Advancing Diagnostic Skills for Technology and Engineering Undergraduates: A Summary of the Validation Data

W Tad Foster firstname.lastname@example.com
Department of Human Resource Development and Performance Technology
Indiana State University

A Mehran Shahhosseini email@example.com
Department of Applied Engineering and Technology Management
Indiana State University

George Maughan
Department of Human Resource Development and Performance Technology
Indiana State University

Advancing Diagnostic Skills for Technology and Engineering Undergraduates: A Summary of the Validation Data
753786CEC69B7B4532C00B18AFE0A5EEdiagnostic skillsconceptual mappingself-guided instructional designsimilarity flooding
Facilitating student growth and development in diagnosing and solving technical problems remains a challenge for technology and engineering educators.With funding from the National Science Foundation, this team of researchers developed a self-guided, computer-based instructional program to experiment with conceptual mapping as a treatment to improve undergraduate technology and engineering students' abilities to develop a plan of action when presented with real technical problems developed in collaboration with industrial partners.The pilot-testing and experimental portions of the project confirmed that the subjects found the training interesting and useful.However, the experimental data did not support the conclusion that the treatment caused an increase in diagnostic performance.Analysis of individual data demonstrated that for a portion of the sample, significant gains occurred.Qualitative analysis demonstrated that the majority of the students treated the experience as a trivial academic exercise, which seriously limited their efforts.A major step forward occurred in the ability to automatically compare student maps with the expert's reference map using a modified version of the similarity flooding algorithm.Student feedback was based on this automatic comparison.In all, the experience has encouraged the team to continue and expand the use of conceptual mapping as a tool for improving diagnostic and problem-solving skills.

Introduction

For some time, technology and engineering educators have focused on improving students' cognitive and metacognitive abilities, especially in the areas of design and problem solving.It is clear that these abilities are essential for personal and professional success.However, an argument can be made that more work is needed.This study was an attempt, supported by funding from the National Science Foundation, to develop and pilot test computer-based, self-paced diagnostic skills training using conceptual mapping techniques developed by Novak (1977).Cognitive abilities are highly successful predictors of job performance, and the development of advanced cognition skills is critical to perform any diagnostic assignment.According to Gabbert, Johnson, and Johnson (2001), one of the ways to address cognitive development is "by examining the strategies students use to complete learning tasks" (p.267).Causal-model theory argues "that, in principle, people are sensitive to causal directionality during learning"; our learning does not involve only "acquiring associations between cues and outcomes irrespective of their causal role" (Waldmann, 2000, p. 53).It is predicted by associative theories that in some learning conditions, our representations are fundamentally flawed.There is evidence that learning from experience seems more of a process of negotiation in which thinking, reflecting, experiencing and action are different aspects of the same process.It is negotiation with oneself and in collaboration with others that one may actually form the basis of learning.(Brockman, 2004, p. 141) Interestingly, this applies as well to the informal learning that occurs in such technical situations as help desk support for information systems.Haggerty (2004) noted that the support and implementation of structured and systematic problem-solving processes and detailed verbal modeling of explanations about the problem are critical to the development of advanced cognition and effective outcomes because they will also increase self-efficacy and satisfaction.

Diagnostic learning (i.e., analyzing causes based on information about the effects) needs to be differentiated from predictive learning (i.e., determining effects based on specific causes)."An important feature of diagnostic inference is the necessity of taking into account alternative causes of the observed effect" (Waldmann, 2000, p. 55).Given that the effects (symptoms) have already occurred and are not necessarily actively recognized by the learner, "diagnostic learning is a test case for humans' competency to form and update mental representation in the absence of direct stimulation" (Waldmann, 2000, p. 73).

Some technical problems are well defined with a clear goal: There is a definite cause and outcome and a proven algorithm to ensure that the problem is solvable.However, many technical problems are ill-defined: The cause or causes, what constitutes relevant data, and the steps to be taken are unclear.Consequently, there is a need for advanced cognitive skills such as analytical, creative, and practical thinking when diagnosing technical problems.Writing from an organizational perspective, Okes (2010) states that a "problem is often not a single problem but many different problems" (p.38).In such cases, the diagnosis will be difficult since there are likely to be multiple causes.The ability to diagnose multiple causes of a common effect versus a common cause for multi [ple] effects requires the technician to learn about these fundamental causal relationships correctly.And this ability is improved through practical experience and the ability to evaluate the causes and solutions of problems.Waldmann (2000) believes that a combination of technical expertise and logical and creative thought processes are essential for diagnosing a problem.Okes (2010) makes a distinction between creative problems and analytical problems: With creative problems, multiple solutions are necessary; however, analytical problems require "the right solutions [that] will not be known until a proper diagnosis is done.[For analytical problems,] it is the [utilization of a] diagnostic process, known as root cause analysis, which finds the causes" (p.37).

Technical Diagnostic Work

"Most people never receive training in root cause analysis, and those who are experts have often learned it through years of experience diagnosing a wide range of problem situations" (Okes, 2010, p. 36-37).One of the ways in which diagnostics skills are developed is through the understanding of cause and effect relationships when performing diagnostics.Typically, technical workers learn "cause and effect relationships resembling symptom-cause troubleshooting charts which they held in memory for use in subsequent troubleshooting" (Green, 2006, Abstract, p. 2).

Troubleshooting requires the technician to utilize problem-solving skills.According to Sharit and Czaja (2000), this is one of the most complex cognitive processes.Haggerty (2004) writes that providing "technical support [is] . . .one mechanism by which [information system] users can gain the necessary knowledge, skills and abilities to use their technology [the system] effectively" (p.iii).As Haggerty (2004) states, "effective support is characterized by a timely and well structured problem solving process where a knowledgeable, sympathetic and patient analyst provides thorough and specific information and explanations, matched to the user's demonstrated level of ability, according to the needs of the specific technical problem" (p.iii).Technical workers, however, often encounter complex, ill-structured problems in their professional efforts to solve technical problems and do not have access to any form of technical support.Three major issues relative to organizational culture's impact on problem solving are identified by Okes (2010), including: how people view problems (e.g., someone to blame), who will be called upon to diagnose and solve the problem, and the ratio of the number of problems to the available personnel to solve them.

Instructional Techniques

Most would agree with Stoyanov and Kirschner (2007) that "solving problems is considered an important competence of students in higher education" (p.49) and that higher education institutions must take on this important task.What seems to be at issue is how to go about the task effectively.Historically, problem-solving instruction has focused mostly on well-defined problems and a rather simple, step-by-step approach.However, most situations that are viewed as problems are typically not well defined, nor do people tend to be successful in solving them using a simplistic problem solving heuristic.What seems to be needed is practice with ill-defined problems within a supportive and instructional environment (Stoyanov & Kirschner, 2007).

To develop diagnostic skills, "the knowledge that one should analyze the problem situation, generate ideas, select the most appropriate, and then implement and evaluate is necessary, but not sufficient" (Stoyanov & Kirschner, 2007, p. 50).It is also important to know how to process information, form and test hypotheses, and make choices based on the data (Johnson, 1994)."The selection and application of these procedures, techniques, and tools depends to a large extent on the desired outcomes of problem-solving determined by the nature of ill-structured problems and the cognitive structures and processes involved in solving them" (Stoyanov & Kirschner, 2007, p. 50)."Instructional design should determine the most effective and efficient conditions of providing both process and operational support to solving ill-structured problems" (p.50).

Writing on the topic of professional development, Mayer (2002) defines problem-based training as providing "realistic problems and the solutions of these problems in a variety of situations" (p.263).He lists the four types of problem-based approaches enumerated by Lohman (2002) that "can be presented in computer-based environments, book-based environments, or live environments" (Mayer, 2002, pp. 263-264): (1) case study, (2) goal-based scenario, (3) problem-based learning, and (4) action learning (p.264).He theorizes that "there are three cognitive steps in problem solving by analogy: (1) recognizing that a target problem is like a source problem you already learned to solve, (2) abstracting a general solution method, and (3) mapping the solution method back onto the target problem" (p.267).

More research is needed "to understand how each of the problem-based training methods [in Lohman's (2002) article] supports the processes described [the three cognitive steps]" (Mayer, 2002, p. 267).The use of graphical structures to help make sense of information is important to problem solving and systems thinking.

Novak, the person credited with the development of conceptual mapping, argues "that the central purpose of education is to empower learners to take charge of their own meaning making" (Novak, 2010, p. 13).His work in conceptual mapping is based on the theoretical work of Ausubel, Piaget, and Vygotsky.Initially developed as a means to collect data about children's knowledge, his research quickly led to the conclusion that concept mapping was useful in helping students learn and helping teachers to organize instruction.It has also been demonstrated to be a useful tool for evaluating what has been learned.Recently, he has applied mapping as planning and problem solving in corporate settings.Siau and Tan (2006) identify "three popular cognitive mapping techniques-causal mapping, semantic mapping, and concept mapping" (p.96).The term cognitive map is developed in psychology as a means of describing an individual's internal mental representation of the concepts and relations among concepts.Cognitive mapping techniques are used in identifying subjective beliefs and to represent these beliefs externally."The general approach is to extract subjective statements from individuals, within specific problem domains, about meaningful concepts and relations among these concepts and then to describe these concepts and relations in some kind of diagrammatical layout [Swan, 1997]" (Siau & Tan, 2008, p. 100).This internal mental representation is used to understand the environment and make decisions accordingly."Causal mapping [emphasis added] is the most commonly used cognitive mapping technique by researchers when investigating the cognition of decision-makers in organizations [Swan, 1997]" (Siau & Tran, 2006, p. 100) because it allows an individual to interpret the environment with salient constructs.The theory argues that individuals, with their own personal system of constructs, use it to understand and interpret events."Semantic mapping [emphasis added], also known as idea mapping, is used to explore an idea without the constraints of a superimposed structure [Buzan, 1993]" (Siau & Tran, 2006, p. 101).With semantic maps, an individual will begin at the center of the paper with the principal idea and work outwards in all directions.This produces an expanding and organized structure consisting of key words and key images.Concept mapping is another cognitive mapping technique, which "is a graphical representation where nodes represent concepts, and links represent the relationships between concepts" (Siau & Tan, 2006, p. 101).Concept mapping is an integral part of systems thinking.

Systems Thinking

Batra, Kaushik, & Kalia (2010) define systems thinking as a holistic way of thinking, fundamentally different from that of traditional forms of analysis in which the observer considers himself the part of reality as a whole system.System[s] thinking resists the breaking down of problems into its component parts for detailed examination and focuses on how the thing being studied interacts with the other constituents of the system. . . .This means that instead of taking smaller and smaller parts or view[s] of the system taken for study, it actually works by expanding its view by taking into account larger and larger numbers of views or parts of the system.(p. 6) "Systems thinking is increasingly being regarded as a 'new way of thinking' to understand and manage the 'natural' and 'human' systems associated with complex problems . . .(Bosch et al., 2007a)" (Nguyen, Bosch, & Maani, 2011).When employing systems thinking to deal with real problems, it is necessary for the trainer to classify real-world problems by system language because the classification can help students or trainees find an appropriate method and methodology to deal with specific problematic situations.According to Batra et al. (2010),

The character / nature / approach of systems thinking makes it extremely effective on the most difficult types of problems to solve. . . .Some of the examples in which system thinking has proven its value include: complex problems that involve helping many actors [learners] see the "big picture" and not just their part; recurring problems or those that have been made worse by past attempts to fix them; issues where an action affects (or is affected by) the environment surrounding the issue, either the natural environment or the competitive environment; and problems whose solutions are not obvious.(p. 6) Batra et al. (2010) discovered that systems thinking leads to: "in-depth search of problem contributors by finding out further reasons for those problems and lead to actual reasons of the same," "solution[s] to all kinds of problems by considering them as a whole system" (p.10), finding the root-cause of a problem by exploring not just analyzing the problem and making assumptions, and "permanent solutions of problems by acting on all possible reasons simultaneously [instead of just making] plans to solve the problem by removing the reasons one by one as per the plan formation" (p.11).

Evaluation of Diagnostic Skills

In order to acquire skills, learning has to take place.According to Kontogiannis and Maoustakis (2002), "most research in artificial intelligence and machine learning has . . .underplayed issues of problem formulation, data collection and inspection of the derived knowledge structures (Langley and Simon 1995)" (p.117).They further state that the stages of inspection and evaluation of knowledge structures are significant because "by making knowledge structures easier to understand or comprehend, we are in a better position to meet criteria of validation, generalization and discovery."(p.133).Basically, the issue of comprehensibility must be part of the evaluation process for technical applications.The learner has to be able to input and output knowledge.Comprehensibility should address transferability to a new context in the future.With the increased use of computer-based diagnostic skills, evaluation is not done separately from the ability of humans to interact with the computer to perform problem-solving tasks.Kontogiannis' and Maoustakis' (2002) "informal approach for refining and elaborating knowledge structures" (p.120, Figure 1) provides some insight on what to consider when performing evaluations of diagnostic skills.The model illustrates the significance of the individual to become an expert in the field to effectively perform diagnostics.It is imperative that the expert is able to employ several diagnostic strategies.This entails "fault-finding strategies and knowledge structures [, which] are dependent upon the amount and kind of data that are available-for example, data regarding equipment reliability data, direction and rate of change of process variables, sequences of change, etc." (p.120).Their model demonstrates the significance of identifying any weakness in the process at the analytical stage.The role of the expert is to determine if the process for solving the problem was justifiable in terms of the principles of operation.At the modification stage, the ability of the learner "to impose a hierarchical structure upon the" (p.123) process is crucial to problem solving.This would entail "providing descriptions of groups of faults that relate functionally to each other" (p.123).Moreover, the creation of subordinate goals and plans are important as they are "high-level objectives or concepts . . . .By progressively breaking up all goals into plans or sequences of checks, the overall task becomes easier to achieve" (Kontogiannis & Maoustakis, 2002, p. 123).Kontogiannis and Maoustakis further explain that "the distance or gap between the top goal and the available responses [, verbal statements of the problem made at a high or detailed level,] becomes smaller" (p.123).The subjects of their study were required to participate in fault-finding activities.This demonstrated the level to which they were able to transfer what was learned.In order to study subjects' diagnostic skills, the authors created three modules: "Module 1-Training," "Module 2-Fault finding test in the manual mode" (p.128), and "Module 3-Transfer to diagnosis in the automatics mode" (p.129).Their findings indicated "that deep goal structures facilitate both the acquisition and the transfer of knowledge" (p.132).

Problem and Purpose of the Study

Research to date supports the assertion that domain-specific problemsolving skills can be developed by educational and training programs.The more experience an individual has with a system and with solving problems that occur in the system, the more proficient a problem solver that individual becomes.What is less certain is the extent to which general problem-solving instruction can result in improvements in near and far transfer of knowledge to novel problem situations.There is strong evidence that the application of conceptual mapping improves learning, memory, and application of knowledge.It is also clear that conceptual mapping enhances systemic understandings.

This project was designed to test the premise that the use of conceptual mapping techniques to force in-depth analysis of technical problems and the creation of a process map of the student's intentions during the diagnosis phase of problem solving would increase the student's level of accuracy when compared to the process used by experts in the particular system.It was argued that developing this expertise in mapping would help the students become more agile in their thinking regarding ill-defined problems.

A quasi-experimental model was used to test the null hypothesis H0 = .In other words, there would be no statistically significant difference between the four groups in terms of the mean difference in percentage accuracy for the two trials.

Method

The project staff developed a 2-hour, computer-based training program using the Lectora authoring software.The first hour of this training was designed to introduce students to systems and troubleshooting problems.During this phase of the training, the subjects were taught to use the Visual Understanding Environment (VUE) open-source software to develop conceptual and process maps.Also during this phase, the subjects were given a "simple" technical problem and asked to map out a plan of action.Using a similarity flooding algorithm, the students' maps was then electronically compared to an expert's map that was already encoded.The information developed by the algorithm was returned to the subjects as feedback so that they would know how well they did.

The second phase of the training took the form of two technical problems developed in collaboration with industrial partners.The first problem involved an electrical grid distribution system, and the second problem focused on a malfunctioning heat exchanger system.In both cases, the subjects were provided with a description of the system and tables containing operational information.From the information presented, the subjects were asked to analyze the system and develop a process map for addressing the apparent malfunction.

The evaluation of this project included pilot-testing the training program after the completion of the first hour, after the completion of the electrical grid problem, and after completion of the full training program.The subjects were provided the program and all necessary software on a memory stick and typically completed the training on a laptop.

Instrumentation

A feedback questionnaire was developed to provide subjects the opportunity to express their reactions to the training.The Likert-type questions were similar to end-of-course student evaluations of instruction.In this case, the focus was on the "look and feel" of the program and the extent to which the students perceived the training to be interesting, relevant, and helpful.Subjects were also encouraged to provide open-ended comments.

Population and Sample

The population for this study was junior and senior undergraduates enrolled in engineering, engineering technology, and closely related majors.The sample was drawn from students enrolled in the College of Technology at Indiana State University (the Principle Investigators' home institution) and four other universities.Faculty members involved with Indiana State University's PhD in Technology Management Consortium and faculty members at the four other institutions accepted invitations to participate.The sample consisted of the classes of those faculty members.

There were four treatments, and the groups were randomized and assigned to the groups of students in the order in which the faculty of these groups agreed to participate.Typically, faculty members would volunteer a particular class that they taught because the training was perceived to be relevant to the focus of the particular course.Subject participation was voluntary, confidential, and anonymous.

Data Collection

Experimental data was collected automatically as the subjects completed the training.The primary dependent variable for the experiment was the difference in percent accuracy for the two attempts for each problem.Upon completion and submission of the subject's map, the map was compared to the expert's map, and percentage accuracy was reported.The subject received this percentage as feedback.One group only received its scores (control condition).The other three groups were given a secondary form of additional feedback: metacognitive cues, review of the expert map, and both.All subjects then were given the opportunity to revise and resubmit their maps.The resubmission was also evaluated for accuracy, and the percentage was reported to the students and recorded.

After completing the training program, the subjects were given a "satisfaction" questionnaire to provide feedback regarding their reactions to the training.There was a range of 1-4 for each of the eight questions asked.Consequently, the overall range for the satisfaction score was 8 to 32 points.

Data Analysis

Student satisfaction data was analyzed using only descriptive statistics that resulted in a mean and standard deviation for each question and the total for all eight questions (i.e., overall satisfaction).Experimental data analysis included testing the differences in percentage accuracy between the first draft and second draft of the subject's process maps.In addition, one-way analysis of variance was used to determine if there were any differences in mean percentage accuracy based on treatment type, university where the subject was studying, and major.

Results

Sample

A total of 130 subjects participated in the experimental portion of this project.Table 1 provides a summary of the demographic data collected.The home institution for this project was Indiana State University.As a result, the majority (55.4%) of the subjects were from that university.Subjects enrolled in mechanical engineering (8.5%) and mechanical engineering technology (44.6%) comprised 53.1% of the total.The majority (81.5%) of the subjects were male.

Satisfaction

Overall, on a scale of 8-32 points, the mean score for subject satisfaction with the training was 25.62 (Sn-1 = 3.25).Each subject's ratings were averaged across the eight questions.The overall mean for all subjects was 3.2.Table 2 provides a summary for each of the eight questions.Of the 126 subjects to complete the satisfaction inventory, only 28 (22%) had an overall mean satisfaction rating below 3.0.The mean satisfaction scores ranged from a low of 1.75 (one subject) to a high of 4.0 (four subject).The majority of the subjects chose not to provide any written comments.Of the 26 subjects who did comment, 11 (42%) made positive comments, and 15 (58%) made negative comments.The negative comments focused on parts of the program that did not work as expected and on needing more time to complete the training.One participant thought that the training would be stronger if it were more challenging.

Experimental Validation

Subjects were offered the opportunity to provide a process map for three problems during the training.The first problem was used as an orientation to the process that they would be using for the two main problems.For the first problem, the subjects were only given the opportunity to provide one map.Based on a comparison with the expert's map for that problem, the overall mean was 18% accuracy (Sn-1 = .16).In other words, the subjects found the task somewhat challenging.

Taken as a whole, the subjects who completed the second problem (i.e., electrical power grid problem) scored an average of 18.8% (Sn-1 = .042)on their first submission and 15.4% (Sn-1 = .043)on their second attempt.A paired samples t-test revealed that the difference was statistically significant (t(129) = 2.919, p < .01),although the reverse of expectations.Subjects who completed the third problem (i.e., heat exchanger problem) scored on average 11.7% (Sn-1 = .021)for their first submission and 12.6% (Sn-1 = .03)for their second submission.This was not found to be a statistically significant difference (t(128) = -1.17,p > .05).In all cases, percent accuracy was based on 100% (i.e., perfect match).The null hypothesis was that the mean difference for the three treatments would be equal to one another and the control group.Based on a one-way analysis of variance, the null hypothesis could not be rejected for the electrical grid problem (F(3, 126) = 1.014, p = .389)nor for the heat exchanger problem (F(3, 126) = 2.315, p = .079).As can be seen, the mean difference for the two submissions for the heat exchanger problem was most pronounced.In addition, no statistically significant differences were found based on gender, major, or the university at which the students were studying.

Although group data confirmed that by and large the subjects did not perform well on the three problems included in this training, analysis of the top performing subjects demonstrated that some students did in fact understand the problem and the process mapping technique reasonably well, as shown in Table 4.

Discussion

As stated earlier, the purpose of this project was to experiment with the development of a self-paced, computer-based program (using the Lectora authoring software) to introduce engineering and technology undergraduates to conceptual mapping techniques to help enhance their diagnostic skills for technical problems.The first hour of this training was designed to introduce students to systems and diagnosing problems.This phase covered the use of the Visual Understanding Environment software to develop conceptual and process maps.Also during this phase, the subjects were given a "simple" technical problem and asked to map a plan of action.Using a similarity flooding algorithm, the students' maps were then electronically compared to an expert's map that was already encoded.The information developed by the algorithm was returned to the subjects as feedback.The second phase of the program included two technical problems developed in collaboration with industrial partners.The first problem involved an electrical grid distribution system, and the second problem focused on a malfunctioning heat exchanger system.In both cases, the subjects were provided with a description of the system and tables containing operational information.From the information presented, the subjects were asked to analyze the system and develop a process map for addressing the apparent malfunction.

The computer program was pilot-tested after completion of the first hour, after completion of the electrical grid problem, and after completion of the full training program.During the quasi-experimental phase, the subjects were asked to complete a satisfaction survey of the finished product and the overall experience.The quasi-experiment involved subjects from five universities and six majors in engineering or technology.The subjects were provided the program and all necessary software on a memory stick and typically completed the training on a laptop.expand the work to all stages in the process from problem identification to solution and follow-up assessment.

Partial support for this work was provided by the National Science Foundation's Transforming Undergraduate Education in Science, Technology, Engineering, and Mathematics (TUES) program under Grant No. 1140677.Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Figure 1 .
1
Figure 1.Sample expert process map (practice problem).

Table 1
1

GenderMajorUniversityFemale9Mech. Eng. Tech.58ISU72(7%)(44.6%)(55.4%)Male106Const. Mgmt.35BGSU35(81.5%)(26.9)(26.9%)NR15Info. Tech.15IUPUI11(11.5%)(11.5%)(8.5%)Mech. Eng.11PU8(8.5%)(6.1%)Pack. Tech.10RHIT4(7.7%)(3.1)Elec. & Comp.1Tech(.8%)

Table 2
2
Mean and Standard Deviation for the Eight Satisfaction Questions
ItemMSD1. The computer based training was interesting3.090.672. The screen design was reasonably attractive3.150.553. The screen layout was logical (i.e., made3.270.57sense)4. All of the program buttons worked as3.210.70expected5. It was easy to navigate my way through the3.290.66program6. The content of the training program was3.120.64meaningful7. This training would be useful to anyone in a3.270.67technical career8. Overall, this was a high quality,3.220.63professional experience

Table 3
3
Mean Differences for the Control Group and Three Treatments for Both Problems
Diff_1Diff_2GroupnMSDMSD135-.06214.176141-.00243.014368232-.00813.123798.04019.129542335-.02497.088989-.01091.101700428-.03775.117151.00764.026403Total130-.03358.131248.00795.085689

Table 4
4

Electric Grid ProblemHeat Exchanger ProblemFirst AttemptSecond AttemptFirst AttemptSecond Attempt0.5600.7340.6510.5350.4610.5260.4970.6920.4230.6520.4910.5540.5150.7510.4190.5370.4930.7410.4750.6480.3420.829
In short, the subjects during pilot testing and during experimentation found on a 4-point scale).However, on average, the subjects' level of performance was much lower than expected.It was clear that about 20% of the subjects clearly understood and found the mapping technique and the feedback provided to help them improve their diagnostic skills.In general, this was not the case.That is, performance on the two main tasks was low and remained low, regardless of the type of feedback given.There were several indications that provide some explanation (i.e., limitations of the work to date).Qualitatively, all of the staff noted that during testing, the majority of the subjects (a) did not have enough time to complete both of the major problem-solving tasks and (b) were treating the exercise as any other academic exercise.A good number of the subjects did nothing more than complete a single map for the problem and simply resubmitted it after reading the feedback.The data, feedback from the subjects, and researcher observations revealed that the electric grid problem was difficult for students to process.In other words, the heat exchanger problem was much more compatible with the simulation capabilities of the training program.Students tended to get lost in the screens provided as a static simulation of the electric grid.Finally, it was clear that at least 2 hours were needed to complete the training as designed.In most cases, the exercise was fitted into a regular class period (75-90 minutes), typically without the professor in attendance.For most, it was not enough time to complete the training and the two problems.A major contribution of this work was the development of automatic student feedback by programming the software using a similarity flooding algorithm(Melnik, Garcia-Molina, & Rahm, 2002).This algorithm provides a means of comparing two graphic items such as conceptual maps.The project team modified the original algorithm to include an automatic thesaurus for synonyms, the use of relative similarity versus full similarity, weighting the nodes, and setting threshold values.Each modification significantly improved the algorithm's accuracy, so that it was possible to compare various expert maps with themselves and get a 100% matching indication.The team validated the algorithm using multiple tests (seeShahhosseini, Ye, Maughan, & Foster, 2014, for a complete report of this work).Technology and engineering educators at all levels are seeking additional ways to help students improve their cognitive and metacognitive reasoning skills.This project was an attempt to determine the extent to which a self-paced, computer-based program would help novice troubleshooters (i.e., students) learn important principles about mapping and diagnostics.The students found the training interesting and useful, but additional work is needed to ensure that the instruction is taken seriously by students.Further, the researchers will work to
System thinking: Strategic planning. A Batra, P Kaushik, L Kalia, SCMS Journal of Indian Management. 742010

Problem solving of machine operators within the context of everyday work: Learning through relationship and community (Doctoral dissertation). J L Brockman, 20043129464Available from ProQuest Dissertations and Theses database

Cooperative learning, group-to-individual transfer, process gain, and the acquisition of cognitive reasoning strategy. B Gabbert, D W Johnson, R T Johnson, 10.1080/00223980.1986.10545253The Journal of Psychology: Interdisciplinary and Applied. 12032001

How refrigeration, heating, ventilation, and air conditioning service technicians learn from troubleshooting (Doctoral dissertation). D F H Green, 20063236882Available from ProQuest Dissertations and Theses database

Towards an understanding of post-training user learning through IT support (Doctoral thesis). N Haggerty, 2004UMI No. NQ96823Available from ProQuest Dissertations and Theses database

Research on problem solving instruction: What works, what doesn't? The Technology Teacher. S D Johnson, 199453

An experimental evaluation of comprehensibility aspects of knowledge structures derived through induction techniques: A case study of industrial fault diagnosis. T Kontogiannis, V Maoustakis, 10.1080/01449290210146728Behaviour & Information Technology. 2122002

Cultivating problem-solving skills through problembased approaches to professional development. M C Lohman, 10.1002/hrdq.1029Human Resource Development Quarterly. 1332002

Similarity flooding: A versatile graph matching algorithm and its application to schema matching. S Melnik, H Garcia-Molina, E Rahm, 10.1109/ICDE.2002.994702Proceedings of the 18th International Conference on Data Engineering. the 18th International Conference on Data EngineeringNew York, NYInstitute of Electrical and Electronics Engineers2002

A theory of education. J D Novak, 1977Cornell University PressIthaca, NY

Learning, creating, and using knowledge: Concept maps as facilitative tools in schools and corporations. J D Novak, 2010RoutledgeNew York, NY2nd ed.

Creating "learning laboratories" for sustainable development in biospheres: A systems thinking approach. N C Nguyen, O J H Bosch, K E Maani, 10.1002/sres.1044Systems Research and Behavioral Science. 2812011

Invited reaction: Cultivating problem-solving skills through problem-based approaches to professional development. R E Mayer, 10.1002/hrdq.1030Human Resource Development Quarterly. 1332002

Common problems with basic problem solving. D Okes, Quality Magazine. 4992010, September

Performance of a computer-based troubleshooting task in the banking industry: Examining the effects of age, task experience, and cognitive abilities. J Sharit, S J Czaja, 10.1207/s15327566ijce0301_1International Journal of Cognitive Ergonomics. 311999

Implementation of similarity flooding algorithm to solve engineering problems using diagnostic skills training technique. A M Shahhosseini, H Ye, G Maughan, W T Foster, 10.1115/IMECE2014-39698International Mechanical Engineering Congress and Exposition. 52014. 2014American Society of Mechanical Engineers

Cognitive mapping techniques for user-database interaction. K Siau, X Tan, 10.1109/TPC.2006.875074IEEE Transactions on Professional Communication. 4922006

Effect of problem solving support and cognitive styles on idea generation: Implications for technology-enhanced learning. S Stoyanov, P Kirschner, 10.1080/15391523.2007.10782496Journal of Research on Technology in Education. 4012007

Competition among causes but not effects in predictive and diagnostic learning. M R Waldmann, 10.1037/0278-73126.96.36.199Journal of Experimental Psychology: Learning, Memory, and Cognition. 2612000


Dosha brain-types: A neural model of individual differences

Frederick T Travis
Center for Brain, Consciousness and Cognition
Maharishi University of Management

Robert Keith Wallace
Department of Physiology
Maharishi University of Management
FairfieldIowaUSA

Dosha brain-types: A neural model of individual differences
10.4103/0975-9476.172385280
This paper explores brain patterns associated with the three categories of regulatory principles of the body, mind, and behavior in Ayurveda, called Vata, Pitta, and Kapha dosha. A growing body of research has reported patterns of blood chemistry, genetic expression, physiological states, and chronic diseases associated with each dosha type. Since metabolic and growth factors are controlled by the nervous system, each dosha type should be associated with patterns of functioning of six major areas of the nervous system: The prefrontal cortex, the reticular activating system, the autonomic nervous system, the enteric nervous system, the limbic system, and the hypothalamus. For instance, the prefrontal cortex, which includes the anterior cingulate, ventral medial, and the dorsal lateral cortices, would exhibit a high range of functioning in the Vata brain-type leading to the possibility of being easily overstimulated. The Vata brain-type performs activity quickly. Learns quickly and forgets quickly. Their fast mind gives them an edge in creative problem solving. The Pitta brain-type reacts strongly to all challenges leading to purposeful and resolute actions. They never give up and are very dynamic and goal oriented. The Kapha brain-type is slow and steady leading to methodical thinking and action. They prefer routine and needs stimulation to get going. A model of dosha brain-types could provide a physiological foundation to understand individual differences. This model could help individualize treatment modalities to address different mental and physical dysfunctions. It also could explain differences in behavior seen in clinical as well as in normal populations.This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.For reprints contact: firstname.lastname@example.com to cite this article: Travis FT, Wallace RK. Dosha brain-types: A neural model of individual differences. J Ayurveda Integr Med 2015;6:280-5.

INTRODUCTION

Typologies have been delineated for body structure (ectomorph, mesomorph, and endomorph), [1] mental functioning (multiple intelligences), [2,3] emotional response (levels of emotional reactivity or sensation-seeking), social relationships (level of need for attachment), and behavioral tendencies (level of stress reactivity). Ayurveda, the traditional medical system of India, has delineated three categories of fundamental regulatory principles of the body, mind, and behavior. These three categories, called doshas, are named Vata, Pitta, and Kapha. [4] The category of Vata dosha includes processes responsible for cell division and cell signaling, movement at all levels of the physiology, excretion of wastes, and also cognition. Vata also regulates the activities of Kapha and Pitta. The category of Kapha dosha includes processes responsible for anabolism, growth and maintenance of structure, storage and stability. The category of Pitta dosha includes processes responsible for metabolism, thermo-regulation, energy homeostasis, pigmentation, vision, and attentional processes. [5] The physical, mental, and behavioral characteristic of each dosha are summarized in Table 1. Notice that the descriptions listed are primarily based on external observation of physiological characteristics and behavior [ Table 1].

PHYSIOLOGICAL PATTERNS SUPPORT THE TRIDOSHIC THEORY

A body of research has grown, which explores patterns of blood chemistry, genetic expression, physiological states, and chronic diseases associated with each dosha type. [6] Triglycerides, total cholesterol, high low-density lipoprotein (LDL), and low high-DL (HDL) concentrations-all common risk factors for cardiovascular disease were reported to be higher in Kapha types compared to Pitta and Vata types. Hemoglobin and red blood count were higher in Pitta compared to other types. Serum prolactin was higher in Vata types. [5] Other research supports these findings that high levels of triglyceride, very LDL and LDL levels and lower levels of HDL cholesterol distinguish Kapha types from other types. [7] Genetic expression also distinguishes dosha types. Genes in the immune response pathways were up-regulated in Pitta types, genes related to cell cycles were up-regulated in Vata types, and genes in the immune signaling pathways were up-regulated in Kapha types. [5] Inflammatory genes were up-regulated in Vata types, whereas up-regulation of oxidative stress pathway genes were observed in Pitta and Kapha types. [8] CD25 (activated B cells) and CD56 (natural killer cells) were higher in Kapha dosha types. CYP2C19 genotypes, a family of genes that help in detoxification and metabolism of certain drugs were down-regulated in Kapha types and up-regulated in Pitta types. [9,10] Physiological patterns also distinguish dosha type. Adenosine diphosphate-induced maximal platelet aggregation was the highest among Vata/Pitta types. [11] In diabetic patients, there were significant decreases in systolic blood pressure in Vata/Pitta, Pitta/Kapha, and Vata/Kapha types after walking (isotonic exercise). The Vata/Pitta types also showed significant decreases in mean diastolic blood pressure. [12] In terms of biochemistry, Kaphas had elevated digoxin levels, increased free radical production and reduced scavenging, increased tryptophan catabolites and reduced tyrosine catabolites, increased glycoconjugate levels, and increased cholesterol: Phospholipid ratio of red blood cell membranes. Pittas showed the opposite biochemical patterns. Vatas showed normal biochemical patterns. [13] A study of basic cardiovascular responses reported that heart rate variability and arterial blood pressure during specific postural changes, exercise, and cold pressor test did not vary with constitutional type. [14] A more recent paper measuring cold pressor test, standing-to-lying ratio, and pupillary responses in light and dark reported that Kapha types have higher parasympathetic activity and lower sympathetic activity in terms of cardiovascular reactivity as compared to Pitta or Vata types. [15] An open question in this discussion is how these genetic, metabolic, and physiological factors express themselves in terms of more expressed lifestyle and behavioral characteristics. Obviously, many genetic and metabolic factors are influenced by the nervous system, which not only controls metabolic and growth factors through the endocrine system but have an impact on the functioning of the immune, digestive, cardiovascular, and virtually every other system in the body. Further, it controls all our mental, emotional, and behavioral systems including attention, learning, memory, and sleep.

THEORY OF DOSHA BRAIN-TYPES

Brain functioning underlies and affects mental and behavior functioning. Thus, the dosha types which are based on observable mental and physical behavior and characteristics, should also be based on different patterns of brain and nervous system functioning. Thus, defining fundamental brain types may give a means to understand individual differences.

For instance, Vata dosha, which is highly variable in behavior and in response to the environment, would be associated with a greater range of functioning of the brain and nervous system. Pitta dosha, which is characterized by dynamism, would be associated with fast, passionate responses of the brain and nervous system to challenges in the environment. Kapha dosha, which is characterized by steadiness, would be associated with stabile activity patterns of the brain and nervous system.

These three different types of brain functioning can be seen as different patterns of functioning of six major systems of the nervous system. Table 2 presents an overview of this model.

The first system is the frontal executive system of the brain, which includes the anterior cingulate gyrus (attention switching and error detection), ventral medial (emotional input), and the dorsal lateral prefrontal cortex (decision making). The Vata Brain-Type exhibits a high range of prefrontal functioning leading to the possibility of being easily overstimulated. They perform activity quickly. Learn quickly and forget quickly. They like to multi-task. Their fast mind gives them an edge in creative problem solving. The Pitta brain-type reacts strongly to all challenges leading to purposeful and resolute actions. They never give up and are very dynamic and goal oriented. The Kapha brain-type is slow and steady leading to methodical thinking and action. They prefer routine and needs stimulation to get going.

The second system is the reticular activating system (RAS) of the brain, which is responsible for arousal level. It determines if we are highly alert, relaxed, or asleep. The Vata brain-type exhibits a high range of arousal levels leading to a sense of over-reacting to the world. They have trouble sleeping soundly. The Pitta brain-type becomes easily aroused and maintains high level of focused arousal to get a task accomplished. The Kapha brain-type is not easily perturbed. They are calm and easy going and seldom get excited.

The third system is the autonomic nervous system that includes the sympathetic (fight-or-flight) and parasympathetic (tend-and-befriend) systems. Ninety percent of our responses to the environment are governed by the autonomic nervous system. This system automatically maintains an optimal level of arousal to deal with any situation from watching a sunset to running after a taxi. The fight-and-flight response is easily turned on in Vata brain-types and is variable in its level of response. The Vata brain-type is very sensitivity to pain and cold temperatures. Their limbs are generally cold and have poor circulation since high sympathetic activation reduces periphery blood flow. The fight-and-flight response turns on to a high level in Pitta brain-type and then return to resting levels again. Autonomic response is tied to purposeful goal oriented behavior it turns on to reach the goal and then turns off. The fight-or-flight response is not easily evoked in the Kapha brain-type. The parasympathetic response is generally high, and the person is very steady. They are sensitive to cold and dampness.

The fourth system is the enteric nervous system, which is responsible for digestion. The enteric nervous system interacts with the microbiome of the gut to modulate immune functioning and activity of the parasympathetic nervous system. The Vata brain-type exhibits a high range of digestive power leading to an irregular appetite, irregular bowel movements, and frequent gas. They are more affected by eating late at night or over eating. That would react more to new foods. The Pitta brain-type has a strong digestion. They are always hunger and can eat at any time and, seemingly, any food. They have loss and frequent bowel movements. The Kapha brain-type is not much affected by what or when they eat. They can easily skip a meal. The enteric nervous system interacts with satiety centers in the hypothalamus to govern feelings of hunger. The fifth system is the limbic system, which is responsible for emotion. It includes many nuclei around the center of the brain: The amygdala for survival and fear response, the hippocampus for anger and spatial awareness, the nucleus accumbens for pleasure, the insula for saliency of experience and tie bodily states to emotions, and hypothalamus that integrates the activity of the autonomic nervous system. The limbic system is highly sensitive to changes in the environment in Vata brain-types. Their emotions are rich and highly variable. When over-activated, the Vata brain-type can have excessive fear and phobias.

The limbic system provides the fire for the Pitta brain-type to react to the world. Their actions are competitive and dynamic. In excess, this can lead to irritability and angry. The Kapha Brain-Type is always smiling. They are seldom in a hurry. Nothing seems to make them angry.

The last system is the hypothalamus, which is responsible for homeostasis. It automatically controls our responses to challenges, freeing us from considering hunger, thirst, and arousal levels. The output of the limbic system feeds into the hypothalamus, which then will activate the autonomic nervous systems as needed and even activate the prefrontal cortex. The hypothalamus is intimately involved in the functioning of the other five brain areas. In Vata brain-types, the hypothalamus is constantly changing the state of mind and body. They will experience bursts of activity and rest, and will frequently snack and drink. In Pitta brain-types, the hypothalamus has a strong on and off switch. When turned on, the autonomic nervous system functions at its maximum to accomplish the goal. There is no half-way point. The hypothalamus maintains a higher core body temperature and dynamic mental and physical activity that leads to the preference for cool foods and drinks in this brain-type. In Kapha brain-types, the hypothalamus maintains a slower metabolism. This can lead to easily gaining weight. There is slower responsiveness to temperature and situations [ Table 2]. This table has been created based on the organizing principles of the three doshas in Ayurveda. If these sub-categories of functioning of brain systems are valid, then other mental and physical typologies should also be explained by the patterns of nervous system functioning described in this table. The explanatory power of this proposed model is demonstrated in consideration of sensation-seeking.

Sensation-seeking in light of the three brain-types

Zuckerman defined the personality trait of sensation-seeking as the tendency to choose varied, complex, and intense sensations leading to increased risk. This trait is exhibited in a range of behaviors including substance use, gambling, and risky sexual practices. [16] High sensation-seekers orient more strongly to novel stimuli and show larger cortical activation patterns. Low sensation-seekers show defensive reactions to strong stimuli and show reduced or unresponsive cortical activation patterns to variations in stimulus intensity. [17] Sensation-seeking is tied to dopamine levels, with higher dopamine leading to more risky behaviors. Dopamine appears to amplifier behavior when there is the chance for large gains, and decreases discrimination that the behavior may also yield large losses. This is seen in rats who press levels for food one yielding one pellet each time, the other leading 4 pellets from 12% to 80% of the time. [18] This was also seen with humans who were given a dopamine agonist, cabergoline, which increases the effect of dopamine in the brain. During conditions with cabergoline compared to placebo, subjects selected the high-risk condition (60-40) when there was the chance for high gains. [19] This effect in humans was only seen in subjects with low levels of sensation-seeking at baseline.

High sensation-seeking is also tied to reduced attention to negative consequences of loss. A functional magnetic resonance imaging (fMRI) study compared blood flow in high and low sensation-seekers during a Wheel of Fortune type test. Both high and low sensation-seekers showed activation in bilateral insular and prefrontal cortical response to winning (activation). However, the high sensation-seekers showed deactivation in these structures to negative outcomes compared to the low group. Ignoring possible negative consequences of a decision may lead to maladaptive choices. [20] High sensation-seekers orient more strongly to novel stimuli and they tend to ignore negative consequences. High sensation-seeking shares many characteristics of the Pitta brain-type with faster activation of the reticular activation that systemically increases arousal, activation of the amygdala that turns on the sympathetic nervous system to increase resource allocation, and activation of the anterior cingulate gyrus to turn your attention to the experience.

Low sensation-seekers have a defensive reaction to novel stimuli, cortical activation is lower, and they consider both positive and negative consequences of an action. Low sensation-seekers shares many characteristics of the Kapha brain-type with less reactivity overall-lower activation of the reticular activation, the amygdala, and the sympathetic nervous system.

The Vata brain-type may lead more to impulsivity rather than to sensation-seeking. Impulsivity is acting without consideration of the consequences of ones actions. Impulsivity and sensation-seeking have different trajectories of development from age 12 to 24. [21] The Vata brain-type is marked by variable response to external conditions that would lead to bursts in attention, in physical energy, and in autonomic response to situations. This type would probably be scored as neither as high nor as low sensation-seeker.

An important consideration in this discussion is that the sensation-seeking trait develops during adolescence. A 12 years longitudinal study of 7640 individuals, age 12 years at baseline, reports substantial variation in the magnitudes of developmental change in sensation-seeking. Some teenagers showing rapid changes in sensation-seeking as they matured and others maintaining relatively constant over the 12 years. [21] These differences could reflect predominance of Vata, Pitta or Kapha brain-types.

A research program to explore physiological correlates of dosha brain-types A research program using physiological and cortical measures could explore the utility of dosha brain-types. Levels of this research program are presented in Table 3. For instance, resting state networks have become a recent focus in neural imaging research. Resting state networks are large-scale cortical circuits that dynamically interact to process experiences a salience network, involving the anterior insula and cingulate gyrus, deactivates the default mode network and activates the central executive network. [22] The salience network could be more variable in Vata brain-types, less reactive in Kapha brain-types, and function more decisively in Pitta brain-types more quickly shutting down the default mode and turning on the central executive network. fMRI could also be used to assess emotional responses to the International Affective Picture System. Pitta brain-types should respond me strongly to stressful pictures. This research program could investigate sympathetic/parasympathetic balance as measured by heart rate variability, which is sensitive to anxiety levels and level of emotional stress. [23] It could also measure cytokine and c-reactive proteins to assess immune functioning in the different brain-types. A focused analysis of brain-types would build the neural basis to understand dosha types [ Table 3].

CONCLUSION

This brain-type model makes specific predictions about patterns of frontal executive functioning, RAS activation, and patterns of autonomic and limbic functioning. These variables can be assessed by standard physiological and electrophysiological measures. Future research will investigate the relation of dosha type to electroencephalographyEEG patterns, autonomic activation and autonomic balance, stress reactivity and behavior to support the proposed model. This line of research could help clarify variable response to drugs and lifestyle modifications in normal and clinical populations and so help target health promotion at all levels of life.

Financial support and sponsorship

Conflicts of interest

There are no conflicts of interest.

Table 1 :
1Description of doshas in AyurvedaVata dosha
Pitta dosha
Kapha dosha
Lean or delicate build,
dry skin, and fine hair
Vibrant and energetic
expression
Light of heart and quick
to speak
Highly creative, and very
changeable in mood,
and flexible in decision
making, and behavior

Medium build,
muscular
Warm friends and
fierce opponents
Are strong willed,
passionate leaders
Tend to be direct
and take charge

Solid frame and
thick bones
Calm, slow to act
and react
Easy personality, and
are loving and caring
Move slowly and
purposefully
Are usually
conservative

Table 2 :
2Patterns of brain functioning for Vata, Pitta, and Kapha brain-typesBrain system
Vata brain-type
Pitta brain-type
Kapha brain-type
Prefrontal
executive circuits

Easily overstimulated
Acts quickly
Learns quickly and forgets quickly
Likes to multi-task

Strong reaction to challenges
Purposeful learning
Tenacious
Goal oriented

Methodical thinking and action
Learns slowly with high level of retention
Prefers routine
Needs stimulation to get going
Reticular
activating system

Is alert but inconsistent
Can lead to nervousness
Hard to fall asleep and stay asleep

Strong dynamic responses to
every situation
Goal oriented behavior
Can see impulsive behavior

Not easily provoked
Easy going
Easily falls asleep and stays asleep

Autonomic
nervous system

The fight-and-flight response is
frequently and easily triggered
Limbs are generally cold and have
poor circulation
Sensitive to pain and cold

The fight-and-flight response
turns on strongly to reach a goal,
and then turns off
Does not like heat
Good peripheral circulation

The fight-or-flight response is not easily
evoked
High parasympathetic response
Sensitive to cold and dampness

Enteric nervous
system

Irregular appetite
Irregular bowel movements, and
frequent gas

Always hungry
Irritable if miss a meal
Loose and frequent bowels

Can easily miss a meal
Regular daily bowel movements

Limbic system
Moods change quickly
Fear and phobias can occur when
in excess

Strong emotions
Competitive and dynamic nature
Irritable and angry when in excess

Calm and happy
Rarely gets angry
Shy and depressed when in excess
Hypothalamic
control

Irregular appetite
Bursts in activity
Frequent snacking and drinking

Always hungry
Strong response
Higher metabolism
Prefers cool foods and water

Can easily miss a meal
Low and steady response
Lower metabolism
Tendency to gain weight

Table 3 :
3Research program to explore physiological, neural, psychological, and behavioral correlates of dosha types fMRI of resting networks fMRI viewing IAPS fMRI during a 2-back task, which measures frontal executive functioning EEG EEG power and coherence patterns ERP during a 2-back task (gives time and functional information) Autonomic measures Sympathetic/parasympathetic balance (heart rate variability) Physiological measures Immune functioning -cytokine and C-reactive proteins Sympathetic reactivity to a speech challenge (cortisol) Psychological measures Profile of mood states Emotional intelligence Cog state: A computerized test of many cognitive measures Behavioral measures Reaction time Psychomotor vigilance task EEG=Electroencephalography, ERP=Event-related potential, fMRI=Functional magnetic resonance imaging, IAPS=International Affective Picture SystemMeasures
Sample research
Neural
imaging

Anthropometry, somatotypes, and aerobic power in ballet, contemporary dance, and dancesport. H Liiv, M A Wyon, T Jürimäe, M Saar, J Mäestu, J Jürimäe, Med Probl Perform Art. 28Liiv H, Wyon MA, Jürimäe T, Saar M, Mäestu J, Jürimäe J. Anthropometry, somatotypes, and aerobic power in ballet, contemporary dance, and dancesport. Med Probl Perform Art 2013;28:207-11.

Multiple Intelligences: The Theory and Practice. H Gardner, New York: Basic BooksGardner H. Multiple Intelligences: The Theory and Practice. New York: Basic Books; 1993.

Multiple Intelligences: New Horizons. New York: Basic Books. H Gardner, Gardner H. Multiple Intelligences: New Horizons. New York: Basic Books; 2010.

. B Dash, R K Sharma, Charaka, Samhita, Caukhambha OrientaliaVaranasi, IndiaDash B, Sharma RK. Charaka Samhita. Varanasi, India: Caukhambha Orientalia; 1995.

Whole genome expression and biochemical correlates of extreme constitutional types defined in Ayurveda. B Prasher, S Negi, S Aggarwal, A K Mandal, T P Sethi, S R Deshmukh, J Transl Med. 648Prasher B, Negi S, Aggarwal S, Mandal AK, Sethi TP, Deshmukh SR, et al. Whole genome expression and biochemical correlates of extreme constitutional types defined in Ayurveda. J Transl Med 2008;6:48.

Prakriti and its associations with metabolism, chronic diseases, and genotypes: Possibilities of new born screening and a lifetime of personalized prevention. S Dey, P Pahwa, J Ayurveda Integr Med. 5Dey S, Pahwa P. Prakriti and its associations with metabolism, chronic diseases, and genotypes: Possibilities of new born screening and a lifetime of personalized prevention. J Ayurveda Integr Med 2014;5:15-24.

Association of constitutional type of Ayurveda with cardiovascular risk factors, inflammatory markers and insulin resistance. N P Mahalle, M V Kulkarni, N M Pendse, S S Naik, J Ayurveda Integr Med. 3Mahalle NP, Kulkarni MV, Pendse NM, Naik SS. Association of constitutional type of Ayurveda with cardiovascular risk factors, inflammatory markers and insulin resistance. J Ayurveda Integr Med 2012;3:150-7.

Potential of ayurgenomics approach in complex trait research: Leads from a pilot study on rheumatoid arthritis. R C Juyal, S Negi, P Wakhode, S Bhat, B Bhat, B K Thelma, PLoS One. 745752Juyal RC, Negi S, Wakhode P, Bhat S, Bhat B, Thelma BK. Potential of ayurgenomics approach in complex trait research: Leads from a pilot study on rheumatoid arthritis. PLoS One 2012;7:e45752.

Traditional medicine to modern pharmacogenomics: Ayurveda Prakriti type and CYP2C19 gene polymorphism associated with the metabolic variability. Y Ghodke, K Joshi, B Patwardhan, Evid Based Complement Alternat Med. Ghodke Y, Joshi K, Patwardhan B. Traditional medicine to modern pharmacogenomics: Ayurveda Prakriti type and CYP2C19 gene polymorphism associated with the metabolic variability. Evid Based Complement Alternat Med 2011;2011:249528.

Indian genome variation consortium. EGLN1 involvement in high-altitude adaptation revealed through genetic analysis of extreme constitution types defined in Ayurveda. S Aggarwal, S Negi, P Jha, P K Singh, T Stobdan, M A Pasha, Proc Natl Acad Sci U S A. 107Aggarwal S, Negi S, Jha P, Singh PK, Stobdan T, Pasha MA. Indian genome variation consortium. EGLN1 involvement in high-altitude adaptation revealed through genetic analysis of extreme constitution types defined in Ayurveda. Proc Natl Acad Sci U S A 2010;107:18961-6.

Ayurvedic concept of constitution) and variations in Platelet aggregation. S Bhalerao, T Deshpande, Thatte U Prakriti, BMC Complement Altern Med. 12Bhalerao S, Deshpande T, Thatte U. Prakriti (Ayurvedic concept of constitution) and variations in Platelet aggregation. BMC Complement Altern Med 2012;12:248-56.

Effect of walking (aerobic isotonic exercise) on physiological variants with special reference to Prameha (diabetes mellitus) as per Prakriti. S Tiwari, S Gehlot, S K Tiwari, G Singh, Ayu. 33Tiwari S, Gehlot S, Tiwari SK, Singh G. Effect of walking (aerobic isotonic exercise) on physiological variants with special reference to Prameha (diabetes mellitus) as per Prakriti. Ayu 2012;33:44-9.

Hypothalamic digoxin, hemispheric chemical dominance, and the tridosha theory. R K Kurup, P A Kurup, Int J Neurosci. 113Kurup RK, Kurup PA. Hypothalamic digoxin, hemispheric chemical dominance, and the tridosha theory. Int J Neurosci 2003;113:657-81.

The basic cardiovascular responses to postural changes, exercise and cold pressor test: Do they vary in accordance with the dual constitutional types of Ayurveda? Evid Based. P K Tripathi, K Patwardhan, G Singh, Complement Alternat Med. 201Tripathi PK, Patwardhan K, Singh G. The basic cardiovascular responses to postural changes, exercise and cold pressor test: Do they vary in accordance with the dual constitutional types of Ayurveda? Evid Based Complement Alternat Med 2011;201:251-9.

Physiological variations in the autonomic responses may be related to the constitutional types defined in Ayurveda. S B Rapolu, M Kumar, G Singh, K Patwardhan, J Humanitas Med. 57Rapolu SB, Kumar M, Singh G, Patwardhan K. Physiological variations in the autonomic responses may be related to the constitutional types defined in Ayurveda. J Humanitas Med 2015;5:e7.

What is the sensation seeker? Personality trait and experience correlates of the Sensation-Seeking Scales. M Zuckerman, R N Bone, R Neary, D Mangelsdorff, B Brustman, J Consult Clin Psychol. 39Zuckerman M, Bone RN, Neary R, Mangelsdorff D, Brustman B What is the sensation seeker? Personality trait and experience correlates of the Sensation-Seeking Scales. J Consult Clin Psychol 1972;39:308-21.

The psychophysiology of sensation seeking. M Zuckerman, J Pers. 58Zuckerman M. The psychophysiology of sensation seeking. J Pers 1990;58:313-45.

Dopaminergic modulation of risk-based decision making. J R St Onge, S B Floresco, Neuropsychopharmacology. 34St Onge JR, Floresco SB. Dopaminergic modulation of risk-based decision making. Neuropsychopharmacology 2009;34:681-97.

Dopamine modulates risk-taking as a function of baseline sensation-seeking trait. A Norbury, S Manohar, R D Rogers, M Husain, J Neurosci. 33Norbury A, Manohar S, Rogers RD, Husain M. Dopamine modulates risk-taking as a function of baseline sensation-seeking trait. J Neurosci 2013;33:12982-6.

High and low sensation seeking adolescents show distinct patterns of brain activity during reward processing. A Cservenka, M M Herting, K L Seghete, K A Hudson, B J Nagel, Neuroimage. 66Cservenka A, Herting MM, Seghete KL, Hudson KA, Nagel BJ. High and low sensation seeking adolescents show distinct patterns of brain activity during reward processing. Neuroimage 2013;66:184-93.

Individual differences in the development of sensation seeking and impulsivity during adolescence: Further evidence for a dual systems model. K P Harden, E M Tucker-Drob, Dev Psychol. 47Harden KP, Tucker-Drob EM. Individual differences in the development of sensation seeking and impulsivity during adolescence: Further evidence for a dual systems model. Dev Psychol 2011;47:739-46.

A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. D Sridharan, D J Levitin, V Menon, Proc Natl Acad Sci U S A. 105Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci U S A 2008;105:12569-74.

Heart rate variability, trait anxiety, and perceived stress among physically fit men and women. R K Dishman, Y Nakamura, M E Garcia, R W Thompson, A L Dunn, S N Blair, Int J Psychophysiol. 37Dishman RK, Nakamura Y, Garcia ME, Thompson RW, Dunn AL, Blair SN. Heart rate variability, trait anxiety, and perceived stress among physically fit men and women. Int J Psychophysiol 2000;37:121-33.