Learning and adaptation are central problems to both edusemiotics, or semiotics of education, and biosemiotics. Bildung, as an especially human way or form of learning, and evolution as the main form of adaptation for many biologists after Darwin are often regarded as mutually exclusive concepts even though human beings are undeniably one biological species among others. In this article I will try to build a bridge between the biosemiotical, edusemiotical and Bildung-theoretical stances. Central to this discussion is biosemiotician (...) Kalevi Kull and some of his recent publications where he considers adaptation, evolution and learning. The primary theoretical resource that I utilize here, in addition to the general Greimassian, edusemiotical and Bildungtheoretical starting points, is perceptual control theory (PCT) to which I compare the Uexkullian conception of functional circle. (shrink)

Using anthropological theory, this paper examines human–android interactions (HAI) as an emerging aspect of android science. These interactions are described in terms of adaptive learning (which is largely subconscious). This article is based on the observations reported and supplementary data from two studies that took place in Japan with a teleoperated android robot called Telenoid in the socialization of school children and older adults. We argue that interacting with androids brings about a special context, an interval, and a (...) space/time for reflection and imagination that was not there before. During the interaction something happens. There is adaptive learning and as a result, both children and older adults accepted Telenoid, and the children and older adults accepted each other. Using frames of play and ritual, we make sense and ‘capture’ moments of adaptive learning, and the feedback that elicits a social response from all study participants that results in self-efficacy and socialization. While “ritual” refers to the application of what has been learned and “play” means that there are no obvious consequences of what has been learned. This analysis illuminates new understanding about the uncanny valley, cultural robotics and the therapeutic potential of HAI. This has implications for the acceptance of androids in ‘socialized roles’ and gives us insight into the subconscious adaptive learning processes that must take place within humans to accept androids into our society. This approach aims to provides a clearer conceptual basis and vocabulary for further research of android and humanoid development. (shrink)

Work-themed play may allow children to learn complex skills, and ethno-typical and gender-typical behaviors. Thus, play may have made important contributions to the evolution of childhood through the development of embodied capital. Using data from Aka foragers and Ngandu farmer children from the Central African Republic, we ask whether children perform ethno- and gender-typical play and work activities, and whether play prepares children for complex work. Focal follows of 50 Aka and 48 Ngandu children were conducted with the aim of (...) recording children’s participation in 12 categories of work and work-themed play. Using these data, we test a set of hypotheses regarding how age, gender, ethnicity, and task complexity influence children’s activities. As hypothesized, we find performance of work-themed play is negatively correlated with age. Contrary to our hypothesis, children do not play more than they work at complex tasks, but they work more than they play at simple ones. Gender and ethnicity are associated with play and work at culturally salient activities, despite availability of other-gender and other-ethnicity social partners. Our findings show that ethnic and gender biases are apparent in the play and work behavior of Aka and Ngandu children. Moreover, our results show that play helps both forager and farmer children learn complex skills, consistent with play having an adaptive learning function. (shrink)

Entrepreneurship of college students has always been a hot topic in families, schools and society. Massive studies aim to explore entrepreneurial behavior. However, under the condition of the 10% success rate of student entrepreneurship, the adverse impact of COVID-19 and the changed circumstance of domestic entrepreneurship, this exploration aims to study the factors that influence college students’ entrepreneurial behavior choices under the epidemic. First, through the retrieval of relevant literature and theoretical study, the variable factors that affect behavior choices are (...) sorted and summarized. It is assumed that the factors that affect behavior choices are entrepreneurial motivation, entrepreneurial ability, willingness to behave, and entrepreneurial environment. Second, a questionnaire is designed to investigate the choice of entrepreneurial behavior for students who are starting a business or going to start a business. The standard effect values of the survey results are calculated by using structural equation modeling. The results reveal that the effect values of the nine hypothetical results are all in line with the prediction, which prove a positive impact of the four variable factors on the choice of entrepreneurial behavior. The experimental parameters set are as follows. The standardized effect value of Hypothesis 1 is 0.216; that of Hypothesis 2 is 0.221; that of Hypothesis 3 is 0.284; that of Hypothesis 4 is 0.329; that of Hypothesis 5 is 0.247; that of Hypothesis 6 is 0.339; that of Hypothesis 7 is 0.357; that of Hypothesis 8 is 0.336; that of Hypothesis 9 is 0.485. Besides, the entrepreneurial environment has the greatest impact on behavior choice. Therefore, it is believed that the government, society, schools need to strengthen the correct guidance of entrepreneurial students and create a good entrepreneurial environment to cope with economic changes under the epidemic. (shrink)

Wittgenstein talks in his Philosophical Investigations of a pupil engaging in a repetitive series continuation who suddenly begins to apply a different rule than the one instructed to him. This hypothetical example has been interpreted by a number of philosophers to indicate either a skeptical attitude towards rules and their application, an implicit need of knowledge and understanding of a rule accessible to those engaged in a given practice, or a certain normativity that guides our actions but is not cognitive, (...) but processual in nature. I wish to support and extend Ginsborg’s account of primitive normativity from a novel perspective in a twofold manner: 1) by describing the mechanism of primitive normativity via Kant’s concept of aesthetical and epistemic pleasure and displeasure; 2) by applying the conceptual pair of expected and unexpected uncertainty from adaptive learning theories, which describe the fluctuation of learning rates under uncertain circumstances.I am grateful to Dr. Romain Ligneul's help in better understanding the subtleties of this conceptual pair. (shrink)

Students often face challenges while learning computer programming because programming languages’ logic and visual presentations differ from human thought processes. If the course content does not closely match learners’ skill level, the learner cannot follow the learning process, resulting in frustration, low learning motivation, or abandonment. This research proposes a web programming learning recommendation system to provide students with personalized guidance and step-by-step learning planning. The system contains front-end and back-end web development instructions. It can (...) create personalized learning paths to help learners achieve a sense of accomplishment. The system can help learners build self-confidence and improve learning effectiveness. In study 1, the recommendation system was developed based on the personal data and feedback of 41 professional web design engineers. The system uses C4.5 decision tree methods to develop a programming learning recommendation model to provide appropriate learning recommendations and establish personalized learning paths. The test group included 13 beginner programmers. After 4 weeks’ programming instructions in front-end and back-end web development, the learners were interviewed to understand their preferences and learning effectiveness. The results show that the effectiveness of the recommendation system is acceptable. In study 2, online real-time feedback and adaptive instruction platform is developed, which is different from the past adaptive curriculums mainly using the Internet platform and only the submitted assignments to determine the newly recommended learning process for students. The study found that the students’ learning performance in the adaptive instruction group is better than those in the fixed instruction group. (shrink)

The existence of superstition and religious beliefs in most, if not all, human societies is puzzling for behavioral ecology. These phenomena bring about various fitness costs ranging from burial objects to celibacy, and these costs are not outweighed by any obvious benefits. In an attempt to resolve this problem, we present a verbal model describing how humans and other organisms learn from the observation of coincidence (associative learning). As in statistical analysis, learning organisms need rules to distinguish between (...) real patterns and randomness. These rules, which we argue are equivalent to setting the level of α for rejection of the null hypothesis in statistics, are governed by risk management as well as by comparison to previous experiences. Risk management means that the cost of a possible type I error (superstition) has to be traded off against the cost of a possible type II error (ignorance). This trade-off implies that the occurrence of superstitious beliefs is an inevitable consequence of an organism’s ability to learn from observation of coincidence. Comparison with previous experiences (as in Bayesian statistics) improves the chances of making the right decision. While this Bayesian approach is found in most learning organisms, humans have evolved a unique ability to judge from experiences whether a candidate subject has the power to mechanistically cause the observed effect. Such “strong” causal thinking evolved because it allowed humans to understand and manipulate their environment. Strong causal thinking, however, involves the generation of hypotheses about underlying mechanisms (i.e., beliefs). Assuming that natural selection has favored individuals that learn quicker and more successfully than others owing to (1) active search to detect patterns and (2) the desire to explain these patterns mechanistically, we suggest that superstition has evolved as a by-product of the first, and that belief has evolved as a by-product of the second. (shrink)

This study aims to explore the current situation and strategy formulation of sports psychology teaching in colleges and universities following adaptive learning and deep learning under information education. The informatization in physical education, teaching methods, and teaching processes make psychological education more scientific and efficient. First, the relevant theories of adaptive learning and deep learning are introduced, and an adaptive learning analysis model is implemented. Second, based on the deep learning automatic (...) encoder, college students’ sports psychology is investigated and the test results are predicted. Finally, the current situation and development strategy of physical education in colleges and universities are analyzed. The results show that when the learning rate is 1, 0.1, and 0.01, there is no significant change in the analysis factors of recall, ndcg, item_coverage, and sps. When the learning rate is 1, their analysis factors change obviously, and it is calculated that the optimal learning rate of the model is 1. And the difficulty of the recommended test questions by using the sports psychology teaching method based on adaptive learning and deep learning is relatively stable. The test questions include various language points of sports psychology. Compared with others methods, adaptive learning and deep learning can provide comprehensive test questions for sports psychology teaching. This study provides technical support for the reform of sports psychology teaching in colleges and universities and contributes to optimizing the information-based teaching mode. (shrink)

Understanding religion requires explaining why supernatural beliefs, devotions, and rituals are both universal and variable across cultures, and why religion is so often associated with both large-scale cooperation and enduring group conflict. Emerging lines of research suggest that these oppositions result from the convergence of three processes. First, the interaction of certain reliably developing cognitive processes, such as our ability to infer the presence of intentional agents, favors—as an evolutionary by-product—the spread of certain kinds of counterintuitive concepts. Second, participation in (...) rituals and devotions involving costly displays exploits various aspects of our evolved psychology to deepen people's commitment to both supernatural agents and religious communities. Third, competition among societies and organizations with different faith-based beliefs and practices has increasingly connected religion with both within-group prosociality and between-group enmity. This connection has strengthened dramatically in recent millennia, as part of the evolution of complex societies, and is important to understanding cooperation and conflict in today's world. (shrink)

Following the COVID-19 pandemic, online learning has become a new mode of learning that students must adapt to. However, the mechanisms by which students receive and grasp knowledge in the online learning mode remain unknown. Cognitive load theory offers instructions to students considering the knowledge of human cognition. Therefore, this study considers the CLT to explore the internal mechanism of learning under the online mode in an experimental study. We recruited 76 undergraduates and randomly assigned them (...) to four groups in which they will watch videos at four different kinds of speed. The study observed and analyzed how video playback speed affected students' learning and cognitive load to obtain the following results: Video playback speed significantly influenced the students' learning effect. The best effect was observed at the speed of 1.25× and 1.5×. The speed that affected the learning effect best differed according to the students' learning abilities. High-level group students performed best at the speed of 1.5×, whereas low-level group students performed best at the speed of 1.25×. The 1.5× speed showed significant differences in the learning effect by students' majors. This indicates that the cognitive load of liberal arts students increased greatly at this speed. A change in playback speed has a significant impact on the cognitive load. Accelerated playback speed increases the cognitive load of students. The highest learning effect is observed under medium cognitive load. (shrink)

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according (...) to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned. (shrink)

To give substance to the rhetoric of ‘learning health systems’, a variety of novel trial designs are being explored to more seamlessly integrate research with medical practice, reduce study duration and reduce the number of participants allocated to ineffective interventions. Many of these designs rely on response adaptive randomisation. However, critics charge that RAR is unethical on the grounds that it violates the principle of equipoise. In this paper, I reconstruct critiques of RAR as holding that it is (...) inconsistent with five important ethical principles. I then argue that these criticisms rest on a faulty view of equipoise encouraged by the idea that a RAR study models the beliefs of a single rational agent about the relative merits of the interventions being studied. I outline a view in which RAR models an idealised health system in which diverse communities of fully informed experts shrink or grow as their constituent members update their expert opinions in light of reliable medical evidence. I show how a proper understanding of clinical equipoise can reconcile this conception of RAR with these five ethical principles. This analysis removes an in-principle objection to RAR and sheds important light on the relationship between clinical equipoise and transient diversity in the scientific community. (shrink)

Research on adaptive rationality has focused principally on inference, judgment, and decision-making that lead to behaviors and actions. These processes typically require cognitive representations as input, and these representations must presumably be acquired via learning. Nonetheless, there has been little work on the nature of, and justification for, adaptively rational learning processes. In this paper, we argue that there are strong reasons to believe that some learning is adaptively rational in the same way as judgment and (...) decision-making. Indeed, overall adaptive rationality can only properly be assessed for pairs of learning and decision processes. We thus present a formal framework for modeling such pairs of cognitive processes, and thereby assessing their adaptive rationality relative to the environment and the agent’s goals. We then use this high-level formal framework on specific cases by demonstrating how natural formal constraints on decision-making can lead to substantive predictions about adaptively rational learning and representation; and characterizing adaptively rational learning for fast-and-frugal one-reason decision-making. (shrink)

Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in natural language processing. Additionally, (...) we share pre-trained legal word embeddings using the word2vec model over large corpora, comprised legislations from UK, EU, Canada, Australia, USA, and Japan among others. (shrink)

Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in natural language processing. Additionally, (...) we share pre-trained legal word embeddings using the word2vec model over large corpora, comprised legislations from UK, EU, Canada, Australia, USA, and Japan among others. (shrink)

If distributed cognition is to become a general analytic frame, it needs to handle more aspects of cognition than just highly efficient problem solving. It should also handle learning. We identify four classes of distributed learning: induction, repurposing, symbiotic tuning, and mutual adaptation. The four classes of distributed learning fit into a two-dimensional space defined by the stability and adaptability of individuals and their environments. In all four classes of learning, people and their environments are highly (...) interdependent during initial learning. At the same time, we present evidence indicating that certain types of interdependence in early learning, most notably mutual adaptation, can help prepare people to be less dependent on their immediate environment and more adaptive when they confront new environments. We also describe and test examples of learning technologies that implement mutual adaptation. (shrink)

It is widely assumed that language in some form or other originated by piggybacking on pre-existing learning mechanism not dedicated to language. Using evolutionary connectionist simulations, we explore the implications of such assumptions by determining the effect of constraints derived from an earlier evolved mechanism for sequential learning on the interaction between biological and linguistic adaptation across generations of language learners. Artificial neural networks were initially allowed to evolve “biologically” to improve their sequential learning abilities, after which (...) language was introduced into the population. We compared the relative contribution of biological and linguistic adaptation by allowing both networks and language to change over time. The simulation results support two main conclusions: First, over generations, a consistent head-ordering emerged due to linguistic adaptation. This is consistent with previous studies suggesting that some apparently arbitrary aspects of linguistic structure may arise from cognitive constraints on sequential learning. Second, when networks were selected to maintain a good level of performance on the sequential learning task, language learnability is significantly improved by linguistic adaptation but not by biological adaptation. Indeed, the pressure toward maintaining a high level of sequential learning performance prevented biological assimilation of linguistic-specific knowledge from occurring. (shrink)

If distributed cognition is to become a general analytic frame, it needs to handle more aspects of cognition than just highly efficient problem solving. It should also handle learning. We identify four classes of distributed learning: induction, repurposing, symbiotic tuning, and mutual adaptation. The four classes of distributed learning fit into a two-dimensional space defined by the stability and adaptability of individuals and their environments. In all four classes of learning, people and their environments are highly (...) interdependent during initial learning. At the same time, we present evidence indicating that certain types of interdependence in early learning, most notably mutual adaptation, can help prepare people to be less dependent on their immediate environment and more adaptive when they confront new environments. We also describe and test examples of learning technologies that implement mutual adaptation. (shrink)

This paper examines whether adults can adapt to novel accents of their native language that contain unfamiliar context-dependent phonological alternations. In two experiments, French participants listen to short stories read in accented speech. Their knowledge of the accents is then tested in a forced-choice identification task. In Experiment 1, two groups of listeners are exposed to newly created French accents in which certain vowels harmonize or disharmonize, respectively, to the rounding of the preceding vowel. Despite the cross-linguistic predominance of vowel (...) harmony over disharmony, the two groups adapt equally well to both accents, suggesting that this typological difference is not reflected in perceptual learning. Experiment 2 further explores the mechanism underlying this type of phonological learning. Participants are exposed to an accent in which some vowels harmonize and others disharmonize, yielding an increased featural complexity. They adapt less well to this regularity, showing that adaptation to novel accents involves feature-based inferences. (shrink)

It is widely assumed that language in some form or other originated by piggybacking on pre-existing learning mechanism not dedicated to language. Using evolutionary connectionist simulations, we explore the implications of such assumptions by determining the effect of constraints derived from an earlier evolved mechanism for sequential learning on the interaction between biological and linguistic adaptation across generations of language learners. Artificial neural networks were initially allowed to evolve “biologically” to improve their sequential learning abilities, after which (...) language was introduced into the population. We compared the relative contribution of biological and linguistic adaptation by allowing both networks and language to change over time. The simulation results support two main conclusions: First, over generations, a consistent head-ordering emerged due to linguistic adaptation. This is consistent with previous studies suggesting that some apparently arbitrary aspects of linguistic structure may arise from cognitive constraints on sequential learning. Second, when networks were selected to maintain a good level of performance on the sequential learning task, language learnability is significantly improved by linguistic adaptation but not by biological adaptation. Indeed, the pressure toward maintaining a high level of sequential learning performance prevented biological assimilation of linguistic-specific knowledge from occurring. (shrink)

Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. (...) To build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial—but not temporal—variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing. (shrink)

Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and (...) the participants had to detect the changes in order to perform well and gain monetary rewards. A cognitive model is constructed that incorporates reinforcement learning with counterfactual reasoning to help quickly adjust the utility of task strategies in response to changes. The results show that the model can accurately explain human data and that counterfactual reasoning is key to reproducing the various effects observed in this change detection paradigm. (shrink)

This paper firstly designs a five-dimensional model of learners’ characteristics and a three-dimensional model of English reading resources’ characteristics in a fragmented learning environment through literature research. At the same time, to make the learning resources meet the characteristics of fragmented learning time and space, the English Level 4 reading resources are reasonably designed and segmented to adapt to the needs of learners’ mobile fragmented learning. Then, combined with machine learning algorithms, an adaptive recommendation (...) model of learning resources in English fragmented reading is constructed. The algorithm-based adaptive recommendation algorithm for English fragmented reading resources is designed. Based on the generated decision trees, the expression rules are parsed to achieve adaptive pushing of resources. The results of this study show that adaptive recommendation of learning resources in English fragmented reading can help teachers to develop future resource recommendation strategies through effective data collection to adaptively push resources that are close to learners’ individual needs. The use of mobile by English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus enhancing different aspects of learners’ reading skills. (shrink)

The recent availability of multiple avian genomes has laid the foundation for a huge variety of comparative genomics analyses including scans for changes and signatures of selection that arose from adaptions to new ecological niches. Nocturnal adaptation in birds, unlike in mammals, is comparatively recent, a fact that makes birds good candidates for identifying early genetic changes that support adaptation to dim‐light environments. In this review, we give examples of comparative genomics analyses that could shed light on mechanisms of adaptation (...) to nocturnality. We present advantages and disadvantages of both “data‐driven” and “hypothesis‐driven” approaches that lead to the discovery of candidate genes and genetic changes promoting nocturnality. We anticipate that the accessibility of multiple genomes from the Genome 10K Project will allow a better understanding of evolutionary mechanisms and adaptation in general. -/- . (shrink)

Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this (...) approach is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest. (shrink)

This paper considers how surprise (or its lack) can be cast as a metacognitive signal with an adaptive function in learning new knowledge and revising belief networks. It reviews the phenomena that may hinder this signal (e.g., hindsight bias) and argues for its extrinsic exploitation in instructional and educational contexts by educators, journalists and parents, who might train learners to internalize the use of surprise to drive explanation‐based learning.

A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function neural network approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics (...) of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme. (shrink)

Prior work has demonstrated that young children in the US and the Ecuadorian Amazon preferentially remember information about the dangerousness of an animal over both its name and its diet. Here we explore if this bias is present among older children and adults in Fiji through the use of an experimental learning task. We find that a content bias favoring the preferential retention of danger and toxicity information continues to operate in older children, but that the magnitude of the (...) bias diminishes with age and is absent in adults. We also find evidence that fitness costs likely impact the types of mistakes that participants make in their attributions of dangerousness and poisonousness. These results suggest that natural selection has shaped the way in which we learn and make inferences about unfamiliar animal species over ontogeny, and that future research is needed on how content biases may vary across the life course. (shrink)

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after (...) they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects. (shrink)

In this article, a new adaptive fuzzy wavelet neural network model is proposed for nonlinear function approximation problems. The AFWNN model is based on the traditional Takagi-Sugeno-Kang fuzzy system. Specifically, this model replaces the membership functions of fuzzy rules with wavelet basis functions, which are known to have time and frequency localization properties, i.e., they can approximate patterns both in the time and frequency domains. The structure of the AFWNN model is derived from that of the adaptive neuro-fuzzy (...) inference system. However, the AFWNN improves over the ANFIS by replacing Gaussian functions in the hidden layer with wavelet basis functions. The AFWNN model is trained using a gradient-based optimization algorithm. The AFWNN is then tested on three function approximation problems of time series prediction. For certain types of nonlinear time series, for instance fractal processes, the AFWNN is found to be substantially more accurate than alternative methods. (shrink)

During the early years a young child gradually becomes a member of a culture by learning how to understand and then produce relevant social practices – particularly through interaction in conversation. This paper examines how one child adapts to the practices surrounding the production of questions and answers. Adopting a longitudinal case-study approach and employing conversation analysis, consideration is given to the question-answer practices this child produces during asymmetric conversations across the period when she is acquiring conversational skills (from (...) 12 months to 3 years 7 months). Through a micro-analytic examination of extract examples across this period, it becomes clear that although initially a child can learn the format of question-answer sequences, it is not until the third year that some recognition of being accountable for the form of an answer becomes evident. Between the ages of 2 and 3, we observe that this child is called to account for answers that are deemed inappropriate or odd. Concluding comments consider these practices as forms of social adaptation within asymmetric interactive contexts. Keywords: child-parent-interaction; asymmetric interaction; learning conversational skills. (shrink)