Selection Theory Research Articles

Methods for optimizing the characteristics of antenna elements and arrays simulating aggregative behavior and evolution of living be-ings in nature

Rigorous analytical solutions to antenna synthesis problems have been obtained for several of their simplest designs, while numerical solutions to specific versions of these problems in a strict electrodynamic formulation for the required practical structures take so much time in most cases that obtaining an optimal solution in an acceptable time is not always possible. For this reason, approximate methods for solving various technical problems, including antenna problems, based on significantly simpler operators than in electrodynamics have become widespread. This allows for a significant gain in time and optimal solutions to synthesis problems in a reasonable time by direct, targeted enumeration of the space of combinations of parameters characterizing the problem under consideration. If it is necessary to increase the accuracy of the solution obtained in this way, approximate methods allow one to select from a possible number of options a significantly smaller number with the best values of the objective function for subsequent analysis of these options by strict methods. A significant place among approximate optimization methods is occupied by evolutionary algorithms (EA), which have been developed since the second half of the last century. They imitate the behavior and evolution of various biological communities, generally obeying the theory of evolution and Darwin's natural selection. The article provides a brief overview of modifications of both well-known algorithms and the control parameters they contain: the genetic algorithm (GA), the method of swarm of biological entities (PSO), the method of differential evolution (DE), with the help of which a number of problems on the synthesis of antennas with discretely and continuously changing parameters have been solved, and a relatively new algorithm – the method of grey wolves (GWO). An example of implementation using the PSO/FDTD optimization algorithm of a dual-frequency and broadband microstrip antenna with an E-shaped plate has been given. A mathematical model and a block diagram of the modified IGWO algorithm have been presented. The superiority of its characteristics in terms of the accuracy of determining the global extremum and the speed of convergence over such well-known algorithms as GWO, SCA, SSA, PSO, FPA, MFO and BA has been shown based on testing these algorithms on twenty-three reference functions.

The dynamic and reciprocal relationship between perceived everyday discrimination and cognitive function in later life

Objectives This study, based on socioemotional selectivity theory and cognitive theory, investigates the dynamic and reciprocal relationship between perceived discrimination and cognitive function in later life. Methods Data were drawn from four waves of the Health and Retirement Study (HRS 2006, 2010, 2014, and 2018). A total of 4,125 people who were 51 and older were included. Cognitive function was measured by the telephone interview for cognitive status (TICS-27). Perceived discrimination was measured using scores of the perceived everyday discrimination scale. Random intercept cross-lagged panel model (RI-CLPM) was utilized. The model was adjusted for a range of covariates. Subgroup analysis by ethnoracial groups was conducted. Results Cross-sectionally, while lower cognitive function was associated with higher perceived discrimination, this relationship was unidirectional. Longitudinally, higher perceived discrimination predicted lower cognitive function in later waves only among non-Hispanic White individuals. Conclusion Results suggested that a decline in cognitive function may precede and contribute to the worsening of perceived discrimination, which may result in further decline in cognitive function. Lifetime experience of discrimination was discussed as a possible source of the racial/ethnic variations in the relationship. Further study is needed to examine whether this relationship holds among people with cognitive impairment and dementia.

Rethinking Instinct Theory from a Darwinian Perspective

The popularity of instinct theory—the core of Thorstein Veblen’s evolutionary methodology—waned with the rise of behavioral psychology in the 1920s, and resurges with the revival of evolutionary psychology in the 1980s. This study reexamines Charles Darwin’s theories of natural and sexual selection within Veblen’s theory. By tracing the origin of emulation, this study explores the human struggle for existence at both inter-species and individual levels, enhancing our understanding of Veblen’s theory as rooted in Darwinism and reinterpreted within Geoffrey Hodgson’s concept of emergence. Differing from Clarence Ayres’ dichotomy, analysis resituates Veblen’s instinct theory within contemporary evolutionary psychology.

Understanding the Unintended Effects of Human-Machine Moderation in Addressing Harassment within Online Communities

ABSTRACT We set out to explore the unintended effects of human-machine moderation in mitigating harassment within online communities. We examine communities that use a block-list type bot to prevent harassment from the source of harassment. Drawing from social categorization and selective exposure theories, we theorize that employing a machine alongside humans for community moderation will create unintended adverse effects. Specifically, within the moderated focal community, we hypothesize an emboldening effect characterized by an increase in harassment among community members directed at their outgroup members. Additionally, we expect a disengaging effect, that is, a downward trend in the focal community’s membership. Finally, in neighboring communities that share the same topic of discussion, we expect a spillover effect, that is, an increase in harassment. Employing Detoxify, a Bidirectional Encoder Representations from Transformers (BERT)-based model, we evaluate harassment scores in the focal community by analyzing 4 million Reddit comments across various communities. These scores serve as inputs for Bayesian Structural Time Series analysis, revealing evidence for both disengaging and spillover effects. For the emboldening effect, we use community-specific keywords in a predefined computer-assisted document classification approach, Keyword Assisted Topic Model (keyATM), to identify the target of harassment. We use mean comparison and regression discontinuity to assess the change in the level of harassment targeting outgroup members before and after the human-machine moderation implementation.

Vegetation biomass and topography are associated with seasonal habitat selection and fall translocation behavior in Arctic hares.

Habitat selection theory suggests that environmental features selected at coarse scales reveal fundamental factors affecting animal fitness. When these factors vary across seasons, they may lead to large-scale movements, including long-distance seasonal migrations. We analyzed the seasonal habitat selection of 25 satellite-tracked Arctic hares from a population on Ellesmere Island (Nunavut, Canada) that relocated over 100 km in the fall. Since no other lagomorph is known to perform such extensive movements, this population offered an ideal setting to test animal movement and habitat selection theory. On summer grounds hares selected low elevation areas, while on winter grounds they selected high vegetation biomass, high elevation, and steep slopes. During fall relocation, they alternated between stopover and traveling behavioral states (ratio 2:1). Stopover locations were characterized by higher vegetation heterogeneity and lower rugosity than traveling locations, while vegetation biomass and elevation interacted to explain stopover locations in a more complex way. The selected combination of environmental features thus varied across seasons and behavioral states, in a way broadly consistent with predictions based on the changing food and safety needs of hares. Although causality was not demonstrated, our results improve our understanding of long-distance movements and habitat selection in Arctic hares, as well as herbivore ecology in the polar desert. Results also provide strong support to animal movement and habitat selection theory, by showing how some important hypotheses hold when tested in a species phylogenetically distinct from most animal models used in this research field.

독거노인의 대인관계 및 사회적 지원요인이 삶의 만족도에 미치는 영향

This study aims to verify the effects of interpersonal relationships and social support factors on the life satisfaction of elderly people over 65 years old divided into three age groups while using Carstensen & Charles' Social Emotional Selection Theory as a foundation. Data from the 8th year supplementary survey of the National Retirement Security Panel (KReIS) were used, and the data of 1,117 elderly people aged 65 years of age or older who were living alone were extracted and analyzed. First, there were differences in educational background, number of children, economic activity, and average monthly income among the examined age groups. Interpersonal relationships, social support factors, and life satisfaction were also all found to differ between the three age groups. Second, it was confirmed that higher social support factors were associated with higher life satisfaction in all age groups. This study is meaningful in that it analyzed the effects of interpersonal relationships and social support factors on life satisfaction according to the characteristics of each age group of elderly people living alone, and based on these results, this study suggested practical intervention measures for social welfare as well as follow-up studies that should be conducted to improve life satisfaction among elderly people living alone

The Relationship between Political Ideology and Social Media Echo Chambers

Purpose: The general purpose of this study was to explore the relationship between political ideology and social media echo chambers. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to political ideology and social media echo chambers. Preliminary empirical review revealed that echo chambers exist on social media platforms, with individuals predominantly exposed to information aligning with their political beliefs. Social Identity Theory and Selective Exposure Theory were instrumental in understanding how users' political affiliations influenced their interactions online, reinforcing echo chambers. These echo chambers, driven by users' desire for ingroup solidarity and selective exposure to like-minded content, posed challenges to democratic discourse and societal cohesion. The study emphasized the importance of addressing these challenges by promoting media literacy, critical thinking, and exposure to diverse viewpoints on social media platforms to foster a more inclusive and deliberative public sphere. Unique Contribution to Theory, Practice and Policy: The Social Identity theory, Selective Exposure theory and Dual Process theory may be used to anchor future studies on the relationship between political ideology and social media echo chambers. The study provided valuable recommendations that contributed to advancing theoretical understanding, informing practical interventions, and shaping policy responses to the challenges posed by echo chambers in contemporary political discourse. It emphasized the integration of social identity theory and selective exposure theory into theoretical frameworks, highlighting the psychological mechanisms driving the formation of echo chambers. Practically, the study underscored the importance of promoting media literacy and critical thinking skills among social media users, while suggesting that social media platforms play a pivotal role in shaping users' information environments. On the policy front, it emphasized the need for evidence-based interventions aimed at mitigating the adverse effects of echo chambers on democratic governance and societal cohesion.

A Theory of Best Choice Selection through Objective Arguments Grounded in Linear Response Theory Concepts

In this study, we propose how to use objective arguments grounded in statistical mechanics concepts in order to obtain a single number, obtained after aggregation, which would allow for the ranking of “agents”, “opinions”, etc., all defined in a very broad sense. We aim toward any process which should a priori demand or lead to some consensus in order to attain the presumably best choice among many possibilities. In order to specify the framework, we discuss previous attempts, recalling trivial means of scores—weighted or not—Condorcet paradox, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), etc. We demonstrate, through geometrical arguments on a toy example and with four criteria, that the pre-selected order of criteria in previous attempts makes a difference in the final result. However, it might be unjustified. Thus, we base our “best choice theory” on the linear response theory in statistical physics: we indicate that one should be calculating correlations functions between all possible choice evaluations, thereby avoiding an arbitrarily ordered set of criteria. We justify the point through an example with six possible criteria. Applications in many fields are suggested. Furthermore, two toy models, serving as practical examples and illustrative arguments are discussed.

Automated content analysis as a tool to compare content in sexual selection research with examples of sexual selection in evolutionary biology textbooks: implications for teaching the nature of science

Background We used college-level evolution textbooks to examine the presentation of sexual selection research—a field with ongoing debates related to sex, sexuality and gender identity. Many classic sexual selection concepts have been criticized for androcentrism and other forms of gender-sex bias, specifically for de-emphasizing the female role in reproductive behaviors and over-reliance on gender-sex binaries. These classic concepts are fundamentally captured in the idea that animal reproductive-related behaviors can be grouped in sex roles (e.g. competitive males and selective females). Recently developed alternative concepts provide a more nuanced understanding of the flexibility of sexual and reproductive-related behaviors, stemming in part from growing attention to a broader range of female behavior. To assess whether students are receiving content reflecting these insights, we measured the congruence between textbook content and the scientific literature, using insects as a case study because of the importance of this group in the development of sexual selection theory, its prevalence in current sexual selection research, and the number of insect examples included in textbooks. We first coded textbook content for sexual selection concepts. We used automated content analysis to analyze a database of citations, keywords and abstracts in sexual selection research published between 1990 and 2014, inclusive of the period covered by the textbooks.ResultsThe textbooks and research literatures prioritized the same taxa (e.g., fruit flies) and sex roles as embodied in classic sexual selection theory. Both the research literature and some textbooks acknowledge androcentrism and other forms of gender-sex bias in classic sexual selection paradigms, especially competitive male and selective female sex roles. Yet, while the research literature included alternative models, textbooks neglected these alternatives, even when researchers had studied both classic and alternative views in the same insect.Conclusions We recommend using this kind of analysis of textbook content to engage students in a conversation around the social factors that impact knowledge construction, a key part of the epistemological understanding they need for a robust grasp of the Nature of Science and of evolutionary theory.

Bayesian joint species distribution model selection for community‐level prediction

AbstractAimJoint species distribution models (JSDMs) are an important tool for predicting ecosystem diversity and function under global change. The growing complexity of modern JSDMs necessitates careful model selection tailored to the challenges of community prediction under novel conditions (i.e., transferable models). Common approaches to evaluate the performance of JSDMs for community‐level prediction are based on individual species predictions that do not account for the species correlation structures inherent in JSDMs. Here, we formalize a Bayesian model selection approach that accounts for species correlation structures and apply it to compare the community‐level predictive performance of alternative JSDMs across broad environmental gradients emulating transferable applications.InnovationWe connect the evaluation of JSDM predictions to Bayesian model selection theory under which the log score is the preferred performance measure for probabilistic prediction. We define the joint log score for community‐level prediction and distinguish it from more commonly applied JSDM evaluation metrics. We then apply the joint community log score to evaluate predictions of 1918 out‐of‐sample boreal forest understory communities spanning 39 species generated using a novel multinomial JSDM framework that supports alternative species correlation structures: independent, compositional dependence and residual dependence.Main conclusionsThe best performing JSDM included all observed environmental variables and compositional dependence modelled using a multinomial likelihood. The addition of flexible residual species correlations improved model predictions only within JSDMs applying a reduced set of environmental variables highlighting potential confounding between unobserved environmental conditions and residual species dependence. The best performing JSDM was consistent across successional and bioclimatic gradients regardless of whether interest was in species‐ or community‐level prediction. Our study demonstrates the utility of the joint community log score to compare the predictive performance of JSDMs and highlights the importance of accounting for species dependence when interest is in community composition under novel conditions.

A quantitative genetic model of background selection in humans.

Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans.

Inferring stochastic group interactions within structured populations via coupled autoregression

The internal behaviour of a population is an important feature to take account of when modelling its dynamics. In line with kin selection theory, many social species tend to cluster into distinct groups in order to enhance their overall population fitness. Temporal interactions between populations are often modelled using classical mathematical models, but these sometimes fail to delve deeper into the, often uncertain, relationships within populations. Here, we introduce a stochastic framework that aims to capture the interactions of animal groups and an auxiliary population over time. We demonstrate the model’s capabilities, from a Bayesian perspective, through simulation studies and by fitting it to predator–prey count time series data. We then derive an approximation to the group correlation structure within such a population, while also taking account of the effect of the auxiliary population. We finally discuss how this approximation can lead to ecologically realistic interpretations in a predator–prey context. This approximation also serves as verification to whether the population in question satisfies our various assumptions. Our modelling approach will be useful for empiricists for monitoring groups within a conservation framework and also theoreticians wanting to quantify interactions, to study cooperation and other phenomena within social populations.

Revisiting niche divergence hypothesis in sexually dimorphic birds: Is diet overlap correlated with sexual size dimorphism?

The evolution of sexual size dimorphism (SSD) is a long-standing topic in evolutionary biology, but there is little agreement on the extent to which SSD is driven by the different selective forces. While sexual selection and fecundity selection have traditionally been proposed as the two leading hypotheses, SSD may also result from natural selection through mechanisms such as sexual niche divergence, which might have reduced resource competition between sexes. Here, we revisited the niche divergence hypothesis by testing the relationship between the sexual overlap in diet and SSD of 56 bird species using phylogenetic comparative analyses. We then assessed how SSD variation relates to the three main hypotheses: sexual selection, fecundity selection, and sexual niche divergence using phylogenetic generalized least squares (PGLS). Then, we compared sexual selection, fecundity selection and niche divergence selection as SSD drivers through phylogenetic confirmatory path analyses to disentangle the possible causal evolutionary relationships between SSD and the three hypotheses. Phylogenetic generalized least squares showed that SSD was negatively correlated with diet overlap, that is, the greater the difference in body size between males and females, the less diet overlap. As predicted by sexual selection theory, the difference in body size between sexes was higher in polygynous species. Confirmatory phylogenetic path analyses suggested that the most likely evolutionary path might include the mating system as a main driver in SSD and niche divergence as a result of SSD. We found no evidence of a role of fecundity selection in the evolution of female-biased SSD. Our study provides evidence that sexual selection has likely been the main cause of SSD and that dietary divergence is likely an indirect effect of SSD.

An Empirical Test of Pedestrian Activity Theories Within Informal Settlements

Pedestrian activity is often measured in the formal parts of cities, yet it has rarely been studied in informal settlements, although they are typically adjacent to formal areas and residents participate in formal urban life. Route optimization and space syntax are two pedestrian activity theories that can be applied to predict path usage in urban areas. These theories have been tested in formal cities, but are they applicable in understudied informal settings? Using motion sensors, we measure pedestrian activity in a Cape Town informal settlement in the early morning and evening hours and test which theory best explains the sensor measurements. Route optimization is weakly correlated with average pedestrian activity, while space syntax performs even more poorly in predicting pedestrian activity. The predictive power of both theoretical calculations further varies by time of day. We find that both theories perform worst at the entrances/exits of the informal settlement—that is, the border between informal and formal. These results indicate that daily movement patterns in informal settlements may differ from formal areas and that the connection between the formal and informal city requires further study to better understand how pedestrian activity links these two types of areas. A new theory of route selection based on such an understanding, which also better incorporates the specific characteristics of informal urban settlements—such as high density, narrow, and constantly changing streets primarily used by residents—may be necessary to understand the needs of pedestrians within informal settlements as compared to formal areas.

A Study on Subtitle Translation Strategies of Kung Fu Panda 2 from the Perspective of Adaptation and Selection Theory

In recent years, with economic globalization, international cultural exchanges are getting closer. Many foreign film works have been introduced into China, especially the English films popular among Chinese audiences. In order to realize film appreciation between different languages, subtitle translation comes into being. However, as an emerging field, subtitle translation studies are not mature compared with traditional translation studies. The Adaptation and Selection Theory is the core theory of the eco-translatology which is a novel and relatively complete system of translation theory put forward by Hu Gengshen, a Chinese scholar. This thesis adopts the Adaptation and Selection Theory as the theoretical basis. Firstly, the research on subtitle translation at home and abroad is reviewed. Secondly, the development, main principles and applicability of the Adaptation and Selection Theory are elaborated. Finally, under the guidance of this theory, this thesis analyzes how translators choose appropriate translation strategies, based on the specific example of subtitle translation in Kung Fu Panda 2, to select and transform from the three dimensions. Besides, how to get a translation with a high “degree of holistic adaptation and selection” are also explored.

Optimization design of brushless DC motor based on improved JAYA algorithm

Brushless direct current motor is widely used in industrial production because of its simple structure, wide speed range and low noise. To improve the operation efficiency of brushless DC motor and reduce the production and application costs, the optimization of brushless DC motor is analyzed by introducing the JAYA algorithm. This method determines the optimal parameters of a brushless DC motor using the theory of electromagnetic structure parameter selection and efficiency calculation. The population diversity of the JAYA algorithm is improved through an empirical learning strategy, and an adaptive strategy is introduced to balance the development ability and search performance of the algorithm. This ensures population diversity and improves convergence speed. The experiment showcases that the improved JAYA algorithm has a lower rank average in unimodal function operations, demonstrating stronger local development ability and better stability. It exhibits strong search ability in many local optima of multimodal functions. Moreover, the motor's average efficiency after optimization is 94.48%. The algorithm reaches the global optimum after approximately 40 iterations and offers faster convergence speed and higher accuracy. The adaptive JAYA algorithm is stable at around 93% when the number of iterations reaches 90, with a maximum efficiency of 95.3%. It is 5–12 percentage points higher than the other three comparison algorithms. The optimal solution of the motor parameters in the adaptive JAYA algorithm is closest to the theoretical parameter optimization value, meeting both the constraints of variables and the constraints of the model. The stator diameter, tooth magnetic induction, winding current density, air gap magnetic induction, and stator yoke magnetic induction values are 201.5 mm, 1.8 T, 2.049 A/mm2, 0.63 T, and 0.91 T, respectively. The research overcomes the problem of parameter optimization in the optimization design of brushless DC motor, improving their economic value of brushless DC motor in industrial production and application.

Instructors’ Understanding of Teaching First-Year Engineering Teaming: Characterization and Redirection

Teaming is increasingly important to teach well in undergraduate engineering education. Teams composed of both majority and minoritized students have an increased risk of majority members harassing minoritized members. Instructors of large classes have a difficult time identifying in which teams such harassment is taking place, and knowing what to do to interrupt it. This paper, part of a bigger project grounded in microaggression theory and selective incivility theory, specifically considers what instructors currently do, and indeed whether it is their job to address teammate harassment. We undertook a rough thematic analysis of interviews with instructors of a large first-year engineering course at a large American research-extensive majority-white university in the Midwest. We found instructors adopted an individual-centric model of teaming, intervened mainly in severe instances, and their interventions tended to be subtle. We offer an early version of an alternative model to structure forthcoming training sessions with instructors, graduate teaching assistants, and peer teachers.

The Exploring Mechanisms of the Formation of Altruistic Behaviors

Altruism is essential to the world. If people do something on account of caring for others more, it is altruistic. Evolution motivates kin altruism. Reciprocal altruism is essential to human society. There also exists altruism that has nothing to do with gene correlation, like vernacular altruism. Different kinds of altruism have different motivations. The glow effect exists because people want to experience again the warm glow they feel when being altruistic, though the neural base needs to be studied. A costly long-term investment is a more complex altruistic behavior in the real world. It can be explained by selective investment theory, which discusses the importance of relationships. What’s more, altruism has its neurological base. The amygdala volume of people with altruistic trait may be more significant. They may also have more excellent neural activity in regions concerning caring and empathy, like midbrain periaqueductal gray and anterior insular. These neurological findings also lay the foundation for some people to be more altruistic than others. Future research can enlarge the investigation on the practical use of altruism, like discovering how to activate people’s altruistic behaviors and what people can do to cultivate and preserve altruism.

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

AbstractSoftware defect detection (SDD) is crucial to ensure the reliability of software systems and identify defects in classification. One of the key challenges in defect detection is to select more informative and relevant features from the vast pool of available software metrics. A novel approach is proposed in this paper that leverages mutation boosted salp swarm optimizer (MBSSO) and Rough Set Theory for feature selection (FS) in SDD. It efficiently explores the search space and incorporates a mutation boosting mechanism to overcome local optima. Rough Set Theory provides a formal framework for analyzing feature relevance and dependency, while MBSSO optimizes the search for the optimal feature subset. The proposed approach involves encoding the feature subsets using binary representation and designing a fitness function. The MBSSO algorithm explores the feature space, iteratively improving the feature subset based on the fitness function. The selected feature subset is then used to train a defect detection model using the Kernel Extreme Learning Machine (KELM) algorithm. The experimental validation is performed by Project Repository for Software Engineering (PROMISE) dataset and compared the performance of proposed approach against other FS methods and baselines. The experimental validation demonstrates that the proposed approach achieves superior performance and selected feature subset improves the accuracy and efficiency. This research contributes to the advancement of SDD by providing an effective and efficient FS technique using MBSSO and Rough Set Theory.

Age-related emotional advantages in encountering novel situation in daily life.

People encounter novel situations throughout their lives that contribute to the acquisition of knowledge and experience. However, novelty can be misaligned with goals and motivation in later adulthood according to socioemotional selectivity theory. This study investigated age differences in emotional reactions associated with novel experiences. Multilevel structural equation models were used to analyze experience-sampling data obtained from an adult sample of 375 participants aged 18-94 years who reported their current situation and momentary emotional experience five times per day for 7 days. On occasions where situations were rated as more novel, people reported reduced positive and increased negative emotion. Those who had more overall exposure to novel situations tended to have more negative emotional experiences in general. Contrary to our hypothesis, there were age differences in individuals' negative emotional reactivity to situations that are perceived as more novel, such that novel situations were reported as less negative among older adults. By applying theoretical understanding of age differences in motivation and well-being in adulthood, our findings illuminate aspects of situations that elicit negative emotions. Findings highlight age-related benefits in emotional well-being, consistent with socioemotional selectivity theory postulates, and further implies that older adults may not be novelty averse. (PsycInfo Database Record (c) 2024 APA, all rights reserved).