Confirmation Bias Research Articles

Gold(I)-Catalyzed Reactions of exo-Glycals with Propargyl Esters toward C-1 Alkenyl Spirocyclopropyl Carbohydrates.

An atom-economic and diazo-free strategy for the construction of novel pseudo anomeric C-1 alkenyl spirocyclopropyl sugars is described. Leveraging the 1,2-migration pathway of propargyl esters under gold(I) catalysis, easily available exo-glycals undergo β-selective alkenylcarbenoid insertion in moderate to excellent yields. Preferential activation of propargyl moieties and concerted [2 + 1] insertion are both favored through ligand choice and electron enrichment of esters. Stereocontrol using conformational bias and rearrangement are also demonstrated.

Semi-Supervised Interior Decoration Style Classification with Contrastive Mutual Learning

Precisely identifying interior decoration styles holds substantial significance in directing interior decoration practices. Nevertheless, constructing accurate models for the automatic classification of interior decoration styles remains challenging due to the scarcity of expert annotations. To address this problem, we propose a novel pseudo-label-guided contrastive mutual learning framework (PCML) for semi-supervised interior decoration style classification by harnessing large amounts of unlabeled data. Specifically, PCML introduces two distinct subnetworks and selectively utilizes the diversified pseudo-labels generated by each for mutual supervision, thereby mitigating the issue of confirmation bias. For labeled images, the inconsistent pseudo-labels generated by the two subnetworks are employed to identify images that are prone to misclassification. We then devise an inconsistency-aware relearning (ICR) regularization model to perform a review training process. For unlabeled images, we introduce a class-aware contrastive learning (CCL) regularization to learn their discriminative feature representations using the corresponding pseudo-labels. Since the use of distinct subnetworks reduces the risk of both models producing identical erroneous pseudo-labels, CCL can reduce the possibility of noise data sampling to enhance the effectiveness of contrastive learning. The performance of PCML is evaluated on five interior decoration style image datasets. For the average AUC, accuracy, sensitivity, specificity, precision, and F1 scores, PCML obtains improvements of 1.67%, 1.72%, 3.65%, 1.0%, 4.61%, and 4.66% in comparison with the state-of-the-art method, demonstrating the superiority of our method.

Problem Behaviours and Relinquishment: Challenges Faced by Clinical Animal Behaviourists When Assessing Fear and Frustration.

Fear and frustration are two emotions thought to frequently contribute to problem behaviour, often leading to relinquishment. Inferring these emotions is challenging as they may present with some similar general signs, but they potentially require different treatment approaches to efficiently address the behaviour of concern. Although behavioural assessment frameworks have been proposed, it is largely unknown how clinical animal behaviourists (CABs) assimilate information about the emotional state of an animal to inform their behavioural assessment. In other fields (such as both in human and veterinary medicine), the use of intuition and gut feelings, without the concurrent use of an assessment framework, can lead to higher rates of error and misdiagnosis. Therefore, this study used semi-structured interviews of ten CABs and qualitative methods to explore the ways they conceptualise, recognise and differentiate fear and frustration in dogs. Although interviewees perceived fear and frustration as negative affective states that lead to changes in an animal's behaviour, there was little consensus on the definition or identification or differentiation of these emotions. The use of a scientific approach (i.e., hypothesis-driven and based on falsification of competing hypotheses) for behavioural assessment was highly variable, with individual assessment processes often characterised by tautology, intuition, circular reasoning and confirmation bias. Assessment was typically based on professional judgment, amalgamating information on interpretation of communicative signals, motivation, learning history, breed, genetics and temperament. Given the lack of consensus in the definition of these states, it is clearly important that authors and clinicians define their interpretation of key concepts, such as fear and frustration, when trying to communicate with others.

Software testing education. Teaching the concept of confirmation bias from a pragmatic perspective of reasoning / La formación en pruebas de software. Enseñanza del concepto de sesgo de confirmación desde la perspectiva del razonamiento pragmático

Wason’s Selection Task, often used by information technology researchers, has traditionally highlighted the perceived inadequacies of software testers from a falsificationist perspective. However, a pragmatic approach to reasoning suggests that human cognition is influenced not only by logic but also by contextual factors. This perspective suggests that testers’ deficiencies may arise from prioritizing information relevance over strict logical adherence. Addressing confirmation bias and contextual influences is crucial for a comprehensive understanding of the issue and for developing new testing strategies. Experiment 1 examined two thematic versions of the Wason task among computer science students, confirming enhanced performance in line with relevance theory. Experiment 2 showed students’ ability to grasp falsification concepts and apply them to abstract tasks. This suggests the Wason test’s value as a didactic tool for illustrating confirmation bias rather than solely assessing reasoning proficiency.

Naive Realism and Its Relationship to Self-Awareness

The current research targets naive realism among university students, self-awareness among university students, and the relationship between naive realism and self-awareness among university students. The researcher suggests the following studies: Like the current research on other samples such as (employees in senior positions, professors, returnees, and displaced persons), and addressing other demographic variables such as (age, education, culture, and social status). It addresses the relationship of naive realism with a number of psychological variables such as (positive work, joint rumination, confirmation bias, cognitive biases, and attentional bias). It addresses the relationship of self-awareness and its relationship with a number of psychological variables such as (reflective thinking, emotional creativity, psychological adjustment, cognitive styles, and parental treatment styles).

Feedback reduces but does not eliminate confirmation bias

Feedback reduces but does not eliminate confirmation bias

Herramientas TIC para la comprensión oral del inglés en diferentes contextos. Fenomenología educativa y aprendizaje significativo

Auditory skills in the teaching-learning of English are a fundamental ability when it comes to learning a second language. In this context, the present exploratory research involves an analysis of several ICT tools and their suitability for teaching auditory skills. Throughout the text, a brief description of each technological tool identified is presented to assess their characteristics and uses in various academic contexts. In methodological terms, this is documentary-based qualitative research, elaborated by means of a survey of documentary sources of proven academic value, under a perspective close to educational phenomenology, interested in describing people's learning experiences without confirmatory theoretical biases. In its general conclusions, the research showed that, in the educational field, the integration of ICT not only facilitates access to authentic and updated materials but also allows for a more personalized and continuous evaluation of students' progress. This does not mean that these tools are infallible or can respond to all the needs and expectations of all learning communities, but beyond this reality, ICTs are an option in constant development.

The role of active and passive music engagement in cognitive development: A systematic review

The aim of this study is to provide a systematic review of the effects of active and passive music engagement on cognitive development. Out of the 66 studies uncovered by the initial search, methods, and conclusions of 12 quantitative, 2 qualitative, and 12 review studies were analyzed. The present review shows active music engagement may improve cognitive skills in near transfer domains, such as verbal and motor skills, as well as auditory discrimination. Improvements in distant transfer domains have also been reported, such as enhanced general IQ, memory skills, numeracy and spatial skills, executive skills, and problem-solving. Benefits for affective and social skill development have also been shown for both active and passive music engagement, while passive engagement is specifically beneficial in clinical settings. However, there are studies that don’t report significant effects. Potential sources of bias are further explored, suggesting that this field may be victim to confirmation and publication bias. Method variety in measurements, sample size, and intervention length, along with the lack of methodological rigor and control groups, prevent us from concluding a direct causal relationship between music engagement and cognitive development. Implications for controlling confounding factors and identifying potential moderator mechanisms are discussed.

Simmering Storm in the East China Sea: Shifting Dynamics in the Great Power Rivalries of East Asia

The East China Sea (ECS) is one of the region’s most significant and oft-times under-addressed potential flashpoints. In this article we take a holistic approach and reexamine the recent actions of East Asia’s two most significant powers, China and Japan, in relation to this important body of maritime space, as well as those of the United States (US), as the incumbent regional hegemon. Specifically, we examine the efficacy of each. This highlights a dynamism in Chinese actions, as well as elucidating how the US and Japan have come to adopt policies that are of dubious efficacy as a means of alleviating or nullifying the escalation of unwanted tensions across the ECS. In so doing, we draw upon two key concepts, immobilism and confirmation bias, that help us to understand why the US and Japan are failing to achieve their assumed objectives of regional stability and the maintenance of the status quo. Ultimately, we argue that a combination of immobilism and confirmation bias in American and Japanese foreign policy, in response to the greater dynamism and adaptability of regional great powers such as China, has led to latent changes in the status quo that risk undermining stability across the ECS.

사회적 연결은 득인가 실인가: 사회적 연결의 이점과 비용에 관한 연구 동향과 과제

Network theory has traditionally focused on social ties as its primary subject of study, investigating patterns of linkages and relationships among actors and the ensuing social structures from various perspectives. This paper reviews the extant literature on both the positive and negative effects of social ties and social networks. It transcends the mainstream research tradition that has predominantly emphasized positive impacts, presenting domestic and international studies that explore the constraints and costs engendered by social ties, including the excessive homogenization they can cause. Specifically, the paper delves into Granovetter’s (1973) “strength of weak ties” theory, Burt’s (1992, 1995) structural hole theory, which addresses both the patterns of connections and relationships between actors and the resultant structures, and Coleman's (1990) concept of network closure, which underscores the overall structure of social networks. In doing so, this paper presents core tenets of these theories and critically evaluates the current state of knowledge as presented in leading domestic and international management and sociology journals. Finally, considering the implications of networks and their social ties for organizational research, alongside the ease with which individuals can now join various networks through the rapid development of online and mobile social networking sites, the paper underscores the importance of focusing on the costs and constraints that actors face when navigating numerous social connections. It also highlights the necessity of being wary of confirmation bias caused by suggestion algorithms embedded in online sites and proposes directions for future research.

Characteristics, Causes and Countermeasures for Victims of Financial Fraud

With the rapid development of Internet technology and the increasing popularity of smart phone applications, the problem of financial fraud has become increasingly prominent, and people of different ages are at risk, which has caused serious impact on society. Victims of financial fraud are usually affected by herd mentality, loss aversion, confirmation bias, etc., while scammers use false publicity to induce investors to make their assets suffer losses. After in-depth analysis of the causes of fraud, this study believes that improving public financial literacy and strengthening financial education become the key to prevent fraud in the whole society. Specifically, college students should deepen their understanding of financial knowledge, and middle-aged and elderly people should improve their awareness of network security and basic knowledge of financial management. Through in-depth research and comprehensive policies, it is expected to build a safe and honest financial environment, ensure the property safety of people of all ages, and promote stable and healthy economic development.

Assessing the Reliability of Machine Learning Explanations in ECG Analysis Through Feature Attribution.

Feature attribution methods stand as a popular approach for explaining the decisions made by convolutional neural networks. Given their nature as local explainability tools, these methods fall short in providing a systematic evaluation of their global meaningfulness. This limitation often gives rise to confirmation bias, where explanations are crafted after the fact. Consequently, we conducted a systematic investigation of feature attribution methods within the realm of electrocardiogram time series, focusing on R-peak, T-wave, and P-wave. Using a simulated dataset with modifications limited to the R-peak and T-wave, we evaluated the performance of various feature attribution techniques across two CNN architectures and explainability frameworks. Extending our analysis to real-world data revealed that, while feature attribution maps effectively highlight significant regions, their clarity is lacking, even under the simulated ideal conditions, resulting in blurry representations.

A delayed diagnosis of hyperthyroidism in a patient with persistent vomiting in the presence of Chiari type 1 malformation.

To present and discuss an uncommon clinical presentation of hyperthyroidism in a female patient with Chiari type 1 malformation. We explore how her medical history influenced the diagnostic process and ultimately contributed to the delayed diagnosis. In this case study, we discuss an unusual presentation of hyperthyroidism in a 35-year-old female with Chiari type 1 malformation. Initially experiencing headaches, tremors, and dizziness, the patient consulted multiple specialists without a clear diagnosis. Later, she developed recurrent vomiting unrelated to food intake, significant weight loss (12 kg), and muscle weakness, leading to her hospitalization. After six months of clinical evaluation with several specialists (neurologists, neurosurgeons, and gastroenterologists), she was,finally, diagnosed with hyperthyroidism by an Internal Medicine physician in another private clinic. Treatment with thiamazole and propranolol led to the improvement of symptoms progressively. This caseemphasizes the vital role of clinical reasoning, crucial problem-solving, and decision-making processes while addressing cognitive biases in medical specialization. Besides, it highlights the need for internist evaluation in outpatient care to ensure comprehensive assessment and prompt specialist referrals if needed. This case accentuates the importance of internist evaluation for comprehensive care and timely specialist referrals. Recognizing unusual presentations, like thyrotoxic vomiting, and addressing cognitive biases, such as confirmation and anchor biases, are crucial for accurate and prompt diagnosis. This approach enhances diagnostic accuracy, minimizing unnecessary tests and costs, and alleviates patient suffering.

Teaching Critical Thinking With Argument Mapping

Critical thinking is the cornerstone of any undergraduate education. One technique for helping students develop critical thinking skills is argument mapping. Argument mapping involves diagraming the arguments for and against a proposition. The map also includes each piece of evidence used to support these arguments. Argument mapping can help students avoid confirmation bias by bringing attention to arguments and evidence which may run counter to their prior beliefs. It also prompts consideration of the strength of arguments and the quality of evidence. We describe how argument mapping can be incorporated in an undergraduate class and then present an example of a classroom exercise. The exercise involves mapping the arguments in a speech by American Secretary of State Colin Powell justifying the use of military force in Iraq before the United Nations Security Council in February 2013.

The influence of psychological factors on investment decision-making: Psychological features of economic relations formation

The article aimed to analyse the influence of psychological factors on the investment decision-making process, which shapes the economic behaviour of investors in Ukraine. The research involved surveys and experimental tasks conducted among investors, with the data analysed using statistical methods to identify the impact of psychological factors on investment decisions. Fear and greed have been found to be most significant emotions affecting investment decisions, whereas fear leads to excessive caution and risk avoidance. In contrast, greed drives investors to take on excessive risks. Overconfidence in one’s knowledge and skills results in an overestimation of opportunities and an increased risk of losses, while confirmation bias leads investors to seek information that supports their previous decisions, ignoring conflicting data. Expert opinions and group behaviour significantly influence investment decisions, particularly under conditions of uncertainty, often resulting in herd behaviour and the formation of economic bubbles. The experimental tasks demonstrated that investors frequently make irrational decisions under the influence of emotions and cognitive biases, even when they can access complete information about risks and opportunities. The survey results also indicated that investors with higher levels of financial literacy are less susceptible to the influence of emotions and cognitive biases, highlighting the importance of financial education in improving the quality of investment decisions. These findings illustrate how psychological factors can distort the investment decision-making process and underscore the necessity of considering these factors when developing investment strategies

Alleviating confirmation bias in perpetually dynamic environments: Continuous unsupervised domain adaptation-based condition monitoring (CUDACoM)

MotivationDeep learning (DL) has revolutionized condition monitoring (CoM) in mechanical systems by reducing manual signal processing. However, DL's industrial integration is limited due to low robustness against distribution shifts. Existing CoM approaches focus on adaptating to one-time distribution shift. We introduce Continuous Unsupervised Domain Adaptation-based CoM (CUDACoM), tackling continuous distribution shifts in systems under perpetually dynamic conditions. MethodologyCUDACoM mitigates confirmation bias, detrimental in long-domain sequences, by introducing two novel strategies (1) Fresh Initialization and (2) In-Domain Pseudo Labeling. Fresh Initialization maintains high model plasticity, while In-Domain pseudo-labeling improves pseudo-label accuracy, enhancing model adaptability. These strategies reduce confirmation bias, crucial for robust self-training, making CUDACoM ideal for long-sequence domain adaptation in perpetually dynamic environments. ResultsCUDACoM outperforms state-of-the-art (SOTA) adversarial and self-training approaches. Validated through two practical case studies: a 200% (RPM) change and gradual sensor degradation across 40 noise levels. These challenging case studies show stronger data shifts than the commonly used standard benchmarking datasets. The second case is especially novel, formulating robustness to noise as a domain adaptation problem. CUDACoM achieved a test accuracy of 0.937 in the RPM case (vs. SOTA's 0.770) and 0.849 in the sensor degradation case (vs. SOTA's 0.751). ImpactThis study addresses the overlooked challenges of employing DL for CoM in perpetually dynamic environments, particularly, the confirmation bias. With CUDACoM's computational efficiency, we provide a practical solution that enhances reliability by facilitating the integration of robust DL into industrial CoM systems.

Dynamic Opinion Formation in Networks: A Multi-Issue and Evidence-Based Approach

In this study, we present a computational model for simulating opinion dynamics within social networks, incorporating cognitive and social psychological principles such as homophily, confirmation bias, and selective exposure. We enhance our model using Dempster–Shafer theory to address uncertainties in belief updating. Mathematical formalism and simulations were performed to derive empirical results from showcasing how this method might be useful for modeling real-world opinion consensus and fragmentation. By constructing a scale-free network, we assign initial opinions and iteratively update them based on neighbor influences and belief masses. Lastly, we examine how the presence of “truth” nodes with high connectivity, used to simulate the influence of objective truth on the network, alters opinions. Our simulations reveal insights into the formation of opinion clusters, the role of cognitive biases, and the impact of uncertainty on belief evolution, providing a robust framework for understanding complex opinion dynamics in social systems.

The replication crisis as mere indicator of two fundamental misalignments: Methodological confirmation bias in hypothesis testing and anthropological oversimplification in theory-building

Psychological research is currently in a phase of transition. The replication crisis has led to the introduction of a number of corrective measures such as preregistration, registered reports, open data and methods in order to make scientific knowledge in psychological science more reliable. In this article, we discuss why these tools remain superficial and provide rather a symptom treatment than a deeper treatment of the causes of the replication crisis. To this end, we address two central misalignments of current psychological research: Confirmation bias, in the sense of overweighting significant, hypothesis-confirming findings over negative ones, and the anthropological oversimplification of the human research subject. We conclude by providing indications of how a paradigm shift in psychological science research and publication practices can help to combat the causes of the replication crisis and poor scientific research practices.

Virtual Category Learning: A Semi-Supervised Learning Method for Dense Prediction With Extremely Limited Labels.

Due to the costliness of labelled data in real-world applications, semi-supervised learning, underpinned by pseudo labelling, is an appealing solution. However, handling confusing samples is nontrivial: discarding valuable confusing samples would compromise the model generalisation while using them for training would exacerbate the issue of confirmation bias caused by the resulting inevitable mislabelling. To solve this problem, this paper proposes to use confusing samples proactively without label correction. Specifically, a Virtual Category (VC) is assigned to each confusing sample in such a way that it can safely contribute to the model optimisation even without a concrete label. This provides an upper bound for inter-class information sharing capacity, which eventually leads to a better embedding space. Extensive experiments on two mainstream dense prediction tasks - semantic segmentation and object detection, demonstrate that the proposed VC learning significantly surpasses the state-of-the-art, especially when only very few labels are available. Our intriguing findings highlight the usage of VC learning in dense vision tasks.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors, i.e., whether testing data is sequentially streamed and whether source model is allowed to be trained with modified loss function. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) protocol, under which we develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. TTAC discovers clusters in both source and target domains and matches the target clusters to the source ones to improve adaptation. When source domain information is strictly absent (i.e., source-free) we further develop an efficient method to infer source domain distributions for anchored clustering. Finally, self-training (ST) has demonstrated great success in learning from unlabeled data and we empirically figure out that applying ST alone to TTT is prone to confirmation bias. Therefore, a more effective TTT approach is introduced by regularizing self-training with anchored clustering, and the improved model is referred to as TTAC++. We demonstrate that, under all TTT protocols, TTAC++ consistently outperforms the state-of-the-art methods on five TTT datasets, including corrupted target domain, selected hard samples, synthetic-to-real adaptation and adversarially attacked target domain. We hope this work will provide a fair benchmarking of TTT methods, and future research should be compared within respective protocols.