The instructional information processing account of digital computation

Fresco, Nir; Wolf, Marty J.

doi:10.1007/s11229-013-0338-5

Cited by 10 publications

(11 citation statements)

References 24 publications

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“…Whatever the "correct" account of digital computation is (Copeland 1996;Fresco and Wolf 2014;Piccinini 2007;Turing 1936), we argue here that, at least to a large extent, computation is an objective phenomenon. Nontrivial computation in memory-based systems is an objective phenomenon, whereas the boundaries of objectivity in memoryless system computation are blurry as is shown below.…”

Section: Introductionmentioning

confidence: 77%

“…Trivial computational systems process data, which need not be structured, by way of exercising a single capacity. Nontrivial computational systems, on the other hand, process instructional information under the right conditions by way of being capable of systematically processing at least two distinct imperative instructions (Fresco and Wolf 2014).…”

Section: Nontrivial Versus Trivial Computationmentioning

confidence: 99%

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Objective Computation Versus Subjective Computation

Fresco

2014

Erkenn

Self Cite

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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 informationprocessing considerations determine what type of computation takes place in physical systems. Abstract. 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.

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Section: Introductionmentioning

confidence: 77%

Section: Nontrivial Versus Trivial Computationmentioning

confidence: 99%

Objective Computation Versus Subjective Computation

Fresco

2014

Erkenn

Self Cite

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“…Note: program-loaded computers process instructional information but it is not about anything outside the computational system; cf. [27]. In particular, the information in question may be merely structural (also called logon-information by MacKay, cf.…”

Section: Cognitive Mechanisms and Their Environmentmentioning

confidence: 99%

Situatedness and Embodiment of Computational Systems

Miłkowski

2017

Entropy

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Abstract:In this paper, the role of the environment and physical embodiment of computational systems for explanatory purposes will be analyzed. In particular, the focus will be on cognitive computational systems, understood in terms of mechanisms that manipulate semantic information. It will be argued that the role of the environment has long been appreciated, in particular in the work of Herbert A. Simon, which has inspired the mechanistic view on explanation. From Simon's perspective, the embodied view on cognition seems natural but it is nowhere near as critical as its proponents suggest. The only point of difference between Simon and embodied cognition is the significance of body-based off-line cognition; however, it will be argued that it is notoriously over-appreciated in the current debate. The new mechanistic view on explanation suggests that even if it is critical to situate a mechanism in its environment and study its physical composition, or realization, it is also stressed that not all detail counts, and that some bodily features of cognitive systems should be left out from explanations.

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“…Four main types of information in this context are given: syntactic Shannon information [30], algorithmic information [2], semantic information [1] (a case for using truth values rather than logical probability distributions is made in [11]) and finally instructional information [10,13].…”

Section: Previous Workmentioning

confidence: 99%

Probabilistic Computation and Emotion as Self-regulation

Haugwitz

Dodig-Crnković

2015

Proceedings of the 2015 European Conference on Software Architecture Workshops

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A treatment of emotion as a means of meta-optimisation in cognitive systems is presented, drawing upon research in neuroscience and reinforcement learning. In particular, emotion is motivated and explained against the background of the freeenergy principle and the Bayesian brain hypothesis, from the perspective of appraisal theory. Various implications of these models are examined in the context of reinforcement learning through a review of recent research. Based on the informationprocessing view of computation, a probabilistic approach to modelling computational systems is tentatively proposed in order to better handle the sort of probabilistic information processing involved in modelling cognition. By taking information gain to be the essential property of computation, it is suggested that a general computing system may be modelled as updates of parameters defining probability distributions.

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The instructional information processing account of digital computation

Abstract: The instructional information processing account of digital computation The instructional information processing account of digital computation

Cited by 10 publications

References 24 publications

Objective Computation Versus Subjective Computation

Objective Computation Versus Subjective Computation

Situatedness and Embodiment of Computational Systems

Probabilistic Computation and Emotion as Self-regulation

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