Causal Processes Research Articles

The role of conflict processing in multisensory perception: behavioural and electroencephalography evidence.

To form coherent multisensory perceptual representations, the brain must solve a causal inference problem: to decide if two sensory cues originated from the same event and should be combined, or if they came from different events and should be processed independently. According to current models of multisensory integration, during this process, the integrated (common cause) and segregated (different causes) internal perceptual models are entertained. In the present study, we propose that the causal inference process involves competition between these alternative perceptual models that engages the brain mechanisms of conflict processing. To test this hypothesis, we conducted two experiments, measuring reaction times (RTs) and electroencephalography, using an audiovisual ventriloquist illusion paradigm with varying degrees of intersensory disparities. Consistent with our hypotheses, incongruent trials led to slower RTs and higher fronto-medial theta power, both indicative of conflict. We also predicted that intermediate disparities would yield slower RTs and higher theta power when compared to congruent stimuli and to large disparities, owing to the steeper competition between causal models. Although this prediction was only validated in the RT study, both experiments displayed the anticipated trend. In conclusion, our findings suggest a potential involvement of the conflict mechanisms in multisensory integration of spatial information. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Coastal Tourism Recovery amid COVID-19: Insights from a Participatory System Dynamics Approach

This study aimed to examine the impacts of COVID-19 on coastal tourism in Nelson Mandela Bay (NMB), South Africa, and propose effective management interventions to enable swift recovery. A participatory system dynamics modelling approach was applied through a qualitative causal mapping processes to support a quantitative model. Multiple stakeholder perspectives were incorporated to gain a holistic understanding of the local impacts. The study revealed that the effects of the pandemic on tourism in NMB were dynamic and accompanied by shifts in governance responses and traveller behaviour. Uncertainty surrounding the rate of recovery in different sectors was observed. Through collaboration with local stakeholders, recovery interventions were identified and tested according to short-to-long-term tourism needs in stages of recovery, revival and growth. The findings highlight the importance of cross-sectoral collaboration in facilitating informed decision-making for sustainable tourism recovery. Moreover, it is encouraged that participatory, multi-stakeholder approaches are adopted to explore the impacts of exogenous factors on the tourism sector, such as those arising from public health, climate, and social–political change. This inclusive and dynamic approach can be used to develop management strategies that are responsive and adaptable to complex and evolving circumstances that can impact the tourism industry.

Biochemical Approach of Acid-Base Disturbances

Background: The current paradigms in arterial blood gas analysis are based on mathematical or physicochemical models that require complex algorithms for interpretation. This leads to avoidance or dislike from health care professionals. Purpose: To formulate a new approach to the analysis and classification of acid base disturbances. Methodology: we conducted a systematic review of the related literature and a paradigm shift for data interpretation. Results: Acid-base disorders can be grouped and managed according to the underlying biochemical processes. Conclusions: This proposal represents a new classification, which groups acid base disorders according to the underlying causal biochemical process. Disorders with an elevated anion gap relate to alterations in cellular respiration (energy/ATP production)- All disturbances with normal anion gap relate to transmembrane ion exchange.

Show Me the Money: Side‐Payments and the Implementation of International Agreements in Federal Systems

Federal systems face specific challenges in fulfilling their international commitments. In cases of shared jurisdiction, the federal government needs the sub-federal level to contribute to the implementation process. Both Canada and the EU have used side-payments to bring and keep on board reluctant and opposing provinces and member states in the implementation of international agreements. However, both cases have experienced the limits of this strategy. This article aims to make a theoretical contribution by identifying the causal conditions and processes that help explain the success and failure of using side-payments to encourage sub-federal support for the implementation of an international agreement. Based on the study of the implementation of the Paris Agreement in Canada and the EU, I develop a two-fold argument. First, side-payments can be an effective tool to persuade sub-federal governments if they are generally interested in contributing to implementation. They do not work for governments of powerful entities that are unwilling to implement. Second, sub-federal governments react to other actors’ conduct. Side-payments can keep reluctant governments of weak entities on board only as long as no alliance of powerful sub-federal entities is formed that resists the implementation of an international agreement.

Integrating utility analysis and workforce strategy research: suggestions for future work

Firms ask their employees to perform a wide variety of tasks, often with daunting time constraints. Research on the firm-level impact of these behaviors – including work in utility analysis (UA) and star employees - has a long and fruitful history, rich with managerial implications. In this paper I comment on research by Joo et al. (2022), who advanced the literature on UA and star employees by highlighting the effects of variance in workforce performance on important firm-level outcomes among 824,924 employees, pooled across 206 samples. In my view this literature can be substantially enhanced by incorporating important moderating and mediating variables that have been identified in the HR strategy literature. In addition, this work can also be improved by developing a better understanding of the causal processes through which star employees’ performance helps to execute strategy, and through the development of better workforce analytics. Advancing this line of research will likely require both qualitative and quantitative research as well as extensive case studies on the identification and implementation of effective workforce strategies.

It takes two to toyi-toyi: one party dominance and opposition party failure in South Africa’s 2019 national election

ABSTRACT Why do dominant parties continue to win elections despite significant governance failures? Scholars of one party democracies tend to locate explanations at the macro-level: manipulation of rules, control over state media, or selective distribution of benefits to supporters. Other scholars emphasize ethnic or religious identities which trump consideration of policy and performance. We employ a multinomial regression model of voter decisions in South Africa’s 2019 general election to explore how the ruling African National Congress managed to secure 58% of the vote amidst a massive corruption scandal and waning public services. We find that dissatisfied government supporters do not ignore poor performance, but must perceive a legitimate alternative amongst the opposition before they switch their vote. Otherwise, they exit the electorate. This allows the governing party to win significant proportions of a diminishing electorate. Thus, decisions about whether to vote are not just a result of resources, mobilization or efficacy, but are also rooted in perceptions of governing and opposition parties. Voter turnout and vote choice are intimately linked, rather than separate causal processes. Moreover, continued one-party dominance may be as much a function of opposition party failure as it is of government control over rules, rents or resources.

Relationship stress, arguments, and sleep quality: A causal process analysis

Using a dyadic intensive longitudinal design, we examined bidirectional links between daily relationship tension and actigraphy-measured sleep quality and whether attachment and post-conflict recovery behavior moderated these effects. A community sample of cohabiting couples ( N = 208 dyads) completed a laboratory-based conflict discussion and a 14-day diary study of arguments and relationship stress. Each night, participants wore actigraphy monitors to assess wakefulness after sleep onset (WASO), an indicator of poor sleep quality. WASO was higher on days of especially high relationship stress but was not associated with next-day relationship stress or arguments. Attachment anxiety and avoidance exacerbated associations between more frequent arguments and higher WASO, and between higher WASO and higher next-day relationship stress. Partner conflict recovery buffered links between higher WASO and more frequent arguments, and between higher relationship stress and higher WASO. This study, one of the few to incorporate behavioral observation and actigraphy-measured sleep quality, identified novel moderators of bidirectional associations between sleep quality and relationship tension, underscoring the significance of dyadic buffering processes on sleep quality.

Should I Stay or Should I Go? The Effect of Allied Brands Negative Publicity on Brand Managers' Decision-Making

This study examines why, and under what conditions, managers of focal brands decide to maintain the alliance with the partner brand when the latter is involved in negative publicity. The literature on brand alliances demonstrates the presence of crisis spillover effects on each partner in the case of negative publicity, suggesting dropping the offending brand to avoid any bad associations related to the negative publicity. However, research evidence on such negative effects in the B2B context is scarce. Drawing on attribution theory and reasoning style, this paper shows through two experiments (Study 1, N = 100; Study 2, N = 160) how the high (vs. low) severity of the allied brand's negative publicity activates an internal causal attribution process, which leads managers of focal brands to drop the offending partner. However, when brand managers are characterized by an analytic (vs. holistic) reasoning style, this path is overturned. Adopting a managerial perspective regarding the implications of negative publicity for B2B relationships, this study provides new insights into how the severity of partner brand negative publicity influences managers' decision-making, via casual attribution. The paper thus offers managers actionable guidelines (i.e., enhancing the analytic reasoning style) to reduce the risk of terminating a profitable alliance.

The memory and identity theory of ICD-11 complex posttraumatic stress disorder.

The 11th version of the International Classification of Diseases (ICD-11) includes complex posttraumatic stress disorder (CPTSD) as a separate diagnostic entity alongside posttraumatic stress disorder (PTSD). ICD-11 CPTSD is defined by six sets of symptoms, three that are shared with PTSD (reexperiencing in the here and now, avoidance, and sense of current threat) and three (affective dysregulation, negative self-concept, and disturbances in relationships) representing pervasive "disturbances in self-organization" (DSO). There is considerable evidence supporting the construct validity of ICD-11 CPTSD, but no theoretical account of its development has thus far been presented. A theory is needed to explain several phenomena that are especially relevant to ICD-11 CPTSD such as the role played by prolonged and repeated trauma exposure, the functional independence between PTSD and DSO symptoms, and diagnostic heterogeneity following trauma exposure. The memory and identity theory of ICD-11 CPTSD states that single and multiple trauma exposure occur in a context of individual vulnerability which interact to give rise to intrusive, sensation-based traumatic memories and negative identities which, together, produce the PTSD and DSO symptoms that define ICD-11 CPTSD. The model emphasizes that the two major and related causal processes of intrusive memories and negative identities exist on a continuum from prereflective experience to full self-awareness. Theoretically derived implications for the assessment and treatment of ICD-11 CPTSD are discussed, as well as areas for future research and model testing. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders

Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc. Real world time series, i.e., large-scale irregular or sparse and intermittent time series, raise significant challenges to existing work attempting to estimate treatment effects. Specifically, the existence of hidden confounders can lead to biased treatment estimates and complicate the causal inference process. In particular, anomaly hidden confounders which exceed the typical range can lead to high variance estimates. Moreover, in continuous time settings with irregular samples, it is challenging to directly handle the dynamics of causality. In this paper, we leverage recent advances in Lipschitz regularization and neural controlled differential equations (CDE) to develop an effective and scalable solution, namely LipCDE, to address the above challenges. LipCDE can directly model the dynamic causal relationships between historical data and outcomes with irregular samples by considering the boundary of hidden confounders given by Lipschitz constrained neural networks. Furthermore, we conduct extensive experiments on both synthetic and real world datasets to demonstrate the effectiveness and scalability of LipCDE.

Do Eyes in the Sky Ensure Peace on the Ground? The Uncertain Contributions of Remote Sensing to Ceasefire Compliance

Abstract In many conflicts, international ceasefire monitors are deployed to mitigate future violence. Increasingly, such monitors use satellite imagery, uncrewed aerial vehicles, and other camera-equipped assets to supplement, and sometimes substitute, human monitoring efforts to document ceasefire violations. To date, we know little about when and how such technology contributes to ceasefire compliance, with scholars offering diverging assessments of the effects. Integrating scholarship on the use of remote sensing in ceasefire monitoring with theories on the causal processes underlying ceasefire monitoring, this analytical essay offers a framework to assess the contribution of remote sensing to ceasefire compliance and illustrates the empirical application of this framework by examining the most technologically advanced ceasefire monitoring mission yet deployed, the Special Monitoring Mission in Ukraine. Focusing on the period prior to the Russian invasion of 2022, our research finds that while the mission’s observational power was expanded, remote sensing technologies ultimately had little effect on modifying conflict party behavior or compliance. While in this case remote sensing technology minimally increased compliance, the study contributes to debates on the use of technology as a conflict management tool, and provides an assessment framework for scholars and for policymakers considering adopting technology in other monitoring contexts.

Toward a causal link between attachment styles and mental health during the COVID-19 pandemic.

Recent research has shown that insecure attachment, especially attachment anxiety, is associated with poor mental health outcomes, especially during the COVID-19 pandemic. Other research suggests that insecure attachment may be linked to nonadherence to social distancing behaviours during the pandemic. The present study aims to examine the causal links between attachment styles (secure, anxious, avoidant), mental health outcomes (depression, anxiety, loneliness) and adherence to social distancing behaviours during the first several months of the UK lockdown (between April and August 2020). We used a nationally representative UK sample (cross-sectional n = 1325; longitudinal n = 950). The data were analysed using state-of-the-art causal discovery and targeted learning algorithms to identify causal processes. The results showed that insecure attachment styles were causally linked to poorer mental health outcomes, mediated by loneliness. Only attachment avoidance was causally linked to nonadherence to social distancing guidelines. Future interventions to improve mental health outcomes should focus on mitigating feelings of loneliness. Limitations include no access to pre-pandemic data and the use of categorical attachment measure. Insecure attachment is a risk factor for poorer mental health outcomes.

Shepherd on reason

ABSTRACT Mary Shepherd assigns reason a central role in her philosophical system, and so to understand that system we must understand her conception of reason. Does she, like Hume, take reason to be a mere matter of factual process that operates over independently contentful representations? Does she, like Descartes, take it to be a process that is intended to track the rational relations among such representations? Or does she, like Kant, take reason to be a structural feature of representations without which they would not have content at all? I argue that Shepherd's understanding of reason is broadly Kantian. Reason is a causal process whereby non-representational sensible qualities are combined in order to form representations of objects as loci of causal powers. These representations represent their objects as the external, continuously and independently existing, causes of the sensible qualities from which they have been constructed, and also as having causal powers to combine with other objects to have other effects. These representations are not only the result of a process of reasoning, but are also constituted by the rational relations into which reason puts them. Reason is both the causal process that forms such representations, and the structure of those representations themselves.

Seven key insights from critical realism and their implications for ecological thinking and action in community psychology.

This article explores some of the possible links between community psychology and critical realism, a relatively new approach to the philosophy of science that has received little attention from community psychologists. Critical realism is presented in relation to seven key insights that can be linked to fundamental tenets of the ecological approach in community psychology. These insights are: (1) A complex reality exists independently of our ideas about it, and this reality is knowable, although imperfectly. (2) Reality is composed of a complex and stratified hierarchy of open systems. (3) Causality is best understood in terms of causal processes that may or may not be directly observable or generalizable; these processes involve complex interactions among generative mechanisms and contextual conditions. (4) Theory and theorizing about causal processes are central to both scientific explanation and practical action. (5) Theory exists at multiple levels of abstraction, ranging from models to metatheory. (6) A diversity of methods can provide evidence in the search for causal processes operating in context. (7) As social scientists, we have an obligation to use social science knowledge to promote human flourishing. Although these insights may be familiar to many community psychologists who adopt an ecological approach to their work, we suggest that clearly articulating these principles can provide more solid foundations for inquiry in the field. We conclude the article by highlighting how critical realism may help to bridge the research-practice gap in community psychology and similar social sciences.

Designing successful agri-environmental schemes: A mechanistic analysis of a collective scheme for eco-system services in the Netherlands

In response to the challenges posed by the fragmentation of habitats and loss of native biodiversity, climate change adaptation and mitigation, diverse agri-environmental measures have been initiated across the European Union (EU) with the aim of fostering agricultural ecosystem service delivery. Previous studies adopting a governance perspective have identified various determinants of successful agri-environmental measures. However, the explanatory value of these studies is limited as the causal processes through which context and scheme design affect implementation success remain grey -boxed. This article uses a mechanism-based approach to uncover the causal processes that underlie actions and interactions within agri-environmental governance arrangements and provides insights into the role the interplay between context and scheme design plays in the successful implementation of agri-environmental measures. The empirical focus is the governance of a successful collective agri-environmental scheme in the Netherlands. In opening the grey box of the causal mechanisms that link contextual and scheme design with their results, the paper applies theory building process tracing methods. Results show that implementation success in the case is explained by the interplay between social learning and trust-building mechanisms. We argue that EU and domestic decision-makers aiming to improve the contribution of agri-environmental measures to climate change adaptation and mitigation must consider the contextual conditions that facilitate increased cooperation between stakeholders and, ultimately, successful implementation of measures.

Implementasi Ajaran Paravidya dan Aparavidya Dalam Memerangi Hoax Sebagai Upaya Membangun Sumber Daya Manusia Hindu Unggul

Social media basically has a big influence on the dimensions of human life, because humans in their various activities cannot be separated from the existence of social media. Social media will have a good impact, if people understand and use it wisely, but it will have a bad impact if they don't use it wisely. This research principally aims to understand how Hindu teachings are to be wise and critical in managing information on social media, bearing in mind that there are two types of knowledge in Hinduism namely Paravidya and Aparavidya. The method used in this study is a qualitative method with a critical discourse analysis approach. The results of this study indicate that Paravidya and Aparavidya knowledge is the main basis that must be understood by Hindus in relation to managing information on social media. By having these two knowledges, humans can have a balance between worldly and spiritual life. In addition, the teachings of Paravidya and Aparavidya are used as a basis for analyzing further information in the process of pratyaksa pramana (namely by direct observation using the five senses), anumana pramana understands the phenomena that occur by examining them through the causal process, then upamana pramana namely through doing the process of comparison or comparison, and pramana's words, namely through instructions from people who have authority about this information. By understanding this, Hindus will be wise in controlling the information they obtain on social media.

Brain Imaging Databases.

The availability of neuroimaging-based databases is helping immensely to understand the brain function in healthy and diseased conditions. This viewpoint highlights the objectives, commonalities, and differences within these existing databases and pointers for researchers to choose a particular database. We introduce a multimodal multidisease database, SWADESH, and its comparison with the existing databases. A futuristic database blueprint is proposed for housing multidisease, multimodal, and longitudinal brain imaging data systematically organized in a matrix form along with neuropsychological assessment scores for the identification of causal disease processes. The information-rich databases will ultimately assist with the systematic identification of prime features linked to causal disease processes, leading to the design of appropriate clinical trials for successful therapeutic interventions.

Causal speech enhancement using dynamical-weighted loss and attention encoder-decoder recurrent neural network.

Speech enhancement (SE) reduces background noise signals in target speech and is applied at the front end in various real-world applications, including robust ASRs and real-time processing in mobile phone communications. SE systems are commonly integrated into mobile phones to increase quality and intelligibility. As a result, a low-latency system is required to operate in real-world applications. On the other hand, these systems need efficient optimization. This research focuses on the single-microphone SE operating in real-time systems with better optimization. We propose a causal data-driven model that uses attention encoder-decoder long short-term memory (LSTM) to estimate the time-frequency mask from a noisy speech in order to make a clean speech for real-time applications that need low-latency causal processing. The encoder-decoder LSTM and a causal attention mechanism are used in the proposed model. Furthermore, a dynamical-weighted (DW) loss function is proposed to improve model learning by varying the weight loss values. Experiments demonstrated that the proposed model consistently improves voice quality, intelligibility, and noise suppression. In the causal processing mode, the LSTM-based estimated suppression time-frequency mask outperforms the baseline model for unseen noise types. The proposed SE improved the STOI by 2.64% (baseline LSTM-IRM), 6.6% (LSTM-KF), 4.18% (DeepXi-KF), and 3.58% (DeepResGRU-KF). In addition, we examine word error rates (WERs) using Google's Automatic Speech Recognition (ASR). The ASR results show that error rates decreased from 46.33% (noisy signals) to 13.11% (proposed) 15.73% (LSTM), and 14.97% (LSTM-KF).

Language Entails Linguistic Relativity.

This commentary addresses the challenge of linking an individual-grounded theory of concepts to a phenomenon that assumes conceptual conventions at population level (linguistic relativity). We distinguish I-concepts (individual, interior, imagistic) from L-concepts (linguistic, labeled, local) and see that quite different causal processes are often conflated under the term "concepts." I argue that the Grounded Cognition Model (GCM) entails linguistic relativity only to the extent that it imports L-concepts into its scope, which it can hardly avoid doing given that practitioners require language to coordinate around their theory and findings. I conclude that what entails linguistic relativity is not the GCM but language itself.

A unified method for assessing the observability of dynamic complex systems

ProblemSystems theory applied to biology and medicine assumes that the complexity of a system can be described by quasi-generic models to predict the behavior of many other similar systems. To this end, the aim of various research works in systems theory is to develop inductive modeling (based on data-intensive analysis) or deductive modeling (based on the deduction of mechanistic principles) to discover patterns and identify plausible correlations between past and present events, or to connect different causal relationships of interacting elements at different scales and compute mathematical predictions. Mathematical principles assume that there are constant and observable universal causal principles that apply to all biological systems. Nowadays, there are no suitable tools to assess the soundness of these universal causal principles, especially considering that organisms not only respond to environmental stimuli (and inherent processes) across multiple scales but also integrate information about and within these scales. This implies an uncontrollable degree of uncertainty. MethodologyA method has been developed to detect the stability of causal processes by evaluating the information contained in the trajectories identified in a phase space. Time series patterns are analyzed using concepts from geometric information theory and persistent homology. In essence, recognizing these patterns in different time periods and evaluating their geometrically integrated information leads to the assessment of causal relationships. With this method, and together with the evaluation of persistent entropy in trajectories in relation to different individual systems, we have developed a method called Φ-S diagram as a complexity measure to recognize when organisms follow causal pathways leading to mechanistic responses. ResultsWe calculated the Φ-S diagram of a deterministic dataset available in the ICU repository to test the method's interpretability. We also calculated the Φ-S diagram of time series from health data available in the same repository. This includes patients' physiological response to sport measured with wearables outside laboratory conditions. We confirmed the mechanistic nature of both datasets in both calculations. In addition, there is evidence that some individuals show a high degree of autonomous response and variability. Therefore, persistent individual variability may limit the ability to observe the cardiac response. In this study, we present the first demonstration of the concept of developing a more robust framework for representing complex biological systems.