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Chapter 27
COGNITIVE SCIENCE
AND EXPL ANATIONS OF
PSYCHOPATHOLO GY
Kelso Cratsley and Richard Samuels
Introduction
The past two decades have witnessed a striking convergence in the interests and methods
of cognitive science and psychiatry. On the one hand, cognitive psychologists and cogni-
tive neuroscientists have increasingly sought to model various forms of psychopathol-
ogy; in part because they promise to illuminate our understanding of normal cognition,
but also because such phenomena are now viewed as intriguing in their own right. At the
same time, researchers within psychiatry have increasingly come to adopt the methods and
assumptions of the cognitive sciences—especially cognitive neuropsychology and cogni-
tive neuroscience—with an attendant commitment to a particular pattern of explanation
which depends upon the construction of mechanistic models (Broome and Bortolotti
2009; Kendler 2008, Kendler et al. 2011). The study of psychopathology has thus become an
important facet of the cognitive sciences, and the cognitive sciences have, in turn, exerted an
important influence on many regions of psychiatry.
While these developments have led to significant research findings, the application of a
cognitive scientific approach to psychopathology is still very much in its infancy, and the
scope and limits of the research strategy remain to be determined. The aim of this chapter,
then, is to explore the prospects for a developed cognitive science of psychopathology. In
“Core explanatory assumptions” we outline the core theoretical assumptions of much cog-
nitive science with a specific focus on the sorts of explanatory strategies that cognitive scien-
tists typically seek to produce. In “Explaining psychopathology” we consider the extension
of these explanatory strategies to the study of psychopathology. Then, in “Some cognitive
accounts of psychopathology” we briefly illustrate how these strategies have been applied
to two specific pathological phenomena: Autism Spectrum Disorder and Major Depressive
Disorder. Finally, in “Two challenges to the cognitive science of psychopathology,” we discuss
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414 Summoning Concepts
a pair of challenges to standard cognitive scientific approaches to psychopathology, one that
focuses on the pervasive role of dissociative data in theory construction, and another, more
“philosophical” challenge which purports to establish in-principle limits on the sorts of
explanations of psychopathology that cognitive scientists have sought to provide.
Core Explanatory Assumptions: Information
Processing and Mechanism
Though cognitive scientists do not share a single vision of how explanation ought to pro-
ceed, large regions of the field cleave to a set of familiar assumptions about the sorts of
models that ought to be developed. For heuristic purposes, we divide up these assumptions
into two related families of commitments. The first concern the idea that cognitive proc-
esses and capacities depend on information processing. The second concern the idea that
cognitive explanations are in some appropriately broad sense mechanistic. Neither family
of assumptions has gone unchallenged in cognitive science. So, for example, advocates of
situated or dynamical approaches sometimes reject them (Chemero 2009). For present pur-
poses, however, we are concerned with research that adheres to these assumptions. And in
what follows, we sketch them in a bit more detail and identify a range of explanatory strat-
egies to which they give rise. In practice these different strategies are seldom pursued in
isolation from each other, but are instead combined in different admixtures within indi-
vidual research programs. Nevertheless, it will be useful to separate them out since in doing
so we will be better placed to appreciate the range of strategies available in understanding
psychopathology.
Information processing
Amongst the core assumptions of much cognitive science is that cognitive capacities –vision,
reasoning, language production, memory, and so on—depend upon information process-
ing of some sort. This general assumption has been articulated in a range of different ways,
invoking different notions of information and different conceptions of the sorts of process-
ing that is involved (see, e.g., Piccinini and Scarantino 2011). But in practice cognitive sci-
entists typically assume that the relevant processes involve representations: roughly, physical
states and structures have semantic contents in that they mean something or denote aspects
of the world (Clark 2000; Thagard 2012; Von Eckardt 1993). Further, it is widely assumed
that the relevant sort of processing is in some sense computational (Cummins and Cummins
2000; McDonald and McDonald 1995). This much is true of the sorts of “classical” computa-
tional models that dominated much early cognitive science (Fodor and Pylyshyn 1988), but
it is also true of mainstream connectionist research as well (Smolensky 1988). Further, even
amongst researchers who are less explicit in their computational assumptions, there is a per-
vasive tendency to characterize cognition in terms of representations and operations ther-
eon. Such assumptions are, for example, widespread in developmental psychology (Carey
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COGNITIVE SCIENCE AND EXPLANATIONS OF PSYCHOPATHOLOGY 415
2009), linguistics (Pinker 1994), and cognitive neuroscience (Gazzaniga 2009), even though
much of this research does not provide any very perspicuous computational characteriza-
tion of the phenomena under discussion.
Suppose, as most cognitive scientists do, that psychological capacities and processes
involve the sort of information processing we sketched earlier. Then it will be possible to
explain cognitive capacities and processes in a range of different ways, or, roughly equiv-
alently, to characterize them at a number of different levels of description. First, it will be
possible to describe cognition at what has variously been called the intentional, semantic
or knowledge level (Newell 1990; Pylyshyn 1984). Roughly put, we can explain what peo-
ple do with reference to the semantic contents of their representational states—for exam-
ple, what they believe and what their goals are—and the meaningful connections that hold
between such states. Thus we might describe the cognitive development of infants in terms
of their learning new concepts or beliefs on the basis of perceptual experience, and we might
explain the decision-making behavior of an adult in terms of their preferences and their
beliefs about the world. Such explanations abstract away from the computational (and neu-
robiological) details of psychological processes, but can nonetheless be exceedingly useful
for making sense of human behavior and cognition.
Second, if an information processing account of cognition is correct, then it will be pos-
sible to describe psychological processes in more computationally perspicuous terms—at
what is sometimes called the representational/algorithmic level (Marr 1982). Commitment
to such a level of description in exceedingly natural in light of the following broadly held
assumptions:
The semantic contents of mental states are somehow encoded by information carrying
states and structures of the brain, what are sometimes called representational vehicles.
Such representational vehicles are created, utilized, and transformed by the
computational processes in which they figure.
Under these assumptions, it is plausible to suppose that psychological capacities and proc-
esses can be explained by characterizing the computations they involve and the properties of
representational vehicles that allow for them to figure in such computations.
To take one well-known example, it is common to characterize aspects of visual percep-
tion—such as binocular vision—by providing some account of the representational vehicles
involved, and a computational characterization of the systems that allow for the exercise
of the capacity—for example, the extraction of depth information from binocular disparity
(Marr 1982). As with intentional level description, this strategy is exceedingly common-
place in cognitive science.
Mechanistic commitments
So far we have sketched some widespread assumptions about cognition, and highlighted
the way in which they give rise to two commonplace explanatory strategies. We now turn
to another commitment, also widespread amongst cognitive scientists, which concerns
the mechanistic character of cognition. To a first approximation, it is widely supposed that
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416 Summoning Concepts
cognitive capacities—such as, language production, memory and visual perception—
depend upon the operation of cognitive mechanisms: roughly, physically realized informa-
tion processing systems that are hierarchically decomposable into functionally specifiable
components, whose individual activities and mutual interactions are responsible for the
production of cognitive phenomena (Bechtel 2008). So characterized, this mechanistic
assumption constitutes a rough-and-ready piece of metaphysics—a claim about the sorts of
entities that cognitive systems are.1 But it is a piece of metaphysics that has important meth-
odological ramifications, and in what follows we spell these out in more detail.
A first ramification of the mechanistic assumption is that any comprehensive account
of cognition must ultimately explain how representations and computational operations
are dependent on—or implemented by—physical structures, states and processes. For if
cognition depends on the activity of physical systems, then presumably there should be
some explanation of how these physical systems—paradigmatically parts of the brain2—
are able to perform the relevant computational operations. Such descriptions are some-
times said to comprise a distinctive implementation or physical level of description
(Marr 1982; Pylyshyn 1984). Crudely put, theories couched at this level are analogous
to descriptions of computational hardware in that they characterize those physical states
and structures in virtue of which a system is able to perform information processing tasks
of the relevant sort.
A second ramification of the mechanistic assumption is that cognitive phenomena can be
explained by affecting a decomposition of the system into subparts (Bechtel and Richardson
1993; Cummins 1975). Roughly put, the idea is that if cognitive capacities depend on hierar-
chically decomposable physical systems, then it should be possible to explain the cognitive
capacity of an organism by decomposing the relevant system into its parts and describing
how their individual activities and mutual interactions give rise to the capacity in question.
Indeed, the typical goal of such decompositions is to “break up” the system into parts so
simple that they manifestly involve no intelligence at all (Fodor 1968).
It is important to be clear, however, that there are two quite different sorts of decomposi-
tional analyses to be found in cognitive science. These two approaches, though not always
clearly distinguished, differ in the sorts of parts that they specify. The first approach, some-
times called functional analysis (Cummins 1975), involves a decomposition of a process—a
system performing a task—into its various component subprocesses.3 So, for example, one
might decompose the process of visual perception into a range of subprocesses, such as
those involved in extracting light intensity gradients from the retinal image, those involved
1 For example, the mechanistic assumption appears to rule out various metaphysical theses about the
mind, such as substance dualism. But it is largely neutral with respect to such issues as whether some
form of functionalism is true, and whether some version of the type-identity theory is correct.
2 Though see Clark (2008) for a defense of the view that much cognition depends on extra-neural
physical structures.
3 Functional analysis is sometimes characterized not in terms of the decomposition of processes
into subprocesses, but in terms of the decomposition of the system’s dispositions into subdispositions,
or capacities into subcapacities. But in the present context such differences are not important. This is
because any exercise of a psychological disposition or capacity that involves the exercise of subcapacities/
disposition will be a process, and any psychological process of this sort will be an exercise of a capacity/
disposition. Thus any given functional analysis can equally well be construed as an analysis of a process
or as an analysis of a capacity/disposition.
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COGNITIVE SCIENCE AND EXPLANATIONS OF PSYCHOPATHOLOGY 417
in determining depth discontinuities, and those involved in determining the spatial orienta-
tion of objects (Marr 1982). Further, these various subprocesses will themselves typically be
decomposed, in iterative fashion, so that the final analysis represents the overall process as
a hierarchically organized informational process of the sort that is naturally represented as a
flowchart or a program (Cummins 2000). For this reason functional analysis is exceedingly
closely related to what we earlier called the algorithmic level. Specifically, such analyses are
often a prelude to—and indeed ultimately represented as—an algorithmic level description
of the system.
The second sort of decomposition does not merely seek to decompose a system perform-
ing a task into subprocesses, but aims to characterize the structures from which the system
is composed. More specifically, such mechanistic decompositions aim to explain a phenom-
enon (or capacity) by decomposing the system into its functionally salient components, and
describing the individual components, the activities in which they engage, and how they
interact with each other in order to produce the phenomena in question. Moreover, such
structural components are themselves typically decomposed further, in iterative fashion,
so that the final analysis describes the system as a hierarchically organized set of causally
interacting structural components—i.e., a mechanism. Thus the present explanatory strat-
egy advocates that in understanding the mind, cognitive scientists in effect pursue the same
approach that might be adopted in explaining the operation of a complex artifact, such as an
internal combustion engine or a digital computer.
In our view, both functional analysis and mechanistic decomposition are legitimate
explanatory enterprises. Indeed, we think that functional analysis is an important step
toward developing mechanistic accounts of cognition. Nonetheless, the discovery of mental
mechanisms is, as we see it, the central task of contemporary cognitive science. And many of
the developments that have occurred over the past few decades—especially those associated
with the emergence of cognitive neuroscience—are motivated largely by this explanatory
goal. Though the identification of mental mechanisms gives rise to many kinds of issues, one
that will figure prominently in later sections of this chapter concerns the extent to which the
study of psychopathology can help illuminate the structure and organization of the human
mind. In view of this, we propose to conclude our overview of cognitive science with a brief
discussion of the methods most commonly deployed in drawing inferences about normal
cognition from the study of pathology.
Evidence for mechanistic hypotheses: The importance
of pathology
There are many different sorts of evidence that are relevant to the assessment of hypoth-
eses about the structure and organization of the human mind, including chronomet-
ric data, developmental evidence, neuroimaging data, and computational simulations.
But arguably the major source of evidence, and one central to the topic of this chapter,
comes from the study of pathology. For just as experimental interventions can help tell
us about the structure of the mind, “natural” interventions, such as disease, nonstandard
development, or accidental damage, can provide opportunities to discover properties of
the mechanisms normally responsible for our mental capacities (Bechtel 2008; Bechtel
and Richardson 1993). A focus on accidental damage is traditionally the business of
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418 Summoning Concepts
cognitive neuropsychology, which has been primarily interested in building models of
normal cognition by studying the deficits incurred by patients with acquired brain inju-
ries (Caramazza 1986; Coltheart 1985; Ellis and Young 1988; Shallice 1988). And a rel-
atively recent variation on this approach, cognitive neuropsychiatry, pursues a similar
agenda by focusing on pathological conditions traditionally classified as psychiatric dis-
orders (David 1993; Frith 2008; Halligan and David 2001).
One central idea common to both cognitive neuropsychology and cognitive neuropsy-
chiatry is that the functional organization of the human mind can be discerned by chart-
ing associations between pathologies and performance on different cognitive tasks. In
particular, it is commonplace to study double dissociations in order to discern the com-
ponent mechanisms responsible for cognitive capacities. In brief, double dissociations are
instances of paired single dissociations, where a pathological condition (or experimental
intervention) affects one particular capacity but not another. More specifically, suppose
that we test at least two different patients (or groups) on at least two different tasks. In a
double dissociation, a patient (or group) X is impaired on Task A but performs normally
on Task B, whereas a patient (or group) Y shows the opposite pattern. In such situations,
it is plausible to infer that the patients (or groups) differed in some cognitive variable that
was differentially tapped by the two tasks. Moreover, the fact that the dissociation goes
in both directions allows us to rule out task difficulty as an explanation of the data.4 As a
consequence, it is often plausible to conclude that the best explanation of the pattern is
that different cognitive mechanisms are involved in the performance of tasks A and B; and
that these mechanisms are differentially impaired (and spared) in the patients (or groups).
Archetypical cases of this kind of inference include the comparison of patients with dam-
age to Broca’s area to patients with damage to Wernicke’s area, or patients with deficits in
long-term memory to those with deficits to short-term memory. (For classic results see
Shallice 1988.)
The virtues of using dissociative data have been debated extensively elsewhere (Coltheart
and Davies 2003; Dunn and Kirsner 2003; Davies 2010; Shallice 1988), and we defer any
critical discussion to the section entitled “Two challenges to the cognitive science of psycho-
pathology.” For the moment, however, two comments are in order. First, notice that double
dissociations appear to provide prima facie evidence for the existence of cognitive mecha-
nisms that are distinct, at least to the extent that they are susceptible to selective impairment
(and sparing). Thus the study of dissociations appears to provide some basis for claims about
the mechanisms responsible for our normal cognitive capacities.5 Second, and by the same
token, double dissociations also appear relevant to understanding the cognitive incapaci-
ties characteristic of different psychopathologies. For it is by establishing conclusions about
deficit cases that one is able to infer conclusions about the normal case. It is to the issue of
explaining psychopathology that we now turn.
4 Single dissociations may simply be the products of a single neural region, with two cognitive tasks
sharing the same physical location, or the products of “resource artifacts” (Shallice 1988), where one task
is impaired simply due to it being more difficult to carry out than the other.
5 These issues are often couched in terms of modularity (e.g., Davies 2005), though in the present
instance the relevant notion of modularity is that of separate modifiability. This notion of modularity
is significantly different from those deployed by Fodor (1983) and others (e.g., Barrett and Kurzban
2006).
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COGNITIVE SCIENCE AND EXPLANATIONS OF PSYCHOPATHOLOGY 419
Explaining Psychopathology
So far we have outlined a range of commonplace explanatory strategies in cognitive sci-
ence that are routinely combined in various admixtures within different research programs.
Specifically, we noted that cognitive capacities and systems can be characterized in the fol-
lowing related ways:
Intentional (semantic or knowledge) level descriptions
Algorithmic level descriptions
Implementation (physical) level descriptions
Functional analyses
Mechanistic descriptions.
In this section we set out a range of different ways in which these various strategies might be
extended to the case of psychopathology. Specifically, in “Three approaches to the explana-
tion of psychopathology” we highlight three importantly different approaches to the expla-
nation of psychopathology, and in “A menu of explanatory strategies” we show how these
approaches cross-classify with the strategies outlined in “Core explanatory assumptions” in
order to yield a menu of different approaches to psychopathology. As with much research in
cognitive science, different explanatory strategies are deployed in different combinations.
In “Some cognitive accounts of psychopathology: Autism and depression” we illustrate this
point by considering a couple of well-known attempts to understand psychopathology in
cognitive scientific terms.
Three approaches to the explanation of psychopathology
There is a number of importantly different ways in which one might apply the resources
of cognitive science to the study of psychopathology, which differ in the assumptions they
make about the relationship between explanations of normal cognition and those invoked
in the case of pathology. In our view, three such approaches are especially important, what
we call deficit-based models, input-based models and direct pathology models.
Deficit-based models
On a deficit-based approach to psychopathology, one starts with a proposal about how the
relevant regions of cognition operate in the normal, non-pathological case and then seek to
explain the phenomena associated with a psychopathological condition by citing respects
in which the normal cognitive system is (selectively) impaired, paradigmatically as a con-
sequence of environmental insult or genetic disorder. In the most extreme cases, deficits are
explained by hypothesizing the complete breakdown of a given cognitive mechanism. To
take a relatively uncontentious case, extreme forms of cortical blindness can be explained
in terms of widespread damage to the visual regions of occipital cortex. Or to take another,
more contentious example, one might explain prosopagnosia—the inability to recognize
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420 Summoning Concepts
faces—in term of the total breakdown of face recognition mechanisms realized in the fusi-
form gyrus (see recent articles in Young et al. 2008).
But deficits need not be an all-or-nothing affair. It need not be the case that the system
either works exactly as it should or is entirely inoperative. Instead, a mechanism can be
damaged in particular respects and to varying degrees; and as a consequence, it may be that
the explanatorily relevant deficit is more fine-grained and partial than complete breakdown.
For example, it may be the case that in specific language impairment (SLI) only a highly dis-
crete component of the language system is damaged, and perhaps only partially so (van der
Lely and Marshall 2010). Indeed, the idea that various core pathological phenomena are a
result of partial impairment has recently become quite common, for example, in research on
delusions (Coltheart et al. 2011; Davies and Coltheart 2000).
Input-based models
A second approach to the explanation of psychopathology is what we call the input-based
approach. Though less commonplace than deficit models, such an approach is still quite
familiar from the literature. On this approach, the pathology is not entirely explained in
terms of a deficit or breakdown of the normal system, but is instead explained, at least in
part, with reference to the character of the inputs to normally functioning cognitive systems,
inputs that are in some sense problematic or at least relatively unusual.6
What is the relationship between input-based and deficit models? Both approaches are
parasitic on some prior conception of how relevant regions of normal cognition operate. But
in contrast to deficit models, input-based models focus on the sorts of inputs that normally
functioning systems receive. In some cases, which we might call pure input-based models,
the divergence between normal and pathological cases is wholly explained in terms of the
character of inputs that normally functioning systems receive. So, for example, in the lit-
erature on depressive disorders, both early formulations of the learned helplessness account
(Seligman 1975), as well as the social competition hypothesis (Price et al. 1994), maintain
that some kinds of depression result from normally functioning cognitive systems receiving
inputs that are in some way nonstandard (e.g., inputs that induce in the subject the percep-
tion that they lack any control over their circumstances). Similarly, in research on addiction
it is routine to explain drug dependencies in terms of the normally functioning reward sys-
tem being “hijacked” by chemical stimulants (see, e.g., papers in Poland and Graham 2011).
But it is important to note that input-based and deficit-based strategies can be combined
in various ways so that the overall explanation of the pathology is a hybrid that recruits
both input-based and deficit-based components. For instance, one sort of hybrid model
proposes that abnormal inputs to normally functioning developmental mechanisms are
responsible for producing malfunctioning cognitive mechanisms. So, for example, in some
cases of language impairment, such as “feral” children growing up in highly impoverished
linguistic environments, the deficits observed in adulthood are plausibly attributed to the
character of the input that the child received in the course of development, as opposed to
any malfunction of the learning mechanisms themselves. Nevertheless, the products of this
6 This approach supposes that some pathologies are strongly analogous to what computer scientists
sometimes call garbage-in-garbage-out (or GIGO) phenomena.
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COGNITIVE SCIENCE AND EXPLANATIONS OF PSYCHOPATHOLOGY 421
developmental process, the mechanisms for language production and comprehension, are
also plausibly viewed as pathological. Thus the overall account of such profound linguistic
deficits plausibly recruits both an input-based component and a deficit-based component.
A second sort of hybrid model proposes that a normally functioning mechanism might be
responsible for producing pathological symptoms because the inputs it receives are gener-
ated by some other mechanism that is malfunctioning. Such proposals are well known from
the literature on delusions. For example, according to Maher’s influential theory of delusion,
although delusional beliefs are the output of systems for reasoning, these systems are in no
way damaged or malfunctioning (Maher 1988). Instead, normally functioning reasoning
systems produce delusional beliefs as a consequence of endeavoring to make sense of the
bizarre sensory inputs that they receive from malfunctioning sensory systems. On this view,
then, delusion is to be explained by combining a deficit-based account of sensory processing
with an input-based account of reasoning processes.
Direct pathology models
The third and final kind of approach to understanding psychopathology is what we call a
direct pathology model. In contrast to deficit and input-based models, direct pathology mod-
els do not seek to explain pathology in terms of some deviation from normal conditions.
Rather, researchers instead seek to model directly the pathological condition or process.
Although direct pathology models are rather less common than the alternatives, it is still the
case that a number of cognitive accounts of pathology conform to this explanatory pattern.
This is especially so of accounts which hold that a given psychiatric condition does not result
from some manipulation to normally functioning cognitive systems, but instead main-
tain that the condition results from the existence of a stable polymorph—a distinct form
of neurocognitive organization—within the relevant human subpopulation. So, for exam-
ple, Annette Karmillof-Smith and her collaborators (D’Souza and Karmiloff-Smith 2011;
Karmiloff-Smith 1992) have argued that for people with developmental disorders, such as
Williams syndrome, the mature brain may be so different that their cognitive systems will
need to be modeled in their own right, as opposed merely to characterized in contrast to
neurotypicals. Such a view is also suggested by some of Baron-Cohen’s more recent work on
autism (Baron-Cohen 2005).
A menu of explanatory strategies
In the “Core explanatory assumptions” section we identified a range of related explanatory
strategies, commonplace in cognitive science. Then in “Three approaches to the explanation
of psychopathology” we drew an orthogonal three-way distinction between approaches to
psychopathology, approaches that differ in the assumptions that they make about the rela-
tionship between the target pathology and normal cognitive processes. When combined,
these distinctions yield a remarkably rich array of explanatory strategies. Indeed, even
ignoring the hybrid approaches discussed, there are at least fifteen distinct strategies, each of
which has been applied within research on psychopathology.
Moreover, these various strategies are routinely combined in various ways by those inter-
ested in psychopathology. For example, amongst those who adopt a deficit approach to a
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422 Summoning Concepts
given pathology, it will be natural to seek: (a) an intentional characterization of both the
normal process and the deficit, (b) to provide algorithmic and functional characterizations
of the normal and pathological processes involved, and (c) to specify the cognitive mech-
anisms on which such processes depend (and the brain organization that allows for the
realization of such mechanisms). Indeed we suspect that there is a widespread consensus
amongst cognitive scientists that all of these various explanatory tasks should ideally be per-
formed in the course of comprehensively characterizing a disorder. As a matter of fact, how-
ever, much extant research only roughly approximates this ideal, and it is quite uncommon
for all of these strategies to receive equal weighting. In the next section we briefly review two
examples that help illustrate the mixed—and often incomplete—nature of many cognitive
models of psychopathology.
Cognitive Accounts of Psychopathology:
Autism and Depression
In the previous section we provided a taxonomy of different cognitive scientific strategies
for explaining and describing psychopathology. We noted that in general these strategies are
not incompatible and that they are often combined within a single research program. These
strategies have been applied to a broad range of psychopathologies, and we encourage read-
ers to see other sources for further illustration (e.g., discussion of delusions in Davies and
Egan, Chapter 42, this volume; Murphy 2006). In this section we very briefly sketch some
well-known accounts of two forms of psychopathology that have long been the focus of cog-
nitive scientific research: autism spectrum disorder (ASD) and major depressive disorder
(MDD). In doing so, we make no attempt at being comprehensive; nor do we aim to adjudi-
cate between competing proposals. Rather our aim is to illustrate the explanatory strategies
outlined earlier.
Autism spectrum disorder
Although cognitive approaches to ASD have produced a range of competing hypotheses, the
most common approach is one in which the social and communicative problems associated
with the disorder are caused by impairment to a “theory of mind” mechanism or module: a
system normally dedicated to processing information about beliefs, desires and other men-
tal states (Baron-Cohen 1995; Frith 1989; Leslie 1994).7
Notice that the sort of model on offer here is deficit-based. As one might expect, then,
much of the evidence comes from studies showing that people with ASD are significantly
impaired on a range of social cognition tasks, including joint attention (following another’s
gaze), the use of pretense, and the understanding of deception (Baron-Cohen 1995). But
perhaps the most frequently cited evidence comes from studies of the so-called “false belief ”
7
For a recent commentary on research in this area see Frith (2012).
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COGNITIVE SCIENCE AND EXPLANATIONS OF PSYCHOPATHOLOGY 423
task, where Leslie, Baron-Cohen, and others have argued that there is dissociative evidence
for the selective impairment of a theory of mind mechanism (or ToMM). In particular,
ToMM theorists argue that high functioning children with ASD perform poorly on this task
compared to children with Down’s syndrome, even though this latter group have far more
profound general cognitive deficits (Baron-Cohen et al. 1985).
In elaborating their account of ASD, ToMM theorists operate at a number of descrip-
tive levels. So, for example, they provide intentional descriptions of the relevant phenomena
(e.g., detailing the supposed inability of patients to form accurate representations of men-
tal states), and they also sketch the processing stages involved in, say, determining the con-
tent of another’s belief. But perhaps the central focus of this approach is on the mechanistic
decomposition of the systems responsible for “mindreading,” and in what follows we focus
on this issue.
Although ToMM theorists agree that there is a neurocognitive mechanism whose impair-
ment is responsible for central aspects of ASD,8 there is considerable disagreement on
a range of related issues. One such issue concerns how best to characterize the functional
and neuroanatomical organization of ToMM. So, for example, it remains a topic of ongoing
research how best to decompose ToMM into its component parts, and what neural regions
are primarily responsible for our capacity to attribute mental states to ourselves and to oth-
ers (Baron-Cohen 2005; Carruthers 2009; Saxe and Kanwisher 2003).
Another issue concerns what mechanisms in addition to ToMM are implicated in
“mindreading”. So, for instance, according to Baron-Cohen’s (1995) influential proposal,
in addition to ToMM, the mindreading system contains highly specialized mechanisms
for processing various sorts of perceptual information, including: an intentionality
detector (ID) that interprets the movement of agent-like stimuli in terms of goals and
desires; an eye-direction detector (EDD) that detects eye-like visual stimuli and tracks
direction of gaze; and a shared attention mechanism (SAM), which takes inputs from ID
and EDD and thereby enables the infant to work out whether they are attending to the
same thing as another person (Baron-Cohen 1995).9 In contrast, Leslie et al. (2004) have
argued that, in addition to whatever specialized modules there are, we must posit a rela-
tively unspecialized “selection processor”—an executive system that is, amongst other
things, responsible for inhibiting a default bias in normal adults toward attributing true
beliefs to others.
A third issue concerns the extent to which the phenomena associated with ASD are
fully explained as a deficit to ToMM. While it has long been recognized that social defi-
cits are central symptoms of this disorder (Wing and Gould 1979), more recent studies
indicate that there are a range of other anomalies—for example, in executive function
and memory (Happe and Frith 2006; Hill 2004). But the precise nature of the relationship
between these anomalies and deficits to ToMM remains unclear. In particular, it remains
a matter of active debate whether these anomalies are a downstream, developmental effect
of ToMM impairment, or whether they are the products of other relatively independent
deficits.
8
For example, there is neuroimaging evidence that areas normally activated during social cognition
are underactive in individuals with ASD (Frith and Frith 2003).
9 For further additions and modifications see Baron-Cohen (2005).
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424 Summoning Concepts
A final issue regarding the ToMM hypothesis concerns its reception amongst ASD
researchers more broadly. In particular, its adequacy has been challenged on a number of
grounds. Some have, for example, recruited evidence about the degree of functional, neu-
roanatomical and developmental overlap between theory of mind capacities and executive
function more broadly in order to argue that ToMM may not be a specialized, independ-
ent system (Carlson and Moses 2001; Perner and Lang 1999). Similarly, Gerrans and Stone
(2008) have argued that normal theory of mind capacities are not supported by a dedicated
ToMM but rather are the product of the developmental interaction between relatively
domain-general, high-level systems, such as those underwriting executive function, and
domain specific modules for low-level perceptual processing (though see Adams 2011 for
critical discussion).
Major depressive disorder
Though a wide array of factors have been implicated in the etiology of MDD, most current
cognitive models generally adhere to a “vulnerability-stress” conceptualization, according
to which depression results from the activation of cognitive biases by stressful life events.
Perhaps the most influential version of this approach was originally proposed by Beck (1967,
1976, 1987), with a model based on the idea that an interplay of dysfunctional “schemas” (or
belief frameworks) and negative life events can lead to a pattern of negatively biased apprais-
als and thoughts. On this view, the characteristic signs and symptoms of MDD—including
feelings of hopelessness and despair, suicidal ideation, and anhedonia—are downstream
effects of these negatively valenced cognitive states.
Notice that the sort of explanation on offer here differs in a range of respects from the
models of ASD considered in the “Autism spectrum disorder” section. First, on the most
natural interpretation, Beck’s model of MDD is substantially input-based in that the impact
of negative life experiences is central to the explanation of depressive episodes. That is, the
input-based approach takes depression to be the result of standard-issue cognition having
fallen prey to the influence of stress and trauma over time. That said, whether the relevant
cognitive “vulnerabilities” constitute a set of underlying deficits remains a point of active
enquiry.10 In which case, it may be that a fully articulated version of the proposal will ulti-
mately constitute a hybrid model.
Second, Beck’s model does not appear to provide any mechanistic decomposition.
Instead, the model seems primarily concerned with recruiting familiar concepts from
cognitive science, such as the notion of a schema, in order to provide an intentional level
description of the processes implicated in MDD. What results is a kind of functional anal-
ysis—a decomposition of the process into intentionally characterized subparts—though
one largely lacking in precise computational analyses. It should be noted, however, that
in recent work Beck and his collaborators (Beck 2008; Clark and Beck 2010) have sought
10 There is a large literature on the cognitive profiles of individuals with MDD, detailing their
divergence from the non-depressed on a range of cognitive tasks, most significantly in the domains of
attention, memory, and aspects of executive functioning (Gotlib and Joorman 2010; Kircanski et al.
2012). This profile of behavioral variation may then signal underlying deficits of some sort.
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COGNITIVE SCIENCE AND EXPLANATIONS OF PSYCHOPATHOLOGY 425
to develop an implementation level account of the neural structures involved in these
processes.
Two Challenges to the Cognitive
Science of Psychopathology
In the previous sections we outlined some of the main explanatory strategies to have
emerged from cognitive science, and illustrated their application to the study of psycho-
pathology. In this section we conclude by briefly considering two challenges to the pros-
pects of a developed cognitive science of psychopathology. The first is methodological in
character, and concerns the pervasive role of dissociative data in theory construction. The
second challenge is rather more philosophical in spirit, and concerns whether cognitive
scientific approaches to psychopathology will turn out to be subject to serious in-principle
limitations.
Challenge 1: The role of dissociative data
As noted in the “Core explanatory assumptions” section, it is exceedingly common, espe-
cially within cognitive neuropsychology, to use double dissociations in order to provide
evidence for the existence of distinct neurocognitive mechanisms, and to identify the under-
lying deficits responsible for neurological and psychiatric disorders. Indeed this method of
dissociation (or MD) is arguably the main source of evidence for such hypotheses. In recent
decades, however, this method has been a focus of much criticism, especially in its applica-
tion of psychiatric disorders. In what follows, we outline three such criticisms (for a more
comprehensive review see Davies 2010).
Objection 1: The instability of symptoms
A first worry with applying the MD to psychiatric disorders concerns the relative instability
of psychiatric symptoms. While these considerations have not been extensively elaborated,
the worry appears to concern the fact that many psychiatric symptoms—for example, sui-
cidal and delusional ideation—tend to wax and wane in their occurrence, form, and sever-
ity (e.g., see Young 2000 on delusions). In view of this, it may be that the MD cannot be
used to support conclusions about the underlying deficits responsible for such symptoms.
Specifically, the idea seems to be that if, on the one hand, symptoms are unstable whilst, on
the other, neurocognitive mechanisms are stable, enduring structures, then dissociative data
will not license inferences regarding which specific underlying deficit is responsible for the
symptoms—i.e., which neurocognitive mechanism is damaged. Thus it may be necessary to
revise, supplement, or even replace the MD in cognitive neuropsychiatry.
Though symptom instability no doubt raises interesting methodological issues, we
doubt that it poses a serious challenge to the use of dissociative data in the study of psy-
chopathology. One obvious, and in our view convincing, response is that when studying
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426 Summoning Concepts
psychopathology it may often be appropriate to characterize deficits in terms of partial
impairment, as opposed to construing symptoms as products of completely impaired sys-
tems (again, see work on delusions by Coltheart et al. 2011; Davies and Coltheart 2000). In
this way, the fact that some psychiatric symptoms appear to wax and wane is explained by
supposing that the dysfunctional informational products of cognitive deficits are occasion-
ally over-ridden—or compensated for—by other processes.11
Objection 2: “Impure” cases
A second challenge to the MD centers on the notion of impure cases, and the epistemic prob-
lems that such cases generate (Van Orden et al. 2001). In brief, a single dissociation is pure
when it involves damage to exactly one neurocognitive mechanism; otherwise it is impure.
A double dissociation is pure, when it combines two pure single dissociations; otherwise it
is impure. In order to appreciate the significance of the distinction, it is important see that
pure and impure double dissociations have quite different inferential properties. If a double
dissociation is pure, then it deductively follows that there are two distinct mechanisms, each
of which is impaired with respect to a task. In which case, if one has strong evidence that a
case is pure, then one also has good reason to conclude that there are two, distinct cognitive
mechanisms, and that the observed pattern of performance is explained by their selective
impairment (and sparing).
The logical implications of impure dissociations are quite different. If a dissociation is
impure, it does not deductively follow that there are two distinct mechanisms whose selec-
tive impairment and sparing is responsible for the pattern of performance. Instead there is a
range of alternatives that are compatible with the impure case. So, for example:
1. It is possible that the dissociable tasks depend at least partially on some shared
resource.
2. It may be that different components within a single mechanism are differentially
affected and that this is responsible for producing different patterns of performance.
3. It is even possible that the dissociable tasks are the products of some kind of
continuous processing space that wholly lacks separate and identifiable mechanisms
(Shallice 1988).
But if this is so, if impure cases are consistent with all these possibilities, then having evi-
dence of such a dissociation does not provide good reason to conclude that the pattern of
performance is produced by the selective impairment of two distinct mechanisms. That is,
impure cases neither provide strong evidence for distinct cognitive mechanisms nor for the
underlying deficit responsible for a given pathology.
What are we to make of these observations? The appropriate response may seem obvi-
ous. If pure cases provide strong evidence and impure cases do not, then in using the MD
we should restrict ourselves to pure cases. But this alone does not resolve the matter. First,
we should expect the vast majority of dissociations to be impure since there is no reason to
11
And this also leaves space open for the study of the role of factors external to the impaired
mechanism, such as environmental stressors, that might explain the more-or-less acute presentation of
certain symptoms.
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COGNITIVE SCIENCE AND EXPLANATIONS OF PSYCHOPATHOLOGY 427
suppose that the causes of neurocognitive insults—for example, blows to the head or chro-
mosomal abnormalities—should respect the boundaries between cognitive mechanisms. In
which case, if impure cases fail to provide a basis for dissociative inferences, then it would
seem that the vast proportion of data that cognitive neuropsychologists have amassed is of
little relevance to the task of discerning cognitive structure.
A second and apparently more serious worry is that in order to pursue a policy of restrict-
ing ourselves to pure cases, it would seem that we need some reasonably reliable method
for determining which cases are pure and which impure. Yet (so the objection continues)
there is no such reliable method; or at any rate, we have no good reason to suppose that there
is. Indeed some go so far as to claim that there exists no noncircular way of determining
whether a given case is pure or otherwise. For in order to know that a double dissociation is
pure one must already have established what the dissociative evidence is supposed to show,
viz., that there are two distinct mechanisms whose impairment is responsible for the pattern
in performance (Van Orden et al. 2001). Thus critics conclude that the MD cannot support
conclusions about the mechanistic structure of the mind or about the underlying causes of
psychopathology.
In our view, the distinction between pure and impure cases does raise problems for the
MD, if only because it suggests that such data typically provide rather less support for con-
clusions about neurocognitive structure than advocates have traditionally supposed. That
said, we are inclined to think that the worries raised by impure cases are somewhat allayed
by noting that the MD may well allow for respectable abductive inferences, even in the
absence of any highly reliable means of discriminating pure from impure cases (Coltheart
and Davies 2003; Davies 2010). This is because although possible alternative explanations
will be routinely available, in many cases the best explanation—the simplest, most conserva-
tive, most powerful explanation—may well appeal to the existence of functionally distinct
neurocognitive mechanisms. Of course, the strength of any particular inference will depend
upon the details of the case. For example, it will depend on the degree of dissociation
between the disorders being compared and between the neural regions that are associated
with these disorders. But if used judiciously, we are inclined to think that, despite the exist-
ence of impure cases, the MD can still yield insight into the functional organization of the
mind/brain and its pathologies.
Objection 3: The problem of developmental dissociations
Another potential challenge to the MD, especially as it extends to psychiatry, comes from
the study of disorders that are thought to be the product of nonstandard development. Until
quite recently, it was commonly assumed that dissociations arising from developmental
psychopathology could provide good evidence for the existence of specialized cognitive
systems. For example, as we saw in “Explaining psychopathology,” many researchers were
led to infer that the impairments in autism were the product of a dysfunctional mechanism
normally responsible for theory of mind. Similarly, Williams syndrome, a developmental
disorder marked by impairments to a child’s spatial recognition capacities, has garnered
considerable theoretical attention in large measure because there appear to be double disso-
ciations between Williams and a range of other disorders, including: autism; prosopagnosia,
which involves deficits in facial recognition; and SLI, which is characterized by syntactic
impairments. Further, these dissociations have been taken to support conclusions regarding
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428 Summoning Concepts
the existence of specific cognitive mechanisms whose selective impairment and sparing is
responsible for the various disorders involved.
Though such inferences have been challenged on a number of grounds, perhaps the
most theoretically interesting concerns the developmental presuppositions implicit in
the application of the MD to developmental disorders. Annette Karmiloff-Smith and her
collaborators (D’Souza and Karmiloff-Smith 2011; Karmiloff-Smith 1992) have argued,
for instance, that the application of the MD to developmental disorders presupposes that
developmental anomalies tend only to affect relatively autonomous neurocognitive sys-
tems, whilst leaving the rest of the brain “residually normal”. In contrast, Karmiloff-Smith
maintains that the brain is more plausibly construed as a massively interconnected system
that develops in holistic fashion, so that early emerging pathological disruption will tend
to be amplified in the course of develop, and ultimately produce comprehensively atypical
neural organization. But if this is so, if developmental disorders, such as Williams, probably
result in comprehensively atypical brains, then it is no longer safe to assume that the signs
and symptoms associated with such disorders are produced by selective impairments to an
otherwise intact or “residually normal” brain. In which case, it would seem that the MD, at
least as typically applied, fails to license conclusions about the existence of specific cogni-
tive mechanisms or about the underlying causes of psychopathology (for further discus-
sion see Machery 2011).
Challenge 2: In-principle limitations?
Let us turn to the second, more principled, of our challenges. Though the cognitive sciences
have made considerable progress in explaining aspects of the human mind and its various
disorders, no one would be so sanguine as to suggest that the project is complete, or even
nearly so. On the contrary, success to date has been fragmentary; and it remains a largely
open question whether—and to what extent—efforts to explain mental disorder will prove
successful. In view of this, we propose to conclude this chapter by discussing considerations
that some (e.g., Murphy 2006) have recently taken to suggest that the cognitive science of
psychopathology may well be subject to serious limitations.
The relevant considerations are familiar to cognitive scientists, and received their canoni-
cal expression almost three decades ago in the final sections of Fodor’s Modularity of Mind
(1983). In this work, Fodor famously advocates a conception of our mental architecture on
which peripheral systems for motor control and low-level perception are modular in char-
acter. That is, they are highly specialized and informationally encapsulated in the sense that
they only utilize a restricted range of the information available to the organism as a whole.
In contrast, Fodor maintains that central systems responsible for such “higher” cognitive
tasks as reasoning and decision-making are radically non-modular, or unencapsulated:
that they can utilize virtually any sort of information in the course of their computations.
According to Fodor, this is likely to have important implications for the scope and limits of
cognitive science. In particular, he maintains that the sort of cognitive science we currently
have, one wedded to an information processing model, is unlikely to make much headway
in providing models of highly unencapsulated central processes. Indeed Fodor goes so far
as to claim “the limits of modularity are also likely to be the limits of what we are going to be
able to understand about the mind, given anything like the theoretical apparatus currently
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COGNITIVE SCIENCE AND EXPLANATIONS OF PSYCHOPATHOLOGY 429
available” (Fodor 1983, p. 126). In what follows, we propose to explore the implications of
this Fodorian pessimism for the cognitive science of psychopathology.
Reasons for Fodorian pessimism?
What reasons are there for supposing that non-modular systems will likely prove recalcitrant
to the explanatory strategies of cognitive science? Fodor provides two main considerations,
and though clearly neither are conclusive, many have found them prima facie compelling.
The first focuses on the prospects of providing implementational (e.g., neurobiological or
neuroanatomical) descriptions of central systems. If a cognitive system is modular and,
hence, processes only a restricted range of inputs, and bears limited informational relations
to other systems, then it is reasonable to suppose that it will have a reasonably well-defined
neural architecture: that it will be localized and have well-defined connections to other sys-
tems. (This appears to be true in the case of early vision, for example.) In contrast, if cen-
tral systems are, as Fodor claims, radically non-modular, bearing elaborate informational
relations every which way, then they are also unlikely to exhibit a clearly articulated neural
architecture. Further, the same will likely be true of the components of such mechanisms.
As a consequence, according to Fodor, lesion studies, deficit studies, neuroimaging, and
other strategies designed to aid in the identification of structurally characterizable units are
unlikely to prove successful.
The second consideration focuses on the prospects of providing algorithmic specifications
of central processes. On the assumption that cognitive systems are information processing
devices, we should expect to be able to specify the sorts of information that a cognitive sys-
tem deploys and the sorts of computations involved in the processing of such information. In
the case of low-level perceptual processes, the task of doing so appears tractable. We are, for
example, able to determine with reasonable clarity what sorts of information are deployed
in early vision, and how it is being processed. According to Fodor, however, the problem we
confront in understanding central processes is that there appear to be few discernible con-
straints on information processing. We are able to think about almost anything, the infor-
mational relations appear to go every which way, and they are highly sensitive to context. In
which case, the task of specifying such processes in information processing terms will likely
prove an extraordinarily difficult one.
Psychopathology in the shadow of Fodorian pessimism
Though the case for Fodorian pessimism is far from overwhelming, it is suggestive enough
to merit consideration of its implications. Let us suppose for the sake of argument, then,
that it is correct, and ask what its implications are for a cognitive science of psychopathol-
ogy. In our view they are likely to be profound. In particular, Fodorian pessimism has bleak
implications for the prospects of cognitive models of psychopathologies that involve central
processes.
The point is perhaps clearest in the case of deficit-based models that seek to describe the
pathology in algorithmic or mechanistic terms. Such models aim to explain how some path-
ological phenomenon is produced by citing some abnormality—or malfunction—in those
mechanisms or computational processes responsible for normal cognition. But such models
presuppose some prior account of normal cognition. In which case if Fodorian pessimism
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430 Summoning Concepts
is correct, then such deficit-based models will be not be forthcoming for pathologies that
involve central cognition. This will simply be because there will be no mechanistic or algo-
rithmic account of normal function to work from in the first place.
Much the same is true of input-based models of psychopathologies that involve central
processes. In such models one seeks to explain some pathological phenomena in terms
of abnormal inputs to normally functioning mechanisms or processes. But again, such
explanations presuppose a prior mechanistic account of normal function. In which case if
Fodorian pessimism is correct about central processes, no such models of the psychopathol-
ogies will be forthcoming.
The issues are perhaps least clear in the case of direct pathology models. Rather than pre-
supposing some account of normal cognition, such models instead proceed directly by con-
structing an account of the pathology itself or of the mechanism(s) that are responsible for
the pathology in question. It is harder to assess the implications of Fodorian pessimism for
such approaches, in large measure because it remains unclear, for any pathology, how much
of central cognition needs to be characterized in order to provide such models. As a conse-
quence, one cannot argue directly from Fodorian pessimism to conclusions about the pros-
pects of such models. For all that, if Fodorian pessimism is correct, we should expect that
direct models of pathologies that crucially depend upon central cognition—for example,
thought disorders and delusional disorders—will be hard to produce in much the same way
as indirect (deficit and input-based) models are.
Of course, none of this precludes the possibility of models of psychopathology that do
not seek to provide mechanistic or algorithmic descriptions of the pathologies involved. But
for present purposes, the point we wish to stress is that the search for such descriptions is
very typically the ultimate goal of cognitive science. And what goes for cognitive science in
general also goes for cognitive scientific explanations of psychopathology. In which case, if
Fodorian pessimism is correct, this explanatory quest may turn out to be forlorn.
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