Sleep restriction therapy (SRT) is an effective treatment for insomnia. The Triple-R theory has been proposed to explain the efficacy of SRT based on three processes: restricting time in bed, regulating sleep and wake times, and reconditioning of the bed-sleep association. However, it is difficult to determine whether the proposed processes explain the efficacy of SRT. Part of this difficulty is because Triple-R is a verbal theory: As verbal theories make few specific predictions, it is difficult to evaluate which data patterns the theory implies. The productive explanation framework proposes that translating a verbal theory into a formal model solves this problem by enabling to simulate data that follows from the theory and as such revealing testable predictions. In the current paper, we apply the productive explanation framework to examine whether the Triple-R theory can account for the efficacy of SRT. We show that the formalized Triple-R theory reproduces data patterns observed in empirical studies, although not all empirical phenomena are reproduced. According to the productive explanation framework, we conclude that the Triple-R theory provides a partial explanation of the efficacy of SRT. In addition, the formalized Triple-R theory makes predictions that can guide new clinical trials. Ultimately, this application of the productive explanation framework provides a better understanding of the mechanisms suggested by the Triple-R theory.

An extension of the Ising model is proposed as a viable alternative for data with values -1, 0 and +1 in the inverse problem, i.e., estimation of the parameters. This model is called the Blume-Capel (BC) model, adapted from physics for small networks. The advantage of the BC model is not only the fact that it is possible to have a neutral (centrist) position on the response scale, but also that this model allows for three stable states. We illustrate magnetisation properties of the BC model using simulations and mean field results. For estimation of the BC parameters, we show that the BC model is part of the exponential family of distributions and show that the model is identified, except for the (inverse) temperature. We then show that combining pseudo-likelihood with lasso yields accurate parameter recovery for the BC model, even in small networks. Moreover, confidence intervals with good coverage properties can be obtained using the desparsified lasso together with sandwich and shrinkage techniques. We apply the methods to data obtained from the online platform Stemwijzer, intended to aid people in deciding for whom to vote.

This article introduces the Learning Ising Attitude Model (LIAM), which explains how attitude networks reduce entropy—the dissonance within an attitude. LIAM extends the network theory of attitudes by adding Hebbian learning, thereby aligning it with classic neural network models like the Hopfield network and the Boltzmann machine. In the original theory, attention and thought temporarily reduce entropy in balanced attitude networks, but it remained unclear why such networks are balanced in the first place. We demonstrate that higher attention, when paired with Hebbian learning, causes the system to evolve toward balanced network structures that effectively reduce entropy. In addition, we model feedback between attitudinal unstableness and attention, incorporate the learning of dispositional tendencies of attitude elements, and illustrate how attitude change can emerge from the model. Finally, we discuss how LIAM contributes to the unification of attitude research, its relation to previous connectionist models, and directions for future work.

Work environments can profoundly influence employee well-being, workload, and performance. As well-being and workload have complex bidirectional interactions, consequently employees can be captured in healthy or unhealthy vicious cycles for their dynamic interplay. Our aim is to model these vicious cycles as competing feedback loops, based on a network modeling perspective from artificial intelligence (AI). In this human-centered AI approach, we specifically focus on contextualization: on how our newly developed context-sensitive competing feedback loops model can capture employee well-being under changing working environments in an organization. We simulate five dynamic contextual factors in the working environment, such as market demand and management support, and evaluate how employee well-being develops over a one-year course. When the working environments became worse, employees were more stuck in an unhealthy burn-out feedback loop, but once there was intervention in some contextual factors, employees switched to the healthy feedback loop again. From these simulations, we conclude that contextualized competing feedback loops can offer a flexible framework to model the dynamics of employee wellbeing and workload depending on working environments. Such contextualized competing feedback loops might serve as a good mental health application tool.

The connectivity hypothesis, central to the increasingly influential symptom network approach to psychopathology, proposes that stronger connectivity among symptoms heightens vulnerability to mental disorders. We provide an analytic derivation of this hypothesis using mean-field Ising models of depression, both in the standard-1/1 formulation and in a 0/1 variant where nodes represent symptoms as absent or present. Applying bifurcation theory, we derive the bifurcation sets and phase transition structure directly from the mean-field equations. This formal characterization elucidates how connectivity shapes system dynamics and, consistent with the network theory of mental disorders, demonstrates that increasing connectivity amplifies the risk of transitions into unhealthy states.

Understanding the reciprocal relationship between practice and skill, or learning success, is essential for designing effective interventions to prevent dropout. Classical learning curves model the effect of practice on skill in the case of enforced learning, where dropout is not an option. However in many learning contexts, such as learning a new hobby or taking an online course, learners can, and often do, drop out. Previous research in educational psychology has suggested a reciprocal relationship between motivation and practice, on the one hand, and achievement and skill, on the other. We provide a formal model of this relationship by integrating classical models of learning and forgetting into a model of the practice-success cycle. We show that in this Reciprocal-Practice-Success (RPS) model of learning, long-term learning outcomes are particularly sensitive to the shape of the learning curve - the risk of dropping out is much higher when the learning curves are sigmoid than when they are concave. Through bifurcation analysis of a simplified formulation of the model, we demonstrate how modifying the minimum practice rate and success sensitivity can mitigate attrition in cases where learning curves are sigmoid. In addition, we explore mechanisms to change the shape of the learning curve to minimize dropout. We also present extensions that incorporate spacing effects and temporal discounting to describe learning dynamics in more realistic contexts. Finally, we outline the empirical support for the model’s key assumptions and predictions and illustrate its use with two datasets.

The modeling of a knowledge domain in a learning environ-ment is a critical step with far-reaching implications, notonly for the system design but also for the granularity withwhich student progress can be tracked. In the [System X]learning environment, games are designed to measure one di-mension of a broad knowledge domain (e.g. clock-reading).However, dimensionality is introduced in the games throughthe practice of specific knowledge components (KC; e.g.,reading digital clocks with half hours). The assignmentof specific items to these KCs is expert-based rather thandata-driven. To investigate the validity of these mappingsof items to KCs, this study employed a hierarchical clus-tering method on person-specific performance at the itemlevel. The results revealed a clear pattern; items associatedwith the same learning goal tend to cluster together. Thissupports the notion that learning goals capture meaningfuland distinct dimensions within the game. Finally, we illus-trate the progress of some players in the game to emphasizethe importance of detailed tracking of multiple abilities foreffective instruction and timely intervention.

In people, the ability to solve analogies such as "body: feet:: table: ?"emerges in childhood, and appears to transfer easily to other domains, such as the visual domain "( : ) :: < : ?". Recent research shows that large language models (LLMs) can solve various forms of analogies. However, can LLMs generalize analogy solving to other domains like people can? To investigate this, we had children, adults, and LLMs solve a series of letter-string analogies (e.g., a b : a c :: j k : ?) in the Latin alphabet, in a near transfer domain (Greek alphabet), and a far transfer domain (list of symbols). Children and adults easily generalized their knowledge to unfamiliar domains, whereas LLMs did not. This key difference between human and AI performance is evidence that these LLMs still struggle with robust human-like analogical transfer.

Study Objectives:Sleep is complex and variable, yet insomnia research and treatment often rely on averages-either across nights or across individuals. Such approaches risk obscuring dynamic features that characterize insomnia as a disorder and its unique manifestation in individuals. In this study, we explore disorder-specific (group-level) and person-specific (individual-level) dynamic phenomena of insomnia among people with insomnia. Methods:We analyzed 8 weeks of sleep diary data from 61 participants with insomnia. Four domains of sleep dynamics were examined at group- and individual-levels: (1) night-to-night variability, (2) temporal dependency of sleep quality, (3) stability of sleep complaints, (4) weekday-weekend variability. We correlated these domains with insomnia severity, pre-sleep arousal, and sleep-related safety behavior. Results:At the group-level, insomnia was characterized by (1) night-to-night fluctuations in sleep parameters, (2) unpredictable sleep quality, (3) frequent co-occurrence and fluctuations in type of sleep complaints, and (4) different sleep patterns on weekdays and weekends. These disorder-specific dynamic phenomena showed medium-sized significant correlations with insomnia indices, ranging from r = -0.25 to 0.41. At the individual-level, all four domains varied markedly across individuals. While the group-level characterizations were fitting for some participants, others showed patterns clearly distinct. We developed a Shiny application which allows readers to explore individual sleep profiles (https://uvasobe.shinyapps.io/PersonalSleepExplorer/). Conclusions:Sleep in insomnia varies from night-to-night and person-to-person. Reliance on averages across nights and across individuals may obscure fluctuations of potential clinical relevance. We call for broader use of sleep diaries to capture dynamic patterns of insomnia and for investigation of their clinical utility. Clinical Trial:Sleep Restriction Treatment for Insomnia.URL: https://clinicaltrials.gov/study/NCT05548907. Registration:NCT05548907.

Psychological network theories provide an important alternative to traditional common cause theories, such as the g-theory of general intelligence and brain-based explanations of depression. Network theories, which are often formalized using the Ising model from statistical physics, have gained significant empirical support. However, the binary nature of nodes in Ising-type models presents a limitation, as many psychological datasets include responses with uncertain or neutral categories (e.g., "don't know" or "not relevant"). Ternary spin models, such as the Blume-Capel model, overcome this constraint by incorporating a third node state, zero, that can represent such responses, enabling more nuanced scale representations. The resulting models exhibit more complex dynamics and provide new insights into research across a range of psychological constructs. We illustrate our approach with examples from three key subdisciplines of psychology. First, we introduce a ternary spin model for attitudes, extending the Ising attitude model. Next, we propose a unified framework encompassing both bipolar disorder and major depressive disorder. Finally, we present a novel ternary network model for understanding knowledge acquisition.

This article illustrates the assumption of path symmetry in current theories of psychopathology and calls for the development of dynamical systems of mental illness that incorporate asymmetry.

In recent years, there has been a growing call to advance psychological theorizing through formal modeling. We answer this by introducing a methodology for developing psychological theories using probabilistic network models (PNMs). Originating in statistical mechanics, PNMs describe networks of interacting elements and have already shaped prominent theories in attitude, emotion, and decision research. We present a systematic guide on how to develop, analyze, and validate PNMs. Central to our framework is a review of nine foundational models that researchers can start from, extend, and adapt to their specific contexts. For each of these models, we discuss existing applications and analyze them using two newly developed tools: a NetLogo model for simulations and an R package for visualizing mean-field dynamics. As a case study, we demonstrate the application of PNMs in theory development before discussing the assumptions and limitations of the framework.

Telling the time is an essential life skill requiring children to integrate language, numeracy, spatial reasoning, temporal sequencing and executive functions. Therefore, understanding how children acquire time-telling skills is crucial. This study builds on previous research by examining (1) how clock features affect time-telling skills, (2) developmental trajectories, (3) common errors and (4) the relationship to other cognitive abilities. We leveraged data from an adaptive learning platform, including ten million responses from 80,000 Dutch-speaking children aged 6–12. Our analysis revealed that the difficulty of clock reading is influenced by clock and task types, time intervals, reference points and daytime information. Contrary to previous research, digital clocks and identification tasks proved more challenging than analogue clocks and production tasks. Additionally, time-telling ability and its relationship to other cognitive abilities changed with age. The results demonstrate how large-scale adaptive learning systems can shed light on individual differences in children’s time-telling performance.

Ambiguous, multistable stimuli can give rise to multiple perceptual interpretations. Bistable visual stimuli, in particular, have long been used to study neurological and cognitive processes. However, many experimental paradigms rely on self-report to assess subjective perceptual experiences, introducing potential issues such as low reliability and interferences of reporting into the processes under investigation. No-report paradigms serve as a method to work around these problems by decoding perceptual content based on physiological data. Here we discuss the strengths and pitfalls of no-report paradigms in the context of research using multistable perception. Focusing on experimental power related to decoding accuracy, we exemplify the current progress of decoding perceptual content using three major domains of physiological data (eye-tracking, M/EEG and fMRI). We find that the predictability of the percept varies significantly depending on the paradigm and stimulus type, showcasing both the potential and limitations of physiological inference in bistable perception research.

In adaptive digital learning environments, it is essential to track learning trajectories. The Elo rating system, known for its computational simplicity, is frequently employed for this purpose. Current Elo-based systems cannot handle rapid changes in ability or are unable to balance accuracy and speed when updating player and item ratings. Changes in Elo ratings depend on the sensitivity parameter $K$. Using fixed $K$ values necessitates a trade-off: Larger values facilitate the tracking of evolving ability levels but introduce greater rating volatility. Smaller values yield more stable estimates, but are slower to reflect actual ability levels. Existing modifications of the Elo system, which diminish $K$ as the number of responses increases, are inadequate in scenarios characterized by considerable ability fluctuation, a common occurrence in digital learning environments. To address this challenge, we introduce a novel approach for dynamically adjusting $K$ values in response to observed trends in rating changes. This method increases $K$ during noticeable upward or downward shifts in ratings and reduces it otherwise. We present a computationally efficient implementation of this idea and validate its superiority over existing $K$ adjustment strategies through simulation studies. Additionally, we describe the implementation of this adaptive $K$ model in a widely-used digital learning platform, Math Garden, which leverages both accuracy and response time in its assessments. By successfully integrating speed and precision, this innovative implementation enhances the effectiveness of digital adaptive learning environments

Introduction: Attitudes about COVID-19 relate to cognitions, feelings, and behaviors regarding the pandemic and vaccination, as well as other factors, such as demographic characteristics, and health- related information. This research uses the Causal Attitude Network (CAN) model to measure attitudes and acceptance of COVID-19 vaccination among 1385 Indonesian people from 15 cities. Methods: Data was obtained from instruments that made in the Netherlands and adapted to the Indonesian language and culture. This research integrates psychometrics with network analysis, an advanced implementation of the field of Statistics to reveal the interaction between psychological factors that shape people's attitudes towards COVID-19 and vaccination in Indonesia. Data analysis used JASP, an open-source statistical analysis software. Results: From this research, it was found that attitude elements regarding trust in vaccine development and awareness of the importance of vaccines in Indonesian society have a high influence on other attitude elements. Attitude elements regarding the habit of wearing masks and awareness about the importance of the COVID-19 vaccine are the attitude elements that have the highest impact on changing other attitude elements. Conclusion: Two attitude elements, trust and awareness, most influence other attitude elements. Trust in the development of the COVID-19 vaccine is related to trust in the experts developing it. In other words, increasing public confidence in the development of a science-appropriate COVID-19 vaccine will be in line with increasing public trust in COVID-19 vaccine developers, and vice versa.

Explanations of polarization often rely on one of the three mechanisms: homophily, bounded confidence, and community-based interactions. Models based on these mechanisms consider the lack of interactions as the main cause of polarization. Given the increasing connectivity in modern society, this explanation of polarization may be insufficient. We aim to show that in involvement-based models, society becomes more polarized as its connectedness increases. To this end, we propose a minimal voter-type model (called I-voter) that incorporates involvement as a key mechanism in opinion formation and study its dependence on network connectivity. We describe the steady-state behaviour of the model analytically, at the mean-field and the moment-hierarchy levels and stress the generality of our findings by considering various extensions and different network topologies.

Analogy-making lies at the heart of human cognition. Adults solve analogies such as \textit{Horse belongs to stable like chicken belongs to ...?} by mapping relations (\textit{kept in}) and answering \textit{chicken coop}. In contrast, children often use association, e.g., answering \textit{egg}. This paper investigates whether large language models (LLMs) solve verbal analogies in A:B::C:? form using associations, similar to what children do. We use verbal analogies extracted from an online adaptive learning environment, where 14,002 7-12 year-olds from the Netherlands solved 622 analogies in Dutch. The six tested Dutch monolingual and multilingual LLMs performed around the same level as children, with MGPT performing worst, around the 7-year-old level, and XLM-V and GPT-3 the best, slightly above the 11-year-old level. However, when we control for associative processes this picture changes and each model's performance level drops 1-2 years. Further experiments demonstrate that associative processes often underlie correctly solved analogies. We conclude that the LLMs we tested indeed tend to solve verbal analogies by association with C like children do.

BACKGROUND:The complex interactions between an individual's drinking behavior and their social environment is crucial but understudied, particularly in mature adult populations. Our aim is to unravel these complexities by investigating how personal drinking patterns are related to those of one's social environment over time, and what the interplay is with personal factors such as occupational prestige and smoking behavior. METHOD:The present study adopts an innovative graphical autoregressive (GVAR) panel network modeling approach to investigate the dynamics between personal drinking habits and social environmental factors, utilizing a comprehensive longitudinal dataset from the Framingham Heart Study with a large sample of predominantly mature adults (N = 1719-5718) connected within a social network. We explored both temporal and contemporaneous associations between individuals' drinking habits (self-reported), smoking behavior (self-reported), perceived job prestige (Treiman prestige score), and the drinking behaviors of their social environment. The latter consists of the proportion of abstaining, moderate drinking, and heavy drinking social connections of each subject. RESULTS:Our findings reveal significant associations between participants' behavior and that of their peers, with reciprocal interactions, substantiating the importance of the influence of one's social network for mature individuals. We found dynamic, reciprocal associations between an individual's drinking behavior and that of their peers, with periods of increased or decreased drinking correlating with increased connections to heavy drinkers or abstainers, respectively. In addition, when individuals drink more than usual, they also tend to consume more cigarettes, and vice versa. CONCLUSIONS:The reciprocal feedback loops identified between an individual's drinking behavior and their social environment highlight the crucial role of social influences in shaping drinking behavior, including among older people. This emphasizes the need to consider social elements in the development of future theories, models, and interventions aimed at addressing problematic alcohol consumption in this vulnerable population.

The Elo rating system (ERS), an intuitive and computationally efficient algorithm, offers a means to effectively update estimates of item difficulties and learner abilities as they evolve. This method proves to be highly advantageous in online learning environments. Computerized adaptive practice (CAP) endeavors to present learners with items that are well-suited to their individual ability levels, with the ultimate goal of enhancing motivation and optimizing learning outcomes. The objective of this paper is to outline common challenges that arise in an Elo-based CAP system and to present the psychometric enhancements implemented in the Prowise Learn environments to address these concerns. More specifically, we focus on three main aspects; 1) the development of a new scoring rule balancing response time and accuracy, 2) a way to fix the item scale to deal with item drift, and 3) an improved adaptive K-factor algorithm to speed up convergence in estimation. Using data from the Prowise Learn environment, analyses were done to illustrate the effect of the enhancements. Results show that these enhancements result in more dynamic tracking of the ratings, solve the issue of item drift, and capture the speed-accuracy trade-off more accurately.