Abstract
In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone.
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Notes
- 1.
It is important to note that this decentering may not apply to other related areas of research, such as issues on confirmation and accommodation, both of which are related to the notion of prediction (see, for instance, Eells 2000). We thank a reviewer for inviting us to note this issue.
- 2.
There are some views of mechanistic models that need not have such strong ontic commitments (e.g, Bechtel 2008) and/or that need not be committed to a manipulationist/counterfactual-dependent account of causation. It is possible that some of the arguments we discuss here do not necessarily apply to these accounts. We don’t discuss these accounts in depth, in part because they are not as thoroughly developed in the philosophy of neuroscience. Thanks to a reviewer for inviting us to clarify this point.
- 3.
We see successful predictions as those which accurately model alternative outcomes (and thus support counterfactuals to some degree), or model future states with accuracy significantly above chance. In short, good predictions estimate outcomes above randomness. Note that, on this view, how-actually and how-possibly models can both yield successful predictions; however, how-actually models may not always make predictions that are perfectly accurate, since their use is often limited to certain contexts (consider the difference between Newtonian and relativistic physics, for example).
- 4.
A clarification: we are not saying that Craver is necessarily committed to the strong reading. As far as we know, partisans of mechanisms have said little as to whether or not predictive models also demand the same ontic commitments that explanatory models do. Our view should rather be seen, then, as an admonition to the effect that even if one adopts a mechanistic stance vis-à-vis the way in which neuroscience ought to be pursued, then the strong ontic commitments that have been argued for explanation need not apply to prediction too.
- 5.
We say that a scale-free architecture “suggests” this organization of individuals because, while not a mathematical guarantee, it appears likely to be so. In a scale-free network architecture, statically speaking, some of the high-degree nodes will be provincial hubs and some of the high-degree nodes will be connector hubs. Granted, it is not the case that networks must not follow this principle; in some scale-free networks, all the high-degree nodes might be connectors. But this seems statistically unlikely as then distinct modules are unlikely to exist. If the high-degree nodes are “randomly” arranged, then some must be connectors and some must be provincial. In other words, in scale-free networks, the nodes at the far end of the distribution have considerable influence over the other nodes in the network, more so than in other kinds of networks with other kinds of degree distributions. Some of these nodes with very many connections are likely to interconnect many different communities and be essential (in the example from the text) for diseases to propagate throughout the network.
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Gessell, B., Stanley, M., Geib, B., De Brigard, F. (2021). Prediction and Topological Models in Neuroscience. In: Calzavarini, F., Viola, M. (eds) Neural Mechanisms. Studies in Brain and Mind, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-54092-0_3
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