Bayesian paradigm has been widely acknowledged as a coherent approach to learning putative probability model structures from a finite class of candidate models. Bayesian learning is based on measuring the predictive ability of a model in terms of the corresponding marginal data distribution, which equals the expectation of the likelihood with respect to a prior distribution for model parameters. The main controversy related to this learning method stems from the necessity of specifying proper prior (...) distributions for all unknown parameters of a model, which ensures a complete determination of the marginal data distribution. Even for commonly used models, subjective priors may be difficult to specify precisely, and therefore, several automated learning procedures have been suggested in the literature. Here we introduce a novel Bayesian learning method based on the predictive entropy of a probability model, that can combine both subjective and objective probabilistic assessment of uncertain quantities in putative models. It is shown that our approach can avoid some of the limitations of the earlier suggested objective Bayesian methods. (shrink)

How does the visual system recognize images of a novel object after a single observation despite possible variations in the viewpoint of that object relative to the observer? One possibility is comparing the image with a prototype for invariance over a relevant transformation set. However, invariance over rotations has proven difficult to analyze, because it applies to some objects but not others. We propose that the invariant transformations of an object are learned by incorporating prior expectations with real-world evidence. We (...) test this proposal by developing an ideal learner model for learning invariance that predicts better learning of orientation dependence when prior expectations about orientation are weak. This prediction was supported in two behavioral experiments, where participants learned the orientation dependence of novel images using feedback from solving arithmetic problems. (shrink)

Parameterization and parameter tuning are central aspects of climate modeling, and there is widespread consensus that these procedures involve certain subjective elements. Even if the use of these subjective elements is not necessarily epistemically problematic, there is an intuitive appeal for replacing them with more objective (automated) methods, such as machine learning. Relying on several case studies, we argue that, while machine learning techniques may help to improve climate model parameterization in several ways, they still require (...) expert judgment that involves subjective elements not so different from the ones arising in standard parameterization and tuning. The use of machine learning in parameterizations is an art as well as a science and requires careful supervision. (shrink)

Previous research shows that people can use the co-occurrence of words and objects in ambiguous situations (i.e., containing multiple words and objects) to learn word meanings during a brief passive training period (Yu & Smith, 2007). However, learners in the world are not completely passive but can affect how their environment is structured by moving their heads, eyes, and even objects. These actions can indicate attention to a language teacher, who may then be more likely to name the attended objects. (...) Using a novel active learning paradigm in which learners choose which four objects they would like to see named on each successive trial, this study asks whether active learning is superior to passive learning in a cross-situational word learning context. Finding that learners perform better in active learning, we investigate the strategies and discover that most learners use immediate repetition to disambiguate pairings. Unexpectedly, we find that learners who repeat only one pair per trial—an easy way to infer this pair—perform worse than those who repeat multiple pairs per trial. Using a working memory extension to an associative model of word learning with uncertainty and familiarity biases, we investigate individual differences that correlate with these assorted strategies. (shrink)

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

Evidence from neurophysiological and psychological studies is coming together to shed light on how we represent and recognize objects. This review describes evidence supporting two major hypotheses: the first is that objects are represented in a mosaic-like form in which objects are encoded by combinations of complex, reusable features, rather than two-dimensional templates, or three-dimensional models. The second hypothesis is that transform-invariant representations of objects are learnt through experience, and that this learning is affected by the temporal sequence in (...) which different views of the objects are seen, as well as by their physical appearance. (shrink)

Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and (...) their labels, where the source is inconsistent by design: It uses both “correct” and “incorrect” object‐label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object‐Label (OL) and the Label‐Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image‐utterance associations, despite the ambiguous learning task. (shrink)

Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering (...) algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost. (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)

Objective: To evaluate one ethics rounds model by describing nurses’ and doctors’ experiences of the rounds. Methods: Philosopher-ethicist-led interprofessional team ethics rounds concerning dialysis patient care problems were applied at three Swedish hospitals. The philosophers were instructed to promote mutual understanding and stimulate ethical reflection, without giving any recommendations or solutions. Interviews with seven doctors and 11 nurses were conducted regarding their experiences from the rounds, which were then analysed using content analysis. Findings: The goal of the rounds (...) was partly fulfilled. Participants described both positive and negative experiences. Good rounds included stimulation to broadened thinking, a sense of connecting, strengthened confidence to act, insight into moral responsibility and emotional relief. Negative experiences were associated with a sense of unconcern and alienation, as well as frustration with the lack of solutions and a sense of resignation that change is not possible. The findings suggest that the ethics rounds above all met the need of a forum for crossing over professional boundaries. The philosophers seemed to play an important role in structuring and stimulating reasoned arguments. The nurses’ expectation that solutions to the ethical problems would be sought despite explicit instructions to the contrary was conspicuous. Conclusion: When assisting healthcare professionals to learn a way through ethical problems in patient care, a balance should be found between ethical analyses, conflict resolution and problem solving. A model based on the findings is presented. (shrink)

This paper presents an implemented computational model of word acquisition which learns directly from raw multimodal sensory input. Set in an information theoretic framework, the model acquires a lexicon by finding and statistically modeling consistent cross‐modal structure. The model has been implemented in a system using novel speech processing, computer vision, and machine learning algorithms. In evaluations the model successfully performed speech segmentation, word discovery and visual categorization from spontaneous infant‐directed speech paired with video images (...) of single objects. These results demonstrate the possibility of using state‐of‐the‐art techniques from sensory pattern recognition and machine learning to implement cognitive models which can process raw sensor data without the need for human transcription or labeling. (shrink)

In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and (...) their effect on the output. The obvious difference is that the common target of a toy and a full-scale model in the sciences is some phenomenon in the world, while the target of a surrogate model is another model. This essential difference makes toy surrogate models (TSMs) a new object of study for theories of understanding, one that is not easily accommodated under current analyses. This paper provides an account of what it means to understand an opaque ML model globally with the aid of such simple models. (shrink)

The achievement of intentional learning is a powerful paradigm for the objectives and methods of the teaching of philosophy. This paradigm sees the objectives and methods of such teaching as based not simply on the mastery of content, but as rooted in attempts to shape the various affective and cognitive factors that influence students’ learning efforts. The goal of such pedagogy is to foster an intentional learning orientation, one characterized by self-awareness, active monitoring of the learning (...) process, and a desire for publicly certified expertise. I provide a number of examples of philosophy-specific teaching strategies that follow this paradigm. (shrink)

The research aims to design a proposed model of learning organizations as an entry point to achieve organizational excellence in the Palestinian universities of Gaza Strip. A random sample of workers were selected from the Palestinian universities consist of (286) employees at recovery rate of (70.3%). The study concluded with a set of results the most important of which: there is a statistically significant relationship between the components of learning organizations and achieving organizational excellence in the Palestinian (...) universities of Gaza Strip from the standpoint of the faculty members. One of the main recommendations of the study: establish the concept of learning organization in leaders and staff of the university through the establishment of specialized courses, and workshops. Turning the status of the university into a learning organization as a strategic objective and the performance of managers are measurement accordingly. Organize visits of the leaders of the university a to the top organizations in this field and to take advantage of their experience and encourage deans and heads of departments to involve their employees in the information on global trends and trends in learning organization through regular meetings. (shrink)

Domjan et al.'s model of how Pavlovian processes regulate social interaction can be extended to social learning, where an individual learns about the value of events, objects, or actions from information provided by another. The conditioned properties of a particular social partner, following from a history of interactions with that partner, can modulate the efficiency and specificity of social learning.

The detection and categorization of animate motions is a crucial task underlying social interaction and perceptual decision making. Neural representations of perceived animate objects are partially located in the primate cortical region STS, which is a region that receives convergent input from intermediate-level form and motion representations. Populations of STS cells exist which are selectively responsive to specific animated motion sequences, such as walkers. It is still unclear how and to what extent form and motion information contribute to the generation (...) of such representations and what kind of mechanisms are involved in the learning processes. The article develops a cortical model architecture for the unsupervised learning of animated motion sequence representations. We demonstrate how the model automatically selects significant motion patterns as well as meaningful static form prototypes characterized by a high degree of articulation. Such key poses are selectively reinforced during learning through a cross talk between the motion and form processing streams. Furthermore, we show how sequence-selective representations are learned in STS by fusing static form and motion input from the segregated bottom-up driving input streams. Cells in STS, in turn, feed their activities recurrently to their input sites along top-down signal pathways. We show how such learned feedback connections enable predictions about future input as anticipation generated by sequence-selective STS cells. Network simulations demonstrate the computational capacity of the proposed model by reproducing several experimental findings from neurosciences and by accounting for recent behavioral data. (shrink)

Aesthetic non-cognitivists deny that aesthetic statements express genuinely aesthetic beliefs and instead hold that they work primarily to express something non-cognitive, such as attitudes of approval or disapproval, or desire. Non-cognitivists deny that aesthetic statements express aesthetic beliefs because they deny that there are aesthetic features in the world for aesthetic beliefs to represent. Their assumption, shared by scientists and theorists of mind alike, was that language-users possess cognitive mechanisms with which to objectively grasp abstract rules fixed independently of human (...) responses, and that cognizers are thereby capable of grasping rules for the correct application of aesthetic concepts without relying on evaluation or enculturation. However, in this article I use Wittgenstein’s rule-following considerations to argue that psychological theories grounded upon this so-called objective model of rule-following fail to adequately account for concept acquisition and mastery. I argue that this is because linguistic enculturation, and the perceptual learning that’s often involved, influences and enables the mastery of aesthetic concepts. I argue that part of what’s involved in speaking aesthetically is to belong to a cultural practice of making sense of things aesthetically, and that it’s within a socio-linguistic community, and that community’s practices, that such aesthetic sense can be made intelligible. (shrink)

In this article learning process is studied as a strategic process. In this we have as a background information Jaakko Hintikka’s interrogative model of learning which understand all reasoning as a strategic searching process in which all the relevant factors have methodologically motivated roles. A learning process takes place in space and time: learning is an active search for new knowledge. To get a better understanding the whole framework has to be schematized. Learning as (...) an active search is object related acting in reality. While acting an actor brings about some changes in reality. Experimentation has to be understood as methodical acting. This is a key to understand learning process as a strategic process: actions of learner and teacher have to be motivated strategically or methodologically. Keywords: Hintikka, strategy, system transformation, high quality learning, high quality teaching, observation, and experimentation. (shrink)

In this article I propose that a postphenomenological approach to science and technology can open new analytical understandings of how material artifacts, embodiment and social agency co-produce learned perceptions of objects. In particle physics, physicists work in huge groups of scientists from many cultural backgrounds. Communication to some extent depends on material hermeneutics of flowcharts, models and other visual presentations. As it appears in an examination of physicists’ scrutiny of visual renderings of different parts of a detector, perceptions vary in (...) relation to social and bodily experiences. Vision in physics has seemingly allowed an objective perception at a convenient distance of the body. This article challenges this view and proposes that the variations can be analysed as cultural at two echelons with the help of a postphenomenological approach combined with cultural psychological theory of artifacts. A third echelon presumably constitutes the phenomenological limit to culture in science. Even this last resort of subjectivity can be embraced by a postphenomenological approach. The process of culturalization in physics can be defined as a process of situating knowledge in a body whose continuous learning of micro-and macro perceptions makes scientific renderings unstable. Taken together postphenomenology, following the distinctions between body one and body two, and combined with cultural psychological learning theory, enables new insight into what constitutes culture in science. (shrink)

Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, choosing the goal or objective function that will maximize performance and a feature-based analysis of the current game board to determine where to place the currently falling zoid so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning models to determine whether different goals (...) result in different feature weights. Two of these optimization strategies quickly rise to performance plateaus, whereas two others continue toward higher but more jagged heights. In Study 2, we compare the zoid placement decisions made by our best CERL models with those made by 67 human players. Across 370,131 human game episodes, two CERL models picked the same zoid placements as our lowest scoring human for 43% of the placements and as our three best scoring experts for 65% of the placements. Our findings suggest that people focus on maximizing points, not number of lines cleared or number of levels reached. They also show that goal choice influences the choice of zoid placements for CERLs and suggest that the same is true of humans. Tetris has a repetitive task structure that makes Tetris more tractable and more like a traditional experimental psychology paradigm than many more complex games or tasks. Hence, although complex, Tetris is not overwhelmingly complex and presents a right-sized challenge to cognitive theories, especially those of integrated cognitive systems. (shrink)

Most e-commerce platforms allow consumers to post product reviews, causing more and more consumers to get into the habit of reading reviews before they buy. These online reviews serve as an emotional feedback of consumers’ product experience and contain a lot of important information, but inevitably there are malicious or irrelevant reviews. It is especially important to discover and identify the real sentiment tendency in online reviews in a timely manner. Therefore, a deep learning-based real online consumer sentiment classification (...) model is proposed. First, the mapping relationship between online reviews of goods and sentiment features is established based on expert knowledge and using fuzzy mathematics, thus mapping the high-dimensional original text data into a continuous low-dimensional space. Secondly, after obtaining local contextual features using convolutional operations, the long-term dependencies between features are fully considered by a bidirectional long- and short-term memory network. Then, the degree of contribution of different words to the text is considered by introducing an attention mechanism, and a regular term constraint is introduced in the objective function. The experimental results show that the proposed convolutional attention–long and short-term memory network model has a higher test accuracy of 83.3% compared with other models, indicating that the model has better classification performance. (shrink)

The present paper shows how statistical learning theory and machine learning models can be used to enhance understanding of AI-related epistemological issues regarding inductive reasoning and reliability of generalisations. Towards this aim, the paper proceeds as follows. First, it expounds Price’s dual image of representation in terms of the notions of e-representations and i-representations that constitute subject naturalism. For Price, this is not a strictly anti-representationalist position but rather a dualist one (e- and i-representations). Second, the paper links (...) this debate with machine learning in terms of statistical learning theory becoming more viable epistemological tool when it abandons the perspective of object naturalism. The paper then argues that machine learning grounds a form of knowing that can be understood in terms of e- and i-representation learning. Third, this synthesis shows a way of analysing inductive reasoning in terms of reliability of generalisations stemming from a structure of e- and i-representations. In the age of Artificial Intelligence, connecting Price’s dual view of representation with Deep Learning provides an epistemological way forward and even perhaps an approach to how knowing is possible. (shrink)

Prior research has shown that people can learn many nouns from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a (...) recent associative model, we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings. (shrink)

The car-sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road (...) and fewer in the parking stations. To achieve that, various machine learning models, namely vector autoregression, support vector regression, eXtreme gradient boosting, k-nearest neighbors, and deep learning models specifically long short-time memory, gated recurrent unit, convolutional neural network, CNN-LSTM, and multilayer perceptron, were performed on different kinds of features. These features include the past usage levels, Chongqing’s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN-LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time. (shrink)

In the current global push to examine the diverse and complex approach in which classroom culture contributes to the shaping of students’ learning cultural identity. Classroom culture plays a fundamental role in constructing students’ learning competencies, perceptions and behaviors. Thus, this study conceptualizes and contextualizes a collective individualism learning model to explicate a specific learning model in classroom culture at Chinese particular context historically and traditionally. The collective individualism model is identified as the (...) individualized learning style of students in Chinese classroom culture characterized by nine symbolic objects, including a textbook, an exercise book, a pen, a blackboard, a screen, a computer, a table, a chair and platform in a Chinese collective class. In addition, two core components of formal collectivism and substantive individualism derived from constructive learning theory, social learning theory, Mezirow’s transformative learning theory and Kolb’s experiential learning theory. The implications for examining the collective individualism model of Chinese classroom culture are likewise linked to innovative talent cultivation and fulfillment of ‘Four Good Teachers’ goal. Both the discussion and conclusion led to the construction of promising classroom culture at Chinese context practically. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with (...) the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Much of the philosophical work on perception has focused on vision. Recently, however, philosophers have begun to correct this ‘tunnel vision’ by considering other modalities. Nevertheless, relatively little has been written about the chemical senses—olfaction and gustation. The focus of this paper is olfaction. In light of new physiological and psychophysical research on olfaction, I consider whether olfactory experience is object-based. In particular, I explore the claim that “odor objects” constitute sensory individuals. It isn’t obvious—at least at the outset—whether they (...) meet the widely accepted principles of object individuation and recognition at work in the visual and auditory cases. Among the general issues I consider, then, is whether these traditional principles form necessary, or simply sufficient, conditions on object perception. As we see, at the very least, considering the object-recognition model of olfaction challenges us to look more closely at well-entrenched models of object perception. But the payoff of doing so is high. We not only learn something about a modality that philosophers have historically neglected; by asking new questions and challenging old assumptions, we also further our understanding of perception in general. (shrink)

Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magnitude: with binary, categorical, or continuous values). (...) In this article, we explore the problem space of these three parameters in the context of a task whose completion entails some combination of 36 state–action pairs, where all intermediate states (i.e., after the initial state and prior to the end state) represent progressive but partial completion of the task. Different choices produce profoundly different learning paths and outcomes, with the strongest effect for moment. Unfortunately, there is little discussion in the literature of the effect of such choices. This absence is disappointing, as the choice of when, what, and how much needs to be made by a modeler for every learning model. (shrink)

The visual world of adults consists of objects at various distances, partly occluding one another, substantial and stable across space and time. The visual world of young infants, in contrast, is often fragmented and unstable, consisting not of coherent objects but rather surfaces that move in unpredictable ways. Evidence from computational modeling and from experiments with human infants highlights three kinds of learning that contribute to infants’ knowledge of the visual world: learning via association, learning via active (...) assembly, and learning via visual-manual exploration. Infants acquire knowledge by observing objects move in and out of sight, forming associations of these different views. In addition, the infant’s own self-produced behavior—oculomotor patterns and manual experience, in particular—is an important means by which infants discover and construct their visual world. (shrink)

Infant language learners are faced with the difficult inductive problem of determining how new words map to novel or known objects in their environment. Bayesian inference models have been successful at using the sparse information available in natural child-directed speech to build candidate lexicons and infer speakers’ referential intentions. We begin by asking how a Bayesian model optimized for monolingual input generalizes to new monolingual or bilingual corpora and find that, especially in the case of the bilingual input, the (...) model shows a significant decrease in performance. In the next experiment, we propose the ME Model, a modified Bayesian model, which approximates infants’ mutual exclusivity bias to support the differential demands of monolingual and bilingual learning situations. The extended model is assessed using the same corpora of real child-directed speech, showing that its performance is more robust against varying input and less dependent than the Intentional Model on optimization of its parsimony parameter. We argue that both monolingual and bilingual demands on word learning are important considerations for a computational model, as they can yield significantly different results than when only one such context is considered. (shrink)

In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and (...) their effect on the output. The obvious difference is that the common target of a toy and a full-scale model in the sciences is some phenomenon in the world, while the target of a surrogate model is another model. This essential difference makes toy surrogate models (TSMs) a new object of study for theories of understanding, one that is not easily accommodated under current analyses. This paper provides an account of what it means to understand an opaque ML model globally with the aid of such simple models. (shrink)

A common finding is that information order influences belief revision (e.g., Hogarth & Einhorn, 1992). We tested personal experience as a possible mitigator. In three experiments participants experienced the probabilistic relationship between pieces of information and object category through a series of trials where they assigned objects (planes) into one of two possible categories (hostile or commercial), given two sequentially presented pieces of probabilistic information (route and ID), and then they had to indicate their belief about the object category before (...) feedback. The results generally confirm the predictions from the Hogarth and Einhorn model. Participants showed a recency effect in their belief revision. Extending previous model evaluations the results indicate that the model predictions also hold for classification decisions, and for pieces of information that vary in their diagnostic values. Personal experience does not appear to prevent order effects in classification decisions based on sequentially presented pieces of information and in belief revision. (shrink)

Through hearing we learn about source events: events in which objects move or interact so that they vibrate and produce sound waves, such as when they roll, collide, or scrape together. It is often claimed that we do not simply hear sounds and infer what event caused them, but hear source events themselves, through hearing sounds. Here I investigate how the idea that we hear source events should be understood, with a focus on how hearing an event relates to hearing (...) the objects involved in that event. I argue that whereas we see events such as rollings and collisions by seeing objects move through space, this cannot be how we hear them, and go on to examine two other possible models. On the first, we hear events but not their participant objects. On the second, to hear an event is to hear the appearance of an object to change. I argue that neither is satisfactory and endorse a third option: to hear a source event is to hear an object as extending through time. (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)

This paper presents research in moral psychology and draws on this research to offer an account of the cognitive systems and processes that generate the perceived objectivity of some moral beliefs. It presents empirical research on the perceived objectivity of moral beliefs, compares different algorithms employed by human cognition in the context of model-free and model-based reinforcement learning, and uses concepts drawn from dual-system and modular theories of cognition. The central claim of the account is that belief (...) in the objectivity of some moral beliefs results from certain ‘modular’ features of cognitive systems. (shrink)

Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...) belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Many NLP tasks rely on accurately estimating word dependency probabilities P(w1|w2), where the words w1 and w2 have a particular relationship (such as verb-object). Because of the sparseness of counts of such dependencies, smoothing and the ability to use multiple sources of knowledge are important challenges. For example, if the probability P(N |V ) of noun N being the subject of verb V is high, and V takes similar objects to V , and V is synonymous to V , then (...) we want to conclude that P(N |V ) should also be reasonably high—even when those words did not cooccur in the training data. (shrink)

This essay introduces the concept of deictic abstraction , taking as a point of departure Husserl’s prototypical but insufficient description of the act of ideation in which a shade of color comes to givenness as an ideal object, i.e., a non-individual or abstract object, on the basis of a perceived individual object. This concept comprises not only color-ideation and ideations of universalities of the sensuous sphere , but all acts founded in perceptions in which ideal objects are directly referred to (...) by means of demonstrative expressions . This allows various types of deictic ideations or abstractions, corresponding to the degree of universality and the kind of ideal object concerned, to be brought to light through the analysis of particular cases of everyday acts of deictic abstraction, including deictic abstractions of predicative universals of the lowest level of universality ; deictic abstractions of non-predicative universals, especially of universal ways of doing something ; and deictic abstractions of individual styles of doing something . Deictic abstractions not only turn out to be constitutive for the formation of perceptual concepts of the lower and lowest levels, but also prove to be foundational for all kinds of learning in which schemata of actions are acquired from a model. (shrink)

David Hume's relatively short essay 'Of the Standard of Taste' deals with some of the most difficult issues in aesthetic theory. Apart from giving a few pregnant remarks, near the end of his discussion, on the role of morality in aesthetic evaluation, Hume tries to reconcile the idea that tastes are subjective (in the sense of not being answerable to the facts) with the idea that some objects of taste are better than others. 'Tastes', in this context, are the pleasures (...) or displeasures that a person can take in the beauties of poems, paintings, and other artistic compositions (though Hume also wants to stress the continuities between tastes, so understood, and the bodily sense of taste). The position at which Hume arrives in the essay (despite some dialectical unclarity) is that some people – the 'true judges'– determine by their 'joint verdict' which works are meritorious. This solution continues to exercise a fascination, as does Hume's complicated route to it. Author Recommends: Paul Guyer, 'The Standard of Taste and the "Most Ardent Desire of Society" ', Values of Beauty (Cambridge: Cambridge University Press, 2005), 37–76. This paper places 'Of the Standard of Taste' in an especially rich context, and asks why Hume concentrates on true judges instead of the improvement of one's own taste. Mary Mothersill, 'Hume: "Of the Standard of Taste" ', Beauty Restored (Oxford: Clarendon Press, 1984), 177–208. This chapter, embedded in an exposition of Mothersill's 'First Thesis' (the denial that there are principles of taste) and 'Second Thesis' (the affirmation that some judgments of taste are genuine judgments), gives a detailed running commentary on Hume's essay. A shorter self-contained version of the chapter appeared as 'Hume and the Paradox of Taste' in Aesthetics: A Critical Anthology , 2nd ed., eds. George Dickie, Richard Sclafani, and Ronald Roblin (New York, NY: St. Martin's Press, 1989, 269–86). Jerrold Levinson, 'Hume's Standard of Taste: The Real Problem', Journal of Aesthetics and Art Criticism (2002): 227–38. An importance recent article, Levinson's piece argues that the 'real' difficulty with Hume's essay has gone unnoticed: why should I care about what Hume's true judges think? Christopher Williams, 'Some Questions in Hume's Aesthetics', Philosophy Compass 2/2 (2007). This article provides a brief overview of the topics discussed under weeks 3–5 in the sample syllabus below. It is intended to provide a roadmap for the particular set of readings listed there. David Wiggins, 'A Sensible Subjectivism?', Needs, Values, and Truth , 3rd ed. (Oxford: Clarendon Press, 1998), 185–214. This is a stimulating paper in moral philosophy that treats Hume's essay on taste as a model for a serious subjectivism. Wiggins then presents his own brand of subjectivism as an alternative to Hume's. Online Materials: Hume's Aesthetics (Ted Gracyk): http://plato.stanford.edu/entries/hume-aesthetics/ Sample Syllabus: Recommended background reading on Hume's historical context: Peter Kivy, The Seventh Sense: Francis Hutcheson and Eighteenth-Century British Aesthetics , 2nd ed. (Oxford: Oxford University Press, 2003), especially Part III. Recommended background reading on the general topic of taste: David A. Whewell, 'Taste', Blackwell Companion to Aesthetics (Oxford: Blackwell, 1992), 415–18. Dabney Townsend and Carolyn Korsmeyer, 'Taste', Encyclopedia of Aesthetics , ed. Michael Kelly (New York, NY and Oxford: Oxford University Press, 1998), 4:355–62. Ted Cohen, 'The Philosophy of Taste: Thoughts on the Idea', Blackwell Guide to Aesthetics , ed. Peter Kivy (Oxford: Blackwell, 2004), 167–73. Week 1: Hume on beauty, art, and aesthetic judgment in the Treatise of Human Nature and the Enquiry Concerning the Principles of Morals The following references are usable for any complete edition of the Treatise or Enquiry Treatise , 2.1.8 ('Of Beauty and Deformity') Treatise , 2.2.5 ('Of Our Esteem for the Rich and Powerful') Treatise , 2.2.8 ('Of Malice and Envy'), final three paragraphs Treatise , 2.2.11 ('Of the Amorous Passion, or Love Betwixt the Sexes') Treatise , 3.1.2 ('Moral Distinctions Deriv'd from a Moral Sense') Treatise , 3.3.1 ('Of the Origin of the Natural Virtues') Treatise , 3.3.5 ('Some Farther Reflexions Concerning the Natural Virtues') Enquiry , Appendix 1 ('Of moral sentiment') Week 2: Hume's essays Essays Moral, Political, and Literary , ed. Eugene Miller (Indianapolis, IN: LibertyClassics, 1985) is the most commonly used edition today. 'Of the Delicacy of Taste and Passion' 'Of Eloquence' 'Of the Rise and Progress of the Arts and Sciences' 'Of Simplicity and Refinement in Writing' 'Of Tragedy' 'Of the Standard of Taste' Week 3: Circularity–Virtuous or Vicious? Peter Kivy, 'Hume's Standard of Taste: Breaking the Circle', British Journal of Aesthetics (1967): 57–66. David Wiggins, 'A Sensible Subjectivism?', Needs, Values, and Truth , 3rd ed. (Oxford: Clarendon Press, 1998), 185–214. Week 4: Rules of Art Mary Mothersill, 'Hume: "Of the Standard of Taste" ', Beauty Restored (Oxford: Clarendon, 1984), 177–208. James Shelley, 'Hume's Double Standard of Taste', Journal of Aesthetics and Art Criticism (1994): 437–45. Nick Zangwill, 'Hume, Taste, and Teleology', The Metaphysics of Beauty (Ithaca, NY: Cornell University Press, 2001), 149–65. Week 5: The True Judge Malcolm Budd, 'Hume and Kant', 'Hume's Standard of Taste', 'Hume and Human Nature', Values of Art (London: Allen Lane, 1995), 16–24 . Paul Guyer, 'The Standard of Taste and the "Most Ardent Desire of Society" ', Values of Beauty (Cambridge: Cambridge University Press, 2005), 37–76. Jerrold Levinson, 'Hume's Standard of Taste: The Real Problem', Journal of Aesthetics and Art Criticism (2002): 227–38. Week 6: Moralism in Aesthetic Judgment: Hume and Beyond Kendall Walton, 'Morals in Fiction and Fictional Morality', Proceedings of the Aristotelian Society (1994): 27–50. Richard Moran, 'The Expression of Feeling in Imagination', Philosophical Review (1994): 75–106. Tamar Szabo-Gendler, 'The Puzzle of Imaginative Resistance', Journal of Philosophy (2000): 55–81. Focus Questions 1. How does Hume distinguish between matters of 'fact' and 'sentiment'? 2. What is a 'rule of art', and are there any rules? 3. Can a bad critic be 'silenced'? 4. What are the characteristics of good critics? 5. Should we expect good critics to agree on the merits of a work, and should I care about becoming a good critic myself? 6. Is it possible to distinguish variations in taste for which we should expect a standard and variations for which it is 'vain' to have such an expectation? 7. How is the excellence of a work related to the exercise of taste? 8. If a work of literature has a moral outlook that differs from our own, should we consider the work defective on literary grounds? (shrink)

The research aimed to produce an appropriate product, namely a cooperative learning model in the rituals of Hajj, to realize an independent Hajj congregation in terms of knowledge, understanding, attitudes, and skills, to specify the level of validity, realism, effectiveness, and attractiveness and so that the problems above can be resolved immediately resolved. This research uses the Research and Development (R&D) approach to achieve the aforementioned objectives. In general, the research and development model is the Four D (...) model. This model consists of four stages of development, i.e., define, design, develop, and develop. The results of the preliminary study / pre-field survey show that in terms of teaching performance of Hajj rituals, it appears that the learning services provided by the instructor/instructor have not been able to accommodate and appreciate the differences in ability (attitude) of Hajj pilgrims. The results of the model development trial show that "(1) it is necessary to improve the teaching performance of the Hajj ritual guide / instructor, (2) the development of the model requires specific teaching performance of Hajj rituals from the supervisor / instructor, and does not want excessive facilities and facilities. (3) there is an increase in the learning activities of Hajj pilgrims in all small groups (high, medium and low), (4) the realization of the optimization of the achievement of learning outcomes for Hajj rituals for Hajj pilgrims, both in the high, medium, and low groups. (shrink)

Production and comprehension have long been viewed as inseparable components of language. The study of vision, by contrast, has centered almost exclusively on comprehension. Here we investigate drawing—the most basic form of visual production. How do we convey concepts in visual form, and how does refining this skill, in turn, affect recognition? We developed an online platform for collecting large amounts of drawing and recognition data, and applied a deep convolutional neural network model of visual cortex trained only on (...) natural images to explore the hypothesis that drawing recruits the same abstract feature representations that support natural visual object recognition. Consistent with this hypothesis, higher layers of this model captured the abstract features of both drawings and natural images most important for recognition, and people learning to produce more recognizable drawings of objects exhibited enhanced recognition of those objects. These findings could explain why drawing is so effective for communicating visual concepts, they suggest novel approaches for evaluating and refining conceptual knowledge, and they highlight the potential of deep networks for understanding human learning. (shrink)

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and (...) thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

Self-relevance has been demonstrated to impair instrumental learning. Compared to unfamiliar symbols associated with a friend, analogous stimuli linked with the self are learned more slowly. What is not yet understood, however, is whether this effect extends beyond arbitrary stimuli to material with intrinsically meaningful properties. Take, for example, stimulus valence an established moderator of self-bias. Does the desirability of to-be-learned material influence self-learning? Here, in conjunction with computational modelling (i.e. Reinforcement Learning Drift Diffusion Model analysis), (...) a probabilistic selection task was used to establish if and how stimulus valence (i.e. desirable/undesirable posters) impacts the acquisition of knowledge relating to object-ownership (i.e. owned-by-self vs. owned-by-friend). Several interesting results were observed. First, undesirable posters were learned more rapidly for self compared to friend, an effect that was reversed for desirable posters. Second, learning rates were accompanied by associated differences in reward sensitivity toward desirable and undesirable choice selections as a function of ownership. Third, decisional caution was greater for self-relevant (vs. friend relevant) responses. Collectively, these findings inform understanding of self-function and how valence and stimulus relevance mutually influence probabilistic learning. (shrink)