appeared in the Leuven norms (de Deyne et al, 2008). Each animal was represented by a vector of 50 continuous-valued features (see Lu et al, 2012, pp.…”

Section: Predicting Human Magnitude Ratingsmentioning

confidence: 99%

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The discovery and comparison of symbolic magnitudes

Chen

Holyoak

2014

Cognitive Psychology

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a b s t r a c tHumans and other primates are able to make relative magnitude comparisons, both with perceptual stimuli and with symbolic inputs that convey magnitude information. Although numerous models of magnitude comparison have been proposed, the basic question of how symbolic magnitudes (e.g., size or intelligence of animals) are derived and represented in memory has received little attention. We argue that symbolic magnitudes often will not correspond directly to elementary features of individual concepts. Rather, magnitudes may be formed in working memory based on computations over more basic features stored in long-term memory. We present a model of how magnitudes can be acquired and compared based on BARTlet, a representationally simpler version of Bayesian Analogy with Relational Transformations (BART; Lu, Chen, & Holyoak, 2012). BARTlet operates on distributions of magnitude variables created by applying dimension-specific weights (learned with the aid of empirical priors derived from pre-categorical comparisons) to more primitive features of objects. The resulting magnitude distributions, formed and maintained in working memory, are sensitive to contextual influences such as the range of stimuli and polarity of the question. By incorporating psychological reference points that control the precision of magnitudes in working memory and applying the tools of signal detection theory, BARTlet is able to account for a wide range of empirical phenomena involving magnitude comparisons, including the symbolic distance effect and the semantic congruity effect. We discuss the role of reference points in cognitive and social decision-making, and implications for the evolution of relational representations.

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Section: How Are Magnitudes Generated?mentioning

confidence: 99%

Section: Predicting Human Magnitude Ratingsmentioning

confidence: 99%

The discovery and comparison of symbolic magnitudes

Chen

Holyoak

2014

Cognitive Psychology

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“…A minimum of 180 features for each concept were collected, for a total of more than 120 concepts. Next, a feature applicability judgment task was performed by 4 subjects who were asked, for each concept-feature pair, to judge whether the feature was applicable to the concept (De Deyne et al, 2008). In this way a feature applicability matrix (or concept-feature matrix) was generated and all properties were verified for all entities.…”

Section: Stimuli and Semantic Cosine Similarity Matrixmentioning

confidence: 99%

“…This matrix contains one column per entity (for a total of more than 120 entities) and one row per property generated by the more than 1000 subjects in response to these entities, yielding a total of 764 rows. Each cell of this matrix contains the sum of the positive responses by 4 subjects who judged the applicability of a given property for a given entity (integer value between 0 and 4) (De Deyne et al, 2008). For use in the current experiment, 24 animate entities from 6 semantic subcategories (birds, marine animals, fish, herpetofauna, insects, and livestock) were selected based on hierarchical clustering analysis (Fig 1) (Bruffaerts et al, 2013b).…”

Section: Stimuli and Semantic Cosine Similarity Matrixmentioning

confidence: 99%

“…Stimuli and properties were derived from a dataset including a large number of animate concepts collected by De Deyne et al (2008). In the De Deyne et al (2008) study, 1003 students were asked to write down 10 features for each of the 10 concrete nouns they received, each noun corresponding to a concept (a concrete entity) (feature generation task).…”

Section: Stimuli and Semantic Cosine Similarity Matrixmentioning

confidence: 99%

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Left perirhinal cortex codes for similarity in meaning between written words: Comparison with auditory word input

et al. 2015

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This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Neuropsychologia following peer review. The definitive publisher-authenticated version (Liuzzi A.G., Bruffaerts R., Dupont P., Adamczuk K., Peeters R., De Deyne S., Storms G., Vandenberghe R. AbstractLeft perirhinal cortex has been previously implicated in associative coding. According to a recent experiment, the similarity of perirhinal fMRI response patterns to written concrete words is higher for words which are more similar in their meaning. If left perirhinal cortex functions as an amodal semantic hub, one would predict that this semantic similarity effect would extend to the spoken modality. We conducted an event-related fMRI experiment and evaluated whether a same semantic similarity effect could be obtained for spoken as for written words. Twenty healthy subjects performed a property verification task in either the written or the spoken modality. Words corresponded to concrete animate entities for which extensive feature generation was available from more than 1000 subjects. From these feature generation data, a concept-feature matrix was derived which formed the basis of a cosine similarity matrix between the entities reflecting their similarity in meaning (called the "semantic cossimilarity matrix"). Independently, we calculated a cosine similarity matrix between the left perirhinal fMRI activity patterns evoked by the words (called the "fMRI cossimilarity matrix"). Next, the similarity was determined between the semantic cossimilarity matrix and the fMRI cossimilarity matrix. This was done for written and spoken words pooled, for written words only, for spoken words only, as well as for crossmodal pairs. Only for written words did the fMRI cossimilarity matrix correlate with the semantic cossimilarity matrix. Contrary to our prediction, we did not find any such effect for auditory word input nor did we find cross-modal effects in perirhinal cortex between written and auditory words. Our findings situate the contribution of left perirhinal cortex to word processing at the top of the visual processing pathway, rather than at an amodal stage where visual and auditory word processing pathways have already converged.

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Semantic representations in inferior frontal and lateral temporal cortex during picture naming, reading, and repetition

Liuzzi,

Meersmans,

Peeters

et al. 2024

Human Brain Mapping

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Reading, naming, and repetition are classical neuropsychological tasks widely used in the clinic and psycholinguistic research. While reading and repetition can be accomplished by following a direct or an indirect route, pictures can be named only by means of semantic mediation. By means of fMRI multivariate pattern analysis, we evaluated whether this well‐established fundamental difference at the cognitive level is associated at the brain level with a difference in the degree to which semantic representations are activated during these tasks. Semantic similarity between words was estimated based on a word association model. Twenty subjects participated in an event‐related fMRI study where the three tasks were presented in pseudo‐random order. Linear discriminant analysis of fMRI patterns identified a set of regions that allow to discriminate between words at a high level of word‐specificity across tasks. Representational similarity analysis was used to determine whether semantic similarity was represented in these regions and whether this depended on the task performed. The similarity between neural patterns of the left Brodmann area 45 (BA45) and of the superior portion of the left supramarginal gyrus correlated with the similarity in meaning between entities during picture naming. In both regions, no significant effects were seen for repetition or reading. The semantic similarity effect during picture naming was significantly larger than the similarity effect during the two other tasks. In contrast, several regions including left anterior superior temporal gyrus and left ventral BA44/frontal operculum, among others, coded for semantic similarity in a task‐independent manner. These findings provide new evidence for the dynamic, task‐dependent nature of semantic representations in the left BA45 and a more task‐independent nature of the representational activation in the lateral temporal cortex and ventral BA44/frontal operculum.

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Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts

Cited by 107 publications

References 104 publications

The discovery and comparison of symbolic magnitudes

The discovery and comparison of symbolic magnitudes

Left perirhinal cortex codes for similarity in meaning between written words: Comparison with auditory word input

Semantic representations in inferior frontal and lateral temporal cortex during picture naming, reading, and repetition

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