Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Animacy plays an important role in cognition (e.g., memory and language). Across languages, a processing advantage for animate words (representing living beings), comparatively to inanimate words (i.e., non-living things), has been found mostly in young adults. Evidence in older adults, though, is still unclear, possibly due to the use of stimuli not properly characterised for this age group. Indeed, whereas several animacy word-rating studies already exist for young adults, these are non-existent for older adults. This work provides animacy ratings for 500 British English and 224 European Portuguese words, rated by young and older adults from the corresponding countries. The comparisons across languages and ages revealed a high interrater agreement. Nonetheless, the Portuguese samples provided higher mean ratings of animacy than the British samples. Also, the older adults assigned, on average, higher animacy ratings than the young adults. The Age X Language interaction was non-significant. These results suggest an inter-age and inter-language consistency in whether a word represents an animate or an inanimate thing, although with some differences, emphasising the need for age- and language-specific word rating data. The animacy ratings are available via OSF: https://osf.io/6xjyv/.
We discuss Telugu Language and Treebanks briefly in this work. Initially, we'll go over the Telugu language briefly. The paninian grammatical model utilized for Telugu dependency representation is then described. Following that, we explain Telugu treebanks and the various formats used to express these treebanks. We also discuss the Telugu language and its representation in the Telugu Dependency Treebank, and we give information on the Telugu language and the Telugu Dependency Treebank. Natural languages are often morphologically rich, and they create sentences in a variety of ways. Researchers have been investigating approaches to annotate text with linguistic knowledge since the advent of machine translation in the 1960s. Previous studies on Indian languages were done at the chunk level. The present shallow parser morphologically parses the input text to the chunk label. Researchers are considering working at the phrase level in the future. They broke the phrases down into smaller parts. The relationship between chunk heads is essential to proceed to sentence-level parsing. This results in reliance parsing.
The outstanding performance recently reached by neural language models (NLMs) across many natural language processing (NLP) tasks has steered the debate towards understanding whether NLMs implicitly learn linguistic competence. Probes, i.e., supervised models trained using NLM representations to predict linguistic properties, are frequently adopted to investigate this issue. However, it is still questioned if probing classification tasks really enable such investigation or if they simply hint at surface patterns in the data. This work contributes to this debate by presenting an approach to assessing the effectiveness of a suite of probing tasks aimed at testing the linguistic knowledge implicitly encoded by one of the most prominent NLMs, BERT. To this aim, we compared the performance of probes when predicting gold and automatically altered values of a set of linguistic features. Our experiments were performed on Italian and were evaluated across BERT’s layers and for sentences with different lengths. As a general result, we observed higher performance in the prediction of gold values, thus suggesting that the probing model is sensitive to the distortion of feature values. However, our experiments also showed that the length of a sentence is a highly influential factor that is able to confound the probing model’s predictions.
Social distance, or perception of the other, is recognized as a dynamic dimension of an interaction, but yet to be widely explored or understood. Through CORAE, a novel web-based open-source tool for COntinuous Retrospective Affect Evaluation, we collected retrospective ratings of interpersonal perceptions between 12 participant dyads. In this work, we explore how different aspects of these interactions reflect on the ratings collected, through a discourse analysis of individual and social behavior of the interactants. We found that different events observed in the ratings can be mapped to complex interaction phenomena, shedding light on relevant interaction features that may play a role in interpersonal understanding and grounding. This paves the way for better, more seamless human-robot interactions, where affect is interpreted as highly dynamic and contingent on interaction history.
L1-L2 parallel dependency treebanks are UD-annotated corpora of learner sentences paired with correction hypotheses. Automatic morphosyntactical annotation has the potential to remove the need for explicit manual error tagging and improve interoperability, but makes it more challenging to locate grammatical errors in the resulting datasets. We therefore propose a novel method for automatically extracting morphosyntactical error patterns and perform a preliminary bilingual evaluation of its first implementation through a similar example retrieval task. The resulting pipeline is also available as a prototype CALL application.
Recent efforts to consolidate guidelines and treebanks in the Universal Dependencies project raise the expectation that joint training and dataset comparison is increasingly possible for high-resource languages such as English, which have multiple corpora. Focusing on the two largest UD English treebanks, we examine progress in data consolidation and answer several questions: Are UD English treebanks becoming more internally consistent? Are they becoming more like each other and to what extent? Is joint training a good idea, and if so, since which UD version? Our results indicate that while consolidation has made progress, joint models may still suffer from inconsistencies, which hamper their ability to leverage a larger pool of training data.
Aesthetic chills are an embodied peak emotional experience induced by stimuli such as music, films, and speeches and characterized by dopaminergic release. The emotional consequences of chills in terms of valence and arousal are still debated and the existing empirical data is conflicting. In this study, we tested the effects of ChillsDB, an open-source repository of chills-inducing stimuli, on the emotional ratings of 600+ participants. We found that participants experiencing chills reported significantly more positive valence and greater arousal during the experience, compared to participants who did not experience chills. This suggests that the embodied experience of chills may influence one's perception and affective evaluation of the context, in favor of theoretical models emphasizing the role of interoceptive signals such as chills in the process of perception and decision-making. We also found an interesting pattern in the valence ratings of participants, which tended to harmonize toward a similar mean after the experiment, though initially disparately distributed. We discuss the significance of these results for the diagnosis and treatment of dopaminergic disorders such as Parkinson's, schizophrenia, and depression.
Recursive Neural Networks (RNNs) are a powerful tool for Natural Language Processing (NLP) that can easily learn and represent recursive structures in sentences. Artificial Neural Networks (ANNs) have been widely used in various NLP tasks, such as language modeling, text classification, and sentiment analysis. With the development of deep learning, RNNs have emerged as a powerful tool to represent structural and syntactic information of sentences. RNNs have attracted a lot of attention due to their ability to learn complex language structures and representation. Consequently, RNNs have achieved great performance in a variety of NLP applications, such as text summarization, machine translation, and question answering. In this paper, we discuss the importance of RNNs in NLP and present different smart performance optimization strategies for RNNs. Firstly, we consider the use of pre-trained language models such as GloVe and word2vec to enhance the representation of words and improve the overall accuracy. Secondly, we implement a variety of regularization techniques, such as dropout and batch normalization, to regularize the learning process of RNNs. Finally, we discuss the approaches used to reduce the complexity and overfitting of RNNs. We compare the performance of different RNNs on the Penn Treebank dataset with different optimization strategies. Experimental results show that our proposed approaches can significantly improve the performance of RNNs and provide a more effective solution for NLP tasks.
Useful information from natural language can be extracted by the process of natural language processing (NLP). NLP contains various tools where parts-of-speech (PoS) tagging plays a significant part. Every word in a sentence can be tagged by the process of PoS which includes noun, adjective, preposition, pronoun, article, verb, adverb and many more. In Penn Treebank have a 48 tagset, so we need to add many more tag sets. Penn Treebank in its eight years of procedures composed of generally seven trillion part-of-speech words tagging markers, three trillion words of underfed translate contents, over two trillion words of content translate for state words format, and 1.6 trillion sentences reproduced verbal content observation for oral communication speech. All these materials consist of such huge genres, for example, IBM PC manuals and WSJ (Wall Street Journal) and so on. This paper defines a review of various techniques, for PoS tagging and the PoS tagging process.
INTRODUCTION: Craving, involving intense and urgent desires to engage in specific behaviours, is a feature of addictions. Multiple studies implicate regions of salience/limbic networks and basal ganglia, fronto-parietal, medial frontal regions in craving in addictions. However, prior studies have not identified common neural networks that reliably predict craving across substance and behavioural addictions. METHODS: Functional magnetic resonance imaging during an audiovisual cue-reactivity task and connectome-based predictive modelling (CPM), a data-driven method for generating brain-behavioural models, were used to study individuals with cocaine-use disorder and gambling disorder. Functions of nodes and networks relevant to craving were identified and interpreted based on meta-analytic data. RESULTS: Craving was predicted by neural connectivity across disorders. The highest degree nodes were mostly located in the prefrontal cortex. Overall, the prediction model included complex networks including motor/sensory, fronto-parietal, and default-mode networks. The decoding revealed high functional associations with components of memory, valence ratings, physiological responses, and finger movement/motor imagery. CONCLUSIONS: Craving could be predicted across substance and behavioural addictions. The model may reflect general neural mechanisms of craving despite specificities of individual disorders. Prefrontal regions associated with working memory and autobiographical memory seem important in predicting craving. For further validation, the model should be tested in diverse samples and contexts.
Constituency parsing is an important task of informing how words are combined to form sentences. While constituency parsing in English has seen significant progress in the last few years, tools for constituency parsing in Indonesian remain few and far between. In this work, we publish ICON (Indonesian CONstituency treebank), the hitherto largest publicly available manually-annotated benchmark Indonesian constituency treebank with a size of 10,000 sentences and approximately 124,000 constituents and 182,000 tokens, which can support the training of state-of-the-art transformer-based models. As part of the process of building the treebank, we review and revamp the constituent and POS tagsets in use in existing treebanks to ensure that the labels are relevant and suitable for the grammatical features of Indonesian. We establish strong baselines on the ICON dataset using the Berkeley Neural Parser with transformer-based pre-trained embeddings, with the best performance of 88.85% F1 score coming from our own version of SpanBERT (IndoSpanBERT). We further analyze the predictions made by our best-performing model to reveal certain idiosyncrasies in Indonesian that pose challenges for constituency parsing.
From the corpus data, we observe that in the real language usage, the particular verb does not appear in all theoretically possible finite and infinite verb forms in the morphologically rich Lithuanian but is used in those forms which are relevant for the verb patterning. On the one hand, by teaching vocabulary, is it important to represent lexis in these relevant forms – frequently used forms, and, on the other hand, in grammar teaching, there is a need to provide learners with appropriate vocabulary, e.g., by teaching infinite forms, to use verbs, in the usage of which, these forms are relevant and frequent.In this paper, we provide language teaching practitioners with the data about the frequently used Lithuanian verbs and show which of them and how often appear in infinite forms (participles in passive and active voice, adverbial participles, half participles). As a research data we use 200 verbs from the Lexical Database of Lithuanian Language Usage which was developed on the basis of the written subcorpus of the Pedagogic corpus of Lithuanian. The investigated verbs belong to the frequent vocabulary: in the corpus of approx. 700,000 tokens, these verbs are used 100 times (and above). First, we analysed, which verbs appear in infinite forms, second, we checked whether frequent and typical infinite forms are included into corpus pattern(s) of these particular verbs, and if there is a link between the infinite form and a particular meaning of the verb.All verbs (except of three verbs with no infinite forms) were included into one of three groups: 1) 11 verbs which occur in the infinite forms frequently (more than 50% of all forms – finite and infinite) and, accordingly, typical; 2) 117 verbs with the infinite forms making up from 10 to 50%; 3) 69 verbs, with the infinite forms making up less than 10% of all verb forms. Interestingly, the verbs of the first group, usually have only one infinite form, e.g., participle in passive voice which makes up more than 50% of all forms of verb. These cases are also frequently observed in the second verb group. Thus, if the verb tends to be used in infinite forms, it is important to know which infinite form is relevant to that particular verb.In the Lexical Database of Lithuanian Language Usage, lexical and grammatical patterning of the word is represented in the form of corpus patterns. In this study, we showed the interrelation between the frequently used infinite forms of the verb and its corpus patterns (also, corpus patterns related to particular meaning of the polysemous verb). We can expect various applications of the provided data in the Lithuanian as a foreign language teaching: the provided data about the verbs typical and frequent in infinite forms and the corpus patterns including these infinite forms can be used for building vocabulary training as well as for developing grammar exercises.
Abstract Viewing a live facial expression typically elicits a similar expression by the observer (facial mimicry) that is associated with a concordant emotional experience (emotional contagion). The model of embodied emotion proposes that emotional contagion and facial mimicry are functionally linked although the neural underpinnings are not known. To address this knowledge gap, we employed a live two-person paradigm (n = 20 dyads) using functional near-infrared spectroscopy during live emotive face-processing while also measuring eye-tracking, facial classifications and ratings of emotion. One dyadic partner, ‘Movie Watcher’, was instructed to emote natural facial expressions while viewing evocative short movie clips. The other dyadic partner, ‘Face Watcher’, viewed the Movie Watcher's face. Task and rest blocks were implemented by timed epochs of clear and opaque glass that separated partners. Dyadic roles were alternated during the experiment. Mean cross-partner correlations of facial expressions (r = 0.36 ± 0.11 s.e.m.) and mean cross-partner affect ratings (r = 0.67 ± 0.04) were consistent with facial mimicry and emotional contagion, respectively. Neural correlates of emotional contagion based on covariates of partner affect ratings included angular and supramarginal gyri, whereas neural correlates of the live facial action units included motor cortex and ventral face-processing areas. Findings suggest distinct neural components for facial mimicry and emotional contagion. This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.
Anthropomorphic appearance is a key factor to affect users’ attitudes and emotions. This research aimed to measure emotional experience caused by robots’ anthropomorphic appearance with three levels – high, moderate, and low – using multimodal measurement. Fifty participants’ physiological and eye-tracker data were recorded synchronously while they observed robot images that were displayed in random order. Afterward, the participants reported subjective emotional experiences and attitudes towards those robots. The results showed that the images of the moderately anthropomorphic service robots induced higher pleasure and arousal ratings, and yielded significantly larger pupil diameter and faster saccade velocity, than did the low or high robots. Moreover, participants’ facial electromyography, skin conductance, and heart-rate responses were higher when observing moderately anthropomorphic service robots. An implication of the research is that service robots’ appearance should be designed to be moderately anthropomorphic; too many human-like features or machine-like features may disturb users’ positive emotions and attitudes.Practitioner Summary: This research aimed to measure emotional experience caused by three types of anthropomorphic service robots using a multimodal measurement experiment. The results showed that moderately anthropomorphic service robots evoked more positive emotion than high and low anthropomorphic robots. Too many human-like features or machine-like features may disturb users’ positive emotions.
This paper demonstrates the development and evaluation of a multilingual neural machine translation system for Indian languages based on the mT5 transformer, successfully utilized to develop multiple state-of-the-art NLP models. We used the modified Asian Language Treebank multilingual dataset to train the system for developing a Machine Translation model capable of translating text in English, Hindi and Bengali amongst each other. Our system was able to achieve acceptable BLEU scores of over 20 in five of the six language pairs, with the English to Bengali system achieving a maximum BLEU score of 49.87 and the Bengali to English system achieving an average BLEU score of 42.43.
Abstract Gender can be considered an embodied social concept encompassing biological and cultural components. In this study, we explored whether the concept of gender varies as a function of different cultural and linguistic norms by comparing communities that vary in their social treatment of gender-related issues and linguistic encoding of gender. In Study 1, Italian, Dutch, and English-speaking participants completed a free-listing task, which showed Italians and Dutch were the most distinct in their conceptualization of gender: Italian participants focused more on socio-cultural features (e.g., discrimination, politics, and power ), whereas Dutch participants focused more on the corporeal sphere (e.g., hormones, breasts, and genitals ). Study 2 replicated this finding focusing on Italian and Dutch and using a typicality rating task: socio-cultural and abstract features were considered as more typical of “gender” by Italian than Dutch participants. Study 3 addressed Italian and Dutch participants’ explicit beliefs about gender with a questionnaire measuring essentialism and constructivism, and consolidated results from Studies 1 and 2 showing that Dutch participants endorsed more essentialist beliefs about gender than Italian participants. Consistent with socio-cultural constructivist accounts, our results provide evidence that gender is conceptualized differently by diverse groups and is adapted to specific cultural and linguistic environments.
We examined the effects of an informative pitch and multisensory contexts as potential factors influencing individuals’ experience of tofu with soy sauce and the amount consumed outside the lab. Two hundred and sixteen participants watched one of two pitches (promoting either vegetarian diets or exercise) and were guided into one of three multisensory contexts (‘sustainable’, ‘meat’, or ‘neutral’ theme). Participants rated the aroma and appearance of soy sauce and the taste of tofu dipped in it using the intuitive ‘one touch’ EmojiGrid valence and arousal measuring tool. Our results showed that the ‘meat’ context increased arousal ratings for soy sauce and the tendency to consume more tofu relative to the other contexts. Pitch did not influence affective ratings or amounts consumed. We conclude that the multisensory context has the potential to positively affect peoples’ choices and perceptions of plant-based and sustainable food and promote its consumption.
Background Reducing the amount of contrast agent needed for contrast-enhanced breast MRI is desirable. Purpose To investigate if generative adversarial networks (GANs) can recover contrast-enhanced breast MRI scans from unenhanced images and virtual low-contrast-enhanced images. Materials and Methods In this retrospective study of breast MRI performed from January 2010 to December 2019, simulated low-contrast images were produced by adding virtual noise to the existing contrast-enhanced images. GANs were then trained to recover the contrast-enhanced images from the simulated low-contrast images (approach A) or from the unenhanced T1- and T2-weighted images (approach B). Two experienced radiologists were tasked with distinguishing between real and synthesized contrast-enhanced images using both approaches. Image appearance and conspicuity of enhancing lesions on the real versus synthesized contrast-enhanced images were independently compared and rated on a five-point Likert scale. P values were calculated by using bootstrapping. Results A total of 9751 breast MRI examinations from 5086 patients (mean age, 56 years ± 10 [SD]) were included. Readers who were blinded to the nature of the images could not distinguish real from synthetic contrast-enhanced images (average accuracy of differentiation: approach A, 52 of 100; approach B, 61 of 100). The test set included images with and without enhancing lesions (29 enhancing masses and 21 nonmass enhancement; 50 total). When readers who were not blinded compared the appearance of the real versus synthetic contrast-enhanced images side by side, approach A image ratings were significantly higher than those of approach B (mean rating, 4.6 ± 0.1 vs 3.0 ± 0.2; P <.001), with the noninferiority margin met by synthetic images from approach A (P <.001) but not B (P >.99). Conclusion Generative adversarial networks may be useful to enable breast MRI with reduced contrast agent dose. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bahl in this issue.
This article reports data from a qualitative study conducted within the elitist English-medium schools in three cities of Pakistan to claim that theory building, and English teaching practices are still modeled on monolingual biases inherent in the orthodox notions of linguistic purism and Anglo-normative traditions of the 1990s. Orthodoxy is manifest in schools’ strong emphasis on direct method and communicative language teaching, and essentialist language management where English is enforced as the sole legitimate language of academic and non-academic transactions. Native language use is attached a stigma and viewed as deficiency in the English language. Such Anglo-normative culture invokes guilty conscience in teachers and students as they disapprove native languages even when they know that they can serve as useful meditating tools for effective content transfer. Crucially, amid English gatekeepers’ celebratory attitudes toward English as the sole legitimate language, some teachers still demonstrate deviation from the pervasive linguistic norms. Showing reflexivity and agency, these teachers ideologically oppose the official policy. They discursively generate spaces for negotiating and appropriating the official policies, which signifies that language policy can be a complex multi-layered process rather than a linear, discrete, or top-down phenomenon. Teachers’ agency is interpreted as ‘decolonial performativity’.
This study aimed to examine whether Japanese participants condition spoken words' meanings to written pseudowords. In Survey 1, we selected spoken words associated with negative (α =.91) and positive (α =.79) features for Experiment 1 and passive (α =.90) and active (α =.80) features for Experiment 2. In Experiment 1, participants evaluated four written pseudowords' emotional valence using a 7-point semantic differential scale (1: negative; 7: positive) before and after conditioning spoken words with negative, neutral, or positive features to each pseudoword. In the conditioning phase, participants read each pseudoword, listened to a spoken word, and verbally repeated each spoken word. The results showed that a pseudoword was conditioned to spoken words with positive and negative features. In Experiment 2, participants evaluated four pseudowords' activeness using a 7-point semantic differential scale (1: passive; 7: active) before and after conditioning spoken words of passive, neutral, and active features to each written pseudoword. In the conditioning phase, the participants read each written pseudoword, listened to a spoken word, and repeated the spoken word. The results showed that the activeness evaluations were more increased for pseudowords conditioned to spoken words of active and neutral features after conditioning than before conditioning but were unchanged for a pseudoword conditioned to those with passive features before and after conditioning. Additonally, Survey 2's results showed that although the positiveness and activeness responses of the words used in Experiments 1 and 2 were controlled well, the lack of significant differences among positiveness responses of words may influence the evaluative conditioning in Experiment 2. That is, when participants condition passive (low arousal) words' activeness (arousal) ratings to those of pseudowords, words' positiveness (valence) ratings would be important in the evaluative conditioning. Our findings suggest that participants can condition spoken word meanings of preference and activeness to those of written pseudowords. It also indicates that linguistically evaluative conditioning's effects are robust in a non-alphabetic language.
The present study aimed to establish and develop an online de novo conditioning paradigm for the measurement of conditioned disgust responses. We further explored the effects of explicit instructions about the CS-UCS contingency on extinction learning and retrieval of conditioned disgust responses. The study included a sample of 115 healthy participants. Geometric figures served as conditioned stimuli (CS) and disgust-evoking pictures as unconditioned stimuli (UCS). During disgust conditioning, the CS+ was paired with the UCS (66% reinforcement) and the CS- remained unpaired; during extinction and retrieval, no UCS was presented. Half of the participants (n = 54) received instructions prior to the disgust extinction stating that the UCS will not be presented anymore. 1-2 days or 7-8 days later participants performed a retrieval test. CS-UCS contingency, disgust and valence ratings were used as dependent measures. Successful acquisition of conditioned disgust response was observed on the level of CS-UCS contingency, disgust and valence ratings. While some decline in valence and disgust ratings during the extinction stage was observed, contingency instructions did not significantly affect extinction performance. Retrieval one week later revealed that contingency instructions increased the discrimination of the CSs. Extinction of conditioned disgust responses is not affected by explicit knowledge of the CS-UCS contingencies. However, contingency instructions prior to extinction seem to have a detrimental effect on long-term extinction retrieval.
The beginnings of words are, in some informal sense, special. This intuition is widely shared, for example, when playing word games. Less apparent is whether the intuition is substantiated empirically and what the underlying organizational principle(s) might be. Here, we answer this seemingly simple question in a quantitatively clear way. Based on arguments about the interplay between lexical storage and speech processing, we examine whether the distribution of information among different speech sounds of words is governed by a critical computational unit for online speech perception and production: syllables. By analyzing lexical databases of twelve languages, we demonstrate that there is a compelling asymmetry between syllable beginnings (onsets) versus ends (codas) in their involvement in distinguishing words stored in the lexicon. In particular, we show that the functional advantage of syllable onset reflects an asymmetrical distribution of lexical informativeness within the syllable unit but not an effect of a global decay of informativeness from the beginning to the end of a word. The converging finding across languages from a range of typological families supports the conjecture that the syllable unit, while being a critical primitive for both speech perception and production, is also a key organizational constraint for lexical storage.
We studied the effect of cutaneous cold stimulus on the perceptual rating of musical chords. Despite the shown influence of music and tactile stimuli on human psychological evaluation, the effect of a cold stimulus on sound perception remains underexplored. We examined the effect of a cold stimulus on four psychological measures (frisson, arousal, pleasantness, and valence) as participants listened to two-note chords (consonance and dissonance). The cold-stimulus condition involved an experimenter touching the back of the participant's neck with a cooling device while listening to the sounds, while the control condition used a cooling device with the power off. For the frisson and arousal measures, the main effect of the stimulus condition was significant, showing that the cold stimulus increased the frisson and arousal measures. For the pleasantness and valence measures, there was a significant main effect of two-note chords, showing that a consonance was perceived as more pleasant than a dissonance; however, there was no significant main effect of stimulus condition, showing that the cold stimulus did not affect pleasantness and valence ratings. The results showed that a cold stimulus could bias frisson and arousal without affecting pleasantness and valence ratings when listening to musical sound.
Languages are known to describe the world in diverse ways. Across lexicons, diversity is pervasive, appearing through phenomena such as lexical gaps and untranslatability. However, in computational resources, such as multilingual lexical databases, diversity is hardly ever represented. In this paper, we introduce a method to enrich computational lexicons with content relating to linguistic diversity. The method is verified through two large-scale case studies on kinship terminology, a domain known to be diverse across languages and cultures: one case study deals with seven Arabic dialects, while the other one with three Indonesian languages. Our results, made available as browseable and downloadable computational resources, extend prior linguistics research on kinship terminology, and provide insight into the extent of diversity even within linguistically and culturally close communities.
INTRODUCTION: Social anxiety disorder (SAD) is characterized by abnormal processing of performance-related social stimuli. Previous studies have shown altered emotional experiences and activations of different sub-regions of the striatum during processing of social stimuli in patients with SAD. However, whether and to what extent social comparisons affect behavioural and neural responses to feedback stimuli in patients with SAD is unknown. MATERIALS AND METHODS: To address this issue, emotional ratings and functional magnetic resonance imaging (fMRI) responses were assessed while patients suffering from SAD and healthy controls (HC) were required to perform a choice task and received performance feedback (correct, incorrect, non-informative) that varied in relation to the performance of fictitious other participants (a few, half, or most of others had the same outcome). RESULTS: Across all performance feedback conditions, fMRI analyses revealed reduced activations in bilateral putamen when feedback was assumed to be received by only a few compared to half of the other participants in patients with SAD. Nevertheless, analysis of rating data showed a similar modulation of valence and arousal ratings in patients with SAD and HC depending on social comparison-related feedback. CONCLUSIONS: This suggests altered neural processing of performance feedback depending on social comparisons in patients with SAD.
Abstract Mental processes underlying people’s responses to Ecological Momentary Assessments (EMA) have rarely been studied. In cognitive psychology, one of the most popular and successful mental process models is the drift diffusion model. It decomposes response time (RT) data to distinguish how fast information is accessed and processed (“drift rate”), and how much information is accessed and processed (“boundary separation”). We examined whether the drift diffusion model could be successfully applied to people’s RTs for EMA questions and could shed light on between- and within-person variation in the mental process components underlying momentary reports. We analyzed EMA data (up to 6 momentary surveys/day for one week) from 954 participants in the Understanding America Study (29,067 completed measurement occasions). An item-response-theory diffusion model was applied to RTs associated with 5 momentary negative affect ratings. As hypothesized, both diffusion model parameters showed moderate stability across EMA measurement occasions. Drift rate and boundary separation together explained a majority of the variance in the observed RTs and demonstrated correspondence across different sets of EMA items, both within and between individuals. The parameters related in theoretically expected ways to within-person changes in activities (momentary work and recreation) and person-level characteristics (neuroticism and depression). Drift rate increased and boundary separation decreased over the study, suggesting that practice effects in EMA consist of multiple distinctive cognitive processes. The results support the reliability and validity of the diffusion model parameters derived from EMA and provide initial evidence that the model may enhance understanding of process underlying EMA affect ratings.
PET/CT scanners with a long axial field-of-view (LAFOV) provide increased sensitivity, enabling the adjustment of imaging parameters by reducing the injected activity or shortening the acquisition time. This study aimed to evaluate the limitations of reduced [18F]FDG activity doses on image quality, lesion detectability, and the quantification of lesion uptake in the Biograph Vision Quadra, as well as to assess the benefits of the recently introduced ultra-high sensitivity mode in a clinical setting. A number of 26 patients who underwent [18F]FDG-PET/CT (3.0 MBq/kg, 5 min scan time) were included in this analysis. The PET raw data was rebinned for shorter frame durations to simulate 5 min scans with lower activities in the high sensitivity (HS) and ultra-high sensitivity (UHS) modes. Image quality, noise, and lesion detectability (n = 82) were assessed using a 5-point Likert scale. The coefficient of variation (CoV), signal-to-noise ratio (SNR), tumor-to-background ratio (TBR), and standardized uptake values (SUV) including SUVmean, SUVmax, and SUVpeak were evaluated. Subjective image ratings were generally superior in UHS compared to the HS mode. At 0.5 MBq/kg, lesion detectability decreased to 95% (HS) and to 98% (UHS). SNR was comparable at 1.0 MBq/kg in HS (5.7 ± 0.6) and 0.5 MBq/kg in UHS (5.5 ± 0.5). With lower doses, there were negligible reductions in SUVmean and SUVpeak, whereas SUVmax increased steadily. Reducing the [18F]FDG activity to 1.0 MBq/kg (HS/UHS) in a LAFOV PET/CT provides diagnostic image quality without statistically significant changes in the uptake parameters. The UHS mode improves image quality, noise, and lesion detectability compared to the HS mode.
Cannabis Use Disorder (CUD) is increasingly prevalent in the United States, while perceived addiction risk and treatment-seeking are declining. Emotional salience of cannabis-use-related problems and benefits likely contribute to motivation to change, but measurement of this process has been limited. The present study sought to validate a novel assessment of emotional appraisal of self-referential cannabis-use-related information across subjective and neurophysiological units of analysis. Non-treatment-seeking individuals with DSM-5 severe CUD (N = 42) completed a task that presented auditory self-referential, personalized cannabis-use-related problem and benefit statements, as well as neutral self-referential statements, during electroencephalography recording. The late positive potential (LPP) was used as a neurophysiological measure of emotional salience. Valence/arousal ratings of each statement, along with their motivational importance in sustaining vs. reducing cannabis use, were also obtained. As predicted, valence and arousal ratings significantly differentiated cannabis-use-related problems and benefits from neutral statements. Partially consistent with predictions, the LPP to cannabis-use-related benefits was significantly larger than LPPs to cannabis-use-related problems and neutral statements, which did not differ from each other. Bonferroni-adjusted exploratory correlations revealed that the LPP to cannabis-use-related problems was sensitive to recent cannabis use frequency. These results provide some support for the validity of this novel multi-method assessment of emotional reactivity to personalized cannabis-use-related self-referential information in non-treatment-seeking individuals with severe CUD. The dissociation between subjective and neurophysiological reactivity to self-referential cannabis-related problem statements should be further explored.
Extensive research has shown that children's early words are learned through sensorimotor experience. Thus, early-acquired words tend to have more concrete meanings. Abstract word meanings tend to be learned later but less is known about their acquisition. We collected meaning-specific concreteness ratings and examined their relationship with age-of-acquisition data from large-scale vocabulary testing with children in grade 2 to college age. Earlier-acquired meanings were rated as more concrete while later-acquired meanings as more abstract, particularly for words typically considered to be concrete. The results suggest that sensorimotor experiences are important to early-acquired word meanings, and other experiences (e.g., linguistic) are important to later-acquired meanings, consistent with a multi-representational view of lexical semantics.
Tinnitus is a multifactorial phenomenon and psychological, audiological, or medical factors can facilitate its onset or maintenance. A growing body of research investigates individuals' perceptions, associations, and experiences of living with tinnitus. This body of research examines tinnitus as a condition rather than a symptom. We examine a sample of chronic tinnitus patients in terms of associations that are induced by neutral sounds. In particular, we investigate how patients with chronic tinnitus ascribe meaning to those neutral sounds. The present study uses Mayring's content analysis to explore the content of psychological associations underlying valence ratings of everyday neutral sounds. Nine tinnitus patients completed a hearing exercise, during which they listened to seven neutral sounds, following which we examined their sound-induced associations using semi-structured interviews. Three groups of factors influenced patients' associations and valence ratings of neutral sounds: affect, episodic memory, and 'other'. The former two factors further comprised two subcategories. In line with previous psychoaudiological research designs, our findings suggest that neutral, everyday auditory stimuli evoke strong affective reactions-possibly through serving as retrieval cues for episodic memories. Based on these findings, we discuss our results in the context of previous psychoaudiological findings and propose further research concerning psychological associations that may specifically underlie the tinnitus sound.
This research paper investigates the function of diminutive morphology in English and Urdu languages with a focus on production, similarities, and differences in inflectional bound morphemes in the noun and adjective categories, and studies the usage patterns and impacts of diminutive forms in Urdu and English on interpersonal communication. The researchers, who are native Urdu speakers and English as a second language speakers, analyze the form and meaning of diminutive morphemes in both languages using Booij’s (2018) Construction Morphology model. Multiple sources are consulted for data collection, including corpora, dictionaries, linguistic databases, literary works, and language resources. The findings suggest that both English and Urdu retain a morphological function, but English has fewer inflectional morphemes than Urdu. Conversely, Urdu employs a large variety of suffixes, particularly for denoting the diminutive aspect, which distinguishes its semantic and pragmatic expressions from those of English. Despite both languages having inflections and using gradient production, English has lost more inflections due to undergoing more periods of change. Further, the usage patterns and impacts of diminutive forms in Urdu and English contribute to the richness and complexity of interpersonal communication. These provide speakers with a range of linguistic resources to express nuances of meaning, convey emotions, and shape social interactions. The paper concludes by noting that this research can help language learners and language scholars better understand the complexities of morphological structures in languages.
Pleasant touching is an important aspect of social interactions that is widely used as a caregiving technique. To address the problems resulting from a lack of available human caregivers, previous research has attempted to develop robots that can perform this kind of pleasant touch. However, it remains unclear whether robots can provide such a pleasant touch in a manner similar to humans. To investigate this issue, we compared the effect of the speed of gentle strokes on the back between human and robot agents on the emotional responses of human participants (n = 28). A robot or a human stroked on the participants’ back at two different speeds (i.e., 2.6 and 8.5 cm/s). The participants’ subjective (valence and arousal ratings) and physiological (facial electromyography (EMG) recorded from the corrugator supercilii and zygomatic major muscles and skin conductance response) emotional reactions were measured. The subjective ratings demonstrated that the speed of 8.5 cm/s was more pleasant and arousing than the speed of 2.6 cm/s for both human and robot strokes. The corrugator supercilii EMG showed that the speed of 8.5 cm/s resulted in reduced activity in response to both human and robot strokes. These results demonstrate similar speed-dependent modulations of stroke on subjective and physiological positive emotional responses across human and robot agents and suggest that robots can provide a pleasant touch similar to that of humans.
Semantic relations between words in a sentence are identified using a technique called dependency parsing. Algorithms called dependency parsers are used to map the words in a sentence to the said semantic roles and to identify the syntactic relations between words. Transition-based dependencies on Indian languages have been relatively less explored, partially attributed to lack of high quality treebanks. Graph Neural Network based parsing techniques have shown relatively high accuracy levels on English language and languages with similar syntactic structures (eg. Spanish), and have potential to show good accuracy with other language syntaxes. Hindi is a language with an extremely rich morphology and a FreeWord order. Such a language, also referred to as a Morphologically Rich, FreeWord Order (MoR-FWO) language, is immensely difficult to parse using traditional methods. Using Graph Neural Networks, we present a state-of-the-art dependency parser for Hindi. We compare performance and efficiency of two approaches in this project- a Machine Learning model and a Neural Network model.
Capturing drivers’ affective responses given driving context and driver-pedestrian interactions remains a challenge for designing in-vehicle, empathic interfaces. To address this, we conducted two lab-based studies using camera and physiological sensors. Our first study collected participants’ (N = 21) emotion self-reports and physiological signals (including facial temperatures) toward non-verbal, pedestrian crossing videos from the Joint Attention for Autonomous Driving dataset. Our second study increased realism by employing a hybrid driving simulator setup to capture participants’ affective responses (N = 24) toward enacted, non-verbal pedestrian crossing actions. Key findings showed: (a) non-positive actions in videos elicited higher arousal ratings, whereas different in-video pedestrian crossing actions significantly influenced participants’ physiological signals. (b) Non-verbal pedestrian interactions in the hybrid simulator setup significantly influenced participants’ facial expressions, but not their physiological signals. We contribute to the development of in-vehicle empathic interfaces that draw on behavioral and physiological sensing to in-situ infer driver affective responses during non-verbal pedestrian interactions.
Neste trabalho, realizamos uma descrição linguística e relatamos o processo de anotação do pronome -se no treebank PetroGold (v3). A atenção especial ao pronome -se se justifica pela necessidade de anotar corretamente os casos em que o pronome indica indeterminação do sujeito, voz passiva sintética ou verbo pronominal, reconhecendo sua importância para diversas tarefas de PLN. Como resultados, discriminamos as 1.960 ocorrências do "se" no corpus por classe sintática e apresentamos os verbos que se associam a cada um (ou mais de um) dos tipos do pronome -se.
Purpose The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis. Design/methodology/approach The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model. Findings On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets. Originality/value The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.
The current literature suggests that some women are uniquely vulnerable to negative effects of hormonal contraception (HC) on affective processes. However, little data exists as to which factors contribute to such vulnerability. The present study evaluated the impact of prepubertal adverse childhood experiences (ACEs) on reward processing in women taking HC (N = 541) compared to naturally cycling women (N = 488). Participants completed an online survey assessing current and past HC use and exposure to 10 different adverse childhood experiences (ACEs) before puberty (ACE Questionnaire), with participants categorized into groups of low (0-1) versus high (≥2) prepubertal ACE exposure. Participants then completed a reward task rating their expected and experienced valence for images that were either erotic, pleasant (non-erotic), or neutral. Significant interactions emerged between prepubertal ACE exposure and HC use on expected (p = 0.028) and experienced (p = 0.025) valence ratings of erotic images but not pleasant or neutral images. Importantly, follow-up analyses considering whether women experienced HC-induced decreases in sexual desire informed the significant interaction for expected valence ratings of erotic images. For current HC users, prepubertal ACEs interacted with HC-induced decreased sexual desire (p = 0.008), such that high ACE women reporting decreased sexual desire on HC showed substantially decreased ratings for anticipated erotic images compared to both high prepubertal ACE women without decreased sexual desire (p < 0.001) and low prepubertal ACE women also reporting decreased sexual desire (p = 0.010). The interaction was not significant in naturally cycling women reporting previous HC use, suggesting that current HC use could be impacting anticipatory reward processing of sexual stimuli among certain women (e.g., high prepubertal ACE women reporting HC-induced decreases in sexual desire). The study provides rationale for future randomized, controlled trials to account for prepubertal ACE exposure to promote contraceptive selection informed by behavioral evidence.
Abstract This paper deals with several aspects of context in lexicography. Section 1 briefly mentions some different approaches to the concept context in various fields. Section 2 puts the focus on different uses and perceptions of the concept context in lexicography, contrasting it with related concepts, such as cotext, contextualization and contextual information. A more comprehensive discussion also covers different aspects of the occurrence of the concept context in dictionary research, with specific reference to central aspects of the so-called inner and outer context. Various portals, dictionaries and dictionary entries will illustrate the above-mentioned approaches. Section 3 approaches the subject from a user perspective. Section 4 addresses the question How can contextual data be extracted or generated? To answer this question, some methods and tools for (automatic) acquisition and analysis of contextual data, – in particular of the local contextual data in terms of Faber and León-Araúz (2016) – are introduced. Examples of these are lexical databases or semantic networks, like WordNet, and corpora, like Sketch Engine, or predictive methods, like Word2vec and similar ones. Some advantages and disadvantages of specific data acquisition tools used for the analysis of local contextual data are indicated. This section also contributes to a more detailed discussion of the automatic generation of the so-called local syntactic-semantic context or word environment, specifically of the building of syntactic-semantic argument patterns and their examples.
In this article, I engage in a discussion of the approaches of the normativists postulating the absence of a linguistic norm and assuming the identification of the norm with usus. I have made an attempt to prove the existence of the norm as a level of internal organisation of language, based on linguistic and mathematical-computational considerations. I accept the need for separate models of language: linguistic and mathematical, which serve different purposes and have different properties. I ponder the dual nature of language manifested by its finite infinity, assuming that only the usus is infinite. I postulate the adoption of a theory (formulated by K. Kłosińska) assuming the existence of an invariant norm together with ‘allonorms’. I also propose the introduction of the probabilistic Gaussian model into the codification procedures of the linguistic norm as a method objectifying the procedure and removing the (qualitative and quantitative) arbitrariness of codifiers.
Constituency parsing plays a fundamental role in advancing natural language processing (NLP) tasks. However, training an automatic syntactic analysis system for ancient languages solely relying on annotated parse data is a formidable task due to the inherent challenges in building treebanks for such languages. It demands extensive linguistic expertise, leading to a scarcity of available resources. To overcome this hurdle, cross-lingual transfer techniques which require minimal or even no annotated data for low-resource target languages offer a promising solution. In this study, we focus on building a constituency parser for $\mathbf{M}$iddle $\mathbf{H}$igh $\mathbf{G}$erman ($\mathbf{MHG}$) under realistic conditions, where no annotated MHG treebank is available for training. In our approach, we leverage the linguistic continuity and structural similarity between MHG and $\mathbf{M}$odern $\mathbf{G}$erman ($\mathbf{MG}$), along with the abundance of MG treebank resources. Specifically, by employing the $\mathit{delexicalization}$ method, we train a constituency parser on MG parse datasets and perform cross-lingual transfer to MHG parsing. Our delexicalized constituency parser demonstrates remarkable performance on the MHG test set, achieving an F1-score of 67.3%. It outperforms the best zero-shot cross-lingual baseline by a margin of 28.6% points. These encouraging results underscore the practicality and potential for automatic syntactic analysis in other ancient languages that face similar challenges as MHG.
The majority of projects fail to achieve their intended objectives, according to research.This could arise for a number of reasons, such as ensuring requirements are managed, excessive documentation of the code, or the difficulty in delivering software that includes all the requested features on time.An effort could be made to overcome such failure rates by establishing a proper management of requirements and concept of reusability.The correct requirements can be identified by checking similarity between the requirements received from the various stakeholders.A reusable software component can result in substantial savings in both time and money.It can be challenging to make a choice regarding the reuse of certain software components.A comparison of the requirements of a new project with those of previous projects prior to starting a new project or even at a later stage during development is useful for identifying reusable components.This paper proposes a framework (ReSim) for identifying software requirements' similarities, in an attempt to improve reusability and identify the correct requirements.A crucial component of ReSim is to measure similarity between software requirements.Different well-known similarity measurement techniques used by the researchers to evaluate the similarity between the software requirements.Some of the methods used to measure this include dice, jaccard, and cosine coefficients, but in this paper, we have used recently developed hybrid method which considers not only semantic information including lexical databases, word embeddings, and corpus statistics, but also implied word order information and produced significant improvements in the results related to the measurement of semantic similarity between words and sentences.As part of the experiments, the study used PURE dataset -in order to demonstrate the efficacy of the proposed framework.As a result, recently developed hybrid method of measuring the requirements similarity is more accurate than Dice, Jaccard, and Cosine, while Cosine is a better choice than Dice, and Jaccard is more accurate than Dice.Thus, ReSim outperforms existing approaches when tested on the PURE dataset, providing the most accurate results for both functional and non-functional requirements.
This study aims to investigate how musical expressions of emotion and individuals’ psychological distress impact subjective ratings of emotional response and subjective appraisals, including familiarity, complexity, and preference. A sample of 123 healthy adults participated in an online survey experiment. After listening to four music excerpts with distinct musical expressions of emotional valence and arousal in a randomized sequence. Participants rated subjective emotions of energy, tension, and valence, as well as subjective appraisals, on a visual analogue scale ranging from 0 to 100. The results of repeated measures ANOVA demonstrated significant differences in emotional responses and appraisals across the ratings for different music excerpts (p > 0.01, respectively). The generalized linear mixed model results further revealed a significant main effect of musical valence on all emotional response dimensions of energy (β = −4.73 **), tension (β = 14.31 ***), valence level (β = −18.81 ***), and subjective appraisal in terms of familiarity (β = −23.06 ***), complexity (β = −6.67 ***), and preference (β = −19.54 ***). Musical arousal showed comparable results except for effects on emotional valence ratings. However, significant effects of psychological distress regarding depression, anxiety, and stress scores were only partially observed. Findings suggest that the expression of emotions through music primarily influences emotional responses and subjective appraisals, while the influence of an individual’s psychological distress level may be relatively subtle.
Background: Music therapy is a promising complementary intervention for addressing various mental health conditions. Despite evidence of the beneficial effects of music, the acoustic features that make music effective in therapeutic contexts remain elusive. Aims: This study aimed to identify and validate distinctive acoustic features of healing music. Methods: We constructed a healing music dataset (HMD) based on nominations from related professionals and extracted 370 acoustic features. Healing-distinctive acoustic features were identified as those that were (1) independent from genre within the HMD, (2) significantly different from music pieces in a classical music dataset (CMD) and (3) similar to pieces in a five-element music dataset (FEMD). We validated the identified features by comparing jazz pieces in the HMD with a jazz music dataset (JMD). We also examined the emotional properties of the features in a Chinese affective music system (CAMS). Results: The HMD comprised 165 pieces. Among all the acoustic features, 74.59% shared commonalities across genres, and 26.22% significantly differed between the HMD classical pieces and the CMD. The equivalence test showed that the HMD and FEMD did not differ significantly in 9.46% of the features. The potential healing-distinctive acoustic features were identified as the standard deviation of the roughness, mean and period entropy of the third coefficient of the mel-frequency cepstral coefficients. In a three-dimensional space defined by these features, HMD's jazz pieces could be distinguished from those of the JMD. These three features could significantly predict both subjective valence and arousal ratings in the CAMS. Conclusions: The distinctive acoustic features of healing music that have been identified and validated in this study have implications for the development of artificial intelligence models for identifying therapeutic music, particularly in contexts where access to professional expertise may be limited. This study contributes to the growing body of research exploring the potential of digital technologies for healthcare interventions.
Dataset and analysis related to the paper Järveläinen, H., and Larrieux, E. Vibrotactile feedback enhances perceived arousal and listening experience in music. Proc. Sound and Music Computing Conference (SMC) 2023, Stockholm, Sweden. The dataset “Cello.reg.df” contains registered continuous measurements of perceived arousal in solo cello performance under varying types and intensities of vibrotactile feedback. Vibrotactile feedback was provided in the Table or in the Chair. See details in the conference paper. Variables: time: time stamp (s) Arousal: Arousal rating, numeric [0,1] Amplitude: factor, levels = High, Low, 0 Vibration: factor, levels = Signal, Noise, No vibration Location (of vibrotactile feedback): factor, levels = Table, Chair sID = subject number (1-30) trial = trial number (redundant, 1-10)
The accelerated growth of cities and urban populations over recent decades and the complexity and diversity of urban areas demands proficient spatial affordance assessment especially for the vulnerable sections of the society. Lately machine learning and computer vision models have become highly competent in analyzing urban images for assessing the built environment. This study harnesses the potential of computer vision techniques to assess the age-friendliness of urban areas. The developed machine learning model utilizes Google’s Street View images and is trained using lived experience-based image ratings provided by elderly participants. Newly assigned urban images are accordingly rated for their level of age-friendliness by the model with an accuracy of 85%. This paper elaborates upon the associated literature review, explains the data collection approach and the developed machine learning model. The success of the implementation is also demonstrated, confirming the validity of the proposed methodology.
Recently, Shirai and Watanabe Royal Society Open Science, 9(1), 211128 (2022) developed OBNIS (Open Biological Negative Image Set), a comprehensive database containing images (primarily animals but also fruits, mushrooms, and vegetables) that visually elicit disgust, fear, or neither. OBNIS was initially validated for a Japanese population. In this article, we validated the color version of OBNIS for a Portuguese population. In study 1, the methodology of the original article was used. This allowed direct comparisons between the Portuguese and Japanese populations. Aside from a few emotional classification mismatches between disgust, fear, or neither-related images, we found that arousal and valence relate distinctively in both populations. In contrast to the Japanese sample, the Portuguese reported increased arousal for more positive valenced stimuli, suggesting that OBNIS images elicit positive emotions in the Portuguese population. These results showed important cross-cultural differences regarding OBNIS. In study 2, a methodological change was introduced: instead of the three classification options used originally (fear, disgust, or neither), six basic emotions were used (fear, disgust, sadness, surprise, anger, happiness), and a "neither" option, to confirm whether some of the originally "neither-related" images are associated with positive emotions (happiness). Additionally, the low-order visual properties of images (luminosity, contrast, chromatic complexity, and spatial frequency distribution) were explored due to their important role in emotion-related research. A fourth image group associated with happiness was found in the Portuguese sample. Moreover, image groups present differences regarding the low-order visual characteristics, which are correlated with arousal and valence ratings, highlighting the importance of controlling such characteristics in emotion-related research.