Exploring Emotion Recognition and the Understanding of Others’ Unspoken Thoughts and Feelings when Narrating Self-Experienced Emotional Events

Emotion decoding competence can be addressed in different ways. In this study, clinical psychology, nursing, or social work students narrated a 2.5–3 min story about a self-experienced emotional event and also listened to another student’s story. Participants were video recorded during the session. Participants then annotated their own recordings regarding their own thoughts and feelings, and they rated recordings by other participants regarding their thoughts and feelings [empathic accuracy, EA, task]. Participants further completed two emotion recognition accuracy (ERA) tests that differed in complexity. The results showed that even though significant correlations were found between the emotion recognition tests, the tests did not positively predict empathic accuracy scores. These results raise questions regarding the extent to which ERA tests tap the competencies that underlie EA. Different possibilities to investigate the consequences of method choices are discussed.

Unsupervised Learning of Brain State Dynamics during Emotion Imagery using High-Density EEG

Here we assume that emotional states correspond to functional dynamic states of brain and body, and attempt to characterize the appearance of these states in high-density scalp electroencephalographic (EEG) recordings acquired from 31 participants during 1-2 hour sessions, each including fifteen 3-5 min periods of self-induced emotion imagination using the method of guided imagery. EEG offers an objective and high-resolution measurement of whatever portion of cortical electrical dynamics is resolvable from scalp recordings. Despite preliminary progress in EEG-based emotion decoding using supervised machine learning methods, few studies have applied data-driven, unsupervised decomposition approaches to investigate the underlying EEG dynamics by characterizing brain temporal dynamics during emotional experience. This study applies an unsupervised approach – adaptive mixture independent component analysis (adaptive mixture ICA, AMICA) that learns a set of ICA models each accounted for portions of a given multi-channel EEG recording. We demonstrate that 20-model AMICA decomposition can identify distinct EEG patterns or dynamic states active during each of the fifteen emotion-imagery periods. The transition in EEG patterns revealed the time-courses of brain-state dynamics during emotional imagery. These time-courses varied across emotions: “grief” and “happiness” showed more abrupt transitions while “contentment” was nearly indistinguishable from the preceding rest period. The spatial distributions of independent components (ICs) of the AMICA models showed higher similarity within-subject across emotions than within-emotion across subjects. No significant differences in IC distributions were found between positive and negative emotions. However, significant changes in IC distributions during emotional imagery compared to rest were identified in brain areas such as the left prefrontal cortex, the posterior cingulate cortex, the motor cortex, and the visual cortex. The study demonstrates the feasibility of AMICA in modeling high-density and nonstationary EEG and its utility in providing data-driven insights into brain state dynamics during self-paced emotional experiences, which have been difficult to measure. This approach can advance our understanding of highly dynamical emotional processes and improve the performance of EEG-based emotion decoding for affective computing and human-computer interaction.

Προσανατολισμός δεσμού και κοινωνική αντίληψη του συναισθήματος: Ο ρόλος των κοινωνικών κινήτρων

Despite the seminal role of emotion perception in social and personal relationships, there is limited understanding of how adult attachment organization affects the decoding of facial emotion expressions. Previousresearch has focused on how insecure attachment-related strategies for emotion regulation influence early stages of emotion information processing. However, recent studies highlight the importance of socialprocesses and motivational factors in the perception of positive and negative emotion (see e.g., Vrtička, Sander, & Vuilleumier, 2012). Based on a critical review of the relevant literature, the present articlepresents findings from a recent series of studies that reveal the effects senders' social moti ves (relational context, social goals) have on emotion decoding accuracy in adults with insecure attachment. The findings from these studies are discussed in the context of theories of motivated social cognition and the social perception of emotion.

Acceptability of Intimate Partner Violence among Male Offenders: The Role of Set-Shifting and Emotion Decoding Dysfunctions as Cognitive Risk Factors

Attitudes towards the acceptability of intimate partner violence against women (IPVAW) contribute to an increased risk of IPVAW perpetration, and these attitudes are common among IPVAW offenders. Research suggests that IPVAW offenders present cognitive deficits related to information processing. Little is known, however, about how these deficits are related to the acceptability of IPVAW. The main aim of this study was to explore the relationship between specific cognitive deficits (i.e., deficits in attention switching, set-shifting, and emotion decoding abilities) and the acceptability of IPVAW in a sample of 84 IPVAW offenders. Results revealed that IPVAW offenders with deficits in attention switching, set-shifting, and emotion decoding abilities demonstrated greater acceptability of IPVAW, and these relationships remained significant after controlling for socio-demographic variables (i.e., age and educational level) and drug consumption. These results highlight the role of cognitive processes in maintaining attitudes of acceptability of IPVAW. Thus, the findings may guide professionals in developing specific intervention programs focused on improving cognitive abilities, in order to reduce the acceptability of IPVAW.