Emotional Representations Research Articles

Социально-философский анализ парадокса репрезентации эмоционального робота в литературе и кинематографе

This article is devoted to the socio-philosophical analysis of the mechanical man – machine or robot ‒ in the representations of modern culture, in particular, in literature and cinema. The key characteristic of such a construction is the paradox of representations of the robot's emotionality, which is conceptualized at the level of human emotions in the context of socio-cultural plot and scenario solutions. The authors pay particular attention to the complex problem of generation, representation, and adequacy of emotions demonstrated by a robot in the course of the development of a literary plot or a cinematic scenario. As a result of the research, the authors delineate and examine a range of socio-philosophical analysis concerning the creation of the mechanical image of a human endowed with emotions, which is intricately linked to the psychological nuances of how this image is perceived by readers or viewers. In conclusion, a selective review of cinematic works from both past and present centuries is conducted, analyzing the socio-philosophical issues arising from the introduction of robots within these narratives. The article also evaluates the prospects for further research in this direction, highlighting the ongoing relevance of emotional robots in modern cultural discourse and their implications for understanding human emotionality and identity in an increasingly mechanized world.

Course of hand pain over four years in patients with hand osteoarthritis depends on BMI, employment and mental wellbeing: The Hand OSTeoArthritis in Secondary care cohort study.

We aimed to characterize hand osteoarthritis (OA) patients with deteriorating or improving hand pain, and to investigate patients achieving good clinical outcome after four years. We used four year annual Australian/Canadian hand osteoarthritis index (AUSCAN) pain subscale (range 0-20) measurements from the HOSTAS cohort (hand OA patients). Pain changes were categorized as deterioration, stable and improvement using the Minimal Clinical Important Improvement (MCII). Good clinical outcome was categorized using the Patient Acceptable Symptom State (PASS). Associations between baseline characteristics (patient and disease characteristics, coping styles, illness perceptions) and outcomes were investigated using multinomial or binary logistic regression, adjusted for baseline pain, age, sex and BMI. 356 patients (83% female, mean age 60.6 years, mean AUSCAN 9.1) were analyzed. Pain improved for 38%, deteriorated for 30% and remained stable for 32% over four years. Four year pain development followed annual trends. At baseline, 44% of patients reached PASS, 49% at follow-up. Higher BMI, coping through comforting cognitions and illness comprehension were positively associated with pain deterioration. Higher AUSCAN function score, mental wellbeing and illness consequences were negatively associated with pain improvement. Employment (positively) and emotional representations (negatively) were associated with both improvement and deterioration. Higher baseline AUSCAN function, tender joint count and symptoms attributed to hand OA were associated negatively with PASS after four years. The pain course of hand OA patients is variable, not inevitably worsening, and various factors may play a role. Whether modification of these risk factors can influence pain outcomes requires further investigation.

Optimized multi-layer self-attention network for feature-level data fusion in emotion recognition

<p>Understanding human emotions across diverse data sources presents challenges in various applications including healthcare, human-machine interaction, security, marketing, and gaming. Prior research has explored fusion techniques to address multimodal data heterogeneity, yet often overlooks the importance of discriminative unimodal information and potential complementarity among fusion strategies. Recognizing emotions from video and audio data poses challenges such as non-verbal cues interpretation, varying expression, ambiguity in context, and the need for nuanced feature extraction to capture subtle emotional nuances accurately. To tackle these issues, it is imperative to employ efficient emotion representation and multimodal fusion techniques, as these tasks have significant importance within the realm of multifaceted recognizing study. This study introduced a novel approach, optimized multi-layer self-attention network for emotion recognition (OMSN-ER), focusing on feature-level data fusion. OMSN-ER precisely assesses emotional states by merging facial and voice data, utilizing a multi-layer progressive dense residual fusion network and a self-attention mountain gazelle convolution neural network. Implemented in Python with the RAVDESS dataset, the methodology achieves exceptional accuracy (0.9908), surpassing benchmarks and demonstrating efficacy in multimodal emotion recognition. This research represents promising advancements in the intricate field of emotion recognition.</p>

Representasi Karakter Utama dalam Film My Idiot Brother 2014

This study aims to analyze the emotional representation of characters with intellectual limitations in the film My Idiot Brother. This study uses a descriptive qualitative approach with the aim of describing the emotional meaning displayed by the characters in the film My Idiot Brother using a semiotic approach. The focus of the research is how the main character, Hendra, displays emotions such as anger, sadness, emotion, and happiness to convey a moral message about acceptance and empathy. Data is collected through repeated observation and documentation of cutouts of relevant scenes. Semiotic analysis is carried out to interpret the symbolic meaning of the expression of the character. The results indicate that the visualized emotions reflect social dynamics within a family with a member who has special needs, underscoring the importance of family support free from stigma. These findings demonstrate the role of film in shaping public perceptions of individuals with disabilities and fostering social inclusion. This study contributes to the discourse on character representation in media and promotes inclusivity

Comparing spiritual wellbeing and illness perceptions between cancer patients from culturally and linguistically diverse and those from mainstream backgrounds in Australia.

Cancer presents a significant health challenge, particularly within culturally and linguistically diverse (CALD) populations. Religious, spiritual, and personal beliefs regarding illness may mitigate its psychological and physical impacts, varying with CALD status. This study aims to assess illness perceptions in Australian cancer patients from both CALD and Anglo-Australian backgrounds and to explore the associations between spiritual wellbeing and illness perceptions within these groups. Cross-sectional study, involving 119 cancer patients from CALD backgrounds (Arabic, Chinese, and Greek) and 50 Anglo-Australians. Spiritual wellbeing and cancer perception were assessed using the World Health Organization Quality of Life-Spirituality, Religiousness, and Personal Beliefs questionnaire and the revised Illness Perception Questionnaire, respectively. Linear regression models analyzed associations. CALD patients perceived their cancer as less enduring (β - 4.74, 95%CI - 6.16, - 3.31) and symptoms as less predictable (- 4.96, 95%CI - 6.12, - 3.86) compared to Anglo-Australians. They had lower confidence in medical interventions (- 2.96, 95%CI - 3.36, - 1.22) and poorer illness understanding (- 4.66, 95%CI - 5.83, - 3.49). In Greek-speaking patients, an increase in spiritual wellbeing was associated with less favourable perceptions of treatment controllability (β - 2.25, 95%CI - 4.39, - 0.09). Conversely, for Chinese-speaking patients, an increase in spiritual wellbeing was associated with improved illness perception across all domains except for consequences and emotional representation. However, no significant association was observed for Arabic patients. Differences in illness perceptions exist among CALD and Anglo-Australians. Spiritual wellbeing appears linked to more favourable perceptions; however, differential effects emerged among groups, emphasizing the need for culturally tailored interventions to improve outcomes.

ARMNet: A Network for Image Dimensional Emotion Prediction Based on Affective Region Extraction and Multi-Channel Fusion.

Compared with discrete emotion space, image emotion analysis based on dimensional emotion space can more accurately represent fine-grained emotion. Meanwhile, this high-precision representation of emotion requires dimensional emotion prediction methods to sense and capture emotional information in images as accurately and richly as possible. However, the existing methods mainly focus on emotion recognition by extracting the emotional regions where salient objects are located while ignoring the joint influence of objects and background on emotion. Furthermore, in the existing literature, when fusing multi-level features, no consideration has been given to the varying contributions of features from different levels to emotional analysis, which makes it difficult to distinguish valuable and useless features and cannot improve the utilization of effective features. This paper proposes an image emotion prediction network named ARMNet. In ARMNet, a unified affective region extraction method that integrates eye fixation detection and attention detection is proposed to enhance the combined influence of objects and backgrounds. Additionally, the multi-level features are fused with the consideration of their different contributions through an improved channel attention mechanism. In comparison to the existing methods, experiments conducted on the CGnA10766 dataset demonstrate that the performance of valence and arousal, as measured by Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²), has improved by 4.74%, 3.53%, 3.62%, 1.93%, 6.29%, and 7.23%, respectively. Furthermore, the interpretability of the network is enhanced through the visualization of attention weights corresponding to emotional regions within the images.

ST-SHAP: A hierarchical and explainable attention network for emotional EEG representation learning and decoding

Background:Emotion recognition using electroencephalogram (EEG) has become a research hotspot in the field of human–computer interaction, how to sufficiently learn complex spatial–temporal representations of emotional EEG data and obtain explainable model prediction results are still great challenges. New methodIn this study, a novel hierarchical and explainable attention network ST-SHAP which combines the Swin Transformer (ST) and SHapley Additive exPlanations (SHAP) technique is proposed for automatic emotional EEG classification. Firstly, a 3D spatial–temporal feature of emotional EEG data is generated via frequency band filtering, temporal segmentation, spatial mapping, and interpolation to fully preserve important spatial–temporal-frequency characteristics. Secondly, a hierarchical attention network is devised to sufficiently learn an abstract spatial–temporal representation of emotional EEG and perform classification. Concretely, in this decoding model, the W-MSA module is used for modeling correlations within local windows, the SW-MSA module allows for information interactions between different local windows, and the patch merging module further facilitates local-to-global multiscale modeling. Finally, the SHAP method is utilized to discover important brain regions for emotion processing and improve the explainability of the Swin Transformer model. Results:Two benchmark datasets, namely SEED and DREAMER, are used for classification performance evaluation. In the subject-dependent experiments, for SEED dataset, ST-SHAP achieves an average accuracy of 97.18%, while for DREAMER dataset, the average accuracy is 96.06% and 95.98% on arousal and valance dimension respectively. In addition, import brain regions that conform to prior knowledge of neurophysiology are discovered via a data-driven approach for both datasets. Comparison with existing methods:In terms of subject-dependent and subject-independent emotional EEG decoding accuracies, our method outperforms several closely related existing methods. Conclusion:These experimental results fully prove the effectiveness and superiority of our proposed algorithm.

Caught between Two Transnationalisms: Mainland Chinese Students’ Emotional Conflicts in Recent Hong Kong Protests

This article discusses minor transnationalism in China through a case study of mainland students’ emotional conflicts in the recent Hong Kong protests. Examining the emotional representations and experiences of mainland students through analysis of media texts and interviews, the article explains how their experiences are a product of tensions between two competing Chinese transnationalisms in Hong Kong against a backdrop of China’s rise. Further unpacking the ways in which mainland students negotiate both nationalist and localist politics through their cross-border mobility, the article reflects on the possibility of a minor transnational positionality that decenters statist configurations of identity. It argues that the case of mainland students in Hong Kong illuminates how a hybridized political subject could emerge amid the effects of the new, Sino-centric version of Chinese transnationalism.

Patients' beliefs as predictors of patient satisfaction and health-related quality of life in pediatric rehabilitation.

This study investigated the predictive value of illness and treatment beliefs for patient satisfaction and health-related quality of life (HRQOL) in adolescents receiving inpatient rehabilitation treatment. In addition, we examined the relationship between fulfilled rehabilitation-related treatment expectations and patient satisfaction. In this longitudinal study (recruitment between April 2019 and March 2020), 170 participants (M = 14.3 years [SD = 1.6]) answered self-report questionnaires before and at the end of rehabilitation (6 weeks later). We applied multiple hierarchical regression analyses, controlling for sociodemographic and diagnoses variables. The results showed fulfilled expectations of treatment success and sustainability to be a significant predictor of patient satisfaction (p < .01). The illness belief dimension of emotional representation predicted HRQOL (p < .01). Rehabilitation-related treatment beliefs were not predictive of any outcome. This study provides a first insight into the relationships between these constructs in the context of inpatient pediatric rehabilitation. However, future research is needed to further examine illness and treatment beliefs in this specific treatment setting. Practical implications concern the incorporation of children's and adolescents' beliefs into treatment management to optimize rehabilitation outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Computational hermeneutics of emotion: a comparative study of emotional landscapes in the Dostoevsky’s novel “Crime and Punishment”

This study employs computational hermeneutics to explore emotional representations in Fyodor Dostoevsky’s Crime and Punishment. Using advanced Natural Language Processing (NLP) techniques, including sentiment analysis powered by the BERT model and other tools, we examine the emotional and existential aspects embedded in the narrative. Our approach combines computational tools with traditional hermeneutic methods to deconstruct the emotional journey of the protagonist, Rodion Raskolnikov. We quantitatively analyze his emotional evolution before and after the pivotal crime, mapping his psychological transition from detachment to engagement. The study focuses on four key emotional dimensions: Attitude, Introspection, Sensitivity, and Temper. By transforming qualitative textual data into a quantitative framework, we provide a novel illustration of Raskolnikov’s psychological progression from a state of depersonalization to full emotional contact. This computational analysis is contextualized within a broader philosophical interpretation, drawing on concepts such as Stoic apatheia and Heideggerian Sorge. Our research serves a dual purpose: it enhances our understanding of Dostoevsky’s work while demonstrating the potential of computational methods in literary analysis. By bridging digital humanities and traditional literary criticism, this study contributes to the growing field of digital hermeneutics and opens new avenues for interdisciplinary research in literature, philosophy, and computational linguistics.

Alexithymia and illness perceptions in persons with multiple sclerosis and their partners

Illness perceptions (IPs) encompass opinions regarding the nature, severity and curability of a disease. The aim of this cross-sectional study was to investigate the association between alexithymia and IPs among persons with multiple sclerosis (PwMS) and their partners, as well as within the dyads composed of PwMS and partners. PwMS referred to the Multiple Sclerosis Center of the University Hospital “Policlinico-San Marco” from 11th August 2021 to 7th January 2022 and their partners completed a battery of questionnaires, including the Toronto Alexithymia Scale-20 and the Illness Perception Questionnaire Revised. A dyadic data analysis (Actor-Partner Interdependence Model) was performed to test the effect of alexithymic traits both on a person’s own illness perceptions (actor effect) and on the partner’s illness perceptions (partner effect). 100 PwMS (71 women; mean age 47.6 ± 10.4 years) and 100 partners (29 women; mean age 49.1 ± 10.8 years), with a mean partnership duration of 20.1 ± 11.7 years, were enrolled. At the dyadic analysis, statistically significant small-to-moderate actor and partner effects were found considering alexithymia (total score and alexithymic facets) and IPs, whereby higher alexithymic traits related to higher negative perceptions (i.e. consequences, emotional representations) and lower positive ones (i.e. coherence, treatment control). Our findings support the relationship between alexithymia and negative illness appraisals. This data may inform therapeutic interventions aimed at reducing alexithymic traits, which in turn may reduce negative, and potentially dysfunctional, illness perceptions.

Visual experience shapes bodily representation of emotion.

Philosophers and experimentalists have long debated whether bodily representation of emotion is grounded in our sensory experience. Indeed, we are used to observe emotional reactions expressed through the bodies of others, yet it is still unknown whether this observation influences how we experience affective states in our own bodies. To delve into this question, we developed a naturalistic haptic task and asked a group of early (n = 20) and late (n = 20) blind, as well as sighted individuals (n = 20) to indicate where in the body they perceive changes associated with affective states. Our results show that visual experience shapes bodily representation of emotion. Blind and sighted individuals attribute different importance to body regions in relation to specific emotional states, as sighted people focus more on visceral sensations, while blind report as more relevant the mouth and the hand areas. We also observe differences in the coherence of bodily maps of specific emotions, such as aggressiveness, for which early and late blind are homogenous in reporting the mouth, while sighted subjects demonstrate a scattered pattern of activation across the body. Finally, our findings show that blind people rely on a different organization of affect, as only sighted categorize bodily maps of emotion through the valence and arousal dimensions. In summary, we demonstrate that sensory experience impacts the bodily representation of affect by modulating the relevance that different body parts have in emotional reactions, modifying the weights attributed to interoceptive and exteroceptive signals, and changing how emotions are conceptualized in the body. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Queering ontological (in)security: a psychoanalysis of political homophobia

ABSTRACT Recent advancements in ontological security (OS) derived from Lacan’s psychoanalysis rethink the concept in terms of the subject’s desire for completeness through attempting stabilised and fixed identities. This approach can offer a novel way to examine the rise of political homophobia where power works pervasively. However, the current conceptualisations of ontological (in)security are not theoretically equipped to unpack and examine such violent politics. This is mainly because ontological (in)security has yet to grapple with the concept of power. This article will address this gap and rethink Lacanian ontological (in)security as a framework for critical power analysis. It equips it with two theoretical moves: Queer studies, which advances the understanding of ‘the other’ and ‘othering’, and emotions research, which demonstrates how emotions work in othering, in International Relations. These two theoretical moves enable the framework to examine how emotions do political work to (re)produce binaries such as subject and object, ‘self’ and ‘other’, and ‘normal’ and ‘perverse’ in the subject production process in heteronormative ‘regimes of normal’. Consequently, Lacanian ontological (in)security can help us understand how and why a gendered and sexualised politics is normalised. The argument will be illustrated through analysis of emotional representations of LGBTQI+ communities in the state-level political discourse in Turkey.

Lexical Analysis of Japanese Children’s Songs from the Perspective of Japanese Language Education

Japanese children's songs can be a valuable resource for adult learners of the language, Firstly, the vocabulary used in children's songs is predominantly high-frequency and foundational, with a large portion falling below the N2 proficiency level. By mastering the words and phrases found in these songs, learners can expand their conversational abilities beyond the confines of traditional textbooks. Secondly, the songs offer a balanced representation of positive, negative, and neutral emotions, reflecting the emotional range and nuances of the Japanese language. This exposure to diverse emotional expressions can enrich learners' understanding of language and cultural nuances. Moreover, the study's semantic analysis reveals frequent word associations and co-occurrences, which can help learners grasp the interconnectedness of Japanese vocabulary and the cultural contexts in which words are used. Importantly, children's songs are deeply rooted in Japanese culture, indirectly exposing learners to various cultural aspects, such as the appreciation of nature, the concept of transience, and the importance of harmony and interconnectedness. This cultural immersion can foster a deeper understanding and appreciation of Japanese values and traditions. Furthermore, the familiar melodies and sing-along nature of these songs can facilitate social bonding and interpersonal connections among learners and native speakers. Engaging in shared cultural experiences, such as singing nursery rhymes together, can create a sense of community and strengthen the bond between learners and the target language culture.

Supplemental Material for Visual Experience Shapes Bodily Representation of Emotion

Supplemental Material for Visual Experience Shapes Bodily Representation of Emotion

CIRG-SL: Commonsense Inductive Relation Graph framework with Soft Labels for Empathetic Response Generation

Empathy, the ability to understand and respond to others’ emotions, is critical in dialogue systems, particularly for applications such as psychological counselling and casual conversation. However, existing approaches often struggle to accurately capture emotional transitions in user utterances and dialogue contexts across multiple levels of granularity. Additionally, the orthogonality of one-hot emotion representations overlooks the subtle relationships between emotion categories. To overcome these limitations, we propose a novel framework: the commonsense inductive relation graph with fine emotional soft labels for empathetic response generation. This framework refines the representation of user utterances by incorporating commonsense knowledge to derive external emotional cues, which are integrated into an adaptive inductive relation graph. This graph explicitly captures and models dynamic emotional trajectories throughout the dialogue. Additionally, we propose a fine-grained soft label construction method that more accurately reflects the nuanced distribution of emotions, replacing the rigid one-hot encoding with a continuous representation that better captures inter-emotion relationships. Our approach achieves strong performance on the benchmark EmpatheticDialogues dataset, with a perplexity score of 27.85, Dist-1 of 1.81, Dist-2 of 6.58, and a dialogue emotion accuracy of 55.65%. These results highlight the effectiveness of our framework in capturing subtle emotions and generating contextually rich, empathetic responses.

Correlation between illness perceptions and self-reported degree-of-worry in somatic ill patients in emergency care: a Danish cross-sectional study.

The study aimed to investigate the correlation between illness perceptions and self-reported degree-of-worry (DOW) in patients presenting to the emergency department with acute somatic illness. A cross-sectional survey study collected data from a survey on DOW, the Brief-illness Perception Questionnaire (B-IPQ) and demographics. An emergency department at a university hospital in the Capital Region of Denmark. 944 patients (≥18 years) presenting to the emergency department with acute somatic illness. Data collection was conducted during day and evening shifts on weekdays from 13 January to 19 December 2020. Correlations between B-IPQ scores and DOW, including (1) the total B-IPQ score, (2) emotional representations (emotion and illness concern combined) and (3) the single items, respectively. Secondary analysis: linear relationships between the single B-IPQ items and DOW. Analyses included the variables sex, age, reason for contact, self-reported comorbidity, employment, highest educational attainment, cohabitation status and region of origin. The study found moderate correlations between the total illness perceptions score and DOW of 0.47 (95% CI 0.42 to 0.52) and emotional representations and DOW of 0.57 (95% CI 0.52 to 0.61). Linear regression showed the largest and most significant changes in DOW with an increasing score of the B-IPQ items for consequence, illness concern and emotion. Illness perceptions overall and perceptions of consequences of symptoms, illness concern and emotional representations are correlated with patients' DOW, with increasing DOW related to more negative illness perceptions. This finding contributes to insight into how illness perceptions are related to illness worry when presenting to the emergency department. Incorporating the novel DOW scale may aid triage and clinical encounters in the emergency department. ClinicalTrails.gov reference: NCT04226040.

Neural Representations of Emotions in Visual, Auditory, and Modality-Independent Regions Reflect Idiosyncratic Conceptual Knowledge.

Growing evidence suggests that conceptual knowledge influences emotion perception, yet the neural mechanisms underlying this effect are not fully understood. Recent studies have shown that brain representations of facial emotion categories in visual-perceptual areas are predicted by conceptual knowledge, but it remains to be seen if auditory regions are similarly affected. Moreover, it is not fully clear whether these conceptual influences operate at a modality-independent level. To address these questions, we conducted a functional magnetic resonance imaging study presenting participants with both facial and vocal emotional stimuli. This dual-modality approach allowed us to investigate effects on both modality-specific and modality-independent brain regions. Using univariate and representational similarity analyses, we found that brain representations in both visual (middle and lateral occipital cortices) and auditory (superior temporal gyrus) regions were predicted by conceptual understanding of emotions for faces and voices, respectively. Additionally, we discovered that conceptual knowledge also influenced supra-modal representations in the superior temporal sulcus. Dynamic causal modeling revealed a brain network showing both bottom-up and top-down flows, suggesting a complex interplay of modality-specific and modality-independent regions in emotional processing. These findings collectively indicate that the neural representations of emotions in both sensory-perceptual and modality-independent regions are likely shaped by each individual's conceptual knowledge.

Talking Face Generation With Audio-Deduced Emotional Landmarks.

The goal of talking face generation is to synthesize a sequence of face images of the specified identity, ensuring the mouth movements are synchronized with the given audio. Recently, image-based talking face generation has emerged as a popular approach. It could generate talking face images synchronized with the audio merely depending on a facial image of arbitrary identity and an audio clip. Despite the accessible input, it forgoes the exploitation of the audio emotion, inducing the generated faces to suffer from emotion unsynchronization, mouth inaccuracy, and image quality deficiency. In this article, we build a bistage audio emotion-aware talking face generation (AMIGO) framework, to generate high-quality talking face videos with cross-modally synced emotion. Specifically, we propose a sequence-to-sequence (seq2seq) cross-modal emotional landmark generation network to generate vivid landmarks, whose lip and emotion are both synchronized with input audio. Meantime, we utilize a coordinated visual emotion representation to improve the extraction of the audio one. In stage two, a feature-adaptive visual translation network is designed to translate the synthesized landmarks into facial images. Concretely, we proposed a feature-adaptive transformation module to fuse the high-level representations of landmarks and images, resulting in significant improvement in image quality. We perform extensive experiments on the multi-view emotional audio-visual dataset (MEAD) and crowd-sourced emotional multimodal actors dataset (CREMA-D) benchmark datasets, demonstrating that our model outperforms state-of-the-art benchmarks.

Decoding ruminative reflection in healthy individuals: The role of triple network connectivity

Ruminative reflection has been linked to enhanced executive control in processing internally represented emotional information, suggesting it may serve as an adaptive strategy for emotion regulation. Investigating the neural substrates of reflection can deepen our understanding of its adaptive properties. This study used network-based statistic (NBS)-Predict methodology to identify resting state functional connectivity (FC)-based predictors of ruminative reflection in a healthy sample. Our results showed that reflection in healthy subjects was predicted by FC within and between the default mode network (DMN), fronto-parietal network (FPN), and salience network (SN). Notably, FC within the FPN and SN, as well as between the FPN and DMN, contributed more significantly to the predictive model. These results underscore the greater influence of FPN and SN connectivity in predicting reflection, providing empirical evidence that increased executive control over internal emotional representations is integral to adaptive reflective processes. Moreover, the triple-network model, particularly the FPN-DMN coupling, emerges as a crucial predictor of ruminative reflection, highlighting the importance of coordinating self-relevant and goal-directed processing in reflective mechanisms. These identified connectivity fingerprints may offer insights into the role of reflective processes in facilitating recovery from depression.