Critical Thinking, Formation, and Change

In this paper, we propose an application of critical thinking (CT) to real-world problems, taking into account personal well-being (PB) and lifelong formation (FO). First, we raise a substantial problem with CT, which is that causal explanation is of little importance in solving everyday problems. If we care about everyday problems, we must treat the identification of causal relationships as a fundamental mechanism and action as a form of solution once the origin of the problem is unequivocally known. Decision-making and problem-solving skills should be the execution of the causal explanations reached. By acting this way, we change reality and achieve our goals, which are none other than those imposed by our PB. However, to achieve changes or results, we must have these fundamental competencies in CT, and these are not innate; we must acquire and develop them, that is, we must train ourselves to have CT competencies according to the demands of today's world. Finally, in this paper we propose a causal model that seeks to identify and test the causal relationships that exist between the different factors or variables that determine the CT-PB-FO relationship. We present some results on the relevance of causality and how to effectively form and address real-world problems from causality. However, there are still questions to be clarified that need to be investigated in future studies.

Towards causal mechanisms of consciousness through focused transcranial brain stimulation

Conscious experience represents one of the most elusive problems of empirical science, namely neuroscience. The main objective of empirical studies of consciousness has been to describe the minimal sets of neural events necessary for a specific neuronal state to become consciously experienced. The current state of the art still does not meet this objective but rather consists of highly speculative theories based on correlates of consciousness and an ever-growing list of knowledge gaps. The current state of the art is defined by the limitations of past stimulation techniques and the emphasis on the observational approach. However, looking at the current stimulation technologies that are becoming more accurate, it is time to consider an alternative approach to studying consciousness, which builds on the methodology of causal explanations via causal alterations. The aim of this methodology is to move beyond the correlates of consciousness and focus directly on the mechanisms of consciousness with the help of the currently focused brain stimulation techniques, such as geodesic transcranial electric neuromodulation. This approach not only overcomes the limitations of the correlational methodology but will also become another firm step in the following science of consciousness.

A Bayesian Network Approach to Explainable Reinforcement Learning with Distal Information

Nowadays, Artificial Intelligence systems have expanded their competence field from research to industry and daily life, so understanding how they make decisions is becoming fundamental to reducing the lack of trust between users and machines and increasing the transparency of the model. This paper aims to automate the generation of explanations for model-free Reinforcement Learning algorithms by answering "why" and "why not" questions. To this end, we use Bayesian Networks in combination with the NOTEARS algorithm for automatic structure learning. This approach complements an existing framework very well and demonstrates thus a step towards generating explanations with as little user input as possible. This approach is computationally evaluated in three benchmarks using different Reinforcement Learning methods to highlight that it is independent of the type of model used and the explanations are then rated through a human study. The results obtained are compared to other baseline explanation models to underline the satisfying performance of the framework presented in terms of increasing the understanding, transparency and trust in the action chosen by the agent.

Zero Sales Resistance: The Dark Side of Big Data and Artificial Intelligence

Big data (BD) is the hue and cry of modern science and society. The impact of such data deluge is huge and far reaching for both science and society. Moreover, given the effort required for collecting and analyzing these data, artificial intelligence (AI) has replaced the human mind in accomplishing the enormous task of deriving insight out of the information. In this article, we analyze the role of BD and AI in steering the world population toward the state of Zero Sales Resistance (ZSR): the inability to exert critical judgment over the most seductive aspects of the aforementioned data deluge. Moreover, we discuss the alarming consequences of presenting the merging of BD and AI as a universal panacea even if, to date, they have proven far more efficient for predicting human decisions and behaviors (predictive analytics) than for solving the most critical problems in science and society. Why? Our answer is simple. The causal structures associated with such challenges command a detailed understanding of the underlying mechanisms (causal explanation), typically acting nonlinearly and on a broad range of scales in space and time. In contrast, personality and behavior can be predicted with no need of a microscopic theory and understanding of the brain-mind system (empirical prediction). This is a direct consequence of the fact that our mind, at least for the intuitive level, uses the same prediction techniques applied by AI (bayesian predictions based on our experience). However, prediction is not explanation, and without joining them it will be impossible to achieve a major advance in our understanding of complex systems.

On Explaining Quantum Correlations: Causal vs. Non-Causal

At the basis of the problem of explaining non-local quantum correlations lies the tension between two factors: on the one hand, the natural interpretation of correlations as the manifestation of a causal relation; on the other, the resistance on the part of the physics underlying said correlations to adjust to the most essential features of a pre-theoretic notion of causation. In this paper, I argue for the rejection of the first horn of the dilemma, i.e., the assumption that quantum correlations call for a causal explanation. The paper is divided into two parts. The first, destructive, part provides a critical overview of the enterprise of causally interpreting non-local quantum correlations, with the aim of warning against the temptation of an account of causation claiming to cover such correlations ‘for free’. The second, constructive, part introduces the so-called structural explanation (a variety of non-causal explanation that shows how the explanandum is the manifestation of a fundamental structure of the world) and argues that quantum correlations might be explained structurally in the context of an information-theoretic approach to QT.

How Does Explanatory Virtue Determine Probability Estimation?-Empirical Discussion on Effect of Instruction

It is important to reveal how humans evaluate an explanation of the recent development of explainable artificial intelligence. So, what makes people feel that one explanation is more likely than another? In the present study, we examine how explanatory virtues affect the process of estimating subjective posterior probability. Through systematically manipulating two virtues, Simplicity-the number of causes used to explain effects-and Scope-the number of effects predicted by causes-in three different conditions, we clarified two points in Experiment 1: (i) that Scope's effect is greater than Simplicity's; and (ii) that these virtues affect the outcome independently. In Experiment 2, we found that instruction about the explanatory structure increased the impact of both virtues' effects but especially that of Simplicity. These results suggest that Scope predominantly affects the estimation of subjective posterior probability, but that, if perspective on the explanatory structure is provided, Simplicity can also affect probability estimation.

When darkness falls at mid-day: young patients' perceptions and meanings of chronic illness and their implications for medical care

BACKGROUND

This study illustrates the perceptions and meanings that patients who have had the onset of certain chronic diseases at young adulthood ascribe to their condition of chronic illness. The paper further examines the implications of such perceptions and construction for medical care.

DESIGN

Qualitative and ethnography.

SETTING

Outpatient chronically ill patients were recruited from the Korle Bu Teaching Hospital, Accra Ghana. Patients were followed up and studied in-depth in their homes.

PARTICIPANTS

Purposive sample of 24 consenting patients diagnosed of cancer, renal disease and stroke.

METHODS

Anthropological data collection techniques mainly in-depth interviews, narratives, conversation and observations were used. Issues explored included patients' perceptions, causal explanations, meanings ascribed to diagnosis, and search for treatment and cure.

RESULTS

Young adults had very low knowledge of chronic illnesses and did not consider themselves at risk to chronic diseases. The search for diagnosis, upon the presentation of symptoms, was long and winding. Families of young patients were more likely to make future search for "spiritual diagnoses" than elderly patients and their families. Keeping silent and maintaining secrecy about diagnosis are important ways by which young adult patients cope with their condition. Irrespective of the prognosis, young patients nurture a strong hope of cure.

CONCLUSION

Young adults diagnosed of certain chronic illnesses ascribe supernatural interpretations to their disease condition. These determine their attitude to their condition as well as health seeking behaviours adopted by them and their families. Responses interfered with their biomedical care and thus have implications for health promotion and healthcare planning and policy.

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History Without Causality. How Contemporary Historical Epistemology Demarcates Itself From the Sociology of Scientific Knowledge. Contemporary proponents of historical epistemology often try to delimit their enterprise by demarcating it from the sociology of scientific knowledge and other sociologically oriented approaches in the history of science. Their criticism is directed against the use of causal explanations which are deemed to invite reductionism and lead to a totalizing perspective on science. In the present article I want to analyse this line of criticism in what I consider are two paradigmatic works of contemporary historical epistemology: Lorraine Daston's und Peter Galison's Objectivity and Hans-Jörg Rheinberger's Toward a History of Epistemic Things. I first present their arguments against the sociological and causal analysis of scientific knowledge and practice and then try to defend sociological work in the history of science against their charges. I will, however, not do so by defending causal explanations directly. Rather, I will show that the arguments against sociological analysis put forward in contemporary historical epistemology, as well as historical epistemology's own models of historical explanation and narration, bear problematic consequences. I argue that Daston, Galison and Rheinberger fail to create productive resonances between macro- and microhistorical perspectives, that they reproduce an internalist picture of scientific knowledge, and finally that Rheinberger's attempt to deconstruct the dichotomy between subject and object leads him to neglect questions about the political dimension of scientific research.

A review of causal inference for biomedical informatics

Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods.