Fifty years after the death of Edmund Husserl, the main founder of the phenomenological current of thought, we present to the public a four book collection showing in an unprecedented way how Husserl's aspiration to inspire the entire universe of knowledge and scholarship has now been realized. These volumes display for the first time the astounding expansion of phenomenological philosophy throughout the world and the enormous wealth and variety of ideas, insights, and approaches it has inspired. The basic commitment to (...) phenomenological concerns found in all this variety makes this collection a most signifi cant historical document. This second volume of the collection bears witness to a deliberate shift of attention from the earlier to the later phase of Husserl's reflections. We see how his issues - intersubjectivity, ethics, human encounter, the societal world, empathy, the sphere of the self, and the surpassing of dichotomies (bodylpsyche, etc.) - are now at the center of attention in the human sciences. Among the authors are H. Tellenbach, A. Rigobello, C. Balzer, C. Bjurvill, V. Molchanov, E. Syristova, O. Balaban, R. Boehm, M. Sancipriano, O. M. Anwar, Y. Okamoto, B. Holyst, T. Sodeika, and M. De Negri. The studies were gathered at the programs held by The World Institute for Advanced Phenomenological Research and Learning in the commemorative year 1988/1989, chiefly at the First World Congress of Phenomenology at Santiago de Compos tela, Spain, with the aim of assessing the current state of phenomenology. A-T. T. (shrink)

This article illustrates the practical usefulness of transdisciplinary work for practical theology by showing how input from an occupational therapist informed my understanding and interpretation of the story of Hannetjie, who had been sexually abused as a child. This forms part of a narrative practical theological research project into the spirituality of female adult survivors of childhood sexual abuse. Transdisciplinary work is useful to practical theologians, as it opens possibilities for learning about matters pastors have to face, but may (...) not have knowledge about. A short retelling of Hannetjie’s story is given to provide information about the context of the research. Next, the transdisciplinary process that was followed is mentioned, and the questions that the transdisciplinary team had to respond to are discussed. Following that, I focus more specifically on the occupational therapist’s answers, and the knowledge gained from her contribution, as an example of how a co-researcher from a divergent discipline can inform a theological study. In this case, knowledge was shared about sensory integration and how the brain processes traumatic stimuli, such as sexual abuse. Lastly, the interrelationship between Hannetjie’s body stories, mind stories and spirit stories is discussed to show how the learning received from occupational therapy affected my thinking about Hannetjie’s stories and the relationships between them. Thus, it is concluded that transdisciplinary work has great value for practical theology, especially in the pastor’s daily work with people who are struggling with difficult stories, because we cannot listen to people’s spirit stories in isolation. They are inextricably intertwined with all our stories about ourselves. (shrink)

Preoperative diagnosis of gastric cancer and primary gastric lymphoma is challenging and has important clinical significance. Inspired by the inductive reasoning learning of the human brain, transfer learning can improve diagnosis performance of target task by utilizing the knowledge learned from the other domains (source domain). However, most studies focus on single-source transfer learning and may lead to model performance degradation when a large domain shift exists between the single-source domain and target domain. By simulating the (...) multi-modal information learning and transfer mechanism of human brain, this study designed a multisource transfer learning feature extraction and classification framework, which can enhance the prediction performance of the target model by using multisource medical data (domain). First, this manuscript designs a feature extraction network that takes the maximum mean difference based on the Wasserstein distance as an adaptive measure of probability distribution and extracts the domain-specific invariant representations between source and target domain data. Then, aiming at the random generation of parameters bringing uncertainties to prediction accuracy and generalization ability of extreme learning machine network, the 1-norm regularization is used to implement sparse constraints of the output weight matrix and improve the robustness of the model. Finally, some experiments are carried out on the data of two medical centers. The experimental results show that the area under curves (AUCs) of the method are 0.958 and 0.929 in the two validation cohorts, respectively. The method in this manuscript can provide doctors with a better diagnostic reference, which has certain practical significance. (shrink)

To introduce this collection of research studies, which stem from the pro grams conducted by The World Phenomenology Institute, we need say a few words about our aims and work. This will bring to light the significance of the present volume. The phenomenological philosophy is an unprejudiced study of experience in its entire range: experience being understood as yielding objects. Experi ence, moreover, is approached in a specific way, such a way that it legitima tizes itself naturally in immediate evidence. (...) As such it offers a unique ground for philosophical inquiry. Its basic condition, however, is to legitimize its validity. In this way it allows a dialogue to unfold among various philosophies of different methodologies and persuasions, so that their basic assumptions and conceptions may be investigated in an objective fashion. That is, instead of comparing concepts, we may go below their differences to seek together what they are meant to grasp. We may in this way come to the things them selves, which are the common objective of all philosophy, or what the great Chinese philosopher Wang Yang Ming called "the investigation of things". It is in this spirit that the Institute's programs include a "cross-cultural" dialogue meant to bring about a profound communication among philosophers in their deepest concerns. Rising above artificial cultural confinements, such dialogues bring scholars, thinkers and human beings together toward a truly human community of minds. Our Institute unfolds one consistent academic program. (shrink)

The paper examines constraints and preferences employed by people in learning decision rules from preclassified examples. Results from four experiments with human subjects were analyzed and compared with artificial intelligence (AI) inductive learning programs. The results showed the people's rule inductions tended to emphasize category validity (probability of some property, given a category) more than cue validity (probability that an entity is a member of a category given that it has some property) to a greater (...) extent than did the AI programs. Although the relative proportions of different rule types (e.g., conjunctive vs. disjunctive) changed across experiments, a single process model provided a good account of the data from each study. These observations are used to argue for describing constraints in terms of processes embodied in models rather than in terms of products or outputs. Thus AI induction programs become candidate psychological process models and results from inductive learning experiments can suggest new algorithms. More generally, the results show that human inductive generalizations tend toward greater specificity than would be expected if conceptual simplicity were the key constraint on inductions. This bias toward specificity may be due to the fact that this criterion both maximizes inferences that may be drawn from category membership and protects rule induction systems from developing over‐generalizations. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Kennedy Institute of Ethics Journal 14.1 (2004) 47-54 [Access article in PDF] Ethical Guidelines for Human Embryonic Stem Cell Research*(A Recommended Manuscript) Adopted on 16 October 2001Revised on 20 August 2002 Ethics Committee of the Chinese National Human Genome Center at Shanghai, Shanghai 201203 Human embryonic stem cell (ES) research is a great project in the frontier of biomedical science for the twenty-first century. Be- cause (...) the research involves the use of human embryos, it triggers serious debate on ethical issues. Opponents consider the embryo to be an early form of human life that should be respected and not destroyed. However, the majority of scientists support embryonic stem cell research, believing that it offers good prospects for the treatment of diseases that have remained incurable until now and so will benefit humankind. The Ethics Committee of the Department of Ethical, Legal, and Social Implications of the Chinese National Human Genome Center at Shanghai seriously discussed the ethical debate initiated by embryonic stem cell research. We concluded that we should support the scientists of our country in actively carrying out human embryonic stem cell research for the noble cause of "medicine being a beneficent art." For the healthy and orderly development of human embryonic stem cell research in our country, we put forward the following recommended Ethical Guidelines for Human Embryonic Stem Cell Research as a reference for leaders, administrative departments, and related scientists. Preface Article 1. Human embryonic stem cells are the primitive cells that play the main role in the growth and development of a human body. These [End Page 47] primitive cells have the potential for infinite proliferation, self-renewal, and multi-directional differentiation. If scientists can discover the mechanism of differentiation of human embryonic stem cells, it will be possible to induce differentiation of human embryonic stem cells to form various types of human cells for clinical cell therapy. If human embryonic stem cell research can be integrated with modern biomedical engineering techniques, it also will be possible to make repair and replacement of human tissues and organs a reality. Article 2. There are two ways to classify human embryonic stem cells. One way is to classify them according to their potential for differentiation. There could be three kinds of stem cells: totipotent stem cells, pluripotent stem cells, and unipotent stem cells.A totipotent stem cell has the potential to develop into a whole individual. It can differentiate into the more than 200 cell types in the whole body, construct any tissue or organ of the body, and finally develop into a whole individual. The fertilized egg and the cleavage cells at the very early stage of embryonic development are totipotent stem cells.A pluripotent stem cell has the potential to differentiate into many cell types derived from the three embryonic layers. However, it has lost the capacity to develop into a complete organism.A unipotent stem cell is derived from the further differentiation of the pluripotent stem cell. It can differentiate only into one cell type such as a hematopoietic stem cell, neural stem cell, and others.The other way is to classify human stem cells according to their source. There could be two kinds of human stem cells: embryonic stem cells and tissue stem cells (also called adult stem cells). The former involves experimentation with embryos, which has serious ethical implications. Ethical issues associated with the latter are mainly expressed in the various opinions regarding the allocation of health resources. Article 3. Human embryonic stem cells are the group of cells called the blastocyst inner cell mass during the early stage of embryonic development. They are the main source of totipotent stem cells, and hence the focal and hot point in stem cell research. Studies on the clinical application of embryonic stem cells probably will involve use of the somatic cell nucleus transfer (SCNT) technique, which destroys the early human embryo. At present, the ethical and moral debate is very serious in human embryonic stem cell research regarding whether the research will develop... (shrink)

This study investigates the joint influences of three factors on the discovery of new word‐like units in a continuous artificial speech stream: the statistical structure of the ongoing input, the initial word‐likeness of parts of the speech flow, and the contextual information provided by the earlier emergence of other word‐like units. Results of an experiment conducted with adult participants show that these sources of information have strong and interactive influences on word discovery. The authors then examine the ability of different (...) models of word segmentation to account for these results. PARSER (Perruchet & Vinter, 1998) is compared to the view that word segmentation relies on the exploitation of transitional probabilities between successive syllables, and with the models based on the Minimum Description Length principle, such as INCDROP. The authors submit arguments suggesting that PARSER has the advantage of accounting for the whole pattern of data without ad‐hoc modifications, while relying exclusively on general‐purpose learning principles. This study strengthens the growing notion that nonspecific cognitive processes, mainly based on associative learning and memory principles, are able to account for a larger part of early language acquisition than previously assumed. (shrink)

This paper describes a novel perspective on the foundations of mathematics: how mathematics may be seen to be largely about “information compression via the matching and unification of patterns”. That is itself a novel approach to IC, couched in terms of nonmathematical primitives, as is necessary in any investigation of the foundations of mathematics. This new perspective on the foundations of mathematics reflects the facts that mathematics is almost exclusively the product of human brains, and has been developed, as (...) an aid to human thinking, mathematics is likely to be consonant with much evidence for the importance of IC in human learning, perception, and cognition. This perspective on the foundations of mathematics has grown out of a long-term programme of research developing the SP Theory of Intelligence and its realization in the SP Computer Model, a system in which a generalised version of ICMUP—the powerful concept of SP-multiple-alignment—plays a central role. This paper shows with an example how mathematics, without any special provision, may achieve compression of information. Then, it describes examples showing how variants of ICMUP may be seen in widely used structures and operations in mathematics. Examples are also given to show how several aspects of the mathematics-related disciplines of logic and computing may be understood as ICMUP. Also discussed is the intimate relation between IC and concepts of probability, with arguments that there are advantages in approaching AI, cognitive science, and concepts of probability via ICMUP. Also discussed is how the close relation between IC and concepts of probability relates to the established view that some parts of mathematics are intrinsically probabilistic, and how that latter view may be reconciled with the all-or-nothing, “exact,” forms of calculation or inference that are familiar in mathematics and logic. There are many potential benefits and applications of the mathematics-as-IC perspective. (shrink)

Medicine's crucial concern with health is perennial, but its reflection, concepts, means change with the advance of science and social life. We present here a fascinating panorama of current medical discussions with their philosophical underpinnings, and queries as they have evolved from the past. The role of Tymieniecka's phenomenology of life is brought forth as the system of philosophical reference.

Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys. The Solomonoff 85th memorial conference was held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff, honouring his various pioneering works - most particularly, his revolutionary insight in the early 1960s that the universality of Universal Turing Machines could be used for universal Bayesian prediction and artificial intelligence. This work continues to increasingly influence (...) and under-pin statistics, econometrics, machine learning, data mining, inductive inference, search algorithms, data compression, theories of intelligence and philosophy of science - and applications of these areas. Ray not only envisioned this as the path to genuine artificial intelligence, but also, still in the 1960s, anticipated stages of progress in machine intelligence which would ultimately lead to machines surpassing human intelligence. Ray warned of the need to anticipate and discuss the potential consequences - and dangers - sooner rather than later. Possibly foremostly, Ray Solomonoff was a fine, happy, frugal and adventurous human being of gentle resolve who managed to fund himself while electing to conduct so much of his paradigm-changing research outside of the university system. The volume contains 35 papers pertaining to the abovementioned topics in tribute to Ray Solomonoff and his legacy. (shrink)

Through mystery, literature reveals to us the Great Unknown. While we are absorbed by the matters at hand with the present enactment of our life, groping for clues to handle them, it is through literature that we discover the hidden strings underlying their networks. Hence our fascination with literature. But there is more. The creative act of the human being, its proper focus, holds the key to the Sezam of life: to the great metaphysical/ontopoietic questions which literature may disclose. (...) First, it leads us to the sublimal grounds of transformation in the human soul, source of the specifically human significance of life (Analecta Husserliana, Volume III, XIX, XXIII, XXVII) Second, it leads us to the unveiling of the hidden workings of life in the twilight of knowing in a dialectic between The Visible and the Invisible, (Volume LXXV, 2002, Analecta Husserliana) down to the ontopoietic truth. (Volume LXXVI, 2002, Analecta Husserliana) This prying into the unknown which provokes the human being as he or she attempts to conquer, step by step, a space of existence, finds its culmination in the phenomenon of mystery as the subject of the present collection. Its formulation brings us to the greatest question of all: the enigmatic solidarity -in-distinctiveness of human cognition and existence. Papers are written by: Tony E. Afejuku, Gary Backhaus, Paul G. Beidler, Matthew J. Duffy, Raffaela Giovagnoli, Jennifer Anna Gosetti-Ferencei, Matti Itkonen, Lawrence Kimmel, Catherine Malloy, Vladimir L. Marchenkov, Nancy Mardas, Howard Pearce, Bernadette Prochaska, Victor Gerald Rivas, M.J. Sahlani, Dennis Skocz, Jadwiga S. Smith, Mara Stafecka, Max Statkiewicz, Mariola Sulkowska, Anna-Teresa Tymieniecka, Leon U. Weinman, Tim Weiss. (shrink)

Objective: Mindfulness-based intervention has been proposed to alleviate loneliness and improve social connectedness. Several randomized controlled trials have been conducted to evaluate the effectiveness of MBI. This study aimed to critically evaluate and determine the effectiveness and safety of MBI in alleviating the feeling of loneliness.Methods: We searched Medline, Embase, PsycInfo, Cochrane CENTRAL, and AMED for publications from inception to May 2020. We included RCTs with human subjects who were enrolled in MBI with loneliness as an outcome. The quality (...) of evidence was assessed using Cochrane's Risk of Bias tool and Grading of Recommendations Assessment, Development, and Evaluation. A random-effects model was used for meta-analysis.Results: Out of 92 articles identified, eight studies involving 815 participants were included in this study. Most trials conducted a minimum of 8 weeks of MBI. Most of the trials used UCLA-Loneliness Scale. A pooled analysis combining three trials and compared with wait-list showed significant improvement in loneliness score reduction using the UCLA-R scale with MD of −6.33 [95% confidence interval : −9.39, −3.26]. Subgroup analysis with only two Cognitively-Based Compassion Training trials also showed similar MD of −6.05. The overall quality of evidence was low.Conclusions: Mindfulness intervention with an average length of 8-week duration significantly improved the population's loneliness level with no mental health issue. However, this evidence had a low GRADE level. (shrink)

The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well-formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary and probabilistic theories of grammaticality. These judgements are gradient in nature, and so cannot be directly accommodated in a binary formal grammar. However, it is also not possible to simply reduce (...) acceptability to probability. The acceptability of a sentence is not the same as the likelihood of its occurrence, which is, in part, determined by factors like sentence length and lexical frequency. In this paper, we present the results of a set of large-scale experiments using crowd-sourced acceptability judgments that demonstrate gradience to be a pervasive feature in acceptability judgments. We then show how one can predict acceptability judgments on the basis of probability by augmenting probabilistic language models with an acceptability measure. This is a function that normalizes probability values to eliminate the confounding factors of length and lexical frequency. We describe a sequence of modeling experiments with unsupervised language models drawn from state-of-the-art machine learning methods in natural language processing. Several of these models achieve very encouraging levels of accuracy in the acceptability prediction task, as measured by the correlation between the acceptability measure scores and mean human acceptability values. We consider the relevance of these results to the debate on the nature of grammatical competence, and we argue that they support the view that linguistic knowledge can be intrinsically probabilistic. (shrink)

The heart of my dissertation project is the proposal of a new updating rule for responding to learning experiences consisting of continuous streams of evidence. I suggest characterizing this kind of learning experience as a continuous stream of stipulated credal derivatives, and show that Continuous Probability Kinematics is the uniquely coherent response to such a stream which satisfies a continuous analogue of Rigidity – the core property of both Bayesian and Jeffrey conditionalization. In the first chapter, I (...) define neighborhood norms of rationality with reference to Kenny Easwaran’s definition of neighborhood properties. I summarize and comment on some of the key arguments in the dispute between time-slice epistemologists, who argue that there are no fundamentally diachronic norms of rationality, and the proponents of diachronic norms. I am sympathetic to two of the key motivations often given in support of the synchronist position: mentalist internalism and the idea that metaphysical disputes about the identity of persons in bizarre puzzle cases should not play a central role in epistemologists’ assessments of the rationality of agents. However, I argue that time-slice epistemology cannot adequately address the rationality of temporally-extended processes like reasoning and learning. Neighborhood norms present a viable third way between these two positions, capturing much of the spirit of the previously-discussed synchronist motivations while still providing just enough temporal structure to meaningfully guide and evaluate temporally-extended rational processes. Continuous Probability Kinematics is an example of one such neighborhood norm. In the second chapter, I develop my updating rule CPK and establish many of its core properties. Of special note here are the deep connections to Jeffrey’s Probability Kinematics, as well as some key differences. The net result of any CPK updating process will always be representable as a Jeffrey shift on the refined partition generated by the propositions that the agent is receiving direct evidence concerning. However, one crucial difference is that CPK provides an intuitive account of how to combine the effects of learning experiences that are each about fundamentally different underlying partitions. In CPK’s formalism, an agent can receive simultaneous evidence streams about an arbitrary (finite) number of propositions, which can themselves be evidentially related in any way. At any given instant, the result of the combination is a simple sum of the effects that learning about the individual propositions would have separately. CPK is concerned with a novel kind of learning experience and involves a novel characterization of evidence. The third and final chapter of this dissertation is concerned with explaining what this characterization of evidence means and with arguing that it can be the basis for genuine learning. I begin by characterizing learning experiences in terms of the Value of Information, and prove a Value of Information theorem for CPK learning experiences under the assumption of a Martingale constraint on the agent’s prior distribution over the signals that they might receive. I examine Timothy Williamson’s arguments that evidence must be propositional and express my skepticism. I then explore two different routes to model agents who update by CPK as if they are learning some propositional content and updating the rest of their credences by Bayesian conditionalization on this content. The second of these two routes provides a very interesting lens to reexamine the evidential commitments that underwrite updating by CPK, which I analyze. (shrink)

The ability to learn cause–effect relations from experience is critical for humans to behave adaptively — to choose causes that bring about desired effects. However, traditional experiments on experience-based learning involve events that are artificially compressed in time so that all learning occurs over the course of minutes. These paradigms therefore exclusively rely upon working memory. In contrast, in real-world situations we need to be able to learn cause–effect relations over days and weeks, which necessitates long-term memory. 413 (...) participants completed a smartphone study, which compared learning a cause–effect relation one trial per day for 24 days versus the traditional paradigm of 24 trials back- to- back. Surprisingly, we found few differences between the short versus long timeframes. Subjects were able to accurately detect generative and preventive causal relations, and they exhibited illusory correlations in both the short and long timeframe tasks. These results provide initial evidence that experience-based learning over long timeframes exhibits similar strengths and weaknesses as in short timeframes. However, learning over long timeframes may become more impaired with more complex tasks. (shrink)

From an early age, exposure to a spoken language has allowed us to implicitly capture the structure underlying the succession of speech sounds in that language and to segment it into meaningful units. Statistical learning, the ability to pick up patterns in the sensory environment without intention or reinforcement, is thus assumed to play a central role in the acquisition of the rule-governed aspects of language, including the discovery of word boundaries in the continuous acoustic stream. Although extensive evidence (...) has been gathered from artificial languages experiments showing that children and adults are able to track the regularities embedded in the auditory input, as the probability of one syllable to follow another syllable in the speech stream, the developmental trajectory of this ability remains controversial. In this work, we have collected Event-Related Potentials while 5-year-old children and young adults were exposed to a speech stream made of the repetition of eight three-syllable nonsense words presenting different levels of predictability to mimic closely what occurs in natural languages and to get new insights into the changes that the mechanisms underlying auditory statistical learning might undergo through the development. The participants performed the aSL task first under implicit and, subsequently, under explicit conditions to further analyze if children take advantage of previous knowledge of the to-be-learned regularities to enhance SL, as observed with the adult participants. These findings would also contribute to extend our knowledge of the mechanisms available to assist SL at each developmental stage. Although behavioral signs of learning, even under explicit conditions, were only observed for the adult participants, ERP data showed evidence of online segmentation in the brain in both groups, as indexed by modulations in the N100 and N400 components. A detailed analysis of the neural data suggests, however, that adults and children rely on different mechanisms to assist the extraction of word-like units from the continuous speech stream, hence supporting the view that SL with auditory linguistic materials changes through development. (shrink)

& Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important (...) probabilistic implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception. (shrink)

It has been argued that dual process theories are not consistent with Oaksford and Chater’s probabilistic approach to human reasoning (Oaksford and Chater in Psychol Rev 101:608–631, 1994 , 2007 ; Oaksford et al. 2000 ), which has been characterised as a “single-level probabilistic treatment[s]” (Evans 2007 ). In this paper, it is argued that this characterisation conflates levels of computational explanation. The probabilistic approach is a computational level theory which is consistent with theories of general cognitive architecture that (...) invoke a WM system and an LTM system. That is, it is a single function dual process theory which is consistent with dual process theories like Evans’ ( 2007 ) that use probability logic (Adams 1998 ) as an account of analytic processes. This approach contrasts with dual process theories which propose an analytic system that respects standard binary truth functional logic (Heit and Rotello in J Exp Psychol Learn 36:805–812, 2010 ; Klauer et al. in J Exp Psychol Learn 36:298–323, 2010 ; Rips in Psychol Sci 12:29–134, 2001 , 2002 ; Stanovich in Behav Brain Sci 23:645–726, 2000 , 2011 ). The problems noted for this latter approach by both Evans Psychol Bull 128:978–996, ( 2002 , 2007 ) and Oaksford and Chater (Mind Lang 6:1–38, 1991 , 1998 , 2007 ) due to the defeasibility of everyday reasoning are rehearsed. Oaksford and Chater’s ( 2010 ) dual systems implementation of their probabilistic approach is then outlined and its implications discussed. In particular, the nature of cognitive decoupling operations are discussed and a Panglossian probabilistic position developed that can explain both modal and non-modal responses and correlations with IQ in reasoning tasks. It is concluded that a single function probabilistic approach is as compatible with the evidence supporting a dual systems theory. (shrink)

Today professionals have to deal with more uncertainties in their field than before. We live in complex and rapidly changing environments. The British philosopher Ronald Barnett adds the term ‘supercomplexity’ to highlight the fact that ‘we can no longer be sure how even to describe the world that faces us’ (Barnett, 2004). Uncertainty is, nevertheless, not a highly appreciated notion. An obvious response to uncertainty is to reduce it—or even better, to wipe it away. The assumption of this approach is (...) that uncertainty has no advantages. This assumption is, however, not correct as several contemporary authors have argued. Rather than problematising uncertainty, I will investigate the pros and cons of embedding uncertainty in educational practice of professional higher education. In order to thoroughly explore the probabilities and challenges that uncertainty poses in education, I will dwell on the radical ideas on uncertainty of the German philosopher Friedrich Nietzsche. In The Birth of Tragedy (1872) he recognises two forces: the Apollinian, that is the pursuit of order and coherence, and the Dionysian, that is the human tendency to nullify all systematisation and idealisation. Uncertainty is part of the Dionysian. I will argue that when educators take Nietzsche's plea to make room for the Dionysian to heart, they can better prepare students for an uncertain world. If, and only if, students are encouraged to deploy both tendencies—the Apollinian and the Dionysian—they can become professionals who are able to stand their ground in an uncertain and changing (professional) world. (shrink)

Statistical learning enables humans to involuntarily process and utilize different kinds of patterns from the environment. However, the cognitive mechanisms underlying the simultaneous acquisition of multiple regularities from different perceptual modalities remain unclear. A novel multidimensional serial reaction time task was developed to test 40 participants’ ability to learn simple first‐order and complex second‐order relations between uni‐modal visual and cross‐modal audio‐visual stimuli. Using the difference in reaction times between sequenced and random stimuli as the index of domain‐general statistical (...) class='Hi'>learning, a significant difference and dissociation of learning occurred between the initial and final learning phases. Furthermore, we used a negative and positive occurrence‐frequency‐and‐reaction‐time correlation to indicate implicit and explicit learning, respectively, and found that learning simple uni‐modal patterns involved an implicit‐to‐explicit segue, while acquiring complex cross‐modal patterns required an explicit‐to‐implicit segue, resulting in a X‐shape crossing of regularity learning. Thus, we propose an X‐way hypothesis to elucidate the dynamic interplay between the implicit and explicit systems at two distinct stages when acquiring various regularities in a multidimensional probability space. (shrink)

This paper presents an effort to induce a Bayesian belief network (BBN) from crime data, namely the national crime victimization survey (NCVS). This BBN defines a joint probability distribution over a set of variables that were employed to record a set of crime incidents, with particular focus on characteristics of the victim. The goals are to generate a BBN to capture how characteristics of crime incidents are related to one another, and to make this information available to domain specialists. (...) The novelty associated with the study reported in this paper lies in the use of a Bayesian network to represent a complex data set to non-experts in a way that facilitates automated analysis. Validation of the BBN’s ability to approximate the joint probability distribution over the set of variables entailed in the NCVS data set is accomplished through a variety of sources including mathematical techniques and human experts for appropriate triangulation. Validation results indicate that the BBN induced from the NCVS data set is a good joint probability model for the set of attributes in the domain, and accordingly can serve as an effective query tool. (shrink)

In the future, the Human Genome Project could eventually open the way to perhaps the determination of the complete wiling diagram of the human brain. This kind of progress may move neuroscience forward into the next level of understanding of human neurophysiology, development and behavior. The next crucial step would be to know, exactly what are the function of this genes, and why its lack or alteration causes a certain disease. Although, genomic has in some way contributed (...) to almost every aspects of neurobiology, the results are both significant and troubling.One of the areas, where HGP will contribute to neuroscience is the mapping of qualitative or complex traits. Complex traits like appearence, intelligence and personality are likely to be affected by hundreds or thousands of different genes, rather than one or a few. On the other hand, the complex organisms are not just the simple result of the sum of their genes, nor do genes alone build the different part of organism or influence behaviour by themselves. In spite of the wide variety of misunderstanding, misinterpretation or possible misusing of these new genetic information, this sort of research may eventually lead to useful practical medical results for humans. This information can contribute to work out therapies to treat learning and memory disorders, neurological disorders like Alzheimer's disease, afflicting more and more people in an increasingly ageing population. Genetics believe, there are 3000 to 4000 hereditary diseases. Gene therapy could one day cure some of them, but only when all the details of their genetic basis are known. Biomedicine in the next century will probably have a completely new role in society based on the beginnings inherent in the HGP.There is a general agreement, that modern neuroscience raises important ethical concerns that not have been adequately addressed either by scientists themselves or by the bioethics community. Some scientists interpreted the rapid progress in neuroscience as additional conformation for a materialist account of human nature, resulting in an attack on traditional belief systems. There is a clear tension between the widely held believes and the intellectual views of many scientists. (shrink)

"No human now gathers for himself or herself the essential resources for life: food, shelter, clothing, and the like. Humans are obligate co-operator, and this has been true for tens of thousands of years; probably much longer. In this regard, humans are very unusual. Cooperation outside the family is rare: though it can be very profitable, it is also very risky, as cooperation makes an agent vulnerable to incompetence and cheating. This book presents a new picture of the emergence (...) of cooperation in our lineage, developing through four fairly distinct phases from a baseline that was probably fairly similar to living great apes, who cooperate, but in fairly minimal ways. As adults, they rarely depend on others when the outcome really matters. This book suggests that cooperation began to be more important for humans through an initial phase of cooperative foraging generating immediate returns from collective action in small mobile bands. This established in our lineage about 1.8 million years ago, perhaps earlier. Over the rest of the Pleistocene, cooperation became more extended in its social scale, with forms of cooperation between bands gradually establishing, and in spatial and temporal scale too, with various forms of reciprocation becoming important. The final phase was the emergence of cooperation in large scale, hierarchical societies in the Holocene, beginning about 12,000 years ago. This picture is nested in a reading of the archaeological and ethnographic record, and twinned to an account of the gradual elaboration of cultural learning in our lineage, making cooperation both more profitable and more stable"--. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:To Recognize the Person: Learning from Narratives of Psychiatric TreatmentLinda J. MorrisonTo know what patients endure at the hands of illness and therefore to be of clinical help requires that doctors enter the worlds of their patients, if only imaginatively, and to see and interpret these worlds from the patient’s point of view(Charon, 2006, p. 9).These narratives of psychiatric hospitalization are rich and evocative. We are fortunate to (...) have twelve descriptions appearing side-by-side, each unique in its focus, outlook, detail and narrative tone. Together their interwoven contexts describe personal and work relationships, internal and external help-seeking, encounters in community and hospital settings, and experiences of the psychiatric (psychiatrized) self as supplicant, victim, recipient, participant, product, and active agent over time (Morrison, 2005).Through their collected narratives, the authors create a distinct counterpoint to the more familiar clinical narratives of psychiatric encounters. Ordinarily the professional narrative drives interpretation of a clinical experience, whether medical or psychiatric. The caregivers populating these narratives would probably tell different stories, and the hospital notes and treatment records yet another (Hinsby & Baker, 2004; VanDerNagel et al., 2009). The disjunction between professional and patient narrative is especially true for psychiatric hospitalizations, in which the inpatient’s active narrative becomes a part of the diagnostic and treatment process (Steslow, 2010).In this symposium, the patient’s narrative is a window for interpretive insights into personal experience of the process. Each is a situated retrospective, in which added dimensions of feeling, thought and interaction provide the reader a fuller and more complex awareness of the experience of psychiatric hospitalization. The narratives provide insights about choice and agency, control and harm, safety and strength, hope and frustration, resilience and recovery, empathy and humanity; they also reveal various ethical issues related to the experience of help-seeking and treatment.It is vital to consider what each person wrote: what choices they made in representing their experience; what aspects they focused on, and what they (we imagine) left out. The authors used their own voices to create their own representations. In each narrative, “the person” provides an important standpoint. Who is this person, what is his or her experience? Where is the person in the situation; in what way does he or she appear to those who provide treatment or care? How, if at all, is the treatment pertinent to the person’s concerns? Does it feel helpful or harmful? How does the person find a way to continue, to push through, with the help of (or in spite of) those we hope are providing care? This is the first sign of agency, of choice, of “dreaming.” [End Page 35]As V. Barnard writes in Dreaming: A Recovery Story, “to make a film, one has to dream it first.” All of the authors in this symposium took the risk and the opportunity to create their own stories: what to include, what to leave out, how to tell what they wanted to say. These choices gave them the power to dream: how it was, how it might have been; how it felt, good and bad; the chance to represent their own reality—to themselves, and to unknown others (Morrison, 2006).Dreaming, Choice and Agency“My psychiatric illness is only one part of me. I am a person with hopes, dreams, love, an artist and a fellow helper of my peers.” V. BarnardV.’s narrative describes the potential for positive action, of dreaming herself as a whole into existence. Her themes of choice and agency emphasize the importance of autonomy and the capacity for autonomous action. On the farm, V. was free to choose and take risks, to take on responsibilities, to be “treated with respect as a human being trying to get better rather than a criminal suffering from something that was optional.” From disconnection, she was able to find connection, on her own terms.The support of peers adds a valuable dimension. Being recognized as a peer, a person who shares similar psychiatric experiences and respects the challenge of everyday life, brings another level to supportive relationships. Negative judgments and stigma are reduced by mutual support and acknowledgement of common goals. Mental health... (shrink)

Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the (...) responses produced by a participant on one trial to generate the stimuli that either they or another participant will see on the next. A formal analysis of this “iterated learning” procedure, based on the assumption that the learners are Bayesian agents, predicts that it should reveal the inductive biases of these learners, as expressed in a prior probability distribution over hypotheses. This article presents a series of experiments using stimuli based on a well-studied set of category structures, demonstrating that iterated learning can be used to reveal the inductive biases of human learners. (shrink)

This lively introductory text exposes the student in the humanities to the world of discrete mathematics. A problem-solving based approach grounded in the ideas of George Pólya are at the heart of this book. Students learn to handle and solve new problems on their own. A straightforward, clear writing style and well-crafted examples with diagrams invite the students to develop into precise and critical thinkers. Particular attention has been given to the material that some students find challenging, such as proofs. (...) This book illustrates how to spot invalid arguments, to enumerate possibilities, and to construct probabilities. It also presents case studies to students about the possible detrimental effects of ignoring these basic principles. The book is invaluable for a discrete and finite mathematics course at the freshman undergraduate level or for self-study since there are full solutions to the exercises in an appendix. (shrink)

What can African sources teach philosophers and related thinkers around the world? In some real ways, both Western philosophers and non-Western advocates of decolonization have failed to appreciate that there is probably a lot to learn from Africa. In my contribution, I explain why neither camp has given African intellectual sources their due and sketch what that would plausibly involve.

Despite the success of artificial intelligence, we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, we follow Bayesian inference to combine (...) intuitive models and new information to make decisions. We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that the probability calculation in Bayesian sense is sensitive to semantic properties of the objects’ combination formed by observation and prior experience. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. First, intuitive models support an individual to use information meaningful ways in a current context. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision making. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. Finally, a progress in data analysis techniques in AI has allowed us to decipher how the human brain valuates different options in complex situations. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. We describe these solutions by reviewing the current research from this perspective. In this study, we outline the basic aspects for human-like AI and we discuss on how science can benefit from AI. The better we understand human’s brain mechanisms, the better we can apply this understanding for building new AI. Both development of AI and understanding of human behavior go hand in hand. (shrink)

Pothos & Busemeyer's (P&B's) query about whether quantum probability can provide a foundation for the cognitive modeling embodies so many underlying implications that the subject is far from exhausted. In this brief commentary, however, I suggest that the conceptual thresholds of the meaningful learning give rise to a typical Boltzmann's weighting measure, which indicates a statistical verisimilitude of quantum behavior in the human cognitive ensemble.

This paper reports an experiment which investigates a possible cognitive antecedent of event-splitting effects (ESEs) experimentally observed by Starmer and Sugden (1993) and Humphrey (1995) – the learning of absolute frequency of event category impacting on the learning of probability of event category – and reveals some evidence that it is responsible for observed ESEs. It is also suggested and empirically substantiated that stripped-down prospect theory will accurately predict ESEs in some decision making tasks, but will not (...) perform well in others. This contention, it is argued, is indicative of fundamental descriptive shortcomings in the economic conception of choice under uncertainty and may entail implications beyond the direct concerns of this paper. (shrink)