Volume 23, Issue 3-4, November - December 2024, Page 245-250.

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)

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed (...) in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach. (shrink)

Learning to perceive is faced with a classical paradox: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the “laws” of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can (...) be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget’s theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget’s theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level. (shrink)

Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts (...) to show how a deep learning mindset can be developed in both oneself and others. Deep learning is an acceptance that our understanding of the world around us is only temporary and is subject to constant scrutiny. Someone who is committed to learning deeply does not simply react to experiences, but engages fully with that experience, knowing that the inevitable disquietude is what leads to efficacy in the world. (shrink)

Meta-learning is learning to learn, which includes the development of capacities to transfer what people learned in one specific domain to other domains. It facilitates finetuning learning parameters and setting priors for effective and optimal learning in novel contexts and situations. Recent advances in research on artificial intelligence have reported meta-learning is essential in improving and optimizing the performance of trained models across different domains. In this paper, I suggest that meta-learning plays fundamental roles (...) in practical wisdom and its cultivation within moral domains. I support my argument with findings from studies in psychology, neuroscience, and computer science. Moreover, I suggested concrete ideas for moral education to foster wisdom cultivation along with meta-learning with evidence from studies across various fields. Particularly, I underscored that exemplars, including role models and mentors, play fundamental roles in education. Finally, I discussed the potential implications of the introduction of the concept, meta-learning, to moral education addressing concerns and issues related to socio-cultural diversity. (shrink)

This volume brings together essays that examine and defend the use of experiential learning activities to teach philosophical terms, concepts, arguments, and practices, and argue that teaching philosophy is about doing philosophy with others.

Natural learning is a countercultural home education approach characterized by the notion of freedom and autonomy in life and learning, intertwined in cultural environments. Using purposive sampling, two single parents from a “township” culture provided an in-depth understanding of natural learning in their context. A case study design was used to collect data, and thematic analysis was used to analyze the data. The results show that home education is a viable space to embed learning and culture (...) and that educational spaces can be creatively transformed to reclaim and reaffirm culture. This study also amplifies the voices of the historically marginalized in home education research. (shrink)

In this new volume in the Oxford Psychology Series, the author presents a highly readable account of the cognitive unconscious, focusing in particular on the problem of implicit learning. Implicit learning is defined as the acquisition of knowledge that takes place independently of the conscious attempts to learn and largely in the absence of explicit knowledge about what was acquired. One of the core assumptions of this argument is that implicit learning is a fundamental, "root" process, one (...) that lies at the very heart of the adaptive behavioral repertoire of every complex organism. The author's goals are to outline the essential features of implicit learning that have emerged from the many studies that have been carried out in a variety of experimental laboratories over the past several decades; to present the various alternative perspectives on this issue that have been proposed by other researchers and to try to accommodate these views with his own; to structure the literature so that it can be seen in the context of standard heuristics of evolutionary biology; to present the material within a functionalist approach and to try to show why the experimental data should be seen as entailing particular epistemological perspectives; and to present implicit processing as encompassing a general and ubiquitous set of operations that have wide currency and several possible applications. Chapter 1 begins with the core problem under consideration in this book, a characterization of "implicit learning" as it has come to be used in the literature. Reber puts this seemingly specialized topic into a general framework and suggests a theoretical model based on standard heuristics of evolutionary biology. In his account, Reber weaves a capsule history of interest in and work on the cognitive unconscious. Chapter 2 turns to a detailed overview of the experimental work on the acquisition of implicit knowledge, which currently is of great interest. Chapter 3 develops the evolutionary model within which one can see learning and cognition as richly intertwining issues and not as two distinct fields with one dominating the other. Finally, Chapter 4 explores a variety of entailments and speculations concerning implicit cognitive processes and their general role in the larger scope of human performance. (shrink)

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of (...) synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

Because corporate social responsibility (CSR) is potentially beneficial for companies, it is important to understand the factors that improve a company’s CSR practice. Scholars hypothesize that facilitating learning organization characteristics, which are divided in characteristics at the organizational and the operational level, may improve CSR implementation. These characteristics stimulate companies and their members to be critical, learn from the past, and embrace change, but there is limited empirical evidence of this approach. This study addresses this gap by surveying 280 (...) CSR professionals and performing bootstrap mediation analyses to test multiple hypotheses. Learning organization characteristics at the organizational level, play a key role in supporting CSR implementation: leadership for learning, system connection, and group learning show a direct relationship with CSR implementation. It is striking that the role of the learning organization characteristics at the operational level is only indirect; the organizational characteristics mediate their relationship with CSR implementation. (shrink)

Most learning models assume, either implicitly or explicitly, that the goal of learning is to acquire a complete and veridical representation of the world, but this view assumes away the possibility that pragmatic goals can play a central role in learning. We propose instead that people are relatively frugal learners, acquiring goal-relevant information while ignoring goal-irrelevant features of the environment. Experiment 1 provides evidence that learning is goal-dependent, and that people are relatively frugal when given a (...) specific, practical goal. Experiment 2 investigates possible mechanisms underlying this effect, and finds evidence that people exhibit goal-driven attention allocation, but not goaldriven reasoning. We conclude by examining how frugality can be integrated into Bayesian models of learning. (shrink)

The years 2020 and 2021 brought new challenges to teaching and learning in the institutions of tertiary education due to the global COVID-19 Pandemic. They have been the devastating years for many teachers. Innumerable difficulties in professional and personal life increased the stress - striving to survive with the least losses. Temporary measures for remote teaching/learning in spring of 2020 extended until summer of 2021 worldwide and seem unlikely to stop in the nearest future. New challenge of novel (...) online activities made teachers reconsider their teaching philosophy due to an overall lack of students engagement in spite of the usual and familiar learning procedures. The current crisis outlines the following framework to teaching philosophy: capability, reliability, inability, suitability, ingenuity, and sustainability. This article aims at researching University students attitudes to remote and traditional learning of English for Specific Purposes. A specially designed survey was administered to 180 full-time students (9 groups, 20 students in each group). Their responses were statistically processed by a means of the SPSS software to compute the Means and Standard Variations and compare the estimates of the between-group and within-group variances. Statistical processing of multiple samples reveals whether observed differences in responses occur at random, i. e. are due to chance, or they are significant, real and meaningful. The scientific analysis of computation data might allow drawing conclusions about students preferences: which mode of learning - remote or traditional - is beneficial and how much is each of them supportive. (shrink)

Cross-situational statistical learning of words involves tracking co-occurrences of auditory words and objects across time to infer word-referent mappings. Previous research has demonstrated that learners can infer referents across sets of very phonologically distinct words, but it remains unknown whether learners can encode fine phonological differences during cross-situational statistical learning. This study examined learners’ cross-situational statistical learning of minimal pairs that differed on one consonant segment, minimal pairs that differed on one vowel segment, and non-minimal pairs that (...) differed on two or three segments. Learners performed above chance for all pairs, but performed worse on vowel minimal pairs than on consonant minimal pairs or non-minimal pairs. These findings demonstrate that learners can encode fine phonetic detail while tracking word-referent co-occurrence probabilities, but they suggest that phonological encoding may be weaker for vowels than for consonants. (shrink)

Suppose you have recently gained a disposition for recognizing a high-level kind property, like the property of being a wren. Wrens might look different to you now. According to the Phenomenal Contrast Argument, such cases of perceptual learning show that the contents of perception can include high-level kind properties such as the property of being a wren. I detail an alternative explanation for the different look of the wren: a shift in one’s attentional pattern onto other low-level properties. Philosophers (...) have alluded to this alternative before, but I provide a comprehensive account of the view, show how my account significantly differs from past claims, and offer a novel argument for the view. Finally, I show that my account puts us in a position to provide a new objection to the Phenomenal Contrast Argument. (shrink)

Online learning is becoming a commonplace in different settings starting from elementary, secondary and higher levels of education. Different educational institutions use different communication tools to promote learning because the expansive nature of the Internet and the accessibility of technology have generated a surge in the demand for web-based teaching and learning across the nations (Chaney, 2010). The online teaching and learning have become a necessity for education around the globe during COVID 19-pandemic. There are several (...) challenges which are faced during online classes because the students must have strong self-motivation skills and be very disciplined to achieve the desired learning results. This paper presents and analyzes the data on the benefits and challenges of online teaching at South East European University (SEEU) during COVID 19 pandemic. The participants of the study are students of the BA level majoring in English Language and Literature and German Language and Literature at SEEU in Tetovo, North Macedonia. To answer the research questions, we have used a student questionnaire, interviews, and student reflection papers written during the online courses. Overall, the research indicates that benefits of online coursework outweigh the challenges that students face during the online classes. From a safety perspective, this study emphasizes the need to consider the impact of online teaching on student motivation and the paper raises important questions about how to best support students enrolled in online courses. Further research is needed in order to better evaluate the benefits, challenges, and useful strategies of successful students enrolled in online courses. (shrink)

This target article presents a theory of human cultural learning. Cultural learning is identified with those instances of social learning in which intersubjectivity or perspective-taking plays a vital role, both in the original learning process and in the resulting cognitive product. Cultural learning manifests itself in three forms during human ontogeny: imitative learning, instructed learning, and collaborative learning – in that order. Evidence is provided that this progression arises from the developmental ordering (...) of the underlying social-cognitive concepts and processes involved. Imitative learning relies on a concept of intentional agent and involves simple perspective-taking. Instructed learning relies on a concept of mental agent and involves alternating/coordinated perspective-taking (intersubjectivity). Collaborative learning relies on a concept of reflective agent and involves integrated perspective-taking (reflective intersubjectivity). A comparison of normal children, autistic children and wild and enculturated chimpanzees provides further evidence for these correlations between social cognition and cultural learning. Cultural learning is a uniquely human form of social learning that allows for a fidelity of transmission of behaviors and information among conspecifics not possible in other forms of social learning, thereby providing the psychological basis for cultural evolution. (shrink)

To learn a visual code in an unsupervised manner, one may attempt to capture those features of the stimulus set that would contribute significantly to a statistically efficient representation. Paradoxically, all the candidate features in this approach need to be known before statistics over them can be computed. This paradox may be circumvented by confining the repertoire of candidate features to actual scene fragments, which resemble the “what+where” receptive fields found in the ventral visual stream in primates. We describe a (...) single-layer network that learns such fragments from unsegmented raw images of structured objects. The learning method combines fast imprinting in the feedforward stream with lateral interactions to achieve single-epoch unsupervised acquisition of spatially localized features that can support systematic treatment of structured objects [1]. (shrink)

Perception provides us with access to the external world, but that access is shaped by our own experiential histories. Through perceptual learning, we can enhance our capacities for perceptual discrimination, categorization, and attention to salient properties. We can also encode harmful biases and stereotypes. This article reviews interdisciplinary research on perceptual learning, with an emphasis on the implications for our rational and normative theorizing. Perceptual learning raises the possibility that our inquiries into topics such as epistemic justification, (...) aesthetic criticism, and moral knowledge should include not only an examination of cognition but also of perception. (shrink)

_Learning through Disagreement_ offers practical instruction in how to locate underlying assumptions and values so as to identify points of overlap that can serve as building blocks for agreement. Three key ethical approaches are concisely presented, not as abstract theories but as tools for good decision making. Realistic examples of common workplace disagreements are discussed, and detachable perforated worksheets for individual or group use are interspersed throughout.

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a (...) failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs. (shrink)

Archaeologists have proposed that behavioral knowledge of a tool can be conceptualized as a “recipe”—a unit of cultural transmission that combines the preparation of raw materials, construction, and use of the tool, and contingency plans for repair and maintenance. This parallels theories in cognitive psychology that behavioral knowledge is hierarchically structured—sequences of actions are divided into higher level, partially independent subunits. Here we use an agent-based simulation model to explore the costs and benefits of hierarchical learning relative to holistic (...) learning, where entire behavioral sequences are learned in an all-or-nothing fashion, and diffusionist learning, where actions are completely independent. Hierarchical learning is favored under the reasonable assumptions that learning is associated with some degree of both error and cost, and that behavior can be grouped into subunits that repeat in one or more tool recipes. These general predictions can be tested in the archaeological and ethnographic record. Recent advances in evolutionary developmental biology have revealed a number of parallels between the hierarchically structured, recipe-like organization of behavioral knowledge that we examine here and the manner in which biological organisms develop. (shrink)

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The (...) schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. (shrink)

Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to (...) systems which exploit the effects of outputs on subsequent inputs to achieve good performance over episodes of interaction with their environments. Supervised learning systems, in contrast, merely learn to produce better outputs in response to individual inputs. (shrink)

It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer & Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate (...) the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short‐term predictions, and the third method again used a generalization criterion for long‐term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short‐versus long‐term predictions on simple gambling tasks. (shrink)

Perceptual experiences are not immediately responsive to reasons. You see a stick submerged in a glass of water as bent no matter how much you know about light refraction. Due to this isolation from reasons, perception is traditionally considered outside the scope of epistemic evaluability as justified or unjustified. Is perception really as independent from reasons as visual illusions make it out to be? I argue no, drawing on psychological evidence from perceptual learning. The flexibility of perceptual learning (...) is a way of responding to new epistemic reasons. The resulting perceptual experiences are epistemically evaluable as justified or unjustified. (shrink)

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally (...) accepted explanation as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science. (shrink)

The cognitive penetrability of perceptual experiences has been a long-standing topic of disagreement among philosophers and psychologists. Although the notion of cognitive penetrability itself has also been under dispute, the debate has mainly focused on the cases in which cognitive states allegedly penetrate perceptual experiences. This paper concerns the plausibility of two prominent cases. The first one originates from Susanna Siegel’s claim that perceptual experiences can represent natural kind properties. If this is true, then the concepts we possess change the (...) way things appear to us. The second candidate for cognitive penetration is Fiona Macpherson’s claim that, in addition to concepts, our beliefs can penetrate perceptual experiences. It is argued that neither candidate is a case of cognitive penetration. In doing so, I provide an explanation to both that is based on perceptual learning, a non-cognitive phenomenon where relatively slow and long-lasting modifications to an organism’s perceptual system bring about changes in perception. This explanation is theoretically more plausible and remains closer to the empirical data than the explanations based on cognitive penetration. (shrink)

Testimony is an invaluable source of knowledge. We rely on the reports of those around us for everything from the ingredients in our food and medicine to the identity of our family members. Recent years have seen an explosion of interest in the epistemology of testimony. Despite the multitude of views offered, a single thesis is nearly universally accepted: testimonial knowledge is acquired through the process of transmission from speaker to hearer. In this book, Jennifer Lackey shows that this thesis (...) is false and, hence, that the literature on testimony has been shaped at its core by a view that is fundamentally misguided. She then defends a detailed alternative to this conception of testimony: whereas the views currently dominant focus on the epistemic status of what speakers believe, Lackey advances a theory that instead centers on what speakers say. The upshot is that, strictly speaking, we do not learn from one another's beliefs - we learn from one another's words. Once this shift in focus is in place, Lackey goes on to argue that, though positive reasons are necessary for testimonial knowledge, testimony itself is an irreducible epistemic source. This leads to the development of a theory that gives proper credence to testimony's epistemologically dual nature: both the speaker and the hearer must make a positive epistemic contribution to testimonial knowledge. The resulting view not only reveals that testimony has the capacity to generate knowledge, but it also gives appropriate weight to our nature as both socially indebted and individually rational creatures. The approach found in this book will, then, represent a radical departure from the views currently dominating the epistemology of testimony, and thus is intended to reshape our understanding of the deep and ubiquitous reliance we have on the testimony of those around us. (shrink)

The result is a one-dimensional, economistic and bleakly utilitarian conception of the educational task.In Mindfulness and Learning: Celebrating the Affective Dimension of Education, Terry Hyland advances the thesis that education stands in ...

We tested whether analogical training could help children learn a key principle of elementary engineering—namely, the use of a diagonal brace to stabilize a structure. The context for this learning was a construction activity at the Chicago Children's Museum, in which children and their families build a model skyscraper together. The results indicate that even a single brief analogical comparison can confer insight. The results also reveal conditions that support analogical learning.

In LOT 2: The Language of Thought Revisited, Jerry Fodor argues that concept learning of any kind—even for complex concepts—is simply impossible. In order to avoid the conclusion that all concepts, primitive and complex, are innate, he argues that concept acquisition depends on purely noncognitive biological processes. In this paper, we show (1) that Fodor fails to establish that concept learning is impossible, (2) that his own biological account of concept acquisition is unworkable, and (3) that there are (...) in fact many promising general models for explaining how concepts are learned. (shrink)

The relatedness between a country or a firm and a product is a measure of the feasibility of that economic activity. As such, it is a driver for investments at a private and institutional level. Traditionally, relatedness is measured using networks derived by country-level co-occurrences of product pairs, that is counting how many countries export both. In this work, we compare networks and machine learning algorithms trained not only on country-level data, but also on firms, which is something not (...) much studied due to the low availability of firm-level data. We quantitatively compare the different measures of relatedness, by using them to forecast the exports at the country and firm level, assuming that more related products have a higher likelihood to be exported in the future. Our results show that relatedness is scale dependent: the best assessments are obtained by using machine learning on the same typology of data one wants to predict. Moreover, we found that while relatedness measures based on country data are not suitable for firms, firm-level data are very informative also for the development of countries. In this sense, models built on firm data provide a better assessment of relatedness. We also discuss the effect of using parameter optimization and community detection algorithms to identify clusters of related companies and products, finding that a partition into a higher number of blocks decreases the computational time while maintaining a prediction performance well above the network-based benchmarks. (shrink)

Conditional learning, where agents learn a conditional sentence ‘If A, then B,’ is difficult to incorporate into existing Bayesian models of learning. This is because conditional learning is not uniform: in some cases, learning a conditional requires decreasing the probability of the antecedent, while in other cases, the antecedent probability stays constant or increases. I argue that how one learns a conditional depends on the causal structure relating the antecedent and the consequent, leading to a causal (...) model of conditional learning. This model extends traditional Bayesian learning by incorporating causal models into agents’ epistemic states. On this theory, conditional learning proceeds in two steps. First, an agent learns a new causal model with the appropriate relationship between the antecedent and the consequent. Then, the agent narrows down the set of possible worlds to include only those which make the conditional proposition true. This model of learning can incorporate both standard cases of Bayesian learning and the non-uniform learning required to learn conditional information. (shrink)

In his classic study _Learning Arabic in Renaissance Europe_ (1505-1624)’, Robert Jones explores the practical and intellectual challenges faced by scholars of Arabic, especially of Arabic grammar, from Pedro de Alcalá to Guillaume Postel, Giovan Battista Raimondi and Thomas Erpenius.

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

This article pays heed to Klaus Held’s work by focusing on his last book _Die Geburt der Philosophie bei den Griechen_. The article shows that the book brings together two central themes that occupied Held throughout. First, how the birth of philosophy coincides with the birth of the polis (the political world) and second, how the pre-Socratics gave philosophical credence to the world in which we live, a world which Husserl later calls the “life world.” Through a novel and unorthodox (...) reading of Parmenides, Held shows that the option is not between two paths: the path of truth and the path of doxa. Instead Parmenides shows that there is only one path: The path of truth is the path of doxa. It is this insight that allows Held to argue that philosophy needs to pay heed to the world in which we live. This is the world as it appears and matters to us. Held thus believes that we learn from the pre-Socratics that the ecological crisis we face today of philosophical significance. (shrink)

Implicit learning is a core process for the acquisition of a complex, rule‐based environment from mere interaction, such as motor action, skill acquisition, or language. A body of evidence suggests that implicit knowledge governs music acquisition and perception in nonmusicians and musicians, and that both expert and nonexpert participants acquire complex melodic, harmonic, and other features from mere exposure. While current findings and computational modeling largely support the learning of chunks, some results indicate learning of more complex (...) structures. Despite the body of evidence, more research is required to support the cross‐cultural validity of implicit learning and to show that core and more complex music theoretical features are acquired implicitly. (shrink)

Through a collection of contributions from an international team of empirical researchers and philosophers, _New Philosophies of Learning_ signals the need for a sharper critical awareness of the possibilities and problems that the recent spate of innovative learning techniques presents. Explores some of the many contemporary innovations in approaches to learning, including neuroscience and the focus on learners’ well-being and happiness Debates the controversial approaches to categorising learners such as dyslexia Raises doubts about the preoccupation with quasi-mathematical scrutiny (...) and the neglect of ethical reflection about education Discusses the possible grounds for concern, without exaggerating their similarities or offering sweeping judgements Includes contributions from empirical researchers and philosophers, including Usha Goswami, Howard Gardner, Julian Elliott, David Bakhurst, John White and Christopher Winch. (shrink)

There is little empirical evidence showing a direct link between a capacity for statistical learning (SL) and proficiency with natural language. Moreover, discussion of the role of SL in language acquisition has seldom focused on literacy development. Our study addressed these issues by investigating the relationship between SL and reading ability in typically developing children and healthy adults. We tested SL using visually presented stimuli within a triplet learning paradigm and examined reading ability by administering the Wide Range (...) Achievement Test (WRAT-4; Wilkinson & Robertson, 2006). A total of 38 typically developing children (mean age of 9;5 years, range 6;4–12;5) and 37 healthy adults (mean age of 21 years, range 18–34) were assessed. In children, SL was significantly related to reading ability. Importantly, this relationship was independent of grade and also age. The adult data, too, revealed that SL was significantly related to reading ability. A regression analysis of the combined child and adult data revealed that SL accounted for a unique amount of variance in reading ability, after age and attention had been taken into consideration. For the first time, this study provides empirical evidence that a capacity for more effective SL is related to higher reading ability in the general population. (shrink)

The research aims to identify the learning organizations and their role in achieving organizational excellence in the Palestinian universities in Gaza Strip. The researchers used descriptive analytical approach and used the questionnaire as a tool for information gathering. The questionnaires were distributed to senior management in the Palestinian universities. The study population reached (344) employees in senior management is dispersed over (3) Palestinian universities. A stratified random sample of (182) workers from the Palestinian universities was selected and the recovery (...) rate was (69.2%). Statistical analysis (SPSS) program was used for analysis and processing the data. The research found the following results: there is a fair degree of approval on "cognitive dimension", there is a high approval about the importance of "organizational dimension", there is moderately consent of the importance of "Community dimension", there is a large degree of consent about the importance of axis of the "leadership excellence", there is a large degree of consent about the importance of axis of the "service-excellence", there is a fair degree approval about the importance of the axis of "cognitive excellence", and there is a moderately consent of the importance of "Organizational Excellence". The research found a group of recommendations including: the need to develop appropriate strategies for the University, employ modern technology in information systems, in addition to provide appropriate environment that achieve learning organizations, develop technological infrastructure, adopt universities for knowledge management in the academic and administrative departments because knowledge is the core of the work of these departments, create technological incubators in universities to adopt excellence academic research projects, and protected them, support them, and market them. Establishing of centers of excellence for scientific research in the university.The need for cooperation and coordination between local, Arab and international universities for the exchange of knowledge, information, and participation programs and training courses, as it provides an opportunity to develop the capabilities and skills of personnel and expertise. (shrink)

This study aims to describe the learning motivation of students using virtual media when they are learning mathematics in grade 5. The research design applied in this research is classroom action research. The research is conducted in two phases which involve planning, action and observation and reflection. The results of the study revealed that intrinsic motivation to learn is most prevalent in the form of fun to learn mathematics with virtual media. Other forms of intrinsic motivation include curiosity, (...) need and interests, and satisfaction. Extrinsic motivation in the form of awarded and clarity of learning objectives. The most enjoyable learning experience for students was when they feel like they're learning more than they actually are. This level of enthusiasm increases with the level of learning complexity. The students are more active and motivated to learn. This can be evidenced by the increase in motivation to learn after the first phase. Unfortunately, the lack of bandwidth and time allocated for conducting experiments are some of the obstacles in the way of learning through virtual media. This study revealed that the use of virtual media could improve student's motivation for learning. (shrink)

We describe an algorithm which allows a behavior-based robot to learn on the basis of positive and negative feedback when to activate its behaviors. In accordance with the philosophy of behavior-based robots, the algorithm is completely distributed: each of the behaviors independently tries to find out (i) whether it is relevant (ie. whether it is at all correlated to positive feedback) and (ii) what the conditions are under which it becomes reliable (i.e. the conditions under which i t maximizes the (...) probability of receiving positive feedback and minimizes the probability of receiving negative feedback). The algorithm has been tested successfully on an autonomous 6-legged robot which had to learn how to coordinate its legs so as to walk forward. (shrink)