With science now recognizing animal consciousness, intelligence, emotion and even morality, there must come an awareness of our own moral responsibilites towards other beings. But there's another reason to consider animals' well-being--because it is intertwined with our own. In Survival at Stake, leading animal rights activist Poorva Joshipura argues passionately that, evolutionarily, humans are far more like other animals than we care to believe. She examines how hunting wildlife leads to pandemics and epidemics, which, in turn, harm us; how (...) the production of meat destroys forests and causes climate change, which, in turn, destoys us; how blood sports hurt both humans and animals; how leather production damages the environment and human health; how animal experimentation is often a threat to public health; how cruelty to animals leads to violent crimes; and so on. It is Joshipura's view that if we reject speciesism--the belief in human superiority--and accept that we are animals too, irrevocably interconnected to other species, from the largest elephant to the smallest bee, and a part of, rather than holding dominance over, nature, we can take the necessary steps towards the betterment of all the planet's inhabitants." From back cover. (shrink)

Cet article propose une sélection de bonnes feuilles du livre de 2013 The Undercommons, actuellement en voie de traduction pour une publication française en 2021. Y sont abordés les thèmes de la politique « encerclée » ( surrounded ), de la gouvernance, du planning et de la policy, de la logistique, ainsi que de ce qui fait de l’étude consacrée aux undercommons une philosophie du toucher.

Computational imagination (CI) conceives imagination as an agent’s simulated sensorimotor interaction with the environment in the absence of sensory feedback, predicting consequences based on this interaction (Marques and Holland in Neurocomputing 72:743–759, 2009). Its bedrock is the simulation hypothesis whereby imagination resembles seeing or doing something in reality as both involve similar neural structures in the brain (Hesslow in Trends Cogn Sci 6(6):242–247, 2002). This paper raises two-forked doubts: (1) neural-level equivalence is escalated to make phenomenological equivalence. Even at an (...) abstract level, many imagined and real actions turn out to be dissimilar. More so, some imagined actions have no corresponding real actions and vice versa, even though neural regions involved in imaginings and real action-perception are the same (Sect. 1). (2) At the implementation level, the hypothesis presents a mutually exclusive view of imagination and perception whereby imagination functions in the absence of the sensory feedback and is action based. Both these issues are contested here: Neither imagination functions in the absence of perception nor all forms of imaginings are action based; it is, rather, about conceiving possibilities which emerge during the perceptual stage itself (Sect. 2). For the modal aspect to arise, it is submitted that an integrative framework is required which Kant can provide for whom imagination is an indispensable part of perception. Kant’s views on concept-formation are presented here to illustrate this aspect (Sect. 3). The Paper is concluded with emphasizing the relevance of Kant’s views to the problems identified in the two sections. (shrink)

The design process in Kashmiri carpet weaving is distributed over a number of actors and artifacts and is mediated by a weaving notation called talim. The script encodes entire design in practice-specific symbols. This encoded script is decoded and interpreted via design-specific conventions by weavers to weave the design embedded in it. The cognitive properties of this notational system are described in the paper employing cognitive dimensions (CDs) framework of Green (People and computers, Cambridge University Press, Cambridge, 1989) and Blackwell (...) et al. (Cognitive technology: instruments of mind—CT 2001, LNAI 2117, Springer, Berlin, 2001). After introduction to the practice, the design process is described in ‘The design process’ section which includes coding and decoding of talim. In ‘Cognitive dimensions of talim’ section, after briefly discussing CDs framework, the specific cognitive dimensions possessed by talim are described in detail. (shrink)

Si on analyse les stratégies publicitaires de la plateforme Netflix il devient possible de mieux comprendre le modèle de spectateur que celles-ci façonnent. En étudiant la comparaison avec la chaine câblée HBO mais aussi le matériel de la campagne promotionnelle TV Got Better, nous observons les promesses que Netflix adresse à son public potentiel (plénitude, participation, prestige et personnalisation) ainsi que son encouragement des pratiques de visionnement « en rafale ».

The concept of lived experience plays a significant role in the social sciences in general and in philosophy in particular. The idea of lived experience as a social reality has been philosophized and given prime importance in the phenomenological tradition of philosophy. However, the work of Alfred Schutz, one of the phenomenologists on lived experience, has not been given adequate attention by either sociologists or philosophers. This paper attempts to understand how lived experiences are not merely individual or subjective experiences (...) but are also collective or social. Firstly, it suggests that lived experiences need to be understood in a broader context, namely that of the social and inter-subjectivity, which can be found in the Philosophy of Alfred Schutz. Thus, there is an attempt to show how Schutz’s philosophy broadly helps us understand lived experience as a social reality. Secondly, to justify how lived experiences are social and collective experiences, illustrations of the studies related to race, gender, and caste are given as a marker for lived experiences throughout the paper. So far, lived experience has been understood within the domain of individual or the subjective or merely in the first-person perspective. Hence, this paper provides some insights into investigating lived experience from the third-person perspective. (shrink)

Si les publics improbables sont, fondamentalement, des publics faibles, ils nous surprennent aussi – et c’est leur principal cadeau, leur caractère exemplaire. Par un art de la transgression, ils rappellent combien la vie publique est une affaire commune, façonnée par les gens à certains moments, en certains lieux, animés par la lutte et l’imagination, et par la joie de se découvrir les uns les autres. Cet article identifie quatre figures d’agentivité sonique pratiquées par ces improbables publics : l’invisible, l’entendu par-dessus (...) l’épaule, l’itinérant et le faible. (shrink)

The design process in Kashmiri carpet weaving is a distributed process encompassing a number of actors and artifacts. These include a designer called naqash who creates the design on graphs, and a coder called talim-guru who encodes that design in a specific notation called talim which is deciphered and interpreted by the weavers to weave the design. The technological interventions over the years have influenced these artifacts considerably and triggered major changes in the practice, from heralding profound cognitive accomplishments in (...) manually driven design process causing major alterations in the overall structure of the practice. The recent intervention is by the digital technology: on the one hand, it has brought precision and speedy processing in the design process, and on the other, it has eliminated some of the crucial actors from the practice. This paper, which forms part of a larger study on the situated and distributed cognitive process in Kashmiri carpet-weaving practice, describes the technological makeover of the design artifacts involved in this practice over the years and their resultant cognitive impact on the design process as well as on the practice. (shrink)

Consumer complaining behavior (CCB) is an important stream of research and practice, as it links the domains of service failure and service recovery. CCB research, although extensive and temporally wide, exhibits a lack of concern for the underlying assumptions of scholarly inquiry. Researchers neither explicitly mention, nor consciously indicate their ontological, epistemological, and methodological assumptions. We systematically identify the extant CCB literature and map it to two well-accepted paradigmatic classifications (Burrell and Morgan 1979; Deetz Organization Science 7(2): 191–207, 1996). Normative (...) or functionalist paradigm with the assumptions of an objective external reality, a positivist epistemology, a determinist view of human nature, and nomothetic methodology emerges as the dominant CCB research paradigm. The implications of this dominance are discussed as a barometer of the future of CCB research and practice. (shrink)

The squared paper or graphs are grid-based design representations used in engineering, industrial and craft design practices wherein designs are drawn over symmetrical grids. This paper reports grid-processing strategies undertaken by actors in a native craft practice, viz. Kashmiri carpet-weaving having three task contexts: (1) _design_, wherein designs are drawn on graph sheets and color scheme given by assigning practice-specific symbolic codes to the motifs by designers; (2) _coding_, wherein a cryptic script, called _talim_, is generated from these encoded graphs (...) by talim-writers; and (3) _weaving_, wherein this script is read, decoded and communicated in an equally cryptic trade-language by weavers among their teams. 32 video-recorded think-aloud sessions were conducted with talim-writers to understand their graph-processing and resultant coding strategies. The observations and qualitative analysis of transcripts revealed highly _situated_ strategies undertaken by experts keen on imbibing visuality of design embedded in the grids, in contrast to _embodied_ strategies undertaken by less-experienced coders keen on processing grids’ structural features and getting overwhelmed by grid-clutter in the process. In this landscape, the findings revealed a negotiated nature of errors instead of being deviations from the underlying grid and thereby objectively assessable. The paper makes recommendations for CAD systems to include more of those grid features exploited by experts during their graph processing to facilitate designers. (shrink)

This article describes various ways actors in Kashmiri carpet weaving practice deploy a range of artifacts, from symbolic, to material, to hybrid, in order to achieve diverse cognitive accomplishments in their particular task domains: information representation, inter and intra-domain communication, distribution of cognitive labor across people and time, coordination of team activities, and carrying of cultural heritage. In this repertoire, some artifacts position themselves as naïve tools in the actors’ environment to the point of being ignored; however, their usage-in-context unfolds (...) their cognitive involvement in the tasks. These usages-in-context are shown through artifact analysis of their routine, improvised, and opportunistic uses, where cognitive artifacts like talim—the central artifact of this practice—are shown to play not only multifunctional roles beyond representation, but are also complemented by trade-specific skills bearing strong cognitive implications in a task. (shrink)

Cognition is predominantly seen as information processing in multidisciplinary landscape of cognition studies, despite having had a formidable opposition from embodied and embedded perspectives in the last few decades. This paper analyses cognitive processes involved in different task domains of Kashmiri carpet weaving practice from the theoretical frameworks of cognitivism and situated-distributed cognition. After introducing the practice and its task domains (Section −1), paradigmatic cognitive activities involved in them are discussed and how these are explained by the two theoretical paradigms (...) and why the latter framework explains these processes better (Section-2). During this discussion (Section – 3), a way via culture impacts the practice and resultant cognitive operations turns out to be workspace of the actors. (shrink)

Plutôt que d’abandonner ou d’écraser l’idée de la liberté de la presse – en la considérant comme naïve ou anachronique – mon but est de la faire revivre et de la redéployer pour plaider en faveur d’une valeur normative particulière : le droit du public à entendre. Je prétends que l’image dominante, historique et professionnalisée de la liberté de la presse – généralement définie comme toutes les libertés dont doivent bénéficier les journalistes pour poursuivre un intérêt public évident – privilégie (...) le droit individuel à la parole sur le droit du public à entendre, alors que ce dernier mérite d’être remis au cœur de nos débats. (shrink)

Chaque époque se caractériserait par des dispositifs rituels de rassemblement des publics qui reflètent une certaine configuration socio-politique : du rassemblement collectif de la tradition théâtrale au modèle individualisé de l’exposition dans le contexte moderne. Comment rassembler des publics face aux défis de notre siècle?

Robert Fogelin claims that interlocutors must share a framework of background beliefs and commitments in order to fruitfully pursue argument. I refute Fogelin’s claim by investigating more thoroughly the shared background required for productive argument. I find that this background consists not in any common beliefs regarding the topic at hand, but rather in certain shared pro-cedural commitments and competencies. I suggest that Fogelin and his supporters mistakenly view shared beliefs as part of the required background for productive argument because (...) these procedural com-mitments become more difficult to uphold when people’s beliefs diverge widely regarding the topic at hand. (shrink)

The “modern” only begins to manifest itself when, in answer to the question, What is distinctively human?, Romanticism replies not by referring to man's eternal capacity for reason and universal rationality, but, ‘instead, to his creative originality, to his individuated capacity to feel and to dream uniquely. The modern begins to emerge when man is seen, not merely as a creature that can discover the world, but also as one who can create new meanings and values, and can thus change (...) himself and fundamentally transform his world, rather than unearth, recover, or “mirror” an essentially unchanging world order. (shrink)

This paper addresses the question of how to educate toward democracy, which has as its defining trait the ability to self-correct. We draw on a study that investigated Deep Reflective Thinking (DRT) as a classroom method for cultivating collective doubt, which is essential for developing students’ capacity for self-correction in a community of inquiry.

Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, today deep learning and machine learning advances are not weakening causality but are creating a new way of finding correlations between indirect factors. This process makes it possible for us to talk about approximate causality, as well (...) as about a situated causality. (shrink)

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction module obtains and displays (...) the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results. (shrink)

The growth of strawberry will be stressed by biological or abiotic factors, which will cause a great threat to the yield and quality of strawberry, in which various strawberry diseased. However, the traditional identification methods have high misjudgment rate and poor real-time performance. In today's era of increasing demand for strawberry yield and quality, it is obvious that the traditional strawberry disease identification methods mainly rely on personal experience and naked eye observation and cannot meet the needs of people for (...) strawberry disease identification and control. Therefore, it is necessary to find a more effective method to identify strawberry diseases efficiently and provide corresponding disease description and control methods. In this paper, based on the deep convolution neural network technology, the recognition of strawberry common diseases was studied, as well as a new method based on deep convolution neural network strawberry disease recognition algorithm, through the normal training of strawberry image feature representation in different scenes, and then through the application of transfer learning method, the strawberry disease image features are added to the training set, and finally the features are classified and recognized to achieve the goal of disease recognition. Moreover, attention mechanism and central damage function are introduced into the classical convolutional neural network to solve the problem that the information loss of key feature areas in the existing classification methods of convolutional neural network affects the classification effect, and further improves the accuracy of convolutional neural network in image classification. (shrink)

In the area of machine learning, different techniques are used to train machines and perform different tasks like computer vision, data analysis, natural language processing, and speech recognition. Computer vision is one of the main branches where machine learning and deep learning techniques are being applied. Optical character recognition is the ability of a machine to recognize the character of a language. Pashto is one of the most ancient and historical languages of the world, spoken in Afghanistan and Pakistan. (...) OCR application has been developed for various cursive languages like Urdu, Chinese, and Japanese, but very little work is done for the recognition of the Pashto language. When it comes to handwritten character recognition, it becomes more difficult for OCR to recognize the characters as every handwritten character’s shape is influenced by the writer’s hand motion dynamics. The reason for the lack of research in Pashto handwritten character data as compared to other languages is because there is no benchmark dataset available for experimental purposes. This study focuses on the creation of such a dataset, and then for the evaluation purpose, a machine is trained to correctly recognize unseen Pashto handwritten characters. To achieve this objective, a dataset of 43000 images was created. Three Feed Forward Neural Network models with backpropagation algorithm using different Rectified Linear Unit layer configurations were trained and tested with this dataset. The simulation shows that Model 1 achieved accuracy up to 87.6% on unseen data while Model 2 achieved an accuracy of 81.60% and 3% accuracy, respectively. Similarly, loss was the lowest for Model 1 with 0.15 and 3.17 for training and testing, followed by Model 2 with 0.7 and 4.2 for training and testing, while Model 3 was the last with loss values of 6.4 and 3.69. The precision, recall, and f-measure values of Model 1 were better than those of both Model 2 and Model 3. Based on results, Model 1 is found to be the most efficient as compared to the other two models in terms of accuracy to recognize Pashto handwritten characters. (shrink)

The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model is constructed to determine the relationship between hidden and explicit variables by determining the relevant indicators and parameter estimation. The prediction model based on Artificial Neural Network and Convolutional Neural Network is constructed. The method’s effectiveness is verified by comparing the (...) prediction model’s loss value and accuracy in training and testing. Meanwhile, 50 pieces of actual cases are tested, and the warning level is determined according to the T-value. The results show that comparing and analyzing ANN, CNN, and the hybrid network of ANN and CNN, the hybrid network’s accuracy is higher than the other two algorithms, 89.1 and 90.1%. Also, the hybrid network has sound prediction effects and accuracy when predicting actual cases. Therefore, the early warning method based on ANN in deep learning has better performance in public health emergencies’ early warning, which is significant for improving early warning capabilities. (shrink)

Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve the (...) network performance by adjusting red/green time split. In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one. The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions. This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others. (shrink)

This chapter examines whether, as Jeff McMahan argues, we should not integrate what he refers to as the “deep morality” of war into our military and international public policies and laws, because of the possible negative consequences of doing so. On the basis of feminist epistemology, I argue that McMahan is wrong to think that publicizing and legalizing the deep morality of war will have the negative consequences that he claims. Through a comparison with the Women's Suffrage Movement (...) in the United States, I argue that McMahan's argument is epistemically biased, in that it reflects and incorporates stereotypical views of poor people as stupid, thoughtless, and brutish. We would do better to think hard about how we conceptualize combatants, and to recognize in our arguments that the abilities to reason well and to act morally are not restricted to the upper echelons of society. (shrink)

Bidirectional deep brain stimulation (DBS) platforms have enabled a surge in hours of recordings in naturalistic environments, allowing further insight into neurological and psychiatric disease states. However, high amplitude, high frequency stimulation generates artifacts that contaminate neural signals and hinder our ability to interpret the data. This is especially true in psychiatric disorders, for which high amplitude stimulation is commonly applied to deep brain structures where the native neural activity is miniscule in comparison. Here, we characterized artifact sources (...) in recordings from a bidirectional DBS platform, the Medtronic Summit RC + S, with the goal of optimizing recording configurations to improve signal to noise ratio (SNR). Data were collected from three subjects in a clinical trial of DBS for obsessive-compulsive disorder. Stimulation was provided bilaterally to the ventral capsule/ventral striatum (VC/VS) using two independent implantable neurostimulators. We first manipulated DBS amplitude within safe limits (2–5.3 mA) to characterize the impact of stimulation artifacts on neural recordings. We found that high amplitude stimulation produces slew overflow, defined as exceeding the rate of change that the analog to digital converter can accurately measure. Overflow led to expanded spectral distortion of the stimulation artifact, with a six fold increase in the bandwidth of the 150.6 Hz stimulation artifact from 147–153 to 140–180 Hz. By increasing sense blank values during high amplitude stimulation, we reduced overflow by as much as 30% and improved artifact distortion, reducing the bandwidth from 140–180 Hz artifact to 147–153 Hz. We also identified artifacts that shifted in frequency through modulation of telemetry parameters. We found that telemetry ratio changes led to predictable shifts in the center-frequencies of the associated artifacts, allowing us to proactively shift the artifacts outside of our frequency range of interest. Overall, the artifact characterization methods and results described here enable increased data interpretability and unconstrained biomarker exploration using data collected from bidirectional DBS devices. (shrink)

Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video (...) sequence and the background image are channel-merged to construct a deep full convolutional network model. Finally, the network model is used to learn the deep features of the video scene and output the pixel-level classification results to achieve moving target detection. This method not only can adapt to complex video scenes of different sizes but also has a simple background extraction method, which effectively improves the detection speed. (shrink)

This study aims to achieve the goal of cultivating and reserving emerging professional talents in social security law, improve the curriculum and mechanism of entrepreneurship education, and improve students’ entrepreneurial willingness and entrepreneurial ability. Deep learning technology is used to study the psychological effects of entrepreneurship education for college students majoring in social security law. Firstly, the concept of entrepreneurial psychology is elaborated and summarized. A related model is designed using the theory of proactive personality and planned behavior through (...) questionnaire survey and regression analysis to explore the relationship between students’ entrepreneurial psychology and entrepreneurial intention. Secondly, an entrepreneurship education method based on deep learning is proposed, and a teaching model of multi-dimensional collaborative entrepreneurship education practice is constructed. On this basis, the deep learning algorithm combines the characteristics of the personalized recommendation algorithm to construct an efficient Problem-Based Learning learning resource recommendation algorithm. Finally, the proposed method is tested. The results show that the Significant value of students who have participated in PBL deep learning courses is less than 0.05, indicating that PBL significantly improves students’ learning ability and the ability to deal with entrepreneurial environments. The results verify the impact of entrepreneurial learning on entrepreneurial intentions. The research on PBL online learning recommendation system shows that the proposed recommendation algorithm is superior to the traditional recommendation algorithm in both roots mean square error value and mean absolute error value on both datasets. The proposed method provides a new idea of reform and innovation to cultivate social security law professionals and the cultivation of the reserve model. (shrink)

This essay focuses on the categerial expression of the metaphysics of ecstatic naturalism and the theology of deep pantheism. Naturalism, the theory that nature is all that there is, becomes the ecstatic form by stressing the continual fissuring between nature naturing and its potencies and nature natured seen as the innumerable orders of the world. Deep pantheism critiques the dualism of supernatural theism as well as the half-way theology of panentheism. Further, deep pantheism is “deep” because (...) it focuses on the churning abyss of the unconscious of nature and it’s archetypal potencies. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Better to Have No Deep Cut Anywhere in the Biopsychosocial SystemDerek Bolton, PhD (bio)It is very good to see theoretical work on the biopsychosocial model, acknowledging the causal role of these three kinds of factors in health and disease. I think Ongaro is right to argue that the biopsychosocial model requires an account of these three also being one—integrated—and that systems theoretic concepts such as dynamic, nonlinear causation (...) are helpful for capturing this. In a recent paper (Bolton, 2023) I have described Engel’s original (1997) paper as visionary but programmatic: visionary in seeing radical theory change afoot, citing von Bertalanffy’s new general system theory, but programmatic because the changes had not happened. The systems theoretic approach applies across the life, behavioral and social sciences, and typically envisages multiple interacting systems and causal pathways, including feedback and feedforward mechanisms, within and between systems. However, linear mechanisms are less complicated to research, and early social epidemiology used standard linear regression models (e.g., see the Whitehall II study [Marmot et al., 1991]). Such models do not capture non-linear connections in which the dependent variable also functions over time as an independent variable. However, the background theory typically would not exclude nonlinear dynamic causation, but on the contrary envisages it. Lower social status raises risk of ill health in a linear model, but it is also a plausible hypothesis that ill health in turn raises risk of lowering social status, which is testable in another or in a composite linear model. As well as social epidemiology, another large-scale theory change was underway at the time Engel wrote, the so-called cognitive revolution in psychology. Axiomatic in this shift was the idea that behavior is a response to the environment as perceived, thus merging the psychological/behavioral with the environmental, including the social. This latter point bears on Ongaro’s main proposal that the biopsychosocial model should envisage social causes as externalist, distinguished from the bio-psychological orientation of current psychiatry—and will be picked up below.Paper 2 makes some bold claims about the positive therapeutic value of construing causes [End Page 321] of mental health problems in external terms that have shared social meaning. These claims are made for functional neurological problems, but with extension to other kinds, in relation to a culture in which the shared social meanings refer to spirit possession, which, as a general type, is found in many non-modern Western cultures, as well as now, in our globalized world, in subcultures worldwide. One of the author’s claims is that within the spirit possession framework the shared framework of external causes enables society to adapt to the problem of living, to make it manageable, by shifting the social environment around the patient. As appropriate for ethnomethodology, there is no reference to controlled studies of outcome or mechanisms and I follow this immersed observational methodology here, with an experiential story that illustrates absence of personal and social acceptance and manageability of problematic conditions. While wandering recently with a friend in the mountains in the northern part of Africa, the local guide, in conversation in the local language and in English, having found out that two of us worked in “psychiatry/psychology,” asked us what we knew about; our brief reply included “obsessive-compulsive disorder” (OCD) and “anxiety”; the guide responded by asking whether OCD was washing hands a lot, and then wanted to discuss with us two people he knew well. One was a relation engaged, since many years, for many hours a day washing and cleaning, avoiding many daily tasks, sometimes childcare; friends and wider kinship structures had tried everything, except modern Western psychiatry (which was unavailable anyway), and, to put it briefly and colloquially, they were not at all happy about the person’s state and wished the person would get better. This person lived at a distance and we could not see them, so we do not know their view, but to judge by people with definite or probable OCD that we are familiar with, they may or may not have been happy in their reclusive state. Also to mention that the local healer had investigated... (shrink)

The philosopher, John Searle (1992, 1990), has challenged some of the most basic tenets of cognitive psychology, especially the notion of a “deep unconscious” defined as mental processes that are in principle inaccessible to consciousness. In previous papers I have argued for a broad concept of the unconscious which includes in addition to mental contents accessible to consciousness under appropriate conditions mental processes that can never become conscious under any conditions. The core of my answer to Searle derives from (...) Millikan’s (1984) theory of proper functions which provides a definition of intentionality independent of consciousness. (shrink)

With the rapid expansion of Internet technology, this research aims to explore the teaching strategies of ceramic art for contemporary students. Based on deep learning, an automatic question answering system is established, new teaching strategies are analyzed, and the Internet is combined with the automatic QA system to help students solve problems encountered in the process of learning. Firstly, the related theories of DL and personalized learning are analyzed. Among DL-related theories, Back Propagation Neural Network, Convolutional Neural Network, Long (...) Short-Term Memory, and Gated Recurrent Unit are compared to implement a single model and a mixed model. Secondly, the collected student questions are selected and processed, and experimental parameters in different models are set for comparative experiments. Experiments reveal that the average accuracy and Mean Reciprocal Rank of traditional retrieval methods can only reach about 0.5. In the basic neural network, the average accuracy of LSTM and GRU structural models is about 0.81, which can achieve better results. Finally, the accuracy of the hybrid model can reach about 0.82, and the accuracy and MRR of the Bidirectional Gated Recurrent Unit Network-Attention model are 0.87 and 0.89, respectively, achieving the best results. The established DL model meets the requirements of the online automatic QA system, improves the teaching system, and helps students better understand and solve problems in the ceramic art courses. (shrink)

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in (...) predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)