Abstract

The detrimental impact of the long-overdue pandemic has been widely acknowledged. However, the varied responses to its unprecedented challenges, particularly among young adults in college, are not well known. This study explores the role of psychological immunity in COVID-related trauma among Indian students. A significant proportion of students reported psychological distress, depression, and stress during the second wave between May and June 2021, with COVID-19 infection and related worries correlating with poorer mental health. Mediation analysis indicated that psychological flexibility negatively predicted distress, depression, and stress, while avoidance coping strategies showed a positive association with these outcomes. These findings suggest that psychological flexibility serves as a protective buffer against the impact of the pandemic, fostering resilience, while avoidant coping exacerbates its adverse effects. Interventions like Acceptance and Commitment Therapy (ACT) may enhance psychological flexibility and mitigate maladaptive coping, improving student mental well-being.

Abstract

The people with Major Depressive Disorder (MDD) exhibit the symptoms of tonal variations in their speech compared to the healthy counterparts. However, these tonal variations not only confine to the state of MDD but also on the language, which has unique tonal patterns. This work analyzes automatic speech-based depression detection across two languages, English and Malayalam, which exhibits distinctive prosodic and phonemic characteristics. We propose an approach that utilizes speech data collected along with self-reported labels from participants reading sentences from IViE corpus, in both English and Malayalam. The IViE corpus consists of five sets of sentences: simple sentences, WH-questions, questions without morphosyntactic markers, inversion questions and coordinations, that can naturally prompt speakers to speak in different tonal patterns. Convolutional Neural Networks (CNNs) are employed for detecting depression from speech. The CNN model is trained to identify acoustic features associated with depression in speech, focusing on both languages. The model's performance is evaluated on the collected dataset containing recordings from both depressed and non-depressed speakers, analyzing its effectiveness in detecting depression across the two languages. Our findings and collected data could contribute to the development of language-agnostic speech-based depression detection systems, thereby enhancing accessibility for diverse populations.

Abstract

Mental health conditions such as depression and anxiety are pervasive issues that affect millions of individuals worldwide. Understanding the intricate relationship between emotional perception and mental health is crucial. This work develops a database named IIITSaint-EmoMDB containing Malayalam language speech samples annotated with emotion ratings at valence and arousal scale using three annotators considering four emotion labels: happy, sad, angry and neutral. In addition to emotion ratings, emotional perception is rated from 150 participants according to the circumplex model of va-lence and arousal. The database also consists of self-reported mental health scores of those participants based on PHQ-9 and GAD-7 questionnaire. Our preliminary analysis of comparing the participants' emotional perception based PHQ-9 and GAD-7 scores revealed that the depressed category showed the highest improvement.

Abstract

Depression, a pervasive mental health issue, highlights the critical need for early detection and effective intervention. A database (InStant-EMDB) is developed to analyze varia tions in the spoken responses of individuals with depression compared to healthy counterparts. Spoken responses are ob tained from English and Malayalam bilingual speakers who respond spontaneously to a set of 15 emotionally evocative words. These words are sourced from the Affective Norms for English Words (ANEW) dataset, which includes valence and arousal ratings for each word. The speech in both English and Malayalam is manually transcribed to accurately reflect the spoken content. The dataset also contains self-reported af fective ratings and data from a mental health survey (PHQ-9) collected from the participants to determine their mental state, which we considered the self-reported depression labels. A preliminary analysis is conducted on the collected speech us ing current state-of-the-art deep learning models such as Con volutional Neural Networks (CNN), Long Short-Term Mem ory networks (LSTM) and Bi-directional Long Short-Term Memory networks (Bi-LSTM). Although distinct linguistic patterns exhibited by individuals struggling with depression are successfully identified by all three models in both Malay alam and English spoken responses, the highest accuracy is achieved by the LSTM models. Our findings and dataset em phasize the potential of linguistic patterns as valuable cues for the early identification and intervention of depression and could contribute to enhancing accessibility for diverse popu lations.

Abstract

The current study examines the interaction between predis- posed mood, perceptual processing, and induced mood us- ing music. We conducted an experiment in which partici- pants were asked to identify stimuli at global or local (G/L) perceptual levels with four different background music con- ditions, which had different valence and arousal ratings. We used BMIS to assess current mood and PHQ-9 and GAD-7 to assess depression and anxiety, and divided the participants into two groups: distress and no distress (encompassing both disor- ders). We found a main effect of background music on mood. However, the distress group showed an overall low mood. Fur- ther, we observed an overarching effect of predisposed mood, encompassing depression and anxiety, on individuals’ transient mood experience and perceptual task performance. Individuals in the non-distress group showed a larger global-precedence effect. The results are discussed in light of emotional reactiv- ity theories and the theory of positive emotion.

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Abstract

The current study aims to understand whether the constraints in the well-defined problem solving task impacts the ill- defined problem solving task of a different knowledge do- main. We chose variants of Raven’s advanced progressive matrices(APM) for well-defined problem-solving. For the ill- defined problem solving task we have chosen creative reason- ing tasks (CRT) which is of same knowledge domain and al- ternate uses task(Guilford, 1967) of a different knowledge do- main. We have collected a sample of 44 participants. We ob- served a significant effect of the variant of APM on CRT per- formance. Higher number of rules in the CRT was observed when it was preceded by cAPM (Mdn = 2) compared to the classic APM (Mdn = 1). Further, we observed a higher CRT Relationship Score under cAPM (Mdn = 78.5) compared to the classic APM (Mdn = 50.5) puzzle conditions. However, the variant of APM did not show a significant effect on AUT, fluency score(p = 0.8 for independent t-test) and elaboration score (p = 0.48 for Mann-Whitney U test). The current results indicate that creativity is more domain-specific than domain- general.

Abstract

Emergency signages are crucial for safe evacuations but have been challenged by inconsistent and ambiguous designs. We conducted two experiments to evaluate a specific emergency signage design, i.e., running man's pictogram, in ease of conveying the direction to exit an unknown building. We compared the running man's pictogram with traditional arrow signage in exp.1, and in exp. 2, we examined the key features that contribute to the running man's composition. In both conditions, the participant's task was to determine the running man's/ arrow's orientation and press the instructed corresponding keys. We observed a faster and more accurate response for the right arrow than the right running man in experiment 1. Further, in exp. 2, the head/face component showed an advantage in identifying the running man's orientation over the leg/hand. Results suggest running man alone in emergency signage may be risky, especially when it is incomplete without a head and face.

Abstract

Is creativity domain-specific or domain-general? Significant studies have approached this problem using self-reported personality and domain-specific and general creative ideation surveys and lack cognitive perspectives. Creativity is defined as the generation of original and useful products that have emerged through the applications of basic cognitive processes to existing knowledge structures. The current study investigates its implications while modifying the basic knowledge structure. Further, we examine the role of the knowledge domain in creative task performance. For prior knowledge structure, we used advanced Raven’s matrices and modified its constraints to create a novel creative advanced Raven’s Matrices (cAPM) to induce creativity. For creativity, we asked participants to perform two tasks: create Raven's like matrices using creative reasoning task (CRT) and Guilford's alternative uses task (AUT). Unlike other creativity tasks like AUT, CRT allows to measure both convergent and divergent thinking in a given task. The CRT and AUT are different from APM puzzles in two ways, i.e., the knowledge domain and the involvement of creative thinking. The fixed order of APM followed by creativity tasks allowed us to evaluate the boundaries of induced creative thinking across the knowledge domain