Nir Fresco

Explaining Computation Without Semantics: Keeping it Simple

This paper deals with the question: how is computation best individuated?1.The semantic view of computation: computation is best individuated by its semantic properties.2.The causal view of computation: computation is best individuated by its causal properties.3.The functional view of computation: computation is best individuated by its functional properties.Some scientific theories explain the capacities of brains by appealing to computations that they supposedly perform. The reason for that is usually that computation is individuated semantically. I criticize the reasons in support of this view and its presupposition of representation and semantics. Furthermore, I argue that the only justified appeal to a representational individuation of computation might be that it is partly individuated by implicitintrinsic representations.

The Explanatory Role of Computation in Cognitive Science

Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation.

A Revised Attack on Computational Ontology

There has been an ongoing conflict regarding whether reality is fundamentally digital or analogue. Recently, Floridi has argued that this dichotomy is misapplied. For any attempt to analyse noumenal reality independently of any level of abstraction at which the analysis is conducted is mistaken. In the pars destruens of this paper, we argue that Floridi does not establish that it is only levels of abstraction that are analogue or digital, rather than noumenal reality. In the pars construens of this paper, we reject a classification of noumenal reality as a deterministic discrete computational system. We show, based on considerations from classical physics, why a deterministic computational view of the universe faces problems (e.g., a reversible computational universe cannot be strictly deterministic).

Concrete Digital Computation: What Does it Take for a Physical System to Compute?

This paper deals with the question: what are the key requirements for a physical system to perform digital computation? Time and again cognitive scientists are quick to employ the notion of computation simpliciter when asserting basically that cognitive activities are computational. They employ this notion as if there was or is a consensus on just what it takes for a physical system to perform computation, and in particular digital computation. Some cognitive scientists in referring to digital computation simply adhere to Turing’s notion of computability. Classical computability theory studies what functions on the natural numbers are computable and what mathematical problems are undecidable. Whilst a mathematical formalism of computability may perform a methodological function of evaluating computational theories of certain cognitive capacities, concrete computation in physical systems seems to be required for explaining cognition as an embodied phenomenon. There are many non-equivalent accounts of digital computation in physical systems. I examine only a handful of those in this paper: (1) Turing’s account; (2) The triviality “account”; (3) Reconstructing Smith’s account of participatory computation; (4) TheAlgorithm Execution account. My goal in this paper is twofold. First, it is to identify and clarify some of the underlying key requirements mandated by these accounts. I argue that these differing requirements justify a demand that one commits to a particular account when employing the notion of computation in regard to physical systems. Second, it is to argue that despite the informative role that mathematical formalisms of computability may play in cognitive science, they do not specify the relationship between abstract and concrete computation.

Computation as Information Processing

It seems incontrovertible that information processing is fundamental to cognitive function. As already observed in Chapter 1, one explanatory strategy for a theory of cognition to take is to view some of the agent’s internal states and processes as carrying information about those relevant aspects of its body and external states of affairs in negotiating its environment (Bechtel, 1998, p. 297). Natural cognitive agents produce new information that is intended, amongst other things, to deal with a variety of environments. It is unsurprising then that semantic accounts of computation that underlie CTM view digital computing systems as engaging in information processing at the symbol level (cf. the FSM and PSS accounts).

An Analysis of the Criteria for Evaluating Adequate Theories of Computation

This paper deals with the question: What are the criteria that an adequate theory of computation has to meet? (1) Smith’s answer: it has to meet the empirical criterion (i.e. doing justice to computational practice), the conceptual criterion (i.e. explaining all the underlying concepts) and the cognitive criterion (i.e. providing solid grounds for computationalism). (2) Piccinini’s answer: it has to meet the objectivity criterion (i.e. identifying computation as a matter of fact), the explanation criterion (i.e. explaining the computer’s behaviour), the right things compute criterion, the miscomputation criterion (i.e. accounting for malfunctions), the taxonomy criterion (i.e. distinguishing between different classes of computers) and the empirical criterion. (3) Von Neumann’s answer: it has to meet the precision and reliability of computers criterion, the single error criterion (i.e. addressing the impacts of errors) and the distinction between analogue and digital computers criterion. (4) “Everything” computes answer: it has to meet the implementation theory criterion by properly explaining the notion of implementation.

A Critical Survey of Some Competing Accounts of Concrete Digital Computation

This paper deals with the question: what are the key requirements for a physical system to perform digital computation? Oftentimes, cognitive scientists are quick to employ the notion of computation simpliciter when asserting basically that cognitive activities are computational. They employ this notion as if there is a consensus on just what it takes for a physical system to compute. Some cognitive scientists in referring to digital computation simply adhere to Turing computability. But if cognition is indeed computational, then it is concrete computation that is required for explaining cognition as an embodied phenomenon. Three accounts of computation are examined here: 1. Formal Symbol Manipulation. 2. Physical Symbol Systems and 3.The Mechanistic account. I argue that the differing requirements implied by these accounts justify the demand that one commits to a particular account when employing the notion of digital computation in regard to physical systems, rather than use these accounts interchangeably.

Setting the Stage: Computation in Cognitive Science

What is computation? This question may seem, at a first glimpse, trivial and uninteresting. There has already been a long discussion about this subject back in the mid-twentieth century. The discussion produced many extensionally equivalent models of computation, such as Turing machines, lambda calculus, cellular automata, Post machines and recursive functions.

Semantic Accounts of Computation Examined

This chapter examines some accounts according to which external semantic content is inherent to the computational process, and so any computational explanation entails content ascription to the explanandum. The goal of these accounts is clear: by ascribing semantic content to digital computing systems proper, proponents of the semantic view of computation aim to explain cognition computationally.

Computation Revisited in the Context of Cognitive Science

In this final chapter, we conclude the book with four theses. First, in Section 8.1, we argue that in the course of nontrivial computation, typically, only implicit intrinsic and mathematical representations are processed. Then, in Section 8.2, we argue that any computational explanation of cognition is unintelligible without a commitment to a single interpretation of ‘digital computation’ as defined by a given account thereof. In Section 8.3, we argue that a blanket dismissal of the key role computation plays in cognitive science is unwarranted. We also argue that the thesis that computationalism, connectionism and dynamicism are mutually exclusive is wrong.