The Formats of Cognitive Representation: A Computational Account
Forthcoming in «Philosophy of Science» please, cite published version
Abstract
Cognitive representations are typically analysed in terms of content, vehicle and format.
While current work on formats appeals to intuitions about external representations, such as
words and maps, in this paper we develop a computational view of formats that does not rely
on intuitions. In our view, formats are individuated by the computational profiles of vehicles,
i.e., the set of constraints that fix the computational transformations vehicles can undergo.
The resulting picture is strongly pluralistic, it makes space for a variety of different formats,
and is intimately tied to the computational approach to cognition in cognitive science and
artificial intelligence.
Author 1:1
Author 2:
Alfredo Vernazzani
Dimitri Coelho Mollo
Institut für Philosophie II
Department for Historical, Philosophical,
Ruhr-Universität Bochum
and Religious Studies
alfredo-vernazzani@daad-alumni.de
Umeå University
https://orcid.org/0000-0002-6458-8478
dimitri.mollo@umu.se
https://orcid.org/0000-0002-0464-3535
1
The authors contributed equally.
1
Acknowledgments: We are indebted to audiences at the Morning Talks series of the Science
of Intelligence Cluster, Spring 2020, Berlin, Germany; at the Weekly Online Chats, Summer
2020, of the Department of Philosophy and Religion, Mississippi State University; at the
Higher Seminar in Philosophy 2022, Umeå, Sweden; at Albert Newen’s Research
Colloquium at the Ruhr-Universität Bochum, and at the Neuromechanisms Online
Workshop 2022. Alfredo Vernazzani would like to thank the German Research Foundation
DFG which supported this research in the context of funding the Research Training Group
“Situated Cognition” (Project number GRK 2185/2).
2
1. Introduction
Representation is a central, and arguably foundational notion in mainstream cognitive
science and artificial intelligence (Burge 2010; Cummins 1989; Neander 2017; Shea 2018).
Appealing to representations internal to biological and artificial systems provides us with
tools to help explain the relational nature of cognition and intelligence: to be cognitive and
intelligent is to behave in such a way as to protect and further the system’s own interests,
satisfying its needs, preserving its existence (and occasionally that of its group) in
interaction with a complex, changing, and often hostile environment. The defining
characteristic of representations is their aboutness, that is to say, the fact that
representations are about something other than themselves. A map can be about the spatial
layout of a region, a sentence can be about the current weather there. Similarly, internal
representations are states and processes within biological and artificial systems that are
about states, processes, and events beyond themselves, typically in the body and the
environment of the system. What representations are about or refer to are their contents
(Shea 2018, 6)2.
While representations are primarily characterised by their contents—a representation of the
location of my office, a representation of Ursula von der Leyen’s face—representations
2
Traditionally it has been preferred to take a non-referential view of content, individuating
contents as conditions of satisfaction instead, which in turn pick out referents. This
difference will not matter for our purposes.
3
can also be characterised in other terms, typically for somewhat different explanatory
purposes. We may be interested in what kinds of physical states and processes carry, or
possess, representational contents. And, perhaps less obviously, we might be interested,
roughly put, in the shape or format a representation takes: is it a map, a photo, a sentence?
In this paper, we will be interested in the latter feature of representations. What are
representational formats? What are they good for? We will investigate such questions
within cognitive science and artificial intelligence research. Our exclusive focus will be on
the representational states and processes going on in brain areas, layers in artificial neural
networks, and the like, which are at the centre of the explanatory and modelling
endeavours in those fields.
We will advance an account of representational formats, which main aim is that of
capturing the epistemic roles that the notion plays, or can play, in the relevant areas of
science and engineering by appeal to the notion of physical computation, i.e., computation
in physical systems (rather than in mathematical theory). Computational views of
representational formats have a long history (Sloman 1978, Larkin and Simon 1987, Fodor
1975). However, such views were often left relatively underdeveloped and/or focused
exclusively on specific kinds of format, with the linguistic/iconic distinction drawing most
4
of the attention (Fodor 2008; Sloman 1978). The latter distinction is still among the most
discussed (Quilty-Dunn 2019; Quilty-Dunn et al. 2022).3
This is unfortunate for at least two reasons. First, extant accounts of formats, including the
ones inspired by the computational approach, have typically taken for granted intuitive
views about formats modelled on external, public representations, such as words, pictures,
and maps. It is debatable, to say the least, that categories applicable to public, external
representations can or should be applied to capturing the goings-on in cognitive and
computational systems. The focus on intuitive distinctions—such as linguistic/pictorial,
analogue/digital—that have marked the literature are a symptom of this (typically implicit)
assumption. Second, and relatedly, an account of representational formats should be
general, and thus able to capture all the formats that are relevant to cognitive (and
computational) processing, rather than being tailored only to account for a subset of
formats.
In this paper, we will try and free our understanding of representational formats from its
intuitive chains. We will do so by developing a computational view of formats that takes as
3
The terminology in the debate is rather confusing. The iconic format is sometimes also
called “depictive” (Kosslyn, Thompson and Ganis 2006), “image-like,” “picture-like” or
“analog” (Quilty-Dunn 2019; Beck 2018; Maley 2011; Paivio 1986; but see Clarke 2019 for
a distinction between iconic and analogue). The discursive or symbolic format is also called
“language-like” (Paivio 1986), “Fregean” (Sloman 1978), or “propositional” (Pylyshyn
1973).
5
its starting point the explanatory needs of the cognitive sciences, rather than common
intuitions. As a consequence, the resulting account yields formats ill-fitted to the categories
traditionally employed in the literature, while positing varieties of representational formats
that have no analogue in external representations. The standard of success for a theory of
representational formats for cognitive science is the epistemic value it has in informing and
guiding research, and not the extent to which the resulting formats fit our pre-theoretic
expectations. The second part of the paper will thus be dedicated to illustrating the
epistemic value of the resulting computational theory of representational formats.
Here is how we will proceed. After presenting our distinctive perspective on the question
of representational formats in 2.1, we will briefly go through the main extant families of
views about their nature, making clear where our own view belongs (sect. 2.2). In 2.3 we
will set out the central explanatory roles played by representational formats in the
cognitive sciences, which, together with broader philosophical considerations, make up a
set of desiderata for any account of representational formats for those fields. We present
and defend the computational view of formats in section 3, while section 4 is dedicated to
illustrating the account by applying it to two case studies: one from neuroscience (the place
cell system), and one from computational modelling (episodic memory recall). Finally, in
section 5 we show that the computational view fulfils the desiderata on theories of
representational formats in the cognitive sciences.
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2. Representational Formats: Nature and Roles
2.1. Three Notions of Representational Format
Coloured pieces of paper, binary code stored in a memory drive, and patterns of neural
activation in the brain can all carry representational content: they can all be
representations, say, of von der Leyen’s face. As carriers of content, these internal states
and processes are called representational vehicles.
Importantly, vehicles are individuated not purely in terms of their physical properties, but
rather in terms of those physical properties to which an interpreter or system is sensitive. In
a paper map, the vehicles are printed shapes and colours, not the type of paper used; in an
electronic computer, the vehicles are ultimately voltage ranges that code for 1s and 0s
during specific time intervals, irrespective of the continuous values voltages take; in a
brain, the vehicles are most likely some aspect(s) of neural activity, such as firing rates, but
not neurons’ colour or smell. Often, different vehicles can carry the same content, thus
representing the same thing; and different things can be represented by the same vehicles.
Qualifying the last sentence with an ‘often’ may seem intuitive enough. It seems
implausible, or at least very doubtful, that a photo of von der Leyen has the very same
content of a verbal description of her facial features. And even if they do, they seem to
represent in very different ways. They also seem to be more appropriate for different uses:
a photo will be better than a verbal description for recognising von der Leyen in a crowd,
while a verbal description will be better if we are interested in a specific, less noticeable
feature.
7
It is not always clear how best to try and accommodate these considerations, especially
when it comes to examples that rely on intuitions about external, public representations
such as photos, words, and maps. One common attempt is to rely on the notion of
representational format to shed light on those and related differences between
representations (Beck 2018; Clarke 2019; Fodor 2007, 2008; Quilty-Dunn 2019). Photos
and verbal descriptions, intuition suggests, belong to different formats, insofar as they
represent different contents, and/or to represent them in different ways. Similar
considerations apply to cognitive science and artificial intelligence (AI) research. Some
kinds of internal representations may have different constraints on what they can and
cannot represent, and/or on how well or efficiently they can represent what they do.
The general shape that an account of representational formats must take plausibly differs
between different domains of application, such as cognitive science and AI on the one
hand, and external, public representations on the other. Even within the former domain, it
is likely that there are differences in terms of epistemic needs and tools when it comes to
the states that the cognitive sciences discover and investigate, and the states that populate
our folk psychology. Failing to keep these two domains separate risks generating
considerable confusion and unclarity.4
4
One way to cash this out is in terms of the personal vs sub-personal distinction. As a
reviewer helpfully pointed out, the distinction can be spelled out in different ways (Drayson
2014). In the remainder of this paper, we shall mainly discuss paradigmatic cases of subpersonal states for ease of exposition, yet our challenge to the mainstream approach to
8
Given their importantly different features, it is to be expected that the expression
‘representational format’ captures fundamentally different constructs in the two domains.
Indeed, an account of the formats of external, public representations is highly likely to
hinge, in complicated ways, on social practices and conventions for the production and
consumption of representations, as well as on individuals’ goals, intentions, and
interpretative abilities. Moreover, in light of the tight connection between social practices
of communication and interpretation, and the posits of folk psychology, it is likely that the
notion of representation format relevant for folk psychological explanations is closer to the
foregoing than it is to that central to the states and processes cognitive science and AI
focus on.
An account of representational formats suitable to cognitive science and AI can rely on
none of the factors mentioned above, on pain of pernicious circularity. For, in these
sciences, the notion of format at play is much more basic, furnishing part of the
representational story that endows systems with the very capacities to engage in social
practices and conventions, to entertain intentions, to interpret, to form goals, and so on.
We will thereby remain silent in what follows on how to account for the representational
formats of public representations, as well as those in folk psychology. The computational
view of formats we propose is designed to capture solely the notion useful for the scientific
study and engineering of cognitive states. Thereby, the standards by which it is to be
formats applies equally to personal-level states. Our account does not depend on the
adequacy or else of the sub-personal/personal distinction.
9
assessed derive from the epistemic value of appeal to formats within those scientific
endeavours.
2.2. Three Approaches to Formats
There are several ways of carving the space of existing theories of representational format.
A popular way of doing so is in terms of the number and kinds of formats that different
views commit to. Some theories recognise only one kind of format (Pylyshyn 1973), some
recognise two (Fodor 2008; Paivio 1986), others more, but not many more (Haugeland
1991). The most commonly mentioned are symbolic, discursive, iconic, analogue, discrete,
and distributed formats. Depending on how each account individuates formats, some of the
terms in that list may be considered to be synonymous (e.g., discrete and symbolic).
Existing theories of representational formats can be grouped into three broad categories,
depending on what conceptual component of the notion of representation they take to be
central to individuating formats: contents, vehicles, or the function from the former to the
latter (Lee et al. 2022).
Some views take representational formats to be tied essentially to the kinds of content a
representation can possess (Haugeland 1991; Peacocke 2019). On such views,
representations are in different formats insofar as they represent different kinds of contents.
In Peacocke’s (2019) content-based view, representations in analogue format are those that
represent magnitudes, i.e., that have magnitudes as their contents. According to
Haugeland’s (1991) picture, there are at least three kinds of formats, individuated by the
kinds of content they represent: logical or discursive representations, which represent
10
‘absolute elements’ (i.e., contents that stand by themselves independently of relations to
other elements); iconic representations, which represent ‘relative elements’ (i.e., contents
intrinsically tied to relations to other contents); and distributed representations, which
represent ‘associative elements’ (i.e., contents associated by similarity or by stimulusresponse patterns).
A more common family of views takes formats to depend on the properties of
representational vehicles (Beck 2019). For instance, if a representational system (only)
employs representational vehicles that come in discrete types, such as the digits/voltage
ranges in digital computers, then that system has a discrete format. If, on the other hand, it
employs vehicles typed in terms of continuous variation across one or more dimensions, as
in a mercury thermometer, the system has an analogue format.
The third family of views, the function-based account, is often conflated with the former
two, and especially with the vehicle-based one. This account has it that representational
formats are individuated by the function that maps vehicles into contents (Lee et al. 2022).
A view along these lines might, for example, identify a type of format in terms of vehicles
structurally resembling, or mirroring, their contents (Beck 2019).
The debate is still open as to which of these approaches, if any, is most adequate.
Challenges have been moved against all of them, typically taking the shape of examples in
which they seem to yield counterintuitive results, such as categorising a format as analogue
that actually seems to be digital (Shimojima 2001). Often such disputes are evaluated in
terms of intuitions about public representations, as in pictures and sentences, or about the
11
nature of our conscious states, such as in perception and thought. We will not delve into
those discussions.
Our purposes here are exclusively constructive, namely to detail and defend a version of
the vehicle-based approach motivated and shaped by the computationalist framework in
mainstream cognitive science, and aimed at producing a notion of representational format
that can be useful and fruitful for cognitive science and artificial intelligence research.
Accordingly, our standards of evaluation for accounts of representational formats rely not
on intuitive judgements about specific cases, but rather on the potential of such accounts to
capture the epistemic roles and needs of cognitive science and AI, and to point toward
fruitful avenues of research. This distinguishes our proposal from most other approaches to
formats—including vehicle-based ones—given the latter’s reliance on intuitions, rather
than on explanatory needs; and their failure to keep apart folk-psychological considerations
from those most relevant to the cognitive sciences, which, as we have pointed out above,
are likely to involve rather different factors and constraints.
We must therefore look more closely at the roles that representational formats play and can
play in the explanatory practices of the cognitive sciences, in order to shed light on the
nature of representational formats in biological and artificial cognitive systems. In other
words, why are representational formats important for explaining cognition and
intelligence? Why aren’t contents and vehicles enough?
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2.3. The Role of Formats
There is widespread agreement that representational formats play key explanatory roles in
the cognitive sciences. Both early (Sloman 1978, 1994, 2002; Larkin and Simon 1987) and
more recent proponents (Fodor 2008) of computation-based approaches to formats have
often characterised formats in analogy to public representations. Sloman (1978, 144-76)
discusses Fregean (discursive) and analogical representational structures (or ‘symbolism’
in his jargon), such as pictures and diagrams. Fodor (2008, 171-73) distinguishes between
discursive representations—modelled on sentences in natural languages—and icons,
understood as akin to pictures (see also Quilty-Dunn 2019; Quilty-Dunn et al. 2022).
Analogies to public representations provide an intuitive grasp on why some explanations
need appeal to formats. As noted earlier, we use different kinds of external representation
depending on what we want to achieve with them: a city map is a more immediate and
flexible means to convey information about spatial layout than a series of sentences.
Let us examine in detail an example of this sort of analogical appeal to formats in science,
more specifically in animal cognition research, discussed by Camp (2009). Some species
of baboon live in troops of varying size, sometimes comprising several dozen members, in
which there are separate hierarchies of dominance-subalternity relations for males and
females. There are dominance-subalternity relations between females belonging to
different families, forming a hierarchy of high-status, mid-status, and low-status families.
Within families, there is also a hierarchy dictated by age, with younger mature females
having higher status than older sisters (with some complications; see Lea et al. 2014).
13
Female baboons are very capable of navigating this complex, two-tiered hierarchy,
behaving according to their ranks across and within families, and both when they are
directly involved in a dispute, or only a kin member is. They also seem to show surprise
when experimenters play calls associated with encounters in which lower-ranking females
challenge higher-ranking ones (Cheney and Seyfarth 2007).
The behaviour of female baboons indicates that they can represent single individuals,
relations of dominance between individuals and families, as well as occasional changes in
the hierarchy. We can safely assume that the representational vehicles are certain features
of neuronal activity in the baboon nervous system. More must be said, however. How are
those contents represented, such that appropriate behaviour is produced, for instance when
there are changes in the dominance relations that call for prompt adaptation to a partially
different social environment?
Some degree of discreteness seems to be required, such that each individual can
independently come to occupy a different place in the represented social hierarchy.
Similarly, some degree of combinatoriality is needed, such that changed social status
changes an individual’s represented dominance-subalternity relations to other individuals
and families. Finally, and more tentatively, it might be expected that the relevant
representations of social hierarchy be in some sense holistic, in the sense that when an
individual’s represented place in the hierarchy changes, all of its represented relations to
other members of the group change in one go, as it were.
14
In light of considerations along these lines, Camp (2009) hypothesises that the format that
the social hierarchy representational system takes in those female baboons is somewhat
akin to that of a tree diagram, similar to the genealogical trees that some humans are quite
keen on cobbling together. Indeed, tree diagrams can represent individuals and their
hierarchical relations, they have combinatorial properties, and when an individual’s
position in the tree changes, their relations to all other individuals change automatically, as
it were.5 (Compare: if such representations were somewhat similar to linguistic
representations, then for each change in dominance relation, a large number of single
representations would have to be updated—X is now higher in the hierarchy than Y; X is
now higher than Z, etc.—which is arguably inefficient and cognitively taxing).
Another explanatory virtue of appealing to tree diagrams or similar formats in this case
study is that it helps explain not only what female baboons can do, but also what they
cannot. If we were to ascribe language-like representations to baboons, insofar as they are
also discrete and combinatorial, we would be left with the puzzle of why they can use such
5
In a similar vein, Boyle (2019) suggests that mindreading in apes may be underlain in
some cases by another format yet, namely map-like representations (see also Camp 2007).
15
a powerful and flexible representational system to represent social hierarchy, while their
behaviour in other tasks indicates less powerful representational capabilities.6
Putting to one side whether it is appropriate to frame the discussion in terms of analogies to
public representations, this case study illustrates that questions about the nature of the
representations employed remain even after determining (or assuming) that the content and
vehicle questions have been answered. These remaining questions are questions about
representational format.
In brief, we need appeal to representational formats in cognitive science and AI because
they play distinctive epistemic roles: they allow us to identify distinctive features of
cognition and intelligence that call for treatment in ways that are not exhausted by appeal
to contents and vehicles. More specifically, representational formats are useful in cognitive
science and AI, at least in large part, insofar as they fulfil the following explanatory roles:
Transformation-based explanation: help explain the workings and
behavioural effects of cognitive states and processes in terms of the specific kinds
of transformation or manipulation available and performed over such states and
processes;
6
For this reason, Camp (2009) rejects Cheney and Seyfarth’s (2007) claim that, in light of
their ability to navigate such a complex social hierarchy, baboons must thereby make use
of language-like representations.
16
Efficiency-based explanation: help explain why certain cognitive states
and processes are more (or less) adequate for a specific task in terms of certain sets
of transformations/manipulations being more efficient, powerful, less taxing and/or
temporally advantageous.7
In addition, a theory of representational formats should be epistemically fruitful (epistemic
fruitfulness). First, in light of the ambiguity of much appeal to representational formats in
the literature, a theory of representational formats should identify a clear, motivated
domain of questions that can or should be tackled by such an appeal. Second, such a theory
of formats needs to strike a balance between overly coarse-grained and overly fine-grained
individuation of formats, so as to secure a distinctive explanatory role to representational
formats, and avoid conflating them with contents or vehicles. Should such an attempt fail,
we would have grounds to be eliminativists about formats, insofar as their job description
could be filled by appeal to contents and vehicles, thus voiding their explanatory purchase.
Third, a theory of representational formats for cognitive science and AI should provide
insight into the nature of representational formats as explanatory posits in those sciences. It
should, in other words, clarify how formats fit with other posits in the cognitive sciences,
including therefore the related notions of representational content, representational vehicle,
and computational process.
7
For a recent account of mechanistic efficiency-based explanations, see Fuentes (2023).
17
These considerations are both a job description and a list of desiderata on theories of
formats suitable for the cognitive sciences. Such theories are to be evaluated in terms of the
extent to which they satisfactorily provide an account that fits that job description.
With this description of the job to do, it is time to put together a job application.
3. The Computational View of Formats
3.1. Computational Vehicles, Functions, and Formats
Views about the nature of representational formats in cognitive systems have typically
relied heavily on the notions of computation, computational process, and computational
transformation. These notions, in contrast to their use in mathematics and computability
theory, are to be understood in concrete, physical terms: they are meant to capture the
physical systems that are computational and carry out computational processes, such as
laptops, smartphones, artificial neural networks, and, plausibly, nervous systems.
Appeal to computation makes it possible to explain the behaviourally adequate transitions
between, and transformations of, representations in a purely mechanical way—in terms,
that is, of following computational rules that are appropriate to the task at hand.
Computational rules are regularities in a physical system that capture the systematic
transitions from inputs (and internal states) to internal states and outputs.
Computational vehicles are individuated by their computational roles, not by the physical
details of their implementation. Individuation of computational systems and the
computations they perform abstracts away from implementational details almost
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completely: computational vehicles and processes may be equivalent in their roles and
effects while differing, even radically, in what kinds of physical states and processes
implement them—voltages, neuronal spike rates, or beads in an abacus. Facial
identification can be achieved by means of the computations performed by populations of
neurons in the fusiform gyrus of the mammalian brain, as well as, arguably, by matrix
operations performed by an electronic computer, as in artificial neural networks. Only
those properties that allow physical states to perform their computational roles are relevant
to their computational properties. These are the degrees of freedom, or dimensions of
variation, of the subset of physical properties of the physical vehicles that are
computationally relevant (Piccinini 2015, 2020; Miłkowski 2013; Fresco 2014, Coelho
Mollo 2018, 2019).
An important, albeit occasionally rejected (Dewhurst 2018), feature of computational
systems is that they can miscompute (Fresco and Primiero 2013). They can fail to compute
what they are supposed to, or, in other words, they can fail to perform the computations it
is their function to perform. An old pocket calculator, say due to some dust in a transistor,
may generate the wrong values, or no value at all, for an arithmetic operation it gets as
input. Functions to compute may derive from design, or from human-independent
processes in the case of biological systems (Piccinini 2015, Coelho Mollo 2019).
To be a computational system therefore just is to be a physical system of a type that can
perform transformations over physical vehicles according to medium-independent rules,
and that has the designed or natural function to do so. Similarly, to be a computational
vehicle or a computational operation just is to be a physical state or process in a
19
computational system individuated in terms of its contributions to its computational
nature.8
According to the mainstream representational-computational approach to cognition,
representational systems in cognitive systems, be them biological or artificial, are
composed of computational states and processes, some of which are also carriers of
content, and thus representational vehicles (Colombo and Piccinini forthcoming).
Computational vehicles are individuated by means of theories of computational
individuation—such as the one briefly presented in this section—while representational
vehicles and contents are individuated by means of theories of cognitive representation
(Shea 2018; Neander 2017; Millikan 2017).9
Cognitive systems are regimented so that the transitions between and transformations of
computational vehicles mirror the behavioural, semantic, or rational constraints relevant to
8
For more detail on and detailed defence of this approach to the individuation of physical
computation, see Piccinini (2015).
9
There is ongoing debate about how best to individuate computation, especially in ways that
avoid computations becoming indeterminate (Fresco, Copeland and Wolf 2021; Shagrir
2001; 2022; Piccinini 2015). Such a debate is beyond the scope of the paper, but see Coelho
Mollo (2018, 2019) for defence of the foregoing view against indeterminacy worries. At any
rate, for our purposes any theory of computational individuation that avoids the
indeterminacy problem would be suitable, be it the one hinted at here or a different one.
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the contents they carry. That is, parts of representational systems can be manipulated
computationally in ways that are appropriate to their contents. In explaining and building
cognitive systems in cognitive science and AI, computation and representation typically go
together, each playing a distinctive explanatory role.
3.2. Individuating Representational Formats Computationally
Representational systems can vary considerably in their computationally relevant
dimensions of variation, depending on the computational vehicles of which they are
composed. Such computational vehicles can have a host of different computationallyrelevant properties. They can vary in the number and nature of the values they might take
across multiple dimensions of variation, and changes in the values they take across one or
more dimensions may lead to constraints on the values that other computational vehicles
may take. We call the limitations over available values across one or more
computationally-relevant dimensions of variation of computational vehicles their inner
constraints; and the mutual constraints between computational vehicles in a
representational system their outer constraints.10
10
Outer constraints bear some similarities to what Lande (2021, 651) calls distributional
properties, i.e., the properties of a mental state that “characterise how states of that type
can, cannot, or must co-occur in a particular system with mental states of other types”
(2021, 651). In contrast to Lande’s account, however, we focus exclusively on the relevant
computational features of cognitive states and processes.
21
In artificial systems, these constraints typically stem from design choices, as well as
engineering convenience and technical limits. In biological systems, on the other hand,
they likely stem from contingent features of evolutionary and developmental history, as
well as the limitations imposed by the ‘wetware’.
As an illustrative analogy, take action figures, a popular kind of toy. One important feature
that distinguishes between action figures is which parts of the puppet can be moved (arms,
legs, head?), and how independent their movements are. Some action figures, often the
cheapest ones, are fully rigid and none of their parts can be moved. Sophisticated ones, on
the other hand, have several mobile parts (legs, arms, neck, etc.), which can typically be
moved independently of the others. Moreover, their limbs may move fluidly and stop at
any specific position, or, less satisfyingly, they may move in jerks, and have predetermined
stop points. Some less sophisticated ones, to great frustration, have more constrained
movements: moving a forearm is impossible without moving the whole arm, or moving a
leg also makes the other leg move.11 The parts that can be moved are what we may call,
with quite some stretch, the ‘vehicles’. The number of relative positions the moving parts
can occupy and the relations between variations over them (such as one leg also making
the other leg move), are their inner and outer constraints, respectively.
11
Incidentally, talk of degrees of freedom is not extraneous to talk of action figures:
indeed, given the former’s correlation with quality (and fun), advertisements for these toys
often mention their degrees of freedom explicitly.
22
We can categorise different types of action figure in terms of their moving parts, the values
that those moving parts can take (which positions can they occupy relative to the body and
to each other?), and the relations between variations over those parts (does moving a leg
also lead to moving the other leg, or rather an arm?), and we can do the same with
computational vehicles. How many parts of the vehicle can be computationally wiggled
and what values can they take? How does wiggling one part affect the possible values of
another part? And how does wiggling values of a vehicle affect (or not) other
computational vehicles, i.e., does changing the values taken by one vehicle affect the
values of the others?12
We can thus type representational systems in ways not unlike how we type action figures,
that is, in terms of their computational (moving) parts, the values (positions) those parts
may take across multiple dimensions, and the mutual constraints between values of the
parts of different vehicles. Representational systems that differ in these respects differ in
what we call their ‘computational profiles’.
12
It is important to keep in mind that only the degrees of freedom that are computationally
relevant are to be considered here (see section 3.1). For instance, even though physical
vehicles in electronic computers can take continuous voltage values, downstream systems
are only sensitive to those values falling within two specific voltage ranges. Therefore, in
such a case there is only one computationally-relevant degree of freedom, i.e., the voltage
range is either ‘0’ or ‘1’.
23
Computational profiles are individuated by the inner and outer constraints of the
computational and representational vehicles in a representational (sub-)system. These
factors determine what computations are available to representational systems, and thus
which kinds of transformations of representations are available to tackle a certain
behavioural task. To type representations in this way, per the foregoing computational
view of formats, is to type them in terms of their representational format.13
In sum, we hold that the proper way of characterising the computational view of formats is
in terms of the following set of claims:
T1: Representational formats are the computational profiles of representational
(sub-)systems in cognitive systems, be them biological or artificial.
T2: Computational profiles, in their turn, are individuated by the inner and outer
constraints of computational vehicles, i.e., the values they can take, and their
mutual constraints.
It is a corollary of the view that different representational formats have different
computational properties. In most cases different formats will be best suited to solving
13
Of course, computations and representations are ultimately implemented by neural
computations in brains or symbolic or numerical computations in AI systems. However, as
pointed out above, the relevant kind of individuation for our purposes is mediumindependent—i.e., computational and representational—rather than implementational.
24
different tasks, and will require different amounts of processing steps—and thus, in realtime systems, of time—than other task-appropriate formats.
To illustrate how our proposed computational view of formats can tackle relevant
questions in the cognitive sciences, we will apply it to a couple of case studies coming
from the cognitive sciences, namely the place cell system in the mammalian brain, and
computational models of episodic memory recall. We will show that this purely
computational approach to the individuation of representational formats makes analogies to
public representations explanatory redundant, and at best of heuristic value (§5).
4. Representational Formats in the Cognitive Sciences
4.1 The Case of Place Cells
Place cells are neurons found in the hippocampus of several mammals, which have a very
interesting property: they fire when the animal occupies specific points in space (O’Keefe
and Dostrovsky 1971; Grieves and Jeffrey 2017). Together, they form a sort of array, with
different (groups of) cells firing when the animal occupies different points in space. Due to
this property, place cells are believed to be part of the ‘cognitive map’ system comprising
the entorhinal cortex and hippocampus, and including other kinds of cells relevant to
spatial cognition, such as grid cells and head-direction cells. In light of its activation
properties, it seems natural to treat this system of brain areas as forming a mechanism for
representing spatial locations and spatial relations in the immediate environment of
mammals, given their abstract similarity to how public maps represent.
25
However, place cells are not spatially arranged in a way that corresponds to the spatial
locations they respond to: there is no map-like correspondence between relative spatial
locations of place cells in the hippocampus and relative spatial locations of points in the
environment. The crucial feature of this system is the coactivation relations cells have to
each other: cells that represent a certain location tend to produce activation in cells that
represent nearby locations, both in online and offline tasks (Shea 2018; Diba and Buzsáki
2007; Dragoi and Tonegawa 2013).
Let us forget for a second that place cell activation correlates with spatial locations, and
that cells that are more likely to be coactivated correlate with nearby spatial locations. Let
us look purely at the computational properties of the vehicles themselves, that is, the
populations of cells and their firing patterns. These patterns constitute a structure of
activation relations, which can be described in terms of probabilistic coactivation relations:
if cell A has firing rate a, then cells B, C, D … N will have firing rates in range x-y with
probabilities p, q, r, .. u. Taken together across the whole system of place cells, these
activation relations constitute a relational structure of computational vehicles.
The computational view allows us to examine the place cell system purely in terms of its
computational features. We have a set of computational vehicles that can vary across one
dimension, and whose values are equivalence classes of firing rates that are treated as the
same by downstream processes. The possible values depend on which and how many such
equivalence classes there are, which hinge in turn on physiological properties of the cell as
well as of the cells it feeds its output to—and are thus to be empirically determined. These
are the inner constraints of the place cell system.
26
The outer constraints are more interesting in this case. If each cell probabilistically
modulates the activity of cells it is strongly connected to, then, in computational terms,
each vehicle’s value stochastically constrains the values a subset of the other vehicles in
the system may take. If vehicle V has value H (a high value, say), then vehicles C, D can
take values in the range, say, M (medium) to H, with specific probabilities assigned to each
downstream vehicle and possible value.14 In other words, we have, roughly, a partially
connected stochastic array of computational vehicles.
In brief, a description of the computationally-relevant features of the place cell system
comprises the following:
● a set of computational vehicles A, …, N, implemented by the place cells;
● their inner constraints: the values that each vehicle may take, i.e., the set of discrete
values a, …, n implemented by different firing rates (assuming that firing rates are
what is computationally relevant);
● their outer constraints, captured by a probabilistic function from values a, …, n of
vehicles A, …, N to values a, …, n of vehicles A, …, N-1.
These computational features determine which kinds of representational roles place cells
can adequately play: any representational task that involves representing concrete or
abstract points in a concrete or abstract space of relations should be a good candidate. Such
a computational profile seems well suited to be employed by representational systems
14
This is of course a simplification, for the sake of ease of illustration.
27
tasked with solving spatial cognition tasks. But there is evidence suggesting that this
system is also employed to solve other kinds of tasks, having to do with ‘distance’ relations
in abstract conceptual spaces (Constantinescu et al. 2016), as well as other behavioural
tasks (Aronov et al. 2017; Mok and Love 2019; Whittington et al. 2020). Place cells may
not always, nor even often, be about places. By the light of the foregoing computational
view, this is to be expected, as the computational profile of that representational system
makes it adequate for a variety of non-spatial tasks.
According to the computational view, these computational features together, that is to say,
the computational profile of the place cell system as a partially connected stochastic array
of vehicles, constitutes a representational format. Analogies with public maps are
misleading for at least three reasons.
First, as noted, there is no spatial-to-spatial correspondence relation between place cells
and what they represent, as in maps. Second, the place cell system has strongly stochastic
features that maps do not have. Third, the analogy to public maps erroneously suggests that
the place cell system is only about space. To talk of the place cell system as having a maplike format—and thus as helping to form ‘cognitive maps’—is thereby misguided: the
analogy with maps is very partial, and overreliance on it obscures important computational
and representational features of the system.
4.2 The Case of Episodic Memory
Episodic memory is a type of declarative memory that concerns, roughly speaking, stored
information about experienced episodes, such as our memory of whom we met yesterday
28
and in what context (Cheng and Werning 2015). Growing evidence suggests that episodic
memory retrieval and recollection are generative processes of scenario construction (Cheng
and Werning 2015; Lackey 2005). This means that memories are reconstructed at each
retrieval through the complex dynamic interaction of different functional areas that encode
different memory traces or engrams (Sekeres et al. 2018).
The main neural locus for episodic memory is the hippocampus and its subregions,
although other brain areas are involved as well (Rolls 2018; Scoville and Milner 1957).
The anterior hippocampus (aHPC) encodes the memory trace about the gist of the episode,
i.e., essential features like the ‘story elements’ that are central to plot coherence (Sekeres et
al. 2018).
For instance, this could be the “story line” of your 10th birthday party—that there were
other children, it was in the afternoon, and so on. The posterior hippocampus (pHPC) and
the neocortex encode the memory trace with fine-grained perceptual-like details; such as
the shape and colour of your birthday cake (Collin et al. 2015; St.-Laurent et al. 2016).
Finally, the aHPC has been shown to interact with the medial prefrontal cortex (mPFC),
which stores the schema engrams, i.e., networks of knowledge structures extracted from
multiple similar experiences (Robin and Moscovitch 2017). In our example, information
about birthday parties in general.
The exact nature of the computations relevant for memory recall in the brain are still
largely unknown (Cheng 2013; Rolls 2018). According to plausible theories about what is
involved in recall, however, we can identify four different components: rich
29
representations of perceptual and semantic information; a representation of the gist of the
episode; an even less informationally-rich memory trace that can reactivate the relevant
episodic gist; and the output representation, namely the reconstructed detailed memory that
is eventually recalled, where the informational detail left out in the gist is ‘filled in’ by
recourse to rich representations of perceptual and semantic information. In other words, we
have, basically, a process of lossy compression followed by a process of decompression
that includes generative elements (Fayyaz et al. 2022).
There have been promising recent attempts at modelling this process in a biologicallyplausible way by means of artificial neural networks: for instance, by combining a
variational autoencoder with a convolutional neural network and simple attentional
selection mechanisms (Fayyaz et al. 2022). The details of such models will not exercise us
here: what matters for our purposes is that they provide a computational story through
which the process of recall as described above may be implemented in brains and/or
artificial systems. And that story plausibly involves transitions between different
representational formats.
In order to shed further light on this case, it is helpful to introduce the notions of vehicular
density and inner repleteness.15 Roughly, a representational system can be more or less
dense depending on whether it admits, for each pair of vehicles, a third vehicle between
them or not, and a further one between this third vehicle and another one, and so on. In its
turn, a vehicle can be more or less replete depending on the range of computationally
15
These notions are inspired by Goodman (1976).
30
relevant dimensions of variation it possesses. A vehicle may be able to take a range of
values in one dimension (like a line), in two dimensions (like a shape), in three dimensions
(like a solid), and so on.
A potential way to build episodic gists from perceptual information is by means of forcing
rich perceptual information into a ‘vehicular funnel’ before storage. That is to say, the
system must move from a format with high density of relatively replete representational
vehicles—which due to these features are able to represent fine-grained details of an
episode—to a format with a rather low density of vehicles with relatively low repleteness,
which encodes only the gist of the episode, and thus requires less storage space and may be
less energetically expensive to access and reactivate. Since information is lost, this is a
lossy compression process.
Memory recall, in its turn, may involve a transformation from a low-density, lowrepleteness format, with its highly compressed representations (the gist), into a higherdensity higher-repleteness format, marked by a qualitatively higher availability of vehicles,
and a larger range of possible values and mutual constraints between them. Since the
compression process involves information loss, recall is partly a generative process. Gist
information can provide pointers to access information stored elsewhere, for instance in
semantic memory, to fill in the information lost during compression (Fayyaz et al. 2022).
This case illustrates that, in computational models, and possibly in cognitive systems,
vehicular density and inner repleteness are computationally relevant properties that help
distinguish different formats. For they involve qualitative differences in the computational
31
profiles of representational systems. Given the lack of detailed knowledge about the
specific features of the vehicles and processes underlying episodic memory recall, the
foregoing computational view of formats can only provide pointers, rather than a precise
specification of the formats involved. However, these pointers can be precious, as they
help identify some of the likely features that the underlying vehicles and processes possess,
and thus the processing signatures that might be expected from their employment (e.g.,
more or less sparse connectivity, higher or lower ranges of values). Moreover, they help
identify the features that need yet to be discovered so that we can have a fuller picture of
the workings of episodic memory recall.
At this juncture, it is worthwhile to point out that small differences in vehicular density and
inner repleteness may be overly fine-grained for the individuation of different formats. For
many explanatory purposes we may wish to generalise over formats, which would be
hindered by an overproliferation of formats, leading to the near impossibility of two
representational systems sharing the same format.
In the life and cognitive sciences, it is often the case that there are no sharp boundaries
fixed by our explanatory concepts. For many explanatory purposes, representational
formats, like other cognitive and biological concepts, should be seen as coarser-grained
and as having fuzzy boundaries: formats are thereby more-or-less well defined clusters of
computational profiles that are significantly similar in their computational capacities to be
considered as identical without explanatory loss. On the other hand, some explanatory
purposes may require finer-grained individuation of formats, say if one wants to examine
small but relevant computational differences between two place cell-like formats.
32
The computational view is thus pluralist in more than one sense. It is pluralist insofar as it
recognises a large variety of different representational formats (instead of the few,
intuition-based ones typically discussed in the literature); and it is pluralist insofar as it
recognises that formats may be individuated in more or less fine-grained ways depending
on the explanatory aims at hand. It is likely that no immediate analogy can be made to the
formats of public representation, but this is no impediment (nor guide) to providing an
epistemically useful notion of representational format for the cognitive sciences.
5. The Explanatory Roles of Representational Formats
5.1. Satisfying the Job Description without Public Representations
The foregoing case studies illustrate that reference to public representational formats—
such as words, pictures, and maps—does not play explanatorily relevant roles and is, at
worst, misleading. We contend that a purely computational approach to formats can fulfil
the explanatory roles identified in section 2.3—transformation-based explanation,
efficiency-based explanation, and epistemic fruitfulness—without any appeal to public
representations. Let us look at each explanatory role in turn.
It should be quite clear that the foregoing computational view is well positioned to meet
transformation-based explanation. After all, it individuates representational formats by
appealing to some of their computational properties, i.e., their computational profiles. And
the notion of computation in cognitive science and AI has as its chief role that of allowing
explanations of internal state-transitions that are rule-based, able to respect semantic,
coherence, and rationality constraints—and all that in naturalistically acceptable ways. The
33
main innovation of the cognitive revolution was not the vindication of the notion of
internal representation, which has a long history in philosophy and science, but rather the
discovery of that of computation, and its ensuing application to explaining how transitions
between representational states can lead, mechanically, to behaviourally-adequate
outcomes (Fodor 1975; Haugeland 1981).
The computational view has it that the proper way of understanding formats is in terms of
the computational transformations that representational systems can undergo, which are
determined in their turn by the nature of the computationally-individuated vehicles that
compose them, and the constraints they pose in light of their computational properties. By
capturing such computational properties, the notion of representational format opens the
way to explaining how computational goings-on in representational systems go along, or
map onto, goings-on in the subject matter represented, such as to lead to adequate
behaviour. Therefore, transformation-based explanation is satisfied: representational
formats capture the computational operations available to representational systems, which
have important consequences for the behavioural appropriateness of their outputs.
There are typically many different possible solutions to one and the same problem. The
same applies to behavioural problems, and the representational and computational states
and processes that can solve them. That of course does not mean that every solution is
equally desirable. There are better or worse, quicker or slower, more or less efficient ways
of solving problems. Rube Goldberg machines, for instance, do solve problems, but in
absurdly, unnecessarily complicated ways. Something similar applies to formats: some
computational solutions to a behavioural problem can be more or less efficient in terms of
34
resources employed, such as (metabolic) energy and time. The more appropriate the
computational profile of a certain representational (sub-)system to a task, the fewer or less
expensive the computations to reach the solution will be.
In brief, by capturing the relevant computational properties of representational systems, the
computational view of formats allows us to explain why certain representational formats
are better suited to specific kinds of tasks—such as spatial navigation—than others. More
appropriate formats will typically involve fewer, less complex, less expensive
computations than less appropriate ones. In consequence, the view satisfies efficiencybased explanation.
Moreover, this sort of consideration can be of quite some epistemic value: even though
natural selection does not typically lead to optimal outcomes, it is in any case to be
expected that it will have led to representational formats that approximate to some extent
the most adequate one for a certain task. Thereby, we can try to reverse-engineer the
representational format at work in a certain behavioural task by trying to find the best
computational solutions to that task, and then assess whether behavioural, psychological,
neuroscientific or explainable AI techniques suggest that something similar is taking place
in the cognitive system at hand. This is one of the aspects that makes the computational
view also fulfil the third and final part of the job description, namely epistemic
fruitfulness.
5.2. Format Pluralism and the Fate of Public Representations
35
Approaches to formats that are modelled on public representations are typically saddled
with dichotomies, such as the much discussed one between propositional and pictorial
formats. However, once we have freed the computational view of formats from the
shackles of intuition, a more pluralistic perspective opens up, in which there are many
different varieties of formats—many more than typically discussed. For instance, in the
case of episodic memory, we have shown that vehicular density and inner repleteness are
computationally relevant properties. Both density and inner repleteness are dimensions of
variation that admit different degrees. There can be computational structures that are more
or less dense, and more or less replete, and these features can be combined in different
ways in different systems.
While we still lack a good understanding of the computational workings of cognitive
systems, a purely computational view of formats sheds light on what we should be looking
for when we look for representational formats in cognitive systems, be them biological or
artificial, namely computational profiles. There are no a priori limitations on what sort of
computationally relevant dimensions of variation may be discovered.
The resulting picture is thus highly pluralistic, since it envisages:
● Multiple computationally-relevant dimensions that must be empirically discovered;
● Graded computationally-relevant dimensions, rather than only all-or-nothing ones;
● Multiple possible combinations of such dimensions.
It is clear that this pluralism about formats goes well-beyond the frequently-discussed
formats based on public representations.
36
Before we conclude, let us briefly return to public representations, and look at what
epistemic roles, if any, they can still play. Consider once again the case of baboon social
navigation. Camp’s reasoning that the format of baboon social cognition is more
diagrammatic than pictorial or language-like may be construed as a heuristic. On the basis
of observable behaviour, we can put forward conjectures about what sorts of properties the
underlying representational structures should possess, such that they can explain the
capacities observed, as well as the capacities that are not displayed by the system under
investigation. Such initial conjectures may helpfully tap into analogies with the
behavioural capacities we display when we use specific types of external, public
representation.
When used as heuristic tools, public representations work as format-schemas, i.e., sketchy,
tentative hypothetical models of the computational profiles that internal representational
systems might possess. Such tentative models can then be improved and adjusted in light
of more fine-grained information (behavioural, psychological, neuroscientific, etc.) about
the cognitive system at hand. This process is likely to generate more advanced explanatory
models that depart considerably from the initial format-schemas based on public
representations, as the heuristically-useful analogies break down.
6. Concluding Remarks
In this paper, we have shown that the computational theory of representational formats—
targeted at capturing an explanatorily useful notion of format for cognitive science and
artificial intelligence research—not only does not require, but can actually be hindered by
37
overbearing analogies to public representations. This computational view offers an account
of what representational formats are: the computational features of physical vehicles that
capture the kinds of transformation/manipulation they can undergo. We have dubbed such
features inner and outer constraints, which together come to form computational profiles.
Per the computational view, representational formats just are the computational profiles of
representational (sub-)systems.
Representational formats can be individuated in coarser- or finer-grained ways, depending
on the explanatory purposes at hand. The computational view also detaches the question of
what formats are, and how many there are, from intuitions based on public representations.
We have taken some preliminary, speculative case studies from current neuroscience and
computational modelling to illustrate the type of analysis that the computational view
provides, and the lines of further empirical investigation that it invites, both in biological
organisms and artificial systems.
38
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