Mace, Caitlin (2025) The Vehicle Indeterminacy Problem. [Preprint]
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Abstract
Proponents of some recent accounts of neural representations avoid classic issues with content indeterminacy by taking an antirealist or pragmatic stance toward representational content and a realist stance toward representational vehicles (e.g., Shea 2018; Egan 2020; Burnston 2020). To establish the reality of representational vehicles, such vehicle realists must rely on the fact that patterns of neural activity are putatively re-identified as the same pattern across systems. Re-identification shows that the pattern exists across means of detection, which shows that the pattern is robust and, therefore, real. But it is entirely unclear by what measure patterns should be determined as similar. Drawing on neuroscientific expertise, I show that determining the relevant details by which to assess similarity is done in practice by coarse-graining vehicles, i.e., compressing, abstracting, or suppressing variations in patterns. I conclude that this coarse-graining strategy vindicates neuroscientific practice but not realists’ appeal to robust vehicles.
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| Item Type: | Preprint | ||||||
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| Keywords: | neural representations, scientific realism, representational vehicles, indeterminacy | ||||||
| Subjects: | Specific Sciences > Cognitive Science > Concepts and Representations Specific Sciences > Neuroscience General Issues > Realism/Anti-realism |
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| Depositing User: | Catilin Mace | ||||||
| Date Deposited: | 29 Aug 2025 13:13 | ||||||
| Last Modified: | 29 Aug 2025 13:13 | ||||||
| Item ID: | 26408 | ||||||
| Subjects: | Specific Sciences > Cognitive Science > Concepts and Representations Specific Sciences > Neuroscience General Issues > Realism/Anti-realism |
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| Date: | 2025 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/26408 |
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