Affective, semantic, frequency, and descriptive norms for 107 face emojis.

We introduce a novel dataset of affective, semantic, and descriptive norms for all facial emojis at the point of data collection. We gathered and examined subjective ratings of emojis from 138 German speakers along five essential dimensions: valence, arousal, familiarity, clarity, and visual complexity. Additionally, we provide absolute frequency counts of emoji use, drawn from an extensive Twitter corpus, as well as a much smaller WhatsApp database. Our results replicate the well-established quadratic relationship between arousal and valence of lexical items, also known for words. We also report associations among the variables: for example, the subjective familiarity of an emoji is strongly correlated with its usage frequency, and positively associated with its emotional valence and clarity of meaning. We establish the meanings associated with face emojis, by asking participants for up to three descriptions for each emoji. Using this linguistic data, we computed vector embeddings for each emoji, enabling an exploration of their distribution within the semantic space. Our description-based emoji vector embeddings not only capture typical meaning components of emojis, such as their valence, but also surpass simple definitions and direct emoji2vec models in reflecting the semantic relationship between emojis and words. Our dataset stands out due to its robust reliability and validity. This new semantic norm for face emojis impacts the future design of highly controlled experiments focused on the cognitive processing of emojis, their lexical representation, and their linguistic properties.

Adding a Piece to the Puzzle? The Allocation of Figurative Language Comprehension into the CHC Model of Cognitive Abilities.

The study aimed to investigate the allocation of figurative language comprehension (FLC) within the Cattell-Horn-Carroll (CHC) model of cognitive abilities, using three newly developed tests: the Reverse Paraphrase Test (RPT), the Literal Paraphrase Test (LPT), and the Proverb Test (PT). The analysis of a sample of 909 participants revealed that the RPT and LPT measured a unidimensional construct of FLC, while the PT was excluded due to insufficient fit. Combining RPT and LPT items, various models were evaluated, with a bifactor S-1 model showing the best fit, indicating the influence of a general factor (representing FLC) and test-specific method factors. The study explored FLC allocation within the CHC model, supporting its consideration as a distinct factor under the g factor. Examining the nomological network, significant correlations emerged between the Intellectual Curiosity and Aesthetic Sensitivity facets of Openness and FLC, which were comparable in size to the relation with general ability. In conclusion, the study enhances the understanding of FLC within the CHC model, advocating its recognition as a distinct factor. Correlations with Openness facets suggest valuable insights into the interplay between cognitive abilities and personality, necessitating further research for a deeper exploration of this relation.

Validation of natural language processing methods capturing semantic incoherence in the speech of patients with non-affective psychosis.

Background: Impairments in speech production are a core symptom of non-affective psychosis (NAP). While traditional clinical ratings of patients' speech involve a subjective human factor, modern methods of natural language processing (NLP) promise an automatic and objective way of analyzing patients' speech. This study aimed to validate NLP methods for analyzing speech production in NAP patients. Methods: Speech samples from patients with a diagnosis of schizophrenia or schizoaffective disorder were obtained at two measurement points, 6 months apart. Out of N = 71 patients at T1, speech samples were also available for N = 54 patients at T2. Global and local models of semantic coherence as well as different word embeddings (word2vec vs. GloVe) were applied to the transcribed speech samples. They were tested and compared regarding their correlation with clinical ratings and external criteria from cross-sectional and longitudinal measurements. Results: Results did not show differences for global vs. local coherence models and found more significant correlations between word2vec models and clinically relevant outcome variables than for GloVe models. Exploratory analysis of longitudinal data did not yield significant correlation with coherence scores. Conclusion: These results indicate that natural language processing methods need to be critically validated in more studies and carefully selected before clinical application.

Stimulus data and experimental design for a self-paced reading study on emoji-word substitutions.

This data paper presents the experimental design and stimuli from an online self-paced reading study on the processing of emojis substituting lexically ambiguous nouns. We recorded reading times for the target ambiguous nouns and for emojis depicting either the intended target referent or a contextually inappropriate homophonous noun. Furthermore, we recorded comprehension accuracy, demographics and a self-assessment of the participants' emoji usage frequency. The data includes all stimuli used, the raw data, the full JavaScript code for the online experiment, as well as Python and R code for the data analysis. We believe that our dataset may give important insights related to the comprehension mechanisms involved in the cognitive processing of emojis. For interpretation and discussion of the experiment, please see the original article entitled "The processing of emoji-word substitutions: A self-paced-reading study".

Modeling Incoherent Discourse in Non-Affective Psychosis.

BACKGROUND: Computational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis. METHODS: Speech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects. RESULTS: Coherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder. CONCLUSIONS: Automated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech.

Whodunnit? Electrophysiological correlates of agency judgements.

Sense of agency refers to the feeling that "I" am responsible for those external events that are directly produced by one's own voluntary actions. Recent theories distinguish between a non-conceptual "feeling" of agency linked to changes in the processing of self-generated sensory events, and a higher-order judgement of agency, which attributes sensory events to the self. In the current study we explore the neural correlates of the judgement of agency by means of electrophysiology. We measured event-related potentials to tones that were either perceived or not perceived as triggered by participants' voluntary actions and related these potentials to later judgements of agency over the tones. Replicating earlier findings on predictive sensory attenuation, we found that the N1 component was attenuated for congruent tones that corresponded to the learned action-effect mapping as opposed to incongruent tones that did not correspond to the previously acquired associations between actions and tones. The P3a component, but not the N1, directly reflected the judgement of agency: deflections in this component were greater for tones judged as self-generated than for tones judged as externally produced. The fact that the outcome of the later agency judgement was predictable based on the P3a component demonstrates that agency judgements incorporate early information processing components and are not purely reconstructive, post-hoc evaluations generated at time of judgement.