2025/12/12 by Max Kistler · 1 voice
Arts and Humanities · Neuroscience · #Philosophy and History of Science #Motor Control and Adaptation #Embodied and Extended Cognition
paper · pdf · doi:10.36446/af.e1168
openalex created_date 2025/12/12 · openalex publication_date 2025/12/12 · openalex updated_date 2026/05/21
Craver (2007) has argued that constitutive relevance can be discovered by mutual manipulability, based on interventions (Woodward, 2003). However, the requirements on interventions make mutual manipulability of mechanisms and their constituents impossible (Baumgartner & Gebharter, 2016). Two proposals for providing empirical criteria for constitutive relevance are examined. Both provide only part of the information needed for constructing models of mechanisms that represent both causal links and constitutive relations. Baumgartner, Casini, and Krickel’s (2020) account is not adequate for mechanisms that contain 1) causal chains of activities and/or 2) activities working in parallel. Furthermore, their requirement to detect two or more activities simultaneously does not fit standard scientific practice. Craver, Glennan, and Povich’s (2021) thesis that constitutive relevance can be reduced to “causal betweenness”, between the input and the output condition of a mechanism, is not adequate for multi-level mechanisms. Models that do not contain any representation of constitution relations might represent the activities of some hypothetical fundamental level. However, both the existence of such a fundamental level and the application of causation to that level are problematic. Craver, Glennan, and Povich’s account of the construction of models for mechanisms leads to the paradoxical result that there are no levels and thus no multi-level mechanisms, but only causal chains of fundamental level activities.The key to understanding how models of multi-level mechanisms can be constructed on the basis of empirical information is that 1) the relevant experiments directly provide only information about causal relations (contrary to what Craver 2007 and Baumgartner, Casini, and Krickel 2020 claim), but that (contrary to what Craver, Glennan, and Povich 2021 claim) this information about causal relations can bear on variables at different levels. A multi-level model is built in two steps. 1) First, partial purely causal models are built for each hypothetical constituent variable Fi, on the basis of top-down and bottom-up experiments that modify or measure Fi in a level-specific way, 2) second, those partial models are merged in a comprehensive model containing both causal and constitution relations between variables, on the basis of information about each variable and spatio-temporal constraints.