This paper defends the mnemonic hypothesis regarding the evolutionary function of episodic memory. According to this view, episodic memory evolved to accurately store and process information about particular past events in order to support its accurate retrieval. Crucially, this information includes the representation of spatiotemporal relations within and among these events. My analysis is grounded in an evolutionary notion of function, which I clarify at the beginning of the paper. I also discuss the types of evidence I will use to evaluate hypotheses about the evolutionary function of a cognitive system. In particular, I focus on four sources of evidence that are typically used in such evaluations: (1) causal-role functional analysis, (2) dysfunctional states, (3) reverse inference from optimality models, and (4) evidence against alternative hypotheses, which in this case are non-mnemonic hypotheses. I argue that the first three types of evidence converge in support of the mnemonic hypothesis. I then critically examine prominent competing accounts, including the imagining-the-future, the counterfactual-thought, the predictive processing, and the universal learning hypotheses. I argue that these alternatives are either inadequately supported by empirical data or are based on conceptual or methodological misunderstandings.
There has been a recent surge of philosophical interest in AI large language models (LLMs) and their relevance for the understanding of human language. In this article, we take the perspective of the philosophy of science on modelling in general to consider whether LLMs are (good) models of language. We establish that LLMs can be considered scientific models, and that as such they are good scientific models of certain aspects of human language, in so far as they can capture the modal structure of natural language. We argue that they can do this even if they do not reveal (all) its causal structure, nomological structure or mechanisms.
Geodesic empirical predictions in General Relativity are remarkably successful. Nonetheless, the status of the geodesic principle is not straightforward. I distinguish the main senses in which geodesic motion is taken as an approximation or idealisation and propose a theory-internal standard for such claims, refining Norton’s (2012) distinction. Theory-internal approximation requires preserving across the approximating process the material bearer undergoing geodesic motion; theory-internal idealisation introduces a distinct dynamically admissible material bearer. I show that standard theorems do not establish geodesicity as approximate or idealised motion within the full, non-linear Einstein-matter theory, though geodesicity is recovered as a controlled leading-order approximation in the perturbative theory.
The goal of this paper is to present, justify and compare to its rivals an account of representational modelling as ensemble-plus-standing-for. The goal is to provide an analysis that is weak enough to be applied to the plethora of different models in empirical science, but strong enough to offer monistic and substantive answers to the problems of directionality, performance and adequacy of scientific representations. The account combines some inherentist and functional aspects adding some essential pragmatic components. In Sect. 1 I start with some introductory and terminological remarks, and the problems and desiderata the account aims to meet. In Sect. 2, I summarize the main features of inherentist and functional accounts, their virtues worth to preserve and their shortcomings to avoid. In Sect. 3 I present the account and how it answers the three problems. In Sect. 4, I assess the account vis a vis its rivals. In the final section I make some concluding remarks.
Historical challenges, such as the pessimistic induction and its cognates, are one of the driving forces in the modern scientific realism debate. To address these challenges, realists have offered various responses, including confining their commitment to certain types of theoretical components, dismissing historical cases as irrelevant to contemporary science, and adopting local approaches, each of which brings its own challenges. In a thought-provoking paper, Sherrilyn Roush draws insight from the discussion of the preface paradox in epistemology and points out the possibility of a new realist stance regarding historical challenges that does not align with these conventional approaches. Despite its uniqueness, the suggested stance does not receive as much attention as it deserves, and its implications have not been fully explored. In this paper, I critically examine Roush’s proposal and argue that it is less appealing than it initially appears. However, rather than simply returning to conventional approaches, I also highlight the possibility of more nuanced realist stances.
Living things are highly complex and organised. This is often taken as an objection to reductionism and a point in favour of organicist views, which argue for the central role of the organism in all biological explanations. This paper argues that biological organisation often either is, or depends on, spatial structure, together with interactions among parts of the system understood in physical terms. We take spatial structure to include position, configuration, shape, directionality, and orientation of objects in space. Important features of the spatial structure of living systems include physical boundaries, such as membranes; cellular structure, which includes physical objects spatially arranged in certain ways; directionality and orientation with respect to internal and environmental signals; location, both of organisms in their environments and of components within the organism; and shape, including the shapes of organisms and their components at atomic, mesoscopic, and larger scales. The importance of spatial structure for explanations of the functioning of biological systems corroborates criticisms of genetic and molecular reductionism, but is compatible with physical reduction – the explanation of biological phenomena in physical terms – and part-whole reduction – the explanation of systems in terms of their parts, spatial structure, and interactions with each other and their environment.
Recent discussion suggests that uncertainty in climate models’ representation of the interaction between external radiative forcing and internal variability prevents scientists from reliably providing local (or regional) climate change information anytime soon. In this paper, I articulate ‘predictable uncertainty’ as an epistemic criterion of reliable climate change information: the ability of climate scientists to systematically characterize and constrain the uncertainty envelope associated with their epistemic situation. There are two related implications. First, internal variability can be an informative signal for future climate change, and controlling for it can help identify the scales at which climate model-based or climate model-supported information may be reliable. Second, the reliability of local climate change information should be evaluated based not primarily on the amount of uncertainty in the interaction between the forced response and internal variability, but rather on climate scientists’ ability to characterize and constrain that uncertainty.
Despite some labeling the Hubble tension as a ‘crisis’, I argue that this tension does not endanger Λ CDM as a permanent contribution to scientific knowledge. Utilizing a Lakatosian framework, I argue that the Λ CDM model represents the latest stage in an evolving research programme characterized by empirical and theoretical progress. I further contend that plausible resolutions to the Hubble tension align with established strategies within this research programme that preserve Λ CDM as a highly accurate approximation. This paper concludes that both the current cosmological research programme and the Λ CDM branch of it are well-positioned to resolve the Hubble tension, thus mitigating concerns of an epistemic crisis.
Recently, Jiang (2024; 2025a) has proposed a novel processual structuralist framework that integrates structuralism (ontic structural realism) with processualism for the metaphysics of biology. In this paper, I further develop the account and shows how it sheds new light on key debates within structuralism and the metaphysics of causation, advancing current discussions in these areas. In particular, I focus on two further implications of this framework: First, drawing inspiration from certain new mechanists (e.g., Illari and Williamson 2013; Krickel 2018), I argue that the framework naturally gives rise to an activity-based account of causation within structuralism, thereby addressing a gap in the literature on structuralism and causation in the special sciences. Although some scholars have explored the relationship between structuralism and causation, particularly in the Special Topical Collection of Synthese (Hüttemann 2017), few have specifically addressed how structuralism might accommodate causation within the special sciences. Here, I show how this novel structuralist account of causation advances beyond previous attempts, such as those by Ladyman and Ross (2007) and French (2014). Furthermore, I argue that this activity-based account of causation offers a novel metaphysical foundation for the interventionist account, filling in another previously underexplored limitation in the existing literature. Interventionism, while widely adopted in the philosophy of science, has often been criticized for its lack of explicit metaphysical grounding (e.g., Kuorikoski 2014). By grounding causation in the activities and processes characterized within a structuralist framework, the account offers the ontological basis that interventionism requires. In doing so, it contributes to the broader philosophical task of furnishing a robust metaphysical framework for interventionism.
The notion of placebo effect plays an important role in medicine, yet there is no widely accepted characterization of it. This paper defends and refines the view, originating with psychiatrist Arthur Shapiro, that placebo effects are characterized by their non-specificity. Although once influential, Shapiro’s view has faced strong criticism and has largely fallen out of favor. I argue, however, that there are two important and defensible senses in which placebo effects are non-specific, both of which can be made precise within Woodward’s interventionist framework of causation. First, placebo effects lack one–one specificity: when a treatment produces a response via the placebo effect, countless other treatments could produce the same outcome, and the treatment could easily produce many different outcomes. Second, the outcome of a placebo effect cannot be attributed specifically to the treatment itself; instead, patient expectations and conditioning history are the more important causes of the outcome. Drawing on the interventionist account of the dimensions that influence our judgments of causal importance, I show that placebos count as mere background conditions of the responses they provoke because of the non-specificity of their effects and their failure to meet closely related interventionist conditions of proportionality and stability. Properly articulated, the non-specificity account of placebo effects has substantial conceptual advantages and fares better than competing views in multiple respects.
In-silico clinical trials (ISCTs) for medical devices and products are computer-based simulations of material clinical trials, often of randomized clinical trials (RCTs) on populations of ‘virtual’ patients. A major aim for developing ISCTs is to use them to complement (or potentially sometimes replace) material trials for providing evidence about the performance of medical products. While these methods are beginning to see use, especially as additional sources of evidence that regulatory agencies allow, they remain relatively under-theorized by philosophers of science. In this paper, we aim to introduce philosophers of science to ISCTs, focusing on ISCTs that generate results using knowledge-based models. Although we identify a number of areas in philosophy of science that we expect can make significant contributions in thinking about ISCTs, from work on measurement and models to that on computer simulations, our own offerings are on issues related to the use of RCTs themselves, like external and internal validity, and on the various regulatory standards for their use. Because of the practical importance of their use as evidence for effectiveness and safety by regulatory agencies, we focus most on how in-silico randomized control trials (ISRCTs) compare with material RCTs done on real patients, which most regulatory agencies see as the gold standard for evidence of intervention effectiveness. Our primary examples are of medical devices since this seems to be the area where suggestions for ISCT use is most developed. Our hope is that this introductory paper will prompt other philosophers of science to delve deeper into this extremely interesting and important area of applied science that can have real practical effects on our lives as ISCT results are increasingly allowed as evidence for medical effectiveness and safety.
Philosophers often hold that causes are things that make a difference to their effects, and on most versions of this view, any event has countless causes. Such a view counts the oxygen in the air as much as the lighted match as a cause of the fire, and counts the big bang as a cause of everything. In practice though most of these causes are ignored and only a few are mentioned. I take the causal selection problem to be that of identifying the principles that justify selecting some few factors as causes of an effect. In this paper I will argue for a set of principles, packaged as a recipe for constructing causal explanations. This recipe will show how causes must be selected as part of explanations, and that appropriate selections depend upon the audience and their goals. These parameters determine not just what causes are selected, but also what effects are identified as explanatory targets, and how causes are represented in models. I shall illustrate how the recipe works by applying it to the investigation of two aircraft accidents.
Chemistry is an experimental science. One cannot learn nor practice chemistry without conducting experiments and spending some time in a laboratory. To appreciate this, I employ the concept of pursuit and show the different ways in which experiments in chemistry have been and continue to be worthy of pursuit. I claim that, beyond theory-testing and development, chemical experimentation has been worthy of pursuit in at least four distinct respects: it is pursued for educational purposes; industrial development; technological innovation; and, the historiography of chemistry. Based on this analysis, I formulate the first explicit taxonomy of pursuit in relation to chemical experimentation. This taxonomy reveals the uniquely important role of experimentation in chemistry. In addition, it shows that the concept of pursuit is a multifaceted and dynamical concept. The analysis of experimentation from the perspective of this taxonomy allows us to consider the value of scientific experimentation in a much broader context than before.
This paper provides a philosophical examination of the integrative field of sociogenomics which seeks to incorporate genomic tools into social science research on individual outcomes. I characterise sociogenomic integration as being guided by two main promises, which I define as the promises of credibility and trustworthiness. I demonstrate that for the social sciences, this type of integration has been associated with the promise of epistemic credibility, especially in the context of claims about causal effects of the social environment. In turn, for behavioural genetics and genomics, the introduction of theoretical resources from disciplines such as sociology is thought to generate ethical trustworthiness by supporting a non-determinist, sociocontextual perspective on the role of genetic factors in individual outcomes. The paper offers a detailed analysis of sociogenomic research on gene-environment interaction (G × E) to illustrate what the two promises entail and to examine whether they are being convincingly realised by this type of inquiry. I argue that sociogenomic studies of G × E fall short on both counts: they cannot be said to measure complex dimensions of social organisation such as equality, and they are also not as clearly opposed to genetic determinism as their advocates maintain. I draw out the implications of this state of affairs for sociogenomic integration and for the role of the social sciences within it.
Aesthetic experiences are slowly but increasingly gaining attention in the contemporary philosophy of science. However, they remain peripheral in debates about research environments dealing with complex socio-environmental problems. Through a socially engaged and empirically oriented approach, I examine the aesthetic experiences in achievements of understanding in two research environments from the sustainability sciences: the ClimArtLab Evolving Futures Owing Our Mess and the Council of Care. I further the debate by presenting a relational account of aesthetics in scientific understanding, where understanding is a triadic engagement between subject, object and environment mediated by aesthetic experiences.
This paper examines the ontological and epistemological status of engineering models to make explicit the often implicit criteria that govern model acceptance and evaluate whether machine learning (ML) models satisfy these criteria. We argue that the distinction engineers draw between "physics-based" and "data-driven" models lacks ontological robustness, and we propose a definition that identifies decision support and evidence of performance as constitutive requirements, while remaining neutral with respect to derivational history and mathematical form. From this foundation, three claims arise. First, ontologically, engineering models are structured compressions of regularities that differ in their degree of theoretical content but not in kind. Second, epistemologically, the warrant for trusting a model in engineering practice derives from calibration, validation, and domain-appropriate uncertainty quantification, and these criteria apply equally to ML models and traditional models. Third, pragmatically, model equivalence should be defined operationally in terms of decision support under reliability constraints rather than physical interpretability. The resistance to ML models in traditional engineering disciplines, thus, may reflect not only philosophical concerns but also an attachment to a conception of proper engineering that ML appears to challenge. This paper concludes that ML models represent not a departure from the engineering modeling tradition but its logical completion, insofar as they make explicit the empiricism that was always implicit in code equations, constitutive laws, and phenomenological models.
This article develops a structural–contextual account of how physical information is generated, stabilized, and rendered objectively determinate. Rather than treating information as ontologically primitive, the paper develops a framework in which quantized action provides a lower physical scale for the discrimination of alternatives, while contextual constraints determine when and how a specific informational value may be objectively attributed. First, the quantum of action ħ is not itself a bit, but sets a characteristic lower action scale for the registration of physically significant distinctions in phase-sensitive physical settings. Second, drawing on the Kochen–Specker theorem, the paper argues that informational values cannot be straightforwardly treated as globally pre-assigned across all contexts; rather, their determinate attribution is context-relative. Third, it introduces the notion of inscription, understood as the stabilization of a contextually generated distinction through a stabilizing structural condition, such as topological protection or metastable energetic separation. Such stabilization turns an otherwise transient distinction into objective, physically stored information. Taken together, these claims support a physically grounded account on which objective physical information is not fundamental but emerges from the interplay of quantized action, structural context, and structural stabilization, in a sense that does not depend on any particular observer’s knowledge or interpretation. The resulting framework complements rather than replaces existing approaches, offering a synthetic reinterpretation of the relationship between dynamics, context, and informational content.
This collection shines a spotlight on the notion of the research environment. How has this concept been understood within the philosophical and scientific literatures so far, and how can it be conceptualised otherwise and going forward? What are its implications for understanding scientific change and knowledge development, particularly given the technological, institutional, methodological and social transformations that the research landscape has undergone over time? Contributions to this collection draw on history, social studies and philosophy of science. One aim of the collection is to develop and explore a variety of conceptualisations of research environments in light of ideas about the environment from biological and cognitive sciences, including the relation between organisms and their environments. Another aim is to demonstrate how conceptualisations of research environments serve as a frame for investigating and understanding scientific inquiry and the conditions under which it is carried out.
The recent pluralist turn in philosophy of science claims that we ought to support and maintain multiple epistemic frameworks for learning about phenomena. This position is especially clear in Massimi’s Perspectival Realism, which argues that it is only by having more than one perspective on a phenomenon that the potential for reliable knowledge about it emerges. This paper applies Perspectival Realism to the case of psychology—with a focus on developmental psychology—arguing first, that the discipline has pursued frameworks almost exclusively within a Minority World perspective, and second, that the lack of alternative perspectives precludes the field from knowing if its methods of knowledge production are reliable. To redress this problem, it advocates the pursuit of Majority World psychological frameworks, and points towards ways in which this could be achieved.
