DANTE – the Database of ANalysed Texts of English – is a lexical database which provides a corpus-based description of the core vocabulary of English. It records the semantic, grammatical, combinatorial, and text-type characteristics of over 42,000 single-word lemmas and 23,000 compounds and phrasal verbs, and it also includes over 27,000 idioms and phrases. Every fact recorded in the database is derived from a systematic analysis of a 1.7 billion-word corpus and supported by corpus examples. The complete text of DANTE from M to R is freely available online (at www.webdante.com), and the full database is available through research or commercial licences

The major aim of the present megastudy of picture-naming norms was to address the shortcomings of the available picture data sets used in psychological and linguistic research by creating a new database of normed colour images that researchers from around the world can rely upon in their investigations. In order to do this, we employed a new form of normative study, namely a megastudy, whereby 1620 colour photographs of items spanning across 42 semantic categories were named and rated by a group of German speakers. This was done to establish the following linguistic norms: speech onset times (SOT), name agreement, accuracy, familiarity, visual complexity, valence, and arousal. The data, including over 64,000 audio files, were used to create the LinguaPix database of pictures, audio recordings, and linguistic norms, which to our knowledge, is the largest available research tool of its kind ( http://linguapix.uni-mannheim.de ). In this paper, we present the tool and the analysis of the major variables.

We developed a set of high quality standardized photographs of objects from six different categories (fruits, vegetables, kitchen utensils, office supplies, toys, and women’s accessories), recorded under two camera viewpoints (top and frontal viewpoints), and five presentation conditions (on its own, held by clean hands, and by hands covered with different substances: sauce, chocolate and mud). Naming normative data and object familiarity ratings are provided from a North American and a Portuguese sample. Further affective evaluations (disgust, arousal and valence) are provided for the various stimuli condition as well as for a context manipulation. Examples of the database and request for access can be made here: https://sites.google.com/view/adaptive-memory-lab/data-databases Here we make available a set of supplementary files containing the following information: S1 Appendix. List of pictures available in each condition and viewpoint. S2 Appendix. Indexes related to the naming task and familiarity ratings obtained for each stimulus and in each sample. S3 Appendix. Responses coded as modal names, the different alternative names provided, along with their corresponding frequencies and percentages of occurrence. S4 Appendix. Arousal, disgust, and valence ratings provided for each stimulus, in each encoding context.

We describe a newly available Hebrew Dependency Treebank, which is extracted from the Hebrew (constituency) Tree-bank. We establish some baseline unlabeled dependency parsing performance on Hebrew, based on two state-of-the-art parsers, MST-parser and MaltParser. The evaluation is performed both in an artificial setting, in which the data is assumed to be properly morphologically segmented and POS-tagged, and in a real-world setting, in which the parsing is performed on automatically segmented and POS-tagged text. We present an evaluation measure that takes into account the possibility of incompatible token segmentation between the gold standard and the parsed data. Results indicate that (a) MST-parser performs better on Hebrew data than Malt-Parser, and (b) both parsers do not make good use of morphological information when parsing Hebrew.

In this work we introduce the analysis of DysList, a language resource for Spanish composed of a list of unique spelling errors extracted from a collection of texts written by people with dyslexia. Each of the errors was annotated with a set of characteristics as well as with visual and phonetic features. To the best of our knowledge, this is the largest resource of this kind in Spanish. We also analyzed all the features of Spanish errors and our main finding is that dyslexic errors are phonetically and visually motivated.

In this article we present a Spanish grammar implemented in the Linguistic Knowledge Builder system and grounded in the theoretical framework of Head-driven Phrase Structure Grammar. The grammar is being developed in an international multilingual context, the DELPH-IN Initiative, contributing to an open-source repository of software and linguistic resources for various Natural Language Processing applications. We will show how we have refined and extended a core grammar, derived from the LinGO Grammar Matrix, to achieve a broad-coverage grammar. The Spanish DELPH-IN grammar is the most comprehensive grammar for Spanish deep processing, and it is being deployed in the construction of a treebank for Spanish of 60,000 sentences based in a technical corpus in the framework of the European project METANET4U (Enhancing the European Linguistic Infrastructure, GA 270893GA; http://www.meta-net.eu/projects/METANET4U/.) and a smaller treebank of about 15,000 sentences based in a corpus from the press.

This study examined how well large language models (LLMs) approximate human psychological ratings for early-acquired English words. We used four state-of-the-art LLMs, including GPT-4o and Meta-Llama-3.1, to evaluate 21 static psychological features for 695 words and compared these estimates with human norms. The results showed that LLMs aligned well with human ratings for some features (e.g., Concreteness, Bodily Interactiveness) in terms of rank correlations (rs >.82) and distributional similarities but diverged notably for others (e.g., Iconicity, Arousal; rs <.48). Compared with content words, function words showed more pronounced discrepancies between human and LLM ratings. We also assessed how similarly human- and LLM-derived psychological features predicted words' age of acquisition (AoA), revealing both strong correspondences and systematic biases, depending on the model (differences in correlations ranged from -.27 to.28). Based on these analyses, we identified which features may be reliably estimated using LLMs, which require further refinement, and what methodological considerations are necessary for applying LLM-based measures in cognitive science. We discuss the implications of using LLMs as methodological tools in psychology and cognitive science, highlighting both their practical advantages (e.g., data coverage and data collection efficiency) and theoretical relevance. The present study provides a novel framework for evaluating the cognitive plausibility of LLMs by using lexical psychological features, complementing existing benchmarks.

Translation equivalents are widely used in bilingual research concerning word processing (e.g., Eddington & Tokowicz, 2013; Jouravlev & Jared, 2020) and second-language vocabulary learning (e.g., Bracken et al., 2017; Degani et al., 2014). Although translation norms exist in several languages, to date there are no Malay-English translation norms. This study presents the first Malay-English translation norms collected with highly proficient Malay-English bilinguals. Furthermore, the study investigates the impact of lexical characteristics on translation ambiguity. The forward translation (FT) task (N = 30) collected English translations for 1004 Malay words selected from the Malay Lexicon Project (Yap et al., 2010), and subsequently the backward translation (BT) task (N = 30) gathered Malay translations for 845 English words obtained from the FT phase. The data revealed a high prevalence of translation ambiguity in both translation directions. Specifically, verbs, adjectives, and class-ambiguous words were more translation-ambiguous than nouns. Furthermore, within-language semantic variability and word length were positively correlated with translation ambiguity, whereas word frequency only correlated with translation ambiguity in FT. Word length and word frequency of the source words and their translations were positively correlated. Intriguingly, only in FT were bilinguals with higher Malay proficiency more likely to provide accurate and dominant translations for the Malay words. These findings contrast with those reported in translation norming studies involving other language pairs. The translation norms provide a useful resource for bilingual language studies involving Malay-English bilinguals.

In this study, we present word prevalence data (i.e., the number of people who know a given word) for 40,777 Catalan words. An online massive visual lexical decision task involving more than 200,000 native speakers of this language was carried out. The characteristics of the participants as well as those of the words which mostly influence word knowledge were examined. Regarding the participants, the analysis of the data revealed that their age was the main factor influencing vocabulary size, followed by their educational level and other variables such as the number of languages spoken and their level of proficiency in Catalan. Concerning the words, by far the most determining factor was lexical frequency, with a minor influence of both length and the size of the orthographic neighborhood. These data mainly agree with those reported in other languages in which the same variables have been analyzed (Dutch, English, and Spanish, thus far). Therefore, the list is increased with Catalan, a language which, due to its use in an essentially bilingual context, is of special interest to researchers interested in the field of bilingualism and second language acquisition.

We present a new lexical resource for the study of preposition behavior, the Pattern Dictionary of English Prepositions (PDEP). This dictionary, which follows principles laid out in Hanks’ theory of norms and exploitations, is linked to 81,509 sentences for 304 prepositions, which have been made available under The Preposition Project (TPP). Notably, 47,285 sentences, initially untagged, provide a representative sample of preposition use, unlike the tagged sentences used in previous studies. Each sentence has been parsed with a dependency parser and our system has near-instantaneous access to features developed with this parser to explore and annotate properties of individual senses. The features make extensive use of WordNet. We have extended feature exploration to include lookup of FrameNet lexical units and VerbNet classes for use in characterizing preposition behavior. We have designed our system to allow public access to any of the data available in the system.

It has been proposed that social experience plays an important role in the grounding of concepts, and socialness has been proffered as a fundamental organisational principle underpinning semantic representation in the human brain. However, the empirical support for these hypotheses is limited by inconsistencies in the way socialness has been defined and measured. To further advance theory, the field must establish a clearer working definition, and research efforts could be facilitated by the availability of an extensive set of socialness ratings for individual concepts. Therefore, in the current work, we employed a novel and inclusive definition to test the extent to which socialness is reliably perceived as a broad construct, and we report socialness norms for over 8000 English words, including nouns, verbs, and adjectives. Our inclusive socialness measure shows good reliability and validity, and our analyses suggest that the socialness ratings capture aspects of word meaning which are distinct to those measured by other pertinent semantic constructs, including concreteness and emotional valence. Finally, in a series of regression analyses, we show for the first time that the socialness of a word's meaning explains unique variance in participant performance on lexical tasks. Our dataset of socialness norms has considerable item overlap with those used in both other lexical/semantic norms and in available behavioural mega-studies. They can help target testable predictions about brain and behaviour derived from multiple representation theories and neurobiological accounts of social semantics.

How words are associated within the linguistic environment conveys semantic content, and it is well known that adultsspeak differently to children than to other adults. We present results from a new word association study in which adultparticipants are instructed to produce either unconstrained or child-directed responses to each cue, where cues included674 nouns, verbs, and adjectives from the McArthur-Bates Communicative Development Inventory (CDI). Child-directedresponses consisted of higher frequency words with fewer letters and earlier ages of acquisition. The correlations amongthe responses generated for each pair of cues differed between unconstrained and child-directed responses, suggestingthat child-directed associations imply different semantic structure. A comparison of growth models guided by semanticnetwork structure revealed that child-directed associations are more predictive of early lexical growth. Thus, these newchild-directed word association norms may provide more clear insight into the semantic context of young children.

The dataset is used to evaluate the predictive power of different English frequency norms on L2 lexical processing data, which were drawn from the data of an L2 English lexical decision task (Chen et al., 2018) conducted among a group of L2 English learners in China. The dataset presented here includes the L2 mean RT and accuracy data for the 370-word stimuli in Chen et al. (2018), their length (including the number of letters, number of syllables), neighboorhood density scores (OLD), the raw corpus frequencies (KF, CELEX, BNC, COCA, ANC, SUBTLEX-UK, SUBTLEX-US, HAL, USENET, WORLDLEX, WORLDLEX-blog, WORLDLEX-Twitter, WORLDLEX-news, GoogleBooks-AmE, GoogleBooks-BrE), as well as the corresponding Zipf values (see van Heuven et al., 2014), and the subjective frequency ratings collected from L2 English learners in China.

Singapore English is a dialect of English spoken by individuals living in Singapore, whose colloquial form (i.e., Singapore Colloquial English) contains unique lexical items not found in dominant dialects of English. The absence of these items from the lexicon of dominant English dialects indicates that lexical-semantic norms central to psycholinguistic research do not exist for these Singapore English concepts. The present paper describes the development of lexical-semantic norms of valence, arousal, concreteness, and humor for a core vocabulary list of approximately 300 words and concepts. The contribution of these lexical-semantic norms to account for lexical processing performance was then evaluated in a visual lexical decision task containing a subset of items from the core list. Results indicated that valence, arousal, and concreteness explained additional variance over and above orthographic similarity and word frequency in the visual lexical decision task. Specifically, Singapore English words that were more positively valenced, highly arousing, and more concrete, were responded to more rapidly and accurately. Overall, this paper provides a case study of how psycholinguistic research can be extended to diverse, understudied dialects of English, and showcases how doing so offers an opportunity for psycholinguistics to examine the importance of various lexical-semantic measures to quantify lexical information in colloquial, informal language.

We present a resource including perceptual strength norms for 1121 Italian words, extracted from the Italian version of the ANEW database. Norms were collected from 57 native-speakers. For each word, participants provided perceptual strength rating for each of the five perceptual modalities, namely hearing, taste, touch, smell and vision. The performance of the perceptual norms in predicting human behaviour was tested in two novel experiments, a lexical decision (n= 30) and a naming task (n=29), in which the same 1121 words were used as stimuli. Here we released our dataset, which contains aggregated mean values of participants' norm ratings, accuracy and reaction times.

This work attempts to extract a viewer’s emotion from the three modalities of a movie: audio, visual and text. A major obstacle for emotion research has been the lack of appropriately annotated databases, limiting the potential of supervised algorithms. To that end we develop and present a database of movie affect, annotated in continuous time, on a continuous valence-arousal scale. Supervised learning methods are proposed to model the continuous affective response using hidden Markov Models and low-level audio-visual features and classify each video frame into one of seven discrete categories (in each dimension); the discrete-valued curves are then converted to continuous values via spline interpolation. A variety of audio-visual features are investigated and an optimal feature set is selected. The potential of the method is verified on twelve 30-minute movie clips with good precision at a macroscopic level. This method proves not suitable to process subtitle information, so we explore the creation of a textual affective model, starting with a fully automated algorithm for expanding an affective lexicon with new entries. Continuous valence ratings are estimated for unseen words under the assumption that semantic similarity implies affective similarity. Starting from a set of manually annotated words, a linear model is trained using the least mean squares algorithm. The semantic similarity between the selected features and the unseen words is computed with various similarity metrics, and used to compute the valence of unseen words. The proposed algorithm performs very well on reproducing the valence ratings of the Affective Norms for English Words (ANEW) and General Inquirer datasets. We then use three simple fusion schemes to combine lexical valence scores into sentence-level scores, producing state-of-the-art results on the sentence rating task of the SemEval 2007 corpus.

Singapore English is a dialect of English spoken by individuals living in Singapore, whose colloquial form (i.e., Singapore Colloquial English) contains unique lexical items not found in dominant dialects of English. The absence of these items from the lexicon of dominant English dialects indicates that lexical-semantic and affective norms central to psycholinguistic research do not exist for these Singapore English concepts, and it is unclear what is the specific influence of these effects when processing Singapore Colloquial English words. The present paper describes the development of valence, arousal, concreteness, and humor norms for a core vocabulary list of approximately 300 words and concepts, via human ratings and probing a Large Language Model, and evaluates the contribution of these norms to account for lexical processing performance in a visual lexical decision task. Results indicated that valence, arousal, and concreteness explained additional variance over and above orthographic similarity and word frequency in the visual lexical decision task. Specifically, Singapore English words that were more positively valenced, highly arousing, and more concrete, were responded to more rapidly and accurately. In addition, although there was generally a high convergence of valence, arousal, and concreteness ratings across human raters and the Large Language Model, humor norms were much less closely aligned. Overall, this paper provides a case study of how psycholinguistic research can be extended to diverse, understudied dialects of English, and showcases how doing so offers an opportunity for psycholinguistics to examine the importance of various lexical-semantic and affective measures to quantify lexical information in colloquial, informal language.

The emotional content of words can affect both true and false memory performance. One hypothesis suggests that the effects of emotion on memory stem from the semantic cohesion of these words. Emotional words are better remembered because they are more inter-related than neutral words (semantic cohesion hypothesis). Although support for this assumption has been found in tasks that measure true memory, less is known about how the structure of lexical knowledge affects emotional false memories. This is partially due to the scarcity of norms that capture the pre-existing knowledge structure of verbal materials commonly used to investigate emotional false memories, such as the Deese/Roediger-McDermott word lists. In this study, we present inter-item association norms for the 44 lists of the Brazilian version of the DRM paradigm. Free-association responses were collected from a sample of 1,042 undergraduates and were used to estimate the level of connectivity among the words present in the DRM lists. Connectivity measures were then used to test the semantic cohesion hypothesis. No significant correlations were found between the emotional measures (valence and arousal) and the connectivity measures. The results do not give support to the semantic cohesion hypothesis and suggest that, for the Brazilian version of DRM lists, inter-item association and emotionality can be independently manipulated.

The purpose of this research was to provide norms for ambiguous spanish words: homographs (words with different meanings unrelated) and polysemous words (words with meanings related). 1n the first study, 104 subjects were asked for defining as many meanings as they knew of a sample of 152 homographs. 1n the second study a sample of 61 polysemous words was presented to 96 subjects who had to build as many sentences as meanings they knew for words. For the purpose of determining the normative frequency of responses representing alternative meanings of the stimulus word we computed the percentage of subjects who answered each meaning. The data were analyzed to determine several indexes: number of meanings, length, graphemic frequency, lexical frequency, and dominance (meanings accessed simultaneously) or equiprobability (equally likely meanings) of each sense for ambiguous words.

The lexical system of Hong Kong Cantonese has been heavily shaped by the local trilingual environment. The development of cultural- and language-specific norms for Hong Kong Cantonese is fundamental for understanding how the speaker population organize semantic memory, how they utilize their semantic resources, and what information processing strategies they use for the retrieval of semantic knowledge. This study presents a normative database of 72 lexical categories in Hong Kong Cantonese produced by native speakers in a category exemplar production task. Exemplars are enlisted under a category label, along with the instance probabilities and word familiarity scores. Possible English equivalents are given to the exemplars for the convenience of non-HKC speaker researchers. Statistics on categories were further extracted to capture the heterogeneity of the categories: the total number of valid exemplars, the number of exemplars covering 90% of the occurrence and the probabilities of the most frequent exemplars in each category. The database offers a direct lexical sketch of the vocabulary of modern Hong Kong Cantonese in a categorical structure. The category-exemplar lists and the comparative statistics together lay the foundations for further investigations on the Hong Kong Cantonese speaking population from multiple disciplines, such as the structure of semantic knowledge, the time-course of knowledge access, and the processing strategies of young adults. Results of this norm can be also used as a benchmark for other age groups. The database can serve as a crucial resource for establishing initial screening tests to assess the cognitive and psychological functioning of the Cantonese-speaking Hong Kong population in both educational and clinical settings. In sum, this normative study provides a fundamental resource for future studies on language processing mechanisms of Hong Kong Cantonese speaking population, as well as language studies and other cross-language/culture studies on Hong Kong Cantonese.

Most large language models are trained on linguistic input alone, yet humans\nappear to ground their understanding of words in sensorimotor experience. A\nnatural solution is to augment LM representations with human judgments of a\nword's sensorimotor associations (e.g., the Lancaster Sensorimotor Norms), but\nthis raises another challenge: most words are ambiguous, and judgments of words\nin isolation fail to account for this multiplicity of meaning (e.g., "wooden\ntable" vs. "data table"). We attempted to address this problem by building a\nnew lexical resource of contextualized sensorimotor judgments for 112 English\nwords, each rated in four different contexts (448 sentences total). We show\nthat these ratings encode overlapping but distinct information from the\nLancaster Sensorimotor Norms, and that they also predict other measures of\ninterest (e.g., relatedness), above and beyond measures derived from BERT.\nBeyond shedding light on theoretical questions, we suggest that these ratings\ncould be of use as a "challenge set" for researchers building grounded language\nmodels.\n

The relationship between the classical affective parameters valence, arousal, dominance and abstractness/concreteness indicators has been conducted on the material of a fairly large number of languages since the end of the 20th century, the implementation of which is conditioned by the availability of specialized resources with quantitative indicators of these affective parameters. For the Russian language, a database of affective norms containing ratings for 1000 words was created recently. At the same time, the availability of a database of abstractness/concreteness ratings allows us to consider the relationship between affective parameters and lexical-semantic and grammatical properties using the material of the Russian language. The main objective of this study is to analyze the correlation between the three affective indicators valence, arousal, dominance and abstractness/concreteness ratings. In the article, a qualitative analysis of lexical units is confirmed by quantitative data. As a result of the study, the features of the distribution of quantitative indicators of affectivity depending on the part-of-speech characteristics of words were revealed. The affective profile of abstract and concrete words is described in detail, which, on the one hand, indicates some specific features of the Russian language in comparison with other languages, and on the other hand, general language tendencies. In addition, the affective features are described based on the analysis of regular thematic groups, which made it possible to determine the dependence of the distribution of words by the parameters valence, arousal, dominance on their lexical and semantic features. The conducted complex of studies demonstrates the possibilities of using the developed database of affective parameters VAD for the Russian language.

This study provides implicit verb consequentiality norms for a corpus of 305 English verbs, for which Ferstl et al. (Behavior Research Methods, 43, 124-135, 2011) previously provided implicit causality norms. An online sentence completion study was conducted, with data analyzed from 124 respondents who completed fragments such as "John liked Mary and so…". The resulting bias scores are presented in an Appendix, with more detail in supplementary material in the University of Sussex Research Data Repository (via https://doi.org/10.25377/sussex.c.5082122 ), where we also present lexical and semantic verb features: frequency, semantic class and emotional valence of the verbs. We compare our results with those of our study of implicit causality and with the few published studies of implicit consequentiality. As in our previous study, we also considered effects of gender and verb valence, which requires stable norms for a large number of verbs. The corpus will facilitate future studies in a range of areas, including psycholinguistics and social psychology, particularly those requiring parallel sentence completion norms for both causality and consequentiality.

Psycholinguistic studies have shown that there are many variables implicated in language comprehension and production. At the lexical level, subjective age of acquisition (AoA), the estimate of the age at which a word is acquired, is key for stimuli selection in psycholinguistic studies. AoA databases in English are often used when testing a variety of phenomena in second language (L2) speakers of English. However, these have limitations, as the norms are not provided by the target population (L2 speakers of English) but by native English speakers. In this study, we asked native Spanish L2 speakers of English to provide subjective AoA ratings for 1604 English words, and investigated whether factors related to 14 lexico-semantic and affective variables, both in Spanish and English, and to the speakers' profile (i.e., sociolinguistic variables and L2 proficiency), were related to the L2 AoA ratings. We used boosted regression trees, an advanced form of regression analysis based on machine learning and boosting algorithms, to analyse the data. Our results showed that the model accounted for a relevant proportion of deviance (58.56%), with the English AoA provided by native English speakers being the strongest predictor for L2 AoA. Additionally, L2 AoA correlated with L2 reaction times. Our database is a useful tool for the research community running psycholinguistic studies in L2 speakers of English. It adds knowledge about which factors-linked to the characteristics of both the linguistic stimuli and the speakers-affect L2 subjective AoA. The database and the data can be downloaded from: https://osf.io/gr8xd/?view_only=73b01dccbedb4d7897c8d104d3d68c46.

We present a database of category production (aka semantic fluency) norms collected in the UK for 117 categories (67 concrete and 50 abstract). Participants verbally named as many category members as possible within 60 seconds, resulting in a large variety of over 2000 generated member concepts. The norms feature common measures of category production (production frequency, mean ordinal rank, first-rank frequency), as well as response times for all first-named category members, and typicality ratings collected from a separate participant sample. We provide two versions of the dataset: a referential version that groups together responses that relate to the same referent (e.g., hippo, hippopotamus) and a full version that retains all original responses to enable future lexical analysis. Correlational analyses with previous norms from the USA and UK demonstrate both consistencies and differences in English-language norms over time and between geographical regions. Further exploration of the norms reveals a number of structural and psycholinguistic differences between abstract and concrete categories. The data and analyses will be of use in the fields of cognitive psychology, neuropsychology, psycholinguistics, and cognitive modelling, and to any researchers interested in semantic category structure. All data, including original participant recordings, are available at https://osf.io/jgcu6/.

The formation of false memories is one of the most widely studied topics in cognitive psychology. The Deese-Roediger-McDermott (DRM) paradigm is a powerful tool for investigating false memories and revealing the cognitive mechanisms subserving their formation. In this task, participants first memorize a list of words (encoding phase) and next have to indicate whether words presented in a new list were part of the initially memorized one (recognition phase). By employing DRM lists optimized to investigate semantic effects, previous studies highlighted a crucial role of semantic processes in false memory generation, showing that new words semantically related to the studied ones tend to be more erroneously recognized (compared to new words less semantically related). Despite the strengths of the DRM task, this paradigm faces a major limitation in list construction due to its reliance on human-based association norms, posing both practical and theoretical concerns. To address these issues, we developed the False Memory Generator (FMG), an automated and data-driven tool for generating DRM lists, which exploits similarity relationships between items populating a vector space. Here, we present FMG and demonstrate the validity of the lists generated in successfully replicating well-known semantic effects on false memory production. FMG potentially has broad applications by allowing for testing false memory production in domains that go well beyond the current possibilities, as it can be in principle applied to any vector space encoding properties related to word referents (e.g., lexical, orthographic, phonological, sensory, affective, etc.) or other type of stimuli (e.g., images, sounds, etc.).

Obtaining norm scores for subjective properties of words can\nbe quite cumbersome as it requires a considerable investment\nproportional to the size of the word set. We present a method\nto predict norm scores for large word sets from a word\nassociation corpus. We use similarities between word pairs,\nderived from this corpus, to construct a semantic space.\nStarting from norm scores for a subset of the words, we\nretrieve the direction in the space that optimally reflects the\nnorm data associated with the words. This direction is used to\northogonally project all the other words in the semantic space\non, providing predictions of the words on the variable of\ninterest. In this study, we predict valence, arousal, dominance,\nage of acquisition, and concreteness and show that the\npredictions correlate strongly with the judgments of human\nraters. Furthermore, we show that our predictions are superior\nto those derived using other methods

This paper presents norms for concreteness and emotional valence of 200 monosyllabic and 199 three-syllabic nouns. Both sets of words were comparable in lexical familiarity. Each word was rated for both characteristics by at least 145 raters. Test-retest and inter-rater reliability proved high for both concreteness and emotional valence ratings. The norms also proved reliable in comparison to three other studies, and they proved effective in two experiments; one with concreteness as an independent variable and one with emotional valence as an independent variable. The nouns, their mean rating and standard deviation arc presented in the appendices, together with two measures of their frequency of occurrence.

A dataset of specificity ratings for English words is hereby presented, analyzed and discussed in relation with other collections of speaker-generated ratings, including concreteness. Both, specificity and concreteness are analyzed in their ability to explain decision latencies in lexical and semantic tasks, showing important individual contributions. Specificity ratings are collected through best-worst scaling method on the words included in the ANEW dataset (Bradley and Lang in Affective norms for English words (ANEW): instruction manual and affective ratings (Tech. Rep.). Technical report C-1, the center for research in psychophysiology, 1999), chosen for its compatibility with many other collections of rating resources, and for its comparability with Italian specificity data (Bolognesi and Caselli in Behav Res Methods 55(7):3531-3548, 2023), allowing for cross-linguistic comparisons. Results suggest that specificity plays an important role in word processing and the importance of taking specificity into consideration when investigating concreteness effects.

We provide new behavioural norms for semantic classification of pictures and words. The picture stimuli are 288 black and white line drawings from the International Picture Naming Project ([Székely, A., Jacobsen, T., D'Amico, S., Devescovi, A., Andonova, E., Herron, D., et al. (2004). A new on-line resource for psycholinguistic studies. Journal of Memory & Language, 51, 247-250]). We presented these pictures for classification in a living/nonliving decision, and in a separate version of the task presented the corresponding word labels for classification. We analyzed behavioural responses to a subset of the stimuli in order to explore questions about semantic processing. We found multiple semantic richness effects for both picture and word classification. Further, while lexical-level factors were related to semantic classification of words, they were not related to semantic classification of pictures. We argue that these results are consistent with privileged semantic access for pictures, and point to ways in which these data could be used to address other questions about picture processing and semantic memory.

En este trabajo se presentan datos normativos para 400 dibujos (Cycowicz, Friedman, Rothstein y Snodgrass, 1997) en una muestra del espanol de Argentina. Las variables estandarizadas son: frecuencia lexica, imaginabilidad, concretud, tipicalidad y categoria semantica. Estos datos se suman a los previamente presentados por Manoiloff, Artstein, Canavoso, Fernandez, Moroni y Segui (2008): acuerdo en el nombre, concordancia en la imagen, familiaridad, complejidad visual, variabilidad de la imagen, edad de adquisicion e idea asociada. Las nuevas variables son de fundamental importancia como instrumento para el trabajo experimental sobre procesamiento del lenguaje, memoria y reconocimiento visual, entre otras areas. Palabras clave Normas Snodgrass Imaginabilidad Concretud ABSTRACT NEW PSYCHOLINGUISTIC NORMS FOR 400 ALARIO AND FERRAND PICTURES IN SPANISH This work presents normative data for 400 pictures taken from Cycowicz, Friedman, Rothstein and Snodgrass (1997) in a native Argentinean Spanish-speaking sample. The standardized variables are lexical frequency, imageability, concreteness, typicality, and semantic category. These data complement those previously presented by Manoiloff, Artstein, Canavoso, Fernandez, Moroni and Segui (2008): Name agreement, image agreement, familiarity, visual complexity, image variability, age of acquisition and verbal associates. The new norms are instruments of fundamental importance for the experimental work in language processing, memory and visual recognition, among other areas. Key words Snodgrass Norms Imageability Concreteness

This paper presents the results of a cross-linguistic idiom norming study based on 150 pairs of Italian and English idioms with similar meanings. Idiom pairs are categorized as lexical (LL), semi-lexical (SL), and post-lexical (PL) based on translatability (Beck, 2020), and annotated to indicate shared syntactic constituents. The variables explored include familiarity, meaningfulness, and objective knowledge (Experience-Based Variables, EBVs), alongside literal plausibility, decomposability, and transparency (Content-Based Variables, CBVs; Hubers, Cucchiarini, Strik, &amp;amp; Dijkstra, 2019). The aims of the present work are multifaceted. First, a replicability check is intended, verifying if idioms show consistent correlational patterns across Italian and English. Second, it examines whether a correlational continuum exists for each CBV, with LL idioms expected to show stronger correlations than SL and PL ones. Finally, the study deepens the comparative perspective on CBVs through exploratory statistical modeling of cross-linguistic rating differences. Results show that correlational patterns are largely replicated between Italian and English, also for variables notoriously difficult to assess. The cross-linguistic correlational continuum is found for all CBVs, and model results show that the difference between Italian and English CBV ratings increases from LL to PL idiom pairs for each CBV, confirming the translatability continuum. Notably, syntax does not significantly impact the difference between Italian and English CBV ratings. Future research avenues are outlined to further explore the relationships between idiomatic variables and expand the comparative study of cross-linguistic idioms.

This project hosts Mime Initiation Latency (MIL) norms for 189 object photographs, developed to support research on motor affordances and embodied cognition. The dataset provides behavioural latency measures indexing the time required for participants to initiate a pantomimed object-use action in response to colour photographs drawn from the Bank of Standardized Stimuli (BOSS; Brodeur et al., 2010, 2014). MIL constitutes a behavioural motor norm intended to complement existing subjective affordance measures (e.g., manipulability, graspability, mimability, body–object interaction). The norms were collected from Thai undergraduate participants using a controlled reaction-time paradigm implemented in E-Prime. After data screening and trimming procedures, normative statistics (means, standard deviations, distributional indices) are reported for 189 manipulable object photographs. Construct validity was evaluated through correlational and regression analyses with established motor and psycholinguistic norms. MIL was moderately to strongly associated with conceptually related motor variables and weakly or not associated with conceptually unrelated motor or lexical-semantic variables, supporting its interpretation as a behavioural index of motor affordance. The repository contains: Aggregated MIL norms (item-level statistics), and Raw data (response times, in milliseconds, for each participant). The shared materials are intended to facilitate stimulus selection, replication, and secondary analyses involving behavioural motor variables. This dataset accompanies the article: Ludington, J. D., &amp; Clarke, A. J. B. (in press). Mime Initiation Latency for Object Photographs: A Behavioral Motor Norm. Journal of Psycholinguistic Research. Full bibliographic details (volume, issue, pages, DOI) will be added upon publication. The norms are released under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence to facilitate reuse with appropriate attribution.

Sensorimotor information plays a fundamental role in cognition. However, the existing materials that measure the sensorimotor basis of word meanings and concepts have been restricted in terms of their sample size and breadth of sensorimotor experience. Here we present norms of sensorimotor strength for 39,707 concepts across six perceptual modalities (touch, hearing, smell, taste, vision, and interoception) and five action effectors (mouth/throat, hand/arm, foot/leg, head excluding mouth/throat, and torso), gathered from a total of 3,500 individual participants using Amazon's Mechanical Turk platform. The Lancaster Sensorimotor Norms are unique and innovative in a number of respects: They represent the largest-ever set of semantic norms for English, at 40,000 words × 11 dimensions (plus several informative cross-dimensional variables), they extend perceptual strength norming to the new modality of interoception, and they include the first norming of action strength across separate bodily effectors. In the first study, we describe the data collection procedures, provide summary descriptives of the dataset, and interpret the relations observed between sensorimotor dimensions. We then report two further studies, in which we (1) extracted an optimal single-variable composite of the 11-dimension sensorimotor profile (Minkowski 3 strength) and (2) demonstrated the utility of both perceptual and action strength in facilitating lexical decision times and accuracy in two separate datasets. These norms provide a valuable resource to researchers in diverse areas, including psycholinguistics, grounded cognition, cognitive semantics, knowledge representation, machine learning, and big-data approaches to the analysis of language and conceptual representations. The data are accessible via the Open Science Framework (http://osf.io/7emr6/) and an interactive web application (https://www.lancaster.ac.uk/psychology/lsnorms/).

Word associations are among the most direct ways to measure word meaning in human minds, capturing various relationships, even those formed by non-linguistic experiences. Although large-scale word associations exist for Dutch, English, and Spanish, there is a lack of data for Mandarin Chinese, the most widely spoken language from a distinct language family. Here we present the Small World of Words-Zhongwen (Chinese) (SWOW-ZH), a word association dataset of Mandarin Chinese derived from a three-response word association task. This dataset covers responses for over 10,000 cue words from more than 40,000 participants. We constructed a semantic network based on this dataset and evaluated concurrent validity of association-based measures by predicting human processing latencies and comparing them with text-based measures and word embeddings. Our results show that word centrality significantly predicts lexical decision and word naming speed. Furthermore, SWOW-ZH notably outperforms text-based embeddings and transformer-based large language models in predicting human-rated word relationships across varying sample sizes. We also highlight the unique characteristics of Chinese word associations, particularly focusing on word formation. Combined, our findings underscore the critical importance of large-scale human experimental data and its unique contribution to understanding the complexity and richness of language.

Words differ with respect to their frequency of-occurrence in the language and with respect to the age at which they are acquired Research has indicated that both variables have a large impact on the speed of processing in a multitude of psycholinguistic tasks. A problem for the research, however, is that the information about the age of acquisition for many types of words is very limited. In Dutch, the information is largely confined to short words Because there is increasing evidence that age of acquisition may be an important variable in the organisation of the semantic system, we present data about 2,332 carefully selected words from 49 different semantic categories. These will provide researchers with the information needed to test a wide range of hypotheses about the origins of the age-of-acquisition effect and to adequately select their stimuli for semantic categorisation tasks.

We present a new set of subjective Age of Acquisition (AoA) ratings for 299 words (158 nouns, 141 verbs) in seven languages from various language families and cultural settings: American English, Czech, Scottish Gaelic, Lebanese Arabic, Malaysian Malay, Persian, and Western Armenian. The ratings were collected from a total of 173 participants and were highly reliable in each language. We applied the same method of data collection as used in a previous study on 25 languages which allowed us to create a database of fully comparable AoA ratings of 299 words in 32 languages. We found that in the seven languages not included in the previous study, the words are estimated to be acquired at roughly the same age as in the previously reported languages, i.e. mostly between the ages of 1 and 7 years. We also found that the order of word acquisition is moderately to highly correlated across all 32 languages, which extends our previous conclusion that early words are acquired in similar order across a wide range of languages and cultures.