Empirically-informed philosophy of mind (EIPM) has become a dominant research style in the twenty-first century. EIPM relies on empirical results in various ways. However, the extant literature lacks an empirical description of how EIPM philosophers rely on empirical results. Moreover, though EIPM is essentially a form of cross-disciplinary research, it has not been analyzed as cross-disciplinary research so far. We aim to fill the above two gaps in the literature by producing quantitative and qualitative descriptions of EIPM as a kind (...) of cross-disciplinary research. Our descriptions aim to enable metaphilosophers to evaluate EIPM methodologically and epistemically. Our analyses use co-citation and categorization analyses informed by the literature on interdisciplinarity. We present five sets of descriptions and identify the three most common types of cross-disciplinary interactions in EIPM. The resulting descriptions enable us to locate two metaphilosophical challenges for EIPM philosophers. One concerns how they should incorporate empirical results in different disciplinary contexts, and the other concerns which theoretical virtue(s) they should aim for when tinkering with scientific theories. (shrink)

We present several new studies focusing on “salience effects”—the decreased tendency to attribute knowledge to someone when an unrealized possibility of error has been made salient in a given conversational context. These studies suggest a complicated picture of epistemic universalism: there may be structural universals, universal epistemic parameters that influence epistemic intuitions, but that these parameters vary in such a way that epistemic intuitions, in either their strength or propositional content, can display patterns of genuine cross-cultural diversity.

1. Biomedical sciences are fast-growing fields with unprecedented speed of research outputs, especially in the quantities of papers. Philosophers aiming to study ongoing biomedical changes face cha...

Grief is a complex emotional experience or process, which is typically felt in response to the death of a loved one, most typically a family member, child, or partner. Yet the way in which grief manifests is much more complex than this. The things we grieve over are multiple and diverse. We may grieve for a former partner after the breakup of a relationship; parents sometimes report experiencing grief when their grown-up children leave the family home. We can also experience (...) grief for people we have never met. Indeed, it is not just persons that we may grieve for. People report feeling grief over the death of their pets, or about the destruction the natural environment. In all these cases one factor that seems to stand out is loss. Despite being about very different things, these various forms of grief all involve a loss of some sort. Yet there is a further aspect of grief, which, on the face of it, does not quite follow this pattern. Grief can also be experienced before a loss has occurred. Grief can be experienced while the person that one is grieving for is still living and before one has (fully) suffered the loss. This phenomenon is known as anticipatory grief. The experience of anticipatory grief is a complex phenomenon, which resists easy classification. Nonetheless, we suggest that mental time travel, our ability to mentally project ourselves into the personal past (episodic memory) and personal future (episodic prospection), is a key mechanism that underpins experiences of anticipatory grief. Anticipatory grief can still be understood in terms of loss, but it is a loss that is brought to mind through memory and imagination. (shrink)

Scientonomy is the field that aims to develop a descriptive theory of the actual process of scientific change (Barseghyan, 2015). Scientometrics is the field that aims to employ statistical methods to investigate the quantitative features of scientific research, especially the impact of scientific articles and the significance of scientific citations (Leydesdorff & Milojević, 2013). In this paper, we aim to illustrate how to methodologically integrate scientonomy with scientometrics to investigate both qualitative and quantitative changes of a scientific community. We will (...) use a case study to achieve our aim. The case study is about a scientific community studying a physiological phenomenon called heart-rate variability (HRV). Moreover, we will argue that this methodological integration outperforms cases in which researchers only employ the resources from one of the two fields. (shrink)

This paper aims to address some methodological issues related to case-based research in the philosophy of contemporary sciences. We focus on the selection processes by which philosophers pick or generate a particular set of papers to conduct their case-based research. We illustrate how to use various quantitative and qualitative methods to improve the epistemic features of the selection processes, and help generate some potential case-based hypotheses for further philosophical investigation.

神經科學哲學 (philosophy of neuroscience) 是科學哲學下的一個新興子領域，主要在探討神經科學研究活動中所牽涉的哲學問題。這裡指的研究活動包含設計實驗、使用特定研究技術、工具或方法、建構模型、推理模式、與科學說明等。本條目首先將簡述神經科學哲學興 起的背景，而後詳述二十一世紀神經科學哲學的發展重點。.

We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates that structure (nonstructural (...) knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. (shrink)

Current development in a deep neural network has given an opportunity to a novel framework for the reconstruction of a holographic image and a phase recovery method with real-time performance. There are many deep learning-based techniques that have been proposed for the holographic image reconstruction, but these deep learning-based methods can still lack in performance, time complexity, accuracy, and real-time performance. Due to iterative calculation, the generation of a CGH requires a long computation time. A novel deep generative adversarial network (...) holography framework is proposed for hologram reconstruction. This novel framework consists of two phases. In phase one, we used the Fresnel-based method to make the dataset. In the second phase, we trained the raw input image and holographic label image data from phase one acquired images. Our method has the capability of the noniterative process of computer-generated holograms. The experimental results have demonstrated that the proposed method outperforms the existing methods. (shrink)

Philosophers of neuroscience have been employing scientific explanation as an epistemic value to evaluate neuroscientific models for the past twenty years. Consequently, they have developed mechanistic and non-mechanistic accounts of neuroscientific explanation. These two types of accounts explicate how to use a specific kind of explanatory value to evaluate the epistemic value of neuroscientific models. This paper presents a case study involving the canonical models from mathematical and computational neuroscience. This case study will show that the above mechanistic and non-mechanistic (...) framework overly focuses on analyzing neuroscientific models as objects with representational contents. As a consequence, it pays less attention to the process of modeling and the epistemic attitudes of modelers; moreover, it can miss some important epistemic values used by modelers. By reconstructing their modeling process, I will identify the relevant modelers' epistemic attitudes and argue that these modelers use different kinds of epistemic values to evaluate the same type of canonical models. Furthermore, among them, one epistemic value is not captured by the relevant mechanistic and non-mechanistic accounts. I develop a processual framework that centers on the modeling process and modelers' epistemic attitudes. This process framework is better because it it accommodates both the mechanistic and non-mechanistic accounts of neuroscientific explanation regarding the canonical model in addition to capturing what both accounts leave out. (shrink)

In this study, an adaptive output feedback fault tolerant control scheme is proposed for a class of multi-input and multioutput nonlinear systems with multiple constraints. The neural network is adopted to handle the unknown nonlinearity by means of its superior approximation capability. Based on it, the state observer is designed to estimate the unmeasured states, and the nonlinear disturbance observer is constructed to tackle the external disturbances. In addition, the Nussbaum function is utilized to cope with the actuator faults, which (...) are coupled with the unknown control directions. Combining with the Lyapunov theory, a NN-based output feedback FTC law is developed for the MIMO nonlinear systems, and the boundedness of all closed-loop system error signals is proved. Simulation results on the unmanned helicopter are performed to demonstrate the effectiveness of the proposed controller. (shrink)