Perturbation graphs, invariant causal prediction and causal relations in psychology.

Networks (graphs) in psychology are often restricted to settings without interventions. Here we consider a framework borrowed from biology that involves multiple interventions from different contexts (observations and experiments) in a single analysis. The method is called perturbation graphs. In gene regulatory networks, the induced change in one gene is measured on all other genes in the analysis, thereby assessing possible causal relations. This is repeated for each gene in the analysis. A perturbation graph leads to the correct set of causes (not nec-essarily direct causes). Subsequent pruning of paths in the graph (called transitive reduction) should reveal direct causes. We show that transitive reduction will not in general lead to the correct underlying graph. We also show that invariant causal prediction is a generalisation of the perturbation graph method and does reveal direct causes, thereby replacing transitive re-duction. We conclude that perturbation graphs provide a promising new tool for experimental designs in psychology, and combined with invariant causal prediction make it possible to re-veal direct causes instead of causal paths. As an illustration we apply these ideas to a data set about attitudes on meat consumption and to a time series of a patient diagnosed with major depression disorder.

Productive explanation: A framework for evaluating explanations in psychological science.

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Dynamical Systems View of Psychiatric Disorders-Theory: A Review.

Importance: Psychiatric disorders may come and go with symptoms changing over a lifetime. This suggests the need for a paradigm shift in diagnosis and treatment. Here we present a fresh look inspired by dynamical systems theory. This theory is used widely to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. Observations: In the dynamical systems view, we propose the healthy state has a basin of attraction representing its resilience, while disorders are alternative attractors in which the system can become trapped. Rather than an immutable trait, resilience in this approach is a dynamical property. Recent work has demonstrated the universality of generic dynamical indicators of resilience that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforests and tipping elements of the climate system. Other dynamical systems tools are used in ecology and climate science to infer causality from time series. Moreover, experiences in ecological restoration confirm the theoretical prediction that under some conditions, short interventions may invoke long-term success when they flip the system into an alternative basin of attraction. All this implies practical applications for psychiatry, as are discussed in part 2 of this article. Conclusions and Relevance: Work in the field of dynamical systems points to novel ways of inferring causality and quantifying resilience from time series. Those approaches have now been tried and tested in a range of complex systems. The same tools may help monitoring and managing resilience of the healthy state as well as psychiatric disorders.

A Dynamical Systems View of Psychiatric Disorders-Practical Implications: A Review.

Importance: Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry. Observations: Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions. Conclusions and Relevance: Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.

Alcohol consumption as a socially contagious phenomenon in the Framingham Heart Study social network.

We use longitudinal social network data from the Framingham Heart Study to examine the extent to which alcohol consumption is influenced by the network structure. We assess the spread of alcohol use in a three-state SIS-type model, classifying individuals as abstainers, moderate drinkers, and heavy drinkers. We find that the use of three-states improves on the more canonical two-state classification, as the data show that all three states are highly stable and have different social dynamics. We show that when modelling the spread of alcohol use, it is important to model the topology of social interactions by incorporating the network structure. The population is not homogeneously mixed, and clustering is high with abstainers and heavy drinkers. We find that both abstainers and heavy drinkers have a strong influence on their social environment; for every heavy drinker and abstainer connection, the probability of a moderate drinker adopting their drinking behaviour increases by [Formula: see text] and [Formula: see text], respectively. We also find that abstinent connections have a significant positive effect on heavy drinkers quitting drinking. Using simulations, we find that while both are effective, increasing the influence of abstainers appears to be the more effective intervention compared to reducing the influence of heavy drinkers.

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.

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.

How compliance with behavioural measures during the initial phase of a pandemic develops over time: A longitudinal COVID-19 study.

In this longitudinal research, we adopt a complexity approach to examine the temporal dynamics of variables related to compliance with behavioural measures during the COVID-19 pandemic. Dutch participants (N = 2399) completed surveys with COVID-19-related variables five times over a period of 10 weeks (23 April-30 June 2020). With these data, we estimated within-person COVID-19 attitude networks containing a broad set of psychological variables and their relations. These networks display variables' predictive effects over time between measurements and contemporaneous effects during measurements. Results show (1) bidirectional effects between multiple variables relevant for compliance, forming potential feedback loops, and (2) a positive reinforcing structure between compliance, support for behavioural measures, involvement in the pandemic and vaccination intention. These results can explain why levels of these variables decreased throughout the course of the study. The reinforcing structure points towards potentially amplifying effects of interventions on these variables and might inform processes of polarization. We conclude that adopting a complexity approach might contribute to understanding protective behaviour in the initial phase of pandemics by combining different theoretical models and modelling bidirectional effects between variables. Future research could build upon this research by studying causality with interventions and including additional variables in the networks.

Tailored interventions into broad attitude networks towards the COVID-19 pandemic.

This study examines how broad attitude networks are affected by tailored interventions aimed at variables selected based on their connectiveness with other variables. We first computed a broad attitude network based on a large-scale cross-sectional COVID-19 survey (N = 6,093). Over a period of approximately 10 weeks, participants were invited five times to complete this survey, with the third and fifth wave including interventions aimed at manipulating specific variables in the broad COVID-19 attitude network. Results suggest that targeted interventions that yield relatively strong effects on variables central to a broad attitude network have downstream effects on connected variables, which can be partially explained by the variables the interventions were aimed at. We conclude that broad attitude network structures can reveal important relations between variables that can help to design new interventions.

Understanding change in COVID-19 vaccination intention with network analysis of longitudinal data from Dutch adults.

Prior research into the relationship between attitudes and vaccination intention is predominantly cross-sectional and therefore does not provide insight into directions of relations. During the COVID-19 vaccines development and enrollment phase, we studied the temporal dynamics of COVID-19 vaccination intention in relation to attitudes toward COVID-19 vaccines and the pandemic, vaccination in general, social norms and trust. The data are derived from a longitudinal survey study with Dutch participants from a research panel (N = 744; six measurements between December 2020 and May 2021; age 18-84 years [M = 53.32]) and analyzed with vector-autoregression network analyses. While cross-sectional results indicated that vaccination intention was relatively strongly related to attitudes toward the vaccines, results from temporal analyses showed that vaccination intention mainly predicted other vaccination-related variables and to a lesser extent was predicted by variables. We found a weak predictive effect from social norm to vaccination intention that was not robust. This study underlines the challenge of stimulating uptake of new vaccines developed during pandemics, and the importance of examining directions of effects in research into vaccination intention.

Control Theory Forecasts of Optimal Training Dosage to Facilitate Children's Arithmetic Learning in a Digital Educational Application.

Education can be viewed as a control theory problem in which students seek ongoing exogenous input-either through traditional classroom teaching or other alternative training resources-to minimize the discrepancies between their actual and target (reference) performance levels. Using illustrative data from [Formula: see text] Dutch elementary school students as measured using the Math Garden, a web-based computer adaptive practice and monitoring system, we simulate and evaluate the outcomes of using off-line and finite memory linear quadratic controllers with constraintsto forecast students' optimal training durations. By integrating population standards with each student's own latent change information, we demonstrate that adoption of the control theory-guided, person- and time-specific training dosages could yield increased training benefits at reduced costs compared to students' actual observed training durations, and a fixed-duration training scheme. The control theory approach also outperforms a linear scheme that provides training recommendations based on observed scores under noisy and the presence of missing data. Design-related issues such as ways to determine the penalty cost of input administration and the size of the control horizon window are addressed through a series of illustrative and empirically (Math Garden) motivated simulations.

Editorial.

During its 128 years of operation, Psychological Review has exerted a powerful and consistent influence on the field under its long-term sponsor, the American Psychological Association (APA). Notwithstanding changes in ownership, it has always been what it is now-the flagship of the Association and the field. Since its inception, the journal has focused on theoretical analyses (e.g., systematic evaluations of alternative theories) and/or developments (e.g., the generation of novel theories) in the psychological sciences. Thus, the objectives of any incoming editor and editorial board remain steadfast: (a) to maintain and enhance the standing of Psychological Review in the field and (b) to correspondingly align its scope, content, and operations with any changes in the Association, the field of psychology in particular, and science and society in general. The journal's new senior editorial team is excited to navigate Psychological Review through the ever-changing landscape of psychology at this time of multiple challenges, referred to by the United Nations Secretary General António Guterres as "the greatest cascade of crises in our lifetime." Although we are initiating a number of changes, we will do our best to maintain Psychological Review's excellence. This will involve our capacity to reflect on and disseminate new theoretical developments, enriched and inspired by current trends in science in general and in psychological science in particular, while maintaining an overarching commitment to advancing the field through the incorporation of diverse perspectives. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Intermediate stable states in substance use.

Many people across the world use potentially addictive legal and illegal substances, but evidence suggests that not all use leads to heavy use and dependence, as some substances are used moderately for long periods of time. Here, we empirically examine, the stability of and transitions between three substance use states: zero-use, moderate use, and heavy use. We investigate two large datasets from the US and the Netherlands on yearly usage and change of alcohol, nicotine, and cannabis. Results, which we make available through an extensive interactive tool, suggests that there are stable moderate use states, even after meeting criteria for a positive diagnosis of substance abuse or dependency, for both alcohol and cannabis use. Moderate use of tobacco, however, was rare. We discuss implications of recognizing three states rather than two states as a modeling target, in which the moderate use state can both act as an intervention target or as a gateway between zero use and heavy use.

Post-error slowing: Large scale study in an online learning environment for practising mathematics and language.

The ability to monitor and adjust our performance is crucial for adaptive behaviour, a key component of human cognitive control. One widely studied metric of this behaviour is post-error slowing (PES), the finding that humans tend to slow down their performance after making an error. This study is a first attempt at generalizing the effect of PES to an online adaptive learning environment where children practise mathematics and language skills. This population was of particular interest since the major development of error processing occurs during childhood. Eight million response patterns were collected from 150,000 users aged 5 to 13 years old for 6 months, across 23 different learning activities. PES could be observed in most learning activities and greater PES was associated with greater post-error accuracy. PES also varied as a function of several variables. At the task level, PES was greater when there was less time pressure, when errors were slower, and in learning activities focusing on mathematical rather than language skills. At the individual level, students who chose the most difficult level to practise and had higher skill ability also showed greater PES. Finally, non-linear developmental differences in error processing were found, where the PES magnitude increased from 6 to 9-years-old and decreased from 9 to 13. This study shows that PES underlies adaptive behaviour in an educational context for primary school students.

A review of mathematical modeling of addiction regarding both (neuro-) psychological processes and the social contagion perspectives.

Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psychological processes, and the social environment. Using mathematical and computational models that allow for surrogative reasoning may be a promising avenue for gaining a deeper understanding of this complex behavior. This paper reviews and classifies a selection of formal models of addiction focusing on the intra- and inter-individual dynamics, i.e., (neuro) psychological models and social models. We find that these modeling approaches to addiction are too disjoint and argue that in order to unravel the complexities of biopsychosocial processes of addiction, models should integrate intra- and inter-individual factors.

The search for causality: A comparison of different techniques for causal inference graphs.

Estimating causal relations between two or more variables is an important topic in psychology. Establishing a causal relation between two variables can help us in answering that question of why something happens. However, using solely observational data are insufficient to get the complete causal picture. The combination of observational and experimental data may give adequate information to properly estimate causal relations. In this study, we consider the conditions where estimating causal relations might work and we show how well different algorithms, namely the Peter and Clark algorithm, the Downward Ranking of Feed-Forward Loops algorithm, the Transitive Reduction for Weighted Signed Digraphs algorithm, the Invariant Causal Prediction (ICP) algorithm and the Hidden Invariant Causal Prediction (HICP) algorithm, determine causal relations in a simulation study. Results showed that the ICP and the HICP algorithms perform best in most simulation conditions. We also apply every algorithm to an empirical example to show the similarities and differences between the algorithms. We believe that the combination of the ICP and the HICP algorithm may be suitable to be used in future research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An Attention-Based Diffusion Model for Psychometric Analyses.

The emergence of computer-based assessments has made response times, in addition to response accuracies, available as a source of information about test takers' latent abilities. The development of substantively meaningful accounts of the cognitive process underlying item responses is critical to establishing the validity of psychometric tests. However, existing substantive theories such as the diffusion model have been slow to gain traction due to their unwieldy functional form and regular violations of model assumptions in psychometric contexts. In the present work, we develop an attention-based diffusion model based on process assumptions that are appropriate for psychometric applications. This model is straightforward to analyse using Gibbs sampling and can be readily extended. We demonstrate our model's good computational and statistical properties in a comparison with two well-established psychometric models.

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

This article aims to improve theory formation in psychology by developing a practical methodology for constructing explanatory theories: theory construction methodology (TCM). TCM is a sequence of five steps. First, the theorist identifies a domain of empirical phenomena that becomes the target of explanation. Second, the theorist constructs a prototheory, a set of theoretical principles that putatively explain these phenomena. Third, the prototheory is used to construct a formal model, a set of model equations that encode explanatory principles. Fourth, the theorist investigates the explanatory adequacy of the model by formalizing its empirical phenomena and assessing whether it indeed reproduces these phenomena. Fifth, the theorist studies the overall adequacy of the theory by evaluating whether the identified phenomena are indeed reproduced faithfully and whether the explanatory principles are sufficiently parsimonious and substantively plausible. We explain TCM with an example taken from research on intelligence (the mutualism model of intelligence), in which key elements of the method have been successfully implemented. We discuss the place of TCM in the larger scheme of scientific research and propose an outline for a university curriculum that can systematically educate psychologists in the process of theory formation.

How to Compare Psychometric Factor and Network Models.

In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples.

Deviations of rational choice: an integrative explanation of the endowment and several context effects.

People's choices are often found to be inconsistent with the assumptions of rational choice theory. Over time, several probabilistic models have been proposed that account for such deviations from rationality. However, these models have become increasingly complex and are often limited to particular choice phenomena. Here we introduce a network approach that explains a broad set of choice phenomena. We demonstrate that this approach can be used to compare different choice theories and integrates several choice mechanisms from established models. A basic setup implements bounded rationality, loss aversion, and inhibition in a natural fashion, which allows us to predict the occurrence of well-known choice phenomena, such as the endowment effect and the similarity, attraction, compromise, and phantom context effects. Our results show that this network approach provides a simple representation of complex choice behaviour, and can be used to gain a better understanding of how the many choice phenomena and key theoretical principles from different types of decision-making are connected.