Dynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system.

Several philosophers have argued that to capture the generality of certain scientific explanations, we must count mathematical facts among their explanantia. I argue that we can better understand these explanations by adopting a more nuanced stance toward mathematical representations, recognizing the role of mathematical representation schemata in representing highly abstract features of physical systems. It is by picking out these abstract but nonmathematical features that explanations appealing to mathematics achieve a high degree of generality. The result is a rich conception of the role of mathematics in scientific explanations that does not require us to treat mathematical facts as explanantia.

This paper provides the first thorough assessment of a physics-based answer, the Bound State Answer (BSA), to the Special Composition Question (SCQ). According to the BSA some objects compose something if they are in a common bound state. The reasons to endorse such an answer, in particular, motivations coming from empirical adequacy and conservativeness, precision, simplicity, and parsimony, are critically addressed. I then go on to compare the BSA to other moderate answers to the SCQ and consider whether objections raised against such answers can be raised against the BSA as well. I finally relate the discussion with mereological pluralism.

This paper accounts for broad definitions of memory, which extend to paradigmatic memory phenomena, like episodic memory in humans, and phenomena in worms and sea snails. These definitions may seem too broad, suggesting that they extend to phenomena that don’t count as memory or illustrate that memory is not a natural kind. However, these responses fail to consider a definition as a hypothesis. As opposed to construing definitions as expressing memory’s properties, a definition as a hypothesis is the basis to test inferences about phenomena. A definition as a hypothesis is valuable when the “kinding” of phenomena is ongoing.

Synthetic biology has a strong modal dimension that is part and parcel of its engineering agenda. In turning hypothetical biological designs into actual synthetic constructs, synthetic biologists reach toward potential biology instead of concentrating on naturally evolved organisms. We analyze synthetic biology’s goal of making biology easier to engineer through the combinatorial theory of possibility, which reduces possibility to (re)combinations of individuals and their attributes in the actual world. While the last decades of synthetic biology explorations have shown biology to be much more difficult to engineer than originally conceived, synthetic biology has not given up its combinatorial approach.

Strong emergentism is the position that certain higher-level properties display a kind of metaphysical autonomy from the lower-level properties in which they are grounded. The prospect of collapse is a problem for strong emergentism. According to those who press the collapse problem any purportedly strongly emergent feature inheres in the emergence base and so is not genuinely autonomous from that base. Umut Baysan and Jessica Wilson (2017) argue that power emergentism avoids the collapse problem. In this paper, I challenge the claim that power emergentism avoids the collapse problem and argue for explanatory emergentism in its place.

Models typically have actual, existing targets. However, some models are viewed as having non-actual targets. I argue that this interpretation comes at various costs and propose an alternative that fares better along two dimensions: (1) agreement with practice and (2) ontological and epistemological parsimony. My proposal is that many of these models actually have actual targets.

Perspectival realists often appeal to the methodology of science to secure a realist account of the retention and continued success of scientific claims through the progress of science (e.g., Massimi [2016]). However, in the context of modern physics, the retention and continued success of scientific claims are typically only definable within a mathematical framework. In this article, I argue that this concern leaves the perspectivist open to Cassirer’s (1910/1923) neo-Kantian critique of the applicability of mathematics in the natural sciences. To support this criticism, I present a case study on the conservation of energy in modern physics.

It is commonly maintained that neuroplastic mechanisms in the brain provide empirical support for the hypothesis of multiple realizability. We show in various case studies that neuroplasticity stems from preexisting mechanisms and processes inherent in the neural (or biochemical) structure of the brain. We argue that not only does neuroplasticity fail to provide empirical evidence of multiple realization, its inability to do so strengthens the mind-body identity theory. Finally, we argue that a recently proposed identity theory called Flat Physicalism can be enlisted to explain the current state of the mind-body problem more adequately.

A phenomenon resulting from a computationally irreducible (or computationally incompressible) process is supposedly unpredictable except via simulation. This notion of unpredictability has been deployed to formulate recent accounts of computational emergence. Via a technical analysis, I show that computational irreducibility can establish the impossibility of prediction only with respect to maximum standards of precision. By articulating the graded nature of prediction, I show that unpredictability to maximum standards is not equivalent to being unpredictable in general. I conclude that computational irreducibility fails to fulfill its assigned philosophical roles in theories of computational emergence.

Accuracy arguments for the core tenets of Bayesian epistemology differ mainly in the conditions they place on the legitimate ways of measuring the inaccuracy of our credences. The best existing arguments rely on three conditions: Continuity, additivity, and strict propriety. In this paper, I show how to strengthen the arguments based on these conditions by showing that the central mathematical theorem on which each depends goes through without assuming additivity.

In analogical reasoning, observations about one or more source domains provide varying degrees of support for a conjecture about a target domain. Norton (2021) challenges the usefulness of formal models of analogical inference. Other philosophers (Dardashti et al. 2019) develop just such formal models in order to show how analogue experiments can confirm a hypothesis, even when the target domain is inaccessible. This paper defends the value of quasi-formal models of analogical reasoning. Such models are broadly compatible with Norton’s position, but help to clarify the structure of analogical reasoning and to identify basic requirements for a good analogical inference.

Despite its potential implications for the objectivity of scientific knowledge, the claim that “scientific instruments are perspectival” has received little critical attention. I show that this claim is best understood as highlighting the dependence of instruments on different perspectives. When closely analyzed, instead of constituting a novel epistemic challenge, this dependence can be exploited to mount novel strategies for resolving two old epistemic problems: conceptual relativism and theory-ladeness. The novel content of this article consists in articulating and developing these strategies by introducing two fine-grained notions of perspectives as the key units of analysis: “broad perspectives” and “narrow perspectives.”

Since Euclid defined a point as “that which has no part” it has been widely assumed that points are necessarily unextended. It has also been assumed that this is equivalent to saying that points or, more properly speaking, degenerate segments, have length zero. We challenge these assumptions by providing models of Euclidean geometry where the points are extended despite the fact that the degenerate segments have null lengths, and observe that whereas the extended natures of the points are not recognizable in the given models, they can be recognized and characterized by structures that are suitable expansions of the models.

In scientific modeling, continuum idealizations bridge scales but at the cost of fundamentally misrepresenting the microstructure of the system. This engenders a mystery. If continuum idealizations are dispensable in principle, this de-problematizes their representational inaccuracy, since continuum properties reduce to lower-scale properties, but the mystery of how this reduction could be carried out endures. Alternatively, if continuum idealizations are indispensable in principle, this is consistent with their explanatory and predictive success but renders their representational inaccuracy mysterious. I argue for a deflationary solution, enlisting the applied scientific method of upscaling as demonstrated in a case from soil hydrology.

Theoretical virtues play an important role in the acceptance and belief of theories in science and philosophy. Philosophers have well-developed views on which virtues ought and ought not to influence one’s acceptance and belief. But what do scientists think? This paper presents the results of a quantitative study with scientists from the natural and social sciences and compares their views to those held by philosophers. Some of the more surprising results are: (i) all three groups have a preference order regarding theoretical virtues, making theory choice a much more determinate matter than what has sometimes been suggested; (ii) the preference orders are very similar for the three groups; (iii) simplicity is viewed as an epistemic virtue particularly by social scientists (but not by philosophers); and (iv) syntactic parsimony is preferred to ontological parsimony by all three groups.

Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains.

While practitioners think highly of randomized studies, some philosophers argue that there is no epistemic reason to randomize. Here I show that their arguments do not entail their conclusion. Moreover, I provide novel reasons for randomizing in the context of interventional studies. The overall discussion provides a unified framework for assessing baseline balance, one that holds for interventional and observational studies alike. The upshot: Practitioners’ strong preference for randomized studies can be defended in some cases, while still offering a nuanced approach to evidence appraisal, one where not all nonrandomized studies are treated equally.

Experimental archaeology is often understood both as testing hypotheses about processes shaping the archaeological record and as generating tacit knowledge. Considering lithic technologies, I examine the relationship between these conceptions. Experimental archaeology is usefully understood via “maker’s knowledge”: archaeological experiments generate embodied know-how enabling archaeological hypotheses to be grasped and challenged, and further, well-positioning archaeologists to generate integrated interpretations. Finally, experimental archaeology involves “material speculation”: the constraints and affordances of archaeologists and their materials shape productive exploration of the capacities of objects and human skill in ways relevant to archaeological questions.

Laurie Paul argues that, when it comes to many of your most significant life-changing decisions, the principles of rational choice are silent. That is because, in these cases, you anticipate that one of your choice options would yield a transformative experience. We argue that such decisions are best seen as ones in which you anticipate awareness growth. You do not merely lack knowledge about which possible outcome will arise from a transformative option; you lack knowledge about what are the possible outcomes. We show how principles of rational choice can be extended to cases of anticipated awareness growth.