Philosophers have been referring to the “Kripke–Putnam” theory of naturalkind terms for over 30 years. Although there is one common starting point, the two philosophers began with different motivations and presuppositions, and developed in different ways. Putnam’s publications on the topic evolved over the decades, certainly clarifying and probably modifying his analysis, while Kripke published nothing after 1980. The result is two very different theories about natural kinds and their names. Both accept that the meaning of a naturalkind term is (...) not given by a description or defining properties, but is specified byits referents. From then on, Putnam rejected even the label, causal theory of reference, preferring to say historical, or collective. He called his own approach indexical. His account of substance identity stops short a number of objections that were later raised, such as what is called the qua problem. He came to reject the thought that water is necessarily H2O, and to denounce the idea of metaphysical necessity that goes beyond physical necessity. Essences never had a role in his analysis; there is no sense in which he was an essentialist. He thought of hidden structures as the usual determinant of natural kinds, but always insisted that what counts as a natural kind is relative to interests. “Natural kind” itself is itself an importantly theoretical concept, he argued. The paper also notes that Putnam says a great deal about what natural kinds are, while Kripke did not. Moreover, a theory about names of natural kinds is to some extent independent of a theory of natural kinds themselves, to the extent that one can accept the one and reject the other, even when both are advanced by the same philosopher. (shrink)

Dans quelle mesure les techniques des instruments modernes d'observation et d'imagerie optique ou numérique développées en physique, biologie et médecine transforment-elles les conceptions philosophiques de l'observation, et le rapport entre cette dernière et la théorie? La philosophie des sciences a conduit ces dernières décennies une réflexion sur le rôle des instruments dans l'observation en s'appuyant essentiellement sur des appareils traditionnels comme les microscopes et télescopes. Le...

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number of (...) subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5 f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

Many people find themselves dissatisfied with recent linguistic philosophy, and yet know that language has always mattered deeply to philosophy and must in some sense continue to do so. Ian Hacking considers here some dozen case studies in the history of philosophy to show the different ways in which language has been important, and the consequences for the development of the subject. There are chapters on, among others, Hobbes, Berkeley, Russell, Ayer, Wittgenstein, Chomsky, Feyerabend and Davidson. Dr (...) class='Hi'>Hacking ends by speculating about the directions in which philosophy and the study of language seem likely to go. The book will provide students with a stimulating, broad survey of problems in the theory of meaning and the development of philosophy, particularly in this century. The topics treated in the philosophy of language are among the central, current concerns of philosophers, and the historical framework makes it possible to introduce concretely and intelligibly all the main theoretical issues. (shrink)

Mathematics plays an inordinate role in the work of many of famous Western philosophers, from the time of Plato, through Husserl and Wittgenstein, and even to the present. Why? This paper points to the experience of learning or making mathematics, with an emphasis on proof. It distinguishes two sources of the perennial impact of mathematics on philosophy. They are classified as Ancient and Enlightenment. Plato is emblematic of the former, and Kant of the latter. The Ancient fascination arises from the (...) sense that mathematics explores something ‘out there’. This is illustrated by recent discussions by distinguished contemporary mathematicians. The Enlightenment strand often uses Kant's argot: ‘absolute necessity’, ‘apodictic certainty’ and ‘a priori’ judgement or knowledge. The experience of being compelled by proof, the sense that something must be true, that a result is certain, generates the philosophy. It also creates the illusion that mathematics is certain. Kant's leading question, ‘How is pure mathematics possible?’, is easily misunderstood because the modern distinction between pure and applied is an artefact of the 19th century. As Russell put it, the issue is to explain ‘the apparent power of anticipating facts about things of which we have no experience’. More generally the question is, how is it that pure mathematics is so rich in applications? Some six types of application are distinguished, each of which engenders its own philosophical problems which are descendants of the Enlightenment, and which differ from those descended from the Ancient strand. (shrink)

Some evil actions are public. Maybe genocide is the most awful. Other evil actions are private, a matter of one person harming another or of self-inflicted injury. Child abuse, in our current reckoning, is the worst of private evils. We want to put a stop to it. We know we can’t do that, not entirely. Human wickedness won’t go away. But we must protect as many children as we can. We want also to discover and help those who have already (...) been hurt. Anyone who feels differently is already something of a monster.We are so sure of these moral truths that we seldom pause to wonder what child abuse is. We know we don’t understand it. We have little idea of what prompts people to harm children. But we do have the sense that what we mean by child abuse is something perfectly definite. So it comes as a surprise that the very idea of child abuse has been in constant flux the past thirty years. Previously our present conception of abusing a child did not even exist. People do many of the same vile things to children, for sure, that they did a century ago. But we’ve been almost unwittingly changing the very definitions of abuse and revising our values and our moral codes accordingly. Ian Hacking, a philosopher, teaches at the Institute for the History and Philosophy of Science and Technology in the University of Toronto. His latest book is entitled The Taming of Chance. (shrink)

Kripke's lectures, published as Wittgenstein on Rules and Private Language , posed a sceptical problem about following a rule, which he cautiously attributed to Wittgenstein. He briefly noticed an analogy between his new kind of scepticism and Goodman's riddle of induction. ‘Grue’, he said, could be used to formulate a question not about induction but about meaning: the problem would not be Goodman's about induction—‘Why not predict that grass, which has been grue in the past, will be grue in the (...) future?’—but Wittgenstein's about meaning: ‘Who is to say that in the past I did not mean grue by “green”, so that now I should call the sky, not the grass, “green”?’. (shrink)

This paper puts together an ancientand a recent approach to classificatory language, thought, and ontology.It includes on the one hand an interpretation of Aristotle's ten categories,with remarks on his first category, called (or translated as) substancein the Categories or What a thing is in the Topics. On the other hand is the ideaof domain-specific cognitive abilities urged in contemporary developmentalpsychology. Each family of ideas can be used to understand the other. Neitherthe metaphysical nor the psychological approach is intrinsically morefundamental; they (...) complement each other. The paper incidentally clarifies distinct uses of the word category in different disciplines, and also attempts to make explicit several notions of domain. It also examines Aristotle's most exotic and least discussed categories, being-in-a-position (e.g., sitting) and having-(on) (e.g., armour). Finally the paper suggests a tentative connectionbetween Fred Sommers' theory of types and Aristotle's first category. (shrink)

The paper argues that philosophers commonly misidentify the substitutivity principle involved in Russell’s puzzle about substitutivity in “On Denoting”. This matters because when that principle is properly identified the puzzle becomes considerably sharper and more interesting than it is often taken to be. This article describes both the puzzle itself and Russell's solution to it, which involves resources beyond the theory of descriptions. It then explores the epistemological and metaphysical consequences of that solution. One such consequence, it argues, is that (...) Russell must abandon his commitment to propositions. (shrink)

A good book may have the power to change the way we see the world, but a great book actually becomes part of our daily consciousness, pervading our thinking to the point that we take it for granted, and we forget how provocative and challenging its ideas once were—and still are. _The Structure of Scientific Revolutions _is that kind of book. When it was first published in 1962, it was a landmark event in the history and philosophy of science. Fifty (...) years later, it still has many lessons to teach. With _The Structure of Scientific Revolutions, _Kuhn challenged long-standing linear notions of scientific progress, arguing that transformative ideas don’t arise from the day-to-day, gradual process of experimentation and data accumulation but that the revolutions in science, those breakthrough moments that disrupt accepted thinking and offer unanticipated ideas, occur outside of “normal science,” as he called it. Though Kuhn was writing when physics ruled the sciences, his ideas on how scientific revolutions bring order to the anomalies that amass over time in research experiments are still instructive in our biotech age. This new edition of Kuhn’s essential work in the history of science includes an insightful introduction by Ian Hacking, which clarifies terms popularized by Kuhn, including paradigm and incommensurability, and applies Kuhn’s ideas to the science of today. Usefully keyed to the separate sections of the book, Hacking’s introduction provides important background information as well as a contemporary context. Newly designed, with an expanded index, this edition will be eagerly welcomed by the next generation of readers seeking to understand the history of our perspectives on science. (shrink)

The conclusion urged in Mr Salmon's recent article is so remarkable that it may be worth recording some difficulties. He claims to rescue Reichenbach's notorious vindication of induction. This is essentially concerned with estimating long-run frequencies. By an estimator let us mean any rule for making estimates appropriate to various bodies of information. Reichenbach thought an estimator is sensible only if it is convergent, that is, roughly speaking, only if its estimates tend to approach the truth as more and more (...) data is collected. Mr Salmon observes that an estimator should, in addition, be independent of the language in which the information is expressed. He contends that this criterion of linguistic invariance, when added to the rest of Reichenbach's train of thought, suffices to justify the “straight rule” for estimation: if m of the n observed A's are B, estimate the long-run frequency of B among A's as m/n. (shrink)

This chapter expounds Hacking’s project of styles of reasoning more systematically than Hacking himself has done, while the following chapters examine its philosophical implications. I shall show that, in addition to the statistical and the laboratory style described in Chap. 3, there exist other four styles of reasoning that share a set of common characterizing features: the algorithmic, the postulational, the historico-genetic and the taxonomic style of reasoning. All the differences notwithstanding, striking parallels can be drawn between these (...) different styles of reasoning: they emerged suddenly at certain points in time, crystallized around communities that shared certain standards of evidence and represent basic and inescapable forms of reasoning. Finally, I shall argue that there are other ways of thinking that differ from styles of reasoning in the sense of Hacking. What I consider a relevant difference is that only styles of reasoning have strong techniques of self-authentication, techniques that are intrinsic to their forms of reasoning. (shrink)

Recently, philosophers and social scientists have turned their attention to the epistemological shifts provoked in established sciences by their incorporation of big data techniques. There has been less focus on the forms of epistemology proper to the investigation of algorithms themselves, understood as scientific objects in their own right. This article, based upon 12 months of ethnographic fieldwork with Russian data scientists, addresses this lack through an investigation of the specific forms of epistemic attention paid to algorithms by (...) data scientists. On the one hand, algorithms are unlike other mathematical objects in that they are not subject to disputation through deductive proof. On the other hand, unlike concrete things in the world such as particles or organisms, algorithms cannot be installed as the objects of experimental systems directly. They can only be evaluated in their functioning as components of extended computational assemblages; on their own, they are inert. As a consequence, the epistemological coding proper to this evaluation does not turn on truth and falsehood but rather on the efficiency of a given algorithmic assemblage. This article suggests that understanding the forms of algorithmic rationality employed in such inquiry is crucial for charting the place of data science within the contemporary academy and knowledge economy more generally. (shrink)

In this paper, I present a version of a sense-data approach to perception, which differs to a certain extent from well-known versions like the one put forward by Jackson. I compare the sense-data view to the currently most popular alternative theories of perception, the so-called Theory of Appearing (a very specific form of disjunctivist approaches) on the one hand and reductive representationalist approaches on the other. I defend the sense-data approach on the basis that (...) it improves substantially on those alternative theories. (shrink)

In days past, epistemologists expended a good deal of effort trying to analyze the basing relation—the relation between a belief and its basis. No satisfying account was offered, and the project was largely abandoned. Younger epistemologists, however, have begun to yearn for an adequate theory of basing. I aim to deliver one. After establishing some data and arguing that traditional accounts of basing are unsatisfying, I introduce a novel theory of the basing relation: the dispositional theory. It begins with (...) the pedestrian observation that beliefs stand or fall with their bases. The theory I offer is an elucidation and refinement of this thought. (shrink)

Biomedical innovation and translation are increasingly emphasizing research using “big data.” The hope is that big data methods will both speed up research and make its results more applicable to “real-world” patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information (...) technologies, predictive algorithms, and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals. (shrink)

When I began to study philosophy sense-data were in the fashion; everybody had some. Nowadays talking about sense-data, like distinguishing between “shall” and “will”, is apt to be regarded as an indication that one has stopped moving with the times. Before abandoning this old habit, I want to consider whether there may not after all be something in a doctrine adopted by so many leading philosophers in pre-war England.

In lieu of an abstract, here is a brief excerpt of the content:Journal of the History of Philosophy 42.4 (2004) 489-490 [Access article in PDF] Ian M. Crystal. Self-Intellection and its Epistemological Origins in Ancient Greek Thought. Aldershot, England: Ashgate Publishing, 2002. Pp. x + 220. Cloth, $79.95. In this excellent re-working of his King's College Ph.D. thesis, Ian Crystal presents an account of the problem of self-intellection in Greek philosophy from Parmenides through Plotinus. The problem, at least as it (...) appears in ancient Greek philosophy, is connected to the way in which a faculty of soul relates to its proper object. For example, the proper object of the visual faculty is light, that of the auditory faculty sound, and it is the difference in kind between these proper objects that requires different faculties of soul in any account of how it is that we come to grasp the two different kinds of sensory data. Reason is itself a faculty of soul that is supposed to correspond to objects of cognition, but since it is possible to grasp, cognitively, the structure of this faculty the question arises: May this faculty function as its own object? The question is famously answered in the affirmative in Aristotle's treatise De anima at 430a2-5 on the grounds that any immaterial object of cognition is identical to thought itself (epi men gar tôn aneu hulês to auto esti to nooun kai to nooumenon).While this may seem like a peculiarly Aristotelian analysis of cognition (relying, as it does, on Aristotle's distinction between matter and form and his rather cryptic claim that the faculties of soul are actually what their proper objects are potentially), Crystal traces the idea both backwards and forwards in time, rooting the problem in Parmenides' general account of faculties of soul and tracing it through Plotinus's sophisticated account of noetic intellect, with visits along the way not only with Aristotle but Plato and the Stoics as well.The book is divided into three parts. In the first ("The Epistemological Origins") Crystal examines the evolution of the philosophical analysis of cognition from Parmenides through Plato. Parmenides, on this account, was the first to offer anything like a philosophical treatment of intellect (Greek nous). In particular, Parmenides distinguishes between a faculty of intellection and an object of that faculty. Plato, consciously working in the Parmenidean tradition, further analyses mind into its faculties and their proper objects but also gives a more detailed analysis of the epistemological subject. Specifically, Plato's treatment of the faculty in the Theaetetus provides an account of the structure of our cognitive faculty and how that structure relates to the metaphysics of the external world. It is here that Crystal recognizes the emergence of a linguistic component in the epistemic subject's cognitive activity (101; cf. Theaetetus189e6-190a6).Part II ("The Emergence of Self-Intellection") consists of a single chapter in which Crystal reads Aristotle's treatment of self-intellection in the De anima as "Aristotle's epistemological response to Plato." Since self-intellection per se really only emerges for the first time in the De anima, this chapter is in some sense the meatiest part of the book. We are given first a careful treatment of Aristotle's account of perception in DA3.2, affection in DA2.5 and De generatione et corruptione1.7, and the central problem of self-consciousness in DA3.2. The argument moves quickly here but Crystal's analysis is precise and clear. Next comes the analysis of the cognitive structure of intellect itself in DA3.4 and Crystal's account of how Aristotle's two aporiai (How can intellect be both a passive affection and something simple and impassive; and Is intellect itself an intelligible object) are responses to Plato's account of cognition at Theaetetus197c1-200d4 (the Aviary passage) on the one hand and to the general lack of an account of intelligible objects in the Theaetetus on the [End Page 489] other. On Crystal's account, Aristotle improves on Plato in the sense... (shrink)

In this Symposium on the Ethics and Epistemology of Big Data, we present four perspectives on the ways in which the rapid growth in size of research databanks—i.e. their shift into the realm of “big data”—has changed their moral, socio-political, and epistemic status. While there is clearly something different about “big data” databanks, we encourage readers to place the arguments presented in this Symposium in the context of longstanding debates about the ethics, politics, and epistemology of biobank, (...) database, genetic, and epidemiological research. (shrink)

A critical discussion of selected chapters of the first volume of Scott Soames’s Philosophical Analysis in the Twentieth Century. It is argued that this volume falls short of the minimal standards of scholarship appropriate to a work that advertises itself as a history, and, further, that Soames’s frequent heuristic simplifications and distortions, since they are only sporadically identified as such, are more likely confuse than to enlighten the student. These points are illustrated by reference to Soames’s discussions of Russell’s logical (...) system and the place of the theory of descriptions in his ontological development. It is then argued that Soames’s interpretation of the point of G.E. Moore’s famous “proof” of an external world, while not straightforwardly undermined by the textual evidence, is nonetheless questionable, and plausibly overlooks what is novel in Moore’s discussion. This, it is argued, in his attempt to offer a common sense “refutation of idealism”, rather than (as is more commonly supposed) an anti-skeptical argument “from differential certainty”. (shrink)

Analytic philosophy arose in the early decades of the twentieth century, with Bertrand Russell and G. E. Moore leading the way. Although some accounts emphasize the role of logic and language in the origin of analytic philosophy, of equal importance is the theme of perception, sense data, and knowledge, which dominated systematic philosophical discussion in the first two decades of the twentieth century in both Britain and America. This chapter examines work on perception and sense data (...) as well as the external world in the analytic tradition in the first half of the twentieth century. After sketching the situation before and just after 1900, it turns to Moore, Russell, and their interlocutors in the teens and twenties, addresses the American scene, looks to subsequent developments in the 1930s and beyond, and finally considers the fates of sense data and the ‘given’ and of the topic of perception more generally. (shrink)

It is sometimes claimed that in his 1912 work, "The Problems of Philosophy" (POP), and possibly as early as “on Denoting”, Russell conceives of the mind's acquaintance with sense-data as providing an indubitable or certain foundation for empirical knowledge. However, although he does say things suggestive of this view in certain of his 1914 works, Russell also makes remarks in POP that conflict with any such broadly "Cartesian" interpretation of this work. This paper attempts to resolve this apparent (...) tension, while also offering a close examination of Russell's views on the nature of acquaintance and their relationship to certain ideas in William James. (shrink)

Examples of sensory illusion show the failure of the attempt of traditional sense-datum theory to account for something's phenomenally appearing to be F by postulating the existence of a sense-datum that is actually F. the Muller-Lyer Illusion cannot be explained by postulating two sensibly presented lines that actually have the lengths the physical lines appear to have. Illusions due to color contrast cannot be explained by postulating sense-data that actually have the colors the physical samples appear (...) to have. (shrink)

This essay examines the physicist John Tyndall’s journal writing in the mid-nineteenth century and focuses on how Tyndall used his journal during a series of transitions that occurred when he was a young man: when he went from being a surveyor to a public school instructor and then from a Ph.D. student and budding experimenter in Germany to Professor of Natural Philosophy at the Royal Institution in London. As well as providing insight into these various transitions, the journal more importantly (...) shows how Tyndall developed a particular ethical conception of self, based on his readings of Carlyle, Emerson, and Fichte, and how that sense of self shaped—and was shaped by—his early experimental practices. Thus, the article is a case study in the development of a particular scientific self that emerged in the mid-nineteenth century, whose novel claim to authority was based on a particular fusion of the ethical and the epistemological. (shrink)

This chapter offers a virtue epistemological framework for making sense of the common complaint that scientism is arrogant, dogmatic, or otherwise epistemically vicious. After characterising scientism in terms of stances, I argue that their components can include epistemically vicious dispositions, with the consequence that an agent who adopts such stances can be led to manifest epistemic vices. The main focus of the chapter is the vice of closed-mindedness, but I go on to consider the idea that arrogance and dogmatism (...) are ‘cooperative vices’, ones liable to be activated by closed-mindedness. I conclude that determining whether or not any given stance is vicious will require sensitivity to the ontology of that stance and the psychology of the agents who adopt them. This would be contribute to our understanding both of scientism and of epistemic virtuous and vicious characters or psychologies. (shrink)

An emerging trend in cognitive science is to explore central epistemological questions using psychological methods. Early work in this growing area of research has revealed that epistemologists’ theories of knowledge diverge in various ways from the ways in which ordinary people think of knowledge. Reflecting the practices of epistemology as a whole, the vast majority of these studies have focused on the concept of propositional knowledge, or knowledge-that. Many philosophers, however, have argued that knowing how to do something is importantly (...) different from knowing that something is the case. Hence, in this paper we turn our attention to people’s concept of knowledge-how. We present data from two experiments that employed a sentence recognition task as an implicit measure of conceptual activation. The data from this implicit measure suggest that, contrary to prominent intellectualist theories of know-how, according to which know-how is a species of propositional knowledge, people’s concept of know-how more closely aligns with anti-intellectualism, the view that knowing how to perform some task consists in having the appropriate skills or abilities. (shrink)

This paper argues that identity which is the locus of emotional and social phenomena of an individual becomes problematic particularly in postmodern society. Postmodern society calls for a socio-cultural and epistemological revolution which permeates the very core of our social existence. Coupled with the immensity and massive effects of the market industry, postmodern culture affects our lives through the dissolutions of boundaries, geographies, and our ethnicities so that our sense of personal and social identity is left into perpetual disintegration, (...) struggles and contradictions. The so-called “inner” and “outer” community which we once cherished inevitably dissolved into the arena of the market industry. Consequently, the sense of “I-am” which Fromm considers as an existential human need already deeply roots itself towards commodity fixations rather than in our group or in our ethnic communal relationships. It is in these contexts that this paper contends a necessity to redeem identity not only as a psychological base but more of an existential human need. Further, this paper maintains that Fromm’s notion of relatedness and rootedness are necessary elements in identity formation since they serve as the existential psychic cores that lead towards being truly at home amidst a fragmented social world. (shrink)