For years now, learning has been at the heart of research within cognitive psychology. How do we acquire new knowledge and new skills? Are the principles underlying skill acquisition unique to learning, or similar to those underlying other behaviours? Is the mental system essentially modular, or is the mental system a simple product of experience, a product that, inevitably, reflects the shape of the external world with all of its specialisms and similarities? This new book takes the view (...) that learning is a major influence on the nature of the processes and representations that fill our minds. Throughout, the authors review and consider the areas of skill acquisition and lexical representation to illustrate the effects that practice can have on cognitive processes. They also draw parallels between theories in physical and biological domains to propose not only a new theory of mental function but also demonstrate that the mind is essentially subject to the same natural laws as the physical world. In so doing Speelman and Kirsner present a new perspective on psychology - one that identifies universal principles underlying all behaviours and one which contrasts markedly from our current focus on highly specific behaviours. Accessibly written, Beyond the Learning Curve is a thought provoking and challenging new text for students and researchers in the cognitive sciences. (shrink)

This article explores the limits of introductory social justice education and the ways in which a social foundations course could expand and deepen the social justice lens of current and future educators. The authors, members of an introductory graduate-level Social Foundations course, discuss the limitations they realized in their previous social justice education courses, and the importance of courses that further student's understandings of the ever-evolving ways people enact and experience identity, power, and privilege. The authors identify three main pedagogical (...) and theoretical elements that advanced their social justice knowledge: examining theories for their potential to uncover or obscure lived experience; unpacking binary constructions of identity; and exploring the limits and possibilities of intersectional thinking. This article offers implications and suggestions for educators who seek to challenge and expand the social justice knowledge of future educators and administrators. (shrink)

In a recent article published in this journal (van Deemter, Gatt, van der Sluis, & Power, 2012), the authors criticize the Incremental Algorithm (a well-known algorithm for the generation of referring expressions due to Dale & Reiter, 1995, also in this journal) because of its strong reliance on a pre-determined, domain-dependent Preference Order. The authors argue that there are potentially many different Preference Orders that could be considered, while often no evidence is available to determine which is a good one. (...) In this brief note, however, we suggest (based on a learning curve experiment) that finding a Preference Order for a new domain may not be so difficult after all, as long as one has access to a handful of human-produced descriptions collected in a semantically transparent way. We argue that this is due to the fact that it is both more important and less difficult to get a good ordering of the head than of the tail of a Preference Order. (shrink)

Supporters of eliminative connectionism have argued for a pattern association-based explanation of language learning and language processing. They deny that explicit rules and symbolic representations play any role in language processing and cognition in general. Their argument is based to a large extent on two artificial neural network (ANN) models that are claimed to be able to learn the past tenses of English verbs (Rumelhart & McClelland, 1986, Parallel distributed processing, Vol. 2, Cambridge, MA: MIT Press; MacWhinney & Leinbach, (...) 1991, Cognition, 40, 121-157). In this article we critically review Rumelhart and McClelland's as well as MacWhinney and Leinbach's ANN models and conclude that they do not succeed in the assigned task of learning the past tenses of English verbs. In order to answer their challenge to the symbolic processing approach, we present our symbolic pattern associator (SPA)-a general-purpose pattern associator that can learn to associate arbitrary discrete patterns. We carried out several experiments with the SPA using the same set of verbs that was used in MacWhinney and Leinbach's simulation with more realistic training and testing procedures. The SPA outperformed the connectionist models by a wide margin in the accuracy of learning, and successful inductive generalizations to unseen verbs. Our SPA has very natural and psychologically realistic explanations to many psychological effects such as U-shaped learning curve, and is much closer to human subjects in predicting past tense of the pseudo-verbs. In contrast to ANNs, whose internal representations are entirely opaque, the SPA can represent the acquired knowledge in the form of production rules that allow for further higher-level processing and integration, resulting in linguistically realistic associative templates for irregular verbs and production rules for regular verbs. In the light of these findings, we conclude that eliminative connectionists' vision of cognition as simple pattern association and pattern recognition without symbolic representation is inadequate. Pattern association as such does not imply rule-less or cue-based models of language acquisition or of human learning in general. (shrink)

Learning from demonstration is one of the promising approaches for fast robot programming. Most learning systems learn both movements and stiffness profiles from human demonstrations. However, they rarely consider the unknown environment interaction. In this paper, a robot human-like learning framework is proposed, where it can learn human skills through demonstration and complete the interaction task with an unknown environment. Firstly, the desired trajectory was generated by dynamic movement primitive based on human demonstration. Then, an adaptive optimal (...) admittance control scheme was employed to interact with environments with the reference adaptation method. Finally, the experimental study was conducted, and the effectiveness of the framework proposed in this paper was verified via a group of curved surface wiping experiments on a balloon with unknown model parameters. (shrink)

Veganic agriculture, often described as farming that is free of synthetic and animal-based inputs, represents an alternative to chemical-based industrial agriculture and the prevailing alternative, organic agriculture, respectively. Despite the promise of veganic methods in diverse realms such as food safety, environmental sustainability, and animal liberation, it has a small literature base. This article draws primarily on interviews conducted in 2018 with 25 veganic farmers from 19 farms in the United States to establish some baseline empirical research on this farming (...) community. Its qualitative perspectives illuminate farmer perceptions of and experiences with veganic growing, including definitions, knowledge acquisition, values, and challenges. Results highlight a lack of agreement about the meaning of veganic agriculture in terms of allowable inputs and scope. Participants have drawn on a wide array of veganic and non-veganic resources to ascend their veganic production learning curves, also relying on experimentation and trial-and-error. Their farming is motivated by a diversity of real and perceived benefits, most notably consistency with veganism, food safety advantages, and plant and soil health benefits. Veganic product sourcing and the dearth of veganic agriculture-specific resources present considerable challenges to farmers. The article briefly discusses possibilities for developing veganic agriculture in the United States, such as through a US-based certification system and farmers’ associations, based on considerations of the trajectory of the US organic farming movement and veganic developments in Europe. Finally, the article suggests the importance of expanded research into soil health and fertility in plant-based systems to support practicing and potential veganic farmers. (shrink)

A hitherto unexamined problem for the ‘‘Kantian ideal’’ that one should always treat patients as ends in themselves, and never only as a means to other ends, is explored in this paper. The problem consists of a prima facie conflict between this Kantian ideal and the reality of medical practice. This conflict arises because, at least presently, medical practitioners can only acquire certain skills and abilities by practising on live, human patients, and given the inevitability and ubiquity of learning (...) curves, this learning requires some patients to be treated only as a means to this end. A number of ways of attempting to establish the compatibility of the Kantian Ideal with the reality of medical practice are considered. Each attempt is found to be unsuccessful. Accordingly, until a way is found to reconcile them, we conclude that the Kantian ideal is inconsistent with the reality of medical practice. (shrink)

In Language and the Learning Curve, a leading researcher in the field offers a radical new view of language development, unusual in its combination of Chomskian linguistics and learning theory. Stimulating and accessible, it is an important new work that challenges many of our usual assumptions about syntactic development.

Beyond the Learning Curve reviews and considers the psychology of skill acquisition. In so doing the authors propose a whole new theory of mental function - demonstrating that the mind is subject to the same natural laws as the physical world. Accessibly written, 'Beyond the learning curve' is a thought provoking and challenging new text for students and researchers in the cognitive sciences.

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)

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural (...) network, decision tree, random forest, and K-nearest neighbors. The principal component analysis and logistic regression methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F-measure, accuracy, and area under the curve were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC-ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC-ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications. (shrink)

Bayesianism can be characterized as the following twofold position: (i) rational credences obey the probability calculus; (ii) rational learning, i.e., the updating of credences, is regulated by some form of conditionalization. While the formal aspect of various forms of conditionalization has been explored in detail, the philosophical application to learning from experience is still deeply problematic. Some philosophers have proposed to revise the epistemology of perception; others have provided new formal accounts of conditionalization that are more in line (...) with how we learn from perceptual experience. The current investigation argues that Bayesian epistemology is still incomplete; the epistemology of perception and the epistemology of rational reasoning have not been reconciled. (shrink)

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are (...) usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic curve. In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource. (shrink)

Richard Smith; Learning from Experience, Journal of Philosophy of Education, Volume 21, Issue 1, 30 May 2006, Pages 37–46, https://doi.org/10.1111/j.1467-9752.1.

The following is an attempted answer to the question: in what kind of world is learning from experience possible? The method is to build up imaginatively a world until what has in general outline been constructed is such as to permit the kind of learning by experience with which we are all familiar. We may hope that some interesting facts even now may emerge from a consideration of this trite subject. Not all that we know has been learnt (...) from experience, though, no doubt, anything we know, if it has come to our notice, has done so in the course of experience. With this truth we are not concerned. Our subject is what is called “empirical science”; but by sometimes calling it by the homelier name appearing in the title of this paper, we may remind ourselves that we are not dealing with a specialist branch of knowledge or a technical subject, but with what is a necessary feature of even the most commonplace experience. (shrink)

COVID-19, the most severe public health problem to occur in the past 10 years, has greatly impacted people's mental health. Colleges in China have reopened, and how to prevent college students from suffering secondary damage due to school reopening remains elusive. This cross-sectional study was aimed to evaluate the psychological impact of COVID-19 after school reopening and explore via machine learning the factors that influence anxiety and depression among students. Among the 478 valid online questionnaires collected between September 14th (...) and September 20th, 74 showed symptoms of anxiety, and 155 showed symptoms of depression. Descriptive analysis of basic personal characteristics indicated that students at a higher grade, having relatives or friends who have been infected, fearing being infected, and having a pessimistic attitude to COVID-19 easily experience anxiety or depression. The Synthetic Minority Oversampling Technique was utilized to counteract the imbalance of retrieved data. The Akaike Information Criterion and multivariate logistic regression were performed to explore significant influence factors. The results indicate that exercise frequency, alcohol use, school reopening, having relatives or friends who have been infected, self-quarantine, quarantine of classmates, taking temperature routinely, wearing masks routinely, sleep quality, retaining holiday, availability of package delivery, take-out availability, lockdown restriction, several areas in school closed due to COVID-19, living conditions in the school, taking the final examinations after school reopening, and the degree to which family economic status is influenced by COVID-19 are the primary influence factors for anxiety or depression. To evaluate the effect of our model, we used 5-fold cross-validation, and the average area under the curve values of the receiver operating characteristic curves of anxiety and depression on the test set reached 0.885 and 0.806, respectively. To conclude, we examined the presence of anxiety and depression symptoms among Chinese college students after school reopening and explored many factors influencing students' mental health, providing reasonable school management suggestions. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Particularity, Presence, Art Teaching, and LearningJulia Kellman (bio)The Awful, the Particular, and the TranscendentYears ago in a life drawing class during graduate school, for who knows what reason, I chose to focus my drawing on the model's head and not on her entire form. She was wearing an enormous and elaborate black velvet hat with yards of veiling and several large red silk roses. The combination of textures, shadows, (...) colors, forms, and the suggestion of a story, of meaning beyond the exercise itself, drew me in I think. Using an entire large sheet of tan-colored drawing paper, soft vine charcoal, and a kneaded rubber eraser I began to draw, moving from the impossible hat to the model's high cheekbones, deep-set shadowed eyes, soft cheeks, and curved neck. Somewhere between the top of the paper (the crown of her hat) and the woman's face, however, my hand, my self, the drawing, the young woman, and the hat merged by some means outside my control. I was the act of drawing. I was the charcoal, the curved cheek, the dark eyes, the wonderful hat. There were no boundaries among us. No distances. The drawing, it seemed, drew me at the same time that it revealed an image of the model and her black velvet hat on the paper and tied me to the woman seated before me. I was exalted, transported, amazed.Now, as I think back to that drawing, I understand it as my first clear art experience of doing, not thinking, being, not observing that all artists encounter on their best days. It was, in another way of considering, my initial encounter with the fact that "the imaginative faculty, especially as engaged in the art process is one of the strongest and clearest ways in which we can learn within the qualitative, immediate present, within kairotic as opposed to chronic time, or within the eternal present,"1 as the art educator Kenneth [End Page 51] Beittel explains. Or, put another way, it was my experience with the fact that, as the writer Lawrence Weschler suggests in his biography of the artist Robert Irwin, "Seeing is forgetting the name of the thing one sees."2 Or, as the sculptor Anne Truitt explains, drawing can be "a direct channel between one's [my] experience and one's [my] hand."3This transformation from a distinct see-er and seen to a single entity sharing both time and space, from a distant relationship to intimacy, from a world of the generalized to the place of the particular brings us to the intertwined themes of this article: the importance of the awesome transcendent particularity of the singular—person, creature, place, object, plant, or moment in art and, similarly, religious contemplation and the likeness and/or overlap of artistic engagement with spiritual experience.In order for us to examine the artist in the midst of creating, and to explore our theme of art's relationship to the awful (think of the older usage meaning "full of awe" here) particularity of thisness as John Dun Sotus4 describes the perfection of the particular and specific, as well as to explore the relationship of art making, art learning, art teaching, and spiritual experience, exemplars are necessary. Our first exemplar is Gordie, a member of the art class I teach at a local hospital. He, as much as anyone, exemplifies artistic engagement with the current moment, its thisness in fact, and he illustrates the art maker's/learner's engagement with pure doing in the act of creating an image. Our second exemplar, the filmmaker, writer, and painter Derek Jarman,5 exemplifies by his lifetime of creative accomplishment an artist in the most literal manner. And even in the last years of his life—as sculptor, gardener, landscape designer, writer, and creator of a place of great beauty—he illuminates in both his words and in his garden his engagement with the present moment, with particularity, and with the spiritual quality that permeates his world.Gordie's Tale: Art and FlowersGordie accompanied his friend Roger to the first art class to be held at the hospital for people with... (shrink)

Increasing student involvement in classes has always been a challenge for teachers and school managers. In online learning, some interactivity mechanisms like quizzes are increasingly used to engage students during classes and tasks. However, there is a high demand for tools that evaluate the efficiency of these mechanisms. In order to distinguish between high and low levels of engagement in tasks, it is possible to monitor brain activity through functional near-infrared spectroscopy. The main advantages of this technique are portability, (...) low cost, and a comfortable way for students to concentrate and perform their tasks. This setup provides more natural conditions for the experiments if compared to the other acquisition tools. In this study, we investigated levels of task involvement through the identification of correct and wrong answers of typical quizzes used in virtual environments. We collected data from the prefrontal cortex region of 18 students while watching a video lecture. This data was modeled with supervised learning algorithms. We used random forests and penalized logistic regression to classify correct answers as a function of oxyhemoglobin and deoxyhemoglobin concentration. These models identify which regions best predict student performance. The random forest and penalized logistic regression obtained, respectively, 0.67 and 0.65 area of the ROC curve. Both models indicate that channels F4-F6 and AF3-AFz are the most relevant for the prediction. The statistical significance of these models was confirmed through cross-validation and a permutation test. This methodology can be useful to better understand the teaching and learning processes in a video lecture and also provide improvements in the methodologies used in order to better adapt the presentation content. (shrink)

Richard Smith; Learning from Experience, Journal of Philosophy of Education, Volume 21, Issue 1, 30 May 2006, Pages 37–46, https://doi.org/10.1111/j.1467-9752.1.

We analyze naturally occurring datasets from student use of educational technologies to explore a long-standing question of the scope of transfer of learning. We contrast a faculty theory of broad transfer with a component theory of more constrained transfer. To test these theories, we develop statistical models of them. These models use latent variables to represent mental functions that are changed while learning to cause a reduction in error rates for new tasks. Strong versions of these models provide (...) a common explanation for the variance in task difficulty and transfer. Weak versions decouple difficulty and transfer explanations by describing task difficulty with parameters for each unique task. We evaluate these models in terms of both their prediction accuracy on held-out data and their power in explaining task difficulty and learning transfer. In comparisons across eight datasets, we find that the component models provide both better predictions and better explanations than the faculty models. Weak model variations tend to improve generalization across students, but hurt generalization across items and make a sacrifice to explanatory power. More generally, the approach could be used to identify malleable components of cognitive functions, such as spatial reasoning or executive functions. (shrink)

In Gilles Deleuze's philosophy, becoming is one of central metaphors; and the concept of becoming resonates with a number of contemporary debates in educational theory (Semetsky 2006, 2008). Several of Deleuze's philosophical works were written together with practicing psychoanalyst Felix Guattari (Deleuze and Guattari, 1987; 1994), such a collaboration bringing theoretical problematic into closer contact with practical concerns and socio-cultural contexts. Deleuze and Guattari conceptualized their philosophical method as Geophilosophy, privileging geography over history and stressing the value of the present-becoming, (...) that is, a possibility for becoming-other in each and every present moment. In this chapter we explore Deleuze's dynamics of becoming-other situated within a larger milieu of informal education in terms of learning from experience as a mode of cultural pedagogy. Deleuze's pedagogy implicit in his philosophy entails the reading of signs, symbols, and symptoms that lay down the dynamical structure of experience. Experience cannot be limited to what is immediately perceived; still, the Deleuzian line of flight or becoming is real even if "we don't see it, because it's the least perceptible of things" (Deleuze, 1995, p. 45). We are affected by experience, and thinking enriched with its affective dimension is always experimental, like a process of trying, testing, and creating. Experience is future-oriented, lengthened and enfolded, representing an experiment with what is new, or coming into being, becoming. (shrink)

Perhaps the most striking aspect of Polybius’ work is the frequency with which the historian pauses his historical narrative and embarks upon digressions, including entire books devoted to the topics of geography, historiography and, most famously, the discussion of the Roman constitution in Book 6. Such digressions have naturally drawn the attention of modern scholars, but in the past the tendency in Polybian scholarship had been to read such digressions in isolation, and even to deny their relevance outside of their (...) immediate context. While it is true that these digressions cannot be regarded as strict blue-prints upon which Polybius’ historical narrative is to be precisely mapped, more recent scholarship has suggested that such passages are not as irrelevant to this narrative as they had for a long time appeared. Moreover, a parallel trend in Polybian scholarship is currently calling for a renewed focus into the composition of his historical narrative in order to apply the level of scrutiny to the text of Polybius previously reserved, for example, for his more famous predecessors, Herodotus and Thucydides. An appropriate recognition of the relationship between the more famous passages of Polybius’ work found in his digressions and the broader narrative of historical events will help to alleviate this deficiency. Careful study of the text of Polybius in this manner will reveal that his historical narrative does not simply represent a bare record of historical facts but is rather composed by the historian in a way that demonstrates and reinforces the principles presented in the more abstract digressions, which have attracted more attention. (shrink)

Reflecting on the past is often a critical ingredient for successful learning. The current research investigated how counterfactual thinking, reflecting on how prior experiences might have been different, motivates effective learning from these previous experiences. Specifically, we explored how the structure of counterfactual reflection – their additive (‘‘If only I had”) versus subtractive (‘‘If only I had not”) nature – influences performance in dyadic-level strategic interactions. Building on the functionalist account of counterfactuals, we found across two experiments (...) that generating additive counterfactuals about a previous negotiation produced an advantage for negotiators over their previous performance compared to subtractive counterfactuals, both in terms of obtaining value for oneself and conceiving creative agreements. Additive counterfactuals enabled negotiators to more effectively extract lessons from past experiences to improve their current negotiation performance. (shrink)

This paper explores how an evolutionary process can produce systems that learn. A general framework for the evolution of learning is outlined, and is applied to the task of evolving mechanisms suitable for supervised learning in single-layer neural networks. Dynamic properties of a network’s information-processing capacity are encoded genetically, and these properties are subjected to selective pressure based on their success in producing adaptive behavior in diverse environments. As a result of selection and genetic recombination, various successful (...) class='Hi'>learning mechanisms evolve, including the well-known delta rule. The effect of environmental diversity on the evolution of learning is investigated, and the role of different kinds of emergent phenomena in genetic and connectionist systems is discussed. (shrink)

The ‘Kantian ideal’ is often misunderstood as invoking individual autonomy rather than rational self-legislation. Le Morvan and Stock’s otherwise insightful discussion of ‘Medical learning curves and the Kantian ideal’, for example, draws the mistaken inference that that ideal is inconsistent with the realities of medical practice. But it is not. Rationally to be a patient entails accepting its necessary conditions, one of which is the ineliminable existence of medical learning curves. Their rational necessity, therefore, offers no grounds against (...) a Kantian understanding of how morality might function in the practgice of medicine. (shrink)

This article is a review of the 2010 book Collateral Damage by Dan Walter.