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Automaticity is still ill-understood, and its relation to habit formation and skill acquisition is highly debated. Recently, the principle of caching has been advanced as a potentially promising avenue for studying automaticity. It is roughly understood as a means of storing direct input-output associations in a manner that supports instant lookup. We raise various concerns that should be addressed before the theoretical progress afforded by this principle can be evaluated. Is caching merely a metaphor for computer caching or is it a computational model that can be used to derive testable predictions? How do the short-term and long-term effects of automaticity relate to the distinction between working memory and long-term memory? Does caching apply to stimulus-response associations - as already suggested by Logan's instance theory - or to algorithms, too? How much practice is required for caching and how does caching depend on the task's type? What is the relation between control processes and caching as these pertain to the possible suppression of automatic processes? Dealing with these questions will arguably also advance our understanding of automaticity.

It is often indeterminate what function a given computational system computes. This phenomenon has been referred to as “computational indeterminacy” or “multiplicity of computations.” In this paper, we argue that what has typically been considered and referred to as the (unique) challenge of computational indeterminacy in fact subsumes two distinct phenomena, which are typically bundled together and should be teased apart. One kind of indeterminacy concerns a functional (or formal) characterization of the system’s relevant behavior (briefly: how its physical states are grouped together and corresponded to abstract states). Another kind concerns the manner in which the abstract (or computational) states are interpreted (briefly: what function the system computes). We discuss the similarities and differences between the two kinds of computational indeterminacy, their implications for certain accounts of “computational individuation” in the literature, and their relevance to different levels of description within the computational system. We also examine the inter-relationships between our proposed accounts of the two kinds of indeterminacy and the main accounts of “computational implementation.”

A single physical process may often be described equally well as computing several different mathematical functions—none of which is explanatorily privileged. How, then, should the computational identity of a physical system be determined? Some computational mechanists hold that computation is individuated only by either narrow physical or functional properties. Even if some individuative role is attributed to environmental factors, it is rather limited. The computational semanticist holds that computation is individuated, at least in part, by semantic properties. She claims that the mechanistic account lacks the resources to individuate the computations performed by some systems, thereby leaving interesting cases of computational indeterminacy unaddressed. This article examines some of these views, and claims that more cases of computational indeterminacy can be addressed, if the system-environment interaction plays a greater role in individuating computations. A new, long-arm functional strategy for individuating computation is advanced.

Computational physical systems may exhibit indeterminacy of computation (IC). Their identified physical dynamics may not suffice to select a unique computational profile. We consider this phenomenon from the point of view of cognitive science and examine how computational profiles of cognitive systems are identified and justified in practice, in the light of IC. To that end, we look at the literature on the underdetermination of theory by evidence and argue that the same devices that can be successfully employed to confirm physical hypotheses can also be used to rationally single out computational profiles, notwithstanding IC.

Do the dynamics of a physical system determine what function the system computes? Except in special cases, the answer is no: it is often indeterminate what function a given physical system computes. Accordingly, care should be taken when the question ‘What does a particular neuronal system do?’ is answered by hypothesising that the system computes a particular function. The phenomenon of the indeterminacy of computation has important implications for the development of computational explanations of biological systems. Additionally, the phenomenon lends some support to the idea that a single neuronal structure may perform multiple cognitive functions, each subserved by a different computation. We provide an overarching conceptual framework in order to further the philosophical debate on the nature of computational indeterminacy and computational explanation.

The idea that the brain is an information processing system raises some challenging questions about whether information exists independently of brains. Answering these questions is relevant for clarifying the theoretical foundations of the sciences of mind and brain, but also for appropriately interpreting and evaluating the evidence about how brains—and other biological systems—work. This article claims that (1) informational descriptions in the sciences of mind and brain can be genuinely explanatory, despite assuming a mind-dependent notion of information; and (2) that either Popperian objectivity or response-dependence in normal receivers is sufficient for grounding the explanatory role that information often plays in these sciences. Hierarchical predictive processing, which has become a central theoretical framework for neurocognitive research, is used as a case study for supporting these two claims.

Is the mathematical function being computed by a given physical system determined by the system’s dynamics? This question is at the heart of the indeterminacy of computation phenomenon (Fresco et al. [unpublished]). A paradigmatic example is a conventional electrical AND-gate that is often said to compute conjunction, but it can just as well be used to compute disjunction. Despite the pervasiveness of this phenomenon in physical computational systems, it has been discussed in the philosophical literature only indirectly, mostly with reference to the debate over realism about physical computation and computationalism. A welcome exception is Dewhurst’s ([2018]) recent analysis of computational individuation under the mechanistic framework. He rejects the idea of appealing to semantic properties for determining the computational identity of a physical system. But Dewhurst seems to be too quick to pay the price of giving up the notion of computational equivalence. We aim to show that the mechanist need not pay this price. The mechanistic framework can, in principle, preserve the idea of computational equivalence even between two different enough kinds of physical systems, say, electrical and hydraulic ones.

Knowledge-how is the kind of knowledge implicated in skill employment and acquisition. Intellectualists claim that knowledge-how is a special type of propositional knowledge. Anti-intellectualists claim that knowledge-how is not propositional. We argue that both views face two open challenges. The first challenge pertains to the relationship between informational states and motor variability. The second pertains to the epistemic function of practice that leads to skill (and knowledge-how). The aim of this paper is to suggest a general conceptual framework based on functional information with both intellectualist and anti-intellectualist features. Our proposal, we argue, avoids the above challenges, and can further the debate on knowledge-how and skill.

Does information exist in the world independently of brains and/or organisms that can interpret it? Answering this question is important for clarifying the theoretical foundations of the sciences of mind and brain. This chapter claims that Popperian objectivity—properly modified—is sufficient for the explanatory role that information often plays in these sciences without endorsing a problematic mind-independent notion of information. There exists, however, a tension between two Popperian views about objectivity: intersubjective agreement and World 3. In order to ground the objectivity of information—as it is used in the sciences of mind and brain, the relation between Popperian intersubjectivity and World 3 objectivity is examined in this chapter. The conclusion is that mind-dependent information can be objective when it is intersubjectively agreed upon, or, to put it differently, when “actualised” information in World 3 stands in the right relation to World 1.

There are many different notions of information in logic, epistemology, psychology, biology and cognitive science, which are employed differently in each discipline, often with little overlap. Since our interest here is in biological processes and organisms, we develop a taxonomy of functional information that extends the standard cue/signal distinction (in animal communication theory). Three general, main claims are advanced here. (1) This new taxonomy can be useful in describing learning and communication. (2) It avoids some problems that the natural/non-natural information distinction faces. (3) Functional information is​ ​produced through exploration and stabilisation processes.

In this article we establish three claims: 1 When the target software is proprietary, in the absence of other overriding ethical considerations, the identification of a vulnerability and the development, sale, and purchase of non-zero-day exploits are ethically justified; 2 when the target software is Free/Libre/Open Source, the buying and selling of vulnerabilities can be ethically justified only in a very narrow situation, while the sale and purchase of non-zero-day exploits is ethically justified absent of any other overriding information; and 3 democratic governments should promote legislation that either incentivizes corporate in-house vulnerability identification and mitigation programs or requires firms to more fully absorb the societal costs of insecure software.

Floridi’s Theory of Strongly Semantic Information posits the Veridicality Thesis (i.e., information is true). One motivation is that it can serve as a foundation for information-based epistemology being an alternative to the tripartite theory of knowledge. However, the Veridicality thesis is false, if ‘information’ is to play an explanatory role in human cognition. Another motivation is avoiding the so-called Bar-Hillel/Carnap paradox (i.e., any contradiction is maximally informative). But this paradox only seems paradoxical, if (a) ‘information’ and ‘informativeness’ are synonymous, (b) logic is a theory of inference, or (c) validity suffices for rational inference; a, b, and c are false.

The question ‘What is computation?’ might seem a trivial one to many, but this is far from being in consensus in philosophy of mind, cognitive science and even in physics. The lack of consensus leads to some interesting, yet contentious, claims, such as that cognition or even the universe is computational. Some have argued, though, that computation is a subjective phenomenon: whether or not a physical system is computational, and if so, which computation it performs, is entirely a matter of an observer choosing to view it as such. According to one view, which we dub bold anti-realist pancomputationalism, every physical object (can be said to) computes every computer program. According to another, more modest view, some computational systems can be ascribed multiple computational descriptions. We argue that the first view is misguided, and that the second view need not entail observer-relativity of computation. At least to a large extent, computation is an objective phenomenon. Construed as a form of information processing, we argue that information-processing considerations determine what type of computation takes place in physical systems.

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).

What is nontrivial digital computation? It is the processing of discrete data through discrete state transitions in accordance with finite instructional information. The motivation for our account is that many previous attempts to answer this question are inadequate, and also that this account accords with the common intuition that digital computation is a type of information processing. We use the notion of reachability in a graph to defend this characterization in memory-based systems and underscore the importance of instructional information for digital computation. We argue that our account evaluates positively against adequacy criteria for accounts of computation.

The phenomenon of digital computation is explained (often differently) in computer science, computer engineering and more broadly in cognitive science. Although the semantics and implications of malfunctions have received attention in the philosophy of biology and philosophy of technology, errors in computational systems remain of interest only to computer science. Miscomputation has not gotten the philosophical attention it deserves. Our paper fills this gap by offering a taxonomy of miscomputations. This taxonomy is underpinned by a conceptual analysis of the design and implementation of conventional computational systems at various levels of abstraction. It shows that ‘malfunction’ as it is typically used in the philosophy of artefacts only represents one type of miscomputation.

3D printing is a process of producing solid objects of various shapes (e.g., spare plastic parts for cars) from a digital model by adding successive layers of material. More recently, 3D bioprinting technology has been used for producing living tissues and organs. 3D bioprinting provides another avenue to analyze the increasingly informational nature of physical objects and the ethical challenges it brings. It uses both specific information provided by the “digital model” and the instructional information of its printing program. While bioprinting holds promise to alleviate shortages of certain biological tissues, in this paper we begin to address ethical challenges that arise, in particular, from the possible avenues of exploiting this information and questions about ownership and quality of as well as accessibility to this information. Further, we suggest that 3D bioprinting brings some urgency to addressing philosophical questions about personal identity.

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.

It is common in cognitive science to equate computation (and in particular digital computation) with information processing. Yet, it is hard to find a comprehensive explicit account of concrete digital computation in information processing terms. An information processing account seems like a natural candidate to explain digital computation. But when ‘information’ comes under scrutiny, this account becomes a less obvious candidate. Four interpretations of information are examined here as the basis for an information processing account of digital computation, namely Shannon information, algorithmic information, factual information and instructional information. I argue that any plausible account of concrete computation has to be capable of explaining at least the three key algorithmic notions of input, output and procedures. Whist algorithmic information fares better than Shannon information, the most plausible candidate for an information processing account is instructional information.

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) The Algorithm 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.

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. The causal view of computation: computation is best individuated by its causal properties. 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 implicit intrinsic representations.

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.

Artefacts do not always do what they are supposed to do, due to a variety of reasons, including manufacturing problems, poor maintenance, and normal wear-and-tear. Since software is an artefact, it should be subject to malfunctioning in the same sense in which other artefacts can malfunction. Yet, whether software is on a par with other artefacts when it comes to malfunctioning crucially depends on the abstraction used in the analysis. We distinguish between “negative” and “positive” notions of malfunction. A negative malfunction, or dysfunction, occurs when an artefact token either does not (sometimes) or cannot (ever) do what it is supposed to do. A positive malfunction, or misfunction, occurs when an artefact token may do what is supposed to do but, at least occasionally, it also yields some unintended and undesirable effects. We argue that software, understood as type, may misfunction in some limited sense, but cannot dysfunction. Accordingly, one should distinguish software from other technical artefacts, in view of their design that makes dysfunction impossible for the former, while possible for the latter.

Piccinini defends a mechanistic account of physical computation intended to distinguish computing from non-computing systems (for both artificial and natural systems). This account—drawing on the m...

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.

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).

In the present chapter, we examine the views of two well-known and influential philosophers concerning the ubiquitous character of concrete computation. They both share a common objective, namely, the falsification of the computationalist thesis that cognition is computational. But in order to accomplish this objective, they in effect undermine the demarcation of digital computation as a subject matter. For, by their lights, there is no fact of the matter about what digital computation is. On their view, every physical object implements any number of programs and, hence, computes every Turing-computable function.

In this chapter, we explore the possible criteria for evaluating the adequacy of candidate accounts of concrete digital computation. According to proponents of CTM, cognition is computational. Before we can judge the plausibility of any particular CTM, we need to understand what notion of computation this theory employs. Although there are extant accounts of computation, any of which may, in principle, serve as a basis for CTM, it is not clear that they are all equivalent or even adequate as accounts of computation proper. By proposing a plausible alternative to Smith’s adequacy criteria, our goal here is to resist his discouraging claim that no adequate account of computation proper is possible.

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.

Computability as an abstract notion, underlying any physical digital computation, was the focus of intense interest during the 1930s by mathematicians such as Godel (1931), Turing (1936), Church (1936) and Post (1936). According to Turing’s analysis, digital computation is the process of effectively calculating the values of a function by some purely mechanical procedure executed by a TM. He argued that computation is reducible to carrying out a calculation by following instructions.

In this chapter, we examine three fairly recent accounts of computation with at least one common characteristic, namely, that they do not posit any extrinsic representational properties. These accounts imply that concrete digital computing systems can be individuated by either their causal properties or their functional/organisational properties.

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.

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.

Wolf and White address different aspects of the paper and in this present reply space only permits making two brief remarks. Starting with White’s intriguing observation: digital computation without erasing information is possible. This clearly has important implications for real-world digital computing systems, since they can be designed and constructed in a more energy efficient manner, if information erasure operations are kept to a minimum. Yet, avoiding information erasure operations completely in the course of computation is another matter. At least prima facie, this introduces difficulties relating to memory storage. Information is conveyed by data and (particularly persistent) data in computing systems occupy space. However, computation that only adds new information without erasing any one will inevitably require infinite memory space. Whilst idealised Turing machines have this capacity by definition, real-world computing systems do not. The second remark is about control information in digital computing systems. White omits the fifth requirement from the list of requirements, which an information processing system has to meet to qualify as a computing system. However, it is the capacity of the physical system to actualise control information that shoulders much of the burden in terms of distinguishing nontrivial digital computing systems from trivial ones. This requirement is the main focus of Wolf’s commentary. Unlike factual information, instructional information is fully compatible with control information. Questions, requests and commands (the building blocks needed for an adequate explanation of concrete digital computation) are examples of control information, since their information processing function involves making something happen. Interestingly, whilst discrete connectionist networks lack any controller unit, they still actualise control information. Typically, the pattern of activating of a set of units depends on the previous activation pattern (in some other possibly overlapping set of units), the weighted connections between the two sets and the units’ threshold functions for combining these weighted influences. Therefore, discrete connectionist