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Teaching Forum - De Bono’s Cognitive Modes in the Classroom:

A Journal of the the Scholarship of Teaching and Learning: Sunday October 26, 2008 Edition

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De Bono’s Cognitive Modes in the Classroom:
Research and Speculations

By Marlow Embree


The utility of Edward De Bono's model of cognitive modes was examined in a classroom situation.  Students' self-perceptions of their relative cognitive strengths were assessed, and relationships between cognitive modes and personality variables were examined.  Implications for the classroom experience, as well as a range of practical applications to academic and personal problem-solving, are explored.



Cognitive styles have long played a significant role within educational and vocational psychology.  Defining the construct as "preferred ways of using the abilities one has" or differing forms of "mental self-government", Sternberg and Grigorenko (1997), in an exhaustive review of the literature, cite dozens of competing (or in some cases overlapping) models of cognitive style differences, including abstract versus concrete learning styles (Harvey, Hunt, and Schroeder, 1961), reflectivity versus impulsivity (Glow, Lange, Glow, and Barnett, 1983), field dependence versus independence (Witkin et al., 1962), depth versus breadth orientations (Schmeck, 1983), and many more.  They note that cognitive styles serve as a means for bridging the concepts of cognition and personality, and that one of the primary motivators for the examination of style differences has been the hope that this concept could be used to help explain variations in student academic performance and to develop strategies for helping at-risk students to excel in the classroom.

Edward de Bono (1970, 1991, 1999), well known and respected as a consulting psychologist within the business world though little known in formal academic circles, is most famous for his model of six different cognitive modes.   De Bono and his certified trainers have provided training in his system, which has come to be known as the Six Hat Method due to the use of the analogy of "wearing different thinking hats", to thousands of business clients and also to numerous grade-schoolers as part of a program to enhance thinking effectiveness among young persons.  In a nutshell, the six De Bono cognitive modes are as follows:

White Hat (objective-factual):  The emphasis in White Hat thinking is on the dispassionate, careful, accurate observation and cataloging of facts and details.  No attempt is made to evaluate, assess, or judge the relative importance or relevance of these facts. 

Red Hat (subjective-emotional):  The focus of Red Hat thinking is emotional awareness, whether the emotions of others (as in empathy or active listening) or one's own emotions (as in introspection and reflection) are in view.  Self-expression of emotions and related subjective states also comes under the Red Hat.

Green Hat (lateral-creative):  The thrust of Green Hat thinking is brainstorming, creativity, idea generation, and "thinking outside the box".   Humor, free association (when the emphasis is on generating ideas), the experience of insight, and related activities are also Green Hat in nature.

Yellow Hat (logical-affirming):  The Yellow Hat involves the use of logical-rational analysis to strengthen an existing idea:  to identify assets and advantages, to find what's working, to combine two flawed ideas to yield one stronger idea.  As such, the Yellow Hat has overtones of optimism and resilience, but is always analytical in nature (as opposed to mere positive emotionality, which is part of the Red Hat).

Black Hat (logical-critical):  The Black Hat also uses logic and rational analysis, but for the purpose of criticism:  to find problems and flaws, to identify lapses in reasoning, to judge or assess the quality of an entity by applying rules.  The Black Hat has a positive social purpose (to spot problems so they can be resolved), but can sometimes strike others as "negative" even though unsupported negative emotionality is Red Hat, not Black Hat.

Blue Hat (metacognitive-strategic):   The Blue Hat is metacognitive (Nelson, 1992) in the sense of "thinking about thinking":  deciding how to approach a task, inventorying and allocating resources, setting goals, establishing and maintaining an agenda, strategizing, and related activities.  Activities oriented around "sharpening the saw" (Covey, 2004) are also within the spirit of the Blue Hat, since they address problems at a meta-level rather than directly and frontally.

De Bono claims that these six different cognitive modes are rooted in six different kinds of brain processes or brain regions.  He does not spell out in detail how this might be so, though one might speculate that there are links to hemispheric lateralization (Schlesinger, 1980):  White and Black Hat thinking have features commonly associated with left-hemisphere processing, while Green and perhaps Red Hat operate in a fashion more evocative of right-hemisphere processing.   De Bono also argues that these cognitive modes are learnable skill sets, which can be enhanced through appropriate targeted practice.  He implies, however, that most people are more comfortable with some modes than others, and that unilateral reliance on a single favored mode is common (the training metaphor is "glueing a hat to your head").  An appropriate balance among the use of all six modes, accompanied by an awareness of which modes fit which kinds of problem-solving situations, is seen as optimal.

Direct references to De Bono's system in the professional literature are scanty (for an exception, see Moseley et al., 2005).  However, much of the existing literature on cognitive modes and cognitive styles can readily be recast into the Six Hat language with minimal difficulty.  For instance, cognitive-experiential self-theory or CEST (Epstein, 1994;  Epstein et al., 1996;  Pacini and Epstein, 1999) posits two contrasting modes of thinking and information processing:  the rational (conscious, analytical, verbal, and generally affectless) and the experiential (unconscious, intuitive, nonverbal, and strongly affective).  In De Bono's terms, the first of these can be construed as fundamentally Black Hat thinking, while the second partakes of both Green and Red Hat elements.  Viswanathan (1993) examines a numerical, detail-driven  mode of thought that is strongly evocative of De Bono's White Hat.  Studies of creativity and divergent thinking, most notably the seminal work of Sternberg (2006) and of Silvia (2008), are strongly reminiscent of De Bono's Green Hat, a conclusion strengthened by the notion that there is a continuum of creativity that includes the intellectual activities of ordinary persons in relatively mundane settings (Beghetto and Kaufman, 2007).  Overtones of the Yellow Hat concept can be found in the positive psychology literature (Foster and Lloyd, 2007), while the metacognition literature suggests the skill sets associated by De Bono with the Blue Hat (Alter et al., 2007).  The counseling literature frequently addresses the question of cognitive-affective balance, which can be interpreted in terms of a contrast between Black and Red Hat thinking (Santostefano and Rieder, 1984). 

Perhaps the closest conceptual match to De Bono's system lies in Jung's (1923) well-known theory of mental functions and their expression within distinct psychological types.  In the Jungian model, there are two distinct types of mental functions or ways of using one's mind:  gathering or generating information about the world without any attempt to classify or categorize that information (Perceiving) and evaluating or prioritizing that information in order to make decisions on the basis of those assessments (Judging).  Each of these two activities can be further subdivided.   There are two fundamentally opposed ways of perceiving:  by means of a sensory focus on the here-and-now details of the concrete situation (Sensing) or by means of a big-picture extrapolation into future possibilities and abstract meanings inherent in the situation (iNtuition).  Similarly, there are two opposed means of judging:  by means of an objective, impersonal, logical analysis that focuses on consequences (Thinking) or by means of a subjective, personal, emotive assessment that focuses on personal or collective values (Feeling).  Jung believed that all humans, early in life, develop or choose a preference for one of these two ways of perceiving and one of these two ways of judging, leading to a psychological quaternity (Sensing-Thinking, Sensing-Feeling, iNtuitive-Thinking, and iNtuitive-Feeling types).  Each of these groups can be further subdivided, leading to the sixteen psychological types measured by the Myers-Briggs Type Indicator (Myers et al., 1998) and similar instruments.

There is, of course, no direct, one-to-one correspondence between the Jungian and the De Bono conceptual frameworks.  The former is fundamentally, despite its foundational discussion of differing mental activities or functions, a theory of personality differences, while the latter focuses almost exclusively on intellectual or cognitive differences.  Yet, as Sternberg and Grigorenko (1997) note, the two are credibly linked;  the same neurological differences may, in part, underlie both.   There do appear to be strong parallels between Jung's Sensing function and De Bono's White Hat mode;  between Jung's iNtuitive function and De Bono's Green Hat;  between Thinking and the Black Hat;  and between Feeling and the Red Hat.  The remaining two cognitive modes in the De Bono system remain without evident parallelism in the Jungian schema;  however, there is enough linkage between the two perspectives to suggest the utility of directly assessing both cognitive style preferences and personality differences among students, as will be done in this study.

As a side note, to an increasing extent within academic psychology, nonclinical personality diversity is conceptualized in terms of a somewhat different model, the so-called Five-Factor Model (FFM), which is oriented around the so-called "Big Five" personality traits of Extraversion, Openness, Agreeableness, Conscientiousness, and Negative Emotionality.  These five personality differences are increasingly seen by scholars of personality as capturing the lion's share of normal personality variability (McCrae and Costa, 1989), and respectively measure an individual's tendency toward sociability versus solitude, emphasis on ideas versus facts, focus on personal-emotional versus impersonal-analytical considerations, desire for versus avoidance of structure and planfulness, and emotional lability versus stability.  In contrast to the Jungian model, which entails assumptions that some empirically inclined psychologists find objectionable or questionable, the Big Five model is preferred within academic psychology circles as a "source metaphor" through which other models of personality diversity can be viewed or subsumed.  It is a generally simple matter to translate the language of the Big Five into Jungian terminology (McCrae and Costa, 1989):  both models include a model of Extraversion;  Openness corresponds to iNtuition; Agreeableness is analogous to Feeling; and Conscientiousness is isomorphic with Judging.  The fifth dimension of the Big Five model (Negative Emotionality) has no direct parallel in the Jungian framework.  While there are some important philosophic differences between the two models (stemming from the conceptual distinctions between trait and type theories generally), for the purposes of a study of this kind a translation between the two schemata seems appropriate.

In sum, the Six Hat Method retains considerable heuristic value for educators.   In the author's experience, students find the model compelling, interesting, and easy to understand.  It provides a common language by which different approaches to life situations can be discussed and analyzed.  The focus of the research presented in this paper is the examination of student self-ratings of relative effectiveness with, and enjoyment of, each of De Bono's cognitive modes.  Possible links between cognitive styles and personality variables are examined to see whether De Bono's system can be matched to other common approaches to understanding student diversity.

Method and Results

Students in introductory courses taught by the author during the academic years 2003-2008 inclusive (a total of 191 students overall) were given a fifty-minute lecture on the Six Hat model.  At the end of this lecture, students were asked to rate their degree of perceived comfort and facility with each of the six cognitive modes using a rank-ordering rubric (ranging from 5 = best to 0 = worst).  Students taking this course during recent semesters (a total of 114 students) were also administered a brief questionnaire measuring the Big Five personality traits of Extraversion, Openness, Agreeableness, Conscientiousness, and Negative Emotionality.  The resulting raw scores were adjusted to a 0-100 score range. 

Table 1 presents descriptive statistics for the six De Bono cognitive modes.  As a group, students rated themselves highest in creative thinking (Green Hat, mean of 3.07) and lowest in critical-analytical thinking (Black Hat, mean of 1.77).  By a t-test, mean Green Hat ratings were significantly greater than expected by chance (t = +4.197, p = .001), while mean Black Hat ratings were significantly lower than expected (t = -6.619, p = .001).  Students appear to perceive themselves as creative, but not particularly analytical.   Classifying students into six groups on the basis of their highest self-rated cognitive mode, 35.1% of students were primarily Green Hat thinkers (the most frequent choice), while only 5.8% rated the Black Hat as their strongest mode of thought (the least frequent).  In terms of the lowest self-rated mode, 26.2% of students (a plurality) rated Black Hat as their weakest mode, while the least frequent choice for weakest mode was the ability to strengthen weak ideas or to see advantages inherent in action options, as represented by the Yellow Hat (8.9%).


Table 1

Cognitive modes (Six Hat self-ratings):  Descriptive statistics

Self-ratings were on a 0-5 scale, from 0 = lowest rating to 5 = highest rating (forced-choice rank ordering).  N = 191 subjects.

Cognitive mode                                           Mean   Standard deviation        Difference from 2.50 (t test)


White Hat (objective-factual)                2.31                 1.64                 -1.566              n.s.

Red Hat (subjective-emotional) 2.66                 1.71                 +1.291            


Green Hat (lateral-creative)                   3.07                 1.89                 +4.197             .001

Yellow Hat (logical-affirming)    2.68                 1.53                 +1.634            


Black Hat (logical-critical)                     1.77                 1.53                 -6.619              .001

Blue Hat (metacognitive-strategic)         2.51                 1.67                 +0.022             n.s.


Intercorrelations between the cognitive mode ratings are presented in Table 2.  It should be borne in mind that, because the original student ratings were ipsative in nature (rank orderings), correlations as a whole are expected to be negative (rating one mode highest necessarily requires that other modes are rated lower).  However, the pattern of correlations remains of interest and is suggestive of general trends.  The two most theoretically compelling contrasts are those between Green and White Hat thinking (r = -.329, p < .01) and between Red and Black Hat thinking (r = -.305, p < .01).  Students who perceive themselves as creative, imaginative, idea-driven, and nonlinear (Green Hat) are unlikely also to perceive themselves as detail-minded, precise, observant, and factual (White Hat), and vice versa;  those who see themselves as subjective, emotive, and relational (Red Hat) are unlikely also to perceive themselves as objective, analytical, and skilled at critical thinking (Black Hat), and vice versa.  In fact, the most frequent combination of highest and lowest self-rated modes was found with students who rated Green Hat highest and White Hat lowest (21 students, or 10.99% of the total sample).  These contrasts appear pedagogically relevant, as outlined below.


Table 2

Cognitive modes (Six Hat self-ratings):  Intercorrelations

Correlations marked with *   were different from zero at a p < .05 level by a r-to-z test.

Correlations marked with ** were different from zero at a p < .01 level by a r-to-z test.

                        Blue                 Black               Yellow             Green              Red

White               -.198**            -.082                -.182*              -.329**            -.166*

Red                  -.279**            -.305**            -.320**            -.017

Green               -.378**            -.303**            -.147*

Yellow             -.089                -.179*

Black               -.014

Cognitive mode                                    Percentage of subjects rating each mode:

                                                            Highest                Lowest

White Hat                                             11.0                 17.3

Red Hat                                               16.8                 15.2

Green Hat                                            35.1                 16.8

Yellow Hat                                           15.2                   8.9

Black Hat                                               5.8                 26.2

Blue Hat                                               16.2                 15.7


Descriptive statistics for the Big Five personality dimensions are summarized in Table 3.  Students as a group were markedly higher than expected on Agreeableness (mean of 60.39, t = +9.387, p < .001) and lower than average on Conscientiousness (mean = 41.03, t = -6.298, p < .001).  This general pattern may contribute to students' self-expressed concerns about procrastination and difficulty with self-efficacy (low Conscientiousness) as well as their apparent desire for interactivity and belonging (high Agreeableness).   Using median split criteria to classify students into Big Five profile groups, the most statistically frequent profile was high E, high O, high A, low C, high N (7.9% of overall sample), which is consistent with the mean scores on each of the individual personality dimensions as well.  This is also consistent with anecdotal confirmation from student services personnel that this is a common profile pattern among students (Hackbarth-Onson, 2008), and may be exacerbated by student self-selection into a class where interest in relationality (high E, high A), a preference for divergent ideas and theories (high O, low C), and concerns about adjustment and well-being (high N) could be factors driving student interest in the discipline.


Table 3

Big Five personality dimensions:  Descriptive statistics

The raw scores were algebraically normalized to yield 0-100 scale scores.  N = 114 subjects.

Personality dimension                Mean   Standard deviation        Difference from 50 (t test)

                                                                                                      t                 p

Extraversion                             56.45               15.64               +4.402             .001

Openness                                 55.60               10.80               +5.530             .001

Agreeableness                          60.39               11.81               +9.387             .001

Conscientiousness                     41.03               15.22               -6.298              .001

Negative Emotionality                54.06               17.94               +2.417             .017


Correlations between the cognitive modes and the Big Five personality dimensions are presented in Table 4.  These correlations, which fit theoretical expectations well, are particularly striking in that students had not been exposed to any lecture information about the Big Five or any related theoretical concepts at the time of making the cognitive mode self-ratings.  Thus, any sources of bias stemming from an implicit theory relating the two ideas are presumably nonexistent.  Yet, the expected pattern of correlations were obtained:  an inverse relationship between White Hat thinking and Openness (r = -.280, p < .01);  a positive relationship between Red Hat thinking and Agreeableness (r = +.268, p < .01);  a positive association between Green Hat thinking and Openness (r = +.374, p < .01);  and an inverse relationship between Black Hat thinking and Agreeableness (r = -.424, p < .01).   As in the previous analysis, this suggests a fourfold classification of students into those favoring each of four academic styles (Green-Red, Green-Black, White-Red, and White-Black), with exact parallels to the Jungian functional divisions (NF, NT, SF, and NT respectively).


Table 4

Big Five personality dimensions:  Correlations with cognitive modes

Correlations marked with *   were different from zero at a p < .05 level by a r-to-z test.

Correlations marked with ** were different from zero at a p < .01 level by a r-to-z test.

                        Extraversion                        Openness                     Agreeableness 

Conscientiousness   Negative Emotionality

White Hat              -.103           -.280**            -.074                +.176                           -.042               

Red Hat                +.173          +.025               +.268**           -.170                            +.232*

Green Hat             +.193*        +.374**           +.076               -.348**                        -.207*

Yellow Hat            +.097          -.056                +.039               +.305**                       -.055

Black Hat              -.239**       -.141                -.424**            +.171                           -.051

Blue Hat                +.004          +.043               +.054               -.028                            +.117



Despite the deceptive simplicity of the rank-ordering exercise, the data presented above confirm that this method actually provides a substantially meaningful window into the cognitive style preferences of college students.  Correlations between cognitive style self-ratings and personality data suggest a systematic, theoretically coherent pattern of relationships.   As predicted, White Hat thinkers were lower in Openness;  Red Hat thinkers were higher in Agreeableness,    Green Hat thinkers were higher in Openness;  and Black Hat thinkers were lower in Agreeableness.

The ease with which the Six Hat Method can be taught to students suggests its usefulness as a classroom heuristic.  It provides a means by which students can be acquainted with the notion of cognitive styles;  helped to recognize that different life situations call for the application of different styles and approaches;  and encouraged to engage in a potentially useful personal inventory of style-related strengths, weaknesses, and habits.  The language of the Six Hats provides a useful shorthand for discussing potentially debilitating classroom deficiencies, such as inattention to detail (weak White Hat), planlessness (weak Blue Hat), and deficits in critical thinking and analysis (weak Black Hat).

Although De Bono does believe that all six cognitive modes are learnable skill sets, it is also a reasonable presumption that strengths in a particular mode often come at the cost of (at least relative) weaknesses in other modes. Prevailing theories of neurological development (Buckingham and Clifton, 2001) strongly suggest this possibility. A Jungian framework also points directly to this sort of necessary tradeoff: in the Jungian model, the development (or so-called "differentiation") of a given cognitive mode or mental function involves an implicit, perhaps unconscious, neglect of the opposing mode or modes (Jung, 1923).

The Big Five correlations noted earlier suggest this kind of opposition in the De Bono cognitive modes as well: Green and White Hat thinking are inversely related (to focus on tangible practical details inherently means not simultaneously attending to big-picture creative possibilities, and vice versa), as are Black and Red Hat thinking (impersonal, analytical, objective logic "short-circuits" a personal, synthetic, subjective orientation to values, and vice versa). Using the Jungian terminology, Intuition and Sensing are opposed, as are Thinking and Feeling, leading to a quaternary of personality type clusters.

Thus, in an ideal pedagogical environment, students could select either a top-down, theory-driven (Green Hat) or a bottom-up, fact-driven (White Hat) approach, as well as either a linear, logic-driven (Black Hat) or a nonlinear, values-driven (Red Hat) approach to any subject. It would be interesting to attempt the development of these four different cognitive-mode tracks for a given course (though it might well involve what is tantamount to four distinct syllabi, with concomitant differences in equalizing the difficulty, scope, and work requirements of each). Yet, it also seems obvious that many topics, courses, and disciplines are inherently skewed in a particular direction; math differs from literature in part because the former focuses more on analytical details (White-Black) while the latter emphasizes broad themes and reader responses (Green-Red).

Students can make use of their Big Five profile in a variety of ways, though it is important to note that the simple self-rating method used in this research only informs students about their relative (not absolute) tendencies, and does not differentiate between ability and motivation. While one might expect ability and motivation to be correlated, it is certainly theoretically possible to imagine a situation in which a person liked, for instance, using the Green Hat without being particularly good at it, or conversely had a high level of skill but found such activities boring, draining, or otherwise demotivating.

First, awareness of one's relative skills and motives can lead to academic self-selection, whether tactically (the choice of particular courses or instructors based on the knowledge of the cognitive modes likely to predominate within a given classroom situation) or strategically (the selection of a given college major or career choice). In the spirit of Peter Drucker's (1999) famous dictum that "it is easier to move from competence to excellence than to move from mediocrity to competence", students might be best served by seeking educational and vocational settings that play to their strong suits. Note that the general education requirements of the traditional university, which force students to sample widely among a broad range of disciplines and, presumably, cognitive modes, may somewhat mitigate against this, though these requirements likely have the salutary effect of helping students to strengthen weak modes through targeted practice - if they can reach a high enough level of competence to persist in what initially may seem an overwhelmingly difficult task.

Second, awareness of the cognitive modes can help students to anticipate likely academic difficulties and to prepare for them, either by identifying a "work-around" that substitutes an indirect path (use of a personal strength that is less obviously relevant to the task at hand) for a more direct one (an approach that requires heavy reliance on a weak mode) or by signaling to them that they need to enhance a weak mode in advance.   In other words, students can adopt the strategy of developing a cognitive "emulator" - the capacity to address difficult tasks in a manner that invisibly plays to one's strengths (Embree, 2006).

Third, recognition of one's cognitive strengths and weaknesses can foster a respect and appreciation for diversity in both the intellectual and the personality realms. Higher education may represent, for many students, the last time in their lives that they will be required to rub shoulders with those who are "wired differently" from themselves. In the adult years, vocational self-selection pressures often create groups that are cognitively similar - to the point where the group becomes dysfunctionally imbalanced and lopsided in the extreme case. Many engineers interact on a daily basis mostly with other engineers, accountants with other accountants, medical professionals with other medical professionals, and so on. Since vocational choice may be strongly governed by underlying cognitive (and correlated motivational) aspects, adults can take their own natural styles for granted or wrongly presume their normativity or universality. The educational experience is an important corrective, but it is most likely to lead to persistent effects if students have a language for classifying and understanding the underlying differences.

Instructors can use the information about cognitive modes in a variety of ways as well. First, it is obviously useful for instructors to be aware of their own cognitive preferences so they can aim for an appropriate level of balance in their teaching styles. Teachers can rationally expect that students who have different preferences may struggle in their courses due to a cognitive mismatch, which can include a tendency to say things in opposing ways. For instance, I am a strongly Green Hat thinker, so my courses tend to emphasize top-down thinking, a theory-driven approach (just enough facts to get by, with minimal emphasis on rote memory) and a strong expectation that students be able to link abstract concepts to specific examples - and generate original examples as well. This would be expected to put White Hat thinkers, many of whom have succeeded in other settings (such as high school) through the use of rote memory techniques, at risk; in addition, the nonlinearity of my teaching style may frustrate more linear (White and/or Black Hat) thinkers. I use various methods (such as online lecture notes) to assist these students in making the necessary transition.

Second, instructors can encourage students to make the distinction between cognitive style differences, which are nonevaluative (the world needs all types of thinkers), from intelligence differences, which are usually depicted as evaluative (more intelligence is better). Students who struggle in a given context can easily attribute this to low intelligence, leading inevitably to a downward "attributional spiral" (Dweck and Leggett, 1988). Many students find it deeply affirming to discover the truth that all of us will excel at some tasks, struggle or fail at others, and that the difference may be attributed to a poor "person-environment fit" (Anderson et al., 2008) rather than a general lack of ability or competence. Rather than giving up altogether, students may thus become motivated to use their self-knowledge to find a better environment by engaging in niche picking (Low et al., 2005).

A further complication stems from the fact that traditional education is not equally friendly to each of the cognitive modes. Historically, there has been a strong emphasis in American education on the retention of facts and specifics, sometimes utilizing rote memorization techniques for this purpose (White Hat). In addition, an emphasis is often placed on the identification of "wrong" or flawed answers (Black Hat), which sometimes fosters a zero-sum educational environment in which students earn grades (and teachers maintain prestige) by being able to spot errors and mistakes in others' responses. It is likely that an overemphasis on those modes to the relative exclusion (or, in some cases, the active disparagement) of other modes has contributed to the creation of "at-risk" students who find education boring, frightening, or otherwise demotivating.

From the standpoint of a cyclical model of history (Strauss and Howe, 1997), it appears that there may be a periodic oscillation within educational circles between White-Black ("back to basics") and Green-Red ("progressive") pedagogical goals and styles. In educational theory, the two extremes are sometimes contrasted under the rubric of "objectivist" versus "constructivist" education (Howard, 1991). In the Strauss-Howe model, the former style is likely to dominate during times in history when, within the wider culture, conformity is valued more than autonomy (as in the 1950s), while the reverse is true during times when the general culture values autonomy over conformity (as in the 1970s). Indeed, the oscillation may stem from our culture's general tendency to overcorrect for perceived errors (Pipher, 1999). This same trend can be seen in many other aspects of the culture not directly germane to the subject matter of this article, such as a cyclical change in modal parenting styles from underprotection to overprotection of children and back again.

Hence, implications of this model for students who prefer different cognitive modes are somewhat historically dependent or historically embedded.  The fact that psychology, whether theoretical or applied, is at least somewhat captive to history was noted rather bombastically by Gergen (1973), leading to the famous "crisis in social psychology" and its rallying cry that "psychology is really just history in disguise". While the extremes of that once shrill debate have somewhat faded in volume and stridency, it remains likely that there is no culture-free or historically universal set of prescriptions for managing the fact of student cognitive diversity. In the current climate (which, if the Strauss-Howe cycle continues to hold sway, is moving toward an increasing emphasis on conformity), Green and Red Hat students are even more at risk than they might be at other times in history, though even at times in history that are friendlier to such students (times when progressive or constructivist education was more strongly advocated), the general tendency of American education to favor White and Black modes probably rendered these students somewhat more challenged than their polar opposites. Especially during times when White-Black education is powerfully reinforced by the educational establishment, provisions to encourage or shelter students whose natural tendency is to think in different ways must be assiduously pursued.

Asked by the instructor to evaluate the use of the different cognitive modes in American education generally, students readily and consistently responded that White and Black modes are encouraged and rewarded by most teachers, while Green and Red modes are ignored or even actively suppressed.  While the present study provides no direct evidence that this is so, the uniformity with which students offer these observations over repeated semesters is interesting in itself.  If true, it raises interesting questions about balance (or the lack thereof) within traditional academia, as well as the possibility that students with certain cognitive style patterns (those with an emphasis on Green and Red modes) may be at risk in the classroom due to the traditional emphasis on White and Black pedagogical styles among a majority of educators.

On the instructor's Web site, students were presented with detailed information about how to use the Six Hat Method in academic, vocational, and personal problem solving.  This material emphasized a systematic, structured use of the cognitive modes to address problems, first by identifying the facts of the current situation (White Hat), then by venting or acknowledging feelings about the facts that might otherwise impede rational problem solving (Red Hat), followed by brainstorming activities designed to generate as many potential solutions as possible (Green Hat), with a deliberate attempt to strengthen or combine solutions (Yellow Hat) before moving to a critical-rational scrutiny of each solution (Black Hat).  (The Blue Hat, in its metacognitive role, is not a step in this process but rather guides the entire process in order to allocate time and other resources efficiently throughout the implementation of the process.)  In optional exercises inviting students to apply this method to real-world problems of their own, many students expressed enthusiasm about the usefulness of the method to their lives.  Again, there are general parallels within the Jungian literature (the "zig-zag" method of Lawrence, 1993), suggesting further links between these two conceptual systems.

However, some caveats are in order.  It is not clear whether all problems should be addressed in such a linear, and such a comprehensive, fashion.  Not only is this approach time-consuming, but it may represent overkill – "using a nuclear weapon to kill a fly" in the famous folk phrasing – with respect to situations that do not call for such an overwhelmingly detailed approach.  Much research into social cognition (e.g., Shah and Oppenheimer, 2008) suggests that in many real-world situations, particularly where the consequences of a suboptimal decision are not extreme and/or when time is pressing, people use a more heuristic, "satisficing" approach to problem-solving to good effect.  It remains to be seen whether the formal Six Hat system is too cumbersome for use in everyday situations in which a simpler approach could yield sufficiently good results.  Further, the kinds of academic decisions that occur within classroom contexts (What and how should I study, and for how long?  How should I tackle this exam question?) may not be readily amenable to the Six Hat approach, though students with a tendency to avoid some important element of decision-making (such as a failure to consider important facts or an unwillingness to "think outside the box") might profit from knowing in Six Hat terms what these characteristic weaknesses might be.

Similarly, in line with De Bono's notion that the cognitive modes represent skill sets that can be improved through targeted practice, the instructor's Web site included detailed suggestions about how to improve weak modes, which students also found useful as evidenced by their responses to classroom exercises.  Making the link back to the Jungian framework, standard advice within the psychological type community about developing weak functions is to utilize an observational approach, watching role models with different preferences who are skilled in a functional area within which one is personally weak (Myers, 1980).  This, too, may suggest a means by which students can be helped to gain skills in a weak Thinking Hat.  In my classes, I have used student work groups as a means of at least implicitly pairing students who are strong versus weak in a particular cognitive mode, with good results.  For instance, as part of a unit on scientific methodology, students have been given practice problems in which a hypothetical research protocol is described, and students working in groups are asked to identify the research design, to classify the variables, to determine how certain variables are operationalized, to note possible research confounds, and the like.  This activity, at which top-down analytical thinkers (Green and Black Hats) tend to excel, is often difficult for those who get mired in the details (White) or who cannot attack the problem in a logically systematic manner (Red).  The opportunities for low-risk observation of other students with different cognitive styles has proven useful as a classroom technique.

The present research is a useful first step in highlighting the relevance and utility of the Six Hat Method, though additional work along these lines is clearly warranted.  Future research might focus on the question of whether students in different kinds of classes or pursuing different majors present different cognitive profiles, or whether might benefit from learning to use the Six Hat Method to analyze the intellectual requirements of different kinds of classes so they can respond accordingly to maximize success.  This might also include an examination of the impact of the similarity between teachers' and students' cognitive modes on academic performance (Furnham and Chamorro-Premuzic, 2005).  The impact of direct, structured interventions to improve students' weak modes could be examined in a more experimentally based study as well.

In addition, it would be interesting to examine the extent to which students with different cognitive styles end up utilizing different "languages" in their approach to academic work, both in terms of internally "translating" instructional material into their natural mode of understanding and processing that information, as well as in terms of student output (spoken or written).  Researchers utilizing a Jungian approach often assert that the different psychological types have different approaches to both generative and receptive language (Myers et al., 1998).  Writing in a somewhat different context, Hall et al. (2004) remind readers that a major reason why members of different communities may fail to understand one another is that distinct groups represent implicitly different language systems and speak in different "insider dialects".  To some extent, the same may be true of individuals with different cognitive styles:  for instance, Green Hat thinkers may be nonlinear and metaphorical in their uses of language, White Hat thinkers may be detail-heavy and concrete, Red Hat thinkers may be emotive with a strong emphasis on personal narrative, and so forth.  Since different academic environments and disciplines probably tend to utilize, and reward the uses of, differing kinds of language, this may be an important mediating factor linking cognitive style differences to academic success, meriting future study.


Alter, A.L.; Oppenheimer, D.M.; and Epley, N. (2007).  Overcoming intuition:  Metacognitive difficulty activates analytic reasoning.  Journal of Experimental Psychology: General, 136(4), 569-576.

Anderson, C.; Spataro, S.E.; and Flynn, F.J.  (2008).  Personality and organizational culture as determinants of influence. Journal of Applied Psychology, 93(3), 702-710.

Beghetto, R.A., and Kaufman, J.C. (2007).  Toward a broader conception of creativity:  A case for 'mini-c' creativity.   Psychology of Aesthetics, Creativity, and the Arts, 1(2), 73-79.

Buckingham, M., and Clifton, D.O.  (2001).  Now, discover your strengths.  New York:  Free Press. 

Covey, S. (2004).  The 7 habits of highly effective people:  Powerful lessons in personal change.  New York:  Free Press. 

De Bono, E. (1970).  Lateral thinking:  A textbook of creativity.  London:  Ward Lock Educational. 

De Bono, E. (1991).  Six action shoes.  New York:  Harper Business. 

De Bono, E. (1999).  Six thinking hats.  Boston:  Back Bay Books. 

Drucker, P.  (1999).  Management challenges for the 21st century.  New York:  Harper Business.

Dweck, C.S., and Leggett, E.L.  (1988).  A social-cognitive approach to motivation and personality.  Psychological Review, 95(2), 256-273.

Embree, M.C.  (2006).  "Developing your personal 'type emulator'."  Type Reporter (September).

Epstein, S. (1994).  Integration of the cognitive and the psychodynamic unconscious.  American Psychologist, 49(8), 709-724.

Epstein, S.; Pacini, R.; and Denes-Raj, V. (1996). Individual differences in intuitive-experiential and analytical-rational thinking styles. Journal of Personality and Social Psychology, 71(2), 390-405.

Foster, S.L., and Lloyd, P.J. (2007).  Positive psychology principles applied to consulting psychology at the individual and group level.  Consulting Psychology Journal: Practice and Research, 59(1), 30-40.

Furnham, A., and Chamorro-Premuzic, T. (2005).  Individual differences in students' preferences for lecturers' personalities.  Journal of Individual Differences, 26(4), 176-184.

Gergen, K.J.  (1973).  Social psychology as history.  Journal of Personality and Social Psychology, 26(2), 309-320.

Glow, R.A.; Lange, R.V.; Glow, P.H.; and Barnett, J.A. (1983). Cognitive and self-reported impulsiveness: Comparison of Kagan's MFFT and Eysenck's EPQ impulsiveness measures. Personality and Individual Differences, 4, 179–187.

Hackbarth-Onson, A.  (2007).  Personal correspondence.

Hall, D.E.;  Koenig, H.G.;  and Meador, K.G.  (2004).  Conceptualizing "religion":  How language shapes and constrains knowledge in the study of religion and health.  Perspectives in Biology and Medicine, 47(3), 386–401. 

Harvey, O.J., Hunt, D.E., and Schroder, H.M. (1961). Conceptual systems and personality organization. New York: Wiley.

Howard, G.S. (1991).  Culture tales:  A narrative approach to thinking, cross-cultural psychology, and psychotherapy.  American Psychologist, 46(3), 187-197.

Jung, C.G.  (1923).    Psychological types.  New York:  Harcourt, Brace and Company. 

Lawrence, G. (1993).  People types and tiger stripes.  Gainesville, FL:  Center for Applications of Psychological Type.

Low, K.S.; Yoon, M.; Roberts, B.W.; and Rounds, J.  (2005).  The stability of vocational interests from early adolescence to middle adulthood:  A quantitative review of longitudinal studies. Psychological Bulletin, 131(5), 713-737.

McCrae, R., and Costa, P. Jr. (1989). Reinterpreting the Myers-Briggs Type Indicator From the Perspective of the Five-Factor Model of Personality. Journal of Personality, 57, 17-40.

Moseley, D.;  Baumfield, V.;  Eliot, J.;  Gregson, M.:  Higgins, S.;  Miller, J.;  and Newton, D.P.  (2005).  Frameworks for thinking:  A handbook for teaching and learning.  New York:  Cambridge University Press. 

Myers, I.B.  (1980).  Gifts differing.  Palo Alto, CA:  Consulting Psychologists Press.

Myers, I.B.;  McCaulley, M.H.;  Quenk, N.L.;  and Hammer, A.L.  (1998).  MBTI manual:  A guide to the development and use of the Myers-Briggs Type Indicator.  Palo Alto, CA:  Consulting Psychologists Press (3rd ed.)

Nelson, T.O.  (1992).  Metacognition:  Core readings.  Boston:  Allyn and Bacon.

Pacini, R., and Epstein, S. (1999).   The relation of rational and experiential information processing styles to personality, basic beliefs, and the ratio-bias phenomenon.  Journal of Personality and Social Psychology, 6(6), 972-987.

Pipher, M.B.  (1999).  Another country: Navigating the emotional terrain of our elders.  New York : Riverhead Books.

Santostefano, S., and Rieder, C. (1984).  Cognitive controls and aggression in children:  The concept of cognitive-affective balance.  Journal of Consulting and Clinical Psychology, 52(1), 46-56. 

Schlesinger, J.S. (1980).  Laterality and myth continued.  American Psychologist, 35(12), 1147-1149.

Schmeck, R. R. (1983). Learning style of college students. In R. F.Dillon & R. R. Schmeck (Eds.), Individual differences in cognition (Vol. 1), 233–279. New York: Academic Press.

Shah, A.K., and Oppenheimer, D.M.  (2008).  Heuristics made easy:  An effort-reduction framework.  Psychological Bulletin, 134(2), 207-222.

Silvia, P.J.; Winterstein, B.P.; and Willse, J.T. (2008).  Assessing creativity with divergent thinking tasks:  Exploring the reliability and validity of new subjective scoring methods.  Psychology of Aesthetics, Creativity, and the Arts, 2(2), 68-85.

Sternberg, R.J. (2006).  Creating a vision of creativity:  The first 25 years.  Psychology of Aesthetics, Creativity, and the Arts, S(1), 2-12.

Sternberg, R.J., and Grigorenko, E.L.  (1997).  Are cognitive styles still in style?  American Psychologist, 52(7), 700-712.

Strauss, W., and Howe, N.  (1997).  The fourth turning:  An American prophecy.  New York:  Broadway Books.

Viswanathan, M. (1993).  Measurement of individual differences in preference for numerical information.  Journal of Applied Psychology, 78(5), 741-752.

Witkin, H.A., Dyk, R.B., Faterson, H.F., Goodenough, D.R., and Karp, S.A. (1962). Psychological differentiation. New York: Wiley.

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Marlowe Embree, Ph.D. is Assistant Professor of Psychology at the University of Wisconsin - Marathon County.