Word Concreteness Research Articles

Transparency and translucency indices for 1,525 pictograms from the Aragonese Portal of Augmentative and Alternative Communication.

This study investigated the transparency and translucency of 1,525 pictograms from the Aragonese Portal of Augmentative and Alternative Communication (ARASAAC). A total of 521 participants took part in tasks that involved providing the word that best described the meaning of a pictogram or rating the relationship between a pictogram and a verbal label. This process allowed us to obtain indices of transparency (the quality of pictograms that makes their meaning easily "guessable" in the absence of their referent) and translucency (the degree of perceived relationship between the pictogram and its referent when the latter is present) which were further analyzed to assess their reliability and comparability with similar studies. Additionally, the relationship of those indices with various visual and psycholinguistic characteristics was explored, particularly focusing on the match between the original ARASAAC pictogram names and the most frequently provided names by the participants (modal names) for the pictograms. Results showed relatively low levels of transparency, as well as high levels of translucency, with nouns displaying the highest values in both metrics. For transparency and translucency, word imageability and concreteness were the most correlated factors, and, together with age of acquisition, they were the most important features related to the name matching with ARASAAC. The norms derived from this study enhance our understanding of pictogram perception, empowering stakeholders to leverage data-driven insights for the development and implementation of pictographic systems, thus improving cognitive accessibility.

Word learning in the wild: App-based evidence for valence and concreteness effects

Abstract Second language (L2) learners need to acquire large vocabularies to approach native-like proficiency. Many controlled experiments have investigated the factors facilitating and hindering word learning; however, few studies have validated these findings in real-world learning scenarios. We use data from the language learning app Lingvist to explore how L2 word learning is affected by valence (positivity/negativity) and concreteness of target words and their linguistic contexts. We found that valence, but not concreteness, affects learning. Users learned positive and negative words better than neutral ones. Moreover, positive words are learned best in positive contexts and negative words in more negative contexts. Word and context valence effects are strongest on the learner’s second encounter with the target word and diminish across subsequent encounters. These findings provide support for theories of embodied cognition and the lexical quality hypothesis and point to the linguistic factors that make learning words, and by extension languages, faster.

Word concreteness modulates bilingual language control during reading comprehension.

Word concreteness modulates bilingual language control during reading comprehension.

Supplemental Material for Word Concreteness Modulates Bilingual Language Control During Reading Comprehension

Supplemental Material for Word Concreteness Modulates Bilingual Language Control During Reading Comprehension

Word Concreteness and Word Originality

Basque is the oldest known spoken language of Western Europe that is not descended from Indo-European. Despite efforts to stop its use from external influences, the Basque people have shown resiliency throughout their history to keep the language alive. However, as is the case with all other languages, Basque has not been immune to influence from neighboring languages; indeed all Basque speakers are bilingual. The question explored here is whether a word’s concreteness using machine translation from Basque to English and English concreteness scores determines if an original Basque word is spoken today. We find that there is a significant difference of concreteness scores between old and contemporary Basque. We also compared old Basque words with common contemporary Basque words in a semantic vector space and found the old words to cluster more tightly than contemporary words suggesting that words with similar semantic vectors are able to retain usage through time.

Reversal of the concreteness effect can be detected in the natural speech of older adults with amnestic, but not non-amnestic, mild cognitive impairment.

Patients with Alzheimer's disease present with difficulty in lexical retrieval and reversal of the concreteness effect in nouns. Little is known about the phenomena before the onset of symptoms. We anticipate early linguistic signs in the speech of people who suffer from amnestic mild cognitive impairment (MCI). Here, we report the results of a corpus-linguistic approach to the early detection of cognitive impairment. One hundred forty-eight English-speaking Singaporeans provided natural speech data, on topics of their choice; 74 were diagnosed with single-domain MCI (38 amnestic, 36 non-amnestic), 74 cognitively healthy. The recordings yield 267,310 words, which are tagged for parts of speech. We calculate the per-minute word counts and concreteness scores of all tagged words, nouns, and verbs in the dataset. Compared to controls, subjects with amnestic MCI produce fewer but more abstract nouns. Verbs are not affected. Slower retrieval of nouns and the reversal of the concreteness effect in nouns are manifested in natural speech and can be detected early through corpus-based analysis. Reversal of the concreteness effect is manifested in patients with Alzheimer's disease (AD) and semantic dementia.The paper reports a corpus-based analysis of natural speech by people with amnestic and non-amnestic mild cognitive impairment (MCI) and cognitively healthy controls.People with amnestic MCI produce fewer and more abstract nouns than people with non-amnestic MCI and healthy controls. Verbs appear to be unaffected.The imageability problem can be detected in natural everyday speech by people with amnestic MCI, which carries a higher risk of conversion to AD.

Diachronic Analysis of a Word Concreteness Rating: Impact of Semantic Change

Diachronic Analysis of a Word Concreteness Rating: Impact of Semantic Change

Shave, shear, shred, or cut: PPA variant differentially impacts erroneous responses produced in a confrontation naming test of verbs

ABSTRACT Background All common variants of primary progressive aphasia (PPA) exhibit naming deficits. Variants are distinguished by relative deficits in repetition (logopenic; lvPPA), object knowledge (semantic; svPPA), and agrammatism or articulation (non-fluent/agrammatic; nfavPPA; Gorno-Tempini et al., 2011). The Hopkins Action Naming Assessment (HANA) is a 30-item verb naming task that can distinguish between variants (Stockbridge et al., 2021). Item-level accuracy is driven by target verb frequency, semantic information density, and conceptual concreteness of the target word (Stockbridge, Venezia, et al., 2022). Aims In this investigation, we examined whether word frequency, semantic density, concreteness, and age of acquisition (AoA) also shaped the incorrect responses patients provided. We hypothesised that error responses would vary in these dimensions as a function of PPA variant. Methods & Procedures The HANA was administered to 271 participants with PPA, resulting in 443 total administrations and 4,529 analysable error responses. Standardised differences between error and target responses for frequency, density, concreteness, and AoA were calculated and averaged for each patient. Analysis of variance (ANOVA) for correlated samples was used to compare variants and planned post-hoc analyses examined the effect of variant on each response quality. Outcomes & Results Participants were similar in age, sex, handedness, and education. There was a significant interaction between PPA variant and the standardised mean differences in lexical qualities (Pillai’s Trace = 0.11, F(9, 747) = 3.19, p < 0.001). Univariate ANOVAs revealed significant differences in the semantic density of error responses relative to the target (F(3) = 7.91, p < 0.001, ηP 2= 0.09), as individuals with lvPPA tended to produce error responses with greater semantic density than the target when compared to the words produced by individuals with nfavPPA (mean difference = 1.45, 95%CI = [0.60,2.29], p < 0.001; Figure 1). PPA variants also differed in the concreteness of their error responses relative to the target (F(3) = 5.99, p<0.001, ηP 2 = 0.07), as error responses produced by individuals with nfavPPA were significantly more concrete than those with lvPPA (mean difference = 0.08, 95%CI = [0.02,0.13], p = 0.003) or svPPA (mean difference=0.08, 95%CI = [0.02,0.14], p = 0.007). Variants did not differ significantly in AoA or frequency of responses relative to their targets. All variants tended to produce more frequent words with a lower AoA than the target verb. Conclusions Error responses tended to be more semantically dense, more concrete, higher frequency, and younger AoA than the target verb. However, PPA variants differed significantly in the extent to which these broader trends held true. These distinct patterns may be included as part of a larger diagnostic picture that to distinguish among PPA variants.

Predicting English word concreteness through its multidimensional perceptual and action strength norms

Many datasets resulting from participant ratings for word norms and also concreteness ratios are available. However, the concreteness information of infrequent words and non-words is rare. This work aims to propose a model for estimating the concreteness of infrequent and new lexicons. Here, we used Lancaster sensory-motor word norms to predict the word concreteness ratios of an English word dataset. After removing the missing values, we employed a stepwise multiple linear regression (SW-MLR) procedure for choosing an optimum number of norms to develop a predictive multiple regression model. Finally, we validate our model using 10-fold cross-validation. The final model could predict concreteness by Residual Mean Standard Error equal to 0.723 and R-Square of 0.515. Also, our results showed that all 11 variables of this dataset except the Head-mouth parameter are useful predictors. In conclusion, as a growing demand to know the concreteness values of non-words and also infrequent words is evident, our statistical method can pave the way for controlled experiments when choosing non-words as a stimulus is critical.

Automated lexical analysis of story recall in healthy controls

AbstractBackgroundImpaired episodic memory is one of the earliest and most prominent symptoms of Alzheimer’s disease (AD). Examining how patients produce words and phrases in story recall tasks is useful for tracking and understanding progression of their disease. Traditional approaches rely on manual assessments of recalled stories which can be limited, time‐consuming, and not applicable in large‐scale studies. Here, we implement an automated, computational approach to measure verbal episodic memory in a digitally recorded story recall task performed by young healthy speakers using natural language processing models.MethodWe analyzed digitized speech samples of pre‐recorded immediate and delayed Craft Story recall tasks performed by healthy speakers (n = 67, mean age = 20.31 years, SD = 2.17, 45 (67%) females). All transcripts were transcribed by trained annotators and automatically scored for number of verbatim and paraphrase recall, total recall score ( = verbatim+paraphrase), and semantic distance from the original story (degree of semantic similarity between the original craft story and the recalled story based on the Word2vec module in python). We also calculated total word count and number of unique words, and rated all recalled words for word familiarity, concreteness, and semantic ambiguity based on published norms. Number of pauses and total durations of speech and silent pauses were also measured.ResultWe found larger semantic distance between successive words in the delayed recall task compared with immediate recall within speaker (p&lt;.001). Delayed recall also elicited lower total recall scores than immediate recall (p = .03). Comparing the effect sizes of semantic distance vs. total recall scores, our model comparison showed a smaller prediction error for semantic distance (AIC = 95.45) than total recall score (AIC = 736.18). In addition, speakers in the delayed recall task performed more paraphrase recall, had higher total word count, number of unique words, more ambiguous words, and produced more speech (all p‐values&lt;.001).ConclusionOur findings suggest that automated speech analysis can provide informative and cost‐effective measures from a recorded story recall task. The study provides an automated and quantitative way to better measure memory abilities by including semantic distance. Future work will test procedures in patients with AD or other neurodegenerative diseases.

Detecting Financial Statement Fraud through Multidimensional Analysis of Text Readability

ABSTRACT This study uses Coh-Metrix to analyze multiple dimensions of readability of the MD&amp;A section of the SEC Form 10-K. We incorporate the five main Coh-Metrix components of text easability (word concreteness, syntactic simplicity, referential cohesion, deep cohesion, and narrativity) into a logistic model to test their predictive power for financial misreporting. We find that compared to the MD&amp;As of nonfraud firms, the MD&amp;As of fraud firms connect clauses and sentences less coherently, use more story-like language, and show a higher number of vague and abstract words. Thus, referential cohesion, narrativity, and word concreteness significantly enhance predictive ability in fraud detection. The Coh-Metrix readability measures enhance the linguistic complexity assessment beyond traditional readability measures, such as the Fog Index and the Flesch Indexes. Financial analysts and investors can utilize the Coh-Metrix readability measures to supplement traditional readability measures and common financial statement variables in predicting financial misreporting. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G32; K42; M41; M48.

The effect of word concreteness on spelling to dictation across adulthood

The effect of word concreteness on spelling to dictation across adulthood

CONcreTEXT norms: Concreteness ratings for Italian and English words in context.

Concreteness is a fundamental dimension of word semantic representation that has attracted more and more interest to become one of the most studied variables in the psycholinguistic and cognitive neuroscience literature in the last decade. Concreteness effects have been found at both the brain and the behavioral levels, but they may vary depending on the constraints of the context and task demands. In this study, we collected concreteness norms for English and Italian words presented in different context sentences to allow better control and manipulation of concreteness in future psycholinguistic research. First, we observed high split-half correlations and Cronbach's alpha coefficients, suggesting that our ratings were highly reliable and can be used in Italian- and English-speaking populations. Second, our data indicate that the concreteness ratings are related to the lexical density and accessibility of the sentence in both English and Italian. We also found that the concreteness of words in isolation was highly correlated with that of words in context. Finally, we analyzed differences between nouns and verbs in concreteness ratings without significant effects. Our new concreteness norms of words in context are a valuable source of information for future research in both the English and Italian language. The complete database is available on the Open Science Framework (doi: 10.17605/OSF.IO/U3PC4).

The influence of word concreteness on acquired positive emotion association: An event-related potential study

The ability to acquire positive emotions from words is essential to psychological well-being. How word concreteness affects the process of positive emotion acquisition remains unknown. Here, using an evaluation conditioning paradigm, participants learned the association between pseudowords and concrete/abstract and positive/neutral words. Behavior and event-related potential data were recorded while participants performed emotional recognition tasks. Behavioral results showed that, for neutral words, concrete words were more accurate than abstract words, whereas for positive words, abstract words were more accurate than concrete words. Moreover, N1 and P2 amplitudes in the pseudowords were modulated by interacting word emotion and concreteness. Specifically, pseudowords associated with neutral concrete words elicited larger N1 and P2 amplitudes than pseudowords associated with neutral abstract words. Conversely, N1 and P2 amplitudes in pseudowords associated with positive abstract words were not significant compared to those in positive concrete words. Additionally, an emotional effect was observed when pseudowords were associated with abstract words, showing higher P3 amplitude for the pseudowords associated with positive abstract words than neutral abstract words. No significant effects were found for the pseudowords associated with positive abstract or concrete words. These findings suggest that association learning may influence the early attention processing of emotion acquisition from words, and emotional information of positive abstract words might boost positive emotion acquisition, thereby eliminating the acquisition advantage from positive concrete words.

Linguistic features of stuttering during spontaneous speech

PurposePrevious work shows that linguistic features (e.g., word length, word frequency) impact the predictability of stuttering events. Most of this work has been conducted using reading tasks. Our study examined how linguistic features impact the predictability of stuttering events during spontaneous speech. MethodsThe data were sourced from the FluencyBank database and consisted of interviews with 35 adult stutterers (27,009 words). Three logistic regression mixed models were fit as the primary analyses: one model with four features (i.e., initial phoneme, grammatical function, word length, and word position within a sentence), a second model with six features (i.e., the features from the previous model plus word frequency and neighborhood density), and a third model with nine features (i.e., the features from the previous model plus bigram frequency, word concreteness, and typical age of word acquisition). We compared our models using the Area Under the Curve statistic. ResultsThe four-feature model revealed that initial phoneme, grammatical function, and word length were predictive of stuttering events. The six-feature model revealed that initial phoneme, word length, word frequency, and neighborhood density were predictive of stuttering events. The nine-feature model was not more predictive than the six-feature model. ConclusionLinguistic features that were previously found to be predictive of stuttering during reading were predictive of stuttering during spontaneous speech. The results indicate the influence of linguistic processes on the predictability of stuttering events such that words associated with increased planning demands (e.g., longer words, low frequency words) were more likely to be stuttered.

Chinese intermediate English learners outdid ChatGPT in deep cohesion: Evidence from English narrative writing

ChatGPT is a publicly available chatbot that can quickly generate texts on given topics, but it remains unknown whether the chatbot is as good as or even superior to human writers in all aspects of writing, and whether the chatbot's writing quality can be significantly improved by updating commands. To investigate these issues, this study compared writing performance on a narrative topic by ChatGPT and Chinese intermediate English (CIE) learners so as to reveal the chatbot's advantages and disadvantages in writing.The data were analyzed by virtue of the five discourse components using Coh-Metrix (a special instrument for analyzing language discourse), and the results revealed that ChatGPT performed better than human writers in narrativity, word concreteness, and referential cohesion, but worse in syntactic simplicity and deep cohesion in its initial version. After its commands were updated, the resulting version was better in its syntactic simplicity, but it still lagged far behind the CIE learners’ writing in deep cohesion. In addition, a correlation analysis of the discourse components suggests that narrativity was correlated with referential cohesion for both ChatGPT and the human writers, but the correlations varied within each group.

Towards hypergraph cognitive networks as feature-rich models of knowledge

Conceptual associations influence how human memory is structured: Cognitive research indicates that similar concepts tend to be recalled one after another. Semantic network accounts provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns (e.g. reading “airplane” makes one think of “air” and “pollution”, and this is represented by links “airplane”-“air” and “airplane”-“pollution”). Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covariate with (and thus contain information about) how similar concepts are along psycholinguistic dimensions like arousal, valence, familiarity, gender and others. We overcome these limits by introducing feature-rich cognitive hypergraphs as quantitative models of human memory where: (i) concepts recalled together can all engage in hyperlinks involving also more than two concepts at once (cognitive hypergraph aspect), and (ii) each concept is endowed with a vector of psycholinguistic features (feature-rich aspect). We build hypergraphs from word association data and use evaluation methods from machine learning features to predict concept concreteness. Since concepts with similar concreteness tend to cluster together in human memory, we expect to be able to leverage this structure. Using word association data from the Small World of Words dataset, we compared a pairwise network and a hypergraph with N= 3586 concepts/nodes. Interpretable artificial intelligence models trained on (1) psycholinguistic features only, (2) pairwise-based feature aggregations, and on (3) hypergraph-based aggregations show significant differences between pairwise and hypergraph links. Specifically, our results show that higher-order and feature-rich hypergraph models contain richer information than pairwise networks leading to improved prediction of word concreteness. The relation with previous studies about conceptual clustering and compartmentalisation in associative knowledge and human memory are discussed.

Measuring the development of lexical richness of L2 Spanish: A longitudinal learner corpus study

AbstractResearch has indicated that lexical richness is an important indicator of second language (L2) proficiency. However, most research has examined written, cross-sectional English L2 corpora and does not necessarily indicate how spoken lexical use develops over time or whether observed trends are stable across L2s. This study adds to previous research on the development of spoken vocabulary by investigating lexical features of L2 Spanish learners over a 21-month period, using the LANGSNAP corpus. Multiple lexical richness indices used in previous studies were examined including lexical diversity, word frequency, word concreteness, and bigram strength of association. Linear mixed-effects models were run to examine changes over time. The results suggest that although some features of lexical richness (e.g., word frequency) see meaningful change over time, others (e.g., bigram T score) may not be indicative of L2 oral development.

Spatiotemporal dynamics of abstract and concrete semantic representations

Dual Coding Theories (DCT) suggest that meaning is represented in the brain by a double code: a language-derived code in the Anterior Temporal Lobe (ATL) and a sensory-derived code in perceptual and motor regions. Concrete concepts should activate both codes, while abstract ones rely solely on the linguistic code. To test these hypotheses, the present magnetoencephalography (MEG) experiment had participants judge whether visually presented words relate to the senses while we recorded brain responses to abstract and concrete semantic components obtained from 65 independently rated semantic features. Results evidenced early involvement of anterior-temporal and inferior-frontal brain areas in both abstract and concrete semantic information encoding. At later stages, occipital and occipito-temporal regions showed greater responses to concrete compared to abstract features. The present findings suggest that the concreteness of words is processed first with a transmodal/linguistic code, housed in frontotemporal brain systems, and only after with an imagistic/sensorimotor code in perceptual regions.

Impact of word properties on list learning: An explanatory item analysis.

A variety of factors affect list learning performance and relatively few studies have examined the impact of word selection on these tests. This study examines the effect of both language and memory processing of individual words on list learning. Item-response data from 1,219 participants, Mage = 74.41 (SD = 7.13), Medu = 13.30 (SD = 2.72), in the Harmonized Cognitive Assessment Protocol were used. A Bayesian generalized (non)linear multilevel modeling framework was used to specify the measurement and explanatory item-response theory models. Explanatory effects on items due to learning over trials, serial position of words, and six word properties obtained through the English Lexicon Project were modeled. A two parameter logistic (2PL) model with trial-specific learning effects produced the best measurement fit. Evidence of the serial position effect on word learning was observed. Robust positive effects on word learning were observed for body-object integration while robust negative effects were observed for word frequency, concreteness, and semantic diversity. A weak negative effect of average age of acquisition and a weak positive effect for the number of phonemes in the word were also observed. Results demonstrate that list learning performance depends on factors beyond the repetition of words. Identification of item factors that predict learning could extend to a range of test development problems including translation, form equating, item revision, and item bias. In data harmonization efforts, these methods can also be used to help link tests via shared item features and testing of whether these features are equally explanatory across samples. (PsycInfo Database Record (c) 2023 APA, all rights reserved).