Multiple roles for executive control in belief-desire reasoning:distinct neural networks are recruited for self perspective inhibition and complexity of reasoning

Belief-desire reasoning is a core component of 'Theory of Mind' (ToM), which can be used to explain and predict the behaviour of agents. Neuroimaging studies reliably identify a network of brain regions comprising a 'standard' network for ToM, including temporoparietal junction and medial prefrontal cortex. Whilst considerable experimental evidence suggests that executive control (EC) may support a functioning ToM, co-ordination of neural systems for ToM and EC is poorly understood. We report here use of a novel task in which psychologically relevant ToM parameters (true versus false belief; approach versus avoidance desire) were manipulated orthogonally. The valence of these parameters not only modulated brain activity in the 'standard' ToM network but also in EC regions. Varying the valence of both beliefs and desires recruits anterior cingulate cortex, suggesting a shared inhibitory component associated with negatively valenced mental state concepts. Varying the valence of beliefs additionally draws on ventrolateral prefrontal cortex, reflecting the need to inhibit self perspective. These data provide the first evidence that separate functional and neural systems for EC may be recruited in the service of different aspects of ToM.

On the completeness of the universal knowledge-belief space

Meier (2008) shows that the universal knowledge-belief space exists. However, besides the universality there is an other important property might be imposed on knowledge-belief spaces, inherited also from type spaces, the completeness. In this paper we introduce the notion of complete knowledge-belief space, and demonstrate that the universal knowledge-belief space is not complete, that is, some subjective beliefs (probability measures) on the universal knowledge-belief space are not knowledge-belief types.

A bizonyosságfüggvények elméletének alkalmazása a pénzügyi kimutatások ellenőrzésében (The application of the theory of belief functions in the audit of financial statements)

A könyvvizsgálati kockázat a téves auditjelentés kiadásának kockázata olyan esetekben, amikor a beszámoló lényeges hibás állítást tartalmaz. Ez a kockázat indirekt módon a hitelintézetek és pénzügyi vállalkozások működésében is megjelenik azokban az esetekben, amikor a lényeges hibás állítást a finanszírozott vállalkozás auditált beszámolója tartalmazza, amelynek az alapján finanszírozási döntést hoznak, vagy a finanszírozás folytatásáról a beszámolóban szereplő, hibás információkból számított hitelkovenánsok alapján döntenek. A könyvvizsgálat kockázatában a vizsgált gazdálkodó üzleti kockázatai tükröződnek vissza, ezért a kockázat felmérése és az ellenőrzés ennek alapján való megtervezése, majd végrehajtása kulcsfontosságú. Jelen tanulmány – kapcsolódva a Hitelintézeti Szemle 2011. évi 4. számához – szintén a kockázat és bizonytalanság témakörét tárgyalja, pontosabban ennek egy gyakorlati vetületét: a bizonyosságfüggvények (belief functions) alkalmazását a könyvvizsgálatban; mindezt a teljesség és a tankönyvszerű rendszerfelépítés igénye nélkül. A módszer ugyanis hazánkban szinte ismeretlen, nemzetközi viszonylatban viszont empirikus kutatásban is rámutattak már az alkalmazás lehetséges előnyeire a hagyományos valószínűségelméleten alapuló számszerű kockázatbecslésekkel szemben. Eszerint a bizonyosságfüggvények jobban reprezentálják a könyvvizsgálóknak a kockázatról alkotott képét, mint a valószínűségek, mert – szemben a hagyományos modellel – nem két, hanem három állapotot kezelnek: a pozitív bizonyíték létezését, a negatív bizonyíték létezését és a bizonyíték hiányának esetét. _______ Audit risk is the risk that the auditor expresses an inappropriate audit opinion when the fi nancial statements are materially misstated. This kind of risk indirectly appears in the fi nancial statements of fi nancial institutions, when the material misstatement is in the fi nanced entity’s statements that serve as a basis for lending decisions or when the decision is made based upon credit covenants calculated from misstated information. The risks of the audit process refl ect the business risks of the auditee, so the assessment of risks, and further the planning and performance of the audit based on it is of key importance. The current study – connecting to No 4 2011 of Hitelintézeti Szemle – also discusses the topic of risk and uncertainty, or to be more precise a practical implementation of the aforementioned: the application of belief functions in the fi eld of external audit. All this without the aim of achieving completeness or textbook-like scrutiny in building up the theory. While the formalism is virtually unknown in Hungary, on the international scene empirical studies pointed out the possible advantages of the application of the method in contrast to risk assessments based on the traditional theory of probability. Accordingly, belief functions provide a better representation of auditors’ perception of risk, as in contrast to the traditional model, belief functions deal with three rather than two states: the existence of supportive evidence, that of negative evidence and the lack of evidence.

On the completeness of the universal knowledge-belief space: A technical note

Meier (2008) shows that the universal knowledge-belief space exists. However, besides the universality there is an other important property might be imposed on knowledge- belief spaces, inherited also from type spaces, the completeness. In this paper we introduce the notion of complete knowledge-belief space, and demonstrate that the universal knowledge-belief space is not complete, that is, some subjective beliefs (probability measures) on the universal knowledge-belief space are not knowledge-belief types.

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Inventing indigenous religious belief and practice within the spaces of Ghanaian Pentecostalism : the Mame Wata healing churches of Half Assini

Goddesses in African religions are spirits that affect humans and demand reverence from them. They are also embodiments of ideas that African people have about women, their powers and their roles in society. This study focused on Mame Wata, a goddess in Half Assini, an Nzema-speaking coastal community in western Ghana. It sought to resolve a paradox, that is, the fact that, the goddess is at the center of a Pentecostalist tradition even though traditional Pentecostalism in Ghana views her as an agent of the devil. The study involved fieldwork in this community of the goddess's female worshippers led by Agyimah, a charismatic man, and an agent of the goddess. The study interpreted the goddess as a post-colonial invented symbol personifying both pre-colonial and emerging ideas about female power. Findings from the study also show that through Mame Wata the followers celebrate the spirituality of the female.

Industrial fault detection and diagnosis using Bayesian belief network

Rapid development in industry have contributed to more complex systems that are prone to failure. In applications where the presence of faults may lead to premature failure, fault detection and diagnostics tools are often implemented. The goal of this research is to improve the diagnostic ability of existing FDD methods. Kernel Principal Component Analysis has good fault detection capability, however it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing. Hence, KPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The diagnosis phase was done in both qualitative and quantitative manner. In qualitative mode, a networked-base causality analysis method was developed to show the causal effect between the most contributing variables in occurrence of the fault. In order to have more quantitative diagnosis, a Bayesian network was constructed to analyze the problem in probabilistic perspective.

Constructs of health belief and disabling distal upper limb pain

Peer reviewed

Religious Education and Freedom of Religion and Belief

What opportunities and challenges are presented to religious education across the globe by the basic human right of freedom of religion and belief? To what extent does religious education facilitate or inhibit ‘freedom of religion’ in schools? What contribution can religious education make to freedom in the modern world? This volume provides answers to these and related questions by drawing together a selection of the papers delivered at the seventeenth session of the International Seminar on Religious Education and Values held in Ottawa in 2010. These reflections from international scholars, drawing upon historical, theoretical and empirical perspectives, provide insights into the development of religious education in a range of national contexts, from Europe to Canada and South Africa, as well as illuminating possible future directions for the subject.

Belief and Bounded Rationality

Predictive accounts of belief ascription, either following the principle of charity or Dennett's intentional stance, have proved popular recently. However, such accounts require us first to treat agents as perfectly rational agents and then revise this assumption as appropriate. I argue that such downwards revision is no easy task and that several proposed accounts are not satisfactory. I propose a way of characterising agent's belief states which shares Dennett's approach but avoids treating agents as perfectly rational, and develop a formal account in terms of fan models.

Belief in a just world and secondary victimization : The role of adolescent deviant behavior

Belief in a Just World research found evidence that one feels threatened whenever one witnesses an innocent victim suffering, often resorting to secondary victimization to neutralize the observed injustice. However, literature has neglected the explanatory power of adolescent deviant behavior in victimization processes. This study (n = 284 students) aims to determine the impact of the adolescents' deviant behavior, BJW and victim's innocence on secondary victimization. Additionally, we analyzed juvenile deviant behavior's impact on victim identification. Juveniles who committed more deviant behaviors identified less with the victim than those with lower deviance levels. The interaction effects show that juveniles who are strong just world believers and have higher delinquency engaged significantly more in secondary victimization when confronted with an innocent victim. These results clarify the role played by adolescent deviant behavior and BJW in secondary victimization judgments regarding situations with innocent and non-innocent victims.