Abstract
Informational theories of semantic content have been recently gaining prominence in the debate on the notion of mental representation. In this paper we examine new-wave informational theories which have a special focus on cognitive science. In particular, we argue that these theories face four important difficulties: they do not fully solve the problem of error, fall prey to the wrong distality attribution problem, have serious difficulties accounting for ambiguous and redundant representations and fail to deliver a metasemantic theory of representation. Furthermore, we argue that these difficulties derive from their exclusive reliance on the notion of information, so we suggest that pure informational accounts should be complemented with (or perhaps substituted by) functional approaches.
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Notes
Of course, Dretske (1981a) was well aware of these problems, and he tried to solve them by distinguishing a learning period (in which misrepresentation is impossible) from a post-learning period. Unfortunately, it is widely agreed that this proposal still faces daunting problems. For one thing, it seems that the same difficulties reappear at the learning period.
Note, however, that the element that is doing the real work in fixing content is the statistical dependence relation; the causal element is mainly introduced in order to avoid certain counterexamples, such as cases involving two mental states with a common cause (Eliasmith 2000, p. 59).
A terminological note: in this essay, we call ‘representational content’ what Eliasmith calls ‘referent’ (he distinguishes ‘referent’ from ‘content’ understood in a different sense).
The connection with Shannon’s measure of information becomes clear once it is noted that 1 and 2 are simplifications of the following inequalities (Usher 2001, p. 321):
-
(a)
\(MI(R_{i};S_{i})=log\frac {P(R_{i}\mid S_{i})}{P(R_{i})}>log\frac {P(R_{i}\mid S_{j})}{P(R_{i})}=MI(R_{i};S_{j})\)
-
(b)
\(MI(R_{i};S_{i})=log\frac {P(S_{i}\mid R_{i})}{P(S_{i})}>log\frac {P(S_{i}\mid R_{j})}{P(S_{i})}=MI(R_{_{j}};S_{i})\)
As Usher notes (p.320), since the logarithm is a monotonic function, and we only make use of ordinal relations, we can rely on exp(MI) that provides the same expression but without the logarithm. As in 1 and 2 in INFO.
-
(a)
Rupert does not formulate his approach in terms of representational states, but as applying to ’terms in a language of thought’. Nonetheless, since the exact nature of the entity that does the representing is irrelevant for our discussion, we describe all accounts as talking about states.
The fact that Info might capture the implicit assumptions made by neuroscientists in establishing hypotheses about representational relations does not mean that SGIT are purely descriptive theories. Eliasmith (2005b), for example, has criticized neuroscientists for excessively relying on what he calls ‘the observer’s perspective’ (i.e. P(R∣S)) and forgetting about the ‘animal’s perspective’ (i.e. P(S∣R)).
We would like to thank an anonymous reviewer for pressing us on this issue.
According to Bayes’ rule, \(P(S_{i}\mid R_{i})=\frac {P(R_{i}\mid S_{i})(S_{i})}{P(R_{i})}\). Thus, to derive P(bee∣Ri) and P(dronefly∣Ri) we need to know P(Ri). Fortunately, as Usher remarks (see foonote 4), in the present context this value is not required, because we are only interested in comparing P(bee∣Ri) and P(dronefly∣Ri), and in both cases the numerator is the same, namely P(Ri). Thus, the fact that P(Ri∣bee)P(bee) < P(Ri∣dronefly)P(dronefly) is enough for showing that P(bee∣Ri) < P(dronefly∣Ri).
As a reviewer suggested, at least one should grant the conceptual possibility of predators representing bees even if they frequently mistake droneflies for bees and the former outnumber the latter. Depending on one’s metaphysical assumptions, the mere fact that this is conceptually possible might be enough for raising a problem for Info.
Some employ the label ’distality problem’ to refer to this difficulty, but this expression is also frequently used for version of the indeterminacy problem (e.g. Neander 2017, ch.9; Schulte 2018). To avoid any sort of misunderstanding, we call this objection the ’wrong distality attribution problem’.
As a reviewer pointed out, one might question whether in all cases the problem we present concerns more distal vs. less distal features (consider, for instance, the contrast between faces and face-looking things). We adopted this terminology here because it is very usual in the literature and in many cases there is clearly a contrast in distality (e.g. faces vs. neuronal patterns). Nonetheless, the name is not important; the key point is that the theory delivers the wrong content, but not in virtue of it being indeterminate.
Actually Rupert (1999, p. 340) accepts a extremely liberal approach to natural kinds, according to which “natural kinds are any kinds that successful non-intentional science finds theoretically interesting and useful”. Thus, he probably faces the first horn of the dilemma.
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Acknowledgements
We would like to thank Axel Barceló, the NCH Mind and Brain conference 2016 and two anonymous referees for their helpful comments and criticisms. Financial support was provided by a Postdoctoral Fellowship at the MCMP-LMU, the fellowship ’formación postdoctoral’ from the Ministerio de Economia y Competividad, the UNAM-DGAPA-PAPIIT programs
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Artiga, M., Sebastián, M.Á. Informational Theories of Content and Mental Representation. Rev.Phil.Psych. 11, 613–627 (2020). https://doi.org/10.1007/s13164-018-0408-1
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DOI: https://doi.org/10.1007/s13164-018-0408-1