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Computation and Computers in the Sciences of Mind and Brain
Abstract
Computationalism says that brains are computing mechanisms, that is, mechanisms that perform computations. At present, there is no consensus on how to formulate computationalism precisely or adjudicate the dispute between computationalism and its foes, or between different versions of computationalism. An important reason for the current impasse is the lack of a satisfactory philosophical account of computing mechanisms. The main goal of this dissertation is to offer such an account. I also believe that the history of computationalism sheds light on the current debate. By tracing different versions of computationalism to their common historical origin, we can see how the current divisions originated and understand their motivation. Reconstructing debates over computationalism in the context of their own intellectual history can contribute to philosophical progress on the relation between brains and computing mechanisms and help determine how brains and computing mechanisms are alike, and how they differ. Accordingly, my dissertation is divided into a historical part, which traces the early history of computationalism up to 1946, and a philosophical part, which offers an account of computing mechanisms. The two main ideas developed in this dissertation are that (1) computational states are to be identified functionally not semantically, and (2) computing mechanisms are to be studied by functional analysis. The resulting account of computing mechanism, which I call the functional account of computing mechanisms, can be used to identify computing mechanisms and the functions they compute. I use the functional account of computing mechanisms to taxonomize computing mechanisms based on their different computing power, and I use this taxonomy of computing mechanisms to taxonomize different versions of computationalism based on the functional properties that they ascribe to brains. By doing so, I begin to tease out empirically testable statements about the functional organization of the brain that different versions of computationalism are committed to. I submit that when computationalism is reformulated in the more explicit and precise way I propose, the disputes about computationalism can be adjudicated on the grounds of empirical evidence from neuroscience.
... Problémem tedy je, že neexistuje abstraktní model výpočtu a zobrazení mezi abstraktním výpočetním procesem a jeho fyzickou implementací. Můžeme se ptát, zda lze vůbec považovat evolucí vytvořená výpočetní zařízení za výpočetní mechanismy (computing mechanisms [10]) ve smyslu např. Turingova stroje. ...
... Piccinini definuje výpočetní mechanismus jako mechanismus, jehož účelem je získat výstupní řetězec symbolů ze vstupního řetězce symbolů na základě obecně platného pravidla, které platí pro všechny vstupy a výstupy [10]. Definuje šest požadavků, které musí fyzikální systém splňovat, aby mohl být výpočetním mechanismem -zejména nezávislý pozorovatel musí být schopen "počítání" identifikovat na základě studia systému. ...
... ANO: Pokud stačí, že pro zadané vstupy získáváme požadované výstupy dle interpretace, kterou jsme zavedli před tím, než byla spuštěna evoluce, potom je RZ výpočetním mechanismem. Pokud je RZ výpočetním mechanismem, potom funkce, které jsou vyčíslitelné pomocí RZ, jsou vyčíslitelné i na Turingově stroji [10]. ...
Fakulta informačních technologií, Vysoké učení technické v Brně Božetěchova 2, 612 66 Brno sekanina@fit.vutbr.cz Abstrakt Evoluční algoritmy je možné využít pro návrh a realizaci číslicových obvodů, a tedy i výpočetních systémů. Výpočetní systémy navržené a fyzicky realizované evolučními technikami však vykazují vlastnosti, jež nenajdeme v existujících výpočetních systémech, které jsou běžně navrhovány inženýry, ani v systémech živé přírody (jako je např. mozek), jejichž chování je také často interpretováno jako výpočet. Článek shrnuje výsledky experimentů, které ukazují, že evoluční algoritmy mohou pro realizaci požadovaného chování využít fyzikální vlastnosti cílové platformy a vlastnosti prostředí (vysoká teplota, radiace), ve kterém je platforma umístěna. Obecně není možné zjistit, jak a proč vyevolvované řešení funguje. Stávající teorie implementace, které řeší vztah mezi abstraktním a fyzickým počítáním, jsou potom zproblematizovány.
... Our brains are physical devices, products of nature. Their behavior is often interpreted as computing (see a thorough review in Piccinini 2003). In comparison with the ordinary computers, they differ in many features. ...
... It basically means that we are not able to see any relation between the internal physical processes and the required behavior which could be formulated via an abstract model. These devices are totally different from those devices and concepts studied in the context of theoretical computer science (Gruska 1997), computationalism (Piccinini 2003) and the so-called ''implementation problem'' (Copeland 1996;Piccinini 2003;Scheutz 1999). Similarly to other classes of computational devices (e.g., abstract computing machines or processors), we should address fundamental questions such as what computational power these devices possess, whether they can be universal and what is limiting for their complexity. ...
... It basically means that we are not able to see any relation between the internal physical processes and the required behavior which could be formulated via an abstract model. These devices are totally different from those devices and concepts studied in the context of theoretical computer science (Gruska 1997), computationalism (Piccinini 2003) and the so-called ''implementation problem'' (Copeland 1996;Piccinini 2003;Scheutz 1999). Similarly to other classes of computational devices (e.g., abstract computing machines or processors), we should address fundamental questions such as what computational power these devices possess, whether they can be universal and what is limiting for their complexity. ...
The evolutionary circuit design is an approach allowing engineers to realize computational devices. The evolved computational
devices represent a distinctive class of devices that exhibits a specific combination of properties, not visible and studied
in the scope of all computational devices up till now. Devices that belong to this class show the required behavior; however,
in general, we do not understand how and why they perform the required computation. The reason is that the evolution can utilize,
in addition to the “understandable composition of elementary components”, material-dependent constructions and properties
of environment (such as temperature, electromagnetic field etc.) and, furthermore, unknown physical behaviors to establish
the required functionality. Therefore, nothing is known about the mapping between an abstract computational model and its
physical implementation. The standard notion of computation and implementation developed in computer science as well as in
cognitive science has become very problematic with the existence of evolved computational devices. According to the common
understanding, the evolved devices cannot be classified as computing mechanisms.
... For an explicit defense of these formulations of functionalism and computationalism, and of their logical independence, seePiccinini (2003a), Ch. 8.2 For a detailed analysis of McCulloch and Pitts's theory, seePiccinini (forthcoming). For a more detailed reconstruction of the early history of computationalism, seePiccinini (2003a), Chs. ...
... For a more systematic list of multiple realizability arguments, none of which establishes the conclusion that the mind is computational, seePiccinini (2003a), Ch. 8. ...
... See, for example, Strogatz (1994).8 For a detailed defense of these statements, seePiccinini (2003a), Chs. 7 and 8. Cf. alsoCopeland (2000Copeland ( , 2002. ...
Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions.
... The position that the universe is a computer, that all processes are computation, is now often called "pancomputationalism," a term probably first used by [65] and [24, p. 566]. In its strongest form, pancomputationalism claims that the universe is literally a computer that computes the changes of the universe according to the physical laws; that all processes are computational processes. ...
The contribution of the body to cognition and control in natural and artificial agents is increasingly described as “off-loading computation from the brain to the body”, where the body is said to perform “morphological computation”. Our investigation of four characteristic cases of morphological computation in animals and robots shows that the ‘off-loading’ perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that facilitates control, (2) morphology that facilitates perception and the rare cases of (3) morphological computation proper, such as ‘reservoir computing.’ where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control – how it contributes to the overall ‘orchestration’ of intelligent behaviour.
... Ascribing computational powers to everything has been called pancomputationalism (Piccinini 2003, 2007, Floridi 2004, and early Putnam 1967 see Müller 2009). For what I have in mind, 'born-again computationalism' is probably a better term, which is the doctrine which says belief in computation brings out an explanation for everything. ...
The paper argues that a computational constraint is one that appeals to control of computational resources in a computationalist explanation. Such constraints may arise in a theory and in its models. Instrumental use of the same concept is trivial because the constraining behavior of any function eventually reduces to its computation. Computationalism is not instrumentalism. Born-again computationalism, which is an ardent form of pancomputationalism, may need some soul searching about whether a genuinely computational explanation is necessary or needed in every domain, because the resources in a computationalist explanation are limited. Computational resources are the potential targets of computational constraints. They are representability, time, space, and, possibly, randomness, assuming that ‘BPP = BQP?’ question remains open. The first three are epitomized by the Turing machine, and manifest themselves for example in complexity theories. Randomness may be a genuine resource in quantum computing. From this perspective, some purported computational constraints may be instrumental, and some supposedly noncomputational or cognitivist constraints may be computational. Examples for both cases are provided. If pancomputationalism has instrumentalism in mind, then it may be a truism, therefore not very interesting, but born-again computationalism cannot be computationalism as conceived here.
Almost no one cites Sellars, while reinventing his wheels with gratifying regularity. (Dennett 1987, 349)
In philosophy of mind, there is functionalism about mental states and functionalism about mental contents. The former — mental State functionalism — says that mental states are individuated by their functional relations with mental inputs, Outputs, and other mental states. The latter — usually called functional or conceptual or inferential role semantics — says that mental contents are constituted by their functional relations with mental inputs, Outputs, and other mental contents (and in some versions of the theory, with things in the environment). If we add to mental State functionalism the popular view that mental states have their content essentially, then mental state functionalism may be seen as a form of functional role semantics and a solution to the problem of mental content, namely, the problem of giving a naturalistic explanation of mental content. According to this solution, the functional relations that constitute contents are physically realized — in a metaphysically unmysterious way — by the functional relations between mental inputs, outputs, and the mental states bearing those contents.
Despite its significance in neuroscience and computation, McCulloch and Pitts's celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts's paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts's contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in computability theory), (ii) a technique that inspired the notion of logic design (a fundamental part of modern computer design), (iii) the first use of computation to address the mind–body problem, and (iv) the first modern computational theory of mind and brain.
In this tutorial, fundamental concepts of evolutionary circuit design and evolvable hardware will be introduced. In particular, we will deal with evolutionary algorithms and reconfigurable devices utilized for hardware evolution as well as with their interactions in typical applications of evolvable hardware. Traditionally, evolvable hardware is interpreted from the perspective of electrical engineering and computer engineering. However, existing results in the field of evolvable hardware have impacts on theoretical computer science. Hence the goal of this tutorial is also to present a non-traditional view on evolvable hardware reflecting these theoretical issues.
The Church–Turing Thesis (CTT) is often employed in arguments for computationalism. I scrutinize the most prominent of such
arguments in light of recent work on CTT and argue that they are unsound. Although CTT does nothing to support computationalism,
it is not irrelevant to it. By eliminating misunderstandings about the relationship between CTT and computationalism, we deepen
our appreciation of computationalism as an empirical hypothesis.
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