The Impact of Mixed Emotions on Creativity in Negotiation: An Interpersonal Perspective

The Mediating and Buffering Effect of Creativity on the Relationship Between Sense of Place and Academic Achievement in Geography

Purpose

This study explored the relationship between sense of place and academic achievement in geography and used a mediation model to verify the mediating role of creativity in this relationship.

Methods

A total of 1,037 upper secondary school students were surveyed using the Sense of Place Scale, the Innovative Behavior Scale, and their geography test scores. SPSS (version 26.0) was used for descriptive statistical analysis and correlation analysis. The PROCESS plug-in (version 4.0) was used to test the mediating effect of creativity.

Results

(1) The correlation analysis showed that sense of place has a positive effect on academic achievement in geography and is related to creativity. Moreover, creativity exerts a positive effect on academic achievement in geography (2). The results of mediation analysis indicated that creativity plays mediating and buffering roles in the relationship between sense of place and academic achievement in geography after controlling for gender and residential address. The direct and indirect effects accounted for 65.708 and 34.292% of the total effect, respectively.

Conclusions

The results indicated that sense of place affected not only academic achievement in geography directly but also creativity indirectly. This conclusion provides certain ideas for the development of geography curriculums. Since academic achievement in geography is related to both sense of place and creativity, it is necessary to pay more attention to integrating sense of place in geography education and to foster creativity in curriculum development and teaching of geography.

Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review

Affective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI), emotion recognition has piqued the interest of academics from a variety of domains. Facial expressions, speech, behavior (gesture/posture), and physiological signals can all be used to identify human emotions. However, the first three may be ineffectual because people may hide their true emotions consciously or unconsciously (so-called social masking). Physiological signals can provide more accurate and objective emotion recognition. Electroencephalogram (EEG) signals respond in real time and are more sensitive to changes in affective states than peripheral neurophysiological signals. Thus, EEG signals can reveal important features of emotional states. Recently, several EEG-based BCI emotion recognition techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of what has been accomplished in this area. It also provides an overview of the datasets and methods used to elicit emotional states. According to the usual emotional recognition pathway, we review various EEG feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods (e.g., convolutional and recurrent neural networks with long short term memory). In addition, EEG rhythms that are strongly linked to emotions as well as the relationship between distinct brain areas and emotions are discussed. We also discuss several human emotion recognition studies, published between 2015 and 2021, that use EEG data and compare different machine and deep learning algorithms. Finally, this review suggests several challenges and future research directions in the recognition and classification of human emotional states using EEG.

Specificity in the Study of Mixed Emotions: A Theoretical Framework

Research on mixed emotions is yet to consider emotion-specificity, the idea that same-valenced emotions have distinctive characteristics and functions. We review two decades of research on mixed emotions, focusing on evidence for the occurrence of mixed emotions and the effects of mixed emotions on downstream outcomes. We then propose a novel theoretical framework of mixed-emotion-specificity with three foundational tenets: (a) Mixed emotions are distinguishable from single-valenced emotions and other mixed emotions based on their emotion-appraisal relationships; (b) Mixed emotions can further be characterized by four patterns that describe relationships between simultaneous appraisals or appraisals that are unique to mixed emotions; and (c) Carryover effects occur only on outcomes that are associated with the appraisal characteristics of mixed emotion. We outline how mixed-emotion-specific effects can be predicted based on the appraisal tendency framework. Temporal dynamics, the application of mixed-emotion-specificity to individual difference research, methodological issues, and future directions are also discussed.

Environmental factors impacting the motivation to innovate: a systematic review

The environments where innovation occurs are often as varied as the areas of endeavors that aspiring innovators could pursue. This systematic review followed the guidelines of the Campbell Collaboration and PRISMA to consolidate the findings of 74 studies into the Expectancy-Value-Cost motivation theoretical framework as a means of usefully isolating for decision-makers the environmental factors that impact the motivation to innovate. The results of this review reveal that additional study of interdisciplinary samples is needed to gather deep narrative and case-driven data that considers the experiences of innovators in addition to organizations. Leaders, including decision-makers, teachers, and supervisors, can set a precedent for their learners and workers to use their past experiences and to feel safe to take intelligent risks and make reasonable mistakes in pursuit of innovating. Ensuring that project teams have a mix of experiences and backgrounds can make for more productive collaborations. Proactively addressing costs can increase workplaces' psychological safety and stability, which enables workers and learners to better focus on the endeavors at hand. The articles' evaluation illustrates that conversation about innovation promotion is dominated by business, which reduces the opportunity to learn from other innovation-driven disciplines or take truly interdisciplinary approaches.

Key concepts for managing organizations and employees turning towards the Fourth Industrial Revolution

The Fourth Industrial Revolution (4IR) brings radical changes for employees, organizations and societies. These changes are accompanied by unforeseeable challenges on cognitive, affective and behavioural levels, as well as fundamental questions that require a response. Work and meaning of work are changing. Organizations have to address employees' perceptions, and deal with rapid technological innovation, new work and employment concepts, as well as global and local power shifts. This conceptual article proposes selected key concepts for managing organizations and employees, namely creativity, positive emotions and solution-focus as keys to meaning-making in the 4IR. The aim of this article is to explore selected key concepts in managing 4IR organizations on a conceptual level and to stimulate future theoretical reflections and discourses of positive psychology (PP) and positive organizational psychology (POP) impacts within the 4IR transformations. Conclusions are drawn and provides recommendations for theory and practice.

EEG-Based BCI Emotion Recognition: A Survey

Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user's emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

Leader strategies for motivating innovation in individuals: a systematic review

Innovation is a topic of intense interest and is seen as key to confronting the vast majority of issues facing humanity. To consolidate the knowledge about approaches promoting innovation, this study conducted a systematic review integrating an all-database (n = 375) search through EBSCOhost completed on April 6th, 2019 in addition to search engine use. Three hundred three studies were full-text reviewed yielding 82 final studies eligible for the inclusion in findings extraction. The findings were synthesized and then organized into the Expectancy-value-cost (EVC) motivation framework to isolate promotive and hindering factors. It is clear that there is an unbalanced primacy in the innovation literature in favor of business and corporate settings with very little representation from the arts or social justice sectors. There is also a common trend of using surveys of individuals in organizations within a single discipline, while interviews are rare. The paucity of studying costs of innovation in the literature is symptomatic of the primarily positive psychology approach taken by studies, rather than a framework like EVC which also considers detractive factors like costs. Numerous studies provide support for the notion that more internal motivations like intrinsic (e.g., interest) and attainment (e.g., importance, fulfillment) were more influential than external motivators like rewards as targets of strategies. Leaders should focus, whenever possible, on topics that engaged curiosity, interest, and satisfaction and, if they choose to use rewards, should focus their strategies to give related rewards; otherwise, they risk sundering the internal motivation to innovate for already interested workers.