Belief Theory Research Articles

Belief Representations for Planning with Contact Uncertainty

Belief Representations for Planning with Contact Uncertainty

Comparing Three Theories of the Gender Gap in Information Technology Careers: The Role of Salience Differences

The information technology (IT) field faces a skills shortage. Only 17% of a projected 3.5 million computing job openings are expected to be filled by 2026 (National Association for Women & Information Technology, 2018). Yet the number of women pursuing IT careers continues to decrease—only 19% of IT bachelor’s degrees in 2016 were awarded to women compared to 57% of bachelor’s degrees overall. We compared three theories that could explain this gender gap in the pursuit of IT careers: expectancy-value theory, role congruity theory, and field-specific ability beliefs theory. We find that women and men are similar in their levels of important factors related to career interest, but that two of these factors—technical learning self-efficacy and agentic goals—have increased salience for women. This suggests that some of the gender gap in the IT field could be addressed by placing more focus on developing technical learning self-efficacy in both men and women. While this could help both women and men, it would likely have an outsized effect on the IT career pursuit of women.

Ensaio para uma Teoria da Crença segundo a Concepção Tripartite do Tempo

O presente artigo tem como principal objetivo a tentativa de pensar a possibilidade de uma teoria da crença entendida como dividida em diferentes modalidades temporais: presente, passado e futuro. Tendo em vista os trabalhos desenvolvidos por Tarski e Popper acerca do problema da verdade e teorias pragmáticas da linguagem como a de Habermas e Austin, gostaríamos de testar os limites da concepção de verdade enquanto correspondência em relação a uma concepção tripartite do tempo, ou seja, nos perguntamos acerca da validade daquela concepção de acordo com a diferente modalidade temporal dos enunciados. Em um primeiro momento, analisamos os fatores para que um enunciado se torne objetivo, isto é, válido para além do âmbito subjetivo, de acordo com a concepção usual de tempo, para em seguida apresentar alguns exemplos de uso prático das modalidades temporais dos enunciados na fala cotidiana, e em algumas instituições públicas regidas por regras aceitas coletivamente. Por fim, nos perguntamos se as mudanças de paradigma em relação à nossa concepção de tempo seriam fatores chave no que diz respeito à determinação das regras de validação dos enunciados que o têm como referência, e as consequências práticas desse tipo de enunciado.

Focal points and their implications for Möbius transforms and Dempster-Shafer Theory

Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a much higher computational burden. A lot of work has been done to reduce the time complexity of information fusion with Dempster’s rule, which is a pointwise multiplication of two zeta transforms, and optimal general algorithms have been found to get the complete definition of these transforms. Yet, it is shown in this paper that the zeta transform and its inverse, the Möbius transform, can be exactly simplified, fitting the quantity of information contained in belief functions. Beyond that, this simplification actually works for any function on any partially ordered set. It relies on a new notion that we call focal point and that constitutes the smallest domain on which both the zeta and Möbius transforms can be defined. We demonstrate the interest of these general results for DST, not only for the reduction in complexity of most transformations between belief representations and their fusion, but also for theoretical purposes. Indeed, we provide a new generalization of the conjunctive decomposition of evidence and formulas uncovering how each decomposition weight is tied to the corresponding mass function.

Big Data, Cognitive Biases, Horror Tropes, and Think Tanks

The present introduction is designed to offer a quick walkthrough of the various scholars’ contributions to the present issue of the Journal of Cognitive Historiography. Among the articles included, two are related to our Extended Open Call for Papers on the topic of “Toxic Traditions: Pathological and Maladaptive Beliefs, Biases, and Behaviours throughout Human History”, i.e., an analysis of the maladaptiveness of theistic beliefs in the Anthropocene and a neuroanthropological examination of the ancient cult of Cybele and Attis. Other articles cover the presence of religious-based cognitive biases and logical fallacies in Scottish philosopher Thomas Reid’s works and the neurocognition of the Stoic silent prayer as recorded by Roman philosopher Seneca. An entire section is dedicated to the critical discussion of both method and theory of Seshat: Global History Databank. The issue also includes a thought-provoking and interdisciplinary conversation on horror studies, a précis, a commentary, and four cutting-edge book reviews. The background to the topic of the call for papers is herein available as an appendix.

A STUDY ON THE LEARNING BELIEFS OF CHINESE AS A SECOND FOREIGN LANGUAGE FOR ENGLISH MAJORED STUDENTS OF BANKING UNIVERSITY HO CHI MINH CITY

The research surveyed the learning beliefs of Chinese as a second foreign language for English majored students of Banking University Ho Chi Minh city. Based on the theory of beliefs in foreign language learning by Horwitz (1985), we conducted a questionnaire survey with 177 students. The questionnaire results indicate that: firstly, Chinese is relatively easy to learn; secondly, children have better language learning capacity than adults; third, focus on phonetics, vocabulary and culture, not grammar; fourth, learning Chinese is useful for yourself. Female students focus on phonetics more than male students. Second-year students focus on phonetics more than third-year students, but not more grammar like third-year students. Unlike students from the central region, students from the northern and southern regions said that they must come to China to learn Chinese. The belief that "Chinese language is easy to learn", the confident and proactive attitude of using Chinese language has a positive impact on students' learning results.

PROBABILISTIC STABILITY, AGM REVISION OPERATORS AND MAXIMUM ENTROPY

AbstractSeveral authors have investigated the question of whether canonical logic-based accounts of belief revision, and especially the theory of AGM revision operators, are compatible with the dynamics of Bayesian conditioning. Here we show that Leitgeb’s stability rule for acceptance, which has been offered as a possible solution to the Lottery paradox, allows to bridge AGM revision and Bayesian update: using the stability rule, we prove that AGM revision operators emerge from Bayesian conditioning by an application of the principle of maximum entropy. In situations of information loss, or whenever the agent relies on a qualitative description of her information state—such as a plausibility ranking over hypotheses, or a belief set—the dynamics of AGM belief revision are compatible with Bayesian conditioning; indeed, through the maximum entropy principle, conditioning naturally generates AGM revision operators. This mitigates an impossibility theorem of Lin and Kelly for tracking Bayesian conditioning with AGM revision, and suggests an approach to the compatibility problem that highlights the information loss incurred by acceptance rules in passing from probabilistic to qualitative representations of belief.

Structural equation modeling to detect correlates of childhood vaccination: A moderated mediation analysis.

This study used a health belief theory derived framework and structural equation model to examine moderators, mediators, and direct and indirect predictors of childhood vaccination. A secondary analysis was conducted using data collected from a cross-sectional survey of a random sample of 1599 parents living in urban and rural areas of Mysore district, India. Applying two-stage probability proportionate-to-size sampling, adolescent girls attending 7th through 10th grades in 23 schools were selected to take home a questionnaire to be answered by their parents to primarily assess HPV vaccine intentions. Parents were also asked whether their children had received one dose of Bacillus Calmette-Guérin; three doses of Diphtheria, Pertussis, Tetanus; three doses of oral Polio vaccine; and one dose of Measles vaccine. In addition, parents were asked about their attitudes towards childhood vaccination. Out of the 1599 parents, 52.2% reported that their children had received all the routine vaccines (fully vaccinated); 42.7% reported their children had missed at least one routine vaccine, and 5.2% reported that their children had missed all routine vaccinations. Perceptions about the benefits/facilitators to childhood vaccination significantly predicted the full vaccination rate (standardized regression coefficient (β) = 0.29) directly and mediated the effect of parental education (β = 0.11) and employment (β = -0.06) on the rate of full vaccination. Parental education was significantly associated indirectly with higher rates of full vaccination (β = 0.11). Parental employment was significantly associated indirectly with decreasing rates of full vaccination (β = -0.05). Area of residence moderated the role of religion (β = 0.24) and the 'number of children' in a family (β = 0.33) on parental perceptions about barriers to childhood vaccination. The model to data fit was acceptable (Root Mean Square Error of Approximation = 0.02, 95% CI 0.018 to 0.023; Comparative Fit Index = 0.92; Tucker-Lewis Index = 0.91). Full vaccination rate was relatively low among children in Mysore, especially among parents who were unsure about the benefits of routine vaccination and those with low educational levels. Interventions increasing awareness of the benefits of childhood vaccination that target rural parents with lower levels of education may help increase the rate of full childhood vaccination in India.

Efficient Computation of Conditionals in the Dempster-Shafer Belief Theoretic Framework.

The Dempster-Shafer (DS) belief theory constitutes a powerful framework for modeling and reasoning with a wide variety of uncertainties due to its greater expressiveness and flexibility. As in the Bayesian probability theory, the DS theoretic (DST) conditional plays a pivotal role in DST strategies for evidence updating and fusion. However, a major limitation in employing the DST framework in practical implementations is the absence of an efficient and feasible computational framework to overcome the prohibitive computational burden DST operations entail. The work in this article addresses the pressing need for efficient DST conditional computation via the novel computational model DS-Conditional-All. It requires significantly less time and space complexity for computing the Dempster's conditional and the Fagin-Halpern conditional, the two most widely utilized DST conditional strategies. It also provides deeper insight into the DST conditional itself, and thus acts as a valuable tool for visualizing and analyzing the conditional computation. We provide a thorough analysis and experimental validation of the utility, efficiency, and implementation of the proposed data structure and algorithms. A new computational library, which we refer to as DS-Conditional-One and DS-Conditional-All (DS-COCA), is developed and harnessed in the simulations.

The Epistemic Significance of Agreement with Exceptional Theistic Philosophers

Suppose that you realize that a substantial majority of the most important philosophers of all time agreed with you on some proposition p. Intuitively, you have gained additional evidence in favor of p and you should increase your confidence that p is true. It turns out that a large number of the most important philosophers of all time (in fact, the vast majority, if we consider, as we will, a recent poll conducted with contemporary philosophers) were theists. In this paper, I explore the epistemic significance of agreement with these philosophers with respect to their theistic beliefs. I argue that agreement with such philosophers does provide evidence in favor of theism.

Social cognition and social functioning in patients with amnestic mild cognitive impairment or Alzheimer's dementia.

The aim of the present study was to examine social cognition and social functioning in a group of amnestic mild cognitive impairment (aMCI) and Alzheimer’s dementia (AD) patients. Thirty one people with aMCI, 29 individuals with AD, and 45 healthy older adults participated in the study. Facial expressions of happiness, anger, fear, disgust, and surprise presented in different intensities had to be labelled. Mentalizing was assessed using first‐order belief theory of mind (ToM) stories and everyday social functioning by the Inventory of Interpersonal Situations (IIS), completed by an informant. aMCI patients were impaired in recognizing the emotions anger, disgust, and fear, while AD patients were impaired in recognizing the emotions anger, disgust, and surprise. More importantly, no significant differences between aMCI and AD patients were found on overall emotion recognition. Both the aMCI and AD patients were impaired on the ToM task, but no differences between the aMCI and AD patients were found. On everyday social functioning, only the AD patients showed impairments. No associations between the IIS and ToM were found, but the IIS and emotion perception were significantly correlated. Regression analysis taking all potentially confounding variables into account showed that only mood, but not the social‐cognitive task performance or any other cognitive variable, predicted social functioning. aMCI and AD patients demonstrated impairments in mentalizing and facial emotion perception, and showed decrements in everyday social functioning. Informing caregivers about these deficits may help them to understand deficits in social cognition that may be present already in the MCI stage of Alzheimer’s disease.

Christian Lay Theodicy and The Cancer Experience

Christian Lay Theodicy and The Cancer Experience

Akratic (epistemic) modesty

Theories of epistemic rationality that take disagreement (or other higher-order evidence) seriously tend to be “modest” in a certain sense: they say that there are circumstances in which it is rational to doubt their correctness. Modest views have been criticized on the grounds that they undermine themselves—they’re self-defeating. The standard Self-Defeat Objections depend on principles forbidding epistemically akratic beliefs; but there are good reasons to doubt these principles—even New Rational Reflection, which was designed to allow for certain special cases that are intuitively akratic. On the other hand, if we construct a Self-Defeat Objection without relying on anti-akratic principles, modest principles turn out not to undermine themselves. In the end, modesty should not be seen as a defect in a theory of rational belief.

On the indeterminacy of the representation of beliefs by probabilities

This paper examines the indeterminacy of the representations of beliefs by subjective probabilities in non-expected utility theories of decision making under uncertainty. These theories include the rank-dependent utility models, probabilistically sophisticated choice and probabilistic sophistication based on event exchangeability.

Knowledge before belief

Research on the capacity to understand others' minds has tended to focus on representations of beliefs, which are widely taken to be among the most central and basic theory of mind representations. Representations of knowledge, by contrast, have received comparatively little attention and have often been understood as depending on prior representations of belief. After all, how could one represent someone as knowing something if one does not even represent them as believing it? Drawing on a wide range of methods across cognitive science, we ask whether belief or knowledge is the more basic kind of representation. The evidence indicates that nonhuman primates attribute knowledge but not belief, that knowledge representations arise earlier in human development than belief representations, that the capacity to represent knowledge may remain intact in patient populations even when belief representation is disrupted, that knowledge (but not belief) attributions are likely automatic, and that explicit knowledge attributions are made more quickly than equivalent belief attributions. Critically, the theory of mind representations uncovered by these various methods exhibits a set of signature features clearly indicative of knowledge: they are not modality-specific, they are factive, they are not just true belief, and they allow for representations of egocentric ignorance. We argue that these signature features elucidate the primary function of knowledge representation: facilitating learning from others about the external world. This suggests a new way of understanding theory of mind - one that is focused on understanding others' minds in relation to the actual world, rather than independent from it.

The strength of de Finetti’s coherence theorem

I show that de Finetti’s coherence theorem is equivalent to the Hahn-Banach theorem and discuss some consequences of this result. First, the result unites two aspects of de Finetti’s thought in a nice way: a corollary of the result is that the coherence theorem implies the existence of a fair countable lottery, which de Finetti appealed to in his arguments against countable additivity. Another corollary of the result is the existence of sets that are not Lebesgue measurable. I offer a subjectivist interpretation of this corollary that is concordant with de Finetti’s views. I conclude by pointing out that my result shows that there is a sense in which de Finetti’s theory of subjective probability is necessarily nonconstructive. This raises questions about whether the coherence theorem can underwrite a legitimate theory of rational belief.

The god-faculty dilemma:challenges for reformed epistemology in the light of cognitive science

ABSTRACT Reformed epistemology (RE) involves a view of knowledge of God which Kelly James Clark and Justin Barrett have brought cognitive science to bear on. They argue that the cognitive science of religion (CSR) indicates that we have a ‘god-faculty’, a notion employed by Alvin Plantinga. Plantinga contends that if there is a God, then we have a specialized god-faculty. Clark and Barrett, by contrast, focus on the empirical evidence and point to a different, less specialized faculty. This difference is significant for how RE and CSR relate. The paper argues that a dilemma arises for those who bring RE and CSR together. A choice must be made between two interpretations of the god-faculty. ‘God-faculty 1ʹ is a specialized system for forming theistic beliefs. Findings in CSR indicate that there is no such system. ‘God-faculty 2ʹ is an unrefined tendency to form beliefs in superhuman agents. This thesis has empirical support in CSR. However, this faculty is unable to deliver the epistemic goods needed for the immediate, non-inferential knowledge of God RE describes. This shows that those who combine central contentions in RE with current research in CSR face a dilemma from which it is hard to escape.

Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion

Background and objective: Breast cancer is a frequently diagnosed cancer in women, contributing to significant mortality rates. Death rates are relatively higher in developing nations due to the shortage of early detection amenities and constraints on access to technical advances combating this disease. The only way to diagnose cancer with certainty is through biopsy performed by pathologists. Computer-aided diagnostic algorithms can assist pathologists in being more productive, objective and consistent in the diagnostic process. The focus of this work is to develop a reliable automated breast cancer diagnosis method which can operate in the prevailing clinical environment. Methods:Nuclei overlap and complex structural organisation of the breast tissue in biopsy images make nuclei segmentation, feature extraction and classification challenging. In this work, a nucleus guided transfer learning (NucTraL) methodology is proposed as a simple and affordable breast tumor classification algorithm. The image feature is represented by fusion of local nuclei features that are extracted using convolutional neural network (CNN) models pretrained on the ImageNet database. The nucleus patch extraction strategy used in this work avoids fine segmentation of the nuclei boundary but provides features with good discriminative power for classification. Classification of the fused features into benign and malignant classes is performed using a support vector machine (SVM) classifier. A belief theory based classifier fusion (BCF) strategy is then employed to combine the outputs arising from the different CNN-SVM combinations to improve accuracy further. Results:Evaluation of results is achieved by executing 100 random trials with 70%–30% train to test division on the publicly available BreaKHis dataset. The proposed framework achieved average accuracy of 96.91%, sensitivity of 97.24% and specificity of 96.18%. Conclusion:It is found that the proposed NucTraL+BCF framework outperforms several recent approaches and achieves results comparable to the state-of-the-art methods even without using high computational power. This qualitative framework based on transfer learning can contribute significantly for developing cost effective and low complexity CAD system for breast cancer diagnosis from histopathological images.

Novel weighted ensemble classifier for smartphone based indoor localization

Indoor localization systems have the capability to change the way of providing location-based services in a closed environment. Though there is no agreed-upon technology that works best in indoor, WiFi signal is an important alternative as most of such places are covered by WiFi Access Points (APs). In this paper, the problem of indoor localization is investigated from the perspective of expert systems through applying machine learning techniques. The significant variation of WiFi signal strength with ambient conditions as well as device configuration badly affects the localization accuracy. Thus, the fingerprinting effort required to train a localization system subject to context heterogeneity is huge. The uncertainty in localization performance due to varying contexts is hardly investigated in the literature. Consequently, the main contribution of this paper is to propose a weighted ensemble classifier based on Dempster–Shafer belief theory to efficiently handle context heterogeneity. Here, the context is defined in terms of different smartphone configurations used for training and testing the system as well as temporal variation of signals. The method presented here utilizes the Dempster–Shafer theory of belief functions to calculate the weights of the base learners in the decision of the ensemble. Belief theory is applied here to handle the inherent uncertainty in WiFi signal variations due to heterogeneous context. Real life experiments are conducted for two datasets, JUIndoorLoc and UJIIndoorLoc at different granularity levels. For JUIndoorLoc, with state-of-the-art classifiers, 86–97% accuracy can be achieved for 10-fold cross-validation. However, when the training context differs from the test conditions, accuracy drops to 62–87%. In such a scenario, the proposed weighted ensemble technique is found to achieve almost 98% localization accuracy when RSSIs, mean and variance of RSSIs are considered as features. The technique can lead to an effective expert system for indoor localization at varying granularity levels. Such systems would be beneficial for pervasive indoor positioning applications as no dedicated infrastructure is needed for positioning.

Evidential-Based Approach for Trajectory Planning With Tentacles, for Autonomous Vehicles

Driving is a combination of continuous mental risk assessment, sensory awareness, and judgment, all adapting to extremely variable surrounding conditions. Several research projects are working on autonomous vehicle to robotize this complex task. The work presented in this paper focuses on reactive local trajectory planning in uncertain environment. The environment uncertainty is one of the challenges that we face in trajectory planning. For autonomous vehicle, to be efficient, they need to be able to deal with this kind of uncertainty. In this paper, we show that the theory of belief functions with its ability to distinguish between different types of uncertainty is able to provide significant advantages in the context of trajectory planning. Using the belief functions, we build evidential grid that represents the surrounding environment. To plan a local trajectory, we generate a set of clothoid tentacles in the egocentred reference frame related to the ego-vehicle. Those tentacles represent possible trajectories that consider the current dynamical state of the vehicle and make a smooth variation in the vehicle dynamic variables. Once the representation of the environment and the possible trajectories are generated, an evaluation of each trajectory is carried out according to several criteria and the choice of the trajectory is made using the decision formalism of Markov decision process. To demonstrate the effectiveness of our evidential approach, we apply it to scenarios where ego-vehicle has to make decision in uncertain dynamical environments, using a driving simulator (SCANeR™ Studio).