Abstract
In this chapter I argue that indicator contents cannot be explanatorily relevant in constitutive mechanistic explanations of cognitive phenomena. The argument relies on the observation that indicator contents are based in conditional probabilities. I first argue that these probabilities must be viewed as physical chances rather than epistemic probabilities or credences. Then I examine the most prominent views on the nature of physical chances to figure out which states of affairs are described by the claim that a neural vehicle carries a particular content. I show that, under both frequentist and propensity interpretations of chances, the property of carrying representational content X is not local to any cognitive phenomena. Furthermore, I show that this property is only mutually dependent with cognitive phenomena under a long-run propensity interpretation of physical chances. However, owing to the failure of locality, I conclude that indicator contents are never explanatorily relevant in constitutive mechanistic explanations in neuroscience.
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Notes
- 1.
Individual differences in learning and developmental plasticity likely equip each subject with a unique set of physical vehicles. This means that the laws in question do not readily generalise to other individuals, or to other token phenomena, as the vehicles involved there will be different.
- 2.
Eliasmith’s and Usher’s accounts are both formulated in terms of contents with mind-to-world direction of fit. For imperative representations, the causal requirement would run in the opposite direction, i.e., Ra causing Sa.
- 3.
An account in the spirit of IC has been hinted at by Godfrey-Smith (1991). Rupert’s (1999) “best-test theory” combines IC’s requirements (a) and (c) but omits (b). In Skyrms’ theory (2010), pointwise mutual information is also used to fix contents of signals, but instead of restricting the contents of a signal to the stimulus with which the signal most covaries, Skyrms views signals as carrying (informational) content about all states with which they covary at all. Since none of these views differ from Eliasmith’s and Usher’s views in terms of compatibility with the mechanistic framework of explanation, I will not consider them further.
- 4.
Where the strength of ‘non-deterministic’ might depend on the grain with which we type events – both setups and outcomes.
- 5.
Instead of “produces”, one might opt for “includes”.
- 6.
The time-dependence is introduced to resolve Humphreys’ paradox – the inconsistency between the fact that probability requires backward conditional probabilities, but propensity is a causal notion, and hence backward conditional propensities are metaphysically suspect. Other solutions would view backward propensities as mathematical fictions (see Humphreys, 1985; Milne, 1986; Salmon, 1979).
- 7.
ty must be earlier than tx, because by tx the result has already been produced, and thus the system has no propensity to produce it at that time anymore.
- 8.
Note, however, that representational content still fails the locality criterion, and so cannot be considered a component of the mechanism for the phenomenon.
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Kohár, M. (2023). Indicator Contents. In: Neural Machines: A Defense of Non-Representationalism in Cognitive Neuroscience. Studies in Brain and Mind, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-031-26746-8_5
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