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  1. Controlling the Unobservable: Experimental Strategies and Hypotheses in Discovering the Causal Origin of Brownian Movement.Klodian Coko - 2024 - In Jutta Schickore & William R. Newman (eds.), Elusive Phenomena, Unwieldy Things Historical Perspectives on Experimental Control. Springer. pp. 209-242.
    This chapter focuses on the experimental practices and reasoning strategies employed in nineteenth century investigations on the causal origin of the phenomenon of Brownian movement. It argues that there was an extensive and sophisticated experimental work done on the phenomenon throughout the nineteenth century. Investigators followed as rigorously as possible the methodological standards of their time to make causal claims and advance causal explanations of Brownian movement. Two major methodological strategies were employed. The first was the experimental strategy of varying (...)
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  • When should researchers cite study differences in response to a failure to replicate?David Colaço, Bradley Walters & John Bickle - 2022 - Biology and Philosophy 37 (5):1-17.
    Scientists often respond to failures to replicate by citing differences between the experimental components of an original study and those of its attempted replication. In this paper, we investigate these purported mismatch explanations. We assess a body of failures to replicate in neuroscience studies on spinal cord injury. We argue that a defensible mismatch explanation is one where a mismatch of components is a difference maker for a mismatch of outcomes, and the components are relevantly different in the follow-up study, (...)
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  • Data quality, experimental artifacts, and the reactivity of the psychological subject matter.Uljana Feest - 2022 - European Journal for Philosophy of Science 12 (1):1-25.
    While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what are (...)
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  • Classification, Kinds, Taxonomic Stability, and Conceptual Change.Jaipreet Mattu & Jacqueline Anne Sullivan - forthcoming - Aggression and Violent Behavior.
    Scientists represent their world, grouping and organizing phenomena into classes by means of concepts. Philosophers of science have historically been interested in the nature of these concepts, the criteria that inform their application and the nature of the kinds that the concepts individuate. They also have sought to understand whether and how different systems of classification are related and more recently, how investigative practices shape conceptual development and change. Our aim in this paper is to provide a critical overview of (...)
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  • Are there Model Behaviours for Model Organism Research? Commentary on Nicole Nelson's Model Behavior.Jacqueline A. Sullivan - 2020 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 82:101266.
    One might be inclined to assume, given the mouse donning its cover, that the behavior of interest in Nicole Nelson's book Model Behavior (2018) is that of organisms like mice that are widely used as “stand-ins” for investigating the causes of human behavior. Instead, Nelson's ethnographic study focuses on the strategies adopted by a community of rodent behavioral researchers to identify and respond to epistemic challenges they face in using mice as models to understand the causes of disordered human behaviors (...)
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  • Neural networks, AI, and the goals of modeling.Walter Veit & Heather Browning - 2023 - Behavioral and Brain Sciences 46:e411.
    Deep neural networks (DNNs) have found many useful applications in recent years. Of particular interest have been those instances where their successes imitate human cognition and many consider artificial intelligences to offer a lens for understanding human intelligence. Here, we criticize the underlying conflation between the predictive and explanatory power of DNNs by examining the goals of modeling.
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  • Understanding the development and use of tools in neuroscience: the case of the tungsten micro-electrode.Juan Manuel Garrido Wainer - 2022 - Synthese 200 (6):1-22.
    The philosophical interest in experimental practice in neuroscience has brought renewed attention to the study of the development and use of techniques and tools for data production. John Bickle has argued that the construction and progression of theories in neuroscience are entirely dependent on the development and ingenious use of research tools. In Bickle's account, theory plays a tertiary role, as it depends on what the tools allow researchers to manipulate, and the tools, in turn, are developed not in order (...)
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  • When remediating one artifact results in another: control, confounders, and correction.David Colaço - 2024 - History and Philosophy of the Life Sciences 46 (1):1-18.
    Scientists aim to remediate artifacts in their experimental datasets. However, the remediation of one artifact can result in another. Why might this happen, and what does this consequence tell us about how we should account for artifacts and their control? In this paper, I explore a case in functional neuroimaging where remediation appears to have caused this problem. I argue that remediation amounts to a change to an experimental arrangement. These changes need not be surgical, and the arrangement need not (...)
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