In this paper, the Author reviewed the typical objections against the claim that brains are computers, or, to be more precise, information-processing mechanisms. By showing that practically all the popular objections are based on uncharitable interpretations of the claim, he argues that the claim is likely to be true, relevant to contemporary cognitive science, and non-trivial.

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

In this paper, we address the question of what current computers are from the point of view of human-computer interaction. In the early days of computing, the Turing machine (TM) has been the cornerstone of the understanding of computers. The TM defines what can be computed and how computation can be carried out. However, in the last decades, computers have evolved and increasingly become interactive systems, reacting in real-time to external events in an ongoing loop. We argue that the TM (...) does not provide a mechanistic explanation for interactive computing. The reason is that the fundamental phenomena relevant to interactive computing are out of the scope of classical computability theory. Part of the explanatory power of the TM relies on what we propose to call an execution model. An execution model belongs to a level of abstraction where it is possible to describe both the functional architecture and the execution in mechanistic terms. An updated execution model is warranted to provide the minimal mechanistic description for interactive computation as a counterpart of what the TM could explain regarding Church-Turing computation. It would support an explanation of the ubiquitous computing devices we know - those interacting with humans, e.g., through digital interfaces. We show that such a model is not available within interactive models of computation and that relevant abstractions and concerns are available in computer engineering but need to be identified and gathered. To fill this void, we propose to reflect on the level of abstraction required to support the mechanistic description of an interactive execution and propose some preliminary requirements. (shrink)

John Searle raised the question whether all computation is observer-relative. Indeed, all of the common views of computation, be they semantical, functional or causal rely on mapping something onto the states of a physical or abstract process. In order to effectively execute such a mapping, this process would have to be observed in some way. Thus a probably syntactical analysis by an observer seems to be essential for judging whether a given process implements some computation or not. In order to (...) be able to explore the nature of these observers in a more formal way, we look at the Computing by Observing paradigm, a theoretical model of computation that includes an observer. We argue that the observers used there, monadic transducers, are good candidates for formalizing the way in which the syntax of a process must be analysed in order to judge whether it is computational. (shrink)

This paper argues that deciding on whether the cognitive sciences need a Representational Theory of Mind matters. Far from being merely semantic or inconsequential, the answer we give to the RTM-question makes a difference to how we conceive of minds. How we answer determines which theoretical framework the sciences of mind ought to embrace. The structure of this paper is as follows. Section 1 outlines Rowlands’s argument that the RTM-question is a bad question and that attempts to answer it, one (...) way or another, have neither practical nor theoretical import. Rowlands concludes this because, on his analysis, there is no non-arbitrary fact of the matter about which properties something must possess in order to qualify as a mental representation. By way of reply, we admit that Rowlands’s analysis succeeds in revealing why attempts to answer the RTM-question simpliciter are pointless. Nevertheless, we show that if specific formulations of the RTM-question are stipulated, then it is possible, conduct su... (shrink)

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. (shrink)

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. (shrink)

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). (shrink)

Varela, Thompson, and Rosch illustrated their original presentation of the enactive theory of cognition with the example of a simple cellular automaton. Their theory was paradigmatically anti-computational, and yet automata similar to the one that they describe have typically been used to illustrate theories of computation, and are usually treated as abstract computational systems. Their use of this example is therefore puzzling, especially as they do not seem to acknowledge the discrepancy. The solution to this tension lies in recognizing a (...) hidden background assumption, shared by both Varela, Thompson, and Rosch and the computational theories of mind which they were responding to. This assumption is that computation requires representation, and that computational states must bear representational content. For Varela, Thompson, and Rosch, representational content is incompatible with cognition, and so from their perspective the automaton that they describe cannot, despite appearances, be computational. However, there now exist several accounts of computation that do not make this assumption, and do not characterize computation in terms of representational content. In light of these recent developments, we will argue that it is quite straightforward to characterize the enactive automaton as a non-representational computing mechanism, one that we do not think they should have any objections to. (shrink)

According to Enactivism, cognition should be understood in terms of a dynamic interaction between an acting organism and its environment. Further, this view holds that organisms do not passively receive information from this environment, they rather selectively create this environment by engaging in interaction with the world. Radical Enactivism adds that basic cognition does so without entertaining representations and hence that representations are not an essential constituent of cognition. Some proponents think that getting rid of representations amounts to a revolutionary (...) alternative to standard views about cognition. To emphasize the impact, they claim that this ‘radicalization’ should be applied to all enactivist friendly views, including, another current and potentially revolutionary approach to cognition: predictive processing. In this paper, we will show that this is not the case. After introducing the problem, we will argue that ‘radicalizing’ predictive processing does not add any value to this approach. After this, we will analyze whether or not radical Enactivism can count as a revolution within cognitive science at all and conclude that it cannot. Finally, in section 5 we will claim that cognitive science is better off when embracing heterogeneity. (shrink)

This paper presents a novel theory of what it is that makes vision a representational affair. Vision is a process of representation; a fact that does not depend on it being "contentfull" or "indirect". Even if it turns out that vision is direct and/or intrinsically "contentless", it is nevertheless defined by features that decisively make it count as a process or representation. The phenomenology of vision is key here: as we see, we are directly presented with aspects of the environment (...) that are at various distances away from us. Through the process of vision, aspects of the environment that would otherwise still be unavailable or “absent”, are made (quasi-)available, or (quasi-)present. This already by itself makes vision deserving of the name ‘representational’, even if those distant aspects of the world are presented to us directly and contentless. In part I, I provide a bit of an overview of the relevant theoretical landscape, and I give my own take on how we may frame and approach some of the issues with respect to mental representation. Then, in part II, I argue that the representational features of vision must primarily be sought at the level of conscious experience. In part III, I elaborate on the two most common theories that are associated with the view that vision is ‘representational’, and proceed to show that there is in fact a third, more direct, way in which vision can be considered as essentially a process of representation. Finally, in part IV, I show that the kind of interaction problems that arise with the more traditional notions of representation also arise with this newer, more direct, notion. (shrink)

In this paper, I show how semantic factors constrain the understanding of the computational phenomena to be explained so that they help build better mechanistic models. In particular, understanding what cognitive systems may refer to is important in building better models of cognitive processes. For that purpose, a recent study of some phenomena in rats that are capable of ‘entertaining’ future paths (Pfeiffer and Foster 2013) is analyzed. The case shows that the mechanistic account of physical computation may be complemented (...) with semantic considerations, and in many cases, it actually should. (shrink)