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)

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)

This research offers a concise analysis of leisure science, leisure studies, and the concept of leisure space-time. It explores the interdisciplinary nature of leisure science, drawing from psychology, sociology, and economics. The research article of corresponding report writing on desk examines leisure studies that contribute to understanding individual and societal leisure behaviors, motivations, and benefits. Additionally, it delves into the notion of leisure space-time, investigating the design and utilization of spaces for leisure activities. This research provides valuable (...) insights into the multifaceted realm of leisure, shedding light on its significance in contemporary society. (shrink)

The literature on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises concerns that these technologies may affect personality, mood, and behavior. We conducted semi-structured interviews with researchers (n = 23) involved in developing next-generation DBS systems, exploring their perspectives on ethics and policy topics including whether DBS/aDBS can cause such changes. The majority of researchers reported being aware of personality, mood, or behavioral (PMB) changes in recipients of DBS/aDBS. Researchers offered varying estimates of the frequency of PMB changes. (...) A smaller majority reported changes in personality specifically. Some expressed reservations about the scientific status of the term ‘personality,’ while others used it freely. Most researchers discussed negative PMB changes, but a majority said that DBS/aDBS can also result in positive changes. Several researchers viewed positive PMB changes as part of the therapeutic goal in psychiatric applications of DBS/aDBS. Finally, several discussed potential causes of PMB changes other than the device itself. (shrink)

The expansion of research on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises important neuroethics and policy questions related to data sharing. However, there has been little empirical research on the perspectives of experts developing these technologies. We conducted semi-structured, open-ended interviews with aDBS researchers regarding their data sharing practices and their perspectives on ethical and policy issues related to sharing. Researchers expressed support for and a commitment to sharing, with most saying that they were either (...) sharing their data or would share in the future and that doing so was important for advancing the field. However, those who are sharing reported a variety of sharing partners, suggesting heterogeneity in sharing practices and lack of the broad sharing that would reflect principles of open science. Researchers described several concerns and barriers related to sharing, including privacy and confidentiality, the usability of shared data by others, ownership and control of data (including potential commercialization), and limited resources for sharing. They also suggested potential solutions to these challenges, including additional safeguards to address privacy issues, standardization and transparency in analysis to address issues of data usability, professional norms and heightened cooperation to address issues of ownership and control, and streamlining of data transmission to address resource limitations. Researchers also offered a range of views on the sensitivity of neural activity data (NAD) and data related to mental health in the context of sharing. These findings are an important input to deliberations by researchers, policymakers, neuroethicists, and other stakeholders as they navigate ethics and policy questions related to aDBS research. (shrink)

A central topic in research at the intersection of development and evolution is the origin of novel traits. Despite progress on understanding how developmental mechanisms underlie patterns of diversity in the history of life, the problem of novelty continues to challenge researchers. Here we argue that research on evolutionary novelty and the closely associated phenomenon of co-option can be reframed fruitfully by: (1) specifying a conceptual model of mechanisms that underwrite character identity, (2) providing a richer and more (...) empirically precise notion of co-option that goes beyond common appeals to “deep homology”, and (3) attending to the nature of experimental interventions that can determine whether and how the co-option of identity mechanisms can help to explain novel character origins. This reframing has the potential to channel future investigation to make substantive progress on the problem of evolutionary novelty. To illustrate this potential, we apply our reframing to two case studies: treehopper helmets and beetle horns. (shrink)

This article starts off with a historical section showing that deep disagreements among notions of social and political justice are a characteristic feature of the history of political thought. Since no agreement or consensus on distributive justice is possible, the article argues that political philosophers should – instead of continuously proposing new normative theories of justice – focus on analyzing the reasons, significance, and consequences of such kinds of disagreements. The next two sections are analytical. The first sketches five (...) possible reasons for deep disagreements among notions of social and political justice. The second discusses the meta-ethical relevance of the lack of consensus on justice and rejects ethical realism and cognitivism based on the argument from deep disagreements. (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)

The aim or goal of science has long been discussed by both philosophers of science and scientists themselves. In The Scientific Image (van Fraassen 1980), the aim of science is famously employed to characterize scientific realism and a version of anti-realism, called constructive empiricism. Since the publication of The Scientific Image, however, various changes have occurred in scientific practice. The increasing use of machine learning technology, especially deep learning (DL), is probably one of the major changes in the last (...) decade. This paper aims to explore the implications of DL-aided research for the aim of science debate. I argue that, while the emerging DL-aided research is unlikely to change the state of classic opposition between constructive empiricism and scientific realism, it could offer interesting cases regarding the opposition between those who espouse truth as the aim of science and those oriented to understanding (of the kind that sacrifices truth). (shrink)

This article starts off with a historical section showing that deep disagreements among notions of social and political justice are a characteristic feature of the history of political thought. Since no agreement or consensus on distributive justice is possible, the article argues that political philosophers should – instead of continuously proposing new normative theories of justice – focus on analyzing the reasons, significance, and consequences of such kinds of disagreements. The next two sections are analytical. The first sketches five (...) possible reasons for deep disagreements among notions of social and political justice. The second discusses the meta-ethical relevance of the lack of consensus on justice and rejects ethical realism and cognitivism based on the argument from deep disagreements. (shrink)

Deep learning is widely used in the fields of information technology and education innovation but there are few studies for young children in the preschool stage. Therefore, we aimed to explore factors that affect children’s learning ability through collecting relevant information from teachers in the kindergarten. Literature review, interview, and questionnaire survey methods were used to determine the influencing factors of deep learning. There were five dimensions for these factors: the level of difficulty of academic, communication skills, level (...) of active collaboration, level of in-depth processing, and reflection level evaluation. Reliability and validity tests were used to analyze the data from questionnaires. In total, 100 valid questionnaires were collected. The Cronbach coefficients for academic challenge, communication, active cooperation, deep processing, and reflective evaluation were 0.801, 0.689, 0.770, 0.758, and 0.665, respectively. Principal component analysis revealed that there were three main factors that affect children’s learning depth: the level of deep processing, the level of reflective evaluation, and the active level of collaboration. In conclusion, there were several factors affecting deep learning in children and further studies are warranted to promote the development of this field. (shrink)

Ethical evaluation of deep brain stimulation as a treatment for Parkinson’s disease is complicated by results that can be described as involving changes in the patient’s identity. The risk of becoming another person following surgery is alarming for patients, caregivers and clinicians alike. It is one of the most urgent conceptual and ethical problems facing deep brain stimulation in Parkinson’s disease at this time. In our paper we take issue with this problem on two accounts. First, we elucidate (...) what is meant by “becoming another person” from a conceptual point of view. After critically discussing two broad approaches we concentrate on the notion of “individual identity” which centers on the idea of “core attitudes”. Subsequently we discuss several approaches to determine what distinguishes core attitudes from those that are more peripheral. We argue for a “foundational-function model” highlighting the importance of specific dependency relations between these attitudes. Our second aim is to comment on the possibility to empirically measure changes in individual identity and argue that many of the instruments now commonly used in selecting and monitoring DBS-patients are inappropriate for this purpose. Future research in this area is advised combining a conceptual and an empirical approach as a basis of sound ethical appraisal. (shrink)

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

The capacity of next-generation closed-loop or adaptive deep brain stimulation devices to read and write shows great potential to effectively manage movement, seizure, and psychiatric disorders, and also raises the possibility of using aDBS to electively modulate mood, cognition, and prosociality. What separates aDBS from most neurotechnologies currently used for enhancement is that aDBS remains an invasive, surgically-implanted technology with a risk-benefit ratio significantly different when applied to diseased versus non-diseased individuals. Despite a large discourse about the ethics of (...) enhancement, no empirical studies yet examine perspectives on enhancement from within the aDBS research community. We interviewed 23 aDBS researchers about their attitudes toward expanding aDBS use for enhancement. A thematic content analysis revealed that researchers share ethical concerns related to safety and security; enhancement as unnecessary, unnatural or aberrant; and fairness, equality, and distributive justice. Most researchers felt that enhancement applications for DBS will eventually be technically feasible and that attempts to develop such applications for DBS are already happening. However, researchers unanimously felt that DBS ideally should not be considered for enhancement until researchers better understand brain target localization and functioning. While many researchers acknowledged controversies highlighted by scholars and ethicists, such as potential impacts on personhood, authenticity, autonomy and privacy, their ethical concerns reflect considerations of both gravity and perceived near-term likelihood. (shrink)

The purpose of the study was to solve the problem of the mismatching between the supply and demand of the talents that universities provide for society, whose major is communication design. The correlations between social post demand and university cultivation, as well as between social post demand and the demand indexes of enterprises for posts, are explored under the guidance of University-Industrial Research Collaboration. The backpropagation neural network is used, and the advantages of the Seasonal Autoregressive Integrated Moving Average (...) model model are combined to design the SARIMA-BPNN model after the relevant parameters are adjusted. Through the experimental analysis, it is found that the error of the root mean square of the designed SARIMA-BP model in post prediction is 7.523 and that of the BPNN model is 16.122. The effect of the prediction model that was designed based on deep learning is smaller than that of the previous model based on the neural network, and it can predict future posts more accurately for colleges and universities. Guided by the “University-Industrial Research Collaboration,” students will have more practice in the teaching process in response to social needs. “University-Industrial Research Collaboration” guides the teaching direction for communication design majors and can help to cultivate communication design talents who are competent for the post provided. (shrink)

Deep Brain Stimulation (DBS) is a relatively new, experimental treatment for patients suffering from treatment-refractory Obsessive Compulsive Disorder (OCD). The effects of treatment are typically assessed with psychopathological scales that measure the amount of symptoms. However, clinical experience indicates that the effects of DBS are not limited to symptoms only: patients for instance report changes in perception, feeling stronger and more confident, and doing things unreflectively. Our aim is to get a better overview of the whole variety of changes (...) that OCD patients experience during DBS treatment. For that purpose we conducted in-depth, semi-structured interviews with 18 OCD patients. In this paper, we present the results from this qualitative study.We list the changes grouped in four domains: with regard to (a) person, (b) (social) world, (c)characteristics of person-world interactions, and (d) existential stance. We subsequently provide an interpretation of these results. In particular, we suggest that many of these changes can be seen as different expressions of the same process; namely that the experience of anxiety and tension gives way to an increased basic trust and increased reliance on one’s abilities. We then discuss the clinical implications of our findings, especially with regard to properly informing patients of what they can expect from treatment, the usefulness of including CBT in treatment, and the limitations of current measures of treatment success. We end by making several concrete suggestions for further research. (shrink)

Default logic has been a very active research topic in artificial intelligence since the early 1980s, but has not received as much attention in the philosophical literature thus far. This paper shows one way in which the technical tools of artificial intelligence can be applied in contemporary epistemology by modeling a paradigmatic case of deep disagreement using default logic. In §1 model-building viewed as a kind of philosophical progress is briefly motivated, while §2 introduces the case of (...) class='Hi'>deep disagreement we aim to model. On the heels of this, §3 defines our formal framework, viz., a refined Horty-style default logic. §4 then uses the framework to model deep disagreement, and finally §5 provides a critical discussion of the result. (shrink)

The deviation of the conveyor belt is a common failure that affects the safe operation of the belt conveyor. In this paper, a deviation detection method of the belt conveyor based on inspection robot and deep learning is proposed to detect the deviation at its any position. Firstly, the inspection robot captures the image and the region of interest containing the conveyor belt edge and the exposed idler is extracted by the optimized MobileNet SSD. Secondly, Hough line transform algorithm (...) is used to detect the conveyor belt edge, and an elliptical arc detection algorithm based on template matching is proposed to detect the idler outer edge. Finally, a geometric correction algorithm based on homography transformation is proposed to correct the coordinates of the detected edge points, and the deviation degree of the conveyor belt is estimated based on the corrected coordinates. The experimental results show that the proposed method can detect the deviation of the conveyor belt continuously with an RMSE of 3.7 mm, an MAE of 4.4 mm, and an average time consumption of 135.5 ms. It improves the monitoring range, detection accuracy, reliability, robustness, and real-time performance of the deviation detection of the belt conveyor. (shrink)

Research ethics education has taken many forms since federal funding agencies issued regulatory guidance directing those supported by these agencies to complete required training. In the absence of a standard training approach among institutions such as universities, the design and content of courses, workshops, and seminars varies widely. Here we describe a southwestern United States research university program that employed six teaching strategies to assist students in deep learning of ethical principles and behavior. Our purpose was to (...) determine how these strategies impact understanding and empowerment among an interdisciplinary group of graduate students. Results suggested that deep learning design can result in learning that requires students not only to remember and understand key ethics concepts but also to analyze and apply them in a broader ethical domain. Key findings inform design and delivery of future research ethics courses for instructional designers and instructors in higher education. (shrink)

Substantial advancement in the diagnosis and treatment of psychiatric disorders may come from assembling diverse data streams from clinical notes, neuroimaging, genetics, and real-time digital footprints from smartphones and wearable devices. This is called “deep phenotyping” and often involves machine learning. We argue that incidental findings arising in deep phenotyping research have certain special, morally and legally salient features: They are specific, actionable, numerous, and probabilistic. We consider ethical and legal implications of these features and propose a (...) practical ethics strategy for managing them. (shrink)

In this article we will draw on experiences from our own research on deep brain stimulation of the central thalamus in the minimally conscious state. We describe ethical challenges faced in clinical research involving medical devices and offer several cautionary notes about its funding and the interplay of market forces and scientific inquiry and suggest some reforms.

For three decades, scholars have investigated the phenomena of organizational misconduct (OM) in the fields of business ethics, management, and organization studies. In recent years, the construct has gained increased attention due to widely reported corruption, bribery, crime, violations, and other acts of immorality undertaken by organizations, especially in emerging markets. Despite its popularity, review studies on OM are sparse, and no systematic review of research on OM in the context of emerging markets exists. This article attempts to fill (...) this void by analyzing the literature on OM in the specific context of emerging markets based on a systematic review methodology and integration approach. Based on the findings of our literature review, we develop an integrative model of the antecedents and consequences of OM in emerging markets and discuss important moderators that may curtail the development of OM and alleviate its consequences. In doing so, we provide scholars with a conceptual model and overview of the current knowledge and findings on the topic. We further discuss important gaps in the literature and provide fruitful avenues for future work that we believe are promising to advance our understanding of OM in the context of emerging markets. (shrink)

Education public opinion information management is an important research focus in the field of Education Data Mining. In this paper, we classify the education data information based on the traditional Flat-OCC model. From the cognitive psychology perspective, we identify up to 12 kinds of emotions, including sadness and happiness. In addition, the EMO-CBOW model is also proposed in this paper to further identify emotion by using various emoticons in educational data sets. The empirical result shows that the proposed Flat-OCC (...) model can classify and identify the emotion of education public opinion data well; and for the recognition of educational emotion classification, the categorization accuracy of the Flat-OCC+EMO-CBOW model is significantly higher than that of a single Flat-OCC model, which reveals that the emotional-pack-based model we propose can enhance our benchmark model. (shrink)

Since entering the new century, People’s living standards are constantly improving, with the continuous improvement of living conditions, people are becoming more and more important in education, which is the embodiment of the enhancement of national strength. The education level is getting higher and higher, and a good education level needs a good teacher–student relationship. To solve these problems, we use the emotional cognition of God’s network to study the teacher–student relationship, and collect and analyze the data of the teacher–student (...) relationship. In this chapter, we use GABP neural network algorithm DHNN algorithm and discrete Hopfield neural network to make the collected data more convenient to be analyzed. The research shows that there is a close relationship between the educational level and the relationship between teachers and students in China, and a good relationship between teachers and students will promote the improvement of educational level. According to the research data, “face-to-face” is the most important way of interaction between tutors and postgraduates in various types of teacher–student relationship. QQ WeChat is also one of the main ways of interaction between students and teachers, which shows that the interaction between students and teachers is talking about the interaction between online and Internet. The education industry is becoming more and more important, and the teacher–student relationship is the most important part of the education industry. Good teacher–student relationship is helpful to cultivate students’ healthy personality. In view of the cold relationship between teachers and students at present, we need to make some measures the relationship between teachers and students and effectively use the relationship between teachers and students to promote the better development of the education industry. (shrink)

_Riassunto_: La _Deep Brain Stimulation _(DBS) è un dispositivo annoverato tra le interfacce-cervello computer e che si qualifica come una promettente soluzione medica per far fronte al decorso di alcune malattie neurodegenerative come il Parikinson. Decenni di utilizzo clinico della DBS hanno permesso di comprenderne gli effetti collaterali e il loro impatto sul PIAAAS (_Personality, Identity, Agency, Authenticity, Autonomy and Self_). In tal senso, per sondare cambiamenti psicologici legati all’uso della DBS si è fatto ricorso a metodi quantitativi, come questionari (...) incentrati su valutazioni di ordine numerico, e metodi qualitativi, che si appoggiano alle esperienze riportate dai pazienti tramite interviste. Questo articolo ha l’obiettivo di evidenziare come, a prescindere dai metodi di indagine utilizzati, la ricerca sia stata sempre focalizzata sugli effetti collaterali della DBS e gli eventuali cambiamenti sulle dimensioni comprese nel PIAAAS, lasciandosi sfuggire l’occasione di usare i numerosi dati raccolti per effettuare un’analisi fenomenologica che potesse restituirci una descrizione della sintesi uomo-macchina. Per colmare questo iato, proponiamo l’utilizzo sinergico di due metodi di raccolta e analisi dati di origine fenomenologica. _Parole chiave_: Deep Brain Stimulation; Effetti collaterali; Sintesi uomo-macchina; Fenomenologia _Deep Brain Stimulation and the human-machine synthesis: A possible phenomenological perspective_ _Abstract_: Deep Brain Stimulation (DBS) is a device included among the brain-computer interfaces and that qualifies as a promising medical solution to cope with the course of some neurodegenerative diseases such as Parikinson’s. Decades of clinical use of DBS have allowed us to understand its side effects and their impact on PIAAAS (_Personality, Identity, Agency, Authenticity, Autonomy and Self_). In this sense, to investigate psychological changes related to the use of DBS, quantitative methods, such as questionnaires focused on numerical assessments, and qualitative techniques, which are based on experiences by patients through interviews, have been used. This article aims to highlight how, regardless of the survey methods used, research has always been focused on the side effects of DBS and possible changes in the concepts included in the PIAAAS, missing the opportunity to use the numerous data collected to carry out a phenomenological analysis that could give us a description of the human-machine synthesis. To bridge this hiatus, we propose the synergistic use of two phenomenological methods of data collection and analysis. _Keywords_: Deep Brain Stimulation; Side Effects; Human-machine synthesis; Phenomenology. (shrink)

Recent research has highlighted the character and importance of the study of agreement. This paper, paralleling work on the more familiar concept of deep disagreement, will provide a first articulation of the character and implications of deep agreements, that is, agreements so deep that disagreement cannot overcome them. To do so, I start by outlining the main features of deep disagreement. I then provide a brief characterization of agreement in general to ground the discussion of (...) the unique characteristics of deep agreements and compare them to deep disagreements. After distinguishing the concept from other more familiar ones, I then point to a few of the major social and theoretical issues the existence of deep agreements poses before concluding with some remarks pointing to valuable areas for future research. (shrink)

People suffering from Obsessive-Compulsive Disorder (OCD) do things they do not want to do, and/or they think things they do not want to think. In about 10 percent of OCD patients, none of the available treatment options is effective. A small group of these patients is currently being treated with deep brain stimulation (DBS). Deep brain stimulation involves the implantation of electrodes in the brain. These electrodes give a continuous electrical pulse to the brain area in which they (...) are implanted. It turns out that patients may experience profound changes as a result of DBS treatment. It is not just the symptoms that change; patients rather seem to experience a different way of being in the world. These global effects are insufficiently captured by traditional psychiatric scales, which mainly consist of behavioural measures of the severity of the symptoms. In this article we aim to capture the changes in the patients’ phenomenology and make sense of the broad range of changes they report. For that we introduce an enactive, affordance-based model that fleshes out the dynamic interactions between person and world in four aspects. The first aspect is the patients’ experience of the world. We propose to specify the patients’ world in terms of a field of affordances, with the three dimensions of broadness of scope (‘width’ of the field), temporal horizon (‘depth’), and relevance of the perceived affordances (‘height’). The second aspect is the person-side of the interaction, that is, the patients’ self-experience, notably their moods and feelings. Thirdly, we point to the different characteristics of the way in which patients relate to the world. And lastly, the existential stance refers to the stance that patients take towards the changes they experience: the second-order evaluative relation to their interactions and themselves. With our model we intend to specify the notion of being in the world in order to do justice to the phenomenological effects of DBS treatment. (shrink)

The deep structure of Confucianism is identified through structuralist analysis in order to provide a conceptual framework for conducting social psychological research in Chinese society. Through understanding and imitating the Way of Heaven (tiendao), Confucians constructed the Way of Humanity (rendao), which consists of two aspects; ethics for ordinary people and ethics for scholars. Ethics for ordinary people adopts the principle of Respecting the Superior for procedural justice and the principle of Favouring the Intimate for distributive justice; the (...) person who occupies the superior position should play the role of decision-maker and should allocate resources by favouring intimate relationships. Because Confucian cosmology suggests that the Way of Humanity corresponds to the Way of Heaven, Confucians required individuals to cultivate themselves with the Way of Humanity. Ethics for scholars further endows Confucian disciples with the mission of benefiting the whole society with the Way of Humanity. (shrink)

Recent breakthroughs in artificial intelligence have primarily resulted from training deep neural networks (DNNs) with vast numbers of adjustable parameters on enormous datasets. Due to their complex internal structure, DNNs are frequently characterized as inscrutable ``black boxes,'' making it challenging to interpret the mechanisms underlying their impressive performance. This opacity creates difficulties for explanation, safety assurance, trustworthiness, and comparisons to human cognition, leading to divergent perspectives on these systems. This chapter examines recent developments in interpretability methods for DNNs, with (...) a focus on interventionist approaches inspired by causal explanation in philosophy of science. We argue that these methods offer a promising avenue for understanding how DNNs process information compared to merely behavioral benchmarking and correlational probing. We review key interventionist methods and illustrate their application through practical case studies. These methods allow researchers to identify and manipulate specific computational components within DNNs, providing insights into their causal structure and internal representations. We situate these approaches within the broader framework of causal abstraction, which aims to align low-level neural computations with high-level interpretable models. While acknowledging current limitations, we contend that interventionist methods offer a path towards more rigorous and theoretically grounded interpretability research, potentially informing both AI development and computational cognitive neuroscience. (shrink)

Deep Brain Stimulation is currently being investigated as an experimental treatment for patients suffering from treatment-refractory AN, with an increasing number of case reports and small-scale trials published. Although still at an exploratory and experimental stage, initial results have been promising. Despite the risks associated with an invasive neurosurgical procedure and the long-term implantation of a foreign body, DBS has a number of advantageous features for patients with SE-AN. Stimulation can be fine-tuned to the specific needs of the particular (...) patient, is relatively reversible, and the technique also allows for the crucial issue of investigating and comparing the effects of different neural targets. However, at a time when DBS is emerging as a promising investigational treatment modality for AN, lesioning procedures in psychiatry are having a renaissance. Of concern it has been argued that the two kinds of interventions should instead be understood as rivaling, yet “mutually enriching paradigms” despite the fact that lesioning the brain is irreversible and there is no evidence base for an effective target in AN. We argue that lesioning procedures in AN are unethical at this stage of knowledge and seriously problematic for this patient group, for whom self-control is particularly central to wellbeing. They pose a greater risk of major harms that cannot justify ethical equipoise, despite the apparent superiority in reduced short term surgical harms and lower cost. (shrink)

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at (...) the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks. (shrink)

We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, (...) as well as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld. (shrink)

There is preliminary evidence, from case reports and investigational studies, to suggest that Deep Brain Stimulation could be used to treat some patients with Anorexia Nervosa. Although this research is at an early stage, the invasive nature of the intervention and the vulnerability of the potential patients are such that anticipatory ethical analysis is warranted. In this paper, we first show how different treatment mechanisms raise different philosophical and ethical questions. We distinguish three potential mechanisms alluded to in (...) the neuroscientific literature, relating to desire, control, and emotion, respectively. We explain why the precise nature of the mechanism has important implications for the patient’s autonomy and personal identity. In the second part of the paper, we consider practical dimensions of offering DBS to patients with AN in certain cases. We first discuss some limited circumstances where the mere offering of the intervention might be perceived as exerting a degree of coercive pressure that could serve to undermine the validity of the patient’s consent. Finally, we consider the implications of potential effects of DBS for the authenticity of the patient’s choice to continue using stimulation to ameliorate their condition. (shrink)

Sociological research on moral panics, long understood as “struggles for cultural power,” has focused on the social groups and media conditions that enable moral panics to emerge, and on the consequences of moral panics for the social control systems of societies. In this article I turn instead to modeling the specific cultural process of how the conditions for a moral panic are turned into an actual moral panic, moving the understanding of moral panic away from its Durkheimian origins and (...) towards a process-relational cultural sociology. Drawing on Roland Barthes’ theory of myth and Kenneth Burke’s dramatism, the paper posits the cultural process of resignification via synecdoche and metanarrative as the driver of the disproportion, concern, hostility, consensus, and volatility of moral panics. This process can be carefully traced in the case of the Salem Witch Trials; a retrospective reading reveals the same process at work in the “Mods and Rockers” panic analyzed by Stanley Cohen. Beyond moral panics, theorizing resignification as a non-exclusive counterpoint to framing and ideational embeddedness enriches the theoretical repertoire of cultural sociology. “Deep culture” and mythological signification can, using the schema proposed here, be understood as practical accomplishments—rhetorical responses to particular situations that, when performed successfully, legitimate violence and other forms of domination. (shrink)

Although the discipline of vice epistemology is only a decade old, the broader project of studying epistemic vices and failings is much older. This paper argues that contemporary vice epistemologists ought to engage more closely with these earlier projects. After sketching some general arguments in section one, I then turn to deep epistemic vices: ones whose identity and intelligibility depends on some underlying conception of human nature or the nature of reality. The final section then offers a case study (...) from a vice epistemic tradition that emerged in early modern English natural philosophy. (shrink)

Over the past decade, it has been discovered that disparate aspects of morphology – often of distantly related groups of organisms – are regulated by the same genetic regulatory mechanisms. Those discoveries provide a new perspective on morphological evolutionary change. A conceptual framework for exploring these research findings is termed ‘deep homology’. A comparative framework for morphological relations of homology is provided that distinguishes analogy, homoplasy, plesiomorphy and synapomorphy. Four examples – three from plants and one from animals (...) – demonstrate that homologous developmental mechanisms can regulate a range of morphological relations including analogy, homoplasy and examples of uncertain homology. Deep homology is part of a much wider range of phenomena in which biological (genes, regulatory mechanisms, morphological traits) and phylogenetic levels of homology can both be disassociated. Therefore, to understand homology, precise, comparative, independent statements of both biological and phylogenetic levels of homology are necessary. (shrink)

Patient narratives from two investigational deep brain stimulation trials for traumatic brain injury and obsessive‐compulsive disorder reveal that injury and illness rob individuals of personal identity and that neuromodulation can restore it. The early success of these interventions makes a compelling case for continued post‐trial access to these technologies. Given the centrality of personal identity to respect for persons, a failure to provide continued access can be understood to represent a metaphorical identity theft. Such a loss recapitulates the pain (...) of an individual's initial injury or illness and becomes especially tragic because it could be prevented by robust policy. A failure to fulfill this normative obligation constitutes a breach of disability law, which would view post‐trial access as a means to achieve social reintegration through this neurotechnological accommodation. (shrink)

Dystonia is a movement disorder that can have a debilitating impact on motor functions and quality of life. There are 250,000 cases in the United States, most with childhood onset. Due to the limited effectiveness and side effects of available treatments, pediatric deep brain stimulation has emerged as an intervention for refractory dystonia. However, there is limited clinical and neuroethics research in this area of clinical practice. This paper examines whether it is ethically justified to offer pDBS to (...) children with refractory dystonia. Given the favorable risk-benefit profile, it is concluded that offering pDBS is ethically justified for certain etiologies of dystonia, but it is less clear for others. In addition, various ethical and policy concerns are discussed, which need to be addressed to optimize the practice of offering pDBS for dystonia. Strategies are proposed to help address these concerns as pDBS continues to expand. (shrink)

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

The report of the President's Council on Bioethics, Human Cloning and Human Dignity, addresses the central ethical, political, and policy issue in human embryonic stem cell research: the moral status of extracorporeal human embryos. The Council members were in sharp disagreement on this issue and essentially failed to adequately engage and respectfully acknowledge each others' deepest moral concerns, despite their stated commitment to do so. This essay provides a detailed critique of the two extreme views on the Council (i.e., (...) embryos have full moral status or they have none at all) and then gives theoretical grounding for our judgment about the intermediate moral status of embryos. It also supplies an account of how to address profound moral disagreements in the public arena, especially by way of constructing a middle ground that deliberately pays sincere respect to the views of those with whom it has deep disagreements. (shrink)

Methods of machine learning (ML) are gradually complementing and sometimes even replacing methods of classical statistics in science. This raises the question whether ML faces the same methodological problems as classical statistics. This paper sheds light on this question by investigating a long-standing challenge to classical statistics: the reference class problem (RCP). It arises whenever statistical evidence is applied to an individual object, since the individual belongs to several reference classes and evidence might vary across them. Thus, the problem consists (...) in choosing a suitable reference class for the individual. I argue that deep neural networks (DNNs) are able to overcome specific instantiations of the RCP. Whereas the criteria of narrowness, reliability, and homogeneity, that have been proposed to determine a suitable reference class, pose an inextricable tradeoff to classical statistics, DNNs are able to satisfy them in some situations. On the one hand, they can exploit the high dimensionality in big-data settings. I argue that this corresponds to the criteria of narrowness and reliability. On the other hand, ML research indicates that DNNs are generally not susceptible to overfitting. I argue that this property is related to a particular form of homogeneity. Taking both aspects together reveals that there are specific settings in which DNNs can overcome the RCP. (shrink)

Seabed mining, the extraction of minerals from the deep-sea floor, is hotly contested. Policymakers have agreed on the need for a regulatory framework. However, traditional ethical theories and principles are not well equipped for the ethics of the alien deep sea. Engaging with the sea means engaging with something abstract that we can only access indirectly. We argue that this invisibility and alienness of the sea and its inhabitants can give new insights into how ethics are done. Rather (...) than getting even more grip on what is already directly known, considering what is outside our usual moral view may be just as valuable. To do so, art can help us think with these unknown and invisible parts of the ecosystem. For that, we describe three cases. The scientist and artist Eugen Ransonnet engaged with the underwater world in the late 19th century. The Victorian flower painter Marianne North's unruly approach greatly influenced the botanic discourse until today. And finally, Christina Stadlbauer's Institute for the Relocation of Biodiversity and her work with the mollusk Pinna Nobilis in Mar Menor, Spain. We describe the Pinna Nobilis video project and the ethical and political opportunities it opens. We propose that engaging with the arts can pave the way towards including the unknown in ethical reflection and, at the same time, how it can rephrase questions that can help us reconsider what is outside of our moral view. (shrink)

This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in terms of ‘algorithmic thought’. Research in deep learning is its case study. This artificial intelligence (AI) technique operates in computational ways that are often opaque. Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience and (...) technoculture tackle the possibility to ‘re-present’ the algorithmic procedures of feature extraction and feature learning to the human mind. The article thus mobilises the notion of incommensurability (originally developed in the philosophy of science) to address explainability as a communicational and representational issue, which challenges phenomenological and existential modes of comparison between human and algorithmic ‘thinking’ operations. (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)

Cultural development is often reflected in the emotional expression of various cultural carriers, such as literary works, movies, etc. Therefore, the cultural development can be analyzed through emotion analysis of the text, so as to sort out its context and obtain its development dynamics. This paper proposes a text emotion analysis method based on deep learning. The traditional neural network model mainly deals with the classification task of short texts in the form of word vectors, which causes the model (...) to rely too much on the accuracy of word segmentation. In addition, the short texts have the characteristics of short corpus and divergent features. A text emotion classification model combing the Bidirectional Encoder Representations from Transformers and Bi-directional Long Short-Term Memory is developed in this work. First, the BERT model is used to convert the trained text into a word-based vector representation. Then, the generated word vector is employed as the input of the BiLSTM to obtain the semantic representation of the context of the relevant word. By adding random dropout, the mechanism prevents the model from overfitting. Finally, the extracted feature vector is input to the fully connected layer, and the emotion category to which the text belongs is calculated through the Softmax function. Experiments show that in processing short texts, the proposed model based on BERT-BiLSTM is more accurate and reliable than the traditional neural network model using word vectors. The proposed method has a better analysis effect on the development of western culture. (shrink)

Deep ecology is an ecological philosophy that promotes an ecocentric lifestyle to remedy the problems of depleting resources and planetary degradation. An integral part of this ecosophy is the process of forming a metaphysical connection to the earth, referred to as self-realisation; an unfolding of the self out into nature to attain a transcendental, non-egoic state. Findings from our research indicate that secondary school students in environment clubs align with the principles of deep ecology, and show a (...) capacity to become student eco-philosophers, and they report empathy for becoming ecocentric beings. This study explores the capacity for students to engage in environmental philosophy. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical (...) problem of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

While deep brain stimulation (DBS) for patients with Parkinson's disease has typically raised ethical questions about autonomy, accountability and personal identity, recent research indicates that we need to begin taking into account issues surrounding the patients’ feelings of authenticity and alienation as well. In order to bring out the relevance of this dimension to ethical considerations of DBS, I analyse a recent case study of a Dutch patient who, as a result of DBS, faced a dilemma between autonomy (...) and authenticity. This case study is meant to point out the normatively meaningful tension patients under DBS experience between authenticity and autonomy. (shrink)