Estimating affective word covariates using word association data

Using Freely Generated Labels Instead of Rating Scales to Assess Emotion in Everyday Life

To measure emotion in daily life, studies often prompt participants to repeatedly rate their feelings on a set of prespecified terms. This approach has yielded key findings in the psychological literature yet may not represent how people typically describe their experiences. We used an alternative approach, in which participants labeled their current emotion with at least one word of their choosing. In an initial study, estimates of label positivity recapitulated momentary valence ratings and were associated with self-reported mental health. The number of unique emotion words used over time was related to the balance and spread of emotions endorsed in an end-of-day rating task, but not to other measures of emotional functioning. A second study tested and replicated a subset of these findings. Considering the variety and richness of participant responses, a free-label approach appears to be a viable as well as compelling means of studying emotion in everyday life.

The good, the bad, and the ambivalent: Extrapolating affective values for 38,000+ Chinese words via a computational model

Word affective ratings are important tools in psycholinguistic research, natural language processing, and many other fields. However, even for well-studied languages, such norms are usually limited in scale. To extrapolate affective (i.e., valence and arousal) values for words in the SUBTLEX-CH database (Cai & Brysbaert, 2010, PLoS ONE, 5(6):e10729), we implemented a computational neural network which captured how words' vector-based semantic representations corresponded to the probability densities of their valence and arousal. Based on these probability density functions, we predicted not only a word's affective values, but also their respective degrees of variability that could characterize individual differences in human affective ratings. The resulting estimates of affective values largely converged with human ratings for both valence and arousal, and the estimated degrees of variability also captured important features of the variability in human ratings. We released the extrapolated affective values, together with their corresponding degrees of variability, for over 38,000 Chinese words in the Open Science Framework ( https://osf.io/s9zmd/ ). We also discussed how the view of embodied cognition could be illuminated by this computational model.

Navigating the Mental Lexicon: Network Structures, Lexical Search and Lexical Retrieval

This paper examines the implications of the association patterns in our understanding of the mental lexicon. By applying the principles of graph theory to word association data, we intend to explore which measures tap better into lexical knowledge. To that end, we had different groups of English as Foreign language learners complete a lexical fluency task. Based on these empirical data, a study was undertaken on the corresponding lexical availability graph (LAG). It is observed that the aggregation (mentioned through human coding) of all lexical tokens on a given topic allows the emergence of some lexical-semantic patterns. The most important one is the existence of some key terms, featuring both high centrality in the sense of network theory and high availability in the LAG, which define a hub of related terms. These communities of words, each one organized around an anchor term, or most central word, are nicely apprehended by a well-known network metric called modularity. Interestingly enough, each module seems to describe a conceptual class, showing that the collective lexicon, at least as approximated by LA Graphs, is organised and traversed by semantic mechanisms or associations via hyponymy or hiperonymy, for instance. Another empirical observation is that these conceptual hubs can be appended, resulting in high diameters compared to same-sized random graphs; even so it seems that the small-world hypothesis holds in LA Graphs, as in other social and natural networks.

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.

Cognitive networks detect structural patterns and emotional complexity in suicide notes

Communicating one's mindset means transmitting complex relationships between concepts and emotions. Using network science and word co-occurrences, we reconstruct conceptual associations as communicated in 139 genuine suicide notes, i.e., notes left by individuals who took their lives. We find that, despite their negative context, suicide notes are surprisingly positively valenced. Through emotional profiling, their ending statements are found to be markedly more emotional than their main body: The ending sentences in suicide notes elicit deeper fear/sadness but also stronger joy/trust and anticipation than the main body. Furthermore, by using data from the Emotional Recall Task, we model emotional transitions within these notes as co-occurrence networks and compare their structure against emotional recalls from mentally healthy individuals. Supported by psychological literature, we introduce emotional complexity as an affective analog of structural balance theory, measuring how elementary cycles (closed triads) of emotion co-occurrences mix positive, negative and neutral states in narratives and recollections. At the group level, authors of suicide narratives display a higher complexity than healthy individuals, i.e., lower levels of coherently valenced emotional states in triads. An entropy measure identified a similar tendency for suicide notes to shift more frequently between contrasting emotional states. Both the groups of authors of suicide notes and healthy individuals exhibit less complexity than random expectation. Our results demonstrate that suicide notes possess highly structured and contrastive narratives of emotions, more complex than expected by null models and healthy populations.

The Nature of Word Associations in Sentence Contexts

How words are interrelated in the human mind is a scientific topic on which there is still no consensus, with different views on how word co-occurrence and semantic relatedness mediate word association. Recent research has shown that lexical associations are strongly predicted by the similarity of those words in terms of valence, arousal, and concreteness ratings. In the current study, we aimed at extending these results to more complex and realistic linguistic scenarios, since human communication is not done with word pairs, but rather through sentences. Hence, the aim of the current study was to verify whether valence, arousal, and concreteness also articulate sentence-level lexical representations. To this end, 32 native Spanish speakers were given cue words and asked to use them in sentences that would provide a meaningful context. The content words of the written sentences were then analyzed. Our results showed that the emotional dimensions (valence and arousal) and concreteness values of the cue words effectively predicted the same values of said dimensions of their sentences' words. In sum, the similarity in the emotional dimensions and concreteness are crucial mechanisms behind word association in the human mind.

No Self Without Salience: Affective and Self-relevance Ratings of 552 Emotionally Valenced and Neutral Dutch Words

It is unknown how self-relevance is dependent on emotional salience. Emotional salience encompasses an individual's degree of attraction or aversion to emotionally-valenced information. The current study investigated the interconnection between self and salience through the evaluation of emotional valence and self-relevance. 56 native Dutch participants completed a questionnaire assessing valence, intensity, and self-relevance of 552 Dutch nouns and verbs. One-way repeated-measures ANCOVA investigated the relationship between valence and self, age and gender. Repeated-measures ANCOVA also tested the relationship between valence and self with intensity ratings and effects of gender and age. Results showed a significant main effect of valence for self-relevant words. Intensity analyses showed a main effect of valence but not of self-relevance. There were no significant effects of gender and age. The most important finding presents that self-relevance is dependent on valence. These findings concerning the relationship between self and salience opens avenues to study an individual's self-definition.

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.

Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised (n = 21,317) and supervised (n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing.

A Chinese Conceptual Semantic Feature Dataset (CCFD)

Memory and language are important high-level cognitive functions of humans, and the study of conceptual representation of the human brain is a key approach to reveal the principles of cognition. However, this research is often constrained by the availability of stimulus materials. The research on concept representation often needs to be based on a standardized and large-scale database of conceptual semantic features. Although Western scholars have established a variety of English conceptual semantic feature datasets, there is still a lack of a comprehensive Chinese version. In the present study, a Chinese Conceptual semantic Feature Dataset (CCFD) was established with 1,410 concepts including their semantic features and the similarity between concepts. The concepts were grouped into 28 subordinate categories and seven superior categories artificially. The results showed that concepts within the same category were closer to each other, while concepts between categories were farther apart. The CCFD proposed in this study can provide stimulation materials and data support for related research fields. All the data and supplementary materials can be found at https://osf.io/ug5dt/ .

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.

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.

The Socio-Moral Image Database (SMID): A novel stimulus set for the study of social, moral and affective processes

A major obstacle for the design of rigorous, reproducible studies in moral psychology is the lack of suitable stimulus sets. Here, we present the Socio-Moral Image Database (SMID), the largest standardized moral stimulus set assembled to date, containing 2,941 freely available photographic images, representing a wide range of morally (and affectively) positive, negative and neutral content. The SMID was validated with over 820,525 individual judgments from 2,716 participants, with normative ratings currently available for all images on affective valence and arousal, moral wrongness, and relevance to each of the five moral values posited by Moral Foundations Theory. We present a thorough analysis of the SMID regarding (1) inter-rater consensus, (2) rating precision, and (3) breadth and variability of moral content. Additionally, we provide recommendations for use aimed at efficient study design and reproducibility, and outline planned extensions to the database. We anticipate that the SMID will serve as a useful resource for psychological, neuroscientific and computational (e.g., natural language processing or computer vision) investigations of social, moral and affective processes. The SMID images, along with associated normative data and additional resources are available at https://osf.io/2rqad/.