Computing and modelling: Analog vs. Analogue

Papayannopoulos, Philippos

doi:10.1016/j.shpsa.2020.05.001

Cited by 22 publications

(5 citation statements)

References 21 publications

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“…Two views are on offer: the continuous conception and the mirroring conception (Beck, 2015). As the name suggests, the first takes analog representation to be essentially (and necessarily) continuous (Haugeland, 1981; Papayannopoulos, 2020). Because analog computers operate on analog representations, this is just the received view of analog computation.…”

Section: Analog Computation In a Nutshellmentioning

confidence: 99%

Computation for cognitive science: Analog versus digital

Maley

2024

WIRES Cognitive Science

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Cognitive science was founded on the idea that the mind/brain can be understood in computational terms. While computational modeling in science is ubiquitous, cognitive science takes the stronger stance that the mind/brain literally performs computations. Moreover, performing computations is crucial to explaining what the mind/brain does, qua mind/brain. Unfortunately, most scientists fail to consider analog computation as a legitimate and theoretically useful type of computation in addition to digital computation; to the extent that analog computation is acknowledged, it is mostly based on a simplistic and incomplete understanding. Taking computation to consist of only one type (i.e., digital) while ignoring another, interestingly distinct type (i.e., analog) leads to an impoverished understanding of what it could mean for minds/brains to compute. A full appreciation and understanding of analog computation—particularly in relation to digital computation—allows researchers to develop computational frameworks and hypotheses in new and exciting ways. Thus, somewhat counterintuitively, looking to the once‐dominant computing paradigm of yesteryear can provide novel computational ways of thinking about the mind and brain.This article is categorized under: Philosophy > Foundations of Cognitive Science

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Section: Analog Computation In a Nutshellmentioning

confidence: 99%

Computation for cognitive science: Analog versus digital

Maley

2024

WIRES Cognitive Science

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“…1 References to analog representation occur in the philosophy of science (Papayannopoulos 2020, Sterrett 2017, Trenholme 1994, philosophy of art (Goodman 1968, Lopes 2018, Thomson-Jones 2015, epistemology (Dretske 1981), philosophy of perception (Beck 2019, Block forthcoming, Peacocke 1986, cognitive science (Dehaene 1997, Feigenson et al 2002, Shagrir 2010), computer science (Vergis et al 1986, Siegelmann & Fishman 1998, Xue 2016, and electrical engineering (Couch 2013). 2 The terms 'iconic', 'pictorial', and 'imagistic' are sometimes used instead of 'analog' and the terms 'digital' and 'discursive' are sometimes used instead of 'symbolic'.…”

Section: Introductionmentioning

confidence: 99%

The structure of analog representation

Lee

Myers

Rabin

2022

Nous

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This paper develops a theory of analog representation. We first argue that the mark of the analog is to be found in the nature of a representational system's interpretation function, rather than in its vehicles or contents alone. We then develop the rulebound structure theory of analog representation, according to which analog systems are those marked by the use of interpretive rules that map syntactic structural features onto semantic structural features. The theory involves three degree-theoretic measures that capture three independent ways in which a system can be more or less analog. We explain how our theory improves upon prior accounts of analog representation, provides plausible diagnoses for novel challenge cases, extends to hybrid systems that are partially analog and partially symbolic, and accounts for some of the advantages and disadvantages of representing analogically versus symbolically.

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“…PALABRAS CLAVE: analógico, representación, Principio de Partes, morfismos, reflejo 1 . Introduction Analog representation and computation play an important role in recent discussions in the philosophy of computation (Isaac 2018;Papayannopoulos 2020;Maley 2023) and in the philosophy of cognitive science, particularly discussions of perception and core cognition (Carey 2009;Beck 2019;Clarke 2022). Nevertheless, basic questions about the nature of analog representation have not yet been answered, including what, if anything, unifies the various types of paradigmatic analog representations.…”

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confidence: 99%

Icons, Magnitudes, and Their Parts

Maley

2023

CRHF

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Analog representations come in different types. One distinction is between those representations that have parts that are themselves representations and those that do not (i.e., those for which the Parts Principle is true and those for which it is not). I offer a unified account of analog representation, showing what all types have in common. This account clarifies when the Parts Principle applies and when it does not, thereby illuminating why the Parts Principle is less interesting than one might have thought. Understanding analog representation instead requires understanding the kinds of magnitudes used in a particular representation, and the kinds of variation possible within a representational scheme.

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Computing and modelling: Analog vs. Analogue

Cited by 22 publications

References 21 publications

Computation for cognitive science: Analog versus digital

Computation for cognitive science: Analog versus digital

The structure of analog representation

Icons, Magnitudes, and Their Parts

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