Model of hierarchical complexity

The model of hierarchical complexity is a framework for scoring how complex a behavior is. It quantifies the order of hierarchical complexity of a task based on mathematical principles of how the information is organized and of information science. This model has been developed by Michael Commons and others since the 1980s.

Overview

The model of hierarchical complexity (MHC), which has been presented as a formal theory,^[1] is a framework for scoring how complex a behavior is. Developed by Michael Lamport Commons,^[2] it quantifies the order of hierarchical complexity of a task based on mathematical principles of how the information is organized,^[3] and of information science.^[4] Its forerunner was the General Stage Model.^[5] It is a model in mathematical psychology.

Behaviors that may be scored include those of individual humans or their social groupings (e.g., organizations, governments, societies), animals, or machines. It enables scoring the hierarchical complexity of task accomplishment in any domain. It is based on the very simple notions that higher order task actions are a) defined in terms the next lower ones (creating hierarchy), b) they organize those actions c) in a non-arbitrary way (differentiating them from simple chains of behavior ensuring a match between the model-designated orders and the real world orders). It is cross-culturally and cross-species valid. The reason it applies cross-culturally is that the scoring is based on the mathematical complexity of the hierarchical organization of information. Scoring does not depend upon the content of the information (e.g., what is done, said, written, or analyzed) but upon how the information is organized.

The MHC is a non-mentalistic model of developmental stages. It specifies 15 orders of hierarchical complexity and their corresponding stages. It is different from previous proposals about developmental stage applied to humans.^[6] Instead of attributing behavioral changes across a person's age to the development of mental structures or schema, this model posits that task sequences of task behaviors form hierarchies that become increasingly complex. Because less complex tasks must be completed and practiced before more complex tasks can be acquired, this accounts for the developmental changes seen, for example, in individual persons' performance of complex tasks. (For example, a person cannot perform arithmetic until the numeral representations of numbers are learned. A person cannot operationally multiply the sums of numbers until addition is learned). Furthermore, previous theories of stage have confounded the stimulus and response in assessing stage by simply scoring responses and ignoring the task or stimulus.

The model of hierarchical complexity separates the task or stimulus from the performance. The participant's performance on a task of a given complexity represents the stage of developmental complexity.

Vertical complexity of tasks performed

One major basis for this developmental theory is task analysis. The study of ideal tasks, including their instantiation in the real world, has been the basis of the branch of stimulus control called psychophysics. Tasks are defined as sequences of contingencies, each presenting stimuli and each requiring a behavior or a sequence of behaviors that must occur in some non-arbitrary fashion. The complexity of behaviors necessary to complete a task can be specified using the horizontal complexity and vertical complexity definitions described below. Behavior is examined with respect to the analytically-known complexity of the task.

Tasks are quantal in nature. They are either completed correctly or not completed at all. There is no intermediate state (tertium non datur). For this reason, the Model characterizes all stages as P-hard and functionally distinct. The orders of hierarchical complexity are quantized like the electron atomic orbitals around the nucleus. Each task difficulty has an order of hierarchical complexity required to complete it correctly, corresponding to the atomic Slater eigenstate. Since tasks of a given quantified order of hierarchical complexity require actions of a given order of hierarchical complexity to perform them, the stage of the participant's task performance is equivalent to the order of complexity of the successfully completed task. The quantal feature of tasks is thus particularly instrumental in stage assessment because the scores obtained for stages are likewise discrete.

Every task contains a multitude of subtasks (Overton, 1990). When the subtasks are carried out by the participant in a required order, the task in question is successfully completed. Therefore, the model asserts that all tasks fit in some configured sequence of tasks, making it possible to precisely determine the hierarchical order of task complexity. Tasks vary in complexity in two ways: either as horizontal (involving classical information); or as vertical (involving hierarchical information).

Horizontal complexity

Classical information describes the number of "yes–no" questions it takes to do a task. For example, if one asked a person across the room whether a penny came up heads when they flipped it, their saying "heads" would transmit 1 bit of "horizontal" information. If there were 2 pennies, one would have to ask at least two questions, one about each penny. Hence, each additional 1-bit question would add another bit. Let us say they had a four-faced top with the faces numbered 1, 2, 3, and 4. Instead of spinning it, they tossed it against a backboard as one does with dice in a game of craps. Again, there would be 2 bits. One could ask them whether the face had an even number. If it did, one would then ask if it were a 2. Horizontal complexity, then, is the sum of bits required by just such tasks as these.

Vertical complexity

Hierarchical complexity refers to the number of recursions that the coordinating actions must perform on a set of primary elements. Actions at a higher order of hierarchical complexity: (a) are defined in terms of actions at the next lower order of hierarchical complexity; (b) organize and transform the lower-order actions (see Figure 2); (c) produce organizations of lower-order actions that are qualitatively new and not arbitrary, and cannot be accomplished by those lower-order actions alone. Once these conditions have been met, we say the higher-order action coordinates the actions of the next lower order.

To illustrate how lower actions get organized into more hierarchically complex actions, let us turn to a simple example. Completing the entire operation 3 × (4 + 1) constitutes a task requiring the distributive act. That act non-arbitrarily orders adding and multiplying to coordinate them. The distributive act is therefore one order more hierarchically complex than the acts of adding and multiplying alone; it indicates the singular proper sequence of the simpler actions. Although simply adding results in the same answer, people who can do both display a greater freedom of mental functioning. Additional layers of abstraction can be applied. Thus, the order of complexity of the task is determined through analyzing the demands of each task by breaking it down into its constituent parts.

The hierarchical complexity of a task refers to the number of concatenation operations it contains, that is, the number of recursions that the coordinating actions must perform. An order-three task has three concatenation operations. A task of order three operates on one or more tasks of vertical order two and a task of order two operates on one or more tasks of vertical order one (the simplest tasks).

Stages of development

The notion of stages or stagecraft is fundamental in the description of human, organismic, and machine evolution.^[7] Previously it has been defined in some ad hoc ways. Here, it is described formally in terms of the Model of Hierarchical Complexity (MHC).

Formal definition of stage

Since actions are defined inductively, so is the function h, known as the order of the hierarchical complexity. To each action A, we wish to associate a notion of that action's hierarchical complexity, h(A). Given a collection of actions A and a participant S performing A, the stage of performance of S on A is the highest order of the actions in A completed successfully at least once, i.e., it is: stage (S, A) = max{h(A) | A ∈ A and A completed successfully by S}. Thus, the notion of stage is discontinuous, having the same transitional gaps as the orders of hierarchical complexity. This is in accordance with previous definitions.^[8]

Because MHC stages are conceptualized in terms of the hierarchical complexity of tasks rather than in terms of mental representations (as in Piaget's stages), the highest stage represents successful performances on the most hierarchically complex tasks rather than intellectual maturity. Table 1 gives descriptions of each stage.

Stages of hierarchical complexity

Table 1. Stages described in the Model of Hierarchical Complexity

Order or stage	What they do	How they do it	End result
0 – calculatory	Exact computation only, no generalization	Human-made programs manipulate 0, 1, not 2 or 3.	Minimal human result. Literal, unreasoning computer programs (at Turing's alpha layer) act in a way analogous to this stage.
1 – sensory or motor	Discriminate in a rote fashion, stimuli generalization, move	Move limbs, lips, toes, eyes, elbows, head; view objects or move	Discriminative establishing and conditioned reinforcing stimuli
2 – circular sensory-motor	Form open-ended proper classes	Reach, touch, grab, shake objects, circular babble	Open ended proper classes, phonemes, archiphonemes
3 – sensory-motor	Form concepts	Respond to stimuli in a class successfully and non-stochastically	Morphemes, concepts
4 – nominal	Find relations among concepts; use names	Find relations among concepts; use names	Single words: ejaculatives & exclamations, verbs, nouns, number names, letter names
5 – sentential	Imitate and acquire sequences; follows short sequential acts	Generalize match-dependent task actions; chain words	Various forms of pronouns: subject (I), object (me), possessive adjective (my), possessive pronoun (mine), and reflexive (myself) for various persons (I, you, he, she, it, we, y'all, they)
6 – preoperational	Make simple deductions; follow lists of sequential acts; tell stories	Count event events and objects; connect the dots; combine numbers and simple propositions	Connectives: as, when, then, why, before; products of simple operations
7 – primary	Simple logical deduction and empirical rules involving time sequence; simple arithmetic	Adds, subtracts, multiplies, divides, counts, proves, does series of tasks on own	Times, places, counts acts, actors, arithmetic outcome, sequence from calculation
8 – concrete	Carry out full arithmetic, form cliques, plan deals	Does long division, short division, follows complex social rules, ignores simple social rules, takes and coordinates perspective of other and self	Interrelations, social events, what happened among others, reasonable deals, history, geography
9 – abstract	Discriminate variables such as stereotypes; logical quantification; (none, some, all)	Form variables out of finite classes; make and quantify propositions	Variable time, place, act, actor, state, type; quantifiers (all, none, some); categorical assertions (e.g., "We all die")
10 – formal	Argue using empirical or logical evidence; Logic is linear, 1 dimensional	Solve problems with one unknown using algebra, logic and empiricism	Relationships (for example: causality) are formed out of variables; words: linear, logical, one-dimensional, if then, thus, therefore, because; correct scientific solutions
11 – systematic	Construct multivariate systems and matrices	Coordinates more than one variable as input; consider relationships in contexts.	Events and concepts situated in a multivariate context; systems are formed out of relations; systems: legal, societal, corporate, economic, national
12 – metasystematic	Construct multi-systems and metasystems out of disparate systems	Create metasystems out of systems; compare systems and perspectives; name properties of systems: e.g. homomorphic, isomorphic, complete, consistent (such as tested by consistency proofs), commensurable	Metasystems and supersystems are formed out of systems of relationships
13 – paradigmatic	Fit metasystems together to form new paradigms	Synthesize metasystems	Paradigms are formed out of multiple metasystems
14 – cross-paradigmatic	Fit paradigms together to form new fields	Form new fields by crossing paradigms	New fields are formed out of multiple paradigms
15 – meta-cross-paradigmatic (performative-recursive)[9]		Observes and understands that by virtue of the cross-paradigms that account for their dynamics,disparate entities ranging from the universe, to paradigms, to species, to social metasystems, to individuals, for example, by their nature and/or with volition, perform recursive procession actions upon themselves, which transform them while and by performing each recursion; transformation may be “positive” or “negative.”

Relationship with Piaget's theory

There are some commonalities between the Piagetian and Commons' notions of stage and many more things that are different. In both, one finds:

1. Higher-order actions defined in terms of lower-order actions. This forces the hierarchical nature of the relations and makes the higher-order tasks include the lower ones and requires that lower-order actions are hierarchically contained within the relative definitions of the higher-order tasks.
2. Higher-order of complexity actions organize those lower-order actions. This makes them more powerful. Lower-order actions are organized by the actions with a higher order of complexity, i.e., the more complex tasks.

What Commons et al. (1998) have added includes:

3. Higher order of complexity actions organize those lower-order actions in a non-arbitrary way.

This makes it possible for the Model's application to meet real world requirements, including the empirical and analytic. Arbitrary organization of lower order of complexity actions, possible in the Piagetian theory, despite the hierarchical definition structure, leaves the functional correlates of the interrelationships of tasks of differential complexity formulations ill-defined.

Moreover, the model is consistent with the neo-Piagetian theories of cognitive development. According to these theories, progression to higher stages or levels of cognitive development is caused by increases in processing efficiency and working memory capacity. That is, higher-order stages place increasingly higher demands on these functions of information processing, so that their order of appearance reflects the information processing possibilities at successive ages (Demetriou, 1998).

The following dimensions are inherent in the application:

1. Task and performance are separated.
2. All tasks have an order of hierarchical complexity.
3. There is only one sequence of orders of hierarchical complexity.
4. Hence, there is structure of the whole for ideal tasks and actions.
5. There are transitional gaps between the orders of hierarchical complexity.
6. Stage is defined as the most hierarchically complex task solved.
7. There are discrete gaps in Rasch Scaled Stage of Performance.
8. Performance stage is different task area to task area.
9. There is no structure of the whole—horizontal decaláge—for performance. It is not inconsistency in thinking within a developmental stage. Decaláge is the normal modal state of affairs.

Orders and corresponding stages

The MHC specifies 15 orders of hierarchical complexity and their corresponding stages, showing that each of Piaget's substages, in fact, are robustly hard stages. Commons also adds four postformal stages: Systematic stage 11, Metasystematic stage 12, Paradigmatic stage 13, and Crossparadigmatic stage 14. It may be the Piaget's consolidate formal stage is the same as the systematic stage. There is one other difference in the orders and stages. At the suggestion of Biggs and Biggs, the sentential stage 5 was added. The sequence is as follows: (0) computory, (1) sensory & motor, (2) circular sensory-motor, (3) sensory-motor, (4) nominal, the new (5) sentential, (6) preoperational, (7) primary, (8) concrete, (9) abstract, (10) formal, and the four postformal: (11) systematic, (12) metasystematic, (13) paradigmatic, and (14) cross-paradigmatic. The first four stages (0–3) correspond to Piaget's sensorimotor stage at which infants and very young children perform. The sentential stage was added at Fischer's suggestion (1981, personal communication) citing Biggs & Collis (1982). Adolescents and adults can perform at any of the subsequent stages. MHC stages 4 through 5 correspond to Piaget's pre-operational stage; 6 through 8 correspond to his concrete operational stage; and 9 through 11 correspond to his formal operational stage.

The three highest stages in the MHC are not represented in Piaget's model. These stages from the Model of Hierarchical Complexity have extensively influenced the field of Positive Adult Development. Few individuals perform at stages above formal operations. More complex behaviors characterize multiple system models.^[10] Some adults are said to develop alternatives to, and perspectives on, formal operations. They use formal operations within a "higher" system of operations and transcend the limitations of formal operations. In any case, these are all ways in which these theories argue for and present converging evidence that some adults are using forms of reasoning that are more complex than formal operations with which Piaget's model ended. However, these new innovations cannot exactly be labelled as Postformal thought, as itself, see latest critique^[11]

Empirical research using the model

The MHC has a broad range of applicability. The mathematical foundation of the model makes it an excellent research tool to be used by anyone examining task performance that is organized into stages. It is designed to assess development based on the order of complexity which the individual utilizes to organize information. The MHC offers a singular mathematical method of measuring stages in any domain because the tasks presented can contain any kind of information. The model thus allows for a standard quantitative analysis of developmental complexity in any cultural setting. Other advantages of this model include its avoidance of mentalistic or contextual explanations, as well as its use of purely quantitative principles which are universally applicable in any context.

The following can use the Model of Hierarchical Complexity to quantitatively assess developmental stages:

• Cross-cultural developmentalists;
• Animal developmentalists;
• Evolutionary psychologists;
• Organizational psychologists;
• Developmental political psychologists;
• Learning theorists;
• Perception researchers;
• History of science historians;
• Educators;
• Therapists;
• Anthropologists.

The following list shows the large range of domains to which the Model has been applied. In one representative study, Commons, Goodheart, and Dawson (1997) found, using Rasch (1980) analysis, that hierarchical complexity of a given task predicts stage of a performance, the correlation being r = 0.92. Correlations of similar magnitude have been found in a number of the studies.

List of examples

List of examples of tasks studied using the Model of Hierarchical Complexity or Fischer’s Skill Theory (1980):

• Algebra (Commons, in preparation)
• Animal stages (Commons & Miller, 2004)
• Atheism (Commons-Miller, 2005)
• Attachment and Loss (Commons, 1991; Miller & Lee, 2000)
• Balance beam and pendulum (Commons, Goodheart, & Bresette, 1995; Commons, Pekker, et al., 2007)
• Contingencies of reinforcement (Commons, in preparation)
• Counselor stages (Lovell, 2004)
• Empathy of Hominids (Commons & Wolfsont, 2002)
• Enterprise Architecture (Evernden, 2013) ^[12]
• Epistemology (Kitchener & King, 1990; Kitchener & Fischer, 1990)
• Evaluative reasoning (Dawson, 2000)
• Four Story problem (Commons, Richards & Kuhn, 1982; Kallio & Helkama, 1991)
• Good Education (Dawson-Tunik, 2004)
• Good Interpersonal (Armon, 1989)
• Good Work (Armon, 1993)
• Honesty and Kindness (Lamborn, Fischer & Pipp, 1994)
• Informed consent (Commons & Rodriguez, 1990, 1993; Commons, Goodheart, Rodriguez, & Gutheil, 2006; Commons, Rodriguez, Adams, Goodheart, Gutheil, & Cyr, 2007).
• Language stages (Commons, et al., 2007)
• Leadership before and after crises (Oliver, 2004)
• Loevinger's Sentence Completion task (Cook-Greuter, 1990)
• Moral Judgment (Armon & Dawson, 1997; Dawson, 2000)
• Music (Beethoven) (Funk, 1989)
• Orienteering (Commons, in preparation)
• Physics tasks (Inhelder & Piaget, 1958)
• Political development (Sonnert & Commons, 1994)
• Relationships (Armon, 1984a, 1984b)
• Report patient's prior crimes (Commons, Lee, Gutheil, et al., 1995)
• Social perspective-taking (Commons & Rodriguez, 1990; 1993)
• Spirituality (Miller & Cook-Greuter, 2000)
• Tool Making of Hominids (Commons & Miller 2002)
• Views of the Agood life@ (Armon, 1984c; Danaher, 1993; Dawson, 2000; Lam, 1995)
• Workplace culture (Commons, Krause, Fayer, & Meaney, 1993)
• Workplace organization (Bowman, 1996a, 1996b)
• Writing (Commons & DeVos, 1985)

Implications of the Model of Hierarchical Complexity

The model of hierarchical complexity by Dr. Michael Commons provides a standard way of examining the universal patterns of development and evolution. It is a quantitative behavioral developmental theory.^[13] The model is used to score the complexity of behavior. Behaviors that may be scored include those of individual humans or their social groupings , other living beings and / or machines. It leads to scoring the hierarchical complexity of task accomplishment in all kinds of domains. The idea behind the model is that higher order task actions are a) described in terms the next lower ones (creating hierarchy), b) they organize those actions c) differentiating them from simple chains of behavior causing a match between the model-designated orders and the real world orders). The scoring is dependent upon how the information is organized and not upon the information context (e.g., what is done, said, written). The main idea behind the model was that mathematical psychology could be used to predict the future.

People and institutes from all the major continents of the world use the model of hierarchical complexity, save for Africa. Because the model is very simple and is based on analysis of tasks and not just performances, it is dynamic.^[14]

Furthermore, with the help of the model, it is possible to quantify the occurrence and progression of transition processes in task performances at any order of hierarchical complexity.^[15]

Criticisms

The descriptions of stages 13–15 have been described as insufficiently precise.^[16]

References

1. Commons & Pekker, 2007
2. (Commons, Trudeau, Stein, Richards, & Krause, 1998)
3. (Coombs, Dawes, & Tversky, 1970)
4. (Commons & Richards, 1984a, 1984b; Lindsay & Norman, 1977; Commons & Rodriguez, 1990, 1993)
5. (Commons & Richards, 1984a, 1984b)
6. (e.g., Inhelder & Piaget, 1958)
8. (Commons et al., 1998; Commons & Miller, 2001; Commons & Pekker, 2007)
9. http://www.academia.edu/3210966/Toward_Defining_Order_15_and_Describing_Its_Performance_for_the_Model_of_Hierarchical_Complexity_draft_
10. (Kallio, 1995; Kallio & Helkama, 1991)
11. Kallio, E. 2011. Integrative thinking is the key: an evaluation of current research into the development of thinking in adults. Theory & Psychology, 21 Issue 6 December 2011 pp. 785 - 801
12. http://www.cutter.com/content/architecture/fulltext/reports/2013/01/index.html Evernden, R. Mastering Complexity to Drive EA Productivity, Cutter Consortium Executive Report, Vol. 16, No. 1, 2013
13. Commons, ML. "INTRODUCTION TO THE MODEL OF HIERARCHICAL COMPLEXITY AND ITS RELATIONSHIP TO POSTFORMAL ACTION" (PDF).
14. Commons, M. "Advances in the model of hierarchical complexity (MHC)" (PDF).
15. Ross, S. "Fractal Model of Nonlinear Hierarchical Complexity: Measuring Transition Dynamics as Fractals of Themselves" (PDF).
16. http://www.academia.edu/3210966/Toward_Defining_Order_15_and_Describing_Its_Performance_for_the_Model_of_Hierarchical_Complexity_draft_

Copyright permissions

Portions of this article are from Applying the Model of Hierarchical Complexity by Commons, M.L., Miller, P.M., Goodheart, E.A., Danaher-Gilpin, D., Locicero, A., Ross, S.N. Unpublished manuscript. Copyright 2007 by Dare Association, Inc. Available from Dare Institute, [e-mail]. Reproduced and adapted with permission of the publisher. Portions of this article are also from "Introduction to the Model of Hierarchical Complexity" by M.L. Commons, in the Behavioral Development Bulletin, 13, 1–6 (http://www.behavioral-development-bulletin.com/). Copyright 2007 Martha Pelaez. Reproduced with permission of the publisher.

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• Kitchener, K.S., & King, P.M. (1990). Reflective judgement: Ten years of research. In M.L. Commons, C. Armon, L. Kohlberg, F.A. Richards, T.A. Grotzer, & J.D. Sinnott (Eds.), Beyond formal operations: Vol. 2. Models and methods in the study of adolescent and adult thought (pp. 63–78). New York: Praeger.
• Kitchener, K.S. & Fischer, K.W. (1990). A skill approach to the development of reflective thinking. In D. Kuhn (Ed.), Developmental perspectives on teaching and learning thinking skills. Contributions to Human Development: Vol. 21 (pp. 48–62).
• Lam, M.S. (1995). Women and men scientists' notions of the good life: A developmental approach. Unpublished doctoral dissertation, University of Massachusetts, Amherst, MA.
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• Miller, P.M., & Lee, S.T. (June, 2000). Stages and transitions in child and adult narratives about losses of attachment objects. Paper presented at the Jean Piaget Society. Montreal, Québec, Canada.
• Overton, W.F. (1990). Reasoning, necessity, and logic: Developmental perspectives. Hillsdale, NJ: Lawrence Erlbaum Associates.
• Oliver, C.R. (2004). Impact of catastrophe on pivotal national leaders' vision statements: Correspondences and discrepancies in moral reasoning, explanatory style, and rumination. Unpublished doctoral dissertation, Fielding Graduate Institute.
• Rasch, G. (1980). Probabilistic model for some intelligence and attainment tests. Chicago: University of Chicago Press.
• Sonnert, G., & Commons, M.L. (1994). Society and the highest stages of moral development. Politics and the Individual, 4(1), 31–55
• Commons ML, Li EL, Richardson AM, Gane-McCalla R, Barker CD, Tuladhar CT. Does the model of hierarchical complexity produce significant gaps between orders and are the orders equally spaced? J Appl Meas. 2014; 15(4):422-49.
• Miller PM, Bener A, Ghuloum S, Commons ML, Burgut FT. Differences and similarities in cross-cultural perceptions of boundaries: a comparison of results from two studies. Int J Law Psychiatry. 2012 Sep-Dec; 35(5-6):398-405.
• Commons ML, Miller PM, Li EY, Gutheil TG. Forensic experts' perceptions of expert bias. Int J Law Psychiatry. 2012 Sep-Dec; 35(5-6):362-71.
• Gutheil TG, Commons ML, Drogin EY, Hauser MJ, Miller PM, Richardson AM. Do forensic practitioners distinguish between testifying and consulting experts? A pilot study. Int J Law Psychiatry. 2012 Sep-Dec; 35(5-6):452-5.
• Drogin EY, Commons ML, Gutheil TG, Meyer DJ, Norris DM. "Certainty" and expert mental health opinions in legal proceedings. Int J Law Psychiatry. 2012 Sep-Dec; 35(5-6):348-53.
• Ghuloum S, Bener A, Commons ML, Miller PM, Burgut FT, Bhugra D. Perceptions of boundaries and cultural influences in Qatar. Int J Soc Psychiatry. 2013 May; 59(3):199-206.
• Spruiell GL, Hauser MJ, Commons ML, Drogin EY. Clinicians imagine a patient's view: Rating disclosures of confidential information. J Am Acad Psychiatry Law. 2011; 39(3):379-86.
• Commons ML, Gutheil TG, Hilliard JT. On humanizing the expert witness: a proposed narrative approach to expert witness qualification. J Am Acad Psychiatry Law. 2010; 38(3):302-4.
• Dawson TL, Goodheart EA, Draney K, Wilson M, Commons ML. Concrete, abstract, formal, and systematic operations as observed in a "Piagetian" balance-beam task series. J Appl Meas. 2010; 11(1):11-23.
• Commons ML, Goodheart EA, Pekker A, Dawson TL, Draney K, Adams KM. Using Rasch scaled stage scores to validate orders of hierarchical complexity of balance beam task sequences. J Appl Meas. 2008; 9(2):182-99.
• Dattilio FM, Commons ML, Adams KM, Gutheil TG, Sadoff RL. A pilot Rasch scaling of lawyers' perceptions of expert bias. J Am Acad Psychiatry Law. 2006; 34(4):482-91.
• Miller PM, Commons ML, Gutheil TG. Clinicians' perceptions of boundaries in Brazil and the United States. J Am Acad Psychiatry Law. 2006; 34(1):33-
• Commons ML, Miller PM, Gutheil TG. Expert witness perceptions of bias in experts. J Am Acad Psychiatry Law. 2004; 32(1):70-
• Commons-Miller LA, Commons ML. Recognizing specialized terminology presented through different modes. J Psychol. 2003 Nov; 137(6):622-36.
• Strasburger LH, Miller PM, Commons ML, Gutheil TG, LaLlave J. Stress and the forensic psychiatrist: a pilot study. J Am Acad Psychiatry Law. 2003; 31(1):18-26.
• Price M, Kafka M, Commons ML, Gutheil TG, Simpson W. Telephone scatologia. Comorbidity with other paraphilias and paraphilia-related disorders. Int J Law Psychiatry. 2002 Jan-Feb; 25(1):37-49.
• Gutheil TG, Commons ML, Miller PM. Personal questions on cross-examination: a pilot study of expert witness attitudes. J Am Acad Psychiatry Law. 2001; 29(1):85-8

External links

• Dare Association, Inc. display text.
• Behavioral Development Bulletin display text.
• Society for Research in Adult Development display text