Probabilistic reasoning

Background-From an affective neuroscience perspective, our understanding of psychiatric illness may be advanced by neuropsychological test paradigms probing emotional processes. Reversal learning is one such process, whereby subjects must first acquire stimulus/reward and stimulus/punishment associations through trial and error and then reverse them. We sought to determine the specificity of previously demonstrated reversal learning impairments in youths with bipolar disorder (BD) by now comparing BD youths to those with severe mood dysregulation (SMD), major depressive disorder (MDD), anxiety (ANX), and healthy controls.

The increasing demand of World Wide Web raises the need of predicting the user's web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.

We review the applicability of Bayesian networks (BNs) for discovering relations between genes, environment, and disease. By translating probabilistic dependencies among variables into graphical models and vice versa, BNs provide a comprehensible and modular framework for representing complex systems. We first describe the Bayesian network approach and its applicability to understanding the genetic and environmental basis of disease. We then describe a variety of algorithms for learning the structure of a network from observational data. Because of their relevance to real-world applications, the topics of missing data and causal interpretation are emphasized. The BN approach is then exemplified through application to data from a population-based study of bladder cancer in New Hampshire, USA. For didactical purposes, we intentionally keep this example simple. When applied to complete data records, we find only minor differences in the performance and results of different algorithms. Subsequent incorporation of partial records through application of the EM algorithm gives us greater power to detect relations. Allowing for network structures that depart from a strict causal interpretation also enhances our ability to discover complex associations including gene-gene (epistasis) and gene-environment interactions. While BNs are already powerful tools for the genetic dissection of disease and generation of prognostic models, there remain some conceptual and computational challenges. These include the proper handling of continuous variables and unmeasured factors, the explicit incorporation of prior knowledge, and the evaluation and communication of the robustness of substantive conclusions to alternative assumptions and data manifestations.

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Ante las dificultades probatorias del nexo causal en supuestos de responsabilidad civil o patrimonial el Derecho obliga a que el peso de la incertidumbre recaiga en su conjunto sobre uno solo de los sujetos implicados: sobre el agente (posiblemente) dañoso, cuando el juzgador rebaja el estándar ordinario de prueba para afirmar un nexo causal dudoso y ordena la reparación total del daño padecido; o sobre la víctima, cuando el órgano judicial mantiene ese estándar ordinario y niega el nexo causal y con ello la responsabilidad. Es el principio del " todo o nada ". La teoría de la pérdida de oportunidad o " chance " altera este planteamiento, pues distribuye el peso de la incertidumbre causal entre las dos partes implicadas: el agente responde sólo en proporción a la probabilidad de que fuera autor del menoscabo; y, correlativamente, la víctima obtiene una reparación acomodada a la probabilidad de no haber padecido el daño de no haber mediado el hecho lesivo. De este modo se brinda una solución equilibrada que pretende adaptarse a una sensibilidad justicial a la que repugna la liberación del agente (posiblemente) dañoso por las dificultades probatorias, pero también que se le conmine a reparar la totalidad de un daño que pudo no haber causado. La profesora de filosofía del derecho Marina Gascón Abellán y el profesor de derecho administrativo Luis Medina Alcoz debaten en torno al fundamento de esta solución, empleada cada vez con más frecuencia (pero con poco rigor) por nuestros jueces y tribunales. La primera acude a la teoría de la prueba y el segundo a la teoría de la causalidad. Para la primera la doctrina de la pérdida de oportunidad no entraña ningún cambio significativo en los planteamientos causales tradicionales; para justificarla basta asumir que el conocimiento judicial de los hechos debe basarse en juicios razonados de probabilidad y acudir a la figura de la presunción iuris tantum del nexo causal. Para el segundo esa doctrina implica una corrección parcial de los planteamientos causales tradicionales traducida en una complicación de los sistemas de imputación causal; junto al " todo o nada " basado en la afirmación o negación vínculo etiológico opera el " ni todo ni nada " basado en la afirmación de causalidades solamente posibles. Palabras clave: Responsabilidad por daños; causalidad; responsabilidad patrimonial de la Administración; responsabilidad proporcional; teoría de la pérdida de oportunidad; causalidad; prueba; incertidumbre; causalidad probabilística.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.

This study explores intuitive ideas about chance and probability in children aged 4 to 6. For this, we conducted an exploratory study of an instructional process with 23 pre-kindergarten and kindergarten students who had not received prior instruction on the subject. The results show that students comprehensively use elementary probabilistic language and have a first informal knowledge about other notions such as sample space, possibility of occurrence, and comparison of probabilities. We conclude that most of this knowledge (basic notions of probability) depends, to a great extent, on visual support in order to compare probability or possibility of occurrence. In other words, it is confirmed that students at these early ages need to start learning the first probabilistic notions in teaching contexts that let them visualise mathematical ideas in a concrete way, using both everyday objects and manipulative materials, and direct action on these objects. In future studies, it will be necessary to continue exploring new data that contribute to the professional development of Early Childhood Education teachers, in order to refine the planning and management of mathematical practices whose purpose is the progressive acquisition of probabilistic literacy at early ages. Ideas Intuitivas sobre el Azar y la Probabilidad en Niños de 4 a 6 Años RESUMEN Este estudio explora las ideas intuitivas sobre el azar y la probabilidad en niños de 4 a 6 años. Para ello, se ha realizado un estudio exploratorio de un proceso de instrucción con 23 alumnos de pre-kindergarten y kindergarten que no han recibido instrucción previa sobre el tema. Los resultados muestran que los alumnos usan comprensivamente lenguaje probabilístico elemental y poseen un primer conocimiento informal acerca de otras nociones como por ejemplo espacio muestral, posibilidad de ocurrencia y comparación de probabilidades. Se concluye que la mayoría de estos conocimientos (nociones básicas de probabilidad) dependen, en gran medida, del apoyo

Decision making both on individual and organizational level is always accompanied by the search of other's opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.

You yourself, or what is the same, your experience is such ``coin'' that, while you aren't questioned, it rotates all the time in ``free flight''. And only when you answer the question the ``coin'' falls on one of the sides: ``Yes'' or ``No'' with the believability that your experience tells you.

Verbal phrases denoting uncertainty are of two kinds: positive, suggesting the occurrence of a target outcome, and negative, drawing attention to its nonoccurrence (Teigen & Brun, 1995). This directionality is correlated with, but not identical to, high and low p values. Choice of phrase will in turn influence predictions and decisions. A treatment described as having "some possibility" of success will be recommended, as opposed to when it is described as "quite uncertain," even if the probability of cure referred to by these two expressions is judged to be the same (Experiment 1). Individuals who formulate their chances of achieving a successful outcome in positive terms are supposed to make different decisions than individuals who use equivalent, but negatively formulated, phrases . Finally, negative phrases lead to fewer conjunction errors in probabilistic reasoning than do positive phrases (Experiment 4). For instance, a combination of 2 "uncertain" outcomes is readily seen to be "very uncertain." But positive phrases lead to fewer disjunction errors than do negative phrases. Thus verbal probabilistic phrases differ from numerical probabilities not primarily by being more "vague," but by suggesting more clearly the kind of inferences that should be drawn. ᭧

Identifying spatio-temporal synchrony in a complex, interacting and oscillatory coupled-system is a challenge. In particular, the characterization of statistical relationships between environmental or biophysical variables with the multivariate data of pandemic is a difficult process because of the intrinsic variability and non-stationary nature of the time-series in space and time. This paper presents a methodology to address these issues by examining the bivariate relationship between Covid-19 and temperature time-series in the time-localized frequency domain by using Singular Value Decomposition (SVD) and continuous cross-wavelet analysis. First, the dominant spatio-temporal trends are derived by using the eigen decomposition of SVD. The Covid-19 incidence data and the temperature data of the corresponding period are transformed into significant eigen-state vectors for each spatial unit. The Morlet Wavelet transformation is performed to analyse and compare the frequency structure of the dominant trends derived by the SVD. The result provides cross-wavelet transform and wavelet coherence measures in the ranges of time period for the corresponding spatial units. Additionally, wavelet power spectrum and paired wavelet coherence statistics and phase difference are estimated. The result suggests statistically significant coherency at various frequencies providing insight into spatio-temporal dynamics. Moreover, it provides information about the complex conjugate dynamic relationships in terms phases and phase differences.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.

This paper proposes a definition of a fuzzy partition element based on the homomorphism between type-1 fuzzy sets and the three-valued Kleene algebra. A new clustering method based on the C-means algorithm, using the defined partition, is presented in this paper, which will be validated with the traditional iris clustering problem by measuring its petals.

We provide a scheme for the synchronization of two chaotic mobile robots when a mismatch between the parameter values of the systems to be synchronized is present. We have shown how meta-heuristic optimization can be used to adapt the parameters in two coupled systems such that the two systems are synchronized, although their behavior is chaotic and they have started with different initial conditions and parameter settings. The controlled system synchronizes its dynamics with the control signal in the periodic as well as chaotic regimes. The method can be seen also as another way of controlling the chaotic behavior of a coupled system. In the case of coupled chaotic systems, under the interaction between them, their chaotic dynamics can be cooperatively self-organized. A synergistic approach to meta-heuristic optimization search algorithm is developed. To avoid being trapped into local optimum and to enrich the searching behavior, chaotic dynamics is incorporated into the proposed search algorithm. A chaotic Levy flight is firstly incorporated in the proposed search algorithm for efficiently generating new solutions. And secondly, chaotic sequence and a psychology factor of emotion are introduced for move acceptance in the search algorithm. We illustrate the application of the algorithm by estimating the complete parameter vector of a chaotic mobile robot.

As we approach the next millennium, and as our problems become too complex to rely only on one discipline to solve them more effectively, multi-disciplinary approaches in the petroleum industry become more of a necessity than professional curiosity. We will be forced to bring down the walls we have built around classical disciplines such as petroleum engineering, geology, geophysics and geochemistry, or at the very least, make them more permeable. Our data, methodologies and approaches to tackle problems will have to cut across various disciplines. As a result, today's AintegrationB, which is based on integration of results, will have to give way to a new form of integration, that is, integration of disciplines. In addition, to solve our complex problem, one needs to go beyond standard techniques and silicon hardware. The model needs to use several emerging methodologies and soft computing techniques: Expert Systems, Artificial Intelligence, Neural Ž . Ž . Ž . Network, Fuzzy Logic GL , Genetic Algorithm GA , Probabilistic Reasoning PR , and Parallel Processing techniques.

Disasters from explosive volcanic eruptions are infrequent and experience in emergency planning and mitigation for such events remains limited. The need for urgently developing more robust methods for risk assessment and decision making in volcanic crises has become increasingly apparent as world populations continue to expand in areas of active explosive volcanism. Nowhere is this more challenging than at Vesuvius, Italy, with hundreds of thousands of people living on the flanks of one of the most dangerous volcanoes in the world. We describe how a new paradigm, evidence-based volcanology, has been applied in EXPLORIS to contribute to crisis planning and management for when the volcano enters its next state of unrest, as well as in long-term land-use planning. The analytical approach we adopted enumerates and quantifies all the processes and effects of the eruptive hazards of the volcano known to influence risk, a scientific challenge that combines field data on the vulnerability of the built environment and humans in past volcanic disasters with theoretical research on the state of the volcano, and including evidence from the field on previous eruptions as well as numerical simulation modelling of eruptive processes. Formal probabilistic reasoning under uncertainty and a decision analysis approach have provided the basis for the development of an event tree for a future range of eruption types with probability paths and hypothetical casualty outcomes for risk assessment. The most likely future eruption scenarios for emergency planning were derived from the event tree and elaborated upon from the geological and historical record. Modelling the impacts in these scenarios and quantifying the consequences for the circumvesuvian area provide realistic assessments for disaster planning and for showing the potential risk-benefit of mitigation measures, the main one being timely evacuation, but include for consideration protecting buildings against dilute, low dynamic pressure surges, and temporary roof supports in the most vulnerable buildings, as well as hardening infrastructure and lifelines. This innovative work suggests that risk-based methods could have an important role in crisis management at cities on volcanoes and small volcanic islands.

The NAC gene family encodes a large family of plant-specific transcription factors with diverse roles in various developmental processes and stress responses in plants. Creation of genome wide prediction tools for NAC proteins will have a significant impact on gene annotation in rice. In the present study, NACSVM, a tool for computational genome-scale prediction of NAC proteins in rice was developed integrating compositional and evolutionary information of NAC proteins. Initially, support vector machine (SVM)based modules were developed using combinatorial presence of diverse protein features such as traditional amino acid, dipeptide (i+1), tripeptide (i+2), four-parts composition and PSSM and an overall accuracy of 79%, 93%, 93%, 79% and 100% respectively was achieved. Later, two hybrid modules were developed based on amino acid, dipeptide and tripeptide composition, through which an overall accuracy of 83% and 79% was achieved. NACSVM was also evaluated using position-specific iterated-basic local alignment search tool which resulted in a lower accuracy of 50%. In order to benchmark NACSVM , the tool was evaluated using independent data test and cross validation methods. The different statistical analyses carried out revealed that the proposed algorithm is an useful tool for annotating NAC proteins in genome of rice.

A new methodology is developed to analyse existing water quality monitoring networks. This methodology incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy reduction. The work starts with a formulation of a conceptual framework for groundwater quality monitoring to represent the methodology's context. This work presents the development of Bayesian techniques for the assessment of groundwater quality. The primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of all available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes' theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.

Suppose that two competing norms, N1 and N2, can be identified such that a given person’s response can be interpreted as correct according to N1 but incorrect according to N2. Which of these two norms, if any, should one use to interpret such a response? In this paper we seek to address this fundamental problem by studying individual variation in the interpretation of conditionals by establishing individual profiles of the participants based on their case judgments and reflective attitudes. To investigate the participants’ reflective attitudes we introduce a new experimental paradigm called the Scorekeeping Task. As a case study, we identify the participants who follow the Suppositional Theory of conditionals (N1) versus Inferentialism (N2) and investigate to what extent internally consistent competence models can be reconstructed for the participants on this basis. After extensive empirical investigations, an apparent reasoning error with and-to-if inferences was found in one of these two groups. The implications of this case study for debates on the proper role of normative considerations in psychology are discussed.

Most so-called “errors” in probabilistic reasoning are in fact not violations of probability theory. Examples of such “errors” include overconfidence bias, conjunction fallacy, and base-rate neglect. Researchers have relied on a very narrow normative view, and have ignored conceptual distinctions—e.g. single case versus relative frequency—fundamental to probability theory. By recognizing and using these distinctions, however, we can make apparently stable “errors” disappear, reappear, or even invert. I suggest what a reformed understanding of judgments under uncertainty might look like.

There are certain 'hard cases' of weakness of will that seem to occur, indeed to be common, but are very difficult to give a non-paradoxical account of.  It is just not clear how they are possible.  This paper is largely an attempt to get them in focus.  What conditions must such weak-willed actions satisfy?  In what sense must they be intentional, for example?  And must the weak-willed agent thinks his action objectively worse than some alternative action open to him?  And must that alternative action actually be open to him?  The paper concludes by considering the condition that a clear case of weakness of will must be in certain sense non-procrastinatory and this turns out to be particularly difficult to satisfy.  Other explanations, ones that do not postulate weakness of will (though they may involve paradoxes of their own), always seem to be available.

Research on probability judgment has traditionally emphasized that people are susceptible to biases because they rely on ''variable substitution'': the assessment of normative variables is replaced by assessment of heuristic, subjective variables. A recent proposal is that many of these biases may rather derive from constraints on cognitive integration, where the capacity-limited and sequential nature of controlled judgment promotes linear additive integration, in contrast to many integration rules of probability theory (Juslin, Nilsson, & Winman, 2009). A key implication by this theory is that it should be possible to improve peoples' probabilistic reasoning by changing probability problems into logarithm formats that require additive rather than multiplicative integration. Three experiments demonstrate that recasting tasks in a way that allows people to arrive at the answers by additive integration decreases cognitive biases, and while people can rapidly learn to produce the correct answers in an additive formats, they have great difficulty doing so with a multiplicative format.

This paper defends two theses about probabilistic reasoning. First, although modus ponens has a probabilistic analog, modus tollens does not -the fact that a hypothesis says that an observation is very improbable does not entail that the hypothesis is improbable. Second, the evidence relation is essentially comparative; with respect to hypotheses that confer probabilities on observation statements but do not entail them, an observation O may favor one hypothesis H 1 over another hypothesis H 2 , but O cannot be said to confirm or disconfirm H1 without such relativization. These points have serious consequences for the Intelligent Design movement. Even if evolutionary theory entailed that various complex adaptations are very improbable, that would neither disconfirm the theory nor support the hypothesis of intelligent design. For either of these conclusions to follow, an additional question must be answered:

The idea of probabilities has been described as a “Janus-faced” concept, which can be thought of either in terms of frequencies or in terms of subjective confidence. This dualism contributes to debates about the nature of human rationality, and therefore the pedagogical assumptions and goals of education. For this reason, the present chapter explores the evidence regarding how quantitative information is intuitively understood in the human mind over the course of elementary school education.

Skin colour detection is frequently been used for searching people, face detection, pornographic filtering and hand tracking. The presence of skin or non-skin in digital image can be determined by manipulating pixels' colour and/or pixels' texture. The main problem in skin colour detection is to represent the skin colour distribution model that is invariant or least sensitive to changes in illumination condition. Another problem comes from the fact that many objects in the real world may possess almost similar skin-tone colour such as wood, leather, skin-coloured clothing, hair and sand. Moreover, skin colour is different between races and can be different from a person to another, even with people of the same ethnicity. Finally, skin colour will appear a little different when different types of camera are used to capture the object or scene. The objective in this study is to develop a skin colour classifier based on pixel-based using RGB ratio model. The RGB ratio model is a newly proposed method that belongs under the category of an explicitly defined skin region model. This skin classifier was tested with SIdb dataset and two benchmark datasets; UChile and TDSD datasets to measure classifier performance. The performance of skin classifier was measured based on true positive (TF) and false positive (FP) indicator. This newly proposed model was compared with Kovac, Saleh and Swift models. The experimental results showed that the RGB ratio model outperformed all the other models in term of detection rate. The RGB ratio model is able to reduce FP detection that caused by reddish objects colour as well as be able to detect darkened skin and skin covered by shadow.

This paper attempts to explore the possibility of using sound signatures for vehicle detection and classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of horns, random but identifiable back ground noises, continuous high energy noises on the back ground are the different challenges encountered in the data collection. Different features were explored out of which smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency ceptral coefficients extracted from fixed regions around the detected peaks along with the manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to predict categories well.

We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC) and RCGA.

11th International Conference on Soft Computing, Artificial Intelligence and Applications (SCAI 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of artificial intelligence, soft computing. The conference looks for significant contributions to all major fields of the artificial intelligence, soft computing in theoretical and practical aspects. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

This chapter discusses the cognitive mechanisms underlying magical beliefs and practices. We first review empirical studies in developmental psychology that address children’s concepts of magic. In particular, these studies focus on how children come to distinguish between events, entities, and agents that violate our intuitive notions of basic causal laws (e.g., gravity) and those that do not. The second part of the chapter reviews anthropological studies on magic (i.e., on witchcraft, shamanism, animism, etc.). The striking feature of these anthropological data is that adults seem to interpret improbable rather than impossible events as magical. From this anthropological evidence, we then suggest that the current theoretical accounts of magic in developmental psychology and cognitive science of religion fail to tackle the pervasiveness of “probabilistic magic”; as a result, it remains to be elucidated why people resort to magical explanations when faced with merely improbable events. To this end, we propose a new probabilistic account of magic, which predicts that supernatural explanations are triggered every time a complexity drop (i.e., a gap between expected and observed complexity) occurs. Finally, we address the question of knowing how “counterintuitive-magic” and “probabilistic-magic” are respectively instantiated across development and cultures.

Real-time problem diagnosis in large distributed computer systems and networks is a challenging task that requires fast and accurate inferences from potentially huge data volumes. In this paper, we propose a cost-efficient, adaptive diagnostic technique called active probing. Probes are end-to-end test transactions that collect information about the performance of a distributed system. Active probing uses probabilistic reasoning techniques combined with information-theoretic approach, and allows a fast online inference about the current system state via active selection of only a small number of most-informative tests. We demonstrate empirically that the active probing scheme greatly reduces both the number of probes (from 60% to 75% in most of our real-life applications), and the time needed for localizing the problem when compared with non-adaptive (pre-planned) probing schemes. We also provide some theoretical results on the complexity of probe selection, and the effect of "noisy" probes on the accuracy of diagnosis. Finally, we discuss how to model the system's dynamics using Dynamic Bayesian networks, and an efficient approximate approach called sequential multifault; empirical results demonstrate clear advantage of such approaches over "static" techniques that do not handle system's changes.

Recent studies indicate that indicative conditionals like "If people wear masks, the spread of Covid-19 will be diminished" require a probabilistic dependency between their antecedents and consequents to be acceptable (Skovgaard-Olsen et al., 2016). But it is easy to make the slip from this claim to the thesis that indicative conditionals are acceptable only if this probabilistic dependency results from a causal relation between antecedent and consequent. According to Pearl (2009), understanding a causal relation involves multiple, hierarchically organized conceptual dimensions: prediction, intervention, and counterfactual dependence. In a series of experiments, we test the hypothesis that these conceptual dimensions are differentially encoded in indicative and counterfactual conditionals. If this hypothesis holds, then there are limits as to how much of a causal relation is captured by indicative conditionals alone. Our results show that the acceptance of indicative and counterfactual conditionals can become dissociated. Furthermore, it is found that the acceptance of both is needed for accepting a causal relation between two co-occurring events. The implications that these findings have for the hypothesis above, and for recent debates at the intersection of the psychology of reasoning and causal judgment, are critically discussed. Our findings are consistent with viewing indicative conditionals as answering predictive queries requiring evidential relevance (even in the absence of direct causal relations). Counterfactual conditionals in contrast target causal relevance, specifically. Finally, we discuss the implications our results have for the yet unsolved question of how reasoners succeed in constructing causal models from verbal descriptions.

A consistent finding in research on conditional reasoning is
that individuals are more likely to endorse the valid modus ponens
(MP) inference than the equally valid modus tollens (MT)
inference. This pattern holds for both abstract task and probabilistic
task. The existing explanation for this phenomenon
within a Bayesian framework (e.g., Oaksford & Chater, 2008)
accounts for this asymmetry by assuming separate probability
distributions for both MP and MT. We propose a novel
explanation within a computational-level Bayesian account of
reasoning according to which “argumentation is learning”.
We show that the asymmetry must appear for certain prior
probability distributions, under the assumption that the conditional
inference provides the agent with new information that
is integrated into the existing knowledge by minimizing the
Kullback-Leibler divergence between the posterior and prior
probability distribution. We also show under which conditions
we would expect the opposite pattern, an MT-MP asymmetry.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.