In the last phase of treatment, Ms. Q indicated that her husband’s infidelity made it easy for her, as he had made her decision for her.

This may be, and much might have been untold about this episode, but I would have preferred to see Ms. Q take responsibility for her decision.

However, it doesn’t matter much what I (or any other therapist) would have preferred; if this construction is what enabled Ms. Q to move on with her life, who is to say that she should view it differently? That brings about a very important question, as to the extent to which a patient’s vision should take precedence over that of the therapist.

There is an exciting new approach (Duncan et al., 2006) that suggests that the patient is always right, and so the patient’s theory of change should be placed before the therapist’s.

Although I am not completely sold on this extreme formulation, there is much to recommend it, and it certainly is worth considering in this case.

It also should be noted that Ingram appears to have taken Ms. Q’s formulation into account, and the therapy progressed from that point forward.

Given the apparent success of the treatment, I would have been very interested in asking Ms. Q, at the last session, what she felt were the mutative factors in the process.

I routinely do that with my patients and never fail to be surprised by what they say. They often mention events as being critical that I have long forgotten, and fail to remark about what I saw as turning points.

There is no right or wrong about this at our current state of knowledge, but it does give one cause to pause and to reconsider strongly held beliefs.

Perhaps this disparity is consistent with the notion of client-directed treatment (Duncan et al., 2006) referred to above, and should give us more reason to listen to our patients at least as much as we would like them to listen to us.


I find it fascinating that Ingram wrote the following: At the time, I was trying to learn a psychodynamic orientation and frequently felt embarrassed and annoyed at myself for violating the rules: I spoke too often, I “broke the frame” by giving opinions and validation of her strengths, and I destroyed opportunities for transference to develop by being too spontaneous, self-disclosing, and genuine. ( p. 25) At the initial meetings of the Society for the Exploration of Psychotherapy Integration (SEPI; http://www.cyberpsych.org/sepi/), this sentiment was repeated frequently.

Therapists trained in several different orientations had found themselves doing things that they would have been reluctant to confess to supervisors, yet that were effective for their patients.

The heart of psychotherapy integration, the movement within which Ingram clearly belongs, is that old rigidities hamper rather than aid the treatment process, and there is much that we can learn from each other.

Ingram’s notion of the “ever-evolving itinerary” within treatment captures the sense of psychotherapy as a process, which is the essence of psychotherapy integration.

Ingram has made an important contribution to psychotherapy integration with her clearly presented approach, and some of her embarrassment, although understandable, might better be seen as a point of pride.


Stricker (2009), Grinfeld (2009), and Lampropoulos (2009) provide three types of commentary on my case of Ms. Q (Ingram, 2009): feedback on my handling of psychotherapy with this high-functioning client; critical evaluation of my “28 core clinical hypotheses” model; and discussion of where this model fits within four categories of integrative psychotherapy—common factors integration, theoretical integration, assimilative integration, and technical eclecticism.

In this response I focus on each topic in turn, and conclude with a discussion of the training of clinical graduate students as integrationally-oriented psychotherapists.


Writing the case of Ms. Q (Ingram, 2009) was an absorbing, enlightening experience.

It not only allowed me to describe how I handle choice points in therapy using my 28 core clinical hypotheses model (to be called “28CCHM” throughout this article; Ingram, 2006), but also enabled me to improve my teaching and supervision of clinical psychology graduate students.

Reading the commentaries of people who share my core value of providing the best possible treatment for each unique client—two highly-regarded experts in integrative psychotherapy (Stricker, 2009; Lampropoulos, 2009), and an extremely knowledgeable and articulate graduate student who resonates to my approach (Grinfeld, 2009)—has been what I can only call, in the language of humanistic psychology, a peak experience.

Their feedback rewarded me with the pleasure of being understood, appreciated, and commended for my contribution; challenged me to sharpen my thinking and increase the clarity of my communication; and invited me to address broad issues that affect the theory, practice, and training of integrative psychotherapy.

Throughout this article I will be referring to specific hypotheses by codes, and the charts at the end of Ingram (2009) will provide further explanations.


Writing a case study for public scrutiny, unlike writing an article on theory or research, puts the writer in a vulnerable, exposed position.

So it was with gratitude and relief that I discovered that Stricker (2009), Grinfeld (2009), and Lampropoulos (2009) each made positive comments, providing me with the enjoyment of being both understood and appreciated.

Stricker praised things that I had not even recognized as praiseworthy, such as not raising the fee when I moved from a nonprofit training clinic to private practice, and providing the client the choice between individual and marital therapy, instead of assuming that individual therapy was the treatment of choice.

He recognized my emphasis on empathy and the therapeutic alliance, even though I had not made that explicit in my case report; appreciated my focus on strengths; and described my ongoing monitoring of progress as “probably the most evidence-based treatment that is available at the present time” (p. 46).

Lampropoulos noted “the level of sophistication in assessing, conceptualizing and treating a client integratively via the use of the 28 core hypotheses,” and recognized that tailoring the conceptualizations to the “needs and dynamics of the client at many different levels” increased the chance that “the client would find them meaningful and beneficial in therapy” (p. 53).

Grinfeld approved that I was “monitoring responses to interventions so that those that the client does not find helpful can be abandoned while those that resonate are pursued” (p. 59).

She also commended my approach for incorporating the three components of the APA’s Evidence-Based Practice in Psychology (EBPP; APA Presidential Task Force, 2006): (1) research evidence, (2) clinical expertise, and (3) patient characteristics, culture and preferences.


All three of the commentators gave me credit for the comprehensiveness of the 28 hypotheses, and the fact that the hypotheses incorporate domains that are usually neglected in clinical practice.

Lampropoulus honored me with the statement “she has done an important job in presenting the gist of major theories and concepts that an integrative/eclectic student or clinician ought to know to practice integratively at a basic level” (p. 53).

Both Grinfeld and Stricker appreciate my inclusion of hypotheses from two of the seven domains, Existential and Spiritual, and Social, Cultural, and Environmental; and Grinfeld, additionally, noted the benefit of the Crisis, Stressful Situations and Transitions category.


Delight at reading the commentaries came not only from positive feedback but also from discovering new language to describe my approach.

From Stricker I learned the succinct term “local clinical scientist model” for what I have been describing as “empirical validation through single case design.” I have been using the term “intermittent” to describe therapy with multiple episodes, and henceforth I will prefer to use his term, “extended sequential therapy.

” I was especially gratified by the way Grinfeld grasped and described a level of the process in my treatment of Ms. Q that I was completely unaware of: Ingram’s choice to follow the client’s “orientation preference” … seems … to constitute an empathic attunement to process [my italics] as well as to content, furthering not just the working alliance but also Ms. Q’s sense that her needs can be acknowledged and met (p. 63).


Those words elegantly described my intentions and their impact, and, incidentally, demonstrated unmatched empathic attunement to my content and process.


I greatly valued the criticism and suggestions from the commentators.

For instance, Stricker rightly said that it would have been desirable to get the client’s explicit permission and have her review the document (unfortunately this was impossible given the length of time since the termination of therapy), and Lampropoulus pointed out the disadvantage of not providing standardized quantitative measures as did Clement (2008) in his case study in a prior issue of this journal.

Grinfeld made the excellent point that most clients, unlike Ms. Q, will not directly provide feedback about which hypotheses resonate and which do not, and may in fact prefer straightforward explanations from a therapist who takes a more authoritative role.

For those clients, the most empathically attuned process would be for the therapist to function in the role of confident expert who makes treatment decisions from a single model, as, in fact, would occur when there is a specific empirically-supported treatment that is the best match for the client’s problem.


Stricker posed challenging questions that allowed me to clarify my thinking on issues I had not previously addressed directly: How important is such external confirmation [of her talent for writing]? As long as Ms. Q is committed to a career in writing, must we (can we) judge how talented she is? As long as she sees her husband as stifling her growth, does it matter whether he really is doing so? ( Stricker, 2009, pp.


These were not rhetorical questions but ones that are open for debate, and the answer must be grounded in specific case data.

In the case of Ms. Q, I believed that it was vitally important to determine that her aspirations were realistic rather than being grandiose fantasies or utopian goals.

In a different case—say, a client who talks with pleasure about her beautiful new house—I would not take a drive to her address to see if I agreed with her opinion.

In the latter example, it is a question of values, taste, and personal definitions.

Moreover, it is a fait accompli; the client has already made a choice, and she is satisfied with it.


In Ms. Q’s case, she was faced with making decisions that would have dramatic repercussions for her marriage, her child, and her lifestyle.

Moreover, the issue was one of defining outcome goals, and the importance that goals be realistic and attainable.

( This topic is discussed in detail in Ingram [2006] in Chapter 12).

I believe that an important task of a therapist is to help clients set goals that are not only possible, but also, when attained, will increase their life satisfaction, enhance their self-worth and self-efficacy, and not result in unexpected collateral damage.

For me, failing to challenge a client’s unrealistic goals would be equivalent to failing to mention a banana peel on the person’s path.


Questions about external verification of the clients’ beliefs allow me to elaborate on the importance I place on working jointly with the client to gather data about “reality” so that the client (not just I) can ascertain the fit of her “model of the world” with the evidence of experience (meaning both her sensory, lived experience, and the “reality” that receives consensual validation from outsiders.) In his article, Stricker wondered if I had gathered details about her impulsivity and inclination to flee situations, noting the importance of distinguishing between an internal state that has never been acted on, and a tendency to action that has been demonstrated in the past.

This client told a highly detailed narrative of her life, including specific episodes that she labeled “fleeing,” including leaving home without advance preparation at 18 and leaving her ex-boyfriend without communicating her intentions.


Had she not been so forthcoming, I would have asked questions like, “How do you define ‘fleeing’?” “ Have you ever done that before?” “ Can you give me an example?” “ Please tell me the story of that experience, from beginning to end.” My knowledge about data-gathering questions comes in part from what Bandler and Grinder (1990) called the “metamodel,” defined as a set of linguistic information-gathering tools, and described in my book (Ingram, 2006, pages 40-44) as an essential interviewing skill.

Questions that require the client to check his or her cognitive map against the data of their personal experience are necessary for evaluating the extent to which that map is faulty (C2—Faulty Cognitive Map).

Questions that allow the therapist to evaluate the client’s information processing skills (C3) not only identify weaknesses, but also “hook into” the reality testing part of the client’s mind and reveal strengths in scientific thinking skills.

Answers to those detail-oriented questions allow the client to flesh out and evaluate her personal narratives, as well as modify faulty thinking such as overgeneralization (“Can you think of an example when that didn’t happen?”) and self-imposed limits created by “can’t” and “should” (“What stops you?” “ What would happen if you did?” “ What would happen if you didn’t?”).


Another challenging question from Stricker was this: “Does the ‘historical truth” of the observation [about early childhood experiences] matter if the ‘narrative truth’ is meaningful to the patient?” He proposed that “coherent and credible attribution for feelings and actions … can be the vehicle for therapeutic progress even in the absence of historical correlates” (Stricker, 2009, p. 48).

Reading this, I felt comfortably in the terrain of Jerome Frank (1961) who suggested that the common elements of successful change interventions were a plausible explanation, and a logically-connected ritual.

I believe that while the lack of verifiable historical data is not an impediment to successful therapy outcomes, credibility and plausibility are enhanced when there is confirming evidence from past experience, and the search for evidence is hugely beneficial for building clients’ ability to evaluate and update their cognitive map (C2) and reframe the experiences that led to maladaptive repetitions of past experiences (P2).


This discussion leads me to pose a question to myself: “How do I reconcile my view that the client is the director or collaborator in the direction of therapy with my belief that I have a responsibility to clear up illusions or misconceptions?”


Stricker pointed out my judgmental position when Ms. Q. decided not to leave the state to enroll in a writing program, but instead to rent an apartment near her child, and live on her own while pursuing her writing career.

He comments, “Ms. Q. was very proud of this choice, and it seems as though Ingram agreed as well.

However, was this a good choice? Who can say?” ( Stricker, 2009, p. 47).


While Stricker would be comfortable putting his preferences aside as long as the outcome of the client’s choices were not obviously destructive, I would be integrating several hypotheses to help the client enhance the quality of her decision by correcting faulty information processing (C3), liberating herself from the need for defensive processes (P4), reducing the need for conditioned emotional avoidance (BL2), and realistically appraising the consequences of choices in terms of values and responsibilities (ES2).


Stricker is correct that I did approve of Ms. Q’s choice, and even showed my approval, because it seemed to be a good compromise between fulfilling her need for freedom and embracing her responsibility to consider the emotional impact on her son. I confess that my tendency to judge according to my own values has been my biggest countertransference challenge, and has put at risk the safety and acceptance that clients need in order to express everything they think and feel. ( I have learned that growing up with a mother who believed “I know what is best for you” was not the best preparation for a psychotherapist.)


One of the weaknesses pointed out by Grinfeld was that the list of 28 hypotheses was not as comprehensive as I believed, citing the omission of process variables and treatment approaches.

Grinfeld’s comments give me a chance to clarify the importance of distinguishing core clinical hypotheses and “treatment approaches that have been shown to be effective in therapy.

” The hypotheses are building blocks for the assessment (also referred to as conceptualization, formulation, or explanation), which is (a) consistent with the data of a specific client; and (b) focused on explaining the cause, maintenance, and potential resolution of specific problems.

The treatment plan then (a) follows logically from specific hypotheses, (b) is focused on attaining outcome goals; (c) specifies in detail strategies, techniques, and therapeutic process dimensions; and (d) is a good match for the specific client’s values, preferences, and abilities.


Grinfeld points to omissions in the list of 28, such as: transference and countertransference, attachment theory, and acceptance-based therapy (e.g., Hayes’ [2004] Acceptance and Commitment Therapy and Linehan’s [1993] Dialectical Behavior Therapy).

I believe that my hypotheses, explained in detail in Ingram (2006), incorporate these ideas.


Transference and Countertransference.

Psychodynamic hypotheses (Ingram, 2006, Chapter 8) lead to the use of countertransference and transference as opportunities to discover, in the here-and-now of the therapist-client relationship, the early childhood origins, repetitious patterns (P2), and unconscious feelings and conflicts (P4) that have led to the client’s current difficulties.

When P3 is applied, the transference represents the activation of unmet early childhood needs, rage over failures of empathic attunement, and the mobilization of hope that the client’s needs for mirroring, soothing, and someone to idealize can be met in a close relationship.

Furthermore, the conversation between client and therapist about perceptions and feelings that can be classified as transference and countertransference help build communication skills (BL3) and foster a healthy, authentic relationship (ES1).


Attachment theory.

This topic is addressed in my book (Ingram, 2006, pp. 302-303) in the section on hypothesis P2, Reenactment of Early Childhood Experiences.

The relationship difficulties of adults can be described in terms of different attachment styles, which have their roots in early childhood experiences with caretakers.

Furthermore, attachment theory contributes to an understanding of the dynamics in family systems (SCE1).


Acceptance and Commitment Therapy.

Hayes’s Acceptance and Commitment Therapy (ACT) can be “unpacked” to illustrate that it is an integration of several hypotheses from my list.

Acceptance stems from a set of beliefs (C2) about what can or cannot be changed by the efforts of the client, and is achieved by using mindfulness strategies, which I describe in my discussion of hypothesis B3 (Mind-Body Connections).

When clients are taught to "just notice," in a detached way, their private events, especially the most distressing ones, they are learning a different way of processing information (C3) and are quieting disturbing self-talk (C4).

The instruction to clients to access the part of the self that is observing and experiencing (rather than identified with thoughts, feelings, sensations, and memories) is consistent with the Internal Parts hypothesis (P1), which embraces theories such as Psychosynthesis, Gestalt Therapy, and Transactional Analysis.

If ACT helps clients to clarify their personal values, take action on them, and bring vitality and meaning to their lives, then it clearly incorporates the existential hypotheses (ES1 and ES2).

I assume that once the individual commits to a goal and begins to take action to achieve it, then issues of conditioned emotional avoidance (BL2) and skill deficits (BL3) can be addressed.


Linehan’s Dialectical Behavior Therapy: Linehan’s (1993) approach is described in my book (Ingram, 2006, p. 434) as an excellent model for defining problems in terms of “skill deficits,” which can then be treated with various skill-building approaches (BL3).

The skills of “emotional regulation” (p. 182) are also discussed in the section on hypothesis B3, Mind-Body Connections, as tools for coping with stress and achieving self-management of arousal level (pp. 91-92).


On the other hand, I accept that the list of 28 is a work in progress.

Whenever I discover a new theory, treatment approach, or orientation, I seek to “unpack” the bundled and branded model, to find the component hypotheses.

It is my hope that as people discover and familiarize themselves with my chosen list of hypotheses, there will be fruitful collaborative work to refine, expand, and reorganize the list, as needed.


The analogy of a computer directory system will increase understanding of the structure of the model of clinical hypotheses and its potential to be expanded to embrace all knowledge relevant to planning effective mental health interventions.

The seven domains (Biological, Cognitive, Psychodynamic, etc.) are like directories in a computerized information system; each hypothesis is like a subdirectory; and within each hypothesis subdirectory there are additional subdirectories which contain files with information about the hypothesis, its associated treatments approaches, when it is a good match, and with which other hypotheses it easily integrates.

Some files have only a paragraph, others comprise the content of books or the experiences of extensive training.


Significantly, the same files can be found within the subdirectories of different hypotheses: this is why it is expected for there to be overlap among hypotheses, and the criterion of mutual exclusivity is not met.

Here is an illustration of a small part of one of the domains.


• Biological Domain


o Mind-Body Connections Hypothesis (B3)


• Treatment approaches


• Acceptance and Commitment Therapy
      • Dialectical Behavior Therapy
      • EMDR
      • Relaxation Training


With this framework in mind, we can see that there is room to conceptually incorporate all past, present, and future models of psychotherapy, as long as we are willing to unpack each theory into its component hypotheses, and agree that brand names of theories, and the loyalty they demand, are detrimental to the advancement of effective integrative therapy.

( Note that in addition to the component mechanisms and processes underlying psychotherapy theories, there is also the issue of the empirical evidence for the efficacy and effectiveness of a theory.

For more on evaluating therapy theories on the basis of the empirical evidence that support them, see APA’s Evidence-Based Practice in Psychology web site [EBPP; APA Presidential Task Force, 2006]).


In a related vein, in his commentary, Lampropoulos made an intriguing suggestion—the idea of a group of experts getting together and building a comprehensive framework.

I of course like his idea of perhaps starting this process with my list of hypotheses.


All three authors described the four models of integration: common factors integration, theoretical integration, assimilative integration, and technical eclecticism, citing, among others, Castonguay, Reid, Halperin and Goldfried (2004); Lampropoulos (2001); Stricker and Gold (2006); and Messer (1992, 2001).

In this context, they all offered their view of where the 28CCHM fits.

Stricker and Grinfeld classify it as technical eclecticism, which, in Stricker’s words, “focuses on a combination of techniques drawn from different therapeutic systems without regard for any specific theoretical approach” (Stricker, 2009, p. 43).

I agree that my approach is similar to technical eclecticism because of the pragmatic approach of drawing techniques from multiple models with an emphasis on resolving specific problems.

However, it differs greatly in that it requires intervening hypotheses (theoretical ideas) between a data base and interventions, as well as deep attention to the client’s complex life history, current life situation, and sociocultural context.


Stricker goes on to describe my approach as closer in style to that of Lazarus’s Multimodal Therapy (Lazarus, 2005a, 2005b) than to other examples of technical eclecticism.

In Lazarus’s approach, a specific technique, drawn from a limited range of theories, is paired with a problem within one of the modalities of behavior, affect, sensation, imagery, cognition, interpersonal relations, and biological functioning.

I place high value on this list (along with an additional spiritual modality) as a tool for comprehensive data gathering (cf.

Ingram, 2006, pp. 34-40), but disagree that it is similar to my list of hypotheses.

With Lazarus’s approach, for example, recognition that “depression” is a syndrome would be unnecessary; the therapist could treat sadness, difficulty with sleep, negative thinking, and psychomotor slowness as separate, unrelated problems.

Furthermore, anyone with that list of symptoms might be treated in the same way; there would be no interest in the various routes by which people arrive at depression, such as difficulties coping with illness and disabilities (B1), stress and trauma (CS2), loss and bereavement (CS4), repetitious patterns of unhappy relationships (P2), loss of meaning (ES1), and past and current experiences with discrimination and other forms of social injustice (SCE5).


I felt best understood by Lampropoulos’s assertion that the 28CCHM fits in the category of theoretical integration models.

He states that it “can probably be most accurately categorized as a comprehensive eclectic model, operating within a broad and clearly defined multitheoretical framework” (Lampropoulos, 2009, p. 53).

He describes its difference from technical eclecticism in that it “focuses on the integration of clinical hypotheses (as opposed to interventions),” accurately stating that my intention is the creation of integrative conceptualizations, not the combination of varied techniques.


Stricker notes that my approach lacks “a guiding theoretical formulation” (p 43).

However, in my book (Ingram, 2006), I provide bases for viewing the 28CCHM as a stand-alone integrative theory that is based on a problem-solving metatheory.

The metatheory has requirements for clear problem identification; specification of achievable and verifiable outcome goals; comprehensive data-gathering; conceptualizations that are creative combinations of ideas extracted from established orientations; treatment strategies that are logically related to the conceptualization and that are matched to the unique needs and characteristics of the client; and a commitment to the process of empirical validation of treatment effectiveness.

Furthermore, combinations of hypotheses specifically designed for a single client is a theoretical formulation, an idea captured in the title of Grinfeld’s (2009) commentary, “A New Therapy for Each Patient.” My approach to psychotherapy integration is to allow each licensed practitioner to bring creativity to each unique case, with the requirement that theoretical foundations are necessary to assure coherent treatment plans, and monitoring of client change is necessary to assure evidence-based practice.


Thanks to the commentators, I realize that I need to walk those extra miles to further develop the 28CCHM to meet standards of a good theoretical model.

I agree with Lampropoulos’s recommendation that “some additional guidelines would be helpful in prioritizing and identifying the possible importance of each of the core hypotheses,” and that more guidance is necessary for combining hypotheses to assure that they “are compatible with each other and that the integration is coherent and acceptable to the client” (Lampropoulos, 2009, p. 54).

Stricker’s suggestion that I attend to the interrelatedness of the domains, and to their cyclical impact, will receive my deepest consideration.


My efforts to decide “Where do I fit?” led me to another question: “Why is it important to hold on to that four-category system?” I don’t mean to minimize the wonderful achievement of developing that framework, and the valuable body of literature that it stimulates, but I’m concerned that we may do ourselves a disservice by trying to classify within those categories the new models that are being created.

The assimilation model is defined in such a way that the therapist must start with a commitment to a “home base” theory; thus people who combine ideas from equally-valued theories by default are described as technical eclectics.

I wonder, perhaps from ignorance, whether the need to uphold this classification system springs less from our desire to improve the quality of integrative therapy, and hence benefit our clients, than from a desire to advance our respectability in the eyes of critics of integrative approaches.

Stricker reminds us that “old rigidities hamper rather than aid the treatment process, and there is much we can learn from each other” (Stricker, 2009, p. 49).

We need to be careful that we are not building orientations of integrationism that create new rigidities.


Two of the commentators pointed out the heavy academic and training requirements for competent application of the 28CCHM.

Lampropoulos states “one might expect that learning a complex multitheoretical integrative model at the conceptual and clinical level would require significant time and supervision” (p. 55).

Grinfeld contrasts deductive and inductive ways of using this model, and shows preference (as I do) for an inductive approach: “Once a clinician has a fund of knowledge about various theoretical approaches, the 28 Core Clinical Hypotheses can be used to synthesize the information already known by the clinician” (p. 65).


Both these commentators help me emphasize the importance of a broad and deep education for psychotherapists.

Without it, the use of the list of hypotheses would be a shallow exercise in labeling and categorizing without the competence to plan effective interventions and evaluate their effectiveness.

As a review of the topics in the Social, Cultural, and Environmental domain (Ingram, 2006, Chapter 9) will reveal, the educational foundations must go beyond traditional psychotherapy and include knowledge of systems (family and organizational), intercultural competence, social psychology, community psychology, and environmental psychology.

I further agree with Zeldow (2009) that competent clinicians need to know something about the human condition ….

( t)he kind of knowledge [that] cannot come from psychology textbooks or the scientific literature alone.

It must come from life experience, clinical experience, and at least some immersion in the humanities.

( p. 6)


Only with years of academic education combined with years of hands-on clinical training can a psychotherapist be qualified to engage in creative, integrative, evidence-based practice.. In the graduate program where I teach (Pepperdine University’s Graduate School of Education and Psychology in Los Angeles), there are many prerequisites before I teach about the 28 hypotheses in both a “techniques of psychotherapy” course and the clinical practicum on-campus meetings.


Grinfeld’s commentary provided the graduate student perspective, describing her frustration in educational environments where “reification of theory is the norm” (p. 59).

Her descriptions of her own graduate training attached concrete images to my assertion of the negative consequences of being forced to choose an orientation.

I have taught at Pepperdine for 31 years, and I was happy to note that this semester, when I asked how many students had been told that eclecticism is bad and they would be in trouble unless they chose an orientation, not one hand went up.

About 10 years ago, every student would have raised a hand.

Grinfeld’s story made me realize that my school is not typical of graduate training program.


The assumption that all therapists start by choosing a single orientation is very deeply ingrained.

Sticker explained that people “who identify with psychotherapy integration …recognize the value of orientations other than their own” (my italics; p. 45).

The implication is that we all start with “our” orientation.

That makes sense for perhaps the majority of senior therapists, whose roots were watered in psychoanalytical institutes or cognitive behavioral laboratories.

I believe it may be appropriate to question the necessity of having a “home base” orientation.

The current cohort of clinical graduate students may be likely to have teachers with different orientations (including integrative therapy) and to rotate through practicum and internship placements with exposure to multiple ways of working with clients.

They see something of value in every approach they learn, and in the process of learning different orientations they may be very comfortable developing integrative treatment plans.

I think this is a good thing.


I cannot find better words for closing this article than to quote what Grinfeld said at the end of her article, when she offered the opinion that the 28CCHM “provides graduate students … the opportunity not only to pursue psychotherapy integration within a structured and practiced framework, but also to pursue identities as ‘integrationally-oriented’ therapists” (Grinfeld, 2009, p. 66). If she is representative of her peers, then we can foresee a future where integrative psychotherapy is widely viewed as the treatment of choice.


Baddeley (1986, 1992) has proposed a muiticomponent model of working memory that consists of three func- tionally independent subsystems: a phonological loop, which maintains speech-based information; a visuospatial scratchpad, which maintains nonverbal information; and a central executive, which coordinates cognitive opera- tions.

Much of the research devoted to testing and refining the subcomponents of Baddeley's model has focused on the phonological loop and the manner in which speech- based material is rehearsed (see Baddeley & Hitch, 1994, for a review).

According to Baddeley and his colleagues, speech-based information is stored and refreshed using subvocal (i.e., covert) articulatory rehearsal (see Logie, 1995, for a discussion).

Although the mechanisms in- volved in the rehearsal of visual and spatial information are not well understood, it has been suggested that im- agery or eye movements may playa role in the mainte- nance of visuo spatial information (for recent reviews, see Baddeley, 1992; Logie, 1995).


Experiments using dual-task procedures suggest that the phonological loop and the visuospatial sketchpad are independent subsystems.

Performance on verbal working memory tasks is disrupted to a much greater extent by secondary verbal tasks than by secondary spatial tasks (Baddeley, Lewis, & Vallar, 1984), and performance on visuospatial working memory tasks is more disrupted by secondary visuospatial tasks than by secondary verbal tasks (Baddeley, Grant, Wight, & Thomson, 1975; Bad- deley & Lieberman, 1980; Logie, 1986; Logie, Zucco, & Baddeley, 1990; Quinn & Ralston, 1986).

However, in many studies (e.g., Baddeley, Grant, et aI., 1975; Badde- ley & Lieberman, 1980; Logie, 1986; Logie et aI., 1990; Quinn & Ralston, 1986), it is unclear whether secondary tasks interfered with encoding, maintenance, or both.

Re- cently, Hale, Myerson, Rhee, Weiss, and Abrams (1996) developed procedures for assessing the amount of inter- ference that results from disrupting the maintenance of information after memory items have been successfully encoded.

These procedures revealed completely selec- tive interference by secondary tasks (i.e., secondary ver- bal tasks interfered with verbal working memory but did not interfere at all with spatial working memory, and vice versa; Hale, Myerson, et aI., 1996, Experiment I).


Research in which the effects of age on working mem- ory was examined has revealed consistent age differ- ences in working memory performance (see Craik & Jennings, 1992; Dempster, 1981; and Salthouse, 1994, for recent reviews).

Several studies have demonstrated that children (Chi, 1976; Dempster, 1985; Hale, Bronik, & Fry, 1997; Kail & Park, 1994), adolescents (Dempster, 1985), and older adults (Salthouse, 1994) all have smaller memory spans than do young adults.

Recent research sug- gests that, relative to young adults, both children and older adults show more pronounced age deficits in spatial mem- ory than in verbal memory (Hale et aI., 1997; Myerson, Hale, Rhee, & Jenkins, in press).


One plausible explanation for the observed age differ- ences in working memory stems from research revealing a relationship between processing speed and working memory in children (Fry & Hale, 1996; Kail & Park, 1994) and in older adults (Salthouse, 1991; Salthouse, Kausler, & Saults, 1988).

It is well established that both children (see, e.g., Hale, 1990; Kail, 1988) and older adults (see, e.g., Cerella, 1990, 1991) are generally slower information processors than young adults (see Cerella & Hale, 1994, for a review) and that both children and older adults show smaller working memory spans than do young adults.

Indeed, age differences in working memory are minimal when processing speed is statistically controlled (Fry & Hale, 1996; Kail & Park, 1994; Salthouse, 1994).

Although the mechanisms underlying the relationship be- tween processing speed and working memory are not fully VERBAL AND SPATIAL WORKING MEMORY understood, it has been argued that faster processors may activate and covertly rehearse information more quickly than slower processors (Dempster, 1981; Salthouse, 1994).


Alternatively, Hasher and Zacks (1988) have suggested that age differences in working memory abilities result from deficits in inhibitory function.

They argued that working memory involves not only the storage and ma- nipulation of"information that is along the goal path," but also involves inhibitory mechanisms that prevent the en- trance of "off-goaI-path" information (Hasher & Zacks, 1988, p. 212).

They hypothesized that age differences in working memory result from interference caused by older adults' decreased ability to inhibit irrelevant infor- mation.

A similar suggestion has been offered regarding inhibitory deficits in children (Dempster, 1992; Tipper, Bourque, Anderson, & Brehaut, 1989).


The present investigation took advantage of the fact that our laboratory has used the same basic procedures, with little or no modification, to assess working memory performance in multiple samples of children and ado- lescents, as well as young and older adults.

Thus, the meta-analysis was able to combine data from seven stud- ies by Hale and her colleagues in which school-age chil- dren (8-year-olds, 10-year-olds, and 12-year-olds), young adults (18- to 22-year-olds), and older adults (65- to 75- year-olds) were administered the digit span and location span working memory tasks first described in Hale, My- erson, et al. ( 1996, Experiment I).

Two of these studies (Hale & Jansen, 1994; Hale, Myerson, Faust, & Fristoe, 1995) were primarily concerned with processing speed and, hence, did not report the working memory data when originally published.

On the basis of the data from the listed in Table 1, both verbal and spatial interfer- ence effects were examined in over 400 subjects.

In both the verbal and the spatial domains, interference effects were measured as the difference between spans with and without secondary tasks.


In all the studies, subjects were exposed to at least four different conditions: digit span, no secondary task; digit span, verbal secondary task; location span, no secondary task; and location span, spatial secondary task.

Exam- ples of the stimuli and procedures are presented in Fig- ure 1.

For the no secondary task conditions, the subjects were shown a series of digits in a box or Xs in a grid, one by one.

The subjects orally recalled the names of the dig- its in the order in which they were presented in the digit span task and marked the locations of the Xs on an empty grid in the location span task.

For the verbal secondary task conditions, in addition to the memory requirements for the no secondary task condition, the subjects were re- quired to indicate the color of each digit or X as it was presented by saying the color name aloud.

For the spatial secondary task conditions, in addition to the memory re- quirements for the no secondary task condition, the sub- jects were required to indicate the color of each digit or X as it was presented by pointing to the matching color in the adjacent palette.


For all seven studies, the subjects initiated the presen- tation of each series of memory items by pressing a but- ton on a response panel.

The presentation of items within a series was computer controlled, and responses were recorded manually by the experimenter.

For all condi- tions, each trial began with the presentation of a fixation square in the center ofthe screen until the subject pressed the ready button.

Following a 250-msec delay, the items in a series were presented for either 1,250 or 1,750 msec, with a 250-msec interitem interval during which the screen was blank.

2 Immediately after all of the items within a series were presented, a recall signal was given.


Testing for all conditions was conducted following the procedure used for the WAIS-R Digit Span subtest (Wechsler, 1981).

That is, the subjects were exposed to two sets of series at an initial length (three items for the digit span and two items for the location span), and if at least one ofthe se- ries was recalled correctly, the length of the series was increased by one.

The experimenter discontinued testing when the subject failed to correctly recall two series of the same length.

The order of presentation of the condi- tions was counterbalanced.


Working memory spans were based on the maximum series length for which at least one series was correctly recalled, without regard to performance on the preceding series.

Ifboth series were correctly recalled at the max- imum length, the span was equal to that length.

If only one series was correctly recalled at the maximum length, the span was equal to that length minus 0.5.

Memory spans for each condition were calculated separately for each subject.


Because the maximum number of digits and locations presented was 9 (in all the studies but Hale et al., 1997), individuals who could remember all 9 items when there was no secondary task might actually have had spans larger than 9.

For such individuals, the resultant underestimation of their span would lead to underesti- mation of their interference effects.

Therefore, individu- als who had digit spans greater than 8.5 were not included in the analyses of verbal working memory.

Similarly, in- dividuals who had location spans greater than 8.5 were not included in the spatial working memory analyses.


None of the subjects from the child samples were ex- cluded for this reason; less than 10% of the young adult subjects and less than 3% of the older adult subjects were excluded.


Mean verbal and spatial working memory spans, with and without secondary tasks for each study, are presented in Table 2.

Mean working memory spans without sec- ondary tasks and interference effects for each age group averaged across all studies are presented in Table 3.

The upper left panel of Figure 2 depicts the mean verbal work- ing memory spans with and without verbal secondary tasks.

As was expected, verbal memory spans when sec- ondary task performance was not required (unshaded bars) increased as the age of the child group increased.

Verbal spans with a secondary task (shaded bars) also in- creased with the age of the child group.

In adults, the older group showed smaller verbal memory spans both with and without a secondary task requirement than did the young adult group.

In all the child and adult groups, verbal memory performance was substantially disrupted when the subjects were required to name the color ofeach letter as it was presented.

When interference effects, were compared across the age groups, 8-year-olds showed the largest interference effects, whereas the l O-year-olds, 12-year-olds, young adults, and older adults all showed


approximately equivalent interference effects (upper right panel of Figure 2).


A similar pattern of results was obtained for the spatial working memory tasks.

That is, spatial memory spans in- creased with the age ofthe child group and decreased with the age ofthe adult group, both when secondary task per- formance was not required and when it was required (lower left panel of Figure 2).

In terms of interference ef- fects, subjects of all ages showed substantial disruption in their memory for spatial locations when they were re- quired to point to the color of each X as it was presented.

As may be seen in the lower right panel of Figure 2, the 8-year-olds, 10-year-olds, 12-year-olds, and young adults all showed similar interference effects, whereas the older adults showed smaller interference effects than all the other age groups.


On the basis of the above results, we would conclude that there is virtually no change in interference from 10 to 20 years of age in either the verbal or the spatial do- mains.

Moreover, there appear to be no consistent age differences (i.e., differences in both domains) in interfer- ence effects across the life span from 8 to 75 years of age.

That is, although the 8-year-olds showed larger in- terference effects than any other age group in the verbal domain, they did not show larger interference effects in the spatial domain.

Similarly,although older adults showed smaller interference effects than all other age groups in the spatial domain, they did not differ from the other groups in the size of their verbal interference effects.


The present results fail to reveal age differences in sus- ceptibility to interference when the absolute size of in- terference effects is compared.

An alternative approach to As was noted in the introduction, when one compares interference in different age groups, one is comparing groups with different baseline levels of memory perfor- mance.

In order to examine the role of age differences in baseline (without transforming the data), we tried com- paring the interference effects of those subjects of dif- ferent ages who had nearly equivalent memory spans when secondary tasks were not required.

This comparison revealed clear age differences in interference between the matched groups: Verbal interference effects decreased systematically with the age of the child subgroup and in- creased with the age of the adult subgroup, and a similar pattern of results was obtained in the spatial domain.


The discrepancy between these results and those of the preceding analysis (which found no systematic age dif- ferences) raises an important new issue.

An analysis that compares subgroups with equal baseline performance is really comparing subgroups of individuals who are sys- tematically unrepresentative of their age groups, and the findings must be interpreted in light of this fact.

For ex-


ample, we matched a subgroup of 8-year-olds with ver- bal spans of 4-5.5 with a subgroup of young adults with the same range of verbal spans.

However, the subgroup of8-year-olds was above average in span, relative to their peers, whereas the matched subgroup of young adults was below average in span, relative to their peers.

Surprisingly, the subgroup of above-average 8-year-olds showed in- terference effects that were not only larger than those of the matched young adults but also larger than their peers.

Also surprisingly, the subgroup of young adults with below-average spans showed interference effects that were not only smaller than those of the matched 8-year-olds, but also smaller than those of their peers.

This may also be seen if one compares the datapoints for subgroups of approximately equal span in the absence of a secondary task in Figure 3.


Taken together, this pattern of results strongly suggests what would otherwise be a counterintuitive idea: Indi- viduals with higher than average spans for their age show larger than average interference effects, and individuals with lower than average spans for their age show smaller than average interference effects.

In order to rigorously evaluate this idea, we next proceeded to examine the pat- tern of individual differences within each group.

In order to do this, subgroups were created within each age group on the basis of verbal and spatial spans in the absence of a secondary task: Subgroups consisted of individuals who had spans of 1-2.5, 3-4.5, 5-6.5, and 7-8.5 items.

These ranges were selected so as to maximize the num- ber of subgroups within each age group, while simulta- neously distributing the total number of subjects in each age group as equally as possible across the subgroups.


We then compared interference effects for all of the different subgroups with ns greater than 5.

The upper and lower panels of Figure 3 show verbal and spatial memory spans with secondary tasks for the different subgroups, plotted as a function of the subgroups' memory spans in the absence of secondary tasks.

As may be seen, when young adults are compared with children and older adults of roughly equivalent span in the absence of a secondary task, the young adults have smaller interference effects.

More importantly, however, interference effects increased with memory ability, as measured by span in the absence of interference in all the age groups.

Within each age group, interference effects were smallest for those with the smallest spans without secondary tasks and largest for those with the largest spans without secondary tasks.

Re- markably similar patterns were observed for both verbal and spatial working memory.


Finally, we analyzed the data from the age group with the largest number of subjects (young adults: verbal n = 153, spatial n = ISO).

The large ns and wide range of spans in this group made it possible to use the techniques of correlational and regression analysis to examine more between age and individual differences in the suscepti- bility of working memory to interference observed in the present analyses is not easily explained on the basis of existing theories of working memory, at least as they are currently formulated.

These theories tend to focus on ei-


precisely the relation between the amount of interference and the size of an individual's memory span in the ab- sence of a secondary task.

The correlation between spans with and without a secondary task was significant for both verbal and spatial working memory tasks (verbal r = .423, spatial r = .333, bothps < .0001).

Importantly, the slope of the regression of span with a secondary task on span without a secondary task was significantly less than 1.0 for both verbal and spatial working memory tasks (verbal slope = 0.501, t = 5.70; spatial slope = 0.421, t = 5.91; both ps < .0001), indicating that the size of the interference effect was an increasing function of span in the absence of a secondary task.


This may be seen clearly in Figure 4, which shows the verbal and spatial regression lines (upper and lower pan- els, respectively) based on all young adult subjects.

In both plots, each point represents the mean and standard error for a subgroup of individuals with exactly the same span in the absence ofa secondary task (e.g., either 5.5, or 6.0, or 6.5, etc.; subgroups with ns < 5 are not shown).

Although, as may be seen, the magnitude of the interfer- ence effect tended to be larger for spatial working memory, the same overall pattern was observed in both domains.

That is, individuals with larger spans in the absence of a secondary task suffered the most interference when per- formance of a secondary task was required.


Collectively, the results of the present meta-analysis re- veal two important findings about developmental and in- dividual differences in susceptibility to interference.

First, verbal and spatial spans measured in the absence of a secondary task increased with age in children and de- creased with age in adults, but interference effects did not change systematically as a function ofage at either end of the life span.

Second, an examination of individual differences within each age group revealed that, in both the verbal and spatial domains, the subjects with larger spans showed larger interference effects than did their peers with smaller spans.


The finding oflarger interference effects in higher span individuals, although counterintuitive, is reminiscent of findings regarding individual differences in the effect of secondary tasks on memory performance reported by Rosen and Engle (1997).

These researchers found that when subjects were required to generate the names of members ofa category (e.g., animals), high-span individ- uals were more affected by a concurrent load on working memory than low-span individuals.

The primary task in- volved retrieval from long-term memory, and it is possi- ble that the concurrent load interfered with the process of retrieval itself or that it interfered with the use of work- ing memory in either the strategic control of retrieval or the evaluation of retrieved information.


In the present case, however, it is clear that the sec- ondary task is interfering with the maintenance of infor- mation in working memory.

The pattern of interaction DISCUSSION ther age or individual differences in working memory and do not deal with the relationship between the two, or they tend to assume that age and individual differences are de- termined by the same mechanisms.


For example, differences in the ability to inhibit irrel- evant information have been used by Dempster (1992) and Hasher and Zacks (1988) to explain age differences in working memory and by Engle (1996) to explain in- dividual differences in working memory.

It is possible that inhibition deficit theories might be able to explain the pattern of age and individual differences in interference effects (i.e., the general lack of age difference in inter- ference effects and the larger interference effects in in- dividuals with larger memory spans) ifthey incorporated additional assumptions.

However, it is not obvious what such assumptions might be.


The difference in interference effects observed between low- and high-span individuals in the present data (as re- flected in the slopes of the regressions in Figure 4) is un- doubtedly exaggerated by measurement error.

Although the reliability ofthe present measures was not determined directly, the digit span task was simply a computerized version of the Digit Span subtest from the WAIS-R (re- liability is equal to or greater than .90 for both young and older adults of ages comparable to those in our sample; Psychological Corporation, 1997), and the location span test followed an analogous procedure.

It is highly unlikely, therefore, that the true slope of the regression between span with a secondary task and span without a secondary task (i.e., the slope corrected for attenuation by measure- ment error) could be greater than 1.0, as is predicted by the inhibition deficit hypothesis.


Alternatively, other researchers have argued that dif- ferences in processing speed are the cause ofdifferences in working memory performance.

Some ofthese research- ers have focused on the relation between age differences in speed and age differences in working memory (e.g., Dempster, 1981; Salthouse, 1996), whereas others have focused on the relation between individual differences in speed and individual differences in working memory (e.g., Miller & Vernon, 1992).

A possible mechanism un- derlying the role of speed in both age and individual dif- ferences in working memory is suggested by the obser- vation that the longer that words take to pronounce, the fewer that can be recalled.

This is known as the word- length effect (Baddeley, Thomson, & Buchanan, 1975).

The word-length effect is often interpreted as implying that the speed with which particular items can be re- hearsed determines how many items can be remembered (e.g., Baddeley, 1986; Baddeley, Thomson, & Buchanan, 1975; but see Brown & Hulme, 1995).

This idea may be extended to imply that any source of difference in rehearsal rate, whether it is attributable to characteristics of indi- vidual items or to age and individual variations, will affect working memory.

Consistent with this idea, developmen- tal differences in children's rehearsal rates are good pre- dictors of developmental differences in memory span (Hulme, Thomson, Muir, & Lawrence, 1984; Kail & Park, 1994; Nicolson, 1981).


Like the inhibition hypothesis, the speed hypothesis may require additional assumptions in order to explain the present findings.

In this case, however, it is more ap- parent what form these assumptions might take.

Most current versions of the speed hypothesis tend to assume that information in working memory will decay in the absence ofrehearsal.

A number ofresearchers (e.g., Bad- deley, 1986; Hulme et aI., 1984; Schweickert & Boruff, 1986) have observed that the number of items that can be recalled in a verbal memory span task is approximately equal to the number of items that can be rehearsed in 2 sec. This is interpreted by Baddeley and others to mean that, in order for an item to be maintained, its trace must be refreshed at approximately 2-sec intervals.

If this is so, when secondary tasks interrupt rehearsal, some items will be lost, and the amount lost will depend on both the rehearsal rate and the speed with which the secondary task is performed (i.e., the length of time that rehearsal is interrupted).


To see this, imagine an individual who can rehearse six items in 2 sec (i.e., three items per second) but cannot rehearse these items while performing a secondary task.

Assume that performance on the secondary task inter- rupts rehearsal for I sec. There is now only 1 sec to re- hearse items before they are lost, and only three items can be rehearsed in that time.

The remaining three items will have gone 2 sec without rehearsal, and their trace strengths may have decayed to the point where the items are unrecoverable.


To see how individual and age differences may affect the amount of interference produced by a secondary task, recall that there are both individual and age differ- ences in processing speed (see, e.g., Cerella & Hale, 1994; Hale & Jansen, 1994) and that there are individual and age differences in rehearsal rate as well (see, e.g., Kail & Park, 1994).

At the group level, both processing speed and rehearsal rate increase with age during child- hood and decrease with age in adulthood.

However, the relationship between processing speed and rehearsal rate in individuals of the same age is not well estab- lished.

Thus, it is unclear to what extent processing speed and rehearsal rate are independent abilities.

Cowan and his colleagues (Cowan et al., 1998) have reported that, although retrieval speed and rehearsal rate both increase with age during childhood, these measures were uncor- related within an age group.

The present findings suggest that the same may be true for processing speed and re- hearsal rate.

If so, this could explain the present pattern of results.


Assume that processing speed governs the amount of time it takes to perform a secondary task.

If the time it takes to perform a secondary task is relatively brief and highly correlated with rehearsal rate, it may be shown that individuals with different rehearsal rates will have more nearly equivalent interference effects.

If, however, the time it takes to perform a secondary task is relatively independent of rehearsal rate, individuals with higher re- hearsal rates will not only have larger spans, they will also show larger interference effects.


These two possibilities are illustrated in Figure 5, which shows two individuals, one with a span of 5 and one with a span of9.

It is assumed that the high-span in- dividual can rehearse more items in the same amount of time than can the low-span individual, that trace strength decays exponentially (i.e., a constant proportion is lost per unit time), and that the rate of decay does not vary appreciably between individuals.

The upper two panels show what would happen if the high-span individual took less time to perform a secondary task than the low-span individual (i.e., rehearsal rate and speed ofsecondary task performance are highly correlated).

The lower two panels show what would happen if both the high- and the low- span individuals take the same amount of time to per- form a secondary task (i.e., rehearsal rate and speed of secondary task performance are relatively independent).


As may be seen, when low-span individuals take more time to perform a secondary task than do high-span in- dividuals, both low- and high-span individuals will show similar interference effects.

In contrast, when low-span and high-span individuals take the same amount of time to perform a secondary task, the high-span individuals will show larger interference effects than low-span indi- viduals.

That is, the difference between the number of items recalled when there is no secondary task (i.e., sec- ondary task duration equals zero) and when there is a secondary task will be larger for high-span individuals than for low-span individuals.


How does this apply to the present problem?

The pat- tern ofage differences is similar to that shown when pro- cessing speed and rehearsal speed are highly correlated (i.e., the pattern shown in the upper panels of Figure 5).

10 10 (/) E ILow Span I Q) 8 8 ::: Interference Effect '5 6 6 Q) .0 E 4 Interference 4 :J Effect Z co 2 2 - ! Hlgh Spanl 0 t- o 0 0 2 3 0 2 3 Duration of Secondary Task 10 10 (/) E ILow Span I ----r .$ 8 8 - 0 6 Interference Effect 6 Q) .0 E 4 Interference 4 :J Z Effect co 2 2 - IHigh Spanl {?- 0 0 0 2 3 0 2 3 Duration of Secondary Task Children, young adults, and older adults all tend to show interference effects of approximately equivalent size, even though children and older adults have smaller spans than young adults.

In contrast, the pattern of individual differences among like-aged individuals is similar to that shown when processing speed and rehearsal speed are relatively independent (i.e., the pattern shown in the lower panels).

Individuals ofa particular age with higher spans tend to show larger interference effects than individuals of the same age with lower spans.

This suggests that de- velopmental changes in rehearsal and processing speed occur in concert but that these changes do not modify the pattern of individual differences in rehearsal and pro- cessing speed.


For example, one child, Diane, may be faster at re- hearsing than her age-mate, Jack, but the two do not dif- fer appreciably in the rate at which they process infor- mation.

Therefore, Diane will have a larger span in the absence of a secondary task and will show a larger inter- ference effect.

This is analogous to the comparison of low- and high-span individuals shown in the lower pan- els of Figure 5, because rehearsal speed is independent of processing speed in the case of Jack and Diane.

As they approach adulthood, Jack and Diane will get faster at both rehearsing and processing, but Diane will remain the faster of the two at rehearsing, whereas there will still be no difference in their processing rates.

Therefore, both Jack and Diane's spans will be larger when there is no secondary task, but Diane will still lose more items from working memory when there is a secondary task.

How- ever, the size of Diane's interference effect will not have changed appreciably from when she was younger.

Al- though she can now rehearse faster, giving her more items to lose, she can also perform the secondary task faster.


Importantly, exactly the same will be true for Jack: His interference effect will also be largely unchanged be- cause, again, the increase in processing speed will can- cel out the increase in rehearsal rate.

The change in Jack's (and Diane's) spans, both with and without a secondary task, is analogous to moving from that shown in the up- per left-hand panel to that shown in the upper right-hand panel of Figure 5.

This is because developmental changes in rehearsal speed and processing speed are highly correlated.


Although the preceding account adequately explains the present findings, it only holds if the time required to perform secondary tasks is short, relative to the time it takes for information in working memory to decay.

Thus, a more rigorous test of these ideas will require actually measuring the amount of time it takes individuals to per- form secondary tasks and the rate at which information decays.

Measures of processing speed, rehearsal speed, and working memory should also be obtained from the same subjects.

Finally, the reliability of all the measures should be assessed in order to determine the true rela- tions between the various measures.

For example, it is pos- sible that the slope of the relation between spans with and without secondary tasks may turn out not to differ appreciably from 1.0, after correcting for measurement error.

If so, this result would be consistent with the speed hypothesis as originally proposed (i.e., with the assump- tion that rehearsal and processing speed are different as- pects of the same ability and, therefore, are highly cor- related).

There would then be no need to modify this hypothesis along the lines outlined above.


The need to examine the interrelations between vari- ous measures is heightened by the fact that, as Brown and Hulme (1995) have recently argued, the case for re- hearsal speed as the determinant of memory span may have been overstated.

These authors show that a simple model can account for the word-length effect without as- suming the existence of covert rehearsal.

In addition, they question other evidence that has been taken as sup- port for a covert rehearsal process.

Although Brown and Hulme focus on verbal working memory, the role of covert rehearsal in visuospatial working memory is also unclear.

As a number of authors have pointed out (Fry & Hale, 1996; Hale, Myerson, et aI., 1996; Logie, 1995), the nature of the mechanism underlying the rehearsal of visuospatial information, assuming that rehearsal is even involved, has never been firmly established.


Even if the covert rehearsal rate does not determine memory span in the absence of a secondary task, the pre- sent findings remain consistent with a speed hypothesis of age differences in working memory.

This hypothesis predicts a correlation between processing speed and mem- ory span and thus predicts relatively little age difference in interference effects.

This is because, as was noted in the case of Jack and Diane, cognitive development in children is associated with faster performance on sec- ondary tasks and larger memory spans, but the faster secondary task performance cancels out the increase in interference effect that would otherwise be associated with an increased span.

The same argument, in reverse, applies to cognitive aging.

However, the present findings are not consistent with a speed hypothesis of individual differences in working memory.

This is because, within an age group, interference effects are actually larger in individuals with larger spans, implying that individuals with large and small spans take approximately the same amount of time to perform the secondary tasks.


Although the generalizability of the current findings remains to be determined, the characteristics of the data- base examined in the present meta-analysis suggest that the present findings regarding the interaction of age and individual differences in working memory are likely to prove highly reliable.

One important characteristic is that both verbal and spatial working memory spans with and without secondary tasks were measured in the same in- dividuals, using the same procedures.

This made it pos- sible to examine the covariation in span and the size of interference effects and minimized the noise that would otherwise occur with procedural variations.

Two other important characteristics of the present database are the number of subjects and the extensive age range that was sampled: Data from over 400 individuals from five child and adult age groups, with at least 50 in each age group, were analyzed.


In summary, the present findings strongly suggest that the absolute amount of interference with working mem- ory that results from secondary tasks does not change systematically as a function of age and that, at all ages, individuals with larger spans show larger interference ef- fects than do their peers with smaller spans. What are the implications of these findings for the measurement of age differences in susceptibility to interference? Should mean interference effects ofdifferent age groups be com- pared directly, despite age differences in baseline perfor- mance, or should interference effects be compared after taking into account baseline differences?


Both ofthese approaches focus exclusively on age dif- ferences, yet the present results suggest that what is needed is research that directly examines the interaction of age and individual differences.

If the speed hypothe- sis is correct, susceptibility to interference is the result of an interaction between the speed with which one pro- cesses secondary task information, on the one hand, and the size of one's memory span in the absence of interfer- ence, on the other.

Ifmemory span and processing speed are related, as the speed hypothesis assumes, an impor- tant question is whether the nature of this relationship changes with age.

Consequently, studies that examine how both age and individual differences in memory span are related to differences in processing speed may be partic- ularly important for clarifying our understanding of the nature of working memory.


Jenkins, Myerson, Hale, and Fry (1999) showed that slopes relating complex spans to simple spans were considerably smaller than one, indicating that persons with higher simple spans suffered more interference when the span task was combined with a processing demand.

They argued that this finding ruled out accounts of working memory based on interference and/or inhibition of interfering information.

We demonstrate that the effect is mainly an artifact from regression to the mean, owing to the low reliability of span scores as used by Jenkins et al. Data from 133 young adults for two verbal and two spatial span tasks show that the slopes relating complex to simple performance are considerably higher for sum scores than for span scores.

Furthermore, an adequate test for an interference or an inhibition account of working memory is to predict interference from complex span tasks, not from simple span tasks.

Interference effects in the verbal span tasks were negatively correlated with an independent measure of working memory capacity, consistent with the interference/inhibition account.


In a recent article, Jenkins, Myerson, Hale, and Fry (1999) presented results that, on their interpretation, question the validity of inhibition-based accounts of working memory capacity.

Basically, a meta-analysis of data from their laboratory showed that the difference between simple spans (i.e., memory spans without secondary processing demand) and complex spans (i.e., spans with an additional processing demand) was larger in people with higher simple spans.

This was reflected in slopes substantially less than one for the regression of complex spans on simple spans.

Jenkins et al. interpreted the difference between simple and complex spans as an interference effect resulting from the secondary task.

Notably, the inverse relationship between simple span and the interference effect held only within age groups.

Differences in simple span between age groups were not accompanied by systematic variation in the interference effect.


Jenkins et al. ( 1999) argued that this finding is incompatible with the idea that individual differences in working memory capacity (at least in age-homogeneous samples) are related to differences in susceptibility to interference and/or the capacity to inhibit irrelevant information.

Accounts based on interference and inhibition, they argued, should predict the opposite pattern: smaller, not larger, interference effects in people with higher spans.

As an alternative, they presented an account based on decay from working memory.

Assuming a negatively accelerated decay function, the absolute amount of decay over a constant time interval is larger for a high-span person than for a low-span person.

If high- and low-span persons do not differ systematically in the speed with which they perform the secondary task (i.e., span and speed are uncorrelated), high spans are expected to suffer a larger interference effect than do low spans.


We disagree with this interpretation for both methodological and conceptual reasons.

On the methodological side, we will argue that the slopes smaller than one are most likely due to a statistical artifact, regression to the mean.

On the conceptual side, we will argue that simple and complex spans do not measure the same construct and that an inhibition account of working memory predicts that people with higher complex spans show less interference but not necessarily that people with higher simple spans show less interference.

To illustrate our point, we will present data that replicate the original findings from Jenkins et al. ( 1999) but, in addition, allow more thorough analyses that point toward an alternative explanation.

Our comment focuses on the issue of interference and inhibition as a potential factor driving individual differences in working memory; it does not challenge the conclusions regarding age differences drawn by Jenkins et al. We do not wish to defend a particular theory that assumes that the ability to inhibit interfering information underlies individual differences in working memory capacity, but only to point out that the data presented by Jenkins et al. do not justify the rejection of such an account.


The regression line relating two variables is crucially dependent on the variable's reliability (Cohen & Cohen, 1983) [1].

In the extreme case, in which one variable has zero reliability (i.e., consists exclusively of error variance), the correlation between predictor and criterion is zero, and the regression line will be flat.

More generally, everything else being equal, the slope of the regression line decreases with decreasing reliability of the variables involved.


The span tasks used in the meta-analysis were scored with the Wechsler digit span procedure, in which tasks of increasing length are presented and the experimenter discontinues testing whenever a participant fails to recall correctly both lists of a given length.

This procedure yields only a single value for each participant, so no direct estimates of reliability are available.

Jenkins et al. ( 1999) briefly discuss this problem and conclude that measurement error does not compromise their conclusions, because the reliability of the traditional WAIS-R Digit Span is known to be above .90.


We think that this is a highly questionable generalization.

The reliability estimates in the Wechsler intelligence test manual are based on a representative sample from the general population.

The studies summarized in Jenkins et al. ( 1999) recruited mainly undergraduates for the young adult groups; this is most likely associated with a restriction of range, thereby reducing the ratio of true variance to total variance.


Moreover, the span tasks used by Jenkins et al. differed from the WAIS-R Digit Span in several ways that could make a difference for their reliability.

For example, their presentation times per item differed from those used in the WAIS-R. Perhaps more important, their complex spans introduced an additional processing demand.

This could turn the task into one qualitatively different from the simple span as measured in the WAIS-R; it might even measure a different construct (Daneman & Carpenter, 1980; Engle, Tuholski, Laughlin, & Conway, 1999) [2][3].


This concern is supported by the fact that the correlations between simple and complex spans were only .42 (for the two verbal tasks) and .33 (for the two spatial tasks).

Given that simple and complex spans are highly similar and, therefore, should share most of their task-specific variance, these correlations are suspiciously low.

This could indicate that at least one of the two spans is not as reliable as is assumed, or alternatively, that the two variables do not measure the same construct.

Either way, the assumption that both spans have reliabilities of at least .90 seems not well grounded.


Since we have no knowledge about the reliability of the span tasks involved in Jenkins et al. 's (1999) meta-analysis, it is difficult to assess to what degree the regression slopes smaller than one are an artifact of measurement error.

Therefore, we decided to replicate their kind of analysis on a data set of our own with simple and complex span tasks for which reliability estimates are available.


We analyzed data from two simple spans (one verbal and one spatial) and their corresponding complex counterparts.

All the tasks were computer administered.


Word span task.

A list of frequent German nouns (none longer than 12 characters) was displayed one by one, each word for 1 sec, in the center of the screen.

Immediately after the last word, the participants attempted to write down the words in correct order on an answer sheet containing the appropriate number of slots for each list length.

Following two practice trials, three trials for each list length between four and eight words were presented in ascending order of list length.


Dual word span task.

The dual-task version differed from the simple word span task in two ways.

First, the memory list was presented simultaneously for 1 sec times the number of words in the list.

Second, the presentation of the memory list was followed by a fixed time interval, during which the participants had to perform a choice reaction time task.

This task involved classifying words as referring to an animal or a plant by pressing one of two keys on the computer keyboard.

New stimuli were presented immediately after each reaction until a total of 5 sec has elapsed.

The participants were instructed to react as quickly and accurately as possible in order to accomplish as many reaction time tasks as possible in the 5-sec interval.

After two practice trials, three trials of each list length between three and seven words were administered in ascending order.


Pattern span task.

A sequence of partially filled 3 X 3 grids was presented.

Each pattern was shown for 3 sec. The series was reproduced immediately after the last pattern by marking the corresponding cells in empty matrices on the answer sheet.

There were five trials each for the list lengths of three to five, presented in ascending order.


Dual pattern span task.

The dual-task version was identical to the simple pattern span, except for an additional processing demand, which consisted of classifying arrows as pointing upward or downward.

The processing task was again administered for a constant interval of 5 sec. There were five trials for each list length from two to four.


In both cases, the simple span task immediately preceded the corresponding dual-task version.

Furthermore, the choice reaction time tasks involved in the dual-task versions were administered as single tasks about half an hour before, so the participants had a moderate amount of practice with both components when they encountered the dual-task versions.


Two additional tasks relevant in the present context were computer administered versions of the reading span task (Daneman & Carpenter, 1980) and its numerical analogue, the computation span task (Turner & Engle, 1989) [2][4].

The reading span task began with the presentation of a number of short sentences, one by one, for 4 sec each.

The participants were required to evaluate the sentences as true or false by pressing an appropriate key and to remember the last word of each sentence.

The last words had to be written down in correct order after the last sentence of an item was presented.

There were 11 trials, with list length ranging from four to seven.

The computation span task used simple equations, involving addition or subtraction of single digits, instead of sentences.

The results of the equations had to be recalled in correct order.

There were 11 trials, with list lengths varying from five to eight.


The participants were 133 students from the University of Mannheim, who took part on 2 days of testing involving various intelligence and working memory tests.

Their mean age was 25.8 years (SD = 3.8), and 44% were female.


For each task, a sum score was computed as follows: For each trial, the number of words or patterns reproduced correctly at the correct ordinal position was counted, and these numbers were added over all the trials.

To compare simple and complex spans on the sum measure, we used only the trials with list lengths administered in both versions (i.e., list lengths from four to seven for word span tasks and from three to four for pattern span tasks).

This means that the list length of eight (word span), list length of three (dual word span), list length of five (pattern span), and list length of two (dual pattern span) were not included in the sum scores.


As a second score, we simulated a span procedure with the data from the two word span tasks: For each list length, we selected the first two of the three trials and scored whether a participant succeeded in recalling perfectly both lists, one list, or none of them.

The third trial of each list length was ignored in order to make our span score parallel the span procedure of Jenkins et al. ( 1999) as much as possible.


Starting with the shortest list length (i.e., four for the single and three for the dual word spans), a participant's span was increased by one for each list length for which he or she recalled at least one of the two lists perfectly.

This was done for increasing list lengths, up to the shortest list length on which the participant failed on both trials.

At this point, the scoring procedure was stopped and all items from longer lists were ignored, because two successive failures on a given list length means that testing is discontinued in the usual span procedure.


The participant was credited with the score reached up to this point (which equaled the list length of the longest list passed at least once before the first double failure).

If only one of the two trials from the previous list length was recalled correctly, span was decreased by 0.5.

Those participants who failed on the two shortest lists were assigned a span equal to the shortest list length minus one.


No span score was formed for pattern span, because there were not enough different list lengths.

Table I summarizes the means and standard deviations for both scores.

In addition, we estimated the reliability of the sum scores by Cronbach's alpha, which corresponds to the mean of all possible split-half reliability values of the items entering into the score.

No such reliability estimation was possible for the span scores, because they were not defined as linear combinations of independent items.

Table 2 presents the correlations of the six variables.


Following the procedure of Jenkins et al. ( 1999), we also conducted all the analyses after exclusion of those participants whose spans could be underestimated because they never reached a list length on which they failed twice.

The largest list length tested was eight for simple word span and seven for dual word span, so we excluded participants with simple word spans larger than seven and those with complex word spans larger than six.

This reduced the sample size to 110.

The analyses with the reduced sample showed signs of a moderate restriction of range but did not alter any of the conclusions.

We therefore decided to present mainly the results from the unrestricted sample and to add data from the reduced sample in parentheses.


Several important observations can be made from these data.

First, complex span scores were not systematically lower than the simple span scores, which is different from what Jenkins et al. ( 1999) found.

This could mean that there was not much interference at all in the complex span tasks.

Alternatively, the interference that did occur was outweighed by the moderate amount of practice with the memory tasks that the participants already had when they first attacked the dual-task versions.

In any case, we will see that the absolute amount of interference is not crucial to our argument.


A second phenomenon to notice is the surprisingly low correlation between the sum scores and the span scores from the same tasks.

These two scores are supposed to measure the same variable.

In addition, the correlations between simple and complex word span tasks were much higher for the sum scores (.72) than for the span scores (.46).

Note that the latter value is comparable with the task intercorrelations reported by Jenkins et al. ( 1999).

Both findings together lead us to suspect that the span scores had low reliability.

We cannot assess their reliability directly, but if we assume that sum scores and span scores measure the same variable, there are two ways to do so indirectly.


An observed correlation robs(x,y) can be expressed as a function of the true correlation rtrue(x,y) and the reliabilities of the variables involved (Cohen & Cohen, 1983, Equation 2.11.4) [1]:


robs (x,y) = rtrue(x,y)√r(x,x)r(y,y).


Let r(x,x) denote the reliability of a sum score and r(y,y) the reliability of the corresponding span score.

Equation 1 can now be solved for the unknown reliability r(y,y):


r(y,y) = robs(x,y)² / rtrue(x,y)² r(x,x).


If sum scores (x) and span scores (y) measure the same variable, their true correlation is one.

Hence, we can insert 1 for rtrue(x,y) and Cronbach's alpha values from Table 1 for r(x,x) to compute an approximation for r(y,y) from Equation 2.

This yields reliability estimates for the span scores of simple word span of .72 and for complex (dual) word span of .74.


A second way to estimate the span scores' reliability is to compute the correlation between the sum scores for simple and complex tasks and correct for attenuation according to Equation 1.

This means solving Equation 1 for rtrue(x,y), which is taken to represent the true correlation between the sum scores of simple and complex spans.

With the reliabilities for the two variables (see Table 1), we can estimate their true correlation from the observed correlation.

This yields an estimate for the true correlation between simple and complex word spans of r = .91.

The same true correlation should hold for the span scores of simple and complex word spans, because they are assumed to measure the same two constructs.

From this, we can obtain an estimation for the joint reliability of the two span scores, r(x,x) r(y,y), by transforming Equation 2:


r(y,y)r(x,x) = robs(x,y)² / rtrue(x,y).


If we assume for simplicity that the two span scores have equal reliability, this reliability equals the square root of r(x,x)r(y,y), which is .51.

The bottom line is that the span scores must be assumed to have a reliability of between .51 and .74, considerably lower than that of the sum scores.


Next, we estimated the regression line for the prediction of complex spans from simple spans.

The results are summarized in Table 3.

The upper panel of Figure 1 shows the scatterplot for the sum scores of word span as an illustrative example.


As can be seen, the slopes were considerably lower than one in all three cases, thus reproducing the findings of Jenkins et al. ( 1999).

It is also clear, however, that the slope was much lower for the span scores than for the sum scores of word span.

This is a direct consequence of the lower task intercorrelation for the span scores and, most likely, a result of the span scores' lower reliability.


It might seem surprising that we could reproduce the pattern found by Jenkins et al. even though there was no mean effect of interference in our data: How can there be more interference in high-span individuals than in low span individuals if there is no evidence of interference overall? The answer is that the phenomenon observed by Jenkins et al.-shallow slopes relating complex to simple spans-does not necessarily reflect the amount of interference suffered by individuals.

The shallow slopes are simply a by-product of the comparatively low correlation between simple and complex spans.

This low correlation is at least in part due to the unreliability of the span measures.


The powerful effect of regression to the mean can be illustrated by reversing the x- and y-axes of the regression lines.

If Jenkins et al. ( 1999) were right in claiming that low-span individuals experience less interference than do high-span individuals, the slopes of simple span plotted over complex span should be larger than one.

Following the line of argument proposed by Jenkins et al., the interference cost in this kind of plot would be the deviation of the regression line above the main diagonal (i.e., the advantage of single task scores above dual task scores).

The lower panel of Figure I shows such a reversed plot for the sum scores of the two word span tasks.

As can be seen, the slope is again smaller than one.

Taken together, the two analyses visualized in Figure I show that regression to the mean can produce slopes considerably smaller than one, regardless of which variable is used as the predictor and which as the criterion!


We believe, however, that reliability and regression to the mean is not the full story.

Even with perfect reliability, simple and complex span tasks will not be perfectly correlated.

This brings us to our second, conceptual argument.

There is good evidence in the literature that simple and complex memory span tasks do not measure the same construct (Cantor, Engle, & Hamilton, 1991; Daneman & Carpenter, 1980; Daneman & Merikle, 1996; Engle et al., 1999; Oberauer, 1993) [2][3][5][6][7].

Simple spans and complex spans tend to load on different factors and correlate with different external criteria.

In particular, simple spans are notoriously bad predictors of complex reasoning, whereas complex spans predict reasoning ability well.

This led investigators to the assumption that complex span tasks, but not simple span tasks, measure working memory capacity (e.g., Daneman & Carpenter, 1980) [2].

Jenkins et al. ( 1999) explicitly disagreed with this position, stating that "simple word span tasks also exemplify the use of working memory" (p. 28) according to Baddeley (1986) [8].

This is certainly true if we consider the whole working memory system as envisioned by Baddeley, including the phonological loop.

Researchers drawing a distinction between simple and complex span measures, however, maintain that simple spans reflect mainly the capacity of a peripheral system, such as the phonological loop, whereas complex spans reflect the capacity of the central component of working memory to a much larger degree (e.g., Cantor et al., 1991) [5].

Therefore, from Baddeley's framework, one would predict that simple and complex spans are not perfectly correlated-although both reflect an aspect of working memory and this is in full accordance with the facts.


Theories of working memory that assign interference and inhibition a central role for individual differences are concerned mainly with the central executive, not with the slave systems in Baddeley's (1986) model (e.g., Engle, Kane, & Tuholski, 1999) [3].

This implies that individual differences in complex span, but not necessarily individual differences in simple span, reflect individual differences in susceptibility to interference and the capacity to inhibit irrelevant information.

And of course, it is only the complex span, not the simple span variant, in which the issue of inhibiting irrelevant information arises at all, because only the complex spans require that the representations used in the secondary tasks be suppressed quickly when no longer necessary.

It follows that an interference/inhibition account of working memory capacity does predict that individuals with higher complex spans will experience less interference than people with lower complex spans. But it does not predict that persons with higher simple spans will experience less interference.


This implies that the slope of the regression line with simple span tasks as predictors and complex tasks as criteria is not an adequate test for an inhibition-based account of working memory.

The complex span tasks, but not the simple span tasks, can be regarded as good measures of the central executive's capacity.

Therefore, a working memory model based on inhibition or interference makes the straightforward prediction that individuals with high complex span will experience less interference than will individuals with low complex span.


In terms of regression analysis, this means that the regression slope for simple span as a function of complex span will be smaller than one (and the intercept will be positive).

This is so because the interference effect, measured as the difference between simple and complex spans, is predicted to decrease with higher complex spans.

Unfortunately, the same artifact of regression to the mean that led Jenkins et al. ( 1999) to reject the inhibition hypothesis will work in favor of the hypothesis in this analysis (for an illustration, see Figure I).


One way to test the inhibition hypothesis could be to measure complex span and the interference effect separately.

The minimal design necessary for this test includes one pair of measures for simple and complex span tasks and an additional, independent measure of complex span.

The first two tasks can be used to obtain the simple-to-complex difference; the second complex task can be used to predict this difference.

An interference/inhibition account should predict a negative covariation between the difference score and the independent estimate of complex span.


Fortunately, there were two additional complex span tasks available in our study that could be used as independent predictors for the interference effects in the word span and the pattern span tasks.

We built sum scores for reading span and computation span and correlated them with the differences between the simple and the complex versions of the two other span tasks (the differences were computed from the sum scores).

The difference of simple and complex word spans correlated -.41 with reading span and -.33 with computation span; both correlations were highly significant.

Figure 2 plots the simple-to-complex differences as a function of reading span.


The difference of simple and complex pattern spans correlated -.05 and .01 with the two predictors, which was not significant.

Thus, the predicted negative correlation between complex span and the interference effect was obtained when all the tasks used material from the verbal-numerical domain.

There was no evidence for a relationship between predictors involving verbal numerical materials and the interference effect computed from spatial tasks.

This difference could be a hint that the ability to resist interference is domain specific. But as long as it is not replicated with a different set of tasks, it could as well be accidental and should, therefore, be interpreted cautiously.

The important point is that there was no positive association between the interference effect and an independent measure of complex span.


The data and analyses presented above show several important points.

The slope of the regression line for complex span scores as a function of simple span scores was consistently smaller than one, in agreement with the findings from Jenkins et al. ( 1999).

When the same data were analyzed using sum scores, the slopes were much higher, but still smaller than one.


The substantial difference between sum scores and span scores is most likely due to the low reliability of the latter.

Although the span's reliability cannot be estimated directly from the present data, we obtained converging indirect evidence that the span scores have low reliability.

This evidence involves the relatively low correlation of span and sum scores computed from the same raw data and the comparatively low correlation between simple and complex span scores.

One reason why the span scores had lower reliability than the sum scores is that the former included only two items for each list length, as in the Jenkins et al. ( 1999) studies, whereas the latter included three.

A more general reason why span scores have low reliability could be that the scoring procedure throws away information.

First, each item is only scored as passed or failed; the number of list elements recalled correctly is not measured.

Second, after a participant fails twice on a given list length, his or her performance on longer lists is no longer considered.


We conclude that the findings of Jenkins et al. ( 1999) do not rule out a working memory model based on interference and inhibition.

The positive covariation between simple span and the "interference effect" they observed is, in large part, an artifact from regression to the mean, owing to low reliability of the span scores.

The remaining true positive covariation is not relevant for a working memory theory based on interference and inhibition, because interference is only predicted to be low for people with high scores on complex span tasks.

An unbiased test of this prediction requires that the difference between simple and complex span scores-that is, the interference effect-be measured independently from the complex span score that is used as its predictor.

Preliminary evidence from our data is consistent with the prediction of the interference/inhibition account.

More systematic research, however, is required to test the generality of this finding-in particular, in a constellation of tasks that elicit larger mean interference costs.