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

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.

A Computational Approach to Identifying Cultural Keywords Across Languages.

Distinctive aspects of a culture are often reflected in the meaning and usage of words in the language spoken by bearers of that culture. Keywords such as душа (soul) in Russian, hati (heart) in Indonesian and Malay, and gezellig (convivial/cosy/fun) in Dutch are held to be especially culturally revealing, and scholars have identified a number of such keywords using careful linguistic analyses (Peeters, 2020b; Wierzbicka, 1990). Because keywords are expected to have different statistical properties than related words in other languages, we argue that a quantitative comparison of word usage across languages can help to identify cultural keywords. To support this claim, we describe a computational method that compares word frequencies across languages, and apply it to both linguistic corpora and word association data. The method identifies culturally specific words that range from "obvious" examples, such as Amsterdam in Dutch, to non-obvious yet independently proposed examples, such as hati (heart) in Indonesian. We show in addition that linguistic corpora and word association data provide converging evidence about culturally specific words. Our results therefore show how computational analyses and behavioral experiments can supplement the methods previously used by linguists to identify culturally salient words across languages.

The "Small World of Words" free association norms for Rioplatense Spanish.

Large-scale word association datasets are both important tools used in psycholinguistics and used as models that capture meaning when considered as semantic networks. Here, we present word association norms for Rioplatense Spanish, a variant spoken in Argentina and Uruguay. The norms were derived through a large-scale crowd-sourced continued word association task in which participants give three associations to a list of cue words. Covering over 13,000 words and +3.6 M responses, it is currently the most extensive dataset available for Spanish. We compare the obtained dataset with previous studies in Dutch and English to investigate the role of grammatical gender and studies that used Iberian Spanish to test generalizability to other Spanish variants. Finally, we evaluated the validity of our data in word processing (lexical decision reaction times) and semantic (similarity judgment) tasks. Our results demonstrate that network measures such as in-degree provide a good prediction of lexical decision response times. Analyzing semantic similarity judgments showed that results replicate and extend previous findings demonstrating that semantic similarity derived using spreading activation or spectral methods outperform word embeddings trained on text corpora.

Unraveling lexical semantics in the brain: Comparing internal, external, and hybrid language models.

To explain how the human brain represents and organizes meaning, many theoretical and computational language models have been proposed over the years, varying in their underlying computational principles and in the language samples based on which they are built. However, how well they capture the neural encoding of lexical semantics remains elusive. We used representational similarity analysis (RSA) to evaluate to what extent three models of different types explained neural responses elicited by word stimuli: an External corpus-based word2vec model, an Internal free word association model, and a Hybrid ConceptNet model. Semantic networks were constructed using word relations computed in the three models and experimental stimuli were selected through a community detection procedure. The similarity patterns between language models and neural responses were compared at the community, exemplar, and word node levels to probe the potential hierarchical semantic structure. We found that semantic relations computed with the Internal model provided the closest approximation to the patterns of neural activation, whereas the External model did not capture neural responses as well. Compared with the exemplar and the node levels, community-level RSA demonstrated the broadest involvement of brain regions, engaging areas critical for semantic processing, including the angular gyrus, superior frontal gyrus and a large portion of the anterior temporal lobe. The findings highlight the multidimensional semantic organization in the brain which is better captured by Internal models sensitive to multiple modalities such as word association compared with External models trained on text corpora.

The Pandemic in Words: Tracking Fast Semantic Changes via a Large-Scale Word Association Task.

Most words have a variety of senses that can be added, removed, or altered over time. Understanding how they change across different contexts and time periods is crucial for revealing the role of language in social and cultural evolution. In this study we aimed to explore the collective changes in the mental lexicon as a consequence of the COVID-19 pandemic. We performed a large-scale word association experiment in Rioplatense Spanish. The data were obtained in December 2020, and compared with responses previously obtained from the Small World of Words database (SWOW-RP, Cabana et al., 2023). Three different word-association measures detected changes in a word's mental representation from Precovid to Covid. First, significantly more new associations appeared for a set of pandemic-related words. These new associations can be interpreted as incorporating new senses. For example, the word 'isolated' incorporated direct associations with 'coronavirus' and 'quarantine'. Second, when analyzing the distribution of responses, we observed a greater Kullback-Leibler divergence (i.e., relative entropy) between the Precovid and Covid periods for pandemic words. Thus, some words (e.g., 'protocol', or 'virtual') changed their overall association patterns due to the COVID-19 pandemic. Finally, using semantic similarity analysis, we evaluated the changes between the Precovid and Covid periods for each cue word's nearest neighbors and the changes in their similarity to certain word senses. We found a larger diachronic difference for pandemic cues where polysemic words like 'immunity' or 'trial' increased their similarity to sanitary/health words during the Covid period. We propose that this novel methodology can be expanded to other scenarios of fast diachronic semantic changes.

Independency of Coding for Affective Similarities and for Word Co-occurrences in Temporal Perisylvian Neocortex.

Word valence is one of the principal dimensions in the organization of word meaning. Co-occurrence-based similarities calculated by predictive natural language processing models are relatively poor at representing affective content, but very powerful in their own way. Here, we determined how these two canonical but distinct ways of representing word meaning relate to each other in the human brain both functionally and neuroanatomically. We re-analysed an fMRI study of word valence. A co-occurrence-based model was used and the correlation with the similarity of brain activity patterns was compared to that of affective similarities. The correlation between affective and co-occurrence-based similarities was low (r = 0.065), confirming that affect was captured poorly by co-occurrence modelling. In a whole-brain representational similarity analysis, word embedding similarities correlated significantly with the similarity between activity patterns in a region confined to the superior temporal sulcus to the left, and to a lesser degree to the right. Affective word similarities correlated with the similarity in activity patterns in this same region, confirming previous findings. The affective similarity effect extended more widely beyond the superior temporal cortex than the effect of co-occurrence-based similarities did. The effect of co-occurrence-based similarities remained unaltered after partialling out the effect of affective similarities (and vice versa). To conclude, different aspects of word meaning, derived from affective judgements or from word co-occurrences, are represented in superior temporal language cortex in a neuroanatomically overlapping but functionally independent manner.

Semantic gender: Norms for 24,000 Dutch words and its role in word meaning.

Semantic gender norms are presented for 24,037 Dutch words. Eighty participants rated 6017 words each on a five-point Likert scale ranging from feminine to masculine. Each word was rated by ten male and ten female participants. The collected norms show high reliability and correlate well with similar norms in English. We show that semantic gender is distinct from other lexical dimensions such as valence, arousal, dominance, concreteness, and age of acquisition. Semantic gender is not the same as the grammatical gender of words, either. The collected norms can be predicted accurately using a semantic space based on word association data. A dimension explaining a good amount of variance is present in this space, indicating that semantic gender is an important component of the human meaning system.

Discovering the structure and organization of a free Cantonese emotion-label word association graph to understand mental lexicons of emotions.

Emotions are not necessarily universal across different languages and cultures. Mental lexicons of emotions depend strongly on contextual factors, such as language and culture. The Chinese language has unique linguistic properties that are different from other languages. As a main variant of Chinese, Cantonese has some emotional expressions that are only used by Cantonese speakers. Previous work on Chinese emotional vocabularies focused primarily on Mandarin. However, little is known about Cantonese emotion vocabularies. This is important since both language variants might have distinct emotional expressions, despite sharing the same writing system. To explore the structure and organization of Cantonese-label emotion words, we selected 79 highly representative emotion cue words from an ongoing large-scale Cantonese word association study (SWOW-HK). We aimed to identify the categories of these emotion words and non-emotion words that related to emotion concepts. Hierarchical cluster analysis was used to generate word clusters and investigate the underlying emotion dimensions. As the cluster quality was low in hierarchical clustering, we further constructed an emotion graph using a network approach to explore how emotions are organized in the Cantonese mental lexicon. With the support of emotion knowledge, the emotion graph defined more distinct emotion categories. The identified network communities covered basic emotions such as love, happiness, and sadness. Our results demonstrate that mental lexicon graphs constructed from free associations of Cantonese emotion-label words can reveal fine categories of emotions and their relevant concepts.

Using Network Science to Understand the Aging Lexicon: Linking Individuals' Experience, Semantic Networks, and Cognitive Performance.

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network-analytical approaches.

Orienting to different dimensions of word meaning alters the representation of word meaning in early processing regions.

Conscious processing of word meaning can be guided by attention. In this event-related functional magnetic resonance imaging study in 22 healthy young volunteers, we examined in which regions orienting attention to two fundamental and generic dimensions of word meaning, concreteness versus valence, alters the semantic representations coded in activity patterns. The stimuli consisted of 120 nouns in written or spoken modality which varied factorially along the concreteness and valence axis. Participants performed a forced-choice judgement of either concreteness or valence. Rostral and subgenual anterior cingulate were strongly activated during valence judgement, and precuneus and the dorsal attention network during concreteness judgement. Task and stimulus type interacted in right posterior fusiform gyrus, left lingual gyrus, precuneus, and insula. In the right posterior fusiform gyrus and the left lingual gyrus, the correlation between the pairwise similarity in activity patterns evoked by words and the pairwise distance in valence and concreteness was modulated by the direction of attention, word valence or concreteness. The data indicate that orienting attention to basic dimensions of word meaning exerts effects on the representation of word meaning in more peripheral nodes, such as the ventral occipital cortex, rather than the core perisylvian language regions.

Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks.

An essential aspect of human communication is the ability to access and retrieve information from ones' 'mental lexicon'. This lexical access activates phonological and semantic components of concepts, yet the question whether and how these two components relate to each other remains widely debated. We harness tools from network science to construct a large-scale linguistic multilayer network comprising of phonological and semantic layers. We find that the links in the two layers are highly similar to each other and that adding information from one layer to the other increases efficiency by decreasing the network overall distances, but specifically affecting shorter distances. Finally, we show how a multilayer architecture demonstrates the highest efficiency, and how this efficiency relates to weak semantic relations between cue words in the network. Thus, investigating the interaction between the layers and the unique benefit of a linguistic multilayer architecture allows us to quantify theoretical cognitive models of lexical access.

Visual and Affective Multimodal Models of Word Meaning in Language and Mind.

One of the main limitations of natural language-based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic-level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both abstract and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed.

Concept Creep and Psychiatrization.

Some aspects of psychiatrization can be understood as forms of concept creep, the progressive expansion of concepts of harm. This article compares the two concepts and explores how concept creep sheds light on psychiatrization. We argue that although psychiatrization is in some respects a broader concept than concept creep, addressing institutional and societal dimensions of the expanding reach of psychiatry in addition to conceptual change, concept creep is broader in other respects, viewing the expansion of psychiatric concepts as examples of the broadening of a more extensive range of harm-related concepts. A concept creep perspective on psychiatrization clarifies the different forms of expansion it involves, the centrality of harm to it, its benefits as well as its costs, its variations across individuals and groups, and the drivers of psychiatrization in the general public and in fields beyond psychiatry.

Representation of associative and affective semantic similarity of abstract words in the lateral temporal perisylvian language regions.

The examination of semantic cognition has traditionally identified word concreteness as well as valence as two of the principal dimensions in the representation of conceptual knowledge. More recently, corpus-based vector space models as well as graph-theoretical analysis of large-scale task-related behavioural responses have revolutionized our insight into how the meaning of words is structured. In this fMRI study, we apply representational similarity analysis to investigate the conceptual representation of abstract words. Brain activity patterns were related to a cued-association based graph as well as to a vector-based co-occurrence model of word meaning. Twenty-six subjects (19 females and 7 males) performed an overt repetition task during fMRI. First, we performed a searchlight classification procedure to identify regions where activity is discriminable between abstract and concrete words. These regions were left inferior frontal gyrus, the upper and lower bank of the superior temporal sulcus bilaterally, posterior middle temporal gyrus and left fusiform gyrus. Representational Similarity Analysis demonstrated that for abstract words, the similarity of activity patterns in the cortex surrounding the superior temporal sulcus bilaterally and in the left anterior superior temporal gyrus reflects the similarity in word meaning. These effects were strongest for semantic similarity derived from the cued association-based graph and for affective similarity derived from either of the two models. The latter effect was mainly driven by positive valence words. This research highlights the close neurobiological link between the information structure of abstract and affective word content and the similarity in activity pattern in the lateral and anterior temporal language system.

A practical primer on processing semantic property norm data.

Semantic property listing tasks require participants to generate short propositions (e.g., [Formula: see text], [Formula: see text]) for a specific concept (e.g., DOG). This task is the cornerstone of the creation of semantic property norms which are essential for modeling, stimuli creation, and understanding similarity between concepts. Despite the wide applicability of semantic property norms for a large variety of concepts across different groups of people, the methodological aspects of the property listing task have received less attention, even though the procedure and processing of the data can substantially affect the nature and quality of the measures derived from them. The goal of this paper is to provide a practical primer on how to collect and process semantic property norms. We will discuss the key methods to elicit semantic properties and compare different methods to derive meaningful representations from them. This will cover the role of instructions and test context, property preprocessing (e.g., lemmatization), property weighting, and relationship encoding using ontologies. With these choices in mind, we propose and demonstrate a processing pipeline that transparently documents these steps, resulting in improved comparability across different studies. The impact of these choices will be demonstrated using intrinsic (e.g., reliability, number of properties) and extrinsic measures (e.g., categorization, semantic similarity, lexical processing). This practical primer will offer potential solutions to several long-standing problems and allow researchers to develop new property listing norms overcoming the constraints of previous studies.

Lexicosemantic, affective, and distributional norms for 1,000 Dutch adjectives.

The research of the word is still very much the research of the noun. Adjectives have been largely overlooked, despite being the second-largest word class in many languages and serving an important communicative function, because of the rich, nuanced qualifications they afford. Adjectives are also ideally suited to study the interface between cognition and emotion, as they naturally cover the entire range of lexicosemantic variables such as imageability (infinite-green), and affective variables such as valence (sad-happy). We illustrate this by showing how the centrality of words in the mental lexicon varies as a function of the words' affective dimensions, using newly collected norms for 1,000 Dutch adjectives. The norms include the lexicosemantic variables age of acquisition, familiarity, concreteness, and imageability; the affective variables valence, arousal, and dominance; and a variety of distributional variables, including network statistics resulting from a large-scale word association study. The norms are freely available from https://osf.io/nyg8v/, for researchers studying adjectives specifically or for whom adjectives constitute convenient stimuli to study other topics, such as vagueness, inference, spatial cognition, or affective word processing.

New Perspectives on the Aging Lexicon.

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, including the role of environmental exposure and several cognitive mechanisms associated with learning, representation, and retrieval of information. We review the current status of research in this field and outline a framework that promises to assess the contribution of both ecological and psychological aspects to the aging lexicon.

Redefining the resolution of semantic knowledge in the brain: Advances made by the introduction of models of semantics in neuroimaging.

The boundaries of our understanding of conceptual representation in the brain have been redrawn since the introduction of explicit models of semantics. These models are grounded in vast behavioural datasets acquired in healthy volunteers. Here, we review the most important techniques which have been applied to detect semantic information in neuroimaging data and argue why semantic models are possibly the most valuable addition to the research of semantics in recent years. Using multivariate analysis, predictions based on patient lesion data have been confirmed during semantic processing in healthy controls. Secondly, this new method has given rise to new research avenues, e.g. the detection of semantic processing outside of the temporal cortex. As a future line of work, the same research strategy could be useful to study neurological conditions such as the semantic variant of primary progressive aphasia, which is characterized by pathological semantic processing.

Left perirhinal cortex codes for semantic similarity between written words defined from cued word association.

Knowledge of visual and nonvisual attributes of concrete entities is distributed over neocortical uni- and polymodal association cortex. Here we investigated the role of left perirhinal cortex in explicit knowledge retrieval from written words. We examined whether it extended across visual and nonvisual properties, animate and inanimate entities, how this differed from picture input and how specific it was for perirhinal cortex compared to surrounding structures. The semantic similarity between stimuli was determined on the basis of a word association-based model. Eighteen participants participated in this event-related fMRI experiment. During property verification, the left perirhinal cortex coded for the similarity in meaning between written words. No differences were found between visual and nonvisual properties or between animate and inanimate entities. Among the surrounding regions, a semantic similarity effect for written words was also present in the left parahippocampal gyrus, but not in the hippocampus nor in the right perirhinal cortex. Univariate analysis revealed higher activity for visual property verification in visual processing regions and for nonvisual property verification in an extended system encompassing the superior temporal sulcus along its anterior-posterior axis, the inferior and the superior frontal gyrus. The association strength between the concept and the property correlated positively with fMRI response amplitude in visual processing regions, and negatively with response amplitude in left inferior and superior frontal gyrus. The current findings establish that input-modality determines the semantic similarity effect in left perirhinal cortex more than the content of the knowledge retrieved or the semantic control demand do. We propose that left perirhinal cortex codes for the association between a concrete written word and the object it refers to and operates as a connector hub linking written word input to the distributed cortical representation of word meaning.