The teleomechanistic view of physical computation offers a solid non-semantic basis that theories of cognitive representation—within the computational-representational framework—can make use of. More generally, having a theory of representation rely on a teleological theory of computation presents many advantages. I will here explore six of the most central ones, occasionally putting them in connection with current theories. My contention is that the computation-based approach to representation is promising in at least two ways: it can modify extant theories of representation such that they are better able to tackle their main shortcomings; and it can suggest compelling theories of representation importantly at odds with current approaches. The rest of this paper will be dedicated to showing why this is so.
A first motivation in favour of computation-based accounts is that the scientific status of computation seems to be less controversial than that of representation, and the prospects for its direct naturalisation seem rosier. Representation is essentially defined by a property that is not easy to make sense of by recourse to the natural sciences. The property of being about something else, of being directed toward something else—aboutness, intentionality—seems importantly different from the kinds of properties at work in basic science, and particularly difficult to explain in naturalistic terms. While significant progress has been made on the naturalisation of representation, the lack of consensus that persists despite decades of focused philosophical work attests to the difficulty of the project.
Computation, on the other hand, is a notion that came to theoretical maturity as already importantly tied to mechanisation (albeit of a purely abstract sort), in the form of the Turing machine, and later on of concretely implementable computational architectures. Its relations to properties and processes amenable to mechanical explanation are much more direct if compared to representation. Using computation as an important stepping stone to account for representation therefore suggests itself as a promising theoretical move. For if computation can be more easily naturalised, we can use it as a starting point for a naturalistic theory of representation.
In light of these considerations, it is curious that most philosophical work in the area tended to start with representation, rather than computation. There is much to be said about the reasons for this, but I would like to mention three tentative ones. First, representation was recognised as central to explaining cognition and intelligence very early in the history of philosophy, starting at least with Plato (e.g. in the Theaetetus); while recognition of the importance of computation came much later, Hobbes being a precursor of the explosion in interest that took place in the second half of the twentieth century, following advances in the theoretical and practical understanding of computational systems. Second, computation is appealed to in helping explain how representations are transformed in task-appropriate ways, with representation’s aboutness taken to play the lion’s share of explaining successful behaviour. Third, as hinted above, the predominant views of computation until recently were extremely liberal and of little explanatory use if not properly constrained, and representation, being already a central part of theories of cognition and intelligence, suggested itself as the natural choice to curb that pernicious liberality.
The teleomechanistic view of computation allows us to do away with this latter motivation for the semantic view. The former two, on their hand, do not justify the semantic view, but merely shed light on the mostly historical grounds for its apparent intuitiveness. They should therefore be weighed against the considerations I brought to bear above in favour of starting with computation instead, which I take to shift the balance toward the computation-based approach. Be as it may, the foregoing line of reasoning is suggestive, but far from decisive. Stronger reasons, I believe, come from the considerations that follow.
Causal relevance and explanation
Giving pride of place to the notion of computation in a theory of representation helps to account for the causal powers of representational vehicles, courtesy of a theory of computational implementation. This has been often seen as the main (if not the only) contribution that the notion of computation makes to the computational-representational framework in the cognitive sciences (Fodor 1975; Haugeland 1981): appeal to computation helps to explain how physical vehicles can behave in ways that respect semantic and rationality constraints, such as preserving truth in deductive arguments, being responsive to practical reasons, or playing good chess. The physical vehicles must be so regimented as to follow computational rules that mirror to a relevant extent semantic or rationality constraints, thus being able to work as representational vehicles, that is, the physical states that carry representational content. Representational vehicles are causally efficacious in virtue of their physical properties. Theories of computational implementation build a bridge between the somewhat abstract level of the computational individuation of a system, and the details of the causally efficacious physical processes that realise, or implement, the computations it performs. Computation, in this way, connects semantic, causally inefficacious properties with physical, causally efficacious ones. Thereby—the accepted picture goes—cognition and intelligence can be physically explained.
This may seem to be an advantage that the teleomechanistic view of computation, in contrast to other non-semantic views, cannot have, since the factors relevant for determining teleological functions are causally inert, being historical rather than occurrent.Footnote 12 Since the teleomechanistic view builds causally inert factors into its conditions of individuation for computation, one may worry that it cannot provide the desired bridge between representational and causal explanation. In this central respect, therefore, the foregoing view may seem to be inferior to its competitors.
Such a worry, I believe, trades on a failure to distinguish individuation from implementation, a distinction that has played an important role in debates about mental causation. While individuative properties need not be causally efficacious, implementational properties do.
Computation, under the teleomechanistic view, is a kind that is partly individuated by historical properties. This makes it so that two physically identical systems—with identical causal powers—may differ in computational nature if one has the teleofunction to compute—a historical property—and the other does not. However, individuation conditions, if they successfully apply to entities in the world, pick out physical systems that possess causal powers. Computational individuation picks out those physical systems that implement computations, insofar as they have the appropriate occurrent and historical properties. The implementing systems have causal powers, and a subset of these causal powers must be such that they fulfil the appropriate parts of the individuation conditions of computation, namely the systematic transformation of inputs into outputs.
Therefore, the teleomechanistic view, as other theories of computation, does not threaten the role of computation in connecting semantic properties with physical, causally efficacious ones via computational implementation. For the mechanistic view appeals to teleology in the individuation of computational systems and computational functions, and not in order to account for computational implementation, which is purely mechanistic (Coelho Mollo 2018; Tucker 2018).Footnote 13
The fact that the causal powers of computational systems hinge on their occurrent, implementational properties, does not make the appeal to teleology idle. For teleology helps to individuate those physical systems that are computational to start with, and thereby that implement physical computations by means of some of their causal properties. Without such a demanding individuation condition, a theory of computation is forced to embrace pancomputationalism—given the ubiquity of causal relations—with all the accompanying problems (see Sect. 4.3 below).Footnote 14
In some cases, such as in structural representation, the causal powers of representational states may be more directly explained in terms of representations being computational structures that stand in structural mapping relations to representational contents. Here, computational structure captures a central part of what it is for physical states and processes to represent, helping explain how they generate appropriate behaviour by means of working as surrogates to their contents (Swoyer 1991; O’Brien 2015). Importantly, a robust mechanistic theory of computation allows us to individuate the causal powers of computational states and processes independently of a theory of representation, thus paving the way, without the threat of liberality or circularity, to basing the causal powers of representations on the causal powers of computations.
A robust, non-liberal theory of physical computation, such as the teleomechanistic view, can help constrain the states and processes in the world that are candidates for representational status. In a theory of representation that takes seriously the computational part of the representational-computational framework, only those states and processes that are part of computational mechanisms can count as representational. This narrowing down of candidates for representational status is something that only views that ascribe computations to an adequately limited set of physical systems can offer. Moreover, the account of computation must be non-semantic, on pain of being circular, or at least uninformative—if we individuate computation by means of representation, appeal to computation is idle in narrowing down the domain of the representational.
The teleomechanistic view is the only non-semantic account of computation that is constrained in this way. Causal mapping accounts, the most sophisticated alternative non-semantic view, lead (at least) to limited pancomputationalism. They fail to narrow down candidates for representational status by means of appeal to computation, since they ascribe computational nature to (almost) all systems. The teleomechanistic view, on the other hand, poses demanding constraints on candidate representational states and processes. Representational states and processes are a subset of computational states and processes, and given that the latter are relatively rare, the claim has considerable bite. Indeed, it excludes all those states and processes of the system that do not contribute to its computational capacities from being candidates for representational status.
In the case of biological cognitive systems, for instance, states and processes that involve glia and blood vessels are not representations, since as far as we know they do not play computational roles—even though they certainly play roles sustaining the states and processes that do play such roles, in partial analogy to what fans and batteries do in electronic computers. In a way true to the spirit of the computational-representational approach to the cognitive sciences, computation helps capture those properties of cognitive systems that are directly relevant to cognition and intelligent behaviour.Footnote 15
In brief, a robust, teleo-based theory of computation helps to delimit the set of physical vehicles that can play representational roles. This is a welcome addition to theories of representation insofar as it helps to avoid the danger of being overly liberal about representational status. This is particularly important for structural representation theories, since the representational relation they rely on, namely mapping relations of some sort, is overly unconstrained, risking to lead both to too many things being bestowed representational status, and to radical indeterminacy of content. Endorsement of the teleomechanistic view of computation contributes to avoiding these unwanted results in at least two ways.
First, it constrains considerably the kinds of physical vehicles that can stand in the content-constituting mapping relations that structural representation theories identify. The teleomechanistic view individuates a relatively small subset of physical systems that are computational, and only a subset thereof will acquire representational status: those computational states and processes that stand in the relevant mapping relation to worldly states of affairs. As should be clear, this is only part of the solution to liberality. For it to succeed, it also requires that the overall theory of representation be able to individuate to some precision the worldly states of affairs that are candidates for standing in the relevant mapping relations. Promising solutions to this issue have been put forward by Ramsey (2007) and Shea (2018), who appeal to organismic embeddedness and behavioural salience to help identify the candidate contents; and by Bielecka and Milkowski (2020), who appeal to error detection mechanisms. Together, teleomechanistic computation and additional requirements such as these further constrain the relevant content-fixing relation, attenuating the problems that structural representation theories have with liberality.
Second, narrowing down the candidate vehicles for cognitive representations indirectly contributes to meeting indeterminacy of content challenges. In light of the fact that the theory of computation individuates the internal computational states and processes of the cognitive system in ways independent from representational status, it helps constrain the kinds of content that can be ascribed to such states and processes. Given the computational profile of such states, how they interface with other computational states and sensory and motor surfaces, only some contents are compatible with the computational workings of the system, many others being implausible insofar as they clash with—or fit rather unnaturally—the computational profile of the vehicles. Admittedly, these constraints by themselves are insufficient to solve content indeterminacy problems, but they help, and given the difficulties involved, any help is welcome.
Objections to teleo-based representation do not apply
It is possible to do justice to the insight that representation is in some way tied to natural teleology, as per teleo-based views of representation, whilst avoiding having to view the connection as directly tied to content determination. Instead, teleological considerations may come into play exclusively in the individuation of physical computation, generating theories that are teleo-based, insofar as they depend on a teleo-based notion of computation, but that do not use teleology to determine content. This can be helpful for preserving a role for teleology in accounting for representation, while avoiding the main lines of objection against teleo-based views. As I show below, appealing to teleology to individuate computation is not liable to the central kinds of objections that have been raised against a similar appeal in individuating representation.
First, since computations are not individuated in terms of representation, the claim that representational contents are finer-grained than what natural teleology can provide is irrelevant. Second, computations, in contrast to representations, are individuated in a relatively coarse-grained way, so that, even assuming that teleology is a rather blunt tool with which to carve nature, no indeterminacy challenge analogous to the one that confronts representation affects computation. Let me expand on this point.
If computations are considered to be a matter of performing logical or mathematical functions, it may seem that similar indeterminacy problems appear. It is plausible that natural teleology cannot distinguish between performance of (some) equivalent logical or mathematical functions, since more than one such function maps equally well onto causal goings-on in physical systems. As Shagrir (2001) and Sprevak (2010) have shown, this is true of the basic logical functions performed by logic gates, the most basic computational components: AND-gates and OR-gates, in isolation, are functionally indistinguishable.
Even if this is true, it should not worry proponents of the teleomechanistic view of computation. For according to influential versions of the view, computations are input-output functions implemented by components of a mechanism. Input-output functions are understood extensionally, in terms of transformations of physical quantities (Dewhurst 2018b), or in terms of functional states and transitions revealed by the functional decomposition of the system (Coelho Mollo 2018; Fresco and Milkowski, forthcoming). Teleofunctions to perform physical or functional input-output functions do not pose indeterminacy worries, since if two such functions are physically/functionally equivalent, they count as the same function, regardless of whether they can be mapped onto different logical or mathematical functions.Footnote 16 Computations can therefore be individuated partly by teleology without this leading to a multiplicity of individuated computations.
In brief, the indeterminacy problems that follow from appealing to teleology in individuating representations do not transpose to individuating computation by analogous means. In contrast to the fine-grainedness of representational individuation—which must (often) distinguish between co-extensional but distinct contents—computational individuation is coarse-grained enough for teleology to carve the computational domain adequately.
Furthermore, physical and functional states and processes are the kinds of states to which selection processes are sensitive; selection processes being at the core of the most promising family of theories of natural teleology, namely selected-effects theories. There is thereby no potentially problematic gap between computational correctness and behavioural success: computational systems have the computational teleofunctions they do because they have been selected for, and they have been selected for because they contribute, often enough, to behavioural success. It seems extremely implausible to claim that there can be no biological functions to perform input-output functions of specific types, since this is arguably what most biological functions, including non-computational ones, consist in (e.g. taking food as input and generating energy as output).Footnote 17 In consequence, worries about a possible mismatch between computational and behavioural success, in the spirit of Burge’s (2010) objection to teleosemantics, do not affect teleo-based theories of computation.
The non-semantic individuation of the internal computational structure of cognitive systems that the teleomechanistic view offers also helps to address an additional, less discussed difficulty that confronts teleo-based views of representation. As Cao (2012) points out, teleosemantics seems ill-suited to capture the internal complexity involved in bringing about cognitive capacities. The intermediate states between sensory input and motor behaviour are, as cognitive psychology and neuroscience have shown, extremely complex both in terms of the number of contributing internal states, and of their interactions, which are marked by a plurality of dependence relations, feedback, excitatory and inhibitory connections, degeneracy, and redundancy. This makes it so that the causal nexus between initial representation producer—in most cases a sensory surface—and the final representation consumer —typically responsible for generating motor behaviour—is highly mediated.
Teleo-based theories of representation may pursue two basic strategies in trying to accommodate this point, none of which is satisfying. On the one hand, focusing only on the initial representation producer and the final representation consumer is unlikely to vindicate the explanatory purchase of representational explanation in cognitive science, as it fails to make sense of the role played by the intermediate vehicles in bringing about complex and intelligent behaviour. Breaking down cognitive systems into their functional and mechanistic components, and shedding light on their contributions to cognition and intelligence, is the main aim of cognitive science. A theory of representation that is silent about intermediate vehicles is thereby inadequate to the cognitive sciences.
On the other hand, recognising the complex organisation of internal producers and consumers that cognitive science reveals makes appeal to selection processes in determining the content of intermediate states implausible. Most of these states, taken singly, make very limited and unspecific contributions to overall behaviour. In consequence, ascription of content to these states cannot be of the externalist kind relevant to the cognitive sciences. Their representational contributions to any instance of behaviour—the representational functions they have been selected to perform—plausibly do not involve entities external to the organism, but rather the proximal states and processes that capture their direct, specific contributions to cognitive processing. These states would thus have as their contents something along the lines of ‘upstream cognitive subsystem in state x’ or ‘upstream neuron active’ (Cao 2012). However, such contents are unlikely to justify appeal to representational explanation, insofar as nothing seems to be lost in terms of explanatory power by shedding representational talk, and appealing rather to causal interactions between cognitive subsystems and/or between neural populations.
It may seen that a teleo-based notion of computation would share the same fate. Given that teleofunctions to compute are similarly dependent on selection processes, and since such processes care only about appropriate external behaviour, it seems that internal components cannot possess specific computational teleofunctions for reasons similar to the above.Footnote 18 Most of the intermediate computational states and processes in cognitive systems make very indirect and variable contributions to external behaviour. Since natural teleology relies on processes sensitive only to appropriate external behaviour, it becomes unclear how such intermediaries, and their computational functions, can be individuated by means of a teleo-based theory of computation.
Mechanistic and functional decomposition can come to the rescue. Although natural teleology, at first glance, can only bestow teleofunctions to compute functions that go from sensory input to motor output, functional and mechanistic decomposition help distribute computational responsibility across intermediate states. Selection processes settle on a complex algorithm or set of algorithms from sensory input to appropriate motor output. In light of the teleofunction to carry out such complex algorithms, there is a breakdown of the contributions that intermediate states and processes make to the overall algorithm. In this way, the theory bestows computational teleofunctions on mediating states and processes in light of the computational contributions they make to the performance of the complex algorithm it is the teleofunction of the whole system to compute. Do these intermediate teleofunctions help identify, in explanatorily powerful ways, the computational contributions that intermediate states and processes are supposed to make to cognition and intelligent behaviour?
In contrast to the case of representational individuation, the answer is positive. For unlike the case of representation, the teleofunctions that intermediate computational states and processes have—according to the teleomechanistic view of computation—are to compute simpler input-output functions that together make up the complex algorithm from sensory input to motor output performed by the whole system. The teleomechanistic view bestows on intermediate subsystems the teleofunction to manipulate in specific ways the inputs they receive—individuated non-semantically, in terms of functional or physical states—and generate outputs to be fed to downstream computational subsystems. These bestowals of computational function are explanatorily powerful. They help explain how the system comes to implement the complicated algorithm that leads from sensory input and internal states to behaviour by recognising the small, partial computational contributions that intermediate states and processes internal to the system make, and which together come to compose the overall function that the whole system computes. While in the case of teleo-based theories of representation, as Cao (2012) argues, we end up with content ascriptions that have little to no explanatory purchase, the teleomechanistic view provides computational ascriptions that play a distinctive explanatory role: they reveal the typically many-stepped, richly-branched algorithm that leads from sensory stimulus to motor behaviour.
The difficulties of teleo-based views of representation in doing justice to internal cognitive complexity are therefore not shared by the teleomechanistic view of computation. A theory of representation can exploit this feature in order to avoid the problem of internal complexity, in at least two mutually-exclusive ways.
First, by relying on the teleomechanistic view of computation, a theory of representation may make use of the non-semantically individuated intermediate computational states and processes, and the contributions they have the teleofunction to make to overall behaviour, to help distribute representational responsibility across the components of the cognitive system. A theory of representation may be able to pick out a subset of computationally-individuated states and processes that play representational roles by standing in exploitable (and exploited) relations to salient features of the environment—such as cognitive maps in entorhinal cortex (Moser et al. 2008).
Alternatively, theories of representation may reject representational ascription to intermediate states and processes, while keeping allegiance to intermediate computations and computational teleofunctions. On this view, the units on which representations are bestowed are whole organisms, or at least whole brains, as Cao (2012) suggests, since it is at this level of description that talk of external behaviour is justified, and thereby appeal to selection processes takes a direct hold. This view is strongly revisionary of mainstream cognitive science, since it rejects subpersonal representational vehicles, and thereby subpersonal representational explanation. However, by means of the teleomechanistic view of computation, it is sensitive to subpersonal explanation cashed out in purely computational terms, thus following mainstream cognitive science at least in its focus on the complex internal goings-on that lead to cognition and intelligent behaviour.
Since my aim in this paper is to open up paths, rather than to tread them, I remain neutral on which route is the most promising.
Misrepresentation and miscomputation
Finally, appealing to natural teleology in accounting for computation allows for a sort of weak normativity already when it comes to computational workings. This can be useful for a theory of representation insofar as it makes it possible to distinguish different factors that may lead to inappropriate behaviour. In particular, it opens the way for distinguishing cases in which misrepresentation depends on computational error, from cases in which misrepresentation takes place despite the fact that the system correctly follows its computational norms (i.e. fulfils its computational teleofunctions).
Applying this point to teleosemantics does not entail that separate selection processes need be responsible for the representational and for the computational teleofunctions of the system. It is compatible with the foregoing that the same kinds of selective pressure led to both the representational and computational organisation of the system—the former hinging on the latter. However, it does not follow that there cannot be dissociations, that is, cases in which there is misrepresentation but no miscomputation, and even, by a fluke, miscomputation without misrepresentation. For instance, if the environment is not the adequate one for the organism, the relations between internal states and world that are relevant for representational success will not obtain, leading to misrepresentation. But this is compatible with the internal computational processes performing the correct computations, since under the teleomechanistic view, the latter are non-semantic.
Moreover, attention to the role of (mis)computation in explaining behaviour can decrease the scope and weight of representational explanation, thereby helping to avoid overly liberal ascription of representational nature. For explanation of behavioural inadequacy or error may be cashed out in terms of pure miscomputation in cases in which the relevant computational states and processes lack representational status. This is important to keep in check the temptation to overextend representational explanation to cases, capacities, or systems for which the notion makes little to no explanatory contribution—thus risking to make appeal to representation lose its distinctive explanatory power, and with it, its theoretical justification.Footnote 19