Possible mechanisms for why desensitization and exposure therapy work

Clinical Psychology Review 25 (2005) 67–95

Possible mechanisms for why desensitization

and exposure therapy work

Warren W. Tryon*

Department of Psychology, Fordham University, Bronx, NY 10458-5198, United States

Received 20 May 2004; received in revised form 8 July 2004; accepted 25 August 2004

Abstract

Rosen and Davison [Rosen, G.M. and Davison, G.C. (2003). Psychology should list empirically

supported principles of change (ESPs) and not credential trademarked therapies or other treatment packages.

Behavior Modification, 27, 300–312] recommended that empirically supported principles be listed instead of

empirically supported treatments because the latter approach enables the creation of putatively new therapies

by adding functionally inert components to already listed effective treatments. This article attempts to

facilitate inquiry into empirically supported principles by reviewing possible mechanisms responsible for the

effectiveness of systematic desensitization and exposure therapy. These interventions were selected because

they were among the first empirically supported treatments for which some attempt was made at

explanation. Reciprocal inhibition, counterconditioning, habituation, extinction, two-factor model, cognitive

changes including expectation, self-efficacy, cognitive restructuring, and informal network-based emotional

processing explanations are considered. Logical problems and/or available empirical evidence attenuate or

undercut these explanations. A connectionist learning-memory mechanism supported by findings from

behavioral and neuroscience research is provided. It demonstrates the utility of preferring empirically

supported principles over treatments. Problems and limitations of connectionist explanations are presented.

This explanation warrants further consideration and should stimulate discussion concerning empirically

supported principles.

D 2004 Elsevier Ltd. All rights reserved.

Keywords: Systematic desensitization; Exposure therapy; Learning-memory mechanism

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W.W. Tryon / Clinical Psychology Review 25 (2005) 67–9568

1. Possible mechanisms for why desensitization and exposure therapy work

Psychologists seem to agree, and our professional ethics require, that only reliable and valid

psychological tests should be used. The new ethics code states that bPsychologists use assessment

instruments whose validity and reliability have been established for use with members of the

population testedQ (APA, 2002, p. 1071). Less agreement exists regarding psychotherapy. The

extent to which interventions are to be based on and informed by scientific research is one of the

primary professional issues facing psychologists today. The effort to establish empirically supported

treatments (ESTs) represents an important attempt to identify interventions that meet minimal

scientific standards (cf. Sanderson, 2003) but has met with serious opposition (Hebert, 2003). One

reason for opposing ESTs is that they do not provide an explanatory context in which to place and

understand a client’s presenting problem. Theories of psychotherapy and personality have

proliferated to address this explanatory need. Absence of empirical support about these theories,

and many interventions based on them, does not diminish the need for an explanatory context.

Psychologists can opt not to administer tests if no reliable and valid test is available for a specific

purpose, but they cannot avoid the need to understand their client’s presenting problem and

consequently they turn to the theoretical tradition in which they were trained even if that position

has limited or no empirical evidence to support its validity or the effectiveness of interventions

based on it. The need to explain seems to take precedence over the desire for empirical support.

The substantial and generally recognized gap between the science and practice of clinical

psychology demonstrates that empirical evidence of outcome alone is insufficient to persuade

clinicians to use ESTs let alone limit their practice to them. Effectiveness of efforts to persuade

clinicians to adopt ESTs may depend substantially on the extent to which science can explain why

ESTs work and thereby provide clinicians with an empirically supported explanatory context in

addition to effective interventions. This is reason enough to engage the explanatory discussion

initiated here.

Rosen and Davison (2003) objected to the current practice of listing ESTs because adding one or

more functionally inert components to an existing intervention based on sound psychological science

can both meet EST requirements and be perceived as a new therapy. This creates two problems. First, yet

another bnewQ therapy appears to have been developed that in fact succeeds because its active

ingredients are those of an already established therapy. Second, causal attribution is frequently made to

the new elements. Rosen and Davison illustrated these problems with a hypothetical intervention called

bPurple Hat TherapyQ (PHT), where the therapist asks the client to wear a large purple hat while

receiving exposure therapy for a phobia. PHT will be more effective than a control treatment because it

entails exposure therapy but PHT proponents will attribute curative powers to wearing the hat and then

establish seminars and proprietary rights to training therapists in this new therapy. Rosen and Davison

cited Eye Movement Desensitization and Reprocessing (EMDR) as a PHT because: (a) EMDR is

superior to control conditions thereby establishing it as an EST, (b) causal attribution has been claimed

for the eye movement component despite a lack of evidence showing it to incrementally add to clinical

outcome, and (c) a burgeoning proprietary training system has also been developed to promote EMDR.

The potential for psychotherapies to proliferate without limit is clear. It is possible that the panoply

of present-day psychotherapies may exist for these reasons. Rosen and Davison proposed that

listing Empirically Supported Principles (ESPs) rather than ESTs may solve this problem and

return our attention to matters of explanation as well as prediction. Their recommendation is

W.W. Tryon / Clinical Psychology Review 25 (2005) 67–95 69

consistent with Shapiro’s (1995) view that a major focus of psychotherapy research should

concern how effective therapies work. Rosen and Davison recognized that considerable

controversy might arise when attempting to identify and categorize psychological principles

but welcomed such debate. They concluded that such argument is useful because, bDebates that

concern principles of change, rather than specific trademarked therapies, will return our attention

to what psychology is aboutQ (p. 308).

Proctor and Capaldi (2001) discussed the complementary yet competing roles of explanation and

prediction in science. Philosophy and religion explain much but predict little that can be

empirically verified and possibly falsified. Hypothesis testing distinguishes science because

it makes empirically testable predictions. However, at some point the resulting facts need

to be placed into an explanatory context. Mature sciences provide explanatory theories in

addition to making testable predictions.1 Proctor and Capaldi noted that British geologists accepted

plate tectonic theory well before American geologists did because they valued explanation as well as

prediction whereas American geologists almost exclusively limited themselves to prediction and

hypothesis testing (cf. Oreskes, 1999). Plate tectonics placed existing geological data into a coherent

explanatory context and provided a plausible mechanism for how certain geological facts came to be.

Proctor and Capaldi implied that American psychologists might learn from the mistakes their geology

colleagues and temper their interest in hypothesis testing with explanatory efforts. This article evaluates

the extent to which various explanations of systematic desensitization and exposure therapy fit with

known facts.2 Novel prediction is not a primary or essential part of this process just as it was not for the

evaluation of plate tectonic theory.

Darwin’s theory of evolution provides further evidence of the importance science places on

explanation. Darwin’s (1859) evolutionary theory was limited to functional statements about the

causal role of variation and selection in the origin and extinction of species and was largely

rejected by the scientific community upon its publication because no plausible proximal causal

mechanism was proposed to explain how variation was instantiated and how selection could

work as described (Bowler, 1983). Darwin’s evolutionary theory remained on the margins of

biology for approximately 75 years until population genetics provided the missing causal

explanatory mechanisms (Mayr, 1982). Tryon (1993b, 2002a) identified parallels with behavioral

psychology. The cognitive revolution in psychology occurred partly because functional

statements made by behaviorists lacked causal mediating explanatory mechanisms. The search

for causal mediators and moderators is evidence that behavioral scientists value explanation in

addition to novel prediction. Systematic desensitization and exposure therapy appear to work but

there is little agreement as to why they work. This article considers several explanatory

possibilities and seeks empirically supported principles for why systematic desensitization and

exposure therapy work. These interventions were selected because they were among the first

empirically supported treatments and because attempts have been made to explain why they

work.

1The dichotomy between hypothesis-testing and explanation is complicated because hypotheses are often derived from explanatory

theories.2The factual status of generalizations based mainly on analog studies can be questioned. The use of nonclinical populations is a major

threat to external validity. However, analogue studies do systematically vary one or more independent variables while controlling for some

confounding variables to reduce threats to internal validity.


2. Explanatory bases and possible principles

We now understand that systematic desensitization depends upon exposure but the two therapies

are treated separately here because that is how the explanatory literature is organized. Most

literature citations regarding the explanatory basis of systematic desensitization are old because

exposure therapy all but replaced it subsequent to Marks (1975) literature review and because

research has focused almost exclusively on outcome research, hypothesis testing, and novel

prediction since then. The relevant literature is enormous and space limitations require reliance on

empirical literature reviews by Borkovec (1972), Brown (1973), Davison and Wilson (1972, 1973),

Emmelkamp (1982, 1994, 2001), Goldfried (1971), Kazdin and Wilcoxon (1976), Kazdin and

Wilson (1978), McGlynn, Mealiea, and Landau (1981), Morgan (1973), Rachman (1965), Spiegler

and Guevremont (1993, pp. 211–214), Wilkins (1971, 1972, 1973a, 1973b), and Yates (1975)

regarding why systematic desensitization is effective. Evidence from single studies is discussed

were pertinent.

The term exposure therapy derives from Marks’s (1975) review of the systematic desensitization

literature where he concluded that mere exposure to aversive cues was as effective as systematic

desensitization. Taylor (2002) identified four categorizes of exposure therapy based on two

dimensions: (1) real vs. imagined stimuli and (2) gradual vs. intense exposure (cf. Table 1).

McGlynn et al. (1981) noted that b. . . exposure theory is not an explanation of therapeutic

desensitization effects. Rather, it is simply a hypothesis concerning the necessary and sufficient

procedural ingredients within the technique. The therapeutic effects of the exposure remain to be

explained (e.g., as extinction, as counterconditioning, as habituation)Q (p. 154). Van Egeren (1971)

organized four of the most common explanations of systematic desensitization into a 2�2 table (cf.

Yates, 1975, p. 165) presented here in modified form as Table 2 and we begin our analysis with

them.

2.1. Reciprocal inhibition

Wolpe (1958, 1995) explained the effectiveness of systematic desensitization using Sherrington’s

(1906/1961) physiological concept of reciprocal inhibition. Reciprocal inhibition has been used

both as a psychological and biological mechanism of action. The psychological mechanism is

based on the premise that two incompatible psychological states cannot occur simultaneously. The

definition of what it means to be relaxed excludes what it means to be anxious. One must be

careful to avoid a tautology here as it is possible to restrict instances of reciprocal inhibition to

only those situations where a strong negative correlation occurs on the basis that other conditions

do not meet the definition. The biological mechanism derives from the study of reflexes. A tap

on the patellar tendon elicits inhibition (relaxation) of the leg’s flexor muscles along with

Table 1

Four types of exposure therapy

Gradual Intense

Imagined stimuli Systematic desensitization Implosion

Real stimuli Graded in vivo exposure Flooding

Table 2

Four theoretical explanations of systematic desensitization

Short-term effects Long-term effects

Antagonistic inhibition present Reciprocal inhibition Counterconditioning

Antagonistic inhibition absent Habituation Extinction


excitation of extensor muscles; a coordination of active inhibition and excitation in different

muscles. Parasympathetic nervous activation is known to inhibit sympathetic nervous activation.

Jacobson’s (1938) deep muscle relaxation technique was the major method Wolpe used to

increase parasympathetic activity, which was hypothesized to inhibit sympathetic activity and

therefore anxiety. However, even temporary paralysis by curare of all muscles did not preclude or

prevent anxiety (Davison, 1966). While a few studies have provided empirical support for the

view that relaxation is a necessary component of systematic desensitization (Davison, 1968; Kass

& Gilner, 1974) others have not (Miller & Nawas, 1970; Nawas, Welsch, & Fishman, 1970).

Agras et al. (1971), Cooke (1968), Craighead (1973), Crowder and Thornton (1970), Freeling and

Shemberg (1970), and Waters, McDonald, and Koresko (1972) reported that phobic anxiety is

reduced whether or not relaxation training is used. It does not appear necessary to pair relaxation

with imagery during desensitization (Aponte & Aponte, 1971). Allowing patients to relax in

between hierarchy items does not appear to alter treatment outcome (Benjamin, Marks, &

Hudson, 1972). Pairing of relaxation with graded imagination seems not to alter treatment

outcome (McGlynn, 1973). Van Egeren (1971) and Van Egeren, Feather, and Hein (1971) found

little empirical support for the reciprocal inhibitory explanation of systematic desensitization. If

reciprocal inhibition was causally necessary then flooding (Miller, 2002) and implosive therapy

(Levis, 2002) should not work because they do not involve any reciprocally inhibitory agent, yet

the parenthetical references cited here indicate that they do. Yates (1975) concluded his literature

as follows: (a) bSystematic desensitization is effective in reducing phobic anxiety, whether

relaxation training is part of the program or notQ (p. 156), and (b) b. . . neither individualized

hierarchies nor any special way of presenting the hierarchies are critical to the success of

desensitizationQ (p. 158). Kazdin and Wilson (1978) concluded that none of the therapeutic

ingredients postulated by Wolpe were in fact necessary (p. 37). In sum, the preponderance of

empirical evidence does not support the principle of reciprocal inhibition by deep muscle

relaxation.

Sexual arousal and aggression are other states that putatively inhibit anxiety but therapeutic

procedures based on them have not been widely developed; probably for ethical and legal reasons.

Assertiveness training and coping skills may also inhibit anxiety to some degree but they do not

appear to totally inhibit anxiety in the same way that leg flexion inhibits extension. A further

difficulty is that Table 2 classifies reciprocal inhibition as having short-term effects whereas it is

supposed to have long-term benefits. An anonymous reviewer objected to this well-established

classification on the basis that Wolpe’s theorizing was Hullian at its core; that reciprocal inhibition

was a theoretical substitute for Hull’s reactive inhibition that built up conditioned inhibition, and

that reactive inhibition was a long-term rather than short-term process. The reviewer argued that

the permanence of conditioned inhibition makes relearning impossible and that creates a serious

explanatory problem.


2.2. Counterconditioning

Wolpe (1958) used Guthrie’s (1952) concept of counterconditioning to explain the long-term

therapeutic effects of systematic desensitization (cf. Davison, 1968). The central concept here is

replacement of an old response with a new one, such as when relaxation putatively replaces

anxiety. The counterconditioning theory of systematic desensitization effects presumes that

decreasing classically conditioned emotionality sets the occasion for reduced instrumental

avoidance of phobic stimuli (Bandura, 1969, pp. 424–425). Table 2 shows that counter-

conditioning is understood to exert long-term effects. Davison (1968) stated that counter-

conditioning is the behavioral equivalent of the neurological process of reciprocal inhibition.

Marks (1975) reviewed the empirical literature and concluded that systematic desensitization

with relaxation is no more effective than graded exposure. The term bexposure therapyQ stems

from this seminal article. Wilkins (1971) had earlier concluded, on the basis of his review of the

empirical literature, that imagination of fearful scenes was the necessary and sufficient ingredient

for successful desensitization. The empirical effectiveness of flooding (Miller, 2002), and

implosive therapy (Levis, 2002) also contradict the counterconditioning explanation of systematic

desensitization as previously mentioned because these forms of treatment are not thought to

replace one emotional state with another. Van Egeren (1971) and Van Egeren et al. found little

empirical support for the counterconditioning explanation of systematic desensitization. These

empirical results along with empirical reviews cited above undercut both the reciprocal inhibition

and counterconditioning explanations of systematic desensitization.

Moreover, McGlynn (2002) noted that Wolpe based his explanation on Hull’s concept of

habits and thereby injected the still unresolved theoretical debates among classical learning

theorists (Clark L. Hull, Edwin R. Guthrie, and Edward C. Tolman) into explanations of the

effectiveness of systematic desensitization (McGlynn et al., 1981). Bandura (1969, p. 431) noted

that the drive-reduction theory of classical conditioning and a fatigue theory of extinction favored

by Wolpe have so far been contradicted by empirical evidence. In sum, the balance of empirical

evidence does not support a counterconditioning explanation of systematic desensitization and the

unresolved theoretical debates among classical learning theorists further diminish the adequacy of

this explanation.

2.3. Habituation

Harris (1943) based his comprehensive review of the early habituation literature on the

following operational definition: bresponse decrement due to repeated stimulationQ (p. 385). Lader

and Mathews (1968) proposed a habituation explanation of systematic desensitization based on

this entirely functional definition of habituation. Emmelkamp and Felten (1985) reported

supportive evidence in that they found that subjective anxiety and physiological arousal

decreased for patients with specific phobias during in vivo exposure. However, Van Egeren

(1971) and Van Egeren et al. found little empirical support for the habituation explanation of

systematic desensitization.

Watts (1979) based his review of the empirical evidence about the habituation explanation of

systematic desensitization on the same functional definition of habituation provided above and

concluded that sufficient supportive evidence existed to make the habituation model a viable


alternative to the reciprocal inhibition model. However, Thompson and Spencer (1966) identified

nine habituation phenomena that are so characteristic of habituation that they need to be present

in order to conclude that habituation is present. This has not been done regarding systematic

desensitization or exposure therapy. A response decrement is required (cf. Harris, 1943). The

habituated response must recover if the stimulus is withheld. Presentation of another, usually

strong, stimulus must cause dishabituation resulting in an increase of response strength.

Hergenhahn and Olson (1993, pp. 4, 12) and Kalat (2003, pp. 383384) noted that habituation

generally entails a short-term response reduction given repeated stimulation. Van Egeren (1971)

and Yates (1975, p. 165) both recognized the short-term effects of habituation. However, massed

habituation trials can result in habituation effects that last approximately 3 weeks (Kandel, 1991).

But sensitization, another short-term process, can restore defensive responses and reverse

habituation (Hergenhan & Olson, 1993, p. 4; Kandel, 1991). In sum, the temporary and

reversible nature of habituation makes it unable to explain durable long-term changes in response

strength.

2.4. Extinction

Extinction entails the lack of onset or offset of stimuli with positive or negative reinforcing

properties contingent upon either the emission or omission of a response. Response decrement is

explained in terms of the lack of reinforcement. Waters et al. (1972) explained the reduction of

phobic behavior subsequent to systematic desensitization in terms of extinction. Marks’s (1975)

concluded on the basis of a review of the empirical literature that exposure to the fearful stimuli

is the only necessary and sufficient condition for anxiety reduction. Emmelkamp (1994) also

concluded, based on a review of the empirical literature that exposure to phobic stimuli without

avoidance is the essential ingredient in effective behavioral treatment for anxiety disorders.

Exposure or exposure plus nonavoidance are partially consistent with an extinction explanation

because a complete extinction explanation needs to: (1) define the target behavior, (2) define the

reinforcer, and (3) show that no onset or offset of the reinforcer occur contingent upon either the

emission or omission of the target behavior. The empirical literature does not strongly support the

third criteria and only partially supports the other two.

More importantly, extinction refers to a functional relationship between response decrement and

absence of reinforcement; it does not explain why this relationship holds, and therefore cannot be

used to explain fear reduction. Behavioral psychologists have intentionally avoided investigation

into processes underlying extinction because formal behavioral explanations are restricted to

functional statements that exclude mediating processes (cf. Plaud & Eifert, 1998; Plaud &

Vogelantz, 1997). An extinction explanation of systematic desensitization must show that the

same mechanism underlies both extinction and systematic desensitization. No such explanation

can presently be provided and consequently systematic desensitization cannot presently be

explained in terms of extinction. The lack of a causal mechanism deprives extinction of

explanatory force.

Bandura (1969) observed that b. . . conventional extinction procedures often involve a form of

unguided counterconditioning b(pp. 429–430) because other stimuli co-occur naturally during

extinction trials. Concurrent cognitions are inevitably associated with, and therefore inextricably

confounded with, extinction procedures in humans. It may therefore not be possible to conduct a


study that avoids this problem. This would preclude establishing extinction as an explanation of

systematic desensitization and exposure therapy in humans. Mahoney (1974) and Meichenbaum

(1974) exploited the fact that people are cognitively active during desensitization by having

participants make corrective self-statements and visualize themselves engaged in adaptive coping

behaviors during desensitization to enhance its effectiveness. The argument that extinction is an

active learning process and therefore consistent with cognitive processing converts it into a

cognitive-behavioral explanation because strict behavioral explanations exclude mediating cognitive

processes from formal explanations of behavior (cf. Plaud & Eifert, 1998; Plaud & Vogelantz,

1997). The current cognitive-behavioral debate (cf. Tryon 1995b, 1996) continues to center on

whether or not to permit mediational processes such as cognition as legitimate scientific

explanations of behavior. Presence and effectiveness of concurrent cognitive processes undercuts

the extinction explanation in humans. Interpreting extinction as counterconditioning is undercut by

the same facts that vitiated the counterconditioning hypothesis reviewed above.

Wolpe (1995) criticized the extinction explanation on two other grounds. First, repeatedly

evoking a fear response in the absence of reinforcement was, in his experience, insufficient to

permanently reduce anxiety. He claimed that the natural histories of phobic patients often include

frequent exposure to fear stimuli with no anxiety reduction. It is possible that these natural

histories also entail avoidance while still anxious followed by subsequent anxiety reduction

thereby reinforcing avoidance and preventing desensitization. Second, Wolpe criticized exposure

explanations for lacking a causal fear reduction mechanism such as reciprocal inhibition.

Evidence presented above constitutes serious empirical and theoretical weaknesses regarding the

extinction explanation of systematic desensitization. The explanatory limitations noted above

suggest that obtaining methodologically rigorous empirical support for the extinction hypothesis in

humans is unlikely.

2.5. Two-factor model

Mowrer’s (1960) two-factor theory is frequently cited as an explanatory basis for exposure

therapies (McAllister & McAllister, 1995; McGlynn, 2002). Mowrer proposed that fears are

acquired according to classical conditioning and are maintained by fear reduction that comes

from escape and avoidance of the phobic object. Menzies and Clarke (1995) critically reviewed

the evidence for and against the traumatic associative conditioning hypothesis regarding the

etiology of phobias and found that the preponderance of the evidence did not support it. The

crucial empirical fact is that few phobic persons can recall a traumatic onset of their phobia. A

patient’s inability to recall relevant trauma does not prove that traumatic events never occurred

but neither does it provide empirical support for the traumatic conditioning etiology of phobias.

The burden of proof remains with the proponents of hypotheses; not with the opponents.

Kheriaty, Kleinknecht, and Hyman (1999) surveyed undergraduate students with blood/injection or

dog phobias regarding traumatic memories using either the Phobia Origins Questionnaire (POQ:

Ost & Hugdahl, 1981) or Phobia Origins Structured Interview (POSI: Kleinknecht, 1994). While

over a quarter (28.6%) of their respondents had no memory for traumatic events the following

evidence of method dependency was reported; 46.0% of respondents had no recall when

examined by structured interview but only 7.3% had no recall when examined by questionnaire.

While 71.8% of respondents to the POQ reported conditioning-like events, 71.8% also reported


vicarious events (e.g., observing trauma in others) and 84.6% reported information events

pertinent to the origin of their phobia. The impact of each type of experience on the intensity of

current phobia remains unknown. For example, Kheriaty et al. (1999) did not demonstrate that

phobic severity was proportional to the severity of conditioning-like events. Kheriaty et al.’s

participants comprised an analog rather than a clinical sample. Perhaps, the same method

dependency exists in clinical samples. Or, recall could be inversely related to severity of

impairment and therefore better in an analog than clinical sample.

A theory may incorrectly explain etiology but correctly explain intervention effects. Anxiety

may develop for reasons other than classical conditioning but once established, anxiety reduction

produced by escape/avoidance conditioning may strengthen fear-related behaviors and emotions.

The empirical evidence first summarized by Marks (1975) indicates that exposure to fear stimuli

and nonavoidance are crucial to fear reduction. One might refer to this as the Exposure and

Nonavoidance empirically supported principle. However, McGlynn et al. (1981) noted that

b. . . exposure theory is not an explanation of therapeutic desensitization effects. Rather, it is

simply a hypothesis concerning the necessary and sufficient procedural ingredients within the

technique. The therapeutic effects of the exposure remain to be explained (e.g., as extinction, as

counterconditioning, as habituation)Q (p. 154). We have already seen that there is little supportive

evidence for these explanations.

2.6. Cognitive changes

Patients are conscious during systematic desensitization and are therefore likely to actively

construe this experience. The following cognitive-behavioral explanations of systematic

desensitization constitute the more popular cognitive-behavioral explanations of systematic

desensitization and exposure therapy. Modeling is not discussed because the more common

therapies do not call for the therapist to model desired behaviors.

2.6.1. Expectation

The psychological principle that persuasion is part of healing has long been accepted (Frank,

1961). However, bOnce it has been established that a placebo intervention is better than no

treatment for a particular problem, then a principle of change has to perform better than placebo

to receive separate recognitionQ (Rosen & Davison, 2003, p. 307). This criterion appears to

distinguish placebo from psychological principles though one might argue that placebo is a

primary psychological principle. Baskin, Tierney, Minami, and Wampold (2003) reported that

structurally equivalent placebos are nearly as effective as treatments. The structural factors

considered were: (a) the number of sessions, (b) length of sessions, (c) format, group vs.

individual, (d) therapist training, (e) whether or not treatment was individualized to the client,

and (f) whether therapists could discuss topics pertinent to the presenting problem or if they

were restricted to neutral topics. Structurally equivalent placebo groups differed from experimental

groups by only 0.149 pooled standard deviations on average; the 95% confidence interval ranged

from 0.055 to 0.292. Placebo interventions that were not structurally equivalent differed from the

experimental groups by 0.465 pooled standard deviations on average; the 95% confidence interval

ranged from 0.309 to 0.621. Structurally equivalent placebos appear to be more credible,

engender greater expectation for change, and to produce larger therapeutic effects.


Wilkins (1971) argued that systematic desensitization works because: (1) therapists foster an

expectation of success, (2) progress up the hierarchy provides confirmatory feedback that success is

occurring, and (3) the patient learns how to control the onset and offset of fearful imagery.

Borkovec (1973) identified nine studies supporting the view that expectancy influenced outcome

and ten studies that did not. Yates (1975, p. 170) also reviewed the literature and found similarly

mixed support. Wilkins (1973b) reviewed the empirical literature regarding expectancy of

therapeutic gain regarding systematic desensitization and concluded that the expectancy explanation

of why systematic desensitization works is unsupported by empirical evidence. On the other hand,

Kazdin and Wilcoxon’s (1976) review of the empirical literature revealed that the large majority of

studies failed to use control groups that were as credible and generated as much expectation of

change as did the systematic desensitization conditions. Systematic desensitization and exposure

therapy may work to some extent because of persuasion but the degree to which this is true is

unknown at this time. Nor has a mechanism of action been offered for how expectation leads to

long-lasting behavioral change. Absence of such an explanatory mechanism deprives expectation of

explanatory force.

A review of the literature reveals that patients with panic disorder and phobias are prone to

over estimate how fearful they will be when exposed to a fear stimulus (Rachman & Bichard,

1988); they expect to be more fearful than they actually are when exposed to the phobic

stimulus. Taylor and Rachman (1994) explained this phenomenon as due to: (a) the over

prediction of danger elements, and (b) the under prediction of safety resources. Exposure therapy

is hypothesized to work because it presumably provides corrective evidence in that participants

see that reality is not as bad as their expectations. This explanation is known as stimulus

estimation theory or match–mismatch theory. Two subsequent studies did not support this

explanation (Arntz, Hildebrand, & van den Hout, 1994; Telch, Valentiner, & Bolte, 1994) but

were criticized by Taylor (1995) on the methodological grounds that they did not obtain both

predictions and reports of danger and safety and they relied on correlational rather than

experimental manipulation. An experimental test of this hypothesis by Wright, Holborn, and

Rezutek (2002) that obtained predictions and reports of danger and safety in snake-fearful

university students provided empirical support for this hypothesis. Effectiveness of systematic

desensitization and exposure therapy can therefore be explained by a consonance seeking process

where fear expectations decrease so as to be more consistent with experience. Mechanism

information about how this learning takes place has not been provided. No explanation has been

given for how exposure brings about lowered fear. The authors imply that some form of

comparison between imagined and experienced states is involved and that this discrepancy

somehow reduces fear. The absence of mechanism information deprives the stimulus estimation

explanation of explanatory force. Another explanatory limitation of the stimulus estimation

explanation is that systematic desensitization and exposure therapy should not reduce fearful

expectations below the fear level experienced in the presence of the fear stimulus yet exposure

therapy frequently appears to reduce fear to lower levels.

2.6.2. Self-efficacy

The coping procedures initiated by Meichenbaum (1974), Mahoney (1974), and others were

designed to promote the kind of cognitive changes that subsequently have been termed self-

efficacy; a positive view of one’s ability to cope. Fear reduction can be explained as the result


of increases in self-efficacy (Bandura, 1977, 1978, 1982, 1998). Systematic desensitization is said

to alter both the response-outcome and efficacy expectation components of self-efficacy. It is

understandable how hierarchy construction from easier to more difficult scenes could improve

one’s response-outcome expectation in favor of expecting that they should eventually be able to

remain calm in the face of fearful cues by the end of treatment. It is also understandable that

successful progression up the hierarchy provides evidence that one is systematically approaching

this clinical goal. What is missing is mechanism information to explain how fear reduction takes

place. How does expectation of fear reduction actually reduce fear? How is it that the individual

can progress up through the hierarchy and thereby validate the new response-outcome

expectation? If the person did not actually become less fearful, then imaginal or actual exposure

to the fearful cues would reconfirm their phobic reaction and reaffirm the original response-

outcome expectation, that they will respond anxiously, and reinforce their original efficacy

expectation that they cannot approach what makes them anxious. Absence of causal mechanism

information regarding self-efficacy changes deprives it of explanatory force.

2.6.3. Cognitive restructuring

The underlying psychological principle for cognitive restructuring appears to be that one

behaves and feels in ways that are consistent with what one thinks and that by changing

cognitions one necessarily changes behaviors and emotions. Rational-emotive behavior therapy

(Ellis & Blau, 1998; Ellis & Whiteley, 1979) and cognitive therapy (Beck, 1976, 1995) aim to

reduce anxiety by restructuring cognitive appraisals. An effort is made to help the person

understand that the phobic object or situation is really not dangerous. Such altered cognitions

may reduce anxiety or persons who become less anxious as a result of systematic desensitization

may think more rationally about their fears. Persons who do not improve as a result of

systematic desensitization may not think more rationally about their fears and therefore may not

be less anxious. Data as to the temporal change sequence and whether cognitions change before

anxiety levels drop or vice versa are needed to decide this matter.

DeRubeis et al. (1990) used Baron and Kenny’s (1986) criteria for establishing mediation as the

methodological basis for determining if changed cognitions mediated the effects of cognitive

therapy. They were unable to satisfy all of Baron and Kenny’s criteria for mediation and

were therefore unable to clearly establish cognitive mediation. These authors did not provide

any causal mechanism to explain how cognitive factors changed emotion and behavior

despite their title bHow does cognitive therapy work?Q thereby failing to make their

explanatory case.

2.6.4. Emotional processing models

Lang (1977, 1979, 1985) and Drobes and Lang (1995) explained the cognitive and emotional

changes produced by systematic desensitization and exposure therapy using an informal cognitive-

emotional-behavioral network theory described as a bioinformational model. These authors

attempted to move beyond the cognitive information processing metaphor by hypothesizing that

fear is mediated by a memory-based network containing information about stimulus character-

istics, verbal and nonverbal response tendencies, feelings, and propositions about the meaning of

these events in different situations. They hypothesized that each information source constitutes a

network node. Excitatory and inhibitory connections were hypothesized to exist among these


nodes and to control behavior and the psychophysiological responses fearful people made while

reading scripts of their traumatic experiences.

Foa and Kozak (1986) proposed that fears entail memory-based networks of associations called

fear structures that integrate perceptual, cognitive, and behavioral tendencies. Activation of a fear

network by a perception is said to motivate avoidance and escape behavior. Foa and Kozak

(1986) and Rachman (1980, 1990) discussed the therapeutic concept of bemotional processingQdefined as the modification of memory-based fear structures associated with thoughts, feelings, and

actions. A gradual reduction in emotional responding over time is expected given (a) repeated

activation of the fear network and (b) incorporation of corrective fear-incongruent information into

the network thereby revising traumatic memories. This is a more active process than extinction

(Forgas, 1999). The author’s nonstandard use of the term habituation incorrectly implies

cumulative stable long-term fear reduction. The nine criteria for habituation established by

Thompson and Spencer (1966) were not met thereby failing to establish a habituation explanation.

Moreover, the reversible nature of habituation documented above precludes explanation of stable

long-term outcomes.

Creamer, Burgess, and Pattison (1992) postulated a similar form of bnetwork resolution

processingQ. Fear networks were presumed to vary in size, structure (interconnectedness), and

accessibility (cf. Foa, Steketee, & Rothbaum, 1989). Chemtob, Roiblat, Hamada, Carlson, and

Twentyman (1988) discussed a four-level cognitive schema network wherein one level influenced

all others through bspreading activationQ thereby interrelating thoughts, feelings, and actions.

All of these informal network theories advanced our understanding because they provided a network

mechanism with causal properties sufficient account for observed functional relationships. However, the

informal presentation of these network theories limits their usefulness. It is not possible to predict

network behavior, and therefore empirically test these theories without further details regarding network

structure, how each node influences all others, and how connections among network nodes change as a

result of learning. Absence of such specifics precludes the possibility of altering these network models

based on empirical research. Formal network theory, discussed below, has made progress regarding all of

these issues and provides a basis for understanding how systematic desensitization and exposure therapy

work.

3. Empirically supported mechanisms

Parallel Distributed Processing Connectionist Neural Network (PDP-CNN) models are memory

mechanisms that learn. Both learning and memory are driven by experience which makes both

processes dependent upon sensation and perception. Hence, action mechanisms associated with the

processes of learning, memory, sensation, and perception are pertinent to our understanding of

how systematic desensitization and exposure therapy work. Some information regarding

empirically supported PDP-CNN mechanisms is needed prior to presenting a connectionist

explanation of how systematic desensitization and exposure therapy work. The first section below

establishes that learning and memory share much in common. The second section documents that

learning entails synaptic change; modeled by modifying connection weights. These changes alter

how the network functions and therefore how psychological and behavioral variables change. The

third section illustrates how activation can cascade across a network. The fourth section discusses


constraints on the learning process and their satisfaction. The fifth section discusses the role of

consonance in network processing. A short list of successful simulations is provided and then a

connectionist explanation of how systematic desensitization and exposure therapy is given.

3.1. Learning-memory

Learning is perhaps the most generic psychological principle and has received an enormous amount of

empirical support from highly controlled laboratory conditions in a wide variety of species regarding a

broad range of behaviors. Our educational system is based upon the principle that people learn. Tryon

(2000) observed that all psychological theories of psychopathology maintain that learning plays an

etiological role in psychological and behavioral disorders and that all persons who benefit from

psychological interventions learn something. That psychologists differ on what is learned and how best

to teach should not obscure our common view that learning is a fundamental empirically supported

principle. All specific conditions under which learning has been demonstrated to occur constitute a list of

necessary and sufficient conditions that psychologists can use to promote therapeutic change.

Cumulative learning presumes retention and that implies some form of memory. Conversely,

memories are learned in that they are derived from experience. Many of the biological structures

necessary for learning are shared with those necessary to form memory. The interdependence of

learning and memory supports reference to a learning-memory principle. The next section

documents that neuroscience has made considerable progress in understanding the mechanisms

that enable learning and memory formation. Cognitive scientists who sought to understand the

bmicrostructure of cognitionQ (e.g., Rumelhart & McClelland, 1986; McClelland, & Rumelhart,

1987) explored parallel distributed processing in artificial neural networks and fostered the

development of formal network learning theory. These and related developments form the basis

for the explanation of systematic desensitization and exposure therapy provided below.

3.2. Learning entails synaptic change

Hebb (1949) hypothesized, and neuroscience subsequently confirmed (Bottjer & Arnold, 1997;

Gluck, Meeter, & Myers, 2003; Kalat, 2001; Kandel, 1991; Kolb & Whishaw, 1998; Krasne,

2002; Martin, Grimwood, & Morris, 2000; Packard & Knowlton, 2002), that learning entails

synaptic change; i.e., brain plasticity. Donahoe and Palmer (1994; pp. 66–67, Note 70; Spitzer,

1993, pp. 42–51), and Rolls and Treves (1998, pp. 322–325) briefly summarize the main cellular

mechanisms responsible for the long-lasting synaptic changes associated with learning. Krasne

(2002), Lynch (2000), Martin, Bartsch, Bailey, and Kandel (2000), and Matzel (2002) provide

more detailed accounts. PDP-CNN models use learning equations to simulate synaptic change

across learning trials. For example, the Hebbian learning equation produces an updated

connection weight equal to the old weight plus the product of the activations of the two

neurons connected by the synapse being altered times a learning rate parameter. This method is

also used to create associative memories (cf. Rolls & Treves, 1998, pp. 42–53). On this view,

learning and memory are two facets of a learning-memory mechanism that appears to be

common to most, if not all, learning including learning due to reinforcement, habituation, and

extinction. That the functional properties of PDP-CNN models depend upon synaptic, connection

weight, changes and resulting activation levels of network nodes has been empirically well

Fig. 1. A hypothetical three-layered feed forward network. The top bSQ layer receives stimulus input. The bhiddenQ middle bOQlayer forms concepts. The bottom bRQ layer represents behavior.


established. Details are provided in introductory texts, such as Bechtel and Abrahamsen (2002),

Martindale (1991), McLeod, Plunkett, and Rolls (1998), O’Reilly and Munakata (2000), and

Spitzer (1999).

3.3. Network architecture and cascade

Synaptic change implies neural network architecture. A PDP-CNN explanatory advance over the

emotional processing models reviewed above is that they provide a detailed description of the network

architecture associated with each model rather than rely on a generic network metaphor. Fig. 1 depicts a

simple feed forward network3 comprised of three layers of nodes, illustrated as circles, and two layers of

connections (synapses), denoted as solid lines. Note that each node in one layer is connected to all nodes

in the next layer and that no connections exist between the top and bottom layers. These connections can

be excitatory, modeled with positive weights, or inhibitory, modeled with negative weights. This

diagram is admittedly a gross over simplification of any real neural structure. However, there is

precedent in science for beginning with simple preparations as initial models of complex phenomena.

Computer simulations based on such simple network structures appear to implement enough by way of

fundamental neuroscience to enable informative simulations of many behavioral and psychological

phenomena. A brief description of how processing cascades across, and is transformed by, each layer of

synapses, connection weights, is prerequisite to understanding the explanation of how systematic

desensitization and exposure therapy work provided below.

Fig. 2 illustrates how activation cascades across the network. Perception, not diagramed here, has

excited the first, third and fifth neurons in the top layer of input nodes as illustrated by the filled circles;

the unfilled circles remain inactive. All connections from inactive neurons have been omitted for clarity

because they do not contribute to cascade processing. This illustration pertains to a single processing

cycle. Therefore, the designated connection, synaptic, weights of 0.1, �0.2, and 0.3 for the first neuron,

�0.2, �0.5, and 0.3 for the middle neuron, and 0.4, 0.2, and 0.1 for the last neuron are presumed to have

3The layers of a network can refer either to layers of neurons (nodes) or synapses (connections). The terms neuron and synapse imply a

biological model whereas the terms nodes and connection weights imply a psychological model. These terms are used interchangeably in this

article. Fig. 1 has three layers of neurons and two layers of synapses and can therefore be described as a three- or two-layered network,

respectively. Fig. 1 is described as a three-layered network to emphasize the different functions performed by each of the three layers of nodes.

Fig. 2. A numerical example of cascade processing.


resulted from prior learning. The activation level of the left neuron in the second, sometimes called

hidden, layer equals its current activation level, not shown, plus the sum of the connection weights4

coming from the active neurons leading into it. The net input now becomes (0.1)+(�0.2)+(0.4)=0.3.

Ignoring the prior level of activation for simplicity and assuming a threshold function such that

cumulative activation greater than zero excites the neuron, this node fires as designated by the filled

circle. Likewise, the sum of the connection weights leading into the middle neuron in the middle layer of

(�2)+(�5)+(2)=�0.5 is less than our zero threshold and therefore this node does not fire as designated

by the unfilled circle. The sum of the connection weights leading into the right neuron in the middle

layer of (0.3)+(0.3)+(0.1)=0.7 exceeds zero and excites this node as designated by the filled circle.

The cascade from the middle to lower layer continues as follows. The connection weights associated

with the active neurons in the middle layer feeding into the left neuron on the bottom level are

(0.5)+(�0.4)=0.1 which exceeds zero and therefore causing it to fire. Notice that the connection weight of

0.2 associated with the second neuron in the middle layer is not involved because that neuron was not

active. The connection weights associated with the active neurons in the middle layer feeding into the right

neuron on the bottom level are (0.3)+(�0.6)=�0.3 which is less than our zero threshold causing this node

to stay off. Hence, the binary input pattern of 1, 0, 1, 0, 1 is transformed into an output pattern of 1, 0.

Transformation is central to cognitive theory. The cascade process is one way that PDP-CNN

networks transform their inputs. The learning-driven connection weight changes noted above alter the

transformation performed as the sign and magnitude of the connection weights change across the

network. Geometric and mathematical parallels with factor analysis provide a way to understand how

and why transformation occurs and why it is dependent upon the sign and magnitude of connection

weights. Consider Fig. 1 from the perspective of factor analysis. Let each circle in the bottom level

represent a single test item and visualize as many circles as the test has items. Let each circle in the

middle level represents an orthogonal factor5 from a factor analysis of those items. Visualize as many

circles as the test has factors. Let the lines connecting the factors to the items constitute factor loadings.

Just as the meaning of each factor depends on the size and sign of each factor loading, the meaning

4Each active neuron, one that fires, is coded 1 and each inactive neuron is coded 0. The activation coming into the left neuron on the

middle level from all three active neurons on the top level is technically (1)(0.1)+(0)(?)+(1)(�0.2)+(0)(?)+(1)(0.3)=0.3. The question marks

refer to the undefined and not shown connections from inactive neurons. These connection weights are unimportant because zero times any

number is zero.5Correlated, also known as oblique, factors can be represented if horizontal connections are allowed among the middle nodes. Strong

parallels exist between structural equation models and neural network architectures.


associated with each node depends on the size and sign of each connection weight.6 Likewise, just as

changing the size and sign of factor loadings changes the concept represented by the factor, so changing

the size and sign of connection weights changes the concept represented by each node in the middle

layer. Let each circle in the top level represents a second-order factor. Visualize fewer circles here than in

the middle layer. Each second-order factor loads on, is connected to, all of the primary factors and forms

a higher-order construct. Its meaning depends upon the size and sign of connections with nodes in the

middle layer.

Processing in Fig. 1 occurs from top down so we need to invert this example and let the stimulus

features correspond to test items, retain our understanding of the nodes in the middle layer as factors, and

consider the bottom layer of nodes as higher order factors. This parallel with factor analysis means that

each network layer transforms input and creates higher-order concepts like factor analysis and higher-

order factor analysis do. Factor loadings provide differential emphasis and in this sense transform the

meaning of item content. Connection weights likewise provide differential emphasis of stimulus

characteristics and consequently determine what the network bthinksQ and bfeelsQ7 about the stimulus

events. Factor loadings are ordinarily computed once after all data are collected. Connection weights can

change after each processing cycle thereby sequentially altering meaning and giving rise to

psychological and behavioral development.

The ability to reduce network processing explanations to physical/chemical processes integrates

psychology into the mature sciences of biology, chemistry, and physics thereby advancing theoretical

consilience (Wilson, 1998). However, network explanations appear to be appropriate psychological

explanations and this presentation is limited to them.

3.4. Constraint satisfaction

Constraints, by definition, limit, restrict, or restrain. Consider a traveling salesman who must visit

several cities and wishes to do so by traveling the fewest miles. The requirement to minimize miles

traveled constrains what route will be taken. This is a single constraint problem. The decision to

purchase a car may entail multiple constraints including: (1) how much money one is willing to pay, (2)

how much performance is important, (3) how much gas mileage is desired, and (4) appearance. Each

facet and the importance placed upon it constrain the choice made. Linear regression positions a straight

line through a data set constrained by the requirement that it minimizes the sum of squared deviations,

vertical distances, between data points and the prediction line. Beliefs and attitudes constituted

constraints in Schultz and Lepper’s (1996) cognitive dissonance reduction network models. PDP-CNNs

attempt to reach the best possible solution by satisfying as many constraints as possible proportional to

their importance. McLeod et al. (1998) stated: bA system which works by constraint satisfaction has a

number of desirable characteristics for modeling human cognition. The main one is that it allows a

decision to be reached by a consensus of evidence, a reasonable fit between input and memory, rather

than requiring an exact matchQ (p. 46). Network nodes usually represent features and connection weightsrepresent their importance. The connection weights constitute constraints because a positive connection

tends to place the receiving node in the same state as the sending node whereas a negative connection

6The mathematics used to compute a factor score is also used to compute what is called netinput to every node in the middle and bottom

layers.7Tryon (1999) discusses how emotions can be encoded into connectionist models.


tends to place the receiving node in the opposite state as the sending node. The actual state of the

receiving node depends upon whether or not the cumulative input of a receiving node exceeds a

threshold level as noted above. Learning entails changing size and sometimes the sign of connection

weights and that changes the constraints during the next processing cycle. Learning functions typically

change connection weights in a way that satisfies as many competing interests as possible and allows the

network to settle into a final activation state that constitutes the best possible compromise solution to the

problem at hand.

3.5. Consonance

Heider (1946, 1958) proposed that consonance is an organizing cognitive principle. Festinger (1957)

described consonance seeking as dissonance reduction. Abelson et al. (1968) edited an 84-chapter

volume entitled bTheories of Cognitive Consistency. Cognitive explanations presume consistency

between cognition and action. For example, cognitive restructuring is based on the view that one can

alter behavior by changing cognition. Changed behavior is explained in terms of changed cognition.

Shultz and Lepper (1996) speculated that bthe study of cognitive consistency seems to have fallen out

of favor, perhaps in part because of an inability to further penetrate its underlying reasoning

mechanismsQ (p. 219). Using connectionist network learning theory, they successfully simulated

important findings of the two major fields of dissonance research, insufficient justification and free

choice. Read and Miller (1998) reviewed connectionist models of social reasoning and social behavior.

Thagard (2000) summarized empirical evidence that coherence pertains to both thought and action.

The process of constraint satisfaction promotes consonance between external environmental

stimulation and current activation states of network nodes. Learning driven changes in connection

weights promotes consonance. More highly valued connections take longer and are more difficult to

change than are less valued connections. Each network iteration attempts to satisfy more constraints and

the process continues until the network stabilizes; i.e., further progress cannot be made. Network output,

behavior, is consequently the result of a consensus process that emphasizes goodness of fit between

current and cumulative prior experience. The resulting compromise provides network models with

clinical relevance.

3.6. Successful simulations

Formal PDP-CNN models have successfully simulated many behavioral (Commons, Grossberg, &

Staddon, 1991; Krasne, 2002) and psychological phenomena (Bechtel & Abrahamsen, 2002; McLeod

et al., 1998). Tryon (1995a) presented an elementary introduction to this field and discussed its

relevance to behavior therapists. Bechtel and Abrahamsen (2002), Martindale (1991), McLeod et al.

(1998), O’Reilly and Munakata (2000), and Spitzer (1999) present more extensive introductions to this

field and successful simulations.

3.7. Explanation of desensitization and exposure therapy

The schematic presentation of a simple three-layer PDP-CNN feed-forward network provided in Fig.

1 is sufficient for a general explanation of how systematic desensitization and exposure therapy can

work.


3.7.1. Pretreatment

People have a long developmental history by the time they seek treatment. Fig. 1 constitutes a

snap shot at a particular point in time just prior to intervention. The top layer of nodes represents

the results of sensation and perception; output from one or more additional networks not shown.

The middle layer of nodes represents one or more facets of cognitions and emotions7 present at the

time the snap shot was taken. The bottom layer of nodes represents one or more facets of the

pretreatment behavioral repertoire at the time the snap shot was taken. The cascade process described

above mediates, explains, how perception gives rise to fear-related, and other, cognitions, emotions, and

behaviors at pre-treatment. The network is in a consonant state because it has gone through many

learning cycles and has stabilized into its present configuration.

3.7.2. Treatment

The therapist creates dissonance by having the person behave in a therapeutic way, e.g.,

remaining relaxed or engaging in the desired behavior, while presenting a fear stimulus. The

stimulus presentation activates the pre-treatment cascade which now conflicts with the state of the

output nodes. Technically, one is said to bclampQ output node activation levels to represent the

desired response while applying the fear stimulus. How this dissonant network state is created is

unimportant from an explanatory perspective but may constitute crucial differences in clinical

technique depending upon the person being treated. Therapeutic change occurs because networks

seek consonance through an iterative gradient descent constraint satisfaction process wherein the

learning process modifies connection (synaptic) weights. Each exposure therapy trial repeats the

process of dissonance formation followed by consonance seeking wherein connection (synaptic)

weights become increasingly consistent with desired behavior as represented by the output layer.

The goal is to change connection weights across the network so that the stimulus cascades into the

desired therapeutic response rather than the pretreatment fear response. Successful cascade changes

causally modify mediating cognitions and emotions as described above.

An important novel prediction is that cognitions, emotions, and behaviors change simultaneously

during each processing cycle. This prediction of simultaneous change contrasts markedly with

sequential expectations by: (a) cognitive models which predict that cognitive changes precede and

mediate behavioral and affective changes, (b) behavioral models which predict that behavior

changes precede and mediate cognitive, and affective changes, and (c) affective models which

predict that emotional changes precede and mediate cognitive and behavioral changes. Simulta-

neous change integrates, and thereby unifies, all three standard models. Further implications are

discussed in Answering clinical questions below.

A treatment corollary of the simultaneity prediction is that interventions should simultaneously

foster cognitive, affective, and behavioral consistency with the desired outcome. Methods of getting

people to think, feel, and act in new ways that are consistent with the desired outcome should be

integrated, pursued simultaneously, in order to promote the physical processes of synaptic change

that alters the real neural network cascade that causally mediates changed perception, cognition,

affect, and behavior.

3.7.3. Principles vs. treatments

The first section of this article argued that certifying ESTs enables the proliferation of seemingly

different clinical interventions whereas ESPs should not. The above section showing that systematic


desensitization and exposure therapy both work because they alter the network cascade process

shows that they work for the same reason, are based upon the same principle, and therefore

constitute a single treatment, not two different ones. Multiple methods for creating dissonance may

constitute different clinical techniques but do not constitute different therapies because they

implement the same empirically supported causal mechanism.

4. Strengths of connectionist explanations

4.1. Empirical support from neuroscience

4.1.1. Synaptic change

The network model presented above is empirically supported by evidence from neuroscience

laboratories that has clearly demonstrated that learning and memory entail synaptic change; i.e.,

brain placticity. Connectionist models are memory systems that learn.

4.1.2. Neuroimaging studies

Kandel (1991) hypothesized that the biological changes produced by learning should be

detectable with modern neuroimaging equipment. Baxter et al. (1992) reported PET results showing

that nine OCD patients treated with CBT for 10 weeks demonstrated the same glucose metabolic

rate decreases as nine patients treated for 10 weeks with oral fluoxetine hydrochloride. Schwartz,

Stoessel, Baxter, Martin, and Phelps (1996) reported a significantly greater bilateral decrease in

caudate glucose metabolic rates for OCD responders than nonresponders to CBT. It is therefore

possible that systematic desensitization and exposure therapy also produce detectable brain changes.

4.2. Empirical support from psychology

The connectionist explanation of systematic desensitization and exposure therapy presented

above is consistent with and is empirically supported by the evidence supporting Mowrer’s (1960)

two-factor theory, Taylor and Rachman’s (1994) match–mismatch model, Lang’s (1977, 1979,

1985) and Drobes and Lang’s (1995) bioinformational model, Foa and Kozak’s (1968) and

Rachman’s (1980, 1990) emotional processing model, and Creamer et al.’s (1992) network

resolution processing model. This constitutes a considerable body of empirical support for the

proposed network theory.

4.2.1. Connectionist model of PTSD

Tryon’s (1998, 1999) bidirectional associative model (BAM) is a connectionist model that has

extended our understanding of Post-traumatic Stress Disorder (PTSD) in ways that satisfy all four of

Jones and Barlow (1990) and all five of Brewin, Dalgleish, and Joseph’s (1996) requirements for a

comprehensive understanding of this disorder.

4.2.2. Novel predictions

The prediction of simultaneous cognitive, affective, and behavioral change discussed above is

novel. Tryon’s (1999) BAM model of PTSD also makes several novel empirically testable


hypotheses. While it is desirable for new theories to make novel predictions, it is not essential that

they do so. The emotional processing informal network models reviewed above gained broad

acceptance even though they did not make novel predictions different from those made by

Mowrer’s two-factor theory. Acceptance of these models appears to have been based on the

additional explanatory power provided by their associated network theories.

4.2.3. Additional mechanism information

Formal connectionist models provide at least two forms of additional mechanism information;

valuable contributions in their own right. First, connectionist models engage the issue of network

architecture; how the network nodes are interconnected. Much evidence supports the view that

network architecture impacts network performance. Second, connectionist models engage the issue

of how best to change the connection weights among network nodes in response to experience.

Various learning equations are used but space does not permit considering the advantages and

disadvantages of currently available options.

4.2.4. Computer simulation and empirical confirmation

Formal network models are sufficiently specific that computer programs can be used to calculate

specific results that can be compared with empirical results. Close agreement between computed results

and empirical findings provides a demonstration proof that the model is capable of explaining the

phenomena under study. Disagreement between computed results and empirical findings sets the

occasion for empirically directed model changes. Commercially available software for connectionist

modeling is increasingly available to construct and test these models (cf. Tryon, 1995a). Some textbooks

provide free software (e.g., McLeod et al., 1998; O’Reilly & Munakata, 2000).

4.2.5. Answering clinical questions

The connectionist explanation of how systematic desensitization and exposure therapy work

presented above makes the novel prediction that cognitive, affective, and behavioral changes occur

simultaneously rather than sequentially as previously noted. This illustrates how knowledge of a

change mechanism can help resolve differential predictions made by behavioral, cognitive, and

affective models of therapeutic change.

DeRubeis et al. (1990) asked bHow does cognitive therapy work?Q but did not provide any causal

mechanism information. The PDP-CNN network cascade model presented above provides a

possible answer. Short-term cognitive changes occur primarily as a result of learning-based synaptic

change; altered connection weights. These modifications change how the middle nodes bloadQ on

stimulus features resulting in changed cognition just as changing the size and sign of factor

loadings changes factor constructs. DeRubeis et al. also reported that cognitive therapy was only

partially explained by cognitive theory in that all of Baron and Kenny’s (1986) criteria for

cognitive mediation were not satisfied. Their understanding of mediation is sequential as noted

above; cognitive changes precede behavioral changes. The Baron and Kenny analytic procedure

uses linear regression methods based on unidirectional linear causation to test for mediation. The

PDP-CNN cascade model presented above entails complex iterative interactions across multiple

network layers while the network settles into a new more congruent configuration. This model

predicts that cognitive, affective, and behavioral changes occur simultaneously during each network

iteration because connection weight changes occur at all network levels during each processing cycle. The


linear regression tests prescribed by Baron and Kenny (1986), and subsequently elaborated by Holmbeck

(1997), were not developed to detect such simultaneous change. The standard mediational model of

sequential change may be incorrect and may need to be replaced by an iterative network cascade model

such as illustrated in Fig. 2.

Changes in neural architecture may answer some etiological questions. For example, synaptic pruning

is a normal developmental process that continues from birth through late adolescence but appears to

continue in persons who become schizophrenic (Huttenlocher, 1979). Connectionist simulations have

shown that excessive synaptic pruning may cause the cognitive distortions found in schizophrenia

(Hoffman & Dobscha, 1989). Traumatic brain injury can alter neural architecture and result in

psychological and behavioral change.

5. Weaknesses and limitations of connectionist explanations

The emerging field of PDP connectionism is not without its limitations and problems. O’Reilly and

Munakata (2000) noted, b. . . the history of neural network modeling has been dominated by periods of

either extreme hype or extreme skepticismQ (p. 413). These authors summarized general and specific

challenges to computational models (pp. 413–421): (a) PDP-CNN models have been criticized as being

too simple thereby omitting potentially important details. Alternatively, simplification has been viewed

as strength on the basis that essential elements are extracted and need to be understood first before

additional complexities can be properly understood (Elman, 1993). (b) PDP-CNN models have been

criticized for being too complex. The interaction of even a few fundamental principles across multiple

network layers is too complicated to fully convey verbally and requires computer simulation to fully

articulate all details, track all developmental changes, and make specific predictions. A balance must be

struck where models are sufficiently complex to capture essential features but simple enough to be

properly studied. (c) PDP-CNN models have been criticized on the basis that they can do anything given

enough free parameters and therefore it is uninteresting to show that they explain so many psychological

and behavioral phenomena. However, PDP-CNN models are frequently based on principled

considerations of how learning occurs rather than ad-hoc parameter fitting as charged. Tests of

generalization to new situations not part of developmental training diminish this criticism. (d) Some

investigators restrict their models to known neuroscience mechanisms whereas other investigators

simulate neuroscience phenomena using methods that are not biologically plausible but that implement

known biological functions. For example, learning is known to modify synaptic functions and back

propagation methods are frequently used to simulate these changes even though back propagation is not

a biologically plausible process. This decision is justified on the basis that it is a way to mathematically

simulate synaptic change in the absence of a complete understanding of how all of these changes

actually take place. PDP-CNNs mainly model function versus exact process. Likewise, mathematical

models are not biologically plausible per se but can be used to simulate biological functions. Moreover,

PDP-CNN models can be purely psychological and need not be based entirely or even partially on

biological facts. (e) PDP-CNN models have been criticized as being reductionistic. This is the mind–

body problem and is not unique to PDP-CNN models. As noted above, while the ability to reduce

network explanations to physical/chemical processes integrates psychology into the mature sciences,

network explanations are appropriate psychological explanations. Some critics remain unconvinced that

any theory of brain can inform theories of mind and behavior and reject mechanistic explanations


generally because they seem to lack free will (cf. Rychlak 1976, 1981). (f) Ilardi (2002) noted that

cognitive psychology has traditionally grounded its theory in terms of symbol manipulation rather than

brain network function. Neuroscience takes the opposite perspective that only brain events are important.

Cognitive neuroscience provides an interactionist synthesis of these opposite positions in its quest to

understand how mind emerges from brain (cf. Ilardi, 2002). PDP-CNN models constitute part of

cognitive neuroscience. They address the bhardQ neuroscience question of how brain networks instantiate

psychology and behavior; how mind and behavior emerge from brain function. Eliminative

connectionism maintains that rules and symbols will be explained in terms of network function. While

it may seem obvious that the brain actually consists of neural networks and therefore our ability to

abstract symbols and follow rules must be understandable in terms of network functions, critics charge

that our current conceptualization of connectionism at best does not yet fully realize this objective and at

worst will never do so (cf. Fodor, 1997; Fodor & McLaughlin, 1990; Fodor & Pylyshyn, 1988; Marcus,

1998a, 1998b, 1999; Quartz, 1993). The exponentially growing interdisciplinary literature reporting

positive findings and demonstration proofs regarding the simulation of diverse psychological and

behavioral phenomena provides positive evidence to the contrary. (g) Most neural network models

pertain to gradual cognitive change and most current models have difficulty with rapid change.

However, given that the brain implements rapid learning it seems that eventually network models should

also be able to do so. (h) PDP-CNN models have been criticized for a lack of cumulative research. This

criticism can be made of all new areas that have not had sufficient time to develop a substantial body of

cumulative findings. This criticism was once true for all currently well-established models at some early

point in their development.

Obstacles to acceptance remain even if all of the issues identified above could be resolved. Garson

(1998, pp. 16–17) identified additional obstacles to the spread of neural network analysis in the social

sciences. His first obstacle concerns explanation. Understanding how a connectionist neural network

model functions after it has gone through its developmental training can be difficult because the

functional properties of a large set of connection weights cannot be readily described. While various

elements of the network can easily be described, their complex interactions require computer simulation

to follow. Simple causal analyses are frequently unavailable in verbal terms. The complex iterative

network cascade causing simultaneous changes at every processing cycle is a good example.

Mathematical analyses are available but they are unfamiliar to most social scientists who are likely to

reject them as not part of their field although mathematical psychologists may take exception to this

position. New analytical methods need to be developed along with ways to teach them to psychologists.A

second obstacle to acceptance identified by Garson (1998) is that there are many implementations

of connectionist models. Conceptually augmenting the S–R model to the S–O–R model was a

simple extension. Explicating the mediating O step with a wide variety of mathematically stated

models not covered in most graduate psychology programs may deter many readers.

A third obstacle identified by Garson (1998) is the slow rate with which neural network models

have become incorporated into common statistical packages such as SPSS and SAS. Access to user

friendly software greatly facilitated the use of complex multivariate statistics such as factor analysis

and multiple regression that are now routinely used by social scientists. Additional software is

becoming available. Some textbooks come bundled with free software (e.g., McLeod et al., 1998;

O’Reilly & Munakata, 2000).

A fourth obstacle identified by Garson (1998) is the new vocabulary needed to discuss connectionist

models. Reluctance to learn new terms may cause some readers to avoid engaging the connectionist


literature thereby rendering its positive contributions unavailable to them. Scientific progress is

frequently left to young new investigators who are learning for the first time (Kuhn, 1996).

Behavioral psychologists may question the value of pursuing network explanations on the basis

that it is just another form of homunculus like all other cognitive explanations they reject. Donahoe

(1991, 1997), Donahoe and Palmer (1994), and Tryon (2002a) have argued against this thesis on the

basis that PDP-CNN models are selectionist systems that are completely consistent with Skinner’s

explanatory style. Donahoe (1997) goes further and argues for bthe necessity of neural networksQ inthe development of behavior theory.

Perhaps the most sweeping criticism is to suggest that the connectionist position presented above does

not advance our understanding of how systematic desensitization and exposure therapy work beyond the

simplistic and obvious assertion that if a psychological intervention has therapeutic effects, then it must

entail biological change. All connectionist explanations of every psychological and behavioral

phenomena can be dismissed in this way. This view: (a) assumes a reductionist perspective that is

not shared by all psychologists (cf. Rycklak, 1976, 1981, 1994), (b) assumes that this perspective is self-

evident rather than a working hypothesis that requires evidential support, and (c) assumes that the details

of how psychology and behavior emerge from biology are unimportant and not worth knowing. Failure

to consider possible proximal causal mechanisms for how systematic desensitization and exposure

therapy work created the knowledge gap that set the occasion for this article. Future focus on empirically

supported principles is a positive step towards filling this knowledge gap and PDP-CNN models provide

a rich source of mechanism information.

6. Conclusions

Reciprocal inhibition, counterconditioning, habituation, extinction, two-factor model, cognitive

changes including expectation, self-efficacy, and cognitive restructuring, and emotional processing were

considered as possible explanatory mechanisms for the effectiveness of systematic desensitization and

exposure therapy. Various problems were identified that attenuate or undercut their explanatory force. A

connectionist network cascade mechanism was presented that provides information beyond that

specified by informal network theories that accords rather well with empirical evidence. It at least

provides a starting position for conducting additional research to critically appraise the merits and

limitations of such an explanation. It also provides a step towards theoretical unification within

psychology based on an empirically supported learning principle (Tryon, 1993a, 2002b). Perhaps this

will increase the number of clinicians who can conceptualize their client’s presenting problem from a

learning-memory perspective and therefore move our field closer to the vision of Eysenck (1964), Wolpe

and Lazarus (1966), and Wolpe (1969) who hoped that intervention would be based on modern learning

theory.

7. Uncited references

Davis & Palladino, 2000

Noll, 1995

Tierney, 1995


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Possible mechanisms for why desensitization and exposure therapy work

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