Category Archives: Cognition

Boyd and the Quantitative Revolution

First impressions of the new book, The John Boyd Roundtable: Debating Science, Strategy, and War are popping up all over the blogosphere. On the second day of its general availability, both Mike Tanji add their thoughts. My chapter in the Roundtable, the History of the OODA loop, was based on an earlier post on my blog.

As was this piece, which criticized the usefulness of the OODA loop:

While I’ll always be a fan of the OODA loop, a great conceptual model of human cognition, it does not help me in predicting outcomes. That’s why I generalized Horn et al to create a domain-knowledge/general-ability/motivation/behavior model of performance.

The OODA loop is certainly a “true” model of two-system processing, where a good Orientation can allow you by bypass conscious Decision making. However, it does not have a good way of telling reasonable applications from just-so stories.

Boyd’s OODA loop was a product of the Cognitive Revolution, that burst through psychology discovering internal mental processes that mediated behavior. However, the OODA loop may become a victim of the Quantitative Revolution, that is currently overthrowing much of the academy and the public schools, and is needed for any form of quality control. As OODA is described as a reaction to the Zero-Defect mentality, an early attempt to bring the Quantitative Revolution to military affaris, this would be an ironic fate.

The Unfairness of Working Memory

Several interrelated posts this morning, including “Intelience and the President of the United States, “Capturing my Thoughts: How could Demographic Warfare me used with 5GW?,” “Fixing Milwaukee Notes: Milwaukee School District Governance,” and “U.S. college panel calls for less focus on SATs.”

The topics all revolve around Working Memory, the capacity of the adult to keep 7 (ish) things in mind at the same time. Some people have more, some have less. Working memory is heritable and impacts life outcomes. Working memory is not “fair.” It is predicted by your class origin, your socio-economic status, your race, and so on while its variance is predicted by your sex. (Being male is risky business.)

Many social problems will be eleviated when we can use retroviruses or stem cell therapy to increase the working memory of the underclass. At the same time, any individual with low working memory can more than compensate by building up his long-term memory (his knowledge and experience), his self-efficacy (how he responds to failure), and his behavior.

A Simple Model of Performance

Over the past year I have worked on grounding John Boyd’s OODA loop in modern psychology. Both here, and more often in academic drafts, I have described Orientation as System 1 or intuitive cognition, Decision as System 2 or deliberate cognition, Observation as perception and Action as behavior. I still think that the OODA model, or something very much like it, is probably the best high-level conceptual model of the human mind that we have available.

However, it does not help us understand what causes variance in the population, in most tasks. The reason for this is that it does not directly address the issue of Motivation. To use a computer metaphor if Orientation or System 1 is the hard drive, controller cards, and BIOS, while System 2 is RAM, then Motivation is the hypervisor, or that thing that controls the ability of everything else to engage in behaviors to achieve a goal. Motivation, or the hypervisor, is useful because it regulates System 1/Orientation/Long-Term Memory’s and System 2/Decision/Working Memory’s control of behavior, which in turn affects performance. A model of cognition that does not include performance misses both motivation’s regulation of behavior, and motivation’s direct impact on performance. A model derived from Horn et al. (1993) may give us a way forward:

The cognitive components of this model can each be broken down into sub-components. Long-term memory includes both procedural knowledge (how to ride a bike, how to tie your shoes) and declarative knowledge (how you would answer questions: what is a bike? what are shoes?). Many tasks require procedural and decalarative knowledge to operate together. Working-memory includes visual working memory, which is in tasks such as imagining the rotation of objects in three-dimensional space, and verbal working memory, which is used to remember lists, numbers or names. As far as I can tell, motivation loads from both self-efficacy, the believe that as of now you can perform specific tasks to reach a goal, and attitudes, especially the enjoyment of a thing (as it relates to consumption) and desire to block out the world (as it relates to production).

This leaves the question of where the Central Executive is. John Sweller has argueed that it exists in Long-Term Memory, and indeed that no central executive is conceivable other than one that operates through a darwinistic random process within System 1 / Orientation. Alan Baddeley asserts it is a third component of working memory, alongside visual and verbal working memory, because central executive functions appear to tax working memory capacity. Albert Bandura asserts that humans are “agents,” and their Central Executive agency must rely within their Motivation. I don’t know.

The harmonization of John Boyd’s OODA loop with this model of what actually predicts performance is an important task for the field of psychology, especially if it can account for creativity. I hope somebody does it.

Some Thoughts on Creative Self-Efficacy

In this post, I outline existing research on the role of self efficacy in creativity and propose future research to further explain the power of creative self-efficacy. To do this, I first discuss the concept of self-efficacy and its measurement, as described by Bandura (1977, 1997), as well as creativity, especially as discussed by (Abuhamdeh and Csikszentmihalyi 2002). I then clarify what is meant by creative self-efficacy, relying primarily on three studies: Tierney and Farmer’s (2002), Choi (2004), and Jaussi, Randel, & Dionne (2007). Lastly, I describe a study that might be done to explore it in the context of creativity, creative self-efficacy, and self-efficacy (Bandura, 2006).

In the first study, Tierney and Farmer (2002) examined how self-efficacy predicts creativity. Understanding creativity as the creation of the useful and the novel in a domain, Tierney and Farmer proposed that creativity in a domain should be predicted both by self-efficacy for that domain and self-efficacy for creativity. The authors proposed that job tenure, education level, job self-efficacy, supervisor support, job complexity, and job tenure would all positively predict creative self-efficacy. All of these hypotheses were confirmed in a study of 584 employees at large corporation except for the last: there was a negative correlation between creative self-efficacy and tenure, which is puzzling if creativity is the result of increased domain-specific learning. A follow up study of 191 workers at a research and development unit of a Midwest chemical company (Tierney & Farmer, 2004) found similar results, with creative self efficacy explaining 35% of employee creativity. In the second study, the correlation of creativity to task expertise was -.11, implying greater task expertise weakened creative performance.

The second study to be examined is Choi (2004). Choi proposed that a number of psychological mediators of creativity, including creative self-efficacy, creative intention, and creative personality, and to test this surveyed 430 students at a business school. Choi’s confirmatory analysis showed that creative self-efficacy explained 34% of the variance in creative performance, while creative intention explained 24%, and creative personality did not explain any additional variation, once other variables such as cautious personality were added to a longitudinal structural model.

The role of creative self-efficacy was further described by Jaussi, Randel, & Dionne (2007). Jaussi and colleagues conducted a treatment on 219 professional senior managers Creative self-efficacy was measured using the Tierney and Farmer (2002) scale, and creativity was measured through co-worker appraisal. Other variables, such as creative identity, stress at work, and gender were also gathered. The authors were primarily interested in the role of creative identity in predicting creativity, though also hypothesized that creative identity would interact with creative self-efficacy in predicting creativity. Hierarchical regression analysis continued to support the role of creative self-efficacy. Creative self-efficacy’s statistical significant impact was apparent after two steps of the analysis, while creative self-identity required another step to appear. However, the effect sizes were small, with all independent variables together only accounting for 14% of the variance in creativity.

Unfortunately, these studies suffer from methodological flaws which limit their generalizability. Consider how the studies treat creativity: Choi’s (2004) definition as “creativity as the generation of novel or original ideas that are useful or relevant” (p. 188), Tierney & Farmer’s (2002) definition as “the generation of domain-specific… novel, and useful outcomes” (p. 138), and Jaussi, Randel, & Dionne’s (2007) definition as “the production novel and useful ideas” (p. 247) are all close to each other, and to definitions of creativity used in other articles (Mayer, 1999). However, all of these paper use reports by instructors (Choi, 2004), a work supervisor (Tierner & Farmer, 2002, 2004), or co-workers (Jaussi, Randel, & Dionne, 2007). Thus, creativity is operationalized as the positive impression one makes on co-workers, rather than paying attention to a field, “all the individuals who act as gatekeepers to a domain,” that are the arbitrators of creativity (Abuhamdeh and Csikszentmihalyi, 2002, p. 37). Consider Abuhamdeh and Csikszentmihalyi’s example of the field in the domain of art: “the field consists of the art critics and art historians, the art dealers and art collectors, and the artists themselves.” Whether or not the field might also include the supervisors of the creative individual, it is clear a field would not be limited to them.

Methodological flaws also weaken the research on creative self-efficacy. Creative self-efficacy is an individual’s belief in his ability to perform a task in order to achieve a goal (Bandura, 1997, 2006). Efficacy varies in terms of the magnitude, generality, and strength of the expectation (Bandura 2006). Self-efficacy can come from an individual’s own accomplishments, observing a model, persuasion, or emotional arousal. The conceptual definitions are less clear, referring to “employees’ beliefs in their ability to be creative in their work” (Tierner & Farmer, 2002, p. 1141), “perceived behavioral control… in the context of creative performance. In other words… a person’s belief that he or she can successfully perform creative behavior in a particular setting” (Choi, 2004, p. 190), or “feelings about whether he or she is creative (feels confident that he or she can be creative in a given task)… the capacity to do a job creatively” (Jaussi, Randel, & Dionne, 2007, p. 249). Examples of the questions used to capture creative self-efficacy were “I have confidence in my ability to solve problems creatively” (Tierney & Farmer, 2002, p. 1141), a formulation also used in Tierney and Farmer (2004) and Jaussi, Randel, & Dionne (2007), as well as “I feel confident that I can introduce new ideas to the class in a convincing manner” and “I feel nervous when I present different views to classmates” (Choi, 2004, p. 139).

Self-efficacy should be defined and operationalized in a way keeping with the established method (Bandura, 2006). If the same construct if measured in different ways in different papers, the research on the topic will become confused. Indeed, as Bandura writes, “The construction of sound efficacy scales relies on a good conceptual analysis of the relevant domain of functioning” (p. 310). Without a good understanding of what it means to be creative, neither creativity nor creative self-efficacy can be measured!

Bandura begins his guide to operationalizaing self-efficacy with the following definition, originally from Bandura (1977): “Perceived self-efficacy is concerned with people’s beliefs in their capabilities to produce given attainments” (p. 307). While no standard self-efficacy scale exists, a standard way of generating them does (Bandura, 2006). Self-efficacy scales should be domain-specific, target factors that help predict proper functioning in the domain, should vary in the difficulty of the rated task so that individual differences in self-efficacy can be examined, and where motivation is a factor should measure self-efficacy to sustain the action. The definition and operationalization of self-efficacy is straight-forward (Bandura, 2006). To measure self-efficacy, questions “should be phrased in terms of can do rather than will do” (p. 308), Self-efficacy questions should ask about the confidence of the individual to perform a specific task as of now. Scales should include 11 steps, beginning at 0 and ending at 100. A practice item should be included to help people understand the scale. After scale construction, the scale should be pre-tested and the Cronbach’s alpha score should be used to determine which items to keep.

Abuhamdeh and Csikszentmihalyi (2002) present a guide for measuring creativity when they describe it as an individual who operates in a domain to gain recognition by the field. Thus, creativity can be measured by the recognition of the field. Interesting, the ELO system that is used by researchers to measure expertise (Charness, Krampe, & Mayr, 1996) is thus a valid measure of creativity. In ELO, every game that a chess player plays against another chess player is recorded. The histories of the players are used to judge the relative difficulty of the game for each. Thus, if a player with a better record defeats a player with a worse record little will change: that is expected. However, if a player with a substantially worse record upsets a player with a better record, it will lead to the previously worse player rising and the previously better player falling. In ELO, therefore, recognition by a domain is objective measured through a player’s recorded interaction with the field.

I propose to expand my prior research in the light of creative self-efficacy. Previously, I conducted a correlational pilot study on 77 blog readers and writers. I was interested in exploring the role of attitudes on creativity, so I measured cognitive and affective attitudes from a standard scale (Crites, et al., 1994), some questions relating to behaviors typical of creative people, and so on. I found that I could explain 20% of variation in creativity using three of the behavioral questions among blog creators, and I could explain 18% of the variation in consumption of blogs among readers by their affective attitude toward blogs. At the time, I was puzzled that affective attitudes. However, I decided that they might do to the concept of flow (Abuhamdeh and Csikszentmihalyi 2002), in which people participate in creative actions in order to avoid shut out the outside world. That is, it might be that blogging is not affectively agreeable, but that not-blogging would be affectively disagreeable. My readings on self-efficacy and creative self-efficacy make me believe that I might be able to explain a large amount of variance in creativity through a combination of withdrawal affect, practice, self-efficacy, and creative self-efficacy. Creativity combines both domain-specific components, such as purposeful practice in a domain, and domain-general components, including many psychological processes (Plucker & Beghetto, 1996). My study would help explain creative performance in terms of both domain-general creative self-efficacy and domain-specific self-efficacy.


Abuhamdeh, S. & Csikszentmihalyi,M. (2002). The artistic personality: a systems perspective, in R.J. Sternberg, E.L. Grigorenco, & J.L. Singer (eds) Creativity: from potential to realization. Washington, D.C. American Psychological Association.

Amabile, T.M. (1988). A model of creativity and innovation in organizations. In B.M. Staw & L.L. Cummings (Eds.), Research in organizational behavior, 10, 123-167. Greenwich, CT: JAI Press.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.). Self-efficacy beliefs of adolescents, 5, pp. 307-337. Greenwich, CT: Information Age Publishing.

Charness, N., Krampe, R., & Mayr, U. (1996). The role of practice and coaching in entrepreneurial skill domains: An international comparison of life-span chess skill acquisition. In The Road to Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games, (K. A. Ericsson, Ed., pp 55-80), Mahwah, NJ: Erlbaum.

Choi, J.N. (2004). Individual and contextual predictors of creative performance: The mediating role of psychological processes. Creativity Research Journal, 16(2 & 3), 187-199.

Crites, S.L., Jr., Fabrigar, L.R., & Petty, R.E. (1994). Measuring the affective and cognitive properties of attitudes: Conceptual and methodological issues. Personality & Social Psychology Bulletin, 20, 619-634.

Jaussi, K.S., Randel, A.E., & Dionne, S.D. (2007). I am, I think I can, I do: The role of personal identity, self-efficacy, and cross-application of experiences in creativity at work. Creativity Research Journal, 19(2 & 3, 247-258).

Mayer, R.E. (1999). Fifty yeras of creativity research. In R.J. Sternberg (Ed.), Handbook of creativity (pp. 449-460). New York: Cambridge University Press.

Plucker, J.A. & Beghetto, R.A. (1996). Why creativity is domain general, why it looks domain specific, and why the distinction does not matter. In The Road to Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games, (K. A. Ericsson, Ed., pp. 153-167), Mahwah, NJ: Erlbaum.

Tierney, P.A. & Farmer, S.M. (2002). Creative self-efficacy: its potential antecedents and relationship to creative performance. Academy of Management Journal, 45(6), 1137-1148.

Tierney, P.A. & Farmer, S.M. (2004). The Pygmalion process and employee creativity. Journal of Management, 30(3), 413-432.

Some thoughts on Albert Bandura’s Research Agenda

The work of Albert Bandura is legendary. The breadth of his theoretical work challenges description. Bandura has described subjects as theoretical as comparing the effects of goals and negative self-efficacy (Bandura & Locke, 2003), to as emotionally volatile as the roots of terrorism (Bandura, 2004). Bandura’s social cognitive theory has been described as a way of understanding mass communication (Bandura, 2001a) and human agency (Bandura, 2006). Bandura’s influence is such that he wrote the review of his own theory for the first the Annual Review of Psychology (Bandura, 2001b).

Bandura’s empirical work is also very large. In his empirical work, Bandura not only outlines theories of the world but executes research practices designed to help understand the world. Thus, in his research papers, Bandura refines both his theoretical reach and his methodological approach. In order to briefly describe these processes, I will briefly online three of Bandura’s papers. First, in Bandura, Ross, & Ross (1961), Bandura describes personality in the context of personality. Second, in gender differences article, Bandura and a co-author examining the development of self-regulatory mechanisms (Bussey & Bandura, 1984). Finally, Bandura examined moral disengagement and the state-sponsored killing of prison inmates (Osofsky, Bandura, & Zimbardo, 2005). For each of these studies, a general background is first present. The theoretical discussion and conclusion of the article is then summarized. Finally, the experimental design is discussed. Following this, the papers are compared and contrasted, to emphasize how Bandura’s theories and methods have evolved over time.

The first article to be examined, “Transmission of aggression through imitation of aggressive models,” bridges the gap between behavioralism and cognitivism. In it, Skinner and his co-authors consciously position their research as presenting original problems to the idea that there are no complex mental structure outside of stimulus-response chains. However, in their discussion of results they stick of behavioralist phrases and ideas. The study itself is part of a chain of research, describing itself as building on Bandura & Huston (1961) and quickly followed by Bandura (1965).

Bandura, Ross, & Ross (1961) argued that imitation is a generalizable form of learning, not limited to the repetition of specific actions. This learning was defined as “sufficiently novel patterns of responses which are unlikely to occur independently of the observation of the behavior of a model and if a subject reproduces these behaviors in a substantially identical form.” The research went beyond publications beyond research which argued that children’s actions were merely imitations of what they had observed (Maccoby, 1959) in order to demonstrate that children would engage in behaviors similar to, but different from, those they had seen modeled. The article challenged the ability of behaviorialism to explain this, nothing that responses to stimulus could only (in the behavioralist view of the world) could only be encouraged after similar responses were already provoked. The modeling research that interested Bandura showed that a learner could observe an action and produce a different action, behaving in a manner that required cognitive manipulation of information.

Methodologically, the study would be well received today. 72 students were examined in a 2x2x2 design with 1 control group. Likewise, half of the subjects were males and alf were females, all less than 70 months old. Half the participants saw a model adult of the same sex, while half saw a model adult of the opposite. Additionally, half saw the model act in a hostile manner towards a doll, while half did not. Along with this, Bandura and his co-authors reported results in way that are typical now and that were typical of the older behaviorists. Tests of t values, p values, and tables of results are still regular features of research journals. However, the authors talk about behavioral measures such as the probability of behavior, rather than later formulations such as the average incidence of some behavior.

The second article, published twenty-three years later, is “Influence of gender constancy and social power on sex-linked modeling.” The theoretical construction of the article is new, reflecting the cognitive revolution. The theory is expanded to develop the role of development, a phenomenon missing from Bandura, Ross, & Ross (1961). However, in accordance with Leahy’s (2004) claim of continuity with the older approach, the methods would be recognizable to a behaviorist. The article itself began a collaboration with Bussey, that would lead to three more publications on gender development (Bussey & Bandura 1992; 1999; 2004).

Theoretically, Bussey & Bandura (1984) builds on Bandura, Ross, & Ross (1961). The new paper extends the analysis of the older research into the development of sex roles in children. This is done in a way that respects the lines of research of both Bandura and his colleague, as they cite earlier work by both (including Bandura, 1969 and Bussey, 1979). The substantive focus of Bussey & Bandura (1984) thus begins to make long stretches of time a meaningful part of the experiment, as younger children are compared to older ones. Finally while some phrases are holdovers from Bandura, Ross, & Ross (such as modeling, patterns of responses or behavior, and so on), others (such as information, cognitive, and capacity) reflect the new reality of psychology after the cognitive revolution

The method of is familiar. Two experiments are conducted in Bussey & Bandura (1984), the first being a two-way analysis and the other being a 2x4x2 analysis of variance design. The second experiment in particular recapitulates Bandura, Ross, & Ross (1961): participants are split into either male or female groups, with either male or female models, in one of four treatment conditions. To make sure the reader does not miss this, Bandura & Ross tie both the theory an the procedure to Bandura’s earlier work: “Social power can exert a strong impact on modeling (Bandura, Ross, & Ross, 1963)” (p. 1297). The behavioralist concern for probability of behavior is gone, and the analysis of variance (ANOVA) test appears with its standard P and p values as results.

The third article, published twenty-one more years later, is titled “The role of moral disengagement in the execution process.” The theory in the paper would be incomprehensible to a behaviorist, rather focusing on high-level mental structures over long periods of time with no special reinforcement. The method of this paper is no longer experimental, but rather a correlational study. Osofsky, Bandura, & Zimbardo (2005) follows Bandura’s continuing work on how socialization impacts moral standards (Bandura, 1999, 2002), and is followed by work on moral disengagement in support of the military (McAlister, Bandura, & Owen, 20060.

The theory of the article continues the generalizing trend that began with the transition from Bandura, Ross, & Ross (1961) to Bussey & Bandura (1984). In the first article, the behavior of small children was study. In the second, the socialization of children as they develop into sex roles was examined. In this final piece, Bandura and his co-authors examine the socialization of adults as they do their jobs. The article also continues the trend toward increasing concern over moral behavior. The first publication discussed its findings in the context of imitation, largely ignoring the moral implications of teaching violence. The second more directly addressed concerns of social power and sex roles, and cites Kohlberg (1996). However, the third openly deals with the disquieting impact on morality of being involved in the imprisonment and death of follow human beings.

The methodology is a correlational study that does not create any new conditions, but observes how factors appear to affect participants. Nearly 250 guards three maximum-security prisons in three different states were studied. Some of the guards had served in the execution process, while others had not. Standard survey tools such as likert scales were used by the guards to rate responses to questions, such as “Murderers who receive the death penaly have forfeited the right to be considered full human beings” (p. 380). From the guards’ responses, Bandura and his colleagues were able to conclude that “Executioners made heaviest use of dehumanization, security and economic justifications and disavowal of personal responsibility” (p. 382). For those who participated in executions, moral disengagement increased with the number of executions with which a guard was involved.

The length of Bandura’s career is striking. At first glance, his experiments (ranging from imitation, to sex-role development, to capital punishment) have little to do with each other. Some have even talked about Bandura having a second and third professional life. With respect, I disagree. Bandura has consistently addressed issues at the intersection of modeling, behavior, and social rules. Bussey & Bandura’s (1984) study is a continuation of Bandura, Ross, & Ross’s (1961) examination on imitative child violence, just as Osofsky, Banduyra, & Zimbardo (2005)’s adult participatory violence naturally extends the study of childhood imitative violence. Bandura’s research style has expanded a bit more in this time, moving beyond the experimental studies of the laboratory to correlational studies of the outside world. This perhaps is the more profound shift, sacrifices the rigor of being able to manipulate variables at well for the external validity of conducting research that matters


Bandura, A., & Huston, A. C. (1961). Identification as a process of incidental learning. Journal of Abnormal and Social Psychology, 63, 311-318.

Bandura, A. (1965). Influence of models’ reinforcement contingencies on the acquisition of imitative responses. Journal of Personality and Social Psychology, 1, 589-595.

Bandura, A. (1969). Social-learning theory of identificatory processes. In D. A. Goslin (Ed.), Handbook of socialization theory and research (pp. 213-262). Chicago: Rand McNally.

Bandura, A. (1999). Moral disengagement. In I. W. Charny (Ed.), Encyclopedia of genocide (pp. 415-418). Santa Barbara, CA: ABC-Clio.

Bandura, A. (2001a). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1-26. In html form from Annual Review Psychology (must be accessed from edu domain).

Bandura, A. (2001b). Social cognitive theory of mass communication. Media Psychology, 3, 265-298.

Bandura, A. (2002). Selective moral disengagement in the exercise of moral agency. Journal of Moral Education, 31, 101-119.

Bandura, A. (2004). Role of selective moral disengagement in terrorism and counterterrorism. In F. M. Mogahaddam & A. J. Marsella (Eds.), Understanding terrorism: Psychological roots, consequences and interventions (pp. 121-150). Washington, DC: American Psychological Association Press.

Bandura, A., & Locke, E. (2003). Negative self-efficacy and goal effects revisited. Journal of Applied Psychology, 88, 87-99.

Bandura, A., Ross, D., & Ross, S. A. (1961). Transmission of aggression through imitation of aggressive models. Journal of Abnormal and Social Psychology, 63, 575-582. Retrieved July 14, 2008, from

Bussey, K. (1979). Same-sex imitation: Fact of fiction? Unpublished doctoral dissertation, University of Queensland, Brisbane, Australia.

Bussey, K., & Bandura, A. (1984). Influence of gender constancy and social power on sex-linked modeling. Journal of Personality and Social Psychology, 47, 1292-1302.

Bussey, K., & Bandura, A. (1992). Self-regulatory mechanisms governing gender development. Child Development, 63, 1236-1250.

Bussey, K., & Bandura, A. (1999). Social cognitive theory of gender development and differentiation, Psychological Review, 106, 676-713.

Bussey, K., & Bandura, A. (2004). Social cognitive theory of gender development and functioning. In A.H. Eagly, A. Beall, & R. Sternberg (Eds.). The psychology of gender (2nd ed., pp.92-119) New York: Guilford.

Kohlberg, L. (1966). A cognitive-developmental analysis of children’s sex-role concepts and attitudes. In E.E. Maccoby (Ed.), The development of sex differences (pp. 82-173). Stanford, CA: Stanford University Press.

Leahey, T.H. (2004). A history of psychology: Main currents in psychological thought (6 ed.). Upper Saddle River, New Jersey: Pearson Prentice Hall.

Maccoby, E.E. (1959). Role-taking in childhood and its consequences for social learning. Child Development, 30, 239-252.

McAlister, A. J., Bandura, A., & Owen, S. V. (2006). Mechanisms of moral disengagement in support of military force: The impact of September 11. Journal of Social and Clinical Psychology, 25(2), 141-165.

Osofsky, M. J., Bandura, A., & Zimbardo, P. G. (2005). The role of moral disengagement in the execution process. Law and Human Behavior, 29, 371-393.

The OODA Loop and Creativity, Try 2

Afer heavily revising my original work tying together the OODA loop to creativity, it has become clear that the project will involve much more than a few references in another paper. So I’m removing it from my current draft, but (for posterity) here were my final thoughts on Creativity in the context of OODA, at least before my work on the taxonomies of creativity changed my perspective.

The OODA loop was originally described to assist in understanding the nature of creativity (Coram, 2002). Creative thoughts, according to Boyd (1992), “permit us to rematch our mental/physical orientation [with the outside world] and grow” in the world (24). Before individuals can be creative, they must be experts. This requires reorientation. However, reorientation that leads to creativity is difficult because expertise requires considerable practice outside of an educational setting. Fortunately, educational psychology’s initial work on dual processing presents a way forward.
Modern research views creativity as a subset of expertise. This formulation is expanded on at length in Herbert Simon’s exploration of creativity in the arts and sciences . Expertise is defined by strong problem solving ability in a particular field, meaning quick scanning of a problem space for any of a large number of remembered patterns (Simon, 2001). Expertise tends to require more than ten years to develop, and a narrowness of focus that can involve both personal and intellectual sacrifices including slowed or reduced growth in other fields that are not focused on or practiced (Simonton, 2003).
The importance of reorientation in creativity is stressed by a recent overview of the growing literature on cognitive load. For instance, van Merrienboer & Sweller (2005) write that “expertise comes from knowledge stored in [long term memory] schemata, not from an ability to engage in reasoning with many elements that have not been organized in long-term memory. Human working memory simply is not able to process many elements” (149-150). Similarly, Sweller (2004) describes how random variations in ideas are necessary for creative products to be new, and not mere memories. The product of such thinking is “change via novelty” (20). Likewise, cognitive load describes originality is seen as random variations in the expression of long-term memory, and original ideas are kept or lost depending on their fitness in the landscape of thought (Sweller, 2004; van Merrienboer & Sweller, 2005). Yet as randomness in thought is assumed to be constant among all individuals, what makes those new ideas more likely to be useful is whether they are grounded in an orientation rich in relevant long term memory.
The scale of the difficult in encouraging creativity is gleaned from a recent discussion on minimal guidance instruction (Kirschner, Sweller, & Clark, 2006). Directly addressing the question of discovery-based styles of instruction, Kirschner and colleagues distinguish expert from novice operating styles. Summarizing research on the expert/novice divide, the authors conclude that attempting to become an expert by replicating the style of experts is not a productive strategy. Yet in an increasingly complex world where many learners will go on to perform tasks that do not currently exist, some form of discovery-based learning will be necessary for the development of expertise in these fields of the future. The task of the educator is not enviable.
Fortunately, educational psychology’s early approaches to dual processing show the way forward. Motivation and good attitude, focused on by Sinatra (2005) and Gregorie (2005), provide an approach that will encourage learners to continue their own reorientation long after they leave the classroom. To put it simply, motivation leads to more practice, more practice loads to more expertise, and more expertise leads to more creativity. In creativity as with other fields of educational psychology, dual processing and OODA do not overturn what we already know. Rather, the Observe-Orient-Decide-Act dual-processing cycle enrich what educational psychology has already discovered about providing the best education possible to our learners.

Boyd, J.R. (1992). The conceptual spiral. Retrieved October 1, 2007, from Belisarius. Web site:
Coram, R. (2002). Boyd: The fighter pilot who changed the art of war. New York: Back Bay Books.
Gregorie, M. (2003). Is it a challenge or a threat? A dual-process model of teachers’ cognition and appraisal process during conceptual change. Educational Psychology Review, 15(2), 147-179.
Kirschner, P.A., Sweller, J., Clark, R.E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75-86.
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Taxonomies of Creativity

After Mark posted his thoughts, I recently completed two books on creativity, talent, and expertise: The Road to Excellence: The Acquistion of Expert Performance in the Arts and Sciences, edited by K. Anders Ericsson, and Creativity: From Potential to Realization, edited by Robert J. Sternberg Elana L. Grigorenko, and Jerome L. Singer. From the chapters in the book, it seems reasonable to divide the study of “creativity” to the study of talent, creativity, expertise, and invention. That is

  • Talent is the potential for Creativity, Expertise, and Invention
  • Creativity = Talent + training
  • Expertise = Creativity+ overtraining
  • Invention = Creativity + profitmotive


What is creativity?

Identifying expertise as a subset of creativity, rather than the reverse, is not something I’ve seen before. But I think it’s valid. Both rely on a high degree of domain-specific knowledge. The difference appears to be that mere Experts are foreclosed to creativity by over rigid mental structures, ignoring conflicting observations, lack of psychopathology, and other things that can be avoided by the looser (but still knowledge-rich) thinking of the creativity.

Beyond this, my other notes are more prosaic and deal with the creativity research itself. Such research is correlational, biographical clinical/on-site, laboratory, or computational. It studies domains such as academics, arts, sports, or professions. It follows the research agenda of cognitivism, social cognitivism, developmentalism, complex dynamic systems. Most researchers view creativity as domain-specific, though some argue it is domain-general.

Intelligence Did Not Matter Much

A recent post by Gene Expression has mind-opening implications, if you read closely:

Gene Expression: Selection, drift, disease and complexity, all rolled into one….
I would have to say that the distributions here are not totally surprising based on other things we know, this is an empirical confirmation to a great extent of rules-of-thumb which many hold because of the theoretical and experimental insights of a century. For example, it is well known that complex-traits which exhibit a continuous distribution and are highly heritable tend to have weak fitness implications. Conversely, Mendelian diseases are usually classified as diseases for a reason! Additionally, the authors find that diseases which are expressed dominantly, that is, one copy results in the disease, have lower values of Dn/Ds, than those which express recessively so that two copies are necessary. This is what we would expect from the fact that when low frequency alleles which only express as homozygotes are segregating within the population randomly most copies are carried within heterozygotes who are not subject to selection; in other words, there is little purification of these genes unless their frequencies are very high as per Hardy-Weinberg. To make the difference between complex-disease loci and Mendelian ones more concrete, think of it in a non-disease context. Height is a quantitative trait, while eye color seems quasi-Mendelian. HMGA2 is a height locus which explains 0.3% of the variation within a population for the trait in question, while the region around OCA2 seems to account for 75% of the variation in blue-brown eye color. In addition the region around OCA2 may have been subject to selection and this selection may explain the difference in eye color across populations. It seems unlikely that we’ll find strong signatures around height loci that explain the variation of height across populations.

General intelligence is a complex trait that has a continuous distribution and is highly heritable.  So, for that matter, do political orientation and personality.  Thus, it is likely that general intelligence, personality, and political orientation did not do much for your ancestors.

Your ancestors were winners, because unlike the vast majority of humans who ever lived, they spawned offpsring who are still alive today.  But the secret of their succeess was probably something other than how quick they were.

Doing Artsy Stuff Isn’t “Creativity”

I’ve talked about creativity before, in the context of the OODA loop, purposeful practice (a form of metacognition that is the opposite of “flow”), and mental illness. Another part of creativity is being recognized as useful by the field of a domain. If you invent a new type of hot water heater, that is being creative. If you’re chess technique allows you to rise in international chess competitions, that’s creativity. If you cure cancer but don’t tell anyone, that’s just wasting your time.

So this article is somewhat off-base:

Why Do Men Share Their Creative Work Online More Than Women? | Scientific Blogging
A recent Northwestern University study has a surprising results – substantially more men are likely to share their creative work online than women even though both genders engage in creative activities at essentially equal rates.

As it confuses artsy-stuff (making music, taking photographs, etc.) with creativity. Certainly artsy-stuff can be a form of practice, therapy, or good old recreation. Perhaps it can lead to creativity one day when you share it with others. But if you sit on it, you’re enjoying yourself, not being creative.