Latent Causes Research Articles

The Ubiquity of Time in Latent-cause Inference.

Humans have an outstanding ability to generalize from past experiences, which requires parsing continuously experienced events into discrete, coherent units, and relating them to similar past experiences. Time is a key element in this process; however, how temporal information is used in generalization remains unclear. Latent-cause inference provides a Bayesian framework for clustering experiences, by building a world model in which related experiences are generated by a shared cause. Here, we examine how temporal information is used in latent-cause inference, using a novel task in which participants see "microbe" stimuli and explicitly report the latent cause ("strain") they infer for each microbe. We show that humans incorporate time in their inference of latent causes, such that recently inferred latent causes are more likely to be inferred again. In particular, a "persistent" model, in which the latent cause inferred for one observation has a fixed probability of continuing to cause the next observation, explains the data significantly better than two other time-sensitive models, although extensive individual differences exist. We show that our task and this model have good psychometric properties, highlighting their potential use for quantifying individual differences in computational psychiatry or in neuroimaging studies.

Computational processes of simultaneous learning of stochasticity and volatility in humans

Making adaptive decisions requires predicting outcomes, and this in turn requires adapting to uncertain environments. This study explores computational challenges in distinguishing two types of noise influencing predictions: volatility and stochasticity. Volatility refers to diffusion noise in latent causes, requiring a higher learning rate, while stochasticity introduces moment-to-moment observation noise and reduces learning rate. Dissociating these effects is challenging as both increase the variance of observations. Previous research examined these factors mostly separately, but it remains unclear whether and how humans dissociate them when they are played off against one another. In two large-scale experiments, through a behavioral prediction task and computational modeling, we report evidence of humans dissociating volatility and stochasticity solely based on their observations. We observed contrasting effects of volatility and stochasticity on learning rates, consistent with statistical principles. These results are consistent with a computational model that estimates volatility and stochasticity by balancing their dueling effects.

Optimizing Public Health through Community Service: Addressing Stunting and Open Defecation in Sidoarjo Regency

Community service in health institutions needs to be guided by six pillars of health transformation, namely to overcome five priority diseases: tuberculosis, stunting, MCH, infectious diseases and non-communicable diseases. One of their focuses is Sidoarjo Regency, where they work closely with the Regional Government to improve public health. Some of the main problems in Sidoarjo Regency include the high rate of stunting and the practice of open defecation (ODF). There is a relationship between stunting and environmental sanitation, especially open defecation. Solutions to overcome this problem include the provision of healthy latrines to the community, increasing the capacity of Posyandu cadres, nutrition management, and promoting clean and healthy living behaviors. This community service activity is based on previous research relevant to stunting and ODF problems. The goal is to reduce stunting rates in five stunting locus villages, increase knowledge about clean and healthy living behaviors, and build 100 healthy family latrines. The method used is Theory, Practice, Follow-up (TPT) which involves training, field practice and advanced mentoring. Conclusion: with the construction of healthy latrines, there has been a decrease in the incidence of diarhe in children. Diarrhea in children is one of the latent causes of stunting rates. By implementing this strategy, it is hoped that a significant increase will be achieved in preventing and reducing stunting rates in Sidoarjo Regency

A qualitative study on apparent and latent contributing factors to driving errors in Iran

Road traffic crashes (RTCs) disproportionately impact low and middle-income countries (LMICs). Current interventions in LMICs primarily target road user behavior, neglecting systemic issues and casting drivers as mere contributors. Given Iran’s unique cultural, financial, and social intricacies, this study aims to explore the latent causes of RTCs, prioritizing drivers’ experiences and encompassing insights from various traffic system components. Applying a qualitative approach, data were collected through 46 semi-structured interviews with 38 participants, including drivers and experts from traffic-related organizations. Content analysis identified ten themes as contributing to driving errors. Direct factors included fatigue influenced by economic and occupational conditions, distraction from internal and external sources, reckless behaviors influenced by traffic culture and environment, and Inadequate driving skills due to training. Macro-scale challenges in road infrastructure, vehicle quality, education, and accident investigations were highlighted. The lack of a centralized traffic safety authority and a predominant focus on penalizing drivers overshadowed systemic issues. The study offers valuable insights into the complex interplay of factors contributing to driving errors in LMICs, advocating for a paradigm shift towards holistic, systemic interventions beyond individual driver behavior and challenging the conventional blame-centric view associated with driving errors.

Climate Research Contributions in Occupational Safety

Objective: This work aimed to identify the additional contributions of the Climate Survey to the Safety area of some companies. Theoretical reference: As a theoretical framework, different questionnaire models were used to research the perceptions of the workforce. Method: A case study was used as a method, whose data collection instrument was a climate questionnaire, constructed from bibliographic research in the ISI Web of Science and Scopus databases, applied to a universe of 455 workers of an offshore organization. Results and Conclusion: Despite being strongly linked to the company culture, the Climate Survey revealed factors underlying risk behaviors, the scope and systemic nature of failures, proving to be an effective diagnostic tool for safety management. Search Implications: This study found that cost reduction policies and focus on financial results tend to reduce organizational investments, leading many companies to adopt reactive management models. And by restricting safety actions to the occurrence of accidents, they ignore unrecorded risks, their scope, latent causes and their systemic nature. Originality/value: This case study is original and assumes an inestimable value due to its scope.

Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction.

Latent-cause inference is the process of identifying features of the environment that have caused an outcome. This problem is especially important in social settings where individuals may not make equal contributions to the outcomes they achieve together. Here, we designed a novel task in which participants inferred which of two characters was more likely to have been responsible for outcomes achieved by working together. Using computational modeling, univariate and multivariate analysis of human fMRI, and continuous theta-burst stimulation, we identified two brain regions that solved the task. Notably, as each outcome occurred, it was possible to decode the inference of its cause (the responsible character) from hippocampal activity. Activity in dorsomedial prefrontal cortex (dmPFC) updated estimates of association between cause-responsible character-and the outcome. Disruption of dmPFC activity impaired participants' ability to update their estimate as a function of inferred responsibility but spared their ability to infer responsibility.

Reconciling shared versus context-specific information in a neural network model of latent causes

It has been proposed that, when processing a stream of events, humans divide their experiences in terms of inferred latent causes (LCs) to support context-dependent learning. However, when shared structure is present across contexts, it is still unclear how the “splitting” of LCs and learning of shared structure can be simultaneously achieved. Here, we present the Latent Cause Network (LCNet), a neural network model of LC inference. Through learning, it naturally stores structure that is shared across tasks in the network weights. Additionally, it represents context-specific structure using a context module, controlled by a Bayesian nonparametric inference algorithm, which assigns a unique context vector for each inferred LC. Across three simulations, we found that LCNet could (1) extract shared structure across LCs in a function learning task while avoiding catastrophic interference, (2) capture human data on curriculum effects in schema learning, and (3) infer the underlying event structure when processing naturalistic videos of daily events. Overall, these results demonstrate a computationally feasible approach to reconciling shared structure and context-specific structure in a model of LCs that is scalable from laboratory experiment settings to naturalistic settings.

Leveraging Directed Causal Discovery to Detect Latent Common Causes in Cause-Effect Pairs.

The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two variables from observational data have been proposed. However, most of these deal only with purely directed causal relationships and cannot detect latent common causes. Here, we devise a general heuristic which takes a causal discovery algorithm that can only distinguish purely directed causal relations and modifies it to also detect latent common causes. We apply our method to two directed causal discovery algorithms, the information geometric causal inference (IGCI) of (Daniusis et al., 2010) and the kernel conditional deviance for causal inference of (Mitrovic et al., 2018), and extensively test on synthetic data-detecting latent common causes in additive, multiplicative and complex noise regimes-and on real data, where we are able to detect known common causes. In addition to detecting latent common causes, our experiments demonstrate that both the modified algorithms preserve the performance of the original in distinguishing directed causal relations.

Investigation of Unexplained Cardiac Arrest: Phenotyping and Genetic Testing.

Unexplained cardiac arrest (UCA) is a working diagnosis that should be replaced by a final diagnosis once evaluation is completed. Complete evaluation of UCA should include high-yield tests like cardiac magnetic resonance imaging, exercise treadmill test, and sodium-channel blocker challenge to identify latent causes of UCA. If no clear etiology is revealed after complete evaluation, idiopathic ventricular fibrillation may be diagnosed, and the strength of its diagnosis can be divided into definitive, probable, and possible based on the number of high-yield tests performed. Care should be provided by a multidisciplinary team with expertise in this area.

Increased syphilis testing and detection of late latent syphilis among women after switching from risk-based to opt-out testing strategy in an urban Australian sexual health clinic: a retrospective observational study

The Melbourne Sexual Health Centre (MSHC) implemented an opt-out syphilis test for women in December 2017. We aimed to examine the differences in syphilis testing uptake and confirmed syphilis cases among women after switching from risk-based to opt-out testing strategies. This was a retrospective study examining all women attending the MSHC for the first time in periods of risk-based testing (2015-2017) and opt-out testing (2018-2020). We calculated the proportion of women who tested for syphilis and the proportion of women with confirmed syphilis in each period. A chi-square test was performed to determine the differences in proportion between the risk-based testing and opt-out periods. A total of 27,481 women (i.e. 13,059 in the risk-based testing period and 14,422 in the opt-out period) were included in the final analysis, and the mean age was 26.8 years (standard deviation=6.9). The proportion of women who were tested for syphilis at their first consultation increased from 52.8% (6890/13,059) in the risk-based testing period to 67.4% (9725/14,422) in the opt-out period (p<0.0001). Syphilis positivity did not differ between the two periods (0.48% [33/6890] vs 0.71% [69/9725], p=0.061) but late latent causes increased from 36.4% [12/33] to 60.9% [42/69] (p=0.033). The opt-out testing strategy increased syphilis testing among women with increased detection of asymptomatic late latent syphilis. The opt-out syphilis testing strategy is beneficial in sexual health services. Health education and awareness may be required to improve syphilis testing uptake. National Health and Medical Research Council.

Key residential construction defects: a framework for their identification and correlated causes

Purpose Construction defects are one of the primary causes of deficient quality performance and constantly yield project cost and schedule overruns mostly because of rework. This study aims to propose a new method for identifying a bespoke checklist of the most recurrent defects in residential construction work and their correlated causes as a basis for developing a framework for practical improvements in managing defect risks. Design/methodology/approach A literature review was first used to ground the defects within the existing theory and develop a new framework for ranking the key defects involved. This was demonstrated and tested by an analysis of Queensland Building Construction Commission (QBCC) archival data for 42,000 reported defects in new Queensland residential buildings. A questionnaire survey of 427 construction practitioners from 37 trades was then conducted to determine their causes. Findings The developed framework consists of preconditions for defective acts, defective supervision and organizational influences and is found to be suitable for analyzing the QBCC archives to rank the 20 most frequent defects. In the demonstration study, a questionnaire survey identified the highest frequency latent causes of defective acts to be workers taking shortcuts to complete tasks, workers’ lack of skill or knowledge level, challenges and limitations of technical constructability, and incorrect material supply; the failure to correct a known problem and poor supervision for defective supervision; and inadequate employee training, low managerial priority for quality and high time pressure and constraints for organizational influences. Originality/value The method is a new approach to identifying the key defects in residential construction work and their correlated causes for developing bespoke checklists as an aid to in managing defect risks.

The Dynamics of Disinformation: Understanding How Pakistani Social Media Users Navigate and React to Fake News

This article is a critical venture into examining the dissemination of misinformation using social media in Pakistan. This study is focused on the responses of social media users in Pakistan to the plethora of fake news and an attempt to unearth latent causes shaping their attitudes. Based on a mixed-methods mechanism, the research design is comprised of a quantitative survey methodology entailing one hundred and fifty users actively operating social media platforms belonging to the universities in Pakistan and is supported by qualitative comprehensive interviews of thirty active social media enthusiasts. The study is intended to delve into the diffusion of fake news and its effects on internet users and the online community in Pakistan. The patterns and trends of the spread of disinformation, factors responsible for generating responses and shaping behaviours, and the stratagems opted by the social media operators in Pakistan have been detected. The study highlights the significance of calculated and timely interventions to ward off disinformation, urging to step up media literacy among the consumers of social media in Pakistan.

Human generalization of internal representations through prototype learning with goal-directed attention.

The world is overabundant with feature-rich information obscuring the latent causes of experience. How do people approximate the complexities of the external world with simplified internal representations that generalize to novel examples or situations? Theories suggest that internal representations could be determined by decision boundaries that discriminate between alternatives, or by distance measurements against prototypes and individual exemplars. Each provide advantages and drawbacks for generalization. We therefore developed theoretical models that leverage both discriminative and distance components to form internal representations via action-reward feedback. We then developed three latent-state learning tasks to test how humans use goal-oriented discrimination attention and prototypes/exemplar representations. The majority of participants attended to both goal-relevant discriminative features and the covariance of features within a prototype. A minority of participants relied only on the discriminative feature. Behaviour of all participants could be captured by parameterizing a model combining prototype representations with goal-oriented discriminative attention.

The performance of unweighted least squares and regularized unweighted least squares in estimating factor loadings in structural equation modeling

In a confirmatory study, researchers are expected to employ the covariance-based structural equation modeling (CB-SEM). One of the key presumptions when utilizing CB-SEM is that the data is multivariate normal. Nevertheless, a perfect normal distribution is rarely observed in real-life data. To resolve this, the unweighted least square (ULS) is designed to specifically deal with non-normal data in SEM. However, ULS often yields improper solutions like negative, or boundary estimates of unique variances since it considers measurement errors in observed variables. The disturbance in SEM is reflected in unique variance, which is random error due to unreliability or measurement error and reliable variation in the item that indicates unknown latent causes. Consequently, this can generate bias in indicator loadings estimates. As an action to disentangle this issue, the present study proposes the implementation of regularization parameters by adding small positive values to the variance-covariance matrix. The ratio of bias to variance in a model can be improved to obtain the best estimation performance. Pro-Active Monte Carlo simulation was used to produce multivariate non-normal data with designated sample sizes and population characteristics. The data were analyzed using R Programming Environment by employing “psych”, “MASS”, “foreign”, “mvrnonnorm”, “purr”, and “semTools” packages with 1000 replications to produce multivariate non-normal data. Next, the “lavaan” package was used for SEM and regularized SEM analyses. The outcome of this study proves the capability of regularized ULS to improve parameter estimation.

How Central is the PISA Outcomes on Human Development?

Unlike the traditional statistical approaches, which imply the existence of a latent common causes that lead to the emergence and covariance of indicators, network modeling suggests that latent traits emerge due to interactions between indicators. Clearly, such kind of handling of the Program for International Student Assessment (PISA) results and other development indicators better reflect the mutual interactions of the indicators. By this aim, the network pattern of development indicators was revealed and graphically represented, most and least important indicators were detected. In addition, the indicators that have closer association with the PISA results were also detected. United Nations Development Program (UNDP) data were used for the analyzes. The 2015 year of data for sixty-six countries were used and consists of thirteen development indicators. The data were analyzed in R statistical program using “qgraph” package. The results showed that the PISA results are not at the central position compared to other development indicators while it was closely associated with gender inequality, secondary school completion rate and unequal life expectations. Those results were discussed based on the existing literature and some recommendations were given to policymakers and for future research.

An Analysis on Serious Incidents and Accidents in Aviation Using Shell Model

A thorough investigation must be conducted in every aviation incident or accident to find the root cause so that recommendations can be drawn to prevent the same incident or accident from occurring again. This research is a descriptive analysis with quantitative methods. Data were collected from the final reports released by the KNKT from 2015 until 2019. The main causes were grouped using the SHELL Model analysis. The results showed that the most prevalent cause of aviation incidents/accidents in Indonesia was the mismatch between liveware-liveware of which the percentage reached 64%. Furthermore, this mismatch was caused by lack of supervision from management as many as 17 events or 74%; 5 events or 22% were caused by unavailability of rules; and lack of coordination with as many as 1 event or 4%. Within 17 events caused by lack of supervision from management, there were 3 phases of which the events occurred namely the landing phase comprising 59% or 10 events, the cruising phase comprising 18% or 3 events, and the approaching phase consisting of 24% or 4 events. Accidents and serious incidents may occur if latent causes such as lack of supervision from management, unavailability of rules and lack of coordination are not mitigated.

Latent-Cause Extraction Model in Maritime Collision Accidents Using Text Analytics on Korean Maritime Accident Verdicts

Maritime collision accidents occur frequently and result in huge damages. Complex collision accidents are especially associated with worse damages. Complex maritime collision accidents involve other types of accidents barring the main accident, such as fire, explosions, capsizes, sinking, and even casualties. When a maritime accident occurs, the maritime accident verdict covers the surveyed facts from the origin of the accident to the consequences. The survey usually reveals the primary cause of the accident; however, complex causes may remain latent. Therefore, this research aims to apply text analytics to maritime verdicts of collision accident cases to identify the latent causes in complex collision accidents. The proposed methods separated the collected corpus into the training dataset and the test dataset. The word propensity database was extracted from the training dataset and applied to sample verdicts of complex maritime collision accidents in the test dataset. The expected results of this research were words that appeared in only complex maritime accidents with a high propensity for additional categories and the relevant context that explains the latent causes that underlie the complexity of the maritime accident. The conclusion suggested that the latent causes derived should be provided to ships to help prevent future complex collision accidents.

Point-estimating observer models for latent cause detection.

The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several "point-estimating" observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by "committing" to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.

أثر تخريج المناط في المعاملات المالية: دراسة تحليلية مقارنة

هدفت الدراسة إلى بيان الأثر العملي الذي ينتج من عملية الكشف عن العلة في مسائل المعاملات المالية، واستخدم الباحث في سبيل ذلك المنهج التحليلي المقارن، حيث لجأ إلى تتبع أدلة معاملات مالية معينة ومقصودة بالذات، ثم تحليل هذه الأدلة عبر مسالك إجرائية ومنهجية مجردة للوقوف على العلل الكامنة وراء الإباحة أو الحظر لهذه المعاملات، ليصار إلى سحب تلك الأحكام الفقهية المستنتجة على معاملات أخرى تشترك مع المعاملات التي تم الكشف عن علتها وتخريج مناطها، ولا ريب أن نوع المعاملات التي اتخذ الباحث منها مادة هذا البحث هي من المسكوت عن علته، أو ما صرحت النصوص بحكمه دون أدنى إشارة إلى مناطه، حيث درس الباحث معاملة بيع الإنسان ما ليس عنده من خلال الأدلة ذات الصلة بها، ثم قام بتخريج مناط هذه المعاملة والكشف عن علتها، لينتهي إلى الأثر الفقهي المترتب على هذا التخريج، وهو إعطاء حكم هذه المعاملة ذاته إلى معاملات أخرى تشاركها في المناط، كبيوع الغرر والبيع قبل القبض، وبيع المعدوم، أو غير المقدور على تسليمه، وبين عدم وجاهة إدخال معاملات أخرى ضمن هذا النسق نفسه؛ لاختلاف المناط. ومثل ذلك في مسألة الأمر برد الشاة مع صاع من تمر، فبعد الكشف عن مناط هذا الأمر وتخريج علته استطاع الباحث أن يؤسس عليه قانون رد المتلفات الفقهي.

CATASTROPHE NOW

ABSTRACTLiterary scholar Eva Horn's The Future as Catastrophe: Imagining Disaster in the Modern Age investigates why modern Western culture so often imagines its own end. Through insightful readings of modern literature, film, and philosophical and sociological discourses, Horn argues that our hunger for apocalypse narratives—chief among them those about the so‐called Last Man—is rooted in a deep‐seated but diffuse mood of risk and crisis that has been generated by contingent and often imperceptible threats, such as impending nuclear disaster and climate change. Without concrete events to anchor it, this anxiety grows and paralyzes action. Representing these possible catastrophes through fiction provides us with cathartic, vivid, and plausible depictions of discrete events. Imagining such scenarios also serves as a practical means of preparing ourselves individually and collectively for possible threats, in turn helping to make them self‐defeating prophecies. One of Horn's central claims is that fiction's capacity to imagine and capture affect, nuance, and detail offers a uniquely powerful means of thinking about the future. This essay challenges that position by arguing that fiction (though it is, as Donna J. Haraway has quipped, often the best political theory) on its own lacks the capacity for critique that connects imagined future catastrophes to their latent causes in presently catastrophic social conditions. Horn illustrates that imagined future catastrophes often illuminate the latent vulnerabilities of the societies that produced them, but her focus on apocalyptic scenarios reproduces, rather than challenges, cultural patterns that obscure more quotidian and destructive forms of “slow violence” and “slow disaster.” If historians and critics are to achieve the consciousness of catastrophic threats against which Horn seeks to mobilize apocalypse fiction, such narratives must also be folded back into a critical history of the present that asks a more pointed political question: for whom is life already catastrophic?