Theories and Models: What They Are, What They Are for, and What They Are About

Erasmus clinical model of the onset and development of stuttering 2.0

A clinical, evidence-based model to inform clients and their parents about the nature of stuttering is indispensable for the field. In this paper, we propose the Erasmus Clinical Model of Stuttering 2.0 for children who stutter and their parents, and adult clients. It provides an up-to-date, clinical model summary of current insights into the genetic, neurological, motoric, linguistic, sensory, temperamental, psychological and social factors (be it causal, eliciting, or maintaining) related to stuttering. First a review is presented of current insights in these factors, and of six scientific theories or models that have inspired the development of our current clinical model. Following this, we will propose the model, which has proven to be useful in clinical practice. The proposed Erasmus Clinical Model of Stuttering visualizes the onset and course of stuttering, and includes scales for stuttering severity and impact, to be completed by the (parent of) the person who stutters. The pathway of the model towards stuttering onset is based on predisposing and mediating factors. In most children with an onset of stuttering, stuttering is transient, but if stuttering continues, its severity and impact vary widely. The model includes the circle of Engel (1977), which visualizes unique interactions of relevant biological, psychological, and social factors that determine the speaker's experience of stuttering severity and its impact. Discussing these factors and their interaction with an individual client can feed into therapeutic targets. The model is supplemented by a lifeline casus.

Athletic Injury Research: Frameworks, Models and the Need for Causal Knowledge

Within applied sports science and medicine research, many challenges hinder the establishment and detailed understanding of athletic injury causality as well as the development and implementation of appropriate athletic injury prevention strategies. Applied research efforts are faced with a lack of variable control, while the capacity to compensate for this lack of control through the application of randomised controlled trials is often confronted by a number of obstacles relating to ethical or practical constraints. Such difficulties have led to a large reliance upon observational research to guide applied practice in this area. However, the reliance upon observational research, in conjunction with the general absence of supporting causal inference tools and structures, has hindered both the acquisition of causal knowledge in relation to athletic injury and the development of appropriate injury prevention strategies. Indeed, much of athletic injury research functions on a (causal) model-blind observational approach primarily driven by the existence and availability of various technologies and data, with little regard for how these technologies and their associated metrics can conceptually relate to athletic injury causality and mechanisms. In this article, a potential solution to these issues is proposed and a new model for investigating athletic injury aetiology and mechanisms, and for developing and evaluating injury prevention strategies, is presented. This solution is centred on the construction and utilisation of various causal diagrams, such as frameworks, models and causal directed acyclic graphs (DAGs), to help guide athletic injury research and prevention efforts. This approach will alleviate many of the challenges facing athletic injury research by facilitating the investigation of specific causal links, mechanisms and assumptions with appropriate scientific methods, aiding the translation of lab-based research into the applied sporting world, and guiding causal inferences from applied research efforts by establishing appropriate supporting causal structures. Further, this approach will also help guide the development and adoption of both relevant metrics (and technologies) and injury prevention strategies, as well as encourage the construction of appropriate theoretical and conceptual foundations prior to the commencement of applied injury research studies. This will help minimise the risk of resource wastage, data fishing, p-hacking and hypothesising after the results are known (HARK-ing) in athletic injury research.

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.

"When" Versus "Whether" Gender/Sex Differences: Insights From Psychological Research on Negotiation, Risk-Taking, and Leadership

We present a conceptual framework of situational moderators of gender/sex effects in negotiation, risk-taking, and leadership-three masculine-stereotypic domains associated with gender/sex gaps in pay and authority. We propose that greater situational ambiguity and higher relevance and salience of gender/sex increase the likelihood of gender/sex-linked behaviors in these domains. We argue that greater ambiguity increases the extent to which actors and audiences must search inwardly (e.g., mental schema, past experience) or outwardly (e.g., social norms) for cues on how to behave or evaluate a situation and thereby widens the door for gender/sex-linked influences. Correspondingly, we propose that gender/sex effects on behavior and evaluations in these domains will be more likely when gender/sex is more relevant and salient to the setting or task. We propose further that these two situational moderators may work jointly or interactively to influence the likelihood of gender/sex effects in negotiation, risk-taking, and leadership. We conclude by discussing applications of our conceptual framework to psychological science and its translation to practice, including directions for future research.

Evaluation of the Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Questionnaire and its Relations to Cannabis-Related Problems

Cannabis use and the prevalence of cannabis use disorder (CUD) among emerging adults are on the rise. Several indicators of cannabis use (e.g., quantity, frequency) as they relate to negative outcomes have been posited in the extant literature. Despite research examining links between indicators and cannabis outcomes, few assessments of cannabis use indicators exist. The Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Inventory (DFAQ-CU) was developed to assess cannabis use across a range of factors. However, the factor structure of the DFAQ-CU has not been replicated. Further, the DFAQ-CU was modeled using reflective strategies despite formative strategies being conceptually appropriate. The present study utilized principal components analyses (PCA) and principal axis factoring (PAF) to evaluate the structure of the DFAQ-CU. PCA yielded a four-component solution; PAF resulted in a five-factor solution. Linear regression found significant relations between PCA components and PAF factors with CUD symptoms and cannabis-related problems; however, effect sizes were larger for the PAF suggesting possible misdisattenuation. The PCA components demonstrated evidence of discriminant and convergent validity with measures of cannabis and alcohol behavior. The study informs research and clinical work through the refinement of cannabis use assessment and enhancing our understanding of the importance of model selection.

Longitudinal interactions between residual symptoms and physiological stress in the remitted symptom network structure of depression

Residual symptoms and stress are amongst the most reliable predictors of relapse in remitted depression. Standard methodologies often preclude continuous stress sampling or the evaluation of complex symptom interactions. This limits knowledge acquisition relative to the day-to-day interactions between residual symptoms and stress. The study aims to explore the interactions between physiological stress and residual symptoms network structure in remitted depression. Twenty-two individuals remitted from depression completed baseline, daily diary (DD), and post-DD assessments. Self-reported stress and residual symptoms were measured at baseline and post-DD. Daily diaries required participants to use a wearable electrodermal activity (EDA) device during waking hours and complete residual symptom measures twice daily for 3-weeks. Two-step multilevel vector auto-regression models were used to estimate contemporaneous and dynamic networks. Depressed mood and concentration problems were central across networks. Skin conductance responses (SCRs), suicide, appetite, and sleep problems were central in the temporal and energy loss in the contemporaneous network. Increased SCRs predicted decreased energy loss. Residual symptoms and stress showed bi-directional interactions. Overall, depressed mood and concentration problems were consistently central, thus potentially important intervention targets. Non-obtrusive bio-signal measures should be used to provide the clinical evidence-base for modelling the interactions between depressive residual symptoms and stress. Practical implications are discussed throughout related to focusing on symptom-specific interactions in clinical practice, simultaneously reducing residual symptom and stress occurrences, EDA as pioneering signal for stress detection, and the central role of specific residual symptoms in remitted depression.

Let's play pretend: Towards effective modelling in experimental psycho(patho)logy

BACKGROUND AND OBJECTIVES

Models are omnipresent in science. We introduce a novel framework to achieve more effective modelling practices in general psychology and experimental psychopathology.

METHOD

We analyze three types of models that are common in psychology: laboratory models, computational models, and cognitive models. We then evaluate two common ways to assess the translational value of models (phenomenological similarity and deep similarity) as well as an arguably underappreciated way (functional similarity). Functional similarity is based on an assessment of whether variables (e.g., the administration of a pharmacological substance) have a similar effect (a) in the model (e.g., in a fear conditioning procedure) and (b) on the real-life target phenomenon (e.g., on real-life anxiety complaints).

CONCLUSIONS

We argue that the assessment of functional similarity is a powerful tool to assess the translational value of models in the field of experimental psychopathology and beyond.

Measuring the active elements of cognitive-behavioral therapies

Understanding how and for whom cognitive-behavioral therapies work is central to the development and improvement of mental health interventions. Suboptimal quantification of the active elements of cognitive-behavioral therapies has hampered progress in elucidating mechanisms of change. To advance process research on cognitive-behavioral therapies, we describe a theoretical measurement framework that focuses on the delivery, receipt, and application of the active elements of these interventions. We then provide recommendations for measuring the active elements of cognitive-behavioral therapies aligned with this framework. Finally, to support measurement harmonization and improve study comparability, we propose the development of a publicly available repository of assessment tools: the Active Elements of Cognitive-Behavioral Therapies Measurement Kit.

Mechanism associated with changes in male reproductive functions during ageing process

Ageing is a natural process with physiological changes in different body parts and has been associated with decreased reproductive capacity. Factors such as imbalance in the antioxidant defence system, vascular diseases, diabetes mellitus, accessory reproductive glands infection, obesity as well as buildup of toxic substances play a role in age-related male reproductive malfunction. Age is inversely proportional to volume of semen, sperm count, sperm progressive motility, sperm viability, normal sperm morphology. The observed negative correlation between ageing and semen indices contributes to male infertility and reproductive decline. Normal levels of ROS, plays crucial role in facilitating sperm function, such as capacitation, hyper-activation, acrosome reaction as well as sperm-oocyte fusion; however, a substantial elevation in the endogenous level of ROS, especially in reproductive tissues, usually instigates destruction of sperm cells and heightened male infertility. Contrarily, antioxidants, such as vitamins C and E, beta-carotene, and micronutrients like zinc and folate, have been found by researchers to facilitate normal semen quality and male reproductive function. Furthermore, the role of hormonal imbalance as a result of the compromised hypothalamic-pituitary-gonadal axis, Sertoli and Leydig cells disorder, and nitric oxide-medicated erectile dysfunction during ageing cannot be undermined.

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.

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.

Future Directions in Youth and Family Treatment Engagement: Finishing the Bridge Between Science and Service

The field has spent more than 50 years investing in the quality of youth mental healthcare, with intervention science yielding roughly 1,300 efficacious treatments. In the latter half of this period, concurrent efforts in implementation science have developed effective methods for supporting front-line service organizations and therapists to begin to bridge the science to service gap. However, many youths and families still do not benefit fully from these strategic investments due to low treatment engagement: nearly half of youths in need of services pursue them, and among those who do, roughly another half terminate prematurely. The negative impact of low engagement is substantial, and is disproportionally and inequitably so for many. We contend that to build a robust and "finished" bridge connecting science and service, the field must go beyond its two historical foci of designing interventions and preparing therapists to deliver them, to include an intentional focus on the youths and families who participate in these interventions and who work with those therapists. In this paper, we highlight the significance of treatment engagement in youth mental healthcare and discuss the current state of the literature related to four priorities: conceptualization, theory, measurement, and interventions. Next, we offer an example from our own program of research as one illustration for advancing these priorities. Finally, we propose recommendations to act on these priorities.

Expanding the system: A brief psychosocial complex systems model of internalising disorder

BACKGROUND

The aetiology of internalising disorders remains poorly understood. Recently, a bottom-up network perspective has suggested mental disorders are best conceptualised as emergent systems, and may be explained by mapping systems of symptoms embedded within a complex biopsychosocial environment. Under this framework, the complex system in which internalising disorders are embedded remains poorly understood. The present research outlines a brief psychosocial system of internalising disorders as a basis for future research.

METHODS

A Mixed Graphical Model was fitted on 15 psychosocial variables (including depression and anxiety) collected during the Christchurch Health and Development Study, a representative population birth cohort of 1265 people born in 1977 in Christchurch, New Zealand.

RESULTS

The model demonstrates that psychosocial risk factors for internalising disorders tend to be inter-related. The psychosocial system accounted for 19.9% of the variance in the diagnostic depression variable, and 5.0% of the variance in diagnostic anxiety. Most variables (10/13) were associated with depression and anxiety either directly or indirectly.

LIMITATIONS

First, the estimated model is undirected, so causal directions are unspecified except for longitudinal relationships. Second, binary diagnostic variables were used for depression and anxiety, meaning the model does include symptom-level complexity. Third, the model does not account for within-person effects.

CONCLUSIONS

This exploratory model may serve as a basis for the mapping of greater (bio) psychosocial complexity around internalising disorders. The model concisely demonstrates the need for researchers to "embrace complexity", but also underscores the conceptual scope that is required to do so on a broader (bio) psychosocial level.

Sensory and Emotional Components in Tourist Memories of Wildlife Encounters: Intense, Detailed, and Long-Lasting Recollections of Individual Incidents

To quantify the role of senses and emotions in creating memorable tourism experiences, we need measurement frameworks that match how memories are created. This study examines that process through directed-content qualitative analysis of tourist encounters with wildlife. Data are derived from: interviews with 20 experienced wildlife tourism experts in 12 countries; 3000 social media posts on tourism enterprise and wildlife encounter websites; and participant observations and records of 168 memorable encounters involving >100 wildlife species, >850 tourists, and ~10,000 h in total, ranging over five decades. Across all data sources, senses and emotions differed between tourist interests and personalities, wildlife species and behaviours, and encounter circumstances. All senses were reported, with the most frequent being sight, followed by sound and smell, and, rarely, touch or taste. Descriptions were fine-grained and complex. The emotions reported were awe, joy, wonder, delight, thrill, amazement, envy, aww (cute-emotion), surprise, elation, satisfaction, interest, boredom, disappointment, sadness, embarrassment, concern, pity, distress, disgust, anxiety, shock, alarm, fear, and panic. Some experiences generated powerful recalls persisting for decades. Short-term, intense, and finely detailed senses and emotions defined experiences, created memories, and determined satisfaction, wellbeing, and subsequent outcomes. More accurate methods are needed to measure and characterise senses, emotions, and memories in tourism experience.

A Systematic Review and Individual Patient Data Network Analysis of the Residual Symptom Structure Following Cognitive-Behavioral Therapy and Escitalopram, Mirtazapine and Venlafaxine for Depression

OBJECTIVE

Consistent evidence suggests residual depressive symptomology are the strongest predictors of depression relapse following cognitive-behavioral therapy (CBT) and antidepressant medications (ADM's). Psychometric network models help detecting and understanding central symptoms that remain post-treatment, along with their complex co-occurrences. However, individual psychometric network studies show inconsistent findings. This systematic review and IPD network analysis aimed to estimate and compare the symptom network structures of residual depressive symptoms following CBT, ADM's, and their combination.

METHODS

PsycINFO, PsycArticles, and PubMed were systematically searched through October 2020 for studies that have assessed individuals with major depression at post-treatment receiving either CBT and/or ADM's (venlafaxine, escitalopram, mirtazapine). IPD was requested from eligible samples to estimate and compare residual symptom psychometric network models post-CBT and post-ADM's.

RESULTS

In total, 25 from 663 eligible samples, including 1,389 patients qualified for the IPD. Depressed mood and anhedonia were consistently central residual symptoms post-CBT and post-ADM's. For CBT, fatigue-related and anxiety symptoms were also central post-treatment. A significant difference in network structure across treatments (CBT vs. ADM) was observed for samples measuring depression severity using the MADRS. Specifically, stronger symptom occurrences were present amongst lassitude-suicide post-CBT (vs. ADM's) and amongst lassitude-inability to feel post-ADM's (vs. CBT). No significant difference in global strength was observed across treatments.

CONCLUSIONS

Core major depression symptoms remain central across treatments, strategies to target these symptoms should be considered. Anxiety and fatigue related complaints also remain central post-CBT. Efforts must be made amongst researchers, institutions, and journals to permit sharing of IPD.Systematic Review Registration: A protocol was prospectively registered on PROSPERO (CRD42020141663; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=141663).

The Function of Literature in Psychological Science

The recent reform debates in psychological science, prompted by a widespread crisis of confidence, have exposed and destabilized the so-called myth of self-correction, that is, the problem that most scientists perceive their disciplines as self-correcting without engaging in actual practices that correct the scientific record. In this paper, building on the idea of self-correction as a myth, I propose another myth common to psychological science: the myth of self-organization. The myth of self-organization is the idea that scientific literature will organize itself into something the community adding to it would recognize as systematic knowledge; while the actual members of those communities do not engage in effective ways of organizing it. I argue for the existence of the myth self-organization by taking a historical look at how the scientific literature was construed by psychologists during the 20th century. In my view, the literature, and behaviors of scientists related to it, becomes a social institution exerting influence over the science it belongs to. I conclude with a critical discussion of self-organization through the debates about preregistration and theory formalization in psychology’s reform movement.

Intervening on psychopathology networks: Evaluating intervention targets through simulations

Identifying the different influences of symptoms in dynamic psychopathology models may hold promise for increasing treatment efficacy in clinical applications. Dynamic psychopathology models study the behavioral patterns of symptom networks, where symptoms mutually enforce each other. Interventions could be tailored to specific symptoms that are most effective at lowering symptom activity or that hinder the further development of psychopathology. Simulating interventions in psychopathology network models fits in a novel tradition where symptom-specific perturbations are used as in silico interventions. Here, we present the NodeIdentifyR algorithm (NIRA) to identify the projected most efficient, symptom-specific intervention target in a network model (i.e., the Ising model). We implemented NIRA in a freely available R package. The technique studies the projected effects of symptom-specific interventions by simulating data while symptom parameters (i.e., thresholds) are systematically altered. The projected effect of these interventions is defined in terms of the expected change in overall symptom activity across simulations. With this algorithm, it is possible to study (1) whether symptoms differ in their projected influence on the behavior of the symptom network and, if so, (2) which symptom has the largest projected effect in lowering or increasing overall symptom activation. As an illustration, we apply the algorithm to an empirical dataset containing Post-Traumatic Stress Disorder symptom assessments of participants who experienced the Wenchuan earthquake in 2008. The most important limitations of the method are discussed, as well as recommendations for future research, such as shifting towards modeling individual processes to validate these types of simulation-based intervention methods.

The Theoretical and Statistical Ising Model: A Practical Guide in R

The “Ising model” refers to both the statistical and the theoretical use of the same equation. In this article, we introduce both uses and contrast their differences. We accompany the conceptual introduction with a survey of Ising-related software packages in R. Since the model’s different uses are best understood through simulations, we make this process easily accessible with fully reproducible examples. Using simulations, we show how the theoretical Ising model captures local-alignment dynamics. Subsequently, we present it statistically as a likelihood function for estimating empirical network models from binary data. In this process, we give recommendations on when to use traditional frequentist estimators as well as novel Bayesian options.