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143268.803288
This chapter explores the similarities and the differences between delusion and self-deception. Delusion and self-deception are similar in some ways. For that reason, it is reasonable to wonder whether delusions are, perhaps, a type of self-deception or whether, conversely, being self-deceived is simply a way of being deluded. It is tempting, in other words, to consider the possibility that we might be able to subsume one of the two conditions under the other one. In this chapter, I will argue that this temptation should be resisted. First, I offer a rough characterisation of both delusion and self-deception. Then, I highlight the respect in which the two conditions are alike: Both conditions seem to involve beliefs which appear to be unresponsive to evidence. Next, I discuss several respects in which the two conditions are different: Firstly, they have different aetiologies. Also, their social impact and their impact on the subject’s well-being is different. The normative aspects of self-deception and delusion are different as well. And, finally, they have different connections to psychopathology, and to the notion of mental illness. For all these reasons, I conclude, delusion and self-deception should be pulled apart as two separate conditions.
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193552.803527
Propositional attitudes have an attitude type (belief, desire, etc. ), and a content. A popular idea in the literature on intentionality is that attitude type is determined by functional role and content in some other way. …
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328960.80356
MeerKAT is an amazing array of 64 radio telescopes in South Africa. Astronomers want to expand this to the Square Kilometer Array, which will actually consist of thousands of telescopes in South Africa and Australia. …
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396116.80359
This paper explores the emerging ethical and privacy challenges posed by brain-computer interfaces (BCIs), focusing on mind-reading BCIs that decode neural activity to interpret thoughts and intentions. As BCI technology progresses from medical applications to consumer markets, the stakes for personal privacy and autonomy rise exponentially. This work examines three unique privacy dilemmas, termed the “Impulsivity Problem”, the “Judgement Problem”, and the “Fingerprint Problem”. These issues emphasize that neural data, with its deeply personal and inextricable link to identity and thought, cannot be treated like conventional forms of information. Drawing on philosophical frameworks, particularly Foucauldian concepts of surveillance and biopower, this paper critically analyzes the potential for BCIs to create a new mode of privacy-infringing observation. To address these concerns, the study proposes a value-sensitive design (VSD) framework and provides a roadmap for ethically aligned BCI development.
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396243.803618
Rasmus Grønfeldt Winther’s latest book, When Maps Become the World, is a valuable contribution to the philosophy of scientific representation. Its central premise is that a philosophical investigation concerning the making and employment of maps may enlighten scientific practices of representation in fields other than cartography. The book is structured around this premise in two main parts. In Part 1 (the ‘philosophy’ part), Winther engages in what he calls ‘map thinking’: a philosophical reflection on what standard geographic maps are and how they are made and used (p. 4). In Part 2 (the ‘science’ part), Winther assesses how the results of his philosophical reflection on maps bear on different cases of scientific representation.
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569526.803663
Del Santo and Gisin have recently argued that classical mechanics exhibits a form of indeterminacy and that by treating the observables of classical mechanics with real number precision we introduce hidden variables that restore determinacy. In this article we introduce the conceptual machinery required to critically evaluate these claims. We present a characterization of indeterminacy which can capture both quantum indeterminacy and the classical indeterminacy of Del Santo and Gisin. This allows us to show that there is an important difference in kind between the two: their classical indeterminacy can be resolved with hidden variables in a manner which is not possible for quantum indeterminacy.
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620375.803697
Today I’d like to dig a little deeper into some ideas from Part 2. I’ve been talking about causal loop diagrams. Very roughly speaking, a causal loop diagram is a graph with labeled edges. I showed how to ‘pull back’ and ‘push forward’ these labels along maps of graphs. …
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627307.803726
A growing body of psychological research suggests that different kinds of explanations of mental illness can have striking and distinctive effects on their audiences’ attitudes and inferences. But it is surprisingly difficult to account for why this is. In this paper, I present a “normative model” of explanatory framing effects, which I claim does a better job of capturing the empirical data than do intuitive alternatives. On this model, different explanations will tend to differently affect their audience’s reasoning because each encodes a different picture of the kind of problem represented by the explanandum, and therefore the kinds of responses to it that are normatively apt to pursue. For example, a biological explanation of depression will convey to its audience that depression is a specifically biological problem, and therefore that appropriate responses to it should be directed at biological facts and norms. The communication of this normative information is, I argue, importantly different from communicating that depression has biological causes. For example, although it seems plausible that most causal explanations can be viewed additively, different characterizations of a problem cannot be so easily combined. This might explain why philosophers and mental health experts sometimes seem to regard different explanations of mental illness as competing or mutually incompatible, despite their appreciation for the causal complexity of these conditions.
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760071.80376
In Part 2, I explained some stuff you can do with graphs whose edges are labeled by elements of a rig. Remember, a rig is like a ring, but it might not have negatives. A great example is the boolean rig, whose elements are truth values:
The addition in this rig is ‘or’ and the multiplication is ‘and’. …
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973578.803795
A central challenge for Neuroscience has been understanding how nervous systems flexibly and reliably generate complex behaviors. How does an animal distinguish a benign encounter from a threat? How is irrelevant information ignored to satisfy its needs? Since the days of Pavlov’s salivating dogs or Skinner’s bar pressing rats, behavioral neuroscientists have constructed highly constrained lab paradigms to study how experience modifies relatively simple behaviors. These behaviors give scientists the benefit of precision and control: by manipulating the temporal relations between stimulus and response, neural activity can be directly tied to the behavior. However, these behaviors are also seen as highly contrived in the sense that there are no levers or bells in the habitats in which rats’ and dogs’ brains evolved, which presumably shaped the neural circuits that generate most behaviors.
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1390266.803826
I’m talking about ‘causal loop diagrams’, which are graph with edges labeled by ‘polarities’. Often the polarities are simply and signs, like here:
But polarities can be elements of any monoid, and last time I argued that things work even better if they’re elements of a rig, so you can not only multiply them but also add them. …
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1488071.803855
In Part 1 I explained ‘causal loop diagrams’, which are graphs with edges labeled by polarities. These are a way to express qualitatively, rather than quantitatively, how entities affect one another. For example, here’s how causal loop diagrams us say that alcoholism ‘tends to increase’ domestic violence:
We don’t need to specify any numbers, or even need to say what we mean by ‘tends to increase’, though that leads to the danger of using the term in a very loose way. …
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1493125.803884
Despite significant advancements in XAI, scholars note a persistent lack of solid conceptual foundations and integration with broader scientific discourse on explanation. In response, emerging XAI research draws on explanatory strategies from various sciences and philosophy of science literature to fill these gaps. This paper outlines a mechanistic strategy for explaining the functional organization of deep learning systems, situating recent advancements in AI explainability within a broader philosophical context. According to the mechanistic approach, the explanation of opaque AI systems involves identifying mechanisms that drive decision-making. For deep neural networks, this means discerning functionally relevant components—such as neurons, layers, circuits, or activation patterns—and understanding their roles through decomposition, localization, and recomposition. Proof-of-principle case studies from image recognition and language modeling align these theoretical approaches with the latest research from AI labs like OpenAI and Anthropic. This research suggests that a systematic approach to studying model organization can reveal elements that simpler (or “more modest”) explainability techniques might miss, fostering more thoroughly explainable AI. The paper concludes with a discussion on the epistemic relevance of the mechanistic approach positioned in the context of selected philosophical debates on XAI.
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1560340.803915
This is a progress report on some joint work with Xiaoyan Li, Nathaniel Osgood and Evan Patterson. Together with collaborators we have been developing software for ‘system dynamics’ modelling, and applying it to epidemiology—though it has many other uses. …
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1658983.80395
The idea that human cognition is, or can be understood as, a form of computation is a useful conceptual tool for cognitive science. It was a foundational assumption during the birth of cognitive science as a multidisciplinary field, with Artificial Intelligence (AI) as one of its contributing fields. One conception of AI in this context is as a provider of computational tools (frameworks, concepts, formalisms, models, proofs, simulations, etc.) that support theory building in cognitive science. The contemporary field of AI, however, has taken the theoretical possibility of explaining human cognition as a form of computation to imply the practical feasibility of realising human(-like or -level) cognition in factual computational systems, and the field frames this realisation as a short-term inevitability. Yet, as we formally prove herein, creating systems with human(-like or -level) cognition is intrinsically computationally intractable. This means that any factual AI systems created in the short-run are at best decoys. When we think these systems capture something deep about ourselves and our thinking, we induce distorted and impoverished images of ourselves and our cognition. In other words, AI in current practice is deteriorating our theoretical understanding of cognition rather than advancing and enhancing it. The situation could be remediated by releasing the grip of the currently dominant view on AI and by returning to the idea of AI as a theoretical tool for cognitive science. In reclaiming this older idea of AI, however, it is important not to repeat conceptual mistakes of the past (and present) that brought us to where we are today.
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1699302.80398
Gingerich uses examples from literature and song to introduce a kind of freedom he calls ‘spontaneous freedom’. He argues that spontaneous freedom is central to our ordinary talk of freedom, but overlooked by the philosophical literature on free will, which focuses on a kind of freedom that is constitutively moral r. I argue that spontaneous freedom is the standard kind of freedom, the constitutively moral kind. What is distinctive about Gingerich’s examples isn’t that they involve a new kind of freedom, but that they involve an agent feeling their freedom.
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1723870.804013
Objective: In reviewing the literature on various topics in the field of ’ageing’, similar issues kept resurfacing. To avoid redundancy, I decided to compile these recurring themes into a single discussion. The goal here is to examine the utility of the current concept of ’ageing’. In particular, this discussion considers how well this concept serves in addressing key objectives, such as measuring ’ageing’, evaluating the validity of ’ageing’ theories, assessing interventions, and examining the validity of experiments conducted in the field of ’ageing’.
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1823439.804042
Are you interested in using category-theoretic methods to tackle problems outside of pure mathematics? Then you might like the Adjoint School. You’ll work online on a research project with a mentor and a team of other students for several months. …
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1954718.804077
Paper accepted to the 2024 edition of the Philosophy of Science Association (New Orleans) and for publication in the related special issue in Philosophy of science. This version of the article has been accepted for publication after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections.
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2185455.804107
Jessica Wilson (2021) offers three characterizations of strong emergence: (1) heuristically, when higher-level features cannot in-principle be deduced from lower-level features, (2) the rejection of Physical Causal Closure in the emergence hexalemma, and (3) when a higher-level feature depends on lower-level features but has a novel power. I explicate Bernard Lonergan (1992 [1957])’s account of emergence to argue that these three characterizations come apart. Lonergan’s account is only weak emergence according to (1), and affirms Physical Causal Closure by denying adjunct premises rather than any of the assumptions of the emergence hexalemma, yet counts as strong emergence according to (3).
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2185565.804136
I argue that the brain does not have its computational structure intrinsically, but only in relation to its environment. I support this view (externalism) with a case study in the neuroscience and evolutionary biology of color vision, showing that which aspects of the brain’s causal structure rise to the level of computation — which features of its causal structure count as part of its functional structure or “wiring diagram” — depends on its environment. I show that this version of externalism helps answer some pressing methodological questions in neuroscience and explainable AI. Along the way I connect some traditional debates about externalism to contemporary cognitive science, and demonstrate the promise of a deflationary approach to cognitive scientific explanation.
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2574823.804169
Polysemy is a phenomenon involving single lexical items with multiple related senses. Much theorizing about it has focused on developing linguistic accounts that are responsive to various compositional and representational challenges in semantics and psychology. We focus on an underexplored question: Why does systematic polysemy cluster in the ways it does? That is, why do we see certain regular patterns of sense multiplicity, but not others? Drawing on an independently motivated view of kind cognition—i.e., the formal structures for different classes of kind representations—we argue for an answer centered on conceptual individuation. Specifically, we argue that classes of kind concepts vary in what they individuate (i.e., counting as one and specifying what makes it the same or different from others). By elucidating these differences, we can explain why a range of patterns of systematic polysemy are found cross-linguistically and why other patterns are not attested. Overall, our account provides an explanatory framework addressing an important question at the interface between language and mind and opens new avenues for future theoretical and empirical research.
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2820164.804203
The question of whether chemical structure is reducible to Everettian Quantum Mechanics (EQM) should be of interest to philosophers of chemistry and philosophers of physics alike. Among the three realist interpretations of quantum mechanics, EQM resolves the measurement problem by claiming that measurements (now interpreted as instances of decoherence) have indeterminate outcomes absolutely speaking, but determinate outcomes relative to emergent worlds (Maudlin, 1995). Philosophers who wish to be sensitive to the practice of quantum chemistry (e.g. Scerri, 2016) should be interested in EQM because Franklin and Seifert (2020) claim that resolving the measurement problem also resolves the reducibility of chemical structure, and EQM is the interpretation which involves no mathematical structure beyond that used by practicing scientists. Philosophers interested in the quantum interpretation debate should be interested in the reducibility of chemistry because chemical structure is precisely the kind of determinate three-dimensional fact which EQM should be able to ground if it is to be empirically coherent (see Allori, 2023). The prospects for reduction of chemical structure are poor if it cannot succeed in EQM; the prospects for EQM as a guide to ontology are poor if it cannot reduce chemical structure.
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2820225.804232
The mechanistic approach in the cognitive and biological sciences emphasizes that scientific explanations succeed by analyzing the mechanisms underlying phenomena across multiple levels. In this paper, we propose a formal strategy to establish such multi-level mechanistic models, which are foundational to mechanistic explanations. Our objectives are twofold: First, we introduce the novel "mLCA" (multi-Level Coincidence Analysis) script, which transforms binary data tables from tests on mechanistic systems into mechanistic models consistent with those tables. Second, we provide several philosophical insights derived from the outcomes generated by this script and its underlying algorithm. Using illustrative examples, we defend the following claims: 1. Inference methods for generating mechanistic models generally require information on how causal factors are assigned to different levels within data tables generated by multi-level structures. 2. The mLCA script successfully produces appropriate mechanistic models from binary data tables, demonstrating the practical application of the philosophical mechanistic approach in the sciences. 3. The number of solutions generated by mLCA increases significantly as the number of relevant factors grows, reflecting adaptations in causal inference methods to meet the demands of multi-level mechanistic modeling. 4. Any further reduction of solutions, if possible, involves pragmatic considerations, a point that carries profound implications for the broader ambitions of the mechanistic approach. By addressing these points, our paper contributes both to the development of practical algorithmic tools and to a deeper philosophical understanding of multi-level mechanistic modeling.
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2820254.804266
This paper investigates whether the philosophical supervenience problem has any bearing on the economic sciences. It first reconstructs some examples of economics normal science that aim at a correct description and explanation of causes of observable phenomena in an economic reference system. Subsequently, the supervenience problem is presented as it is known from the philosophy of biology and the philosophy of mind. A formulation of the problem for economic causes is then developed in an analogous way, even though the ontological commitments of economics are less obvious. The main hypotheses defended in this paper are the following ones: (i) Economic models are amenable to causal interpretations and (ii) the efficacy of economic causes characterized by such models is fundamentally problematic from a metaphysical point of view, analogously to that of biological and mental causes. Moreover, it is shown that (iii) the problem of causal exclusion is even more drastic for economic causes than for biological or mental causes due to a non-localizability and an overlap of economic events.
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2834383.804299
1: Caravaggio, in his religious art, reminded people
that these miracles had transpired neither in primary colors, nor in brilliantly hued paintings of sanitized saints and celestial fireworks, but in dusty streets and dark rooms much like the streets and rooms in which they lived. …
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2873047.804326
This paper examines a form of talking about speech acts, mental states, and other features so far unexplored in philosophy: quotative be like. Quotative be like is the use of like and to be that occurs in constructions such as "Ellen was like "I'm leaving!"" We argue that neglect of quotative be like represents a gap in our understanding of our ways of characterizing the minds and speech of ourselves and others. Further, we show that quotative be like is not reducible to more familiar forms of direct discourse or indirect discourse. Mapping out a number of different options for theorizing about quotative be like, we argue for an account on which the quoted material in quotative be like picks out properties.
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2929139.804353
The relation between sensing/cognition and mental disorders (āfāt al-dhihn) receives special attention in Avicenna’s writings on psychology and medicine. Avicenna identifies two ways of diagnosing mental disorders: one way is in relation to the function of the senses, while the other is in relation to the internal faculties. A psychological phenomenon commonly exhibited in such disorders is the experience of hallucinatory content, that is, registering perceptible content that does not exist to what assumed to be the correspond to an existing object in external reality. In this chapter, I set out to investigate the cognitive process underlying the experience of hallucinatory content, and to show the significant roles that compositive imagination plays in creating and imposing this content upon sensory experience.
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3050874.80439
Relevance Theory (RT; Gutt, 1989, 1991, 2000) stipulates that translation is an act of interpretive language use, establishing interlingual interpretive resemblance between source and target language utterances, rather than describing, assessing or transferring truth values of utterances. In an extension to the original RT framework, Gutt (2004, 2005) distinguishes between two modes of translation - a stimulus mode (S-mode) and an interpretive mode (I-mode) - by which translators establish interpretive resemblances across languages. S-mode translation is tightly linked to linguistic forms, while I-mode translation appeals to the translator’s (self) awareness of the cognitive/cultural environment in which the translation unfolds. In this chapter, I argue that interlingual resemblance and contentful representation, as in descriptive language use, are two incompatible categories and that translation – defined as interlingual interpretive resemblance – can be seen as a form of non-representational language production. I suggest that translation as interpretive language use is heavily based in priming processes. While perceptual/semantic/affective priming mechanisms drive S-mode translation, the phenomenal consciousness of subjective experiences underly I-mode translation.
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3050998.804419
The extant literature on AI (and popular culture more generally) has a few popular slogans that seek to dismiss the cognitive capacities of current large-language models (LLMs). Here, from a conceptual standpoint, we assess whether two such slogans have any teeth. The first such slogan is that “LLMs can only predict next-tokens”. The second is that “AIs are stochastic parrots”. We will briefly explain these two slogans, and argue that, in plausible construals, they do not imply fundamental limitations to cognition and semantic grounding (which of course does not imply anything positive about current AI’s cognitive capacities). The difference between our approach and that of the burgeoning literature reaching a similar conclusion is that we base our arguments on the idea of ‘knowledge-first epistemology’.