Table Of ContentStudies in Applied Philosophy,
Epistemology and Rational Ethics
Nir Fresco
Physical
Computation and
Cognitive Science
Studies in Applied Philosophy, Epistemology
and Rational Ethics
Volume 12
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Nir Fresco
Physical Computation
and Cognitive Science
ABC
NirFresco
SchoolofHumanities&Languages,
UniversityofNewSouthWales
Sydney
Australia
ISSN2192-6255 ISSN2192-6263 (electronic)
ISBN978-3-642-41374-2 ISBN978-3-642-41375-9 (eBook)
DOI10.1007/978-3-642-41375-9
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To my beloved wife, Calanit, and my lovely daughters, Leah & Noa
Foreword
Nir Fresco tackles the nontrivial question of “What is computation?”. His
thorough review includes explication and critique of accounts of computation
ranging from Turing's, as is embodied by the Turing machine, to more recent
accounts from the computational theory of mind. He offers his own account of
concrete digital computation that is based on a critical analysis of the role
information plays in computation and how differing notions of information fare in
the context of computation. His two-pronged approach will appeal to
philosophically inclined computer scientists who want to understand better
common theoretical claims in cognitive science. This book provides a thorough
and timely analysis of differing accounts of computation while advancing the
important role that information plays in understanding computation.
--Marty J. Wolf, Professor of Computer Science,
Bemidji State University
Preface
It is my great pleasure to write this preface for Dr. Nir Fresco’s book “Physical
Computation and Cognitive Science” based on his doctoral thesis “Concrete Digital
Computation: Competing Accounts and its Role in Cognitive Science” of which I
was the supervisor. Nir was enrolled in Philosophy at UNSW as a part-time PhD
student while working full-time as a computing professional, a very reflective one.
Not only did he submit his thesis early he also found time to publish six papers
related to it on the way through. A truly remarkable achievement.
Nir’s main project in this book is to clarify the nature of computation, but he
has a special interest in its explanatory role in cognitive science and understanding
cognition. Computation is an ambiguous concept and computer scientists,
philosophers and cognitive scientists who use the concept can contest some claim
using it and not realise they are not actually in disagreement with each other, even
though it looks as though they are. This cuts both ways: sometimes they talk past
each other and think they are in agreement when they are not. A practical aim of
this book is to help with this problem by clarifying the nature of computation.
In very brief compass but sufficient to point to the substantial scope of this
volume, here is a selective description of its eight chapters.
The first sets the stage and discusses computation in cognitive science and a
distinction between three kinds of representation in computation: intrinsic,
mathematical and extrinsic. The central question to be answered is what does it
take for a system to perform physical digital computation and some important
distinctions start the discussion here.
Chapters 2 through to 7 form the heart of the book’s answer to this question.
Chapter 2, propaedeutic in nature, discusses adequacy criteria for evaluating
accounts of computation. The criteria proposed by five influential theorists are
considered: Brian Cantwell Smith, Gualtiero Piccinini, John von Neumann, David
Chalmers and Matthias Scheutz. Three of Smith’s criteria are discussed, six of
Piccinini’s and three of von Neumann’s as well as the single criterion proposed by
Chalmers and Scheutz. There is some overlap among these and Nir argues
cogently for six criteria broadly in agreement with Piccinini.
These six criteria concern what an adequate account should explain and
classify. They are, using Nir’s terminology, the following: the conceptual,
implementation, dichotomy, miscomputation, taxonomy and program execution
criteria. The first requires being able to explain basic concepts in computation
such as that of a computer program, algorithm and computer architecture. The
X Preface
second concerns the implementation by physical systems of abstract computation.
The third requires the accurate classification of paradigmatic computing systems
distinguishing them from non-computing systems. The fourth and fifth require
accounting for miscomputation and appropriately classifying computational
systems of differing powers, respectively. The sixth requires an account to
explain the relation between program execution and concrete digital computation.
Adequacy criteria having been chosen, we are in a position to see how well
competing accounts of computation measure up. Chapter 3, as Nir says in its title,
starts at the beginning and examines Alan Turing’s account. Unsurprisingly at this
stage, but informatively his account does not do well. Turing was answering a
related but different question.
Chapter 4 may be seen as a kind of interlude in the proceedings, not really
discussing a serious account of computation. Titled, ‘The Triviality “Account”
Examined’, it discusses attempts by two philosophers John Searle and Hilary
Putnam to undermine the computational view of cognition by arguing in effect
that every physical object (of sufficient everyday complexity) computes,
trivialising the notion of what it is to compute. The exercise of arguing against this
view is instructive.
The next three chapters deal successively with semantic accounts of
computation in Chapter 5, information processing accounts in Chapter 6 and more
heterogeneously causal and functional accounts in Chapter 7.
None of the three semantic accounts in Chapter 5 passes muster as accounts of
concrete computation. On these accounts of computation, representation is the
central notion and, more specifically, extrinsic (roughly, external world)
representation. These semantic accounts are the physical symbol system account
(PSS), the formal symbol manipulation account (FSM) and a reconstruction of
B.C. Smith’s participatory account. FSM has been the most influential of these
accounts especially in cognitive science and the critique here should contribute to
a useful rethink.
Two accounts do better, one each from chapters 6 and 7. In Chapter 6 the
Instructional Information Processing account (IIP) does well as does the
Mechanistic account in Chapter 7. Neither of these appeals to extrinsic
representation for the explanation of computation.
Chapter 6 on computation as information processing explores what this
common view could mean. Nir distinguishes four kinds of information: two non-
semantic kinds (Shannon and algorithmic) and two semantic kinds (declarative
and instructional). He argues that only an instructional information processing
account, a view he introduces and develops here, can satisfy the adequacy criteria.
Chapter 7 focuses on three fairly recent non-semantic accounts of concrete digital
computation. The first two of these are primarily causal and the last causal and
functional. The first, called the Gandy-Sieg account, synthesises the older views of
Robin Gandy and the more recent work of Wilfried Sieg. Concentrating on machine
computation this view requires the system’s operation to be describable as a
sequence of state transitions and has an emphasis on physical requirements for
computation and, hence, (on some interpretations) implementation.
Preface XI
The second of these accounts is the algorithm execution account. As the name
suggests this view ties computation to acting in accordance with an algorithm.
There is an older view of Robert Cummins that is significantly refined by Jack
Copeland so as not to succumb to trivialisation (as in Chapter 3). To this end
Copeland puts constraints on implementation, requiring a specification of the
basic architecture of the system and an algorithm specific to the primitive
operations of the architecture as well as the possibility of a labelling of the
system’s parts subject to special conditions.
The third view is Piccinini’s mechanistic account. This non-semantic not
exclusively causal view focuses on the functional/organisational features of the
physical computing system. A central notion is that of a ‘digit’, a stable discrete
state of a component of the system whose type can be reliably distinguished by it.
Concatenations of digits are called ‘strings’ and can be either data or rules
depending on their functional role in the system. On this view, a digital computing
system is a mechanism with the function of mapping input strings (paired perhaps
with internal states) into output strings according to general rules.
This quick review of these important accounts omits the instructive discussions
of the way they fail or pass the six criteria of adequacy.
The subtlety of thought on show in the first seven chapters bears further fruit in
the last when applied, among other things, to the role of computation in cognitive
science. Chapter 8 is titled ‘Computation Revisited in the Context of Cognitive
Science’ and covers a lot of ground. Two early positions defended in the first two
sections are firstly that nontrivial computations typically only process implicit
intrinsic and mathematical representations, not extrinsic ones and secondly that
computational explanations of cognition will be unintelligible unless there is
commitment to a single interpretation of the key phrase ‘digital computation’. The
largest third section on the explanatory role of computation in cognitive science
discusses the explanatory frameworks of computationalism, connectionism and
dynamicism arguing that they are not mutually exclusive and opening the way for
their integration. This section also discusses computational neuroscience, neural
computation and the nature of mechanistic and non-mechanistic explanations. The
chapter closes with some general remarks on computation and cognition.
A final remark about this book and its provenance and author. In the second
half of 2013 Nir was awarded the international Goldberg Memorial Prize. This
prize, awarded by the International Association for Computing and Philosophy, is
for outstanding graduate research. As noted above, Nir already had 6 publications
written during his PhD candidacy. Ideas from all of these contributed to his
doctorate and provided some of the rationale for this prestigious award. They will
find a wider audience here.
Dr. Phillip Staines
Philosophy Discipline
School of Humanities and Languages
University of New South Wales
Abstract
There are currently considerable confusion and disarray about just how we should
view computationalism, connectionism and dynamicism as explanatory
frameworks in cognitive science. A key source of this ongoing conflict among the
central paradigms in cognitive science is an equivocation on the notion of
computation simpliciter. ‘Computation’ is construed differently by
computationalism, connectionism, dynamicism and computational neuroscience. I
claim that these central paradigms, properly understood, can contribute to an
integrative cognitive science. Yet, before this claim can be defended, a better
understanding of ‘computation’ is required.
‘Digital computation’ is an ambiguous concept. It is not just the classical
dichotomy between analogue and digital computation that is the basis for the
equivocation on ‘computation’ simpliciter in cognitive science, but also the
diversity of extant accounts of digital computation. What does it take for a
physical system to perform digital computation? There are many answers to this
question ranging from Turing machine computation, through the formal
manipulation of symbols, the execution of algorithms and others, to strong
pancomputationalism, according to which every physical system computes every
Turing-computable function. Despite some overlap among them, extant accounts
of concrete digital computation are intensionally and extensionally non-
equivalent, thereby rendering ‘digital computation’ ambiguous.
The objective of this book is twofold. First, it is to promote a clearer
understanding of concrete digital computation. Accordingly, the main underlying
thesis of the book is that not only are extant accounts of concrete digital
computation non-equivalent, but most of them are inadequate. In the course of
examining several key accounts of concrete digital computation, I also propose the
instructional information processing account, according to which nontrivial digital
computation is the processing of discrete data in accordance with finite
instructional information. The second objective is to establish the foundational
role of computation in cognitive science whilst rejecting the extrinsically
representational nature of computation proper.
Keywords: Cognitive Science, Computability, Computationalism, Concrete
Computation, Connectionism, Data, Digital Computation, Information, Turing
Machine.
