Philosophers are fond of saying that their field boosts critical thinking. Two of them decided to put that claim to the test.

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We investigate the epistemic role of coherence in scientific reasoning, focusing on its use as a heuristic for filtering evidence. Using a novel computational model based on Bayesian networks, we simulate agents who update their beliefs under varying levels of noise and bias. Some agents treat reductions in coherence as higher-order evidence and interpret such drops as signals that something has gone epistemically awry, even when the source of error is unclear. Our results show that this strategy can improve belief accuracy in noisy environments but tends to mislead when evidence is systematically biased. We explore the implications for the rationality of coherence-based reasoning in science.

Many successful explanations show how causally individuated parts are responsible for the occurrence of the phenomena that scientists seek to explain. On this view, parts that are chosen only by convention, and related only through correlations, cannot possibly figure in successful explanations. This is because without some form of causal grounding, it seems unintelligible why any explanatory relation between these parts and the phenomenon of interest would hold. This problem is particularly pronounced in functional connectivity models (FC) in neuroscience. These models typically represent time series of recurrent neural activity in conventionally determined spatial regions (as a network’s nodes) and synchronization likelihoods among these time series (as its edges). Many neuroscientists and philosophers maintain that because of this, FC models cannot provide explanations. We formulate this problem more precisely and then show that it rests on an impoverished interpretation of scientific models in general and FC models in particular. We then provide a positive account of how FC models provide a variety of neuroscientific explanations.

This paper explores the development and evaluation of physics-specific large-scale AI models, which we refer to as large physics models (LPMs). These models, based on foundation models such as large language models (LLMs) are tailored to address the unique demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing insights from physical theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability through testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining large physics models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

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The Philosophy of Science Association (PSA) is continuing its excellent expert "office hour" series this semester---a program other academic associations should consider adopting. "The PSA Office Hour aims to facilitate interactions between our graduate student membership and prominent philosophers of science, and in a more controlled, accessible, and carbon-conscious setting than is provided by our