Michael Commons

Michael Lamport Commons (1939) is a theoretical behavioral scientist and a complex systems scientist. He developed the Model of Hierarchical Complexity. He also is the founder of the Journal of Adult Development, Society for the Quantitative Analyses of Behavior and the Society for Research in Adult Development, the European Society for Research in Adult Development and co-editor of the journal Behavioral Development Bulletin.

Life and work

Michael Lamport Commons was born in 1939 in Los Angeles, and grew up in Hollywood. Commons holds two B.A.s from University of California at Los Angeles (UCLA), one in mathematics, the other in psychology. He earned his M.A., and M.Phil. and in 1973 received his Ph.D., in psychology from Columbia University. Currently, he is Assistant Clinical Professor, Department of Psychiatry at Beth Israel Deaconess Medical Center a teaching hospital of Harvard Medical School, and Director of the Dare Institute, Cambridge, MA.

His research interest is the quantitative analysis of psychological reality as it develops across the life span and evolutionarily. With Francis Asbury Richards, Edward Trudeau, and Alexander Pekker, he developed the Model of Hierarchical Complexity, a mathematical psychology model.

He is one of the cofounders of Society for Quantitative Analysis of Behavior, the Society for research in adult development, the European Society for Research in Adult Development, the Society for Terrorism Research and the Special Interest Group, Developmental Behavior Analysis in the Association for Behavior Analysis International.

He is on the governing board of the Journal of Behavior Analysis Online. He is past co-editor of the Journal of Behavior Analysis Online. He was a senior editor of Quantitative Analyses of Behavior, Volumes 1-11 and of four volumes on Adult Development including ‘’Beyond Formal Operations: Late Adolescent and Adult Cognitive Development’’ and ‘’Clinical Approaches to Adult Development,’’ as well as associate editor for a special issue of Journal of the Experimental Analysis of Behavior on the nature of reinforcement. He is the Consulting Editor of ‘’Moral Development Series.’’

Michael Common's Association

Dare Association Inc

Michael has been a part of some renowned companies. One of those is Dare Association Incorporate. The Dare Association is an independent, not-for-profit organization. Dare association supports endeavors in the science and arts.

In 1979, to comply with Work-Study requirements, the Dare Institute was founded. In 1979 Joel R Peck discovered that the Federal Work-Study Program (FWSP) could fund his and others' work at the Dare Association's behavior and decision analysis laboratory that is how the foundation stone for Dare Association was laid. Before that, the Dare Association had paid the full wages of its employees. Under FWSP, the government pays 2/3rd wages for students, and the Dare Association pays the remaining 1/3rd. As of now the Dare Institute conducts research on decision making development in humans within such contexts as academia, economics, politics, institutions, businesses, medicine, and the law, under the experienced leadership of Michael Commons and Patrice M Miller.Studies in the Dare Institute concentrate on the people’s perception of values and causality development within the domain of the above-mentioned contexts. Experimental tests are provided to the participants — children, adolescents and adults — to gather data.

Dare Association’s Scientific Activities

Dare Association has been actively involved in various scientific activities which include multiple behavioral science programs. These programs have evolved and grown into external groups. These groups include

1) Society for Research In Adult Development

2) Society for Quantitative Analysis of Behavior

Dare Association's Artistic Activities

The artistic activities of the Dare Association includes:

1) Kirana Institute for North Indian Music

2) The theatre of external music

3) Settima Practica Center

Applied Behavioral Science Activities

Another activity involvement of the Dare Association is the applied behavioral science activity. This involves:

1) The International Health, Education and Development Division of Dare Association:

This division has the broad goal of improving the people’s well-being in developing countries.

2) Grupo para Terceira Idade e Infacncia (Group for the Third Age and Childhood (GATIII)) an organization for elderly-abuse prevention.

3) Mindfulness connections: supports and encourages the accessibility, integration and application of the practices and the principles of mindfulness, awareness, compassion and wisdom in all aspects of life.

Dare Institute

Dare Institute is a group led by the Dare Association. This group is devoted to research in psychological topics such as human development, psychiatry and the law, political psychology, behavioral economics, and cognitive science.

Objectives of the Dare Association

The main objectives of the Dare Association are to :

Help people to design research projects and analyze data.

Help people to get their papers published.

Train students ranging from junior high to postdoctoral (in the behavioral sciences.)

Teach people to construct instruments and interviews that assess performance stages, informed consent, sexual harassment, and other topics.

Act as an ancillary research facility to the Program in Psychiatry and the law

People come to the Dare Institute from all over the world seeking help with the above tasks and more.^[1]

Core Complexity Assessments

Besides the Dare Association, Dr. Commons is also associated with Core Complexity Assessments. Core Complexity pairs people with a suitable job. The company bring insights from 30 years of research in developmental psychology to pair candidates with jobs. Core Complexity Assessments’ tools are created in a fashion which helps companies and managers hire smarter, retain workers, invest in employee development and human resources planning, and shaping the future organizational structure of the company.^[2]

Michael Common's Patent

Intelligent control with hierarchical stacked neural networks

Patent number: 9129218

Type: Grant

Filed: July 18, 2014

Issued: September 8, 2015

Inventors: Michael Lamport Commons, Mitzi Sturgeon White

Invention Summary

This invention relates to the use of hierarchical stacked neural networks that develop new tasks and learn through processing information in a mode that triggers cognitive development in the human brain in identifying atypical messages, for example, spam messages in email and similar services. Neural networks are useful in constructing systems that learn and create complex decisions in the same methodology as the brain.

This invention applies models of the ordered stages that the brain moves through during development that causes it to execute highly complex tasks at higher stages of development to the task of identifying atypical messages, such as email spam. In this process, actions performed at some point of development are developed by ordering, altering and combining the tasks executed in the preceding phase. Because of this process, at each stage of development more complicated tasks can be executed than those performed at the preceding phase.

Implications

It is an object of the present invention to provide hierarchical stacked neural networks that overcome the limitations of the neural networks of the prior art.It is another object of the present invention to provide linked but architecturally distinct hierarchical stacked neural networks that simulate the brain's capacity to organize lower-order actions hierarchically by combining, ordering, and transforming the actions to produce new, more complex higher-stage actions.

Another aim of the invention is to provide hierarchical stacked neural networks that are ordered in a non-arbitrary way so that tasks executed by neural networks at a higher level are the result of a concatenation of tasks executed by lower-level networks in the hierarchy.In addition the tasks executed by a neural network in the stacked hierarchy are a result of amalgamating, ordering, and altering tasks executed by the neural network that precedes it at a lower level in the stacked hierarchy.Furthermore, another aim of the invention is that neural networks at higher levels in the hierarchy execute highly complex actions and tasks than neural networks that precede them at a lower level in the hierarchy.

Intelligent control with hierarchical stacked neural networks

Patent number: 9053431

Type: Grant

Filed: July 2, 2014

Issued: June 9, 2015

Inventor: Michael Lamport Commons

This is a system and a method of identifying an abnormally deviant message . An ordered set of words within the message is recognized. The set of words observed within the message is associated with a set of anticipated words, the set of anticipated words having semantic characteristics. A set of grammatical structures illustrated in the message is recognized, based on the ordered set of words and the semantic characteristics of the corresponding set of anticipated words. A cognitive noise vector consisting of a quantitative measure of a deviation between grammatical structures illustrated in the message and a measure (unexpected) of grammatical structures for a message of the type is then discerned. The cognitive noise vector could be processed by higher levels of the neural network and/or an outer processor.

Implications

An aim of this invention to provide hierarchical stacked neural networks that are ordered in a non-arbitrary way so that tasks executed by neural networks at a higher level are the result of a concatenation of tasks executed by lower-level networks in the hierarchy. We can say, lower level neural networks would be able to send output that would be useful as input in the higher levels.

This invention provides an architecture of hierarchically linked, neural networks created for spam filtering stacked one on top of the other. Every neural network in the hierarchical stack keeps track not only of the data it can glean from the input, as in previous art neural networks, but it also concentrates on "cognitive noise" and develops an error vector or a same means of determining the degree of the imperfections in the information transmitted.

In this invention, higher-level neural networks interact with lower level neural networks in the hierarchical stacked neural network. The higher-level neural networks responds to the lower-level neural networks to calibrate connection weights, thus improving the precision of the tasks executed at the lower levels. The higher-level neural networks can also demand that additional information be fed to the lowest neural network in the stacked hierarchy.

Intelligent control with hierarchical stacked neural networks

Patent number: 9015093

Type: Grant

Filed: October 25, 2011

Issued: April 21, 2015

Inventor: Michael Lamport Commons

This is a method of processing information which involves receiving a message; processing the message with a trained artificial neural network based processor, having at least one set of outputs which represent information in a non-arbitrary organization of actions based on an architecture of the artificial neural network based processor and the training; representing as a noise vector at least one data pattern in the message which is represented incompletely in the non-arbitrary organization of actions; analyzing the noise vector distinctly from the trained artificial neural network; scrutinizing one database minimum; and developing an output in dependence on said analyzing and said searching.

The present invention relates to the field of cognitive neural networks, and to hierarchical stacked neural networks configured to imitate human intelligence.

Implications

One goal of the invention to is provide linked but architecturally distinguishable hierarchical stacked neural networks that emulate the capacity of the brain to rearrange lower-order actions hierarchically by combining, ordering, and changing the tasks to produce new, highly complex higher-stage actions.These lower levels of neural networks complete simpler tasks than higher levels.

Furthermore, this invention also provides hierarchical stacked neural networks that are ordered in a non-arbitrary manner so that tasks executed by neural networks at a higher level are the consequence of a concatenation of tasks executed by lower-level networks in the hierarchy. We can say, lower level neural networks would provide output that would be useful as input in the higher levels.

Intelligent control with hierarchical stacked neural networks

Patent number: 8788441

Type: Grant

Filed: November 3, 2009

Issued: July 22, 2014

Inventors: Michael Lamport Commons, Mitzi Sturgeon White

It is a continuation of the previous patent

Summary and Implications

The goal of the invention to provide hierarchical stacked neural networks that overpower the restraints of the neural networks of the previous art. Another aim of the invention is to provide associated but distinguishable hierarchical stacked neural networks that imitate the brain's volume to arrange lower-order actions hierarchically by combining, ordering, and changing the tasks to develop more compound higher-stage tasks.

Another aim is to provide hierarchical stacked neural networks which are ordered in a non-arbitrary way so that actions executed by neural networks at a higher level are the result of a concatenation of tasks executed by lower-level networks in the hierarchy.In addition, another task is that the tasks executed by a neural network in the stacked hierarchy are a result of amalgamating, ordering, and altering task executed by the neural network which precedes it at a lower level in the stacked hierarchy.

Furthermore, neural networks at higher levels in the hierarchy execute highly complex tasks than neural networks that precede them at a lower level in the hierarchy.

This invention provides an architecture of hierarchically linked, distinguishable neural networks stacked one on top of the other. Every neural network in the hierarchical stack uses the neuron-based methodology of previous art neural networks. The tasks that every neural network executes and the order in which they execute are based on human cognitive development.

Intelligent control with hierarchical stacked neural networks

Patent number: 8775341

Type: Grant

Filed: October 25, 2011

Issued: July 8, 2014

Inventor: Michael Lamport Commons

Oct 25, 2011

It is a structure and method of identifying abnormal message. An organized set of words within the message is identified. The set of words observed within the message is associated to a corresponding set of anticipated parable, the set of anticipated words having semantic characteristics. A set of grammatical compositions illustrated in the message is identified, based on the ordered set of words and the semantic characteristics of the corresponding set of anticipated words. A cognitive noise vector encompassing a quantitative measure of a deviation between grammatical structures illustrated in the message and an anticipated measure of grammatical structures for a message of the type is then discerned. The cognitive noise vector may be processed by higher levels of the neural network and/or an outer processor.

Implications

In this invention lower-level neural networks interact with higher level neural networks in the hierarchical stacked neural network. The higher-level neural networks responds to the lower-level neural networks to regulate coupling weights as a result boosting the precision of the tasks executed at the lower levels. The higher-level neural networks can also demand that more information be fed to the lowest neural network in the stacked hierarchy.Another aim of this invention is to deliver linked but architecturally distinguishable hierarchical stacked neural networks which imitate the brain's volume to categorize lower-order actions hierarchically by amalgamating, ordering, and altering the tasks to develop complex higher-stage actions. As a result, lower levels of neural networks complete easier tasks as compared to higher levels. For example, in spam filtering, lower levels would concentrate on identifying text as text, distinguishing text into letters, and arranging text into strings of letters, while higher level neural networks would identify and understand words and higher levels would identify a surplus of poorly structured words or sentences.Furthermore, another goal of the invention to give hierarchical stacked neural networks that are ordered in a non-arbitrary manner so that tasks executed by neural networks at a higher level are the result of a coupling of tasks executed by lower-level networks in the hierarchy. We can also say that lower level neural networks can give output that would be useful as input in the higher levels.

Intelligent control with hierarchical stacked neural networks

Patent number: 7613663

Type: Grant

Filed: December 18, 2006

Issued: November 3, 2009

Inventors: Michael Lamport Commons, Mitzi Sturgeon White

Summary and Implications

The goal of the invention is to provide hierarchical stacked neural networks that overcome the limitations of the neural networks of the previous art.Another goal is to provide associated but architecturally different hierarchical stacked neural networks which imitate the brain's measurable volume to arrange lower-order actions hierarchically by incorporating, ordering, and altering the tasks to develop new, more complex higher-stage actions. This invention also provides hierarchical stacked neural networks which are ordered in a non-arbitrary manner so that tasks executed by neural networks at a higher level are the consequence of coupling of actions executed by lower-level networks in the hierarchy. Another aim is that the tasks executed by a neural network in the stacked hierarchy are a resultant of amalgamating, ordering, and altering tasks executed by the neural network that precedes it at a lower level in the stacked hierarchy.It is another aim of the model that neural networks at higher levels in the hierarchy execute highly complex tasks as compared to neural networks that precede them at a lower level in the hierarchy.

Intelligent control with hierarchical stacked neural networks

Patent number: 7152051

Type: Grant

Filed: September 30, 2002

Issued: December 19, 2006

Inventors: Michael Lamport Commons, Mitzi Sturgeon White

Summary and Implications

The invention to provides hierarchical stacked neural networks which overcome the limitations of the neural networks of the previous art. It also provides linked but architecturally distinct hierarchical stacked neural networks that imitate the volume and magnitude of the brain to organize lower-order actions hierarchically by ordering, combining and altering the actions to develop more complex higher-stage tasks.

Moreover, the invention also provides hierarchical stacked neural networks which are ordered in a non-arbitrary manner so that tasks performed by neural networks at a higher level are the resultant of a concatenation of actions executed by lower-level networks in the hierarchy.Another aim of the invention is that the tasks executed by a neural network in the stacked hierarchy are a consequence of amalgamating, ordering, and altering tasks executed by the neural network that precedes it at a lower level in the stacked hierarchy.In addition another aim of the invention is that the neural networks at higher levels in the hierarchy execute highly complex actions as compared to neural networks that precede them at a lower level in the hierarchy.^[3]

Publications

Commons has also contributed chapters to a number of books, and written a number of peer-reviewed papers.^[4] Books:

• 1984, Beyond formal operations: Vol. 1. Late adolescent and adult cognitive development. with F. A. Richards & C. Armon (Eds.), New York: Praeger.

Articles, a selection

• 1982, "Systematic and metasystematic reasoning: A case for a level of reasoning beyond Piaget's formal operations". With Richards, F. A., & Kuhn, D. in: Child Development, 53, 1058-1069.
• 1990. "Equal access" without "establishing" religion: The necessity for assessing social perspective-taking skills and institutional atmosphere". With J.A. Rodriguez In: Developmental Review, 10, 323-340.
• 1991, "A comparison and synthesis of Kohlberg's cognitive-developmental and Gewirtz's learning-developmental attachment theories". In: J. L. Gewirtz & W. M. Kurtines (Eds.), Intersections with attachment (pp. 257–291). Hillsdale, NJ: Erlbaum.
• 1993. "The development of hierarchically complex equivalence classes". With J.A. Rodriguez In: Psychological Record, 43, 667-697.
• 1993, "Atmosphere and stage development in the workplace". With Krause, S. R., Fayer, G. A., & Meaney, M. In: J. Demick & P. M. Miller (Eds.). Development in the workplace (pp. 199–220). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
• 1995. "Formal, systematic, and metasystematic operations with a balance-beam task series: A reply to Kallio's claim of no distinct systematic stage". With others. In: Adult Development, 2 (3), 193-199.
• 1995. "Moral stage of reasoning and the misperceived "duty" to report past crimes (misprision)". With others. In: International Journal of Law and Psychiatry, 18(4), 415-424.
• 1997. Psychophysics of Stage: Task Complexity and Statistical Models. With: Goodheart, E. A., & Dawson T. L.. Paper presented at the International Objective Measurement Workshop at the Annual Conference of the American Educational Research Association, Chicago, IL.
• 1998. "Hierarchical Complexity of Tasks Shows the Existence of Developmental Stages". With others. In: Developmental Review, 8(3), 237-278.
• 2001, "A quantitative behavioral model of developmental stage based upon hierarchical complexity theory". With P.A. Miller. In: Behavior Analyst Today, 2(3), 222-240.
• 2002. "A complete theory of human evolution of intelligence must consider stage changes: A commentary on Thomas Wynn’s Archeology and Cognitive Evolution". With P.A. Miller. In: Behavioral and Brain Sciences. 25(3), 404-405.
• 2004. "Development of behavioral stages in animals". With P.A. Miller. In: Marc Bekoff (Ed.). Encyclopedia of animal behavior. (pp. 484–487). Westport, CT: Greenwood Publishing Group.
• 2006. "Informed Consent: Do you know it when you see it?" With others in: Psychiatric Annals, June, 430-435.
• 2007. "Using Rasch scaled stage scores to validate orders of hierarchical complexity of balance beam task sequences". With others. In: E. V. Smith, Jr. & R. M. Smith (Eds.). Rasch measurement: Advanced and specialized applications (pp. 121–147). Maple Grove, MN: JAM Press

List of some publications related to the Model of Hierarchical Complexity or Michael Commons

• • Armon, C. (1984a). Ideals of the good life and moral judgment: Ethical reasoning across the life span. In M.L. Commons, F.A. Richards, & C. Armon (Eds.), Beyond formal operations: Vol. 1. Late adolescent and adult cognitive development (pp. 357–380). New York: Praeger.
  • Armon, C. (1984c). Ideals of the good life and moral judgment: Evaluative reasoning in children and adults. Moral Education Forum, 9(2).
  • Armon, C. (1989). Individuality and autonomy in adult ethical reasoning. In M.L. Commons, J.D. Sinnott, F.A. Richards, & C. Armon (Eds.), Adult development, Vol. 1. Comparisons and applications of adolescent and adult developmental models, (pp. 179–196). New York: Praeger.
  • Armon, C. (1993). The nature of good work: A longitudinal study. In J. Demick & P.M. Miller (Eds.), Development in the workplace (pp. 21–38). Hillsdale, NJ: Erlbaum.
  • Armon, C. & Dawson, T.L. (1997). Developmental trajectories in moral reasoning across the life-span. Journal of Moral Education, 26, 433–453.
  • Biggs, J. & Collis, K. (1982). A system for evaluating learning outomes: The SOLO Taxonomy. New York: Academic Press.
  • Bowman, A.K. (1996b). Examples of task and relationship 4b, 5a, 5b statements for task performance, atmosphere, and preferred atmosphere. In M.L. Commons, E.A. Goodheart, T.L. Dawson, P.M. Miller, & D.L. Danaher, (Eds.) The general stage scoring system (GSSS). Presented at the Society for Research in Adult Development, Amherst, MA.
  • Commons, M.L. (1991). A comparison and synthesis of Kohlberg's cognitive-developmental and Gewirtz's learning-developmental attachment theories. In J.L. Gewirtz & W.M. Kurtines (Eds.), Intersections with attachment (pp. 257–291). Hillsdale, NJ: Erlbaum.
  • Commons, M.L., Goodheart, E.A., & Bresette, L.M. with Bauer, N.F., Farrell, E.W., McCarthy, K.G., Danaher, D.L., Richards, F.A., Ellis, J.B., O'Brien, A.M., Rodriguez, J.A., and Schraeder, D. (1995). Formal, systematic, and metasystematic operations with a balance-beam task series: A reply to Kallio's claim of no distinct systematic stage. Adult Development, 2 (3), 193–199.
  • Commons, M.L., Goodheart, E.A., & Dawson T.L. (1997). Psychophysics of Stage: Task Complexity and Statistical Models. Paper presented at the International Objective Measurement Workshop at the Annual Conference of the American Educational Research Association, Chicago, IL.
  • Commons, M.L., Goodheart, E.A., Pekker, A., Dawson, T.L., Draney, K., & Adams, K.M. (2007). Using Rasch scaled stage scores to validate orders of hierarchical complexity of balance beam task sequences. In E.V. Smith, Jr. & R.M. Smith (Eds.). Rasch measurement: Advanced and specialized applications (pp. 121–147). Maple Grove, MN: JAM Press.
  • Commons, M.L., Goodheart, E.A., Rodriguez, J.A., Gutheil, T.G. (2006). Informed Consent: Do you know it when you see it? Psychiatric Annals, June, 430–435.
  • Commons, M.L., Krause, S.R., Fayer, G.A., & Meaney, M. (1993). Atmosphere and stage development in the workplace. In J. Demick & P.M. Miller (Eds.). Development in the workplace (pp. 199–220). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
  • Commons, M.L., Lee, P., Gutheil, T.G., Goldman, M., Rubin, E. & Appelbaum, P.S. (1995). Moral stage of reasoning and the misperceived "duty" to report past crimes (misprision). International Journal of Law and Psychiatry, 18(4), 415–424.
  • Commons, M.L., & Miller, P.A. (2001). A quantitative behavioral model of developmental stage based upon hierarchical complexity theory. Behavior Analyst Today, 2(3), 222–240.
  • Commons, M.L., Miller, P.M. (2002). A complete theory of human evolution of intelligence must consider stage changes: A commentary on Thomas Wynn's Archeology and Cognitive Evolution. Behavioral and Brain Sciences. 25(3), 404–405.
  • Commons, M.L., & Miller, P.M. (2004). Development of behavioral stages in animals. In Marc Bekoff (Ed.). Encyclopedia of animal behavior. (pp. 484–487). Westport, CT: Greenwood Publishing Group.
  • Commons, M.L., & Pekker, A. (2007). Hierarchical Complexity: A Formal Theory. Manuscript submitted for publication.
  • Commons, M.L., & Richards, F.A. (1984a). A general model of stage theory. In M.L. Commons, F.A. Richards, & C. Armon (Eds.), Beyond formal operations: Vol. 1. Late adolescent and adult cognitive development (pp. 120–140). New York: Praeger.
  • Commons, M.L., & Richards, F.A. (1984b). Applying the general stage model. In M.L. Commons, F.A. Richards, & C. Armon (Eds.), Beyond formal operations: Vol. 1. Late adolescent and adult cognitive development (pp. 141–157). New York: Praeger.
  • Commons, M.L., Richards, F.A., & Kuhn, D. (1982). Systematic and metasystematic reasoning: A case for a level of reasoning beyond Piaget's formal operations. Child Development, 53, 1058–1069.
  • Commons, M.L., Rodriguez, J.A. (1990). AEqual access" without "establishing" religion: The necessity for assessing social perspective-taking skills and institutional atmosphere. Developmental Review, 10, 323–340.
  • Commons, M.L., Rodriguez, J.A. (1993). The development of hierarchically complex equivalence classes. Psychological Record, 43, 667–697.
  • Commons, M.L., Rodriguez, J.A. (1990). "Equal access" without "establishing" religion: The necessity for assessing social perspective-taking skills and institutional atmosphere. Developmental Review, 10, 323–340.
  • Commons, M.L., Trudeau, E.J., Stein, S.A., Richards, F.A., & Krause, S.R. (1998). Hierarchical Complexity of Tasks Shows the Existence of Developmental Stages. Developmental Review, 8(3), 237–278.
  • Commons, M.L., & De Vos, I.B. (1985). How researchers help writers. Unpublished manuscript available from [e-mail].
  • Commons-Miller, N.H.K. (2005). The stages of atheism. Paper presented at the Society for Research in Adult Development, Atlanta, GA.
  • Cook-Greuter, S.R. (1990). Maps for living: Ego-development theory from symbiosis to conscious universal embeddedness. In M.L. Commons, J.D. Sinnott, F.A. Richards, & C. Armon (Eds.). Adult Development: Vol. 2, Comparisons and applications of adolescent and adult developmental models (pp. 79–104). New York: Praeger.
  • Coombs, C.H., Dawes, R.M., & Tversky, A. (1970). Mathematical psychology: An elementary introduction. Englewood Cliffs, New Jersey: Prentice-Hall.
  • Danaher, D. (1993). Sex role differences in ego and moral development: Mitigation with maturity. Unpublished dissertation, Harvard Graduate School of Education.
  • Dawson, T.L. (2000). Moral reasoning and evaluative reasoning about the good life. Journal of Applied Measurement, 1 (372–397).
  • Dawson Tunik, T.L. (2004). "A good education is" The development of evaluative thought across the life span. Genetic, Social, and General Psychology Monographs, 130, 4–112.
  • Demetriou, A. (1998). Cognitive development. In A. Demetriou, W. Doise, K.F.M. van Lieshout (Eds.), Life-span developmental psychology (pp. 179–269). London: Wiley.
  • Fischer, K.W. (1980). A theory of cognitive development: The control and construction of hierarchies of skills. Psychological Review, 87(6), 477–531.
  • Funk, J.D. (1989). Postformal cognitive theory and developmental stages of musical composition. In M.L. Commons, J.D. Sinnott, F.A. Richards & C. Armon (Eds.), Adult Development: (Vol. 1) Comparisons and applications of developmental models (pp. 3–30). Westport, CT: Praeger.
  • Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence: An essay on the development of formal operational structures. (A. Parsons, & S. Seagrim, Trans.). New York: Basic Books (originally published 1955).
  • 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
  • Kallio, E. (1995). Systematic Reasoning: Formal or postformal cognition? Journal of Adult Development, 2, 187–192.
  • Kallio, E., & Helkama, K. (1991). Formal operations and postformal reasoning: A replication. Scandinavian Journal of Psychology. 32(1), 18–21.
  • 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.
  • Lamborn, S., Fischer, K.W., & Pipp, S.L. (1994). Constructive criticism and social lies: A developmental sequence for understanding honesty and kindness in social relationships. Developmental Psychology, 30, 495–508.
  • Lindsay, P.H., & Norman, D.A. (1977). Human information processing: An introduction to psychology, (2nd Edition), New York: Academic Press.
  • Lovell, C.W. (1999). Development and disequilibration: Predicting counselor trainee gain and loss scores on the Supervisee Levels Questionnaire. Journal of Adult Development.
  • Miller, M. & Cook Greuter, S. (Eds.). (1994). Transcendence and mature thought in adulthood. Lanham: MN: Rowman & Littlefield.
  • 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

References

1. "DARE | Home". www.dareassociation.org. Retrieved 2015-09-21.
2. "Core Complexity Assessments". corecomplexity.com. Retrieved 2015-09-21.
3. "Patents by Inventor Michael Lamport Commons - Justia Patents Database". patents.justia.com. Retrieved 2015-09-21.
4. Papers

External links

• Dare Association
• Society for Quantitative Analyses of Behavior (Click here for Wikipedia page)
• Proceedings of the Society for Quantitative Analyses of Behavior published in Behavioral Processes
• Society for Terrorism Research
• Behavior Analyst Online
• Behavior Analyst Online listing on the Association for Behavior Analysis International website
• Behavioral Development Bulletin
• Behavioral Development Special Interest Group
• Society for Research in Adult Development
• European Society for Research in Adult Development