Information processing, computation, and cognition.

Abstract

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both - although others disagree vehemently. Yet different cognitive scientists use 'computation' and 'information processing' to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism, connectionism, and computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates' empirical aspects.

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Information Processing, Computation, and Cognition


Gualtiero Piccinini
Department of Philosophy, Center for Neurodynamics, and Department of Psychology
University of Missouri – St. Louis
St. Louis, MO, USA
Email: piccininig@umsl.edu

Andrea Scarantino
Department of Philosophy and Neuroscience Institute
Georgia State University
Atlanta, GA, USA
Email: ascarantino@gsu.edu

This is a preprint of an article whose final and definitive form will be published in Journal of
Biological Physics; Journal of Biological Physics is available online at:
http://www.springerlink.com/content/102921/

Abstract:
Computation and information processing are among the most fundamental notions in cognitive
science. They are also among the most imprecisely discussed. Many cognitive scientists take it
for granted that cognition involves computation, information processing, or both – although
others disagree vehemently. Yet different cognitive scientists use ‘computation’ and
‘information processing’ to mean different things, sometimes without realizing that they do. In
addition, computation and information processing are surrounded by several myths; first and
foremost, that they are the same thing. In this paper, we address this unsatisfactory state of
affairs by presenting a general and theory-neutral account of computation and information
processing. We also apply our framework by analyzing the relations between computation and
information processing on one hand and classicism and connectionism/computational
neuroscience on the other. We defend the relevance to cognitive science of both computation,
at least in a generic sense, and information processing, in three important senses of the term.
Our account advances several foundational debates in cognitive science by untangling some of
their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way
for the future resolution of the debates’ empirical aspects.

Keywords: classicism, cognitivism, computation, computational neuroscience, computational
theory of mind, computationalism, connectionism, information processing, meaning, neural
computation, representation.

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1. Information Processing, Computation, and the Foundations of Cognitive
Science
Computation and information processing are among the most fundamental notions in cognitive
science. Many cognitive scientists take it for granted that cognition involves computation,
information processing, or both. Many others, however, reject theories of cognition based on
either computation or information processing [1-7]. This debate has continued for over half a
century without resolution.
An equally long-standing debate pitches classical theories of cognitive architecture [8-
13] against connectionist and neurocomputational theories [14-21]. Classical theories draw a
strong analogy between cognitive systems and digital computers. The term ‘connectionism’ is
primarily used for neural network models of cognitive phenomena constrained solely by
behavioral (as opposed to neurophysiological) data. By contrast, the term ‘computational
neuroscience’ is primarily used for neural network models constrained by neurophysiological
and possibly also behavioral data. We are interested not so much in the distinction between
connectionism and computational neuroscience as in what they have in common: the
explanation of cognition in terms of neural networks and their apparent contrast with classical
theories. Thus, for present purposes connectionism and computational neuroscience may be
considered together.
For brevity’s sake, we will refer to these debates on the role information processing,
computation, and neural networks should play in a theory of cognition as the foundational
debates.
In recent years, some cognitive scientists have attempted to get around the
foundational debates by advocating a pluralism of perspectives [22-23]. According to this kind
of pluralism, it is a matter of perspective whether the brain computes, processes information, is
a classical system, or is a connectionist system. Different perspectives serve different purposes
and different purposes are legitimate. Hence, all sides of the foundational debates can be
retained if appropriately qualified.
Although pluralists are correct to point out that different descriptions of the same
phenomenon can in principle complement one another, this kind of perspectival pluralism is
flawed in one important respect. There is an extent to which different parties in the
foundational debates offer alternative explanations of the same phenomena—they can’t all be
right. Nevertheless, these pluralists are responding to something true and important: the
foundational debates are not merely empirical; they cannot be resolved solely by collecting
more data because they hinge on how we construe the relevant concepts. The way to make
progress is therefore not to accept all views at once, but to provide a clear and adequate
conceptual framework that remains neutral between different theories. Once such a
framework is in place, competing explanations can be translated into a shared language and
evaluated on empirical grounds.
Lack of conceptual housecleaning has led to the emergence of a number of myths that
stand in the way of theoretical progress. Not everyone subscribes to all of the following
assumptions, but each is widespread and influential:

(1) Computation is the same as information processing.

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(2) Semantic information is necessarily true.
(3) Computation requires representation.
(4) The Church-Turing thesis entails that cognition is computation.
(5) Everything is computational.
(6) Connectionist and classical theories of cognitive architecture are mutually exclusive.

We will argue that these assumptions are mistaken and distort our understanding of
computation, information processing, and cognitive architecture.
Traditional accounts of what it takes for a physical system to perform a computation or
process information [19, 24-26] are inadequate because they are based on at least some of (1)-
(6). In lieu of these traditional accounts, we will present a general account of computation and
information processing that systematizes, refines, and extends our previous work [27-37].
We will then apply our framework by analyzing the relations between computation and
information processing on one hand and classicism and connectionism/computational
neuroscience on the other. We will defend the relevance to cognitive science of both
computation, at least in a generic sense we will articulate, and information processing, in three
important senses of the term. We will also argue that the choice among theories of cognitive
architecture is not between classicism and connectionism/computational neuroscience, but
rather between varieties of neural computation, which may be classical or non-classical.
Our account advances the foundational debates by untangling some of their conceptual
knots in a theory-neutral way. By leveling the playing field, we pave the way for the future
resolution of the debates’ empirical aspects.
2. Getting Rid of Some Myths
The notions of computation and information processing are often used interchangeably. Here
is a representative example: “I … describe the principles of operation of the human mind,
considered as an information-processing, or computational, system” *38, p. 10, emphasis
added]. This statement presupposes assumption (1) above. Why are the two notions used
interchangeably so often, without a second thought?
We suspect the historical reason for this conflation goes back to the cybernetic
movement’s effort to blend Shannon’s information theory [39] with Turing’s [40] computability
theory (as well as control theory). Cyberneticians did not clearly distinguish either between
Shannon information and semantic information or between semantic and non-semantic
computation (more on these distinctions below). But at least initially, they were fairly clear
that information and computation played distinct roles within their theories. Their idea was
that organisms and automata contain control mechanisms: information is transmitted within
the system and between system and environment, and control is exerted by means of digital
computation [41, 42].
Then the waters got muddier. When the cybernetic movement became influential in
psychology, AI, and neuroscience, ‘computation’ and ‘information’ became ubiquitous
buzzwords. Many people accepted that computation and information processing belong
together in a theory of cognition. After that, many stopped paying attention to the differences
between the two. To set the record straight and make some progress, we must get clearer on

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the independent roles computation and information processing can fulfill in a theory of
cognition.
The notion of digital computation was imported from computability theory into
neuroscience and psychology primarily for two reasons: first, it seemed to provide the right
mathematics for modeling neural activity [43]; second, it inherited mathematical tools
(algorithms, computer program, formal languages, logical formalisms, and their derivatives,
including many types of neural networks) that appeared to capture some aspects of cognition.
These reasons are not sufficient to actually establish that cognition is digital computation.
Whether cognition is digital computation is a difficult question, which lies outside the scope of
this essay.
The theory that cognition is computation became so popular that it progressively led to
a stretching of the operative notion of computation. In many quarters, especially
neuroscientific ones, the term ‘computation’ is used, more or less, for whatever internal
processes explain cognition. Unlike ‘digital computation,’ which stands for a mathematical
apparatus in search of applications, ‘neural computation’ is a label in search of a theory. Of
course, the theory is quite well developed by now, as witnessed by the explosion of work in
computational and theoretical neuroscience over the last decades [20, 44-45]. The point is that
such a theory need not rely on a previously existing and independently defined notion of
computation, such as ‘digital computation’ or even ‘analog computation’ in its most
straightforward sense.
By contrast, the various notions of information (processing) have distinct roles to play.
By and large, they serve to make sense of how organisms keep track of their environments and
produce behaviors accordingly. Shannon’s notion of information can serve to address
quantitative problems of efficiency of communication in the presence of noise, including
communication between the external (distal) environment and the nervous system. Other
notions of information—specifically, semantic information—can serve to give specific semantic
content to particular states or events. This may include cognitive or neural events that reliably
correlate with events occurring in the organism’s distal environment as well as mental
representations, words, and the thoughts and sentences they constitute.
Whether cognitive or neural events fulfill all or any of the job descriptions of
computation and information processing is in part an empirical question and in part a
conceptual one. It’s a conceptual question insofar as we can mean different things by
‘information’ and ‘computation’, and insofar as there are conceptual relations between the
various notions. It’s an empirical question insofar as, once we fix the meanings of
‘computation’ and ‘information’, the extent to which computation and the processing of
information are both instantiated in the brain depends on the empirical facts of the matter.
Ok, but do these distinctions really matter? Why should a cognitive theorist care about
the differences between computation and information processing? The main theoretical
advantage of keeping them separate is to appreciate the independent contributions they can
make to a theory of cognition. Conversely, the main cost of conflating computation and
information processing is that the resulting mongrel concept may be too messy and vague to do
all the jobs that are required of it. As a result, it becomes difficult to reach consensus on
whether cognition involves either computation or information processing.

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assumptions (2)-(6).
Assumption (2) is that semantic information is necessarily true; there is no such thing as
false information. This “veridicality thesis” is defended by most theorists of semantic
information [46-49]. But as we shall point out in Section 4, (2) is inconsistent with one
important use of the term ‘information’ in cognitive science *37+. Therefore, we will reject (2)
in favor of the view that semantic information may be either true or false.
Assumption (3) is that there is no computation without representation. Most accounts
of computation rely on this assumption [19, 24-26, 38]. As one of us has argued extensively
elsewhere [27, 28, 50], however, assumption (3) obscures the core notion of computation used
in computer science and computability theory—the same notion that inspired the
computational theory of cognition—as well as some important distinctions between notions of
computation. The core notion of computation does not require representation, although it is
compatible with it. In other words, computational states in the core sense may or may not be
representations. Understanding computation in its own terms, independently of
representation, will allow us to sharpen the debates over the computational theory of cognition
as well as cognitive architecture.
Assumption (4) is that cognition is computation because of the Church-Turing thesis [51,
52]. The Church-Turing thesis says that any function that is computable in an intuitive sense is
recursive or, equivalently, computable by some Turing machine [40, 53, 54].1 Since Turing
machines and other equivalent formalisms are the foundation of the mathematical theory of
computation, many authors either assume or attempt to argue that all computations are
covered by the results established by Turing and other computability theorists. But recent
scholarship has shown this view to be fallacious [29, 55]. The Church-Turing thesis does not
establish whether a function is computable. It only says that if a function is computable in a
certain intuitive sense, then it is computable by some Turing machine. Furthermore, the
intuitive sense in question has to do with what can be computed by following an algorithm (a
list of explicit instructions) defined over sequences of digital entities. Thus, the Church-Turing
thesis applies directly only to algorithmic digital computation. The relationship between
algorithmic digital computation and digital computation simpliciter, let alone other kinds of
computation, is quite complex, and the Church-Turing thesis does not settle it.
Assumption (5) is pancomputationalism: everything is computational. There are two
ways to defend (5). Some authors argue that everything is computational because describing
something as computational is just one way of interpreting it, and everything can be
interpreted that way [19, 23]. We reject this interpretational pancomputationalism because it
conflates computational modeling with computational explanation. The computational theory
of cognition is not limited to the claim that cognition can be described (modeled)
computationally, as the weather can; it adds that cognitive phenomena have a computational
explanation [28, 31, 34]. Other authors defend (5) by arguing that the universe as a whole is at
Debates on computation, information processing, and cognition are further muddied by

1 The Church-Turing thesis properly so called—i.e., the thesis supported by Church, Turing, and Kleene’s
arguments—is sometimes confused with the Physical Church-Turing thesis. The Physical Church-Turing thesis [58]
lies outside the scope of this paper. Suffice it to say that the Physical Church-Turing thesis is controversial and in
any case does not entail that cognition is computation in a sense that is relevant to cognitive science [29].