Word associations: Norms for 1,424 Dutch words in a continuous task

Deyne, Simon De; Storms, Gerrit

doi:10.3758/brm.40.1.198

Cited by 116 publications

(135 citation statements)

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“…We selected 4,300 Dutch words from various sources (De Deyne & Storms, 2008;Fontaine, Poortinga, Setiadi, & Suprapti, 2002;Fontaine et al, 2007;Frijda, Kuipers, & ter Schure, 1989;Hermans & De Houwer, 1994;Keuleers, Diependaele, & Brysbaert, 2010;Osgood et al, 1957;Rouckhout & Schacht, 2000; http://synoniemen.net/). The selection of words was guided by the idea that in addition to neutral words, we needed as many words as possible with a marked value for each of the three affective variables.…”

Section: Materials and Proceduresmentioning

confidence: 99%

Norms of valence, arousal, dominance, and age of acquisition for 4,300 Dutch words

et al. 2012

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This article presents norms of valence/pleasantness, activity/arousal, power/dominance, and age of acquisition for 4,300 Dutch words, mainly nouns, adjectives, adverbs, and verbs. The norms are based on ratings with a 7-point Likert scale by independent groups of students from two Belgian (Ghent and Leuven) and two Dutch (Rotterdam and Leiden-Amsterdam) samples. For each variable, we obtained high split-half reliabilities within each sample and high correlations between samples. In addition, the valence ratings of a previous, more limited study (Hermans & De Houwer, Psychologica Belgica, 34:115-139, 1994) correlated highly with those of the present study. Therefore, the new norms are a valuable source of information for affective research in the Dutch language.

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Section: Materials and Proceduresmentioning

confidence: 99%

Norms of valence, arousal, dominance, and age of acquisition for 4,300 Dutch words

et al. 2012

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“…Fig. 1 Visual representation of the semantic clusters and semantic distances between entities, based on the feature generation data collected by Deyne and Storms (2008). For visualization, data reduction of the similarity matrix to two dimensions was performed by means of multidimensional scaling (MDS) (Matlab 2011b, Statistics Toolbox).…”

Section: Stimuli and Semantic Cosine Similarity Matrixmentioning

confidence: 99%

“…We evaluated the effect of semantic similarity on the fMRI activity pattern in two ways: In a representational similarity analysis (RSA) (Kriegeskorte et al, 2008;Bruffaerts et al, 2013b), we examined the cosine similarity between the cosine similarity matrix based on the fMRI activity patterns (called the "fMRI cossimilarity matrix") and the semantic cossimilarity matrix (De Deyne et al, 2008). We did so for the fMRI cossimilarity matrix for written and spoken words pooled, for written words only, for spoken words only, and for exclusively crossmodal written-spoken word pairs.…”

Section: Semantic Similarity Analysismentioning

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). A minimum of 180 features for each concept were collected, for a total of more than 120 concepts.…”

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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“…It was emphasized that there is no one correct answer. In order to reduce the risk of chaining (i.e., responding with associations based on a previous response rather than responding to the cue; see De Deyne & Storms, 2008;McEvoy & Nelson, 1982), participants were shown three examples in which the cue was repeated in every response (e.g., cue: een kopje … 'a cup of …', responses: een kopje koffie, een kopje thee, v e r h a g e n e t a l .…”

Section: Methodsmentioning

confidence: 99%

Predictive language processing revealing usage-based variation

et al. 2018

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a b st r a c t While theories on predictive processing posit that predictions are based on one's prior experiences, experimental work has effectively ignored the fact that people differ from each other in their linguistic experiences and, consequently, in the predictions they generate. We examine usagebased variation by means of three groups of participants (recruiters, job-seekers, and people not (yet) looking for a job), two stimuli sets (word sequences characteristic of either job ads or news reports), and two experiments (a Completion task and a Voice Onset Time task). We show that differences in experiences with a particular register result in different expectations regarding word sequences characteristic of that register, thus pointing to differences in mental representations v e r h a g e n e t a l . 330of language. Subsequently, we investigate to what extent different operationalizations of word predictability are accurate predictors of voice onset times. A measure of a participant's own expectations proves to be a significant predictor of processing speed over and above word predictability measures based on amalgamated data. These findings point to actual individual differences and highlight the merits of going beyond amalgamated data. We thus demonstrate that is it feasible to empirically assess the variation implied in usage-based theories, and we advocate exploiting this opportunity.k e y w o r d s : individual differences, surprisal, cloze probabilities, completion task, voice onset times.

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Word associations: Norms for 1,424 Dutch words in a continuous task

Cited by 116 publications

References 32 publications

Norms of valence, arousal, dominance, and age of acquisition for 4,300 Dutch words

Norms of valence, arousal, dominance, and age of acquisition for 4,300 Dutch words

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

Predictive language processing revealing usage-based variation

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