COGNITIVE SCIENCE AND PSYCHIATRY:
Dan J. Stein, M.D.
Dept of Psychiatry
College of Physicians and Surgeons, Columbia University,
and the New York State Psychiatric Institute,
722 W 168 St
NY, NY 10032
Running Title: Cognitive Science and Psychiatry
Cognitive science is a multidisciplinary field, comprising cognitive
psychology, artificial intelligence, linguistics, neuroscience, and
anthropology. In recent years, cognitive science has become a
predominant paradigm in studies of the mind. This paper reviews work
at the emerging interface between cognitive science and psychiatry.
It is argued that cognitive science has significant potential as an
integrative framework for theorizing and researching psychiatric
disorders and their treatment.
Cognitive science is a relatively new field that incorporates concepts
and methods from philosophy, cognitive psychology, artificial
intelligence, linguistics, neuroscience and anthropology (Gardner,
1985; Posner, 1989). Whereas behaviorism dominated the psychological
sciences during the first part of this century, cognitive science has
become a central paradigm of the latter part. This "cognitive
revolution" was fostered by the promise of cognitive science as an
integrated and fertile approach to the mind. Indeed, departments of
cognitive science have become important loci of interdisciplinary
research and have generated a prodigous literature.
During the period of the cognitive revolution, significant changes
also took place in psychiatry (Lipowski, 1989). During the 1950s,
psychoanalysis comprised a dominant school in many departments of
psychiatry. In subseqent decades, however, a community-focused model
was widely touted. Most recently, neurobiological approaches have
been given pride of place. Certainly, there have been calls for a
synthetic or "biopsychosocial" approach to psychiatric disorders and
treatment. Nevertheless, there has been little progress on the
construction of an integrative framework for such an approach.
On the whole, cognitive scientists have paid little attention to
clinical phenomena, while clinicians have in turn only occasionally
employed the concepts and methods of cognitive science. In recent
years, however, dialogue between the two fields has increased (Breger,
1969; Mahoney and Freedman, 1985; Ingram, 1986; Horowitz, 1988a;
Williams, Fraser, MacLeod, and Mathews, 1988; Magaro, 1991; Stein and
Young, 1992a). In this paper I review research at this intersection,
and discuss its potentials and limitations. I suggest that cognitive
science can provide an important integrative framework for psychiatry.
What exactly is cognitive science? The following three sections
attempt to address this question. I briefly discuss descriptions and
definitions of cognitive science, its origins and subdisciplines, and
then contrast and compare cognitive science with behaviorist,
psychoanalytic and neurobiological models (Stein, 1992a).
a) Descriptions and Definitions
In general, cognitive scientists are interested in mental structures
and processes, their representational significance, and their physical
instantiation (Stillings, Feinstein, Garfield, Rissland, Rosenbaum,
Weisler, and Baker-Ward, 1987). Several authors have attempted more
rigorous definitions of cognitive science. A cognitive
information-processing account, for example, views the mind as an
information-processing system that selects, transforms, encodes,
stores, retrieves, and generates information and behavior (Lachman,
Lachman, and Butterfield, 1979). A computational view, on the other
hand, emphasizes that, "Cognitive science, sometimes explicitly, and
sometimes implicitly, tries to elucidate the workings of the mind by
treating them as computations, not necessarily of the sort that is
carried out be the digital computer, but of a sort that lies within
[the] broader theory of computation" (Johnson-Laird, 1988, p. 9).
Restrictive definitions of cognitive science may, however, include
only one or other of the divergent models that cognitive scientists
have developed. Early cognitive scientists viewed the mind as a
sequential processor, similar to the early digital computer. The mind
was seen as a passive recipient of information, which was registered
in a short-term memory, and perhaps encoded in a long-term memory.
More recent cognitive scientists have, however, pointed out that the
mind is a parallel processor (Rumelhart, Hinton, and the PDP Research
Group, 1986), and have emphasized that mental structures are active
and that they occur within a particular context (Neisser, 1976;
Lakoff, 1987). Such work may be excluded by a definition of cognitive
science that focuses solely on information-processing and computation.
On the hand, too broad a definition of cognitive science may prevent a
constrast between cognitive science models and other models often used
by clinicians. It may be useful to at least specify the broad
categories into which cognitive science models fall. Cognitive
science models typically specify cognitive architecture in one of two
ways, symbolic and connectionist. The elements of symbolic systems
are symbols, which are stored in associative structures. Symbolic
systems include levels-of-processing models (Craik and Lockhart,
1972), spreading activation constructs (Collins and Loftus, 1975), and
schema approaches (Neisser, 1976). The elements of connectionist
systems are simplified and schematized neurons which are
interconnected in a network. Again, a variety of these parallel
processing models have been developed (Rumelhart et al., 1986). In
subsequent sections the application of both symbolic and connectionist
architectures to clinical theory and practice is reviewed.
b) Origins and Subdisciplines
Further understanding of cognitive science can be gained by discussing
its origins. The development of cognitive science models was
encouraged by various factors, including the failure of behaviorism,
the invention of the computer, and various theoretical advances
(Gardner, 1985). One of the most important of these theoretical
advances was the development of computer science. The father of this
field was Turing (1936), a British mathematician who described a
simple machine (the Turing machine) that executed instructions in
binary code, and proposed that such a machine could in principle
perform any computational task. He also described the idea of a
universal Turing machine which takes a coded version of other Turing
machines as input and then emulates their behavior.
Computer science can immediately be seen as relevant to psychology,
for the question arises of whether the human mind can be instantiated
on a universal Turing machine. If so, computer science would
constitute a basic science for exploring the hypothesis (Craik, 1943)
that the mind is a symbol manipulating device. Turing (1950)
suggested it might be possible to program a machine so that a user
communicating with the machine and with a person would be unable to
differentiate the two (the Turing machine test).
A number of other developments consolidated the importance of
computational constructs for psychology. Thus, it was proposed that
certain concepts were useful in explaining both computing machines and
human brains. This was clearly formulated at a seminal meeting in
1948, the Hixon Symposium on "Cerebral Mechanisms in Behavior"
(Jeffress, 1951) at which McCulloch, a professor of psychiatry, and
Von Neumann were the opening speakers. For example, the all-or-none
property of neuronal activation can be compared with the determination
of Boolean statements as either true or false. Furthermore, neuronal
networks and Boolean statements can be described in elecrical terms as
current that passed or failed to pass in a circuit.
In addition, it was argued that certain constructs were useful in
explaining both computing machines and human minds. Hebb (1949), for
example, suggested that synaptic strengthening in neuronal networks
led to learning. Wiener (1947) proposed that machines and minds that
had feedback mechanisms that displayed purposefulness. Central to
control and communication engineering was the notion of the message,
whether this was transmitted by electrical, mechanical, or neural
means. Information, Shannon (1938) showed, was independent of its
It has been suggested that cognitive science was founded in September
1956, at the Symposium on Information Theory at MIT (Gardner, 1985).
Significant research presented at the conference included papers by
the cognitive psychologist, Miller, artificial intelligence workers,
Newell and Simon, and the linguist, Chomksy. In his paper, Miller
illustrated the importance of empirical studies of cognition by
discussing work on the constraints of short-term memory processes.
Work by other psychologists in the 1950s, including Broadbent and
Cherry in Britain, and Bruner and colleagues at Harvard, gave further
impetus to the development of cognitive psychology, and established
the discipline and its empirical methodologies as a cornerstone of
cognitive science. Newell and Simon argued that artifial intelligence
was possible, and drew comparisons between artificial and human
problem solving processes. Together with Minsky and McCarthy they
pioneered the field of artificial intelligence, and helped establish
the importance of a computational methodology for cognitive science.
Finally, Chomsky described his theory of a grammar based on linguistic
transformations. This work, together with his scathing review
(Chomsky, 1959) of Skinner's work on verbal behavior buttressed the
shift from behaviorism to cognitivism, and established linguistics as
a useful frame from which the issues of cognitive science could be
Other core disciplines of cognitive science include neuroscience,
anthropology, and philosophy. Neuroscience comprises a lower limit
for cognitive science, for human cognitive structures and processes
are ultimately based in and constrained by neuroanatomy and
neurochemistry. Anthropology and social psychology constitute an
upper limit for cognitive science, for cognitive structures and
processes vary from place to place and time to time. Finally, many of
the questions studied by cognitive scientists were first raised by
philosophers. Philosophers have also addressed the foundational
principles of the discipline, and more recently, as cognitive science
has advanced, have been faced with the meta-cognitive question of what
the mind and the world are like for this scientific advance to have
Developmental psychology is not usually listed as a central discipline
of cognitive science. Nevertheless, one of the most important
pre-cognitivists was the developmental psychologist Piaget. Piaget
detailed the transformation of mental structures from sensorimotor
reflexes to the operations of formal thought (Piaget, 1952).
Contemporary dialogue between developmental psychology and cognitive
science is increasing (Sternberg, 1984; Meltzoff, 1990; Globerson and
c) Contrasts and Comparisons
Cognitive science models can be contrasted and compared with the
clinically familiar models of behaviorism, psychoanalysis, and
neurobiology. Behaviorism, for example, self-consciously makes the
mind into a "black box", asserting that only observable stimuli and
responses can be studied. In contrast, the cognitive model holds that
what is most interesting are the mental structures in the "black box"
and the processes (operations) whereby they generate cognitive
products (thoughts and feelings).
Nevertheless, it is possible to see a continuity between behaviorist
and cognitive science models. While the behaviorist model is limited
to inputs (stimuli) and outputs (responses), the cognitivist model is
concerned with inputs, processing, and outputs. Like behaviorists,
cognitivists have adopted an empirical stance, which emphasizes the
importance of careful measurement and laboratory experimentation.
The classical psychoanalytic model of the mind was an energy-based
one. For Freud (1894), "in mental functions something is to be
distinguished - a quota of affect or sum of excitation - which
possesses all the characteristics of a quantity (though we have no
means of measuring it), which is capable of increase, dimunition,
displacement and discharge, and which is spread over the memory-traces
of ideas somewhat as an electric charge is spread over the surface of
a body". Freud described how the forces of the unconscious are
expressed, transformed, or repressed, resulting in everyday behaviors
Again, however, it is possible to demonstrate a continuity between
psychoanalysis and cognitive science. Cognitive science and
psychoanalysis both focus on the structures of the mind, and the way
in which these determine mental phenomena. Furthermore, in Freud's
later work he describes affect not in terms of energy, but in terms of
its role as a signal. Finally, post-Freudian psychoanalysts,
including the self psychologists, the objects relations school, and
the interpersonalists, have increasingly employed cognitively oriented
constructs such as self and other representations (Stein, 1992b).
Neurobiological models attempt to explain the biological basis of
mental processes and products. Proponents of these models may state
that all psychological explanations are reducible to neurobiological
ones. This view contrasts with the cognitive science argument that
cognitive phenomena necessarily require psychological explanations.
Psychological events are emergent phenomena, and psychological
explanations are not reducible to biological ones.
Once again, however, it is possible to see a continuity between the
biological model and cognitive science. Earlier neuroscience was
described as comprising a lower limit of cognitive science. While
some cognitivists are interested in information-processing in only the
most abstract sense, many others are interested specifically in how
information-processing occurs in the human neurobiological substrate.
This interest is particularly apparent in connectionist work informed
by neuroscience (Rumelhart et al, 1986).
CLINICAL COGNITIVE SCIENCE
For the clinician, the question immediately arises of whether
cognitive science models are useful in accounting for clinical
phenomena. However, standard texts of cognitive science make little
reference to psychiatric disorders and treatment. Nevertheless, a
number of developments in cognitive science have been instrumental in
allowing a bridge to clinical phenomena. This section briefly reviews
some of these bridges from cognitive science to the clinic, including
research on emotional and unconscious processing, and relevant work in
each of the subdisciplines of cognitive science.
Cognitive science has been characterized by a disregard for emotion
(Gardner, 1985). Nevertheless, humans are not only the most
intelligent of the animals, they are perhaps also the most emotional
(Hebb, 1946). Furthermore, it may be suggested that this relationship
between intelligence and emotionality is a necessary one. In a
seminal paper, Simon (1967) argued that emotion in humans is
comparable to the prioritized interruption of different processes by
one another in complex artificial intelligence systems with multiple
goals and limited resources. The idea that cognitive design problems
are solved by emotional processes remains popular in contemporary
cognitive science attempts to theorize emotion (Oatley and
Johnson-Laird, 1987; Sloman, 1987), and has helped generate a
rapidly growing empirical literature on cognitive-affective
processing. Cognitivist work on emotion has subsequently been
employed in order to understand affective experience and change in the
clinic (Mandler, 1975; Greenberg and Safran, 1990).
Another important area within cognitive science that has clinical
relevance is the study of unconscious processing. Research on the
unconscious is not new, and includes the pioneering work of Helmholtz,
Janet, and Freud (Ellenberger, 1970). However, it was not until the
emergence of cognitive science that the "cognitive unconscious"
(Kihlstrom, 1987) became a respectable area of study. Research has
focused on such areas as selective attention, subliminal perception,
implicit memory, hypnotic suggestion, and dreaming (Erdelyi, 1985;
Foulkes, 1985; Kihlstrom, 1987). While this work immediately brings
psychoanalysis to mind, there appear to be important differences
between the cognitive unconscious and the psychoanalytic unconscious
(Bowers and Meichenbaum, 1984). According to Freud, the unconscious
is a set of drives, affects, and motives that has an organization and
content more primitive than those of the consciousness, but that
nevertheless has latent the potential for interacting with other
elements of the psyche. According to cognitive science, the
unconscious is a set of cognitive processes, including attitudes and
predispositions, that act prior to consciousness, so actively
organizing and ordering experience and behavior. Nevertheless,
contemporary work on unconscious processing establishes a dialogue
between cognitive and clinical scientists (Bowers and Meichenbaum,
1984; Eagle, 1986; Horowitz, 1988b; Safran and Greenberg, 1986; Uleman
and Bargh, 1989; Singer, 1990; Prigatano and Schachter, 1991; Cloitre,
1992; Stein, 1992b).
A number of early cognitivists have been interested specifically in
psychiatric disorders. Ruesch and Bateson (1968) were among the first
to apply cybernetic concepts to psychopathology and psychotherapy.
Within cognitive psychology, Hilgard (1977), whose work will be
discussed later, employed Janet's notion of dissociation and developed
a neodissociative model of the dissociative disorders. This work
exemplified the potential utility of employing cognitive psychology
constructs to conceptualize psychiatric disorders.
In artificial intelligence, Colby (1981) pioneered the investigation
of clinical phenomena. He developed a program, PARRY, which
incorporated a model of the paranoid process, and succeeded in
simulating a paranoid patient. The difficulty of such work, and
Colby's achievement, is illustrated by the limited amount of
subsequent research in this area (Tomkins and Messick, 1963;
Clippinger, 1977). More recently, however, a number of authors have
employed computer implemented neural networks to model psychopathology
and psychotherapy (Hoffman, 1987; Hestenes, 1991; Cohen and
Servan-Schreiber, 1992l; Williams and Oaksford, 1992; Caspar,
Rothenfluh, and Segal, unpublished manuscript).
Linguists, neuroscientists, anthropologists and social psychologists
have long been interested in clinical phenomena, and some of this work
has fallen within the realm of clinical cognitive science.
Cognitivist models of the aphasias have their origins in the work of
the "diagram-makers", who constructed modular models of the mind at
the turn of the century (Morton, 1984). Current cognitive studies of
language may contribute to various clinical areas, such as treatment
of dyslexia (Swerling and Sternberg, 1992), or understanding clinical
dialogue and the mechanisms of the "talking cure" (Oatley, 1992).
Contemporary cognitive neuropsychologists continue to hold that study
of neurologically impaired patients contributes to cognitive theories
of mind (Caramazza, 1992). Clinical anthropology has tended not to
adopt cognitivist models (Stein, 1992c), but cognitively oriented work
in social psychology has been particularly relevant to clinical
science, and has tackled such important concerns as self and other
representations, and their role in mood disorders (Markus, 1977;
Cantor and Kihlstrom, 1981; Sorrentino and Higgins, 1986; Westen,
1988). Finally, it may be argued that philosophy, too, constitutes an
important foundational discipline for clinical cognitive research
(Mahoney, 1991; Lyddon, 1988; Stein, 1992d).
Developmental psychologists have long been interested in developmental
arrests and abnormalities. Since Piaget, cognitive concepts have
increasingly been used in this area. Developmental research that
draws on cognitive science constructs and focuses on clinical
implications has been particularly influential (Greenspan, 1980;
Stern, 1985; Emde, 1983).
The work of cognitive scientists interested in clinical phenomena
provides the basis for employing cognitive constructs to conceptualize
psychiatric disorders, assessment, and treatment. I will discuss each
of these areas in turn. While a variety of psychiatric disorders have
been studied from a cognitive science perspective, I will focus on
only a few in order to highlight some important methods and issues in
clinical cognitive science.
a) Major Depression
Since their introduction by Ellis, Beck and others, cognitive
therapies have become increasingly accepted as an effective treatment
of depression (Mahoney and Freeman, 1985). While these therapies are
not necessarily formal extensions of cognitive science, much of this
work has been influenced by the cognitive revolution. The growing
dialogue between cognitive science and cognitive models of depression
(Ingram and Reed, 1986; Ingram and Wisnicki, 1991) provides the most
comprehensive exemplar of the use of symbolic architectures from
cognitive science to understand psychopathology.
Symbolic architectures specify the mind in terms of cognitive
structures, operations, and processes. Cognitive structures that have
been hypothesized to be central in depression include associative
networks (Ingram, 1984; Teasdale, 1983) and self-schemas (Beck, 1967;
Kuiper, Derry, and MacDonald, 1982). Associative network theories
characterize depression as having increased availability and/or
accessiblity of negative constructs about the self. Self-schema
theory characterizes depression as activation of a self-structure that
is negative in content. While evidence for negative self-schemas in
depression is good, there is less evidence that such schemas are
present in the absence of current depression (Ingram and Reed, 1986;
Ingram and Wisnicki, 1991).
Research also suggests that cognitive processes in depression are
characterized by a negative selectivity bias (Ingram and Reed, 1986;
Ingram and Wisnicki, 1991). Studies of encoding may be divided into
studies of personal semantic information (i.e. information related to
concepts of self) and episodic information (i.e. information related
to autobiographical events). Studies of semantic encoding indicate
that enhanced negative encoding is a key factor in information
processing in depression, while studies of episodic encoding indicate
that less accurate encoding of positive information is critical.
Studies of retrieval, on the other hand, suggest that negative
information is more efficiently and positive information is less
efficiently retrieved in depression. In addition, depressed patients
selectively monitor negative information with a decrease in attention
to positive stimuli.
Finally, cognitive products in depression include attributions and
automatic thoughts (Ingram and Reed, 1986; Ingram and Wisnicki, 1991).
Depressed patients are more likely to make dysfunctional attributions
as to the cause of events. Negative automatic thoughts appear to be
characteristic of subclinical and clinical depression, and they remit
Cognitive science models of depression influence our conceptualization
of psychotherapy, and direct further empirical research. The finding,
for example, that depressed schemas are not present in the absence of
depression suggests that cognitive therapy works not by structural
changes in underlying schemas, but by changes in the activation level
of negative self-schemas. While cognitive structures such as schemas
are difficult to measure, methodological progress has been made
(Segal, 1988). Considerable theoretical and empirical research on
cognitive science models of depression nevertheless remains to be
done. In particular, cognitive science models of depression have,
with few exceptions (Klein, 1976), failed to incorporate
b) Dissociative and Conversion Disorders
Symbolic architectures, including schema theory, have not only been
adopted by the cognitive therapy tradition, but have also been
employed in psychoanalytically informed research (Wachtel, 1982; Slap
and Saykin, 1983; Segal, 1988; Horowitz, 1988a; Horowitz, 1991). The
area in which there is perhaps most clearly an intersection between
the constructs of symbolic architecture and psychodynamic theory is
the study of dissociative and conversion disorders.
The first detailed psychological theory of dissociative and conversion
disorders was developed by Janet (1907). Like Freud, Janet studied at
Charcot's clinic at the Salpetriere. He too was interested in the
notion of cognitive processes that took place outside of awareness,
for which he coined the term "subconscious". Janet described the mind
in terms of "automatisms", or elementary structures controlling
experience, thought, and action in various domains, and which together
make up the flow of consciousness. At times of stress or exhaustion,
one or more automatisms separate from the rest, breaking the unity of
consciousness. The dissociated automatisms continue to operate, but
are no longer accessible to phenomenal awareness or amenable to
voluntary control, resulting in various dissociative or conversion
Hilgard (1977) used concepts from cognitive psychology to provide a
neodissociative account of these disorders. He described the mind in
terms of subordinate cognitive structures each with a degree of
autonomy, a hiererarchical control managing the competition between
these structures, and a central monitoring and controlling structure
(the executive ego). Subordinate cognitive structures can operate
outside of awareness, or they may be processed consciously through the
executive ego. Certain physiological (e.g. anesthesia) or
psychological (e.g. hypnosis) conditions lead to disruptions in the
connections between the subordinate structures. Information
processing in the subordinate structures may then take place outside
of phenomenal awareness or voluntary control. More recently,
Kihlstrom (1990) has used an associative-network model of memory to
further elaborate this view of the dissociative and conversion
disorders. He describes how disruption of links between
representations of memory or of sensory input and representations of
self may result in lack of awareness of particular memories or
sensations, with continued processing of related representations.
While psychoanalytic theory has also emphasized the importance of
unconscious processes in the genesis of these disorders,
neodissociative theory has a view of the unconscious that is
closer to that of cognitive science. Although there may be
theoretical difficulties with the psychoanalytic energy based view,
the success of the cognitivist revival of Janet's ideas ultimately
depends on its ability to generate empirical work on these relatively
c) Obsessive-Compulsive Disorder
Earlier the question of the relationship between cognitive science and
neurobiological models was raised. This becomes particular relevant
for the clinical cognitive science of disorders about which
neurobiological knowledge is advancing. Current research on OCD, for
example, has demonstrated involvement of the serotonergic system and
orbitofrontal-basal ganglia-thalamic pathways, and the disorder is
increasingly seen as having a neurobiological basis (Stein and
Cognitive science constructs may, however, be helpful in thinking
through the relationship between neurobiological findings and the
psychology of OCD. Several authors have, for example, suggested that
neuroanatomical structures involved in OCD play a role in
goal-feedback mechanisms (Gray, 1982; Rapoport and Wise, 1988).
Disruption of cybernetic control in OCD results in psychological
features such as repetitive symptoms and strategies of seeking control
Work on neuropsychiatric impairment and serotonergic dysfunction in
OCD has led Stein and Hollander (1992) to propose that OCD may involve
a biologically based impairment in determining or in evaluating
goal-feedback responses. Some patients, perhaps those with high
neurological soft signs, visuospatial difficulties and rapid erroneous
responses on neuropsychological testing, may have difficulty in
determining goal-feedback responses. Other patients, perhaps those
with serotonergic dysfunction and slow correct responses on
neuropyschological testing, may overevaluate harm associated with
This model suggests that both biological approaches (normalizing
serotonergic function) and psychological approaches (changing harm
assessment) may be helpful in the treatment of OCD. On the other
hand, in patients where goal-feedback response impairment is
associated with evidence of structural brain damage, treatment may be
less successful. The model provides a heuristic for further research
correlating biological, cognitive, and clinical variables in OCD.
d) Psychotic Disorders
Sincle the time of Kraepelin and Jung, psychiatrists have used
techniques from experimental psychology to explore
information-processing deficits in psychotic disorders. More
recently, cognitive science methods have been used to study these
disorders. There is now a large literature on information-processing
in schizophrenia (Magaro, 1980; Neuchterlein and Dawson, 1984;
Saccuzzo, 1986), and some work on mania (Grossman and Harrow, 1991).
In their review, Neuchterlein and Dawson (1984) highlight two types of
information-processing deficits in schizophrenia - poorer detection of
single, highly familiar stimuli during vigilance, and increases in the
time required to recognize an uncomplicated stimulus.
Information-processing tasks may constitute indicators of
vulnerability, may have prognostic value, and may be useful measures
of medication response. Information-processing deficits in
schizophrenia may also be conceptualized in schema terms (Magaro,
1980). Finally, a number of theorists have attempted to integrate
information-processing and neurobiological findings (Neuchterlein and
Of considerable current interest, however, is work that employs
connectionist architectures from cognitive science to study psychotic
disorders. This work is of particular interest insofar as
connectionist models succeed in incorporating neurobiological
knowledge. In an early paper Hoffman noted that different
perturbations of a particular kind of network (the Hopfield network)
led to different forms of neural dysfunction (Hoffman, 1987). Memory
overload led to disturbances similar to those seen in schizophrenia,
while increased randomness led to disturbances similar to those seen
in mania. Neural dysfunction in perturbed Hopfield networks is,
however, only loosely similar to that seen in psychosis, and the
neurobiological viability of these perturbations is arguable.
A more recent connectionist model is that of Cohen and
Servan-Schreiber (1992). Their neural network simulates performance
of schizophrenic patients on tasks related to the processing of
context. Furthermore, their network incorporates the neurobiological
finding that dopamine has a modulatory effect on prefrontal cortex by
influencing the responsivity, or gain, of cells in this region. Thus,
their model simultaneously posits that schizophrenia is characterized
by a particular information-processing deficit with a specific
neurobiological underpinnning, and provides a novel and robust 17
methodology to explore this deficit.
e) Other Disorders
There are several other disorders which can be understood in terms of
cognitive science constructs. Space considerations preclude a
discussion of anxiety disorders (Rapee, 1991; Eysenck, 1991), autism
(Litrownik and McInnis, 1986), impulsive (Barratt, 1987) and
antisocial (Gorenstein, 1991) behavior, learning disability (Swerling
and Sternberg, 1992), memory disorders (Shimamura, 1989), personality
disorders (Stein and Young, 1992b), and substance abuse (Hull and
Conceptualizing various psychiatric disorders in terms of cognitivist
constructs immediately raises the question of employing cognitive
science in clinical assessment. Cognitive science has assessment
constructs that differ from those of more traditional frameworks
(106). In general, cognitivists attempt to use empirical methods to
assess the particular psychological structures and processes
responsible for generating mental events. Many of these methods may
be useful in clinical assessment, for example, of neuropsychological
impairment (Delis, Kramer, Fridlund, Kaplan, 1990), or of maladaptive
schemas of self and other (Young, 1990; Horowitz, 1991).
The variety and range of paradigms and techniques developed by
cognitive scientists holds promise for their application to clinical
measurement (Merluzzi, Rudy, and Glass, 1981). It may however be
necessary to modify cognitive science laboratory methods before
employing them in the clinic. For example, early
information-processing studies in patients with depression did not to
use stimuli with negative and depressive content, and therefore failed
to elucidate relevant cognitive structures and processes (Ingram and
On the other hand, the clinical setting may allow for more precise
assessment than is available to most cognitive scientists. For
example, measurement of schemas in personality disorder by self-report
questionnaires is problematic, as patients may not be aware of or may
avoid their schemas. However, during psychotherapy schemas are
explored in various ways and links are drawn between the patient's
behaviors and these schemas (Young, 1990). In this way psychotherapy
techniques allow assessment to move beyond laboratory based cognitive
How can a cognitive science approach to psychiatric disorders and
their assessment contribute to psychiatric treatment? Clearly, the
reformulation of any particular disorder in cognitivist terms will
lead to conceptualization of the treatment of that disorder in the
same terms. Thus, the paper has already alluded to the treatment of
negative self-schemas in depression, and the treatment of maladaptive
person schemas in personality disorder. Perhaps more important,
however, is the idea that cognitive science contributes to the theory
of treatment integration. In recent years there have been frequent
calls for psychotherapy integration and for the integration of
psychotherapy and pharmacotherapy, but there has been little work on
the development of theoretical frameworks that allow such an
integration to take place.
It is notable, however, that within contrasting schools of therapy,
there have been shifts to an increasingly cognitive orientation. The
cognitive-behavioral treatment tradition has moved from behaviorist
models to linear cognitive models to more complex cognitive models
(Dobson, 1988). Psychoanalysis has moved away from energy based
models to more cognitivist ones, and several psychoanalysts, beginning
with Blum (1961) and Peterfreund (1971), have pointed to the value of
cognitive science for psychoanalytic meta-theory (Clippinger, 1977;
Erdelyi, 1985; Westen, 1988; Horowitza, 1988; Stein, 1992b). Finally,
neurobiological findings are increasingly being incorporated into
clinical cognitive science models.
Schema theory, for example, has allowed both cognitive-behaviorists
(Beck, 1967; Young, 1990; Beck, Freeman and Associates, 1991) and
psychoanalysts (Horowitz, 1988a, 1991; Klein, 1976; Wachtel, 1982;
Slap and Saykin, 1983) to focus on mental structures, their biological
basis, their development and change, and on the way in which they
direct psychological events (Stein, 1992e). Similarly, neural network
models may allow an integrative conceptual approach to these issues
(Caspar, Rothenfluh, and Segal, unpublished manuscript; Forrest,
1991). Insofar as cognitive science has conceptual continuities with
other approaches, but does not have their weakness (e.g., emphasizes
empirical methods but avoids reduction of mind to stimulus-response
chains, focuses on mental structures but avoids an energy based
meta-psychology), it may have theoretical advantages. Furthermore,
conceptual reformulation may also foster technical integration.
During psychotherapy, for example, schema change can then be
undertaken by means of affective, cognitive, behavioral, and
interpersonal interventions (Young, 1990).
A number of advantages of clinical cognitive science are immediately
apparent. First, it offers integrative theoretical models of clinical
disorders, assessment, and treatment. Whereas so much contemporary
clinical science may be criticized for being either mindless or
brainless (Lipowski, 1989), clinical cognitive science offers a
multifaceted approach that incorporates and extends earlier paradigms.
In addition, it employs a number of methodologies which encourage
empirical research. Clinical cognitivists have researched clinical
phenomena outside the office, and have introduced new scales and
measures into the clinic. Conversely, the study of clinical phenomena
may enrich cognitive science (Stein, 1992a). Nevertheless, a number
of criticisms of clinical cognitive science may also be offered. The
foundations of cognitive science have been extensively debated in its
subdiscipline of philosophy; here I will focus on more clinically
A first objection states that clinical cognitive science is overly
general or that it is "old wine in a new bottle". The psychoanalytic
clinician may feel that he or she uses cognitive science concepts and
methods, but simply employs an older terminology. Clinicians with a
cognitive-behavioral orientation may feel that clinical work is
primarily a pragmatic exercise and that it is unnecessary to introduce
extraneous theoretical baggage. Thus cognitive science may be seen as
only reframing cinical theory, rather than advancing it. I would
argue, however, that more accurate reframing constitutes an essential
part of scientific progress. Psychoanalysts who reject energy based
models, for example, are faced with the choice of discarding
metapsychology or of viewing psychoanalysis as a hermeneutic rather
than scientific enterprise. In providing a possible theoretical
conceptualization of psychodynamic work, cognitive science immediately
goes beyond mere translation of older terminology (Stein, 1992b).
Similarly, while cognitive therapists and cognitive scientists could
conceivably work independently of one another, reframing clinical
phenomena in terms of cognitive science may have immediate advantages.
Successful reframing leads to more comprehensive and powerful models,
and encourages novel methods of treatment and research (Stein, 1992e).
A converse objection is that cognitivist and computational models are
overly restricted or insufficiently complex. The word "cognitivist"
itself points to a lack of focus on emotion. Computational machines
operate along automatic and rigid lines, whereas humans have
context-dependent and fallible minds. Clinicians with a strong
neurobiological or sociocultural orientation may doubt the ability of
cognitivist models to include biological or sociocultural findings.
Clinicians with an experiential or phenomenological orientation are
particularly likely to question the value of a computational model in
understanding human perceptions, feelings, and experiences.
I would suggest that this criticism is partially correct and partially
incorrect. The criticism is wrong insofar as it suggests that the
object of cognitive science models is to replicate human minds. It is
a truism that there are differences between computers and humans, but
it does not follow that computational models cannot help us understand
human minds. This distinction can be illustrated by the debate
between Colby (1977), the author of PARRY, and Weizenbaum (1976), who
developed a program called ELIZA, that simulated a psychotherapy
session. Colby is a strong supporter of clinical cognitive science.
However, when Weizenbaum noted that people took his program as an
indication that psychotherapists could be replaced by computers, he
emphasized the limitations of cognitive science, noting that computers
should not be allowed to take over certain human tasks. However, the
issue is not whether computers can or should replace minds, but rather
whether cognitive science models can and ought to be used to
conceptualize and investigate minds. It is noteworthy that whereas
PARRY was based on a specific model of paranoid processes (and
therefore directly contributed to the science of psychopathology),
ELIZA was not a model of psychotherapy per se (and did not make a
direct contribution to the science of psychotherapy), but rather a
study of the rules of dialogue.
What is right about this criticism is that it points to the complexity
of human minds. Furthermore, early cognitivist models conceived of
the mind as a linear information-processor and may have contributed to
the development of overly simplistic clinical cognitive models.
However, current cognitive science models and clinical cognitive
models have become increasingly sophisticated. Gardner (1) has
described these developments in terms of the "computational paradox";
namely, that it took the early formal cognitivist models to describe
and elaborate the differences between the serial digital computer and
the human mind. These advances are increasingly apparent in clinical
cognitive science as well. Thus clinical cognitive science is
beginning to address both the neurobiological and the sociocultural
limits of cognitive processing. Furthermore, it may be argued that
clinical cognitive science models have begun to succeed in
incorporating both positivist concerns with empirical measurement and
hermeneutic concerns with meaning and context (72). The complexity of
human minds therefore constitutes a challenge rather than an a priori
objection to clinical cognitive science.
An important criticism of clinical cognitive science that employs
symbolic architectures involves questions of theoretical precision.
Cognitive science models of mental structures and processes vary in
important ways, and their adoption by clinicians has led to further
diversification. For example, whereas Horowitz's (1988a) division of
schemas into motivational, role, and value schemas is consistent with
Freud's division of the psyche into id, ego, and superego; Beck and
colleagues' (1991) division of schemas into cognitive, affective, and
action schemas is consistent with their view of the linear progression
of thoughts, feelings, and behaviors. Furthermore, a variety of
related concepts such as scripts, self-systems, personal constructs,
plans, and frames are popular in the cognitive and clinical
literature. This diversity may in part reflect the theoretical
fertility of cognitive science, and may be helpful in providing a
range of ideas and models. Nevertheless, the diversity also reflects
a lack of agreement about the taxonomy of representation, and a lack
of detailed knowledge about the complexities of human cognitive
architecture. Clearly, further work is necessary to clarify and
consolidate the theoretical apparatus of clinical cognitive science.
Theoretical diversity contributes to the difficulties that researchers
have experienced in measuring constructs such as schemas. Problems in
measurement are to some extent expectable with research on posited
entities which underly observable behavior. Nevertheless, there are
important unresolved complexities in determining how best to approach
the processing of information that differs in target, favorability,
content, congruence, and personal relevance (Ingram and Wisnicki,
1991). Detailed clinical observation and advances in assessment
techniques will be necessary to resolve these methodological
difficulties (Horowitz, 1991).
Clinical cognitive science that employs connectionist architectures
has emerged only recently. This field has the potential advantages of
methodological precision and of close links with neuroscience.
However, current paradigms sometimes appear removed from clinical
experience and the dialogue between connectionism and neurobiology
remains rudimentary. Further work is necessary to narrow the gap
between behavior of neural nets and clinical phenomenology, and to
integrate mechanisms of neural net perturbation with knowledge of
pathogenic mechanisms in biological psychiatry.
The interdisciplinary nature and rapid development of cognitive
science is of immediate interest to psychiatry. In this paper, I have
reviewed areas in which cognitive science and clinical science have
established a dialogue. Clinical cognitive science appears to provide
an integrative and sophisticated framework for conceptualizing and
researching psychiatric disorders and their treatment. Further
theoretical and empirical work needs to be done to consolidate this
early work. Nevertheless, there has been sufficient progress to
suggest the promotion of cognitive science as an important theoretical
framework for psychiatry, and to encourage further exploration of this
Barratt, E.S. (1987). Impulsiveness and anxiety: Information
processing and electroencephalograph topography. Journal of
Research in Personality, 21:453-463.
Beck, A.T. (1967). Depression: Clinical, Experimental, and
Theoretical Aspects. New York: Harper and Row.
Beck, A.T., & Freeman, A., and Associates (1991). Cognitive Therapy
of Personality Disorders. New York: Guilford.
Blum, G.S. (1961). A Model of the Mind. New York: Wiley.
Bowers, K.S., & Meichenbaum, D. (eds) (1984). The Unconscious
Reconsidered. New York: Wiley.
Breger, L. (ed) (1969). Clinical-cognitive Psychology: Models and
Integrations. Englewood Cliffs, NJ: Prentice-Hall.
Cantor, N., Kihlstrom, J. (eds) (1981). Personality, cognition, and
social interaction. Hillsdale, NJ: Erlbaum.
Caramazza, A. (1992). Is cognitive neuropsychology possible? Journal
of Cognitive Neuroscience, 4:80-94.
Caspar, F., Rothenfluh, T., & Segal, Z. (unpublished). The appeal of
connectionism for clinical psychology.
Chomsky, N. (1959). A review of B.F. Skinner's Verbal Behavior.
Clippinger, J. (1977). Meaning and Discourse: A Computer Model of
Psychoanalytic Speech and Cognition. Baltimore: Johns Hopkins
Cloitre, M. (1992). The avoidance of emotional processing: A
cognitive science perspective, in Stein, D.J., & Young,
J.E. (eds), Cognitive Science and Clinical Disorders. San Diego:
Cohen, J.D., & Servan-Schreiber, D. (1992), Context, cortex, and
dopamine: A connectionist approach to behavior and biology in
schizophrenia. Psychol. Rev., 99:45-77.
Colby, K.M. (1981). Modeling a paranoid mind. Behavioral and Brain
Collins, A.M., & Loftus, E.F. (1975). A spreading-activation theory
of semantic processing. Psychol. Rev., 82:407-428.
Craik, F.I.M., & Lockhart, R.S. (1972). Levels of processing: A
framework for memory research. J. Verb. Learning Verb. Behav.,
Craik, K. (1943). The Nature of Explanation. Cambridge: Cambridge
Delis, D.C., Kramer, J.H., Fridlund, A.J., & Kaplan, E. (1990). A
cognitive science approach to neuropsychological assessment, in
McReynolds, P., Rosen, J.C., & Chelune, G.J. (eds), Advances in
Psychological Assessment. New York: Plenum Press.
Dobson, K.S. (1988). Historical and philosophical bases of the
cognitive-behavioral therapies, in Dobson, K.S. (ed). Handbook of
Cognitive-Behavioral Therapies. New York, Guilford.
Eagle, M.N. (1986). The psychoanalytic and the cognitive
unconscious, in Stern, R. (ed), Theories of the Unconscious and
Theories of the Self. Hillsdale, NJ: Erlbaum.
Ellenberger, H.F. (1970). The Discovery of the Unconscious: The
History and Evolution of Dynamic Psychiatry. New York: Basic
Emde, R.N. (1983). The prerepresentational self and its affective
core. Psychoanalytic Study of the Child. New Haven: Yale
Erdelyi, M.H. (1985). Psychoanalysis: Freud's Cognitive Psychology.
New York: WH Freeman.
Eysenck, M.W. (1991). Anxiety and cognitive functioning: A
multifaceted approach, in Lister, R.G., & Weingartner, H.J. (eds),
Perspectives on Cognitive Neuroscience. Oxford: Oxford University
Forrest, D.V. (1991). Mind, brain, and machine: Object recognition.
J. Am. Acad. Psychoanal., 19:555-577.
Foulkes, D. (1985). Dreaming: A cognitive-psychological analysis.
Hillsdale, NJ: Erlbaum.
Freud, S. (1894). The neuro-psychoses of defense. Standard Edition,
3. London: Hogarth Press, 1962
Gardner, H. (1985). The Mind's New Science: A History of the
Cognitive Revolution. New York: Basic Books.
Globerson, T., & Zelniker, T. (eds) (1989). Cognitive Style and
Cognitive Development. Norwood, NJ: Ablex.
Gorenstein, E.E. (1991). A cognitive perspective on antisocial
personality, in Magaro, P.A. (ed), Cognitive Bases of Mental
Disorders. Newbury Park, CA: Sage.
Gray, J.A. (1982). The Neuropsychology of Anxiety. New York:
Oxford University Press.
Greenberg, L.S., & Safran, J.D. (1990). Emotion in Psychotherapy.
New York: Guilford.
Greenspan, S.I. (1980). Intelligence and Adaptation: An Integration
of Psychoanalytic and Piagetian Developmental Psychology. New
York: International Universities Press.
Grossman, L.S., & Harrow, M. (1991). Thought disorder and cognitive
processes in mania, in Magaro, P.A. (ed), Cognitive Bases of Mental
Disorders. Newbury Park, CA: Sage.
Hebb, D.O. (1946). On the nature of fear. Psychol. Rev.,
Hebb, D.O. (1949). The Organization of Behavior. New York: Wiley.
Hestenes, D. (1991). A neural network theory of manic-depressive
illness, in Levine D.S., Leven, S.J. (eds), Motivation, Emotion,
and Goal Direction in Neural Networks. Hillside, NJ: Lawrence
Hilgard, E.R. (1977). Divided Consciousness: Multiple Controls in
Human Thought and Action. New York: Wiley.
Hoffman, R.E. (1987). Computer simulations of neural information
processing and the schizophrenia-mania dichotomy. Arch. Gen.
Horowitz, M.J. (ed) (1988a). Psychodynamics and Cognition. Chicago:
University of Chicago Press.
Horowitz, M.J. (1988b). Introduction to psychodynamics: A new
synthesis. New York: Basic Books.
Horowitz, M.J. (1991). Person schemas and maladaptive interpersonal
behavior patterns. Chicago: Chicago University Press.
Hull, J.G, & Reilly, N.P. (1986). An information processing approach
to alcohol use and its consequences, in Ingram, R.E. (ed),
Information Processing Approaches to Clinical Psychology. San
Diego: Academic Press.
Ingram, R.E. (1984). Toward an information processing analysis of
depression. Cognitive Therapy and Research, 8:443-478.
Ingram, R.E. (ed) (1986). Information Processing Approaches to
Clinical Psychology. San Diego: Academic Press.
Ingram, R.E., & Reed, M.E. (1986). Information encoding and
retrieval processes in depression: Findings, issues, and future
directions, in Ingram, R.E. (ed), Information Processing Approaches
to Clinical Psychology. San Diego: Academic Press.
Ingram, R.E., & Wisnicki, K. (1991). Cognition in depression, in
Magaro, P.A. (1991). Cognitive Bases of Mental Disorders.
Newbury Park, CA: Sage.
Janet, P. (1907). The Major Symptoms of Hysteria. New York:
Jeffress, L.A. (ed) (1951). Cerebral Mechanisms in Behavior. The
Hixon Symposium. New York: Wiley.
Johnson-Laird, P.N. (1988). The Computer and the Mind: An
Introduction to Cognitive Science. Cambridge: Harvard University
Kihlstrom, J.F. (1987). The cognitive unconscious. Science,
Kihlstrom, J.F., & Hoyt, I.P. (1990). Repression, dissociation, and
hypnosis, in Singer, J.L. (ed), Repression and Dissociation:
Implications for Personality Theory, Psychopathology, and Health.
University of Chicago Press: Chicago.
Klein, G.S. (1976). Psychoanalytic Theory: An Exploration of
Essentials. New York: International Universities Press.
Koukkou, M. (1988). A psychophysiological information-processing
model of cognitive dysfunction and cognitive treatment in
depression, in Perris, C., Blackburn, I.M., & Perris, H. Cognitive
Psychotherapy: Theory and Practice. Berlin: Springer Verlag.
Kuiper, N.A., Derry, P.A., & MacDonald, M.R. (1982). Self-reference
and person perception depression, in Weary, G., Mirels, H. (eds),
Integrations of Clinical and Social Psychology. New York: Oxford
Lachman, R., Lachman, J.L., & Butterfield, E.C. (1979). Cognitive
Psychology and Information Processing: An Introduction.
Hillsdale, NJ: Erlbaum.
Lipowski, Z.J. (1989). Psychiatry: Mindless or brainless, both or
neither? Can. J. Psychiatry, 34:249-254.
Lakoff, G. (1987). Women, Fire, and Dangerous Things: What
Categories Reveal about the Mind. Chicago: University of Chicago
Litrownik, A.J., McInnis, E.T. (1986). Information processing and
autism, in Ingram, R.E. (ed), Information Processing Approaches to
Clinical Psychology. San Diego: Academic Press.
Lyddon, W.J. (1988). Information-processing and constructivist
models of cognitive therapy: A philosophical divergence. Journal
of Mind and Behavior, 9:137-166.
Magaro, P.A. (1980). Cognition in schizophrenia and paranoia: The
integration of cognitive processes. Hillsdale, NJ: Erlbaum.
Magaro, P.A. (ed) (1991). Cognitive Bases of Mental Disorders.
Newbury Park, CA: Sage.
Mahoney, M.J. (1991). Human Change Processes. New York: Basic
Mahoney, M.J., & Freeman, A. (eds) (1985). Cognition and
Psychotherapy. New York: Plenum.
Mandler, G. (1975). Mind and Emotion. New York: Wiley.
Markus, H. (1977). Self-schemata and processing information about the
self. J. Pers. Soc. Psychol., 35:63-78.
Meltzoff, A.N. (1990). Towards a developmental cognitive science:
The implications of cross-modal matching and imitation for the
development of representation and memory in infancy. Ann. N.Y.
Acad. Sci., 608:1-37.
Merluzzi, T.V., Rudy, T.E., & Glass, C.R. (1981). The information
processing paradigm: Implications for cognitive science, in
Merluzzi, T.V., Glass, C.R., & Genest, M. (eds), Cognitive
Assessment. New York: Guilford Press.
Morton, J. (1984). Brain-based and non-brain-based models of
language, in Caplan, D., Lecours, A.R., Smith, A. (eds), Biological
Perspectives on Language. Cambridge: MIT Press.
Neisser, U. (1976). Cognition and Reality. San Francisco: WH
Neuchterlein, K.H., & Dawson, M.E. (1984). Information processing
and functioning in the developmental course of schizophrenic
disorders. Schizophr. Bull., 10:160-203.
Oatley, K. (1991). Best Laid Plans. Cambridge: Cambridge University
Oatley, K., & Johnson-Laird, P.N. (1987). Towards a cognitive theory
of emotion. Cognition and Emotion, 1:29-50.
Peterfreund, E. (1971). Informations, Systems, and Psychoanalysis:
An Evolutionary Biological Approach to Psychoanalytic Theory. New
York: International Universities Press.
Piaget, J. (1952). The Origins of Intelligence in Children. New
York: International Universities Press.
Pitman, R. (1987). A cybernetic model of obsessive-compulsive
psychopathology. Compr. Psychiatry, 28:334-343.
Posner, M.I. (ed) (1989). The Foundations of Cognitive Science.
Cambridge: MIT Press.
Prigatano, G.P., & Schachter, D.L. (eds) (1991). Awareness of
Deficit after Brain Injury: Clinical and Theoretical Issues.
Oxford University Press: Oxford.
Rapee, R.M. (1991). Psychological factors in generalized anxiety, in
Rapee, R.M., Barlow, D.M. (eds), Chronic Anxiety: Generalized
Anxiety Disorder and Mixed Anxiety-Depression. New York:
Rapoport, J., & Wise, S. (1988). Obsessive-compulsive disorder: A
basal ganglia disease? in Rapoport, J. (ed), Obsessive-Compulsive
Disorder in Children and Adolescents. Washington, DC: APPI Press.
Ruesch, J., & Bateson, G. (1968). Communication: The Social Matrix
of Psychiatry. New York: Norton.
Rumelhart, D.E., Hinton, G.E., and the PDP Research Group (1986).
Parallel Distributed Processing: Explorations in the
Microstructure of Cognition. Cambridge: MIT Press.
Saccuzzo, D.P. (1986). An information processing interpretation of
theory and research in schizophrenia, in Ingram, R.E., Information
Processing Approaches to Clinical Psychology. San Diego: Academic
Safran, J.D., & Greenberg, L.S. (1986). Affect and the unconscious:
A cognitive perspective, in Stern, R. (ed), Theories of the
Unconscious and Theories of the Self. Hillsdale, NJ: Erlbaum.
Shannon, C.E. (1938). A symbolic analysis of relay and switching
circuits. Master's thesis, Massachusetts Institute of Technology.
Shimamura, A.P. (1989). Disorders of memory: the cognitive science
perspective, in Boller, F., & Grafman, J. (eds), Handbook of
Neuropsychology, 3. Amsterdam: Elsevier Science Publications.
Simon, H. (1967). Motivational and emotional controls of cognition.
Psychol. Rev., 74:29-39.
Singer, J.L. (ed) (1990). Repression and Dissociation: Implications
for Personality Theory, Psychopathology, and Health. University of
Chicago Press: Chicago.
Slap, J.W., & Saykin, A.J. (1983). The schema: Basic concept in a
nonmetapsychological model of the mind. Psychoanalysis and
Contemporary Thought, 6:305-325.
Sloman, A. (1987). Motives, mechanisms, and emotions. Cognition and
Sorrentino, R.M., & Higgins, E.T. (eds) (1986). Handbook of
Motivation and Cognition: Foundations of Social Behavior. New
York: Guilford Press.
Stein, D.J. (1992a). Clinical cognitive science: Possibilities and
limitations. In Stein, D.J., & Young, J.E. (eds), Cognitive
Science and Clinical Disorders. San Diego: Academic Press, 1992.
Stein, D.J. (1992b). Psychoanalysis and cognitive science:
Contrasting models of the mind. J. Am. Acad. Psychoanal.,
Stein, D.J. (1992c). Medical anthropology and psychotherapy
integration: A cognitivist approach. Presented at the Annual
Meeting of the Society for the Exploration of Psychotherapy
Integration, San Diego.
Stein, D.J. (1992d). Cognitive science and clinical knowledge.
Stein, D.J. (1992e). Schemas in the cognitive and clinical sciences:
An integrative construct. Journal of Psychotherapy Integration
Stein, D.J., Hollander, E. (1992). Cognitive science and
obsessive-compulsive disorder, in Stein D.J., Young, J.E. (eds),
Cognitive Science and Clinical Disorders. San Diego: Academic
Stein, D.J., & Young, J.E. (eds) (1992a). Cognitive Science and
Clinical Disorders. San Diego: Academic Press.
Stein, D.J., & Young, J.E. (1992b). A schema-focused approach to
personality disorder, in Stein, D.J., & Young, J.E. (eds),
Cognitive Science and Clinical Disorders. San Diego: Academic
Stern, D. (1985). The Interpersonal World of the Infant. New York:
Sternberg, R.J. (ed) (1984). Mechanisms of Cognitive Development.
New York: WH Freeman.
Stillings, N.A., Feinstein, M.H., Garfield, J.L., Rissland, E.L.,
Rosenbaum, D.A., Weisler, S.E., & Baker-Ward, L. (1987). Cognitive
Science: An Introduction. Cambridge: MIT Press.
Swerling, L.C., & Sternberg, R.J. (1992). Information processing,
experience, and reading disability, in Stein, D.J., & Young, J.E.
(eds), Cognitive Science and Clinical Disorders. DJ, Young JE.
San Diego: Academic Press.
Teasdale, J.D. (1983). Negative thinking in depression: Cause,
effect, or reciprocal relationship? Advances in Behavioural
Research and Therapy, 5:3-25.
Tomkins, S.S., & Messick, S. (eds) (1963). Computer Simulation of
Personality: Frontier of Psychological Theory. New York: Wiley.
Turing, A. (1936). On computable numbers, with an application to the
Entscheidungsproblem. Proceedings of the London Mathematical
Society, 2nd Series, 42:230-265.
Turing, A. (1950). Computing machinery and intelligence. Mind,
Uleman, J.S., & Bargh, J.A. (eds) (1989). Unintended Thought. New
York: Guilford Press.
Wachtel, P.L. (1982). Resistance: Psychodynamic and behavioral
approaches. New York: Plenum Press.
Weizenbaum, J. (1976). Computer Power and Human Reason. San
Francisco: WH Freeman.
Westen, D. (1988). Transference and information-processing. Clinical
Psychology Review, 8:161-179.
Wiener, N. (1947). Cybernetics. Cambridge: MIT Press.
Williams, J.M.G., Fraser, N.W., MacLeod, C., & Matthews, A. (1988).
An Information Processing Analysis of the Emotional Disorders. New
Williams, J.M.G, & Oaksford, M. (1992). Cognitive science, anxiety,
depression: From experiments to connectionism, in Stein, D.J., &
Young, J.E. (eds), Cognitive Science and Clinical Disorders.
San Diego: Academic Press.
Young, J.E. (1990). Cognitive therapy for personality disorders: A
schema-focused approach. Sarasota: Professional Resource