Theory Construction Methodology: A Practical Framework for Building Theories in Psychology

Common Cause Versus Dynamic Mutualism: An Empirical Comparison of Two Theories of Psychopathology in Two Large Longitudinal Cohorts

Mental disorders are among the leading causes of global disease burden. To respond effectively, a strong understanding of the structure of psychopathology is critical. We empirically compared two competing frameworks, dynamic-mutualism theory and common-cause theory, that vie to explain the development of psychopathology. We formalized these theories in statistical models and applied them to explain change in the general factor of psychopathology (p factor) from early to late adolescence (N = 1,482) and major depression in middle adulthood and old age (N = 6,443). Change in the p factor was better explained by mutualism according to model-fit indices. However, a core prediction of mutualism was not supported (i.e., predominantly positive causal interactions among distinct domains). The evidence for change in depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences.

The "central" importance of loneliness in mental health: A network psychometric study

Loneliness is linked to negative mental health outcomes like depression and social anxiety. However, it is unclear how loneliness and these conditions are connected. This study aims to address two questions: (a) Are there pathways connecting loneliness to these conditions? (b) What symptoms play a role in these pathways? Using network analyses, the study examined a representative sample of 962 adults from the UK (mean age = 46, females = 492). The network analysis revealed four dimensions: depression, social anxiety and two dimensions for loneliness (isolation and social connectedness). Two distinct pathways were identified for the transmission of symptoms between loneliness, social anxiety and depression. The depression-isolation pathway involved a node representing the perceived loss of social connections. The social anxiety-social connectedness pathway was characterised by intimate interaction contexts. These findings suggest that loneliness is associated with different symptom pathways, potentially contributing to comorbidity between loneliness, social anxiety and depression.

Mapping visual working memory models to a theoretical framework

The body of research on visual working memory (VWM)-the system often described as a limited memory store of visual information in service of ongoing tasks-is growing rapidly. The discovery of numerous related phenomena, and the many subtly different definitions of working memory, signify a challenge to maintain a coherent theoretical framework to discuss concepts, compare models and design studies. A lack of robust theory development has been a noteworthy concern in the psychological sciences, thought to be a precursor to the reproducibility crisis (Oberauer & Lewandowsky, Psychonomic Bulletin & Review, 26, 1596-1618, 2019). I review the theoretical landscape of the VWM field by examining two prominent debates-whether VWM is object-based or feature-based, and whether discrete-slots or variable-precision best describe VWM limits. I share my concerns about the dualistic nature of these debates and the lack of clear model specification that prevents fully determined empirical tests. In hopes of promoting theory development, I provide a working theory map by using the broadly encompassing memory for latent representations model (Hedayati et al., Nature Human Behaviour, 6, 5, 2022) as a scaffold for relevant phenomena and current theories. I illustrate how opposing viewpoints can be brought into accordance, situating leading models of VWM to better identify their differences and improve their comparison. The hope is that the theory map will help VWM researchers get on the same page-clarifying hidden intuitions and aligning varying definitions-and become a useful device for meaningful discussions, development of models, and definitive empirical tests of theories.

Towards a qualitative theory of the interruption of eating behavior change

The poor maintenance of eating behavior change is one of the main obstacles to minimizing weight regain after weight loss during diets for non-surgical care of obese or overweight patients. We start with a known informal explanation of interruption in eating behavior change during severe restriction and formalize it as a causal network involving psychological variables, which we extend with energetic variables governed by principles of thermodynamics. The three core phenomena of dietary behavior change, i.e., non-initiation, initiation followed by discontinuation and initiation followed by non-discontinuation, are expressed in terms of the value of the key variable representing mood or psychological energy, the fluctuation of which is the result of three causal relationships. Based on our experimental knowledge of the time evolution profile of the three causal input variables, we then proceed to a qualitative analysis of the resulting theory, i.e., we consider an over-approximation of it which, after discretization, can be expressed in the form of a finite integer-based model. Using Answer Set Programming, we show that our formal model faithfully reproduces the three phenomena and, under a certain assumption, is minimal. We generalize this result by providing all the minimal models reproducing these phenomena when the possible causal relationships exerted on mood are extended to all the other variables (not just those assumed in the informal explanation), with arbitrary causality signs. Finally, by a direct analytical resolution of an under-approximation of our theory, obtained by assuming linear causalities, as a system of linear ODEs, we find exactly the same minimal models, proving that they are also equal to the actual minimal models of our theory since these are framed below and above by the models of the under-approximation and the over-approximation. We determine which parameters need to be person-specific and which can be considered invariant, i.e., we explain inter-individual variability. Our approach could pave the way for universally accepted theories in the field of behavior change and, more broadly, in other areas of psychology.

Do We Need a Novel Framework for Classifying Psychopathology? A Discussion Paper

Introduction

The ICD-11 and DSM-5 are the leading systems for the classification of mental disorders, and their relevance for clinical work and research, as well as their impact for policy making and legal questions, has increased considerably. In recent years, other frameworks have been proposed to supplement or even replace the ICD and the DSM, raising many questions regarding clinical utility, scientific relevance, and, at the core, how best to conceptualize mental disorders.

Method

As examples of the new approaches that have emerged, here we introduce the Hierarchical Taxonomy of Psychopathology (HiTOP), the Research Domain Criteria (RDoC), systems and network approaches, process-based approaches, as well as a new approach to the classification of personality disorders.

Results and Discussion

We highlight main distinctions between these classification frameworks, largely related to different priorities and goals, and discuss areas of overlap and potential compatibility. Synergies among these systems may provide promising new avenues for research and clinical practice.

Repositioning Construct Validity Theory: From Nomological Networks to Pragmatic Theories and Their Evaluation by Explanatory Means

In this article, I argue for a number of important changes to the conceptual foundations of construct validity theory. I begin by suggesting that construct validity theorists should shift their attention from the validation of constructs to the process of evaluating scientific theories. This shift in focus is facilitated by distinguishing construct validation (understood as theory evaluation) from test validation, thereby freeing it from its long-standing focus on psychological measurement. In repositioning construct validity theory in this way, researchers should jettison the outmoded but superficially popular notion that theories are nomological networks in favor of a more plausible pragmatic view of their natures, such as the idea that theories are explanatorily coherent networks. Consistent with this shift in understanding the nature of theories, my recommendation is that construct validation should embrace an explanationist perspective on the theory evaluation process to complement its focus on hypothetico-deductive theory testing. On this view, abductive research methods have an important role to play. The revisionist perspective on construct validity proposed here is discussed in light of relevant developments in scientific methodology and is applied to an influential account of the validation process that has shaped research practice.

Towards a general modeling framework of resource competition in cognitive development

The current paper presents an integrated formal model of typical and atypical development based on the mechanisms of mutualism and resource competition. The mutualistic network model is extended with the dynamics of competition for limited resources, such as time and environmental factors. The proposed model generates patterns that resemble established phenomena in cognitive development: the positive manifold, developmental phases, developmental delays and lack of early indicators in atypical development, developmental regression, and "quasi-autism" caused by extreme environmental deprivation. The presented modeling framework fits a general movement towards formal theory construction in psychology. The model is easy to replicate and develop further, and we offer several avenues for future work.

Formalizing theories of child development: Introduction to the special section

Here we introduce a Special Section of Child Development entitled "Formalizing Theories of Child Development." This Special Section features five papers that use mathematical models to advance our understanding of central questions in the study of child development. This landmark collection is timely: it signifies growing awareness that rigorous empirical bricks are not enough; we need solid theory to build the house. By stating theory in mathematical terms, formal models make concepts, assumptions, and reasoning more explicit than verbal theory does. This increases falsifiability, promotes cumulative science, and enables integration with mathematical theory in allied disciplines. The Special Section contributions cover a range of topics: the developmental origins of counting, interactions between mathematics and language development, visual exploration and word learning in infancy, referent identification by toddlers, and the emergence of typical and atypical development. All are written in an accessible manner and for a broad audience.

Formalizing developmental phenomena as continuous-time systems: Relations between mathematics and language development

We demonstrate how developmental theories may be instantiated as statistical models, using hierarchical continuous-time dynamic systems. This approach offers a flexible specification and an often more direct link between theory and model parameters than common modeling frameworks. We address developmental theories of the relation between the academic competencies of mathematics and language, using data from the online learning system Mindsteps. We use ability estimates from 160,164 observation occasions, across N = 4623 3rd to 9th grade students and five ability domains. Model development is step-by-step from simple to complex, with ramifications for theory and modeling discussed at each step.

The new measures of interoceptive accuracy: A systematic review and assessment

Conscious interoception, the perception of internal bodily states, is thought to contribute to fundamental human abilities (e.g., decision-making and emotional regulation). One of its most studied dimensions is interoceptive accuracy: the objective capacity to detect internal bodily signals. In the past few years, several labs across the world have started developing new tasks aimed at overcoming limitations inherent in classical measures of interoceptive accuracy. In this systematic review, we identified these tasks (since 2015) for the cardiac, respiratory, and gastrointestinal domains. For each identified task, we discuss their strengths and weaknesses, and make constructive suggestions for further improvement. In the general discussion, we discuss the (potentially elusive) possibility of reaching high validity in the measurement of interoceptive accuracy. We also point out that interoceptive accuracy may not be the most critical dimension for informing current theories, and we encourage researchers to investigate other dimensions of conscious interoception.

Use of Assistive Technologies and Alternative Means by Older People: The "Actional Model of Older People´s Coping with Health-Related Declines"

This paper presents the "Actional Model of Older people´s Coping with Health-Related Declines" to explain the use of a broad range of action alternatives of older persons for dealing with current or anticipated diseases, functional declines, activity limitations and participation restrictions. The general background is the action-theoretical model of intentional self-regulation of human development (e.g., Brandtstädter, 2006; Rothermund & Brandtstädter, 2019). Yet, our model provides an increased specification of major model components toward the situation of older people coping with current or anticipated health-related declines. The model development follows an adapted theory construction methodology (TCM) by Borsboom et al. (Perspectives on Psychological Science, 16(4), 756-766, 2021) and adapted principles for constructing practically useful theories by Berkman & Wilson (Perspectives on Psychological Science, 16(4), 864-874, 2021). Regarding content, we further draw on models of the use of assistive technologies (ATs) and medical services, qualitative studies on reasons for using ATs, and quantitative studies on health-related goals. The resulting model includes these components: (1) Discrepancies between perceived or anticipated and desired health-related development, (2) health-related discrepancy reduction and prevention goals, (3) action possibilities for reducing or preventing health-related discrepancies, (4) further motivating and demotivating goals, (5) beliefs about effective means for reaching the goals (2) and (4), (6) generation of the particular coping actions by goals (2) and (4) in combination with beliefs about effective means, (7) external context factors, and (8) modes of joint decision-making and decision-making on behalf of older people. The explanatory and practical value of the model are discussed as well as its implications for future research and geropsychology teaching.

Research-Problem Validity in Primary Research: Precision and Transparency in Characterizing Past Knowledge

Four validity types evaluate the approximate truth of inferences communicated by primary research. However, current validity frameworks ignore the truthfulness of empirical inferences that are central to research-problem statements. Problem statements contrast a review of past research with other knowledge that extends, contradicts, or calls into question specific features of past research. Authors communicate empirical inferences, or quantitative judgments, about the frequency (e.g., "few," "most") and variability (e.g., "on the one hand," "on the other hand") in their reviews of existing theories, measures, samples, or results. We code a random sample of primary research articles and show that 83% of quantitative judgments in our sample are vague and do not have a transparent origin, making it difficult to assess their validity. We review validity threats of current practices. We propose that documenting the literature search, reporting how the search was coded, and quantifying the search results facilitates more precise judgments and makes their origin transparent. This practice enables research questions that are more closely tied to the existing body of knowledge and allows for more informed evaluations of the contribution of primary research articles, their design choices, and how they advance knowledge. We discuss potential limitations of our proposed framework.

Using machine learning to identify early predictors of adolescent emotion regulation development

As 20% of adolescents develop emotion regulation difficulties, it is important to identify important early predictors thereof. Using the machine learning algorithm SEM-forests, we ranked the importance of (87) candidate variables assessed at age 13 in predicting quadratic latent trajectory models of emotion regulation development from age 14 to 18. Participants were 497 Dutch families. Results indicated that the most important predictors were individual differences (e.g., in personality), aspects of relationship quality and conflict behaviors with parents and peers, and internalizing and externalizing problems. Relatively less important were demographics, bullying, delinquency, substance use, and specific parenting practices-although negative parenting practices ranked higher than positive ones. We discuss implications for theory and interventions, and present an open source risk assessment tool, ERRATA.

Formalizing psychological interventions through network control theory

Despite the growing deployment of network representation to comprehend psychological phenomena, the question of whether and how networks can effectively describe the effects of psychological interventions remains elusive. Network control theory, the engineering study of networked interventions, has recently emerged as a viable methodology to characterize and guide interventions. However, there is a scarcity of empirical studies testing the extent to which it can be useful within a psychological context. In this paper, we investigate a representative psychological intervention experiment, use network control theory to model the intervention and predict its effect. Using this data, we showed that: (1) the observed psychological effect, in terms of sensitivity and specificity, relates to the regional network control theoretic metrics (average and modal controllability), (2) the size of change following intervention negatively correlates with a whole-network topology that quantifies the "ease" of change as described by control theory (control energy), and (3) responses after intervention can be predicted based on formal results from control theory. These insights assert that network control theory has significant potential as a tool for investigating psychological interventions. Drawing on this specific example and the overarching framework of network control theory, we further elaborate on the conceptualization of psychological interventions, methodological considerations, and future directions in this burgeoning field.

On the scope of scientific hypotheses

Hypotheses are frequently the starting point when undertaking the empirical portion of the scientific process. They state something that the scientific process will attempt to evaluate, corroborate, verify or falsify. Their purpose is to guide the types of data we collect, analyses we conduct, and inferences we would like to make. Over the last decade, metascience has advocated for hypotheses being in preregistrations or registered reports, but how to formulate these hypotheses has received less attention. Here, we argue that hypotheses can vary in specificity along at least three independent dimensions: the relationship, the variables, and the pipeline. Together, these dimensions form the scope of the hypothesis. We demonstrate how narrowing the scope of a hypothesis in any of these three ways reduces the hypothesis space and that this reduction is a type of novelty. Finally, we discuss how this formulation of hypotheses can guide researchers to formulate the appropriate scope for their hypotheses and should aim for neither too broad nor too narrow a scope. This framework can guide hypothesis-makers when formulating their hypotheses by helping clarify what is being tested, chaining results to previous known findings, and demarcating what is explicitly tested in the hypothesis.

Modeling vulnerability and intervention targets in the Borderline Personality Disorder system: A network analysis of in silico and in vivo interventions

Modeling psychopathology as a complex dynamic system represents Borderline Personality Disorder (BPD) as a constellation of symptoms (e.g., nodes) that feedback and self-sustain each other shaping a network structure. Through in silico interventions, we simulated the evolution of the BPD system by manipulating: 1) the connectivity strength between nodes (i.e., vulnerability), 2) the external disturbances (i.e., stress) and 3) the predisposition of symptoms to manifest. Similarly, using network analysis we evaluated the effect of an in vivo group psychotherapy to detect the symptoms modified by the intervention. We found that a network with greater connectivity strength between nodes (more vulnerable) showed a higher number of activated symptoms than networks with less strength connectivity. We also found that increases in stress affected more vulnerable networks compared to less vulnerable ones, while decreases in stress revealed a hysteresis effect in the most strongly connected networks. The in silico intervention to symptom alleviation revealed the relevance of nodes related to difficulty in anger regulation, nodes which were also detected as impacted by the in vivo intervention. The complex systems methodology is an alternative to the common cause model with which research has approached the BPD phenomenon.

Perspectives on scientific error

Theoretical arguments and empirical investigations indicate that a high proportion of published findings do not replicate and are likely false. The current position paper provides a broad perspective on scientific error, which may lead to replication failures. This broad perspective focuses on reform history and on opportunities for future reform. We organize our perspective along four main themes: institutional reform, methodological reform, statistical reform and publishing reform. For each theme, we illustrate potential errors by narrating the story of a fictional researcher during the research cycle. We discuss future opportunities for reform. The resulting agenda provides a resource to usher in an era that is marked by a research culture that is less error-prone and a scientific publication landscape with fewer spurious findings.

Introducing Meta-analysis in the Evaluation of Computational Models of Infant Language Development

Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant data. Thus, it is desirable to have evaluation methodologies that could account for robust empirical reference data, across multiple infant capabilities. Moreover, there is a need for practices that can compare developmental trajectories of infants to those of models as a function of language experience and development. The present study aims to take concrete steps to address these needs by introducing the concept of comparing models with large-scale cumulative empirical data from infants, as quantified by meta-analyses conducted across a large number of individual behavioral studies. We formalize the connection between measurable model and human behavior, and then present a conceptual framework for meta-analytic evaluation of computational models. We exemplify the meta-analytic model evaluation approach with two modeling experiments on infant-directed speech preference and native/non-native vowel discrimination.

Using the increasing vaccination model and the moderating roles of chronic medical conditions and education to understand COVID-19 vaccination uptake in Singapore

COVID-19 vaccination programmes have helped reduce deaths and morbidity from the pandemic and allowed for the resumption of normal life. However, vaccine hesitancy remains an issue even with recurrent surges in COVID-19 cases due to new SARS-CoV-2 variants. Purpose: To elucidate psychosocial factors that contribute to our understanding of vaccine hesitancy. 676 Participants in Singapore took part in an online survey on vaccine hesitancy and uptake between May and June 2021. Data on demographics, perception of the COVID-19 pandemic, and vaccine willingness and hesitancy factors were collected. The responses were analyzed using structural equation modeling (SEM). The study found that confidence in the COVID-19 vaccines and risk perception of the COVID-19 situation are significantly associated with vaccination intention, while vaccination intention is also significantly associated with reported vaccination status. Additionally, certain chronic medical conditions moderate the relationship between vaccine confidence/risk perception and vaccine intention. This study contributes to our understanding of factors behind vaccination uptake which can help anticipate challenges to future vaccination campaigns for the next pandemic.

From Metaphor to Computation: Constructing the Potential Landscape for Multivariate Psychological Formal Models

For psychological formal models, the stability of different phases is an important property for understanding individual differences and change processes. Many researchers use landscapes as a metaphor to illustrate the concept of stability, but so far there is no method to quantify the stability of a system's phases. We here propose a method to construct the potential landscape for multivariate psychological models. This method is based on the generalized potential function defined by Wang et al. (2008) and Monte Carlo simulation. Based on potential landscapes we define three different types of stability for psychological phases: absolute stability, relative stability, and geometric stability. The panic disorder model by Robinaugh et al. (2019) is used as an example, to demonstrate how the method can be used to quantify the stability of states and phases, illustrate the influence of model parameters, and guide model modifications. An R package, simlandr, was developed to provide an implementation of the method.

Towards a Framework for Unifying Research in Theoretical Psychology

Current criticism of theoretical psychology concerns, in particular, the state of its foundations and the construction of theories, while the evolution of the discipline leads to its disunity. According to one of the points of view discussed, psychological knowledge must be unified in order to overcome theoretical fragmentation. Those who are concerned about the lack of a unified theory in psychology explore various aspects of disunity and the possibility of unifying theoretical psychology. In recent decades, a number of approaches to unification have been developed, and now theoretical studies of unification need to be harmonized. I believe that in order to develop a solid framework, it makes sense to reach a consensus on the general, conceptual and methodological ideas of unification. On this basis, it would be possible to develop a program for creating an approach to unification in theoretical psychology, and I propose an outline of three possible ones.

Perception and action as viewed from the Theory of Event Coding: a multi-lab replication and effect size estimation of common experimental designs

The Theory of Event Coding (TEC) has influenced research on action and perception across the past two decades. It integrates several seminal empirical phenomena and it has continued to stimulate novel experimental approaches on the representational foundations of action control and perceptual experience. Yet, many of the most notable results surrounding TEC originate from an era of psychological research that relied on rather small sample sizes as judged by today's standards. This state hampers future research aiming to build on previous phenomena. We, therefore, provide a multi-lab re-assessment of the following six classical observations: response-effect compatibility, action-induced blindness, response-effect learning, stimulus-response binding, code occupation, and short-term response-effect binding. Our major goal is to provide precise estimates of corresponding effect sizes to facilitate future scientific endeavors. These effect sizes turned out to be considerably smaller than in the original reports, thus allowing for informed decisions on how to address each phenomenon in future work. Of note, the most relevant results of the original observations were consistently obtained in the present experiments as well.

An integrative systematic review of employee silence and voice in healthcare: what are we really measuring?

The history of inquiries into the failings of medical care have highlighted the critical role of communication and information sharing, meaning that speaking up and employee silence have been extensively researched. However, the accumulated evidence concerning speaking-up interventions in healthcare indicates that they achieve disappointing outcomes because of a professional and organizational culture which is not supportive. Therefore, there is a gap with regard to our understanding of employee voice and silence in healthcare, and the relationship between withholding information and healthcare outcomes (e.g., patient safety, quality of care, worker wellbeing) is complex and differentiated. The following integrative review is aimed at addressing the following questions; (1) How is voice and silence conceptualized and measured in healthcare?; and (2) What is the theoretical background to employee voice and silence?. An integrative systematic literature review of quantitative studies measuring either employee voice or employee silence among healthcare staff published in peer-reviewed journals during 2016-2022 was conducted on the following databases: PubMed, PsycINFO, Scopus, Embase, Cochrane Library, Web of Science, CINAHL and Google Scholar. A narrative synthesis was performed. A review protocol was registered on the PROSPERO register (CRD42022367138). Of the 209 initially identified studies for full-text screening, 76 studies met the inclusion criteria and were selected for the final review (N = 122,009, 69.3% female). The results of the review indicated the following: (1) concepts and measures are heterogenous, (2) there is no unifying theoretical background, and (3) there is a need for further research regarding the distinction between what drives safety voice versus general employee voice, and how both voice and silence can operate in parallel in healthcare. Limitations discussed include high reliance on self-reported data from cross-sectional studies as well as the majority of participants being nurses and female staff. Overall, the reviewed research does not provide sufficient evidence on the links between theory, research and implications for practice, thus limiting how research in the field can better inform practical implications for the healthcare sector. Ultimately, the review highlights a clear need to improve assessment approaches for voice and silence in healthcare, although the best approach to do so cannot yet be established.

Rational learners and parochial norms

Parochial norms are narrow in social scope, meaning they apply to certain groups but not to others. Accounts of norm acquisition typically invoke tribal biases: from an early age, people assume a group's behavioral regularities are prescribed and bounded by mere group membership. However, another possibility is rational learning: given the available evidence, people infer the social scope of norms in statistically appropriate ways. With this paper, we introduce a rational learning account of parochial norm acquisition and test a unique prediction that it makes. In one study with adults (N = 480) and one study with children ages 5- to 8-years-old (N = 120), participants viewed violations of a novel rule sampled from one of two unfamiliar social groups. We found that adults judgments of social scope - whether the rule applied only to the sampled group (parochial scope), or other groups (inclusive scope) - were appropriately sensitive to the relevant features of their statistical evidence (Study 1). In children (Study 2) we found an age difference: 7- to 8-year-olds used statistical evidence to infer that norms were parochial or inclusive, whereas 5- to 6-year olds were overall inclusive regardless of statistical evidence. A Bayesian analysis shows a possible inclusivity bias: adults and children inferred inclusive rules more frequently than predicted by a naïve Bayesian model with unbiased priors. This work highlights that tribalist biases in social cognition are not necessary to explain the acquisition of parochial norms.

Dark-ego-vehicle principle: Narcissism as a predictor of anti-sexual assault activism

In this preregistered study, we tested the dark-ego-vehicle principle. This principle states that individuals with dark personalities, such as high narcissistic traits, are inclined to become involved in certain kinds of ideologies and political activism. We argue that narcissistic individuals can be attracted to anti-sexual assault activism because this form of activism may provide them with opportunities to obtain positive self-presentation (e.g., virtue signaling), gain status, dominate others, and engage in social conflicts to get their thrills. A diverse US sample (N = 313) completed online measures of narcissistic traits and involvement in anti-sexual assault activism. In addition, relevant covariates were assessed (i.e., age, gender, adult sexual assault history, sexual harassment myth acceptance, and altruism), and the interaction between narcissistic traits and gender was considered. The results of the multiple regression analysis showed that higher narcissistic traits predicted an individual’s higher involvement in anti-sexual assault activism over and above the covariates. However, this relationship was evident only for the women in this sample. Notably, a higher level of altruism in an individual was also substantially associated with higher involvement in anti-sexual assault activism. We discuss how the narcissism-by-gender interaction may be in line with the dark-ego-vehicle principle.

A manifesto for applying behavioural science

Recent years have seen a rapid increase in the use of behavioural science to address the priorities of public and private sector actors. There is now a vibrant ecosystem of practitioners, teams and academics building on each other's findings across the globe. Their focus on robust evaluation means we know that this work has had an impact on important issues such as antimicrobial resistance, educational attainment and climate change. However, several critiques have also emerged; taken together, they suggest that applied behavioural science needs to evolve further over its next decade. This manifesto for the future of applied behavioural science looks at the challenges facing the field and sets out ten proposals to address them. Meeting these challenges will mean that behavioural science is better equipped to help to build policies, products and services on stronger empirical foundations-and thereby address the world's crucial challenges.

Understanding left-wing authoritarianism: Relations to the dark personality traits, altruism, and social justice commitment

In two pre-registered studies, we investigated the relationship of left-wing authoritarianism with the ego-focused trait of narcissism. Based on existing research, we expected individuals with higher levels of left-wing authoritarianism to also report higher levels of narcissism. Further, as individuals with leftist political attitudes can be assumed to be striving for social equality, we expected left-wing authoritarianism to also be positively related to prosocial traits, but narcissism to remain a significant predictor of left-wing authoritarianism above and beyond those prosocial dispositions. We investigated our hypotheses in two studies using cross-sectional correlational designs. Two nearly representative US samples (Study 1: N = 391; Study 2: N = 377) completed online measures of left-wing authoritarianism, the Dark Triad personality traits, and two variables with a prosocial focus (i.e., altruism and social justice commitment). In addition, we assessed relevant covariates (i.e., age, gender, socially desirable responding, and virtue signaling). The results of multiple regression analyses showed that a strong ideological view, according to which a violent revolution against existing societal structures is legitimate (i.e., anti-hierarchical aggression), was associated with antagonistic narcissism (Study 1) and psychopathy (Study 2). However, neither dispositional altruism nor social justice commitment was related to left-wing anti-hierarchical aggression. Considering these results, we assume that some leftist political activists do not actually strive for social justice and equality but rather use political activism to endorse or exercise violence against others to satisfy their own ego-focused needs. We discuss these results in relation to the dark-ego-vehicle principle.

New Approaches to 3D Vision

New approaches to 3D vision are enabling new advances in artificial intelligence and autonomous vehicles, a better understanding of how animals navigate the 3D world, and new insights into human perception in virtual and augmented reality. Whilst traditional approaches to 3D vision in computer vision (SLAM: simultaneous localization and mapping), animal navigation (cognitive maps), and human vision (optimal cue integration) start from the assumption that the aim of 3D vision is to provide an accurate 3D model of the world, the new approaches to 3D vision explored in this issue challenge this assumption. Instead, they investigate the possibility that computer vision, animal navigation, and human vision can rely on partial or distorted models or no model at all. This issue also highlights the implications for artificial intelligence, autonomous vehicles, human perception in virtual and augmented reality, and the treatment of visual disorders, all of which are explored by individual articles. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Influence of Public Sports Services on Residents' Mental Health at Communities Level: New Insights from China

It is generally believed that sports play an important role in healing and boosting mental health. The provision of public sports services is important for enhancing residents' physical fitness and mental health, and for promoting their satisfaction with government public services. To build and strengthen a high-quality sports service-oriented society, it is important to explore whether community public sports services influence residents' mental health. To explore this phenomenon, the study gathered data from China and employed multi-level regression models to meet the study objective. The results show that the residents' age difference is 0.03, and the average daily exercise time is 0.02, which is significantly correlated with residents' mental health. The results show that the lower the availability and greening of sports facilities, and the fewer rest facilities there are, the higher the mental distress of residents may be. Conversely, the improvement of the greening and availability of sports facilities can facilitate the promotion of residents' mental health levels. Moreover, it was found that the mental health of residents is mainly and positively affected by the cleanliness of sports facilities. The street environment affects mental health and is attributed to the damage to sports facilities. Neighborhood communication also improves residents' mental health, and trust between neighbors has the greatest impact on reducing mental distress. Finally, the study proposes that the government should propose strategies to optimize the provision of community public sports services in the study area to boost both social and mental health benefits.

Promoting Liberal Education through Introductory Psychology: The Perspective-Based Approach

Introductory psychology is typically presented to undergraduates as a set of loosely related topics reflecting the organization of most textbooks. The empirically based evidence presented in the topical format is likely to be limited by progressive knowledge obsolesce and replicability challenges impacting contemporary science. We suggest that psychology instructors consider an alternative course format organized by theories or perspectives. A perspective-centered introductory psychology provides a consistent and pluralistic view of the field and embeds research findings and methodology within psychology’s major explanatory theories. Current trends in undergraduate higher education emphasize career readiness. Development of critical thinking and applying psychological principles to real-world situations, including students’ own lives, may be better achieved through a perspective-based approach. Given that the typical introductory psychology student is a non-major taking the course to fulfil liberal arts requirements, a perspective approach offers several advantages. A focus on understanding human behavior from multiple vantage points is a skill valued by prospective employers of new bachelor’s degree recipients. With its emphasis on divergent and dialectical reasoning, a perspectivecenteredintroductory psychology course can become the centerpiece of the general education curriculum. In addition to promoting post-formal reasoning, a perspective-centered introductory psychology illustrates the diverse epistemologies that have shaped our field.

A network approach to climate change anxiety and its key related features

Research has pointed to startling worldwide rates of people reporting considerable anxiety vis-à-vis climate change. Yet, uncertainties remain regarding how climate anxiety's cognitive-emotional features and daily life functional impairments interact with one another and with climate change experience, pro-environmental behaviors, and general worry. In this study, we apply network analyses to examine the associations among these variables in an international community sample (n = 874). We computed two network models, a graphical Gaussian model to explore network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the variables. Both network models pointed to the cognitive-emotional features of climate anxiety as a potential hub bridging general worry, the experience of climate change, pro-environmental behaviors, and the functional impairments associated with climate anxiety. Our findings offer data-driven clues for the field's larger quest to establish the foundations of climate anxiety.

Refining Research and Representation of Sexual and Gender Diversity in Neuroscience

There are opportunities to improve neuroscience that include lesbian, gay, bisexual, and transgender (LGBT) people. In this review, we briefly describe how the history of LGBT people in psychiatry has influenced neuroimaging approaches; how these attitudes have shifted over time; and what we can do to ensure that our future work is rigorous, ethical, and in service of the LGBT community. We suggest ways to refine neuroimaging methodologies to improve our understanding of marginalization and stigma while shifting away from research that focuses solely on the "etiology" or origins of LGBT identities. We also offer suggestions for conducting representative research that is LGBT-inclusive, regardless of the population of interest.

Beyond two modes of thought: A quantum model of how three cognitive variables yield conceptual change

We re-examine the long-held postulate that there are two modes of thought, and develop a more fine-grained analysis of how different modes of thought affect conceptual change. We suggest that cognitive development entails the fine-tuning of three dimensions of thought: abstractness, divergence, and context-specificity. Using a quantum cognition modeling approach, we show how these three variables differ, and explain why they would have a distinctively different impacts on thought processes and mental contents. We suggest that, through simultaneous manipulation of all three variables, one spontaneously, and on an ongoing basis, tailors one's mode of thought to the demands of the current situation. The paper concludes with an analysis based on results from an earlier study of children's mental models of the shape of the Earth. The example illustrates how, through reiterated transition between mental states using these three variables, thought processes unfold, and conceptual change ensues. While this example concerns children, the approach applies more broadly to adults as well as children.

Recovering Within-Person Dynamics from Psychological Time Series

Idiographic modeling is rapidly gaining popularity, promising to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: first, time series models will arguably always be misspecified, meaning it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest a modeling methodology that goes beyond fitting time series models alone and puts formal theories at the center of theory development.

Good scientific practice in EEG and MEG research: Progress and perspectives

Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.

Continuous-Time Modeling of the Bidirectional Relationship Between Incidental Affect and Physical Activity

BACKGROUND

Previous research suggests that there is a bidirectional relationship between incidental affect (i.e., how people feel in day-to-day life) and physical activity behavior. However, many inconsistencies exist in the body of work due to the lag interval between affect and physical activity measurements.

PURPOSE

Using a novel continuous-time analysis paradigm, we examined the temporal specificity underlying the dynamic relationship between positive and negative incidental affective states and moderate-to-vigorous physical activity (MVPA).

METHODS

A community sample of adults (n = 126, Mage = 27.71, 51.6% Male) completed a 14-day ambulatory assessment protocol measuring momentary positive and negative incidental affect six times a day while wearing a physical activity monitor (Fitbit). Hierarchical Bayesian continuous-time structural equation modeling was used to elucidate the underlying dynamics of the relationship between incidental affective states and MVPA.

RESULTS

Based on the continuous-time cross-effects, positive and negative incidental affect predicted subsequent MVPA. Furthermore, engaging in MVPA predicted subsequent positive and negative incidental affect. Incidental affective states had a greater relative influence on predicting subsequent MVPA compared to the reciprocal relationship. Analysis of the discrete-time coefficients suggests that cross-lagged effects increase as the time interval between measurements increase, peaking at about 8 h between measurement occasions before beginning to dissipate.

CONCLUSIONS

The results provide support for a recursive relationship between incidental affective states and MVPA, which is particularly strong at 7-9 hr time intervals. Future research designs should consider these medium-term dynamics, for both theory development and intervention.

A survey on how preregistration affects the research workflow: better science but more work

The preregistration of research protocols and analysis plans is a main reform innovation to counteract confirmation bias in the social and behavioural sciences. While theoretical reasons to preregister are frequently discussed in the literature, the individually experienced advantages and disadvantages of this method remain largely unexplored. The goal of this exploratory study was to identify the perceived benefits and challenges of preregistration from the researcher's perspective. To this end, we surveyed 355 researchers, 299 of whom had used preregistration in their own work. The researchers indicated the experienced or expected effects of preregistration on their workflow. The results show that experiences and expectations are mostly positive. Researchers in our sample believe that implementing preregistration improves or is likely to improve the quality of their projects. Criticism of preregistration is primarily related to the increase in work-related stress and the overall duration of the project. While the benefits outweighed the challenges for the majority of researchers with preregistration experience, this was not the case for the majority of researchers without preregistration experience. The experienced advantages and disadvantages identified in our survey could inform future efforts to improve preregistration and thus help the methodology gain greater acceptance in the scientific community.

Some Recommendations on the Use of Daily Life Methods in Affective Science

Real-world emotions are often more vivid, personally meaningful, and consequential than those evoked in the lab. Therefore, studying emotions in daily life is essential to test theories, discover new phenomena, and understand healthy emotional functioning; in short, to move affective science forward. The past decades have seen a surge of research using daily diary, experience sampling, or ecological momentary assessment methods to study emotional phenomena in daily life. In this paper, we will share some of the insights we have gained from our collective experience applying such daily life methods to study everyday affective processes. We highlight what we see as important considerations and caveats involved in using these methods and formulate recommendations to improve their use in future research. These insights focus on the importance of (i) theory and hypothesis-testing; (ii) measurement; (iii) timescale; and (iv) context, when studying emotions in their natural habitat.

Revisiting the theoretical and methodological foundations of depression measurement

Depressive disorders are among the leading causes of global disease burden, but there has been limited progress in understanding the causes and treatments for these disorders. In this Perspective, we suggest that such progress crucially depends on our ability to measure depression. We review the many problems with depression measurement, including limited evidence of validity and reliability. These issues raise grave concerns about common uses of depression measures, such as diagnosis or tracking treatment progress. We argue that shortcomings arise because depression measurement rests on shaky methodological and theoretical foundations. Moving forward, we need to break with the field's tradition that has, for decades, divorced theories about depression from how we measure it. Instead, we suggest that epistemic iteration, an iterative exchange between theory and measurement, provides a crucial avenue for depression measurement to progress.

Psychology needs to get tired of winning

Psychological science is on an extraordinary winning streak. A review of the published literature shows that nearly all study hypotheses are supported. This means that either all the theories are correct, or the literature is biased towards positive findings. Results from large-scale replication projects and the prevalence of questionable research practices indicate the latter. This is a problem because science progresses from being wrong. For decades, there have been calls for better theories and the adoption of a strong inference approach to science. However, there is little reason to believe that psychological science is ready to change. Although recent developments like the open science movement have improved transparency and replicability, they have not addressed psychological science's method-oriented (rather than problem-oriented) mindset. Psychological science still does not embrace the scientific method of developing theories, conducting critical tests of those theories, detecting contradictory results, and revising (or disposing of) the theories accordingly. In this article, I review why psychologists must embrace being wrong and how the Registered Report format might be one strategy for stopping psychology's winning streak.

What Can Educational Psychology Learn From, and Contribute to, Theory Development Scholarship?

One of the field of psychology’s stated goals is to produce scholarship with findings that benefit the world. Over the last 10 years, psychology scholarship and its presumed societal benefits have been called into question due to the field’s history of questionable research practices, racism, and epistemic oppression. Calls for methodological, ethical, and practical reforms are essential to building a psychological science that is just, effective, reliable, and beneficial. Recently, these calls have been complemented by a push to reform how theory is developed and used in psychology. There is great need for better understanding and implementation of theory development, both in the field of psychology broadly as well as in its subdisciplines, including educational psychology. At the same time, educational psychologists have much to contribute to the tripartite calls for methodological, ethical, and theory reforms. In this paper, I explore theory development in psychology by discussing the importance of developing both the descriptive and explanatory aspects of theory as well as the need to refine how theory is evaluated and integrated. Then, I review how innovations and practices in educational psychology can inform the broader theory reform movement in psychology writ large. Finally, I identify important future directions for further advancing theory reform in psychology, including addressing ableism in the field, increasing the field’s hospitability for theory revision, furthering theory development transparency, and more deeply integrating theory development into preparation programs.