Monthly Archives: January 2024

Rate-type Models of Fluid Flow in Porous Media

DOI: 10.31038/NAMS.2024711

Abstract

The paper develops a modelling for fluid and heat flows also in connection with diffusion processes in nanodevices. The approach involves rate equations that generalize those of Maxwell fluid and Maxwell-Cattaneo heat flux and is based on two basic principles of continuum physics: objectivity and thermodynamic consistency. From the technical side, the paper follows the view that a convenient procedure should be grounded on the theory of mixtures. Accordingly, within the classical theory of mixtures, the constitutive equations are established for stress tensor and heat flux in fluid-solid mixtures with relaxation properties. The results are then combined with models occurring in the literature about flow in porous media.

Keywords

Relaxation in nanosystems, Rate-type models, Porous media, Mixtures, Thermodynamic consistency, Objective derivative

Introduction

Among the features of nanodevices is the inefficient dissipation of heat, which can lead to material degradation. Consistently, the decrease of the thermal conductivity (see, e.g., [1-3]), which hinders heat exchange, calls for more involved materials models. Nanoscale systems with dimensions comparable to the mean-free path of particles (or phonons) nonlocal effects are required to be inserted in the model. Furthermore, in microdevices working at high frequencies also relaxation effects occur so that realistic models need to account for the time delay of relaxation processes. Diffusion processes are also of interest in nanodevices and this indicates that a proper modelling of fluid flow in porous media is required.

In essence, nonlocality and relaxation are modelled by means of spatial and time derivatives of suitable order in the equation of motion and the balance of energy. This paper develops a modelling for fluid and heat flows through an approach that is based on two principles: objectivity and thermodynamic consistency. Objectivity means that constitutive equations are form-invariant under the group of Euclidean transformations [4,5]. Thermodynamic consistency means that, granted the validity of the balance equations, the constitutive equations make the entropy production non-negative. For definiteness, from the technical side, this paper follows the view that a convenient approach should be grounded on the theory of mixtures. That is why we begin with the main points of the theory of mixtures. Next the constitutive equations are established for stress tensor and heat flux in fluid-solid mixtures with relaxation properties. The results are combined with models occurring in the literature about flow in porous media.

Notation and Balance Equations for Mixtures

The body under consideration is a mixture of n constituents occupying a time-dependent region of the three-dimensional space. The subscript α= 1, 2,…, n labels the fields pertaining to the α-th constituent and Σ_αis a shorthand for Σⁿ_α=1The compact notation is used; for any pair of vectors u, v the symbol u·v denotes the inner product, and likewise for tensors, The symbol ∇ denotes the gradient, ∇· the divergence, and ⊗ the dyadic product.

Denote by the subscript α= 1, 2, …, n the quantities pertaining to the α-th constituent. For any function the dashed symbol denotes the material derivative relative to the pertinent constituent, viz.

The conservation of mass of single constituents results into the n continuity equations

The equations of motion are written in the form

where T_α is the (Cauchy) stress tensor, b_α the body force, m_α the interaction force, or growth, between constituents. The growths are subject to

No body couples are considered and then the balance of angular momentum results in

Let be the specific internal energy. The local version of the balance of energy eventually reads

where is the energy supply and

Lastly we look at the second law of thermodynamics which, also for mixtures, places restrictions on the admissible constitutive equations. For any α-th constituent let be the absolute temperature and the specific entropy. The balance of entropy is derived by the general view that the entropy change equals the entropy transfer plus the entropy production. This is made formal by letting j_α be the entropy flux, the entropy supply and the entropy production so that

The set of functions

constitutes a thermodynamic process. The axiom, known as entropy principle or second law of thermodynamics, about the increase of entropy in a closed system is stated by saying 2 that the entropy production is non-negative for any thermodynamic process consistent with the balance equations. Formally, for mixtures the second law of thermodynamics requires that

for any thermodynamic process.

This statement is based on refs [6-8]. Following [9] and [5], §9.3, we let the entropy productions be given by constitutive equations, as is done for the entropy fluxes {j_α} after [7].

If the constitutive equations make the inequality non-valid then those constitutive equations are not admissible. That is why we can see the second law as the selection of physically admissible constitutive models.

For technical convenience we put

k_α being referred to as extra-entropy flux. Hence we can write eq. (4) as

Substitution of from (3) results in

Using the Helmholtz free energy we have

Hence the second law is expressed by the Clausius-Duhem (CD) inequality

We now investigate the thermodynamic requirements on the pertinent constitutive equations.

Solid-fluid mixtures

With the view of modelling porous media, we consider a binary mixture with a solid and a fluid; we denote by the subscripts f, s the quantities pertaining to the fluid and solid constituents. The fluid is viscous and compressible. To describe relaxation effects, both stress and heat flux are modelled through rate equations as is the case for the Maxwell (or Maxwell-Wiechert) fluid and the Maxwell-Cattaneo equation of the heat flux; account of nonlocality through higher-order derivative is developed in [10] via the Guyer-Krumhansl form.

Owing to objectivity, the rate has to be expressed through an objective time derivative. The simplest one is the corotational derivative namely

for vectors a and tensors A while W is the pertinent spin tensor (W_f or W_s). Hence we assume

7,8

where K_s ∈ Sym is non-singular. If the rates vanish then eqs (7) and (8) reduce to the Navier-Stokes and Fourier laws; for formal simplicity the longitudinal viscosity coefficient is taken to be zero.

To frame these assumptions in a consistent thermodynamic setting we let

and derive the constitutive equations for of the fluid and the solid. We let θ_f = θ_s = θ while ∇ θ_f ≠ ∇ θ_s. Then we observe that

we put m_f = − β(|u|)u, u = u_f − u_s, and hence

With these assumptions the extra-entropy fluxes k_f, k_s turn out to be zero; to save writing we omit them. Hence the CD inequality takes the form

compute above

Compute and observe that the relations

com down

follow as a consequence of the linearity and arbitrariness of θ`_f, θ`_s trD_f, (∇θ_f)`, (∇θ_s)`, `D_f . Next we recall the identity `E_s = F^T_sD_sF_s and notice that, by (7) and (8),

7, 8 down

and the like for q_s. Thus we can write the remaining terms of the CD inequality as

and down

We first consider the dependence on W_fand W_s,

The linearity on W_f,W_s and the arbitrariness of q_f, q_s imply that each term has to vanish; the vanishing of the second and third terms results in

Next since W ∈ Skw then for any tensors A,B we have

hence implies that

The linearity and arbitrariness of D_f,D_s, Δθ_f, Δθ_s imply

11, 12

The CD inequality then reduces to

and hence each term has to be non-negative.

By using (11) and (12) we find

The symmetry conditions (9) and (10) hold identically while (13) holds if and only if

namely the expected relations for heat conductivities K_s, , shear viscosity μ_f, and interaction force coefficient β.

Dynamics of Viscous Fluids in Porous Media

The dynamics of the fluid is governed by the balance equations. With reference to the literature (e.g. [11] and refs therein), to simplify the notation we restrict attention to the fluid, omit the subscript f and use a superposed dot, ε˙, instead of a slash, ε’. The continuity equation and the equation of motion read

read down

where g is the acceleration gravity. The function βv generalizes Darcy’s model through the Forchheimer function β while, as usual, it is assumed v_s = 0. According to (15) we have found that any β ≥ 0 is consistent with thermodynamics.

Things are more involved with the balance of energy, namely

By definition,

Hence the balance of energy involves which requires that the rate equations (7), (8) are applied.

Conclusions

The thermodynamic analysis provides a complete scheme of dynamic equations for the flow of fluids in solids. Yet the general scheme so obtained is quite cumbersome thus justifying some approximations applied in the literature. Quite often β is taken to be constant but, foremost, the fluid is taken to incompressible, ∇ · v = 0, while

The dependence of ε on and q is not considered and (see, e.g., [12]) ε is assumed to depend only on the temperature θ.

According to eqs (14) and (16) the free energy ε is independent of and q if

c₁, c₂ being positive parameters possibly dependent on . Though this looks a very specific model, eq. (17) is the necessary assumption that makes ε (θ, ρ ) thermodynamically consistent if T_f and q_f are subject to the rate equations (7) and (8).

Acknowledgments

The research leading to this work has been developed under the auspices of INDAM-GNFM.

References

Hennessy MG, Myers TG (2020) Guyer-Krumhansl heat conduction in thermoreflectance experiments, in Multidisciplinary Mathematical Modelling. Applications of Mathematics to the Real World, F. Font and T.G. Myers eds, pp. 21-34, Springer Cham.
Cahill DG et al (2014) Nanoscale thermal transport, II Appl Phys Rev 1: 011305.
Dong Y (2015) Dynamical Analysis of Non-Fourier Heat Conduction and Its Application in Nanosystems, Springer, New York.
Truesdell C (1984) Rational Thermodynamics, Springer, New York.
Morro A, Giorgi C (2023) Mathematical Modelling of Continuum Physics, Birkh¨auser, Cham.
Truesdell C (1969) Rational Thermodynamics: A Course Of Lectures On Selected Topics, McGraw-Hill, New York.
M¨uller I (1968) A thermodynamic theory of mixtures of fluids. Arch Rational Mech Anal 28: 1-39.
Bowen RM (1968) Thermochemistry of reacting materials. J Chem Phys 49: 1625-1637.
Morro A (2022) Diffusion models of continuum physics. Nanotechnol Adv Mater Sci. 5: 1-6.
Morro A (2023) On the Modelling of Heat Conduction in Crystals via Higher-grade Terms. Nanotechnol Adv Mater Sci 6 (3): 1-4.
[11] Straughan B (2023) Thermal convection in a Brinkman-Darcy-Kelvin-Voigt fluid with a generalized Maxwell-Cattaneo law. Ann Univ Ferrara 69: 521-540.
Payne LE, Song JC (1997) Continuous dependence on initial-time geometry and spatial geometry in generalized heat conduction. J Math Anal Appl 214: 173-190.

What are the ASPIRE Principles and Why Do They Matter for Post-Pandemic Education?

Leave a reply

DOI: 10.31038/PSYJ.2024611

Although they were less likely to fall ill, the pandemic exacerbated difficulties for many young people across the world in terms of mental health, connection with others and widening gaps between the privileged and disadvantaged. Many governments were keen for students to ‘catch up’ on curriculum targets but others advocated for social and emotional issues to be addressed as a priority. Negative emotions inhibit cognitive pathways and learning is more accessible with higher levels of wellbeing. Referring to six principles of positive education, summarised by the acronym ASPIRE, this invited paper explored what happened to high school students worldwide in lockdown and what they need in education in a post-COVID world.

ASPIRE is an acronym for Agency, Safety, Positivity, Inclusion, Respect and Equity. These principles, when threaded through everything that happens in a school, can enhance both wellbeing and learning. They are based in the positive psychology literature [1-3] and also aligned with healthy child development.

Agency

This can be defined as having a voice and choice about what concerns you, the opposite of having actions and decisions made by others. Self-determination is now accepted as a pillar of wellbeing [4] and comprises autonomy, relatedness and competence. What happened in the pandemic was out of the control of young people and for some this had a negative impact on their wellbeing. Others however found a role in having a greater say in family life, such as supporting younger siblings. What is promising is that in many countries young people are taking a lead in post-COVID recovery with several initiatives highlighted in the article. Schools with a strong commitment to the pupil voice have reported positive outcomes, including more pro-social behaviour, stronger relationships and improved attainment and attendance [5].

Safety

Safety embraces physical, emotional and psychological safety. Although measures were taken to protect physical wellness for everyone in the pandemic, safety was compromised in many other ways with less access to avenues of support. This included increases in family violence, child abuse, on-line bullying and models of misogynistic behaviours. Going back to school was positive for some young people at risk but constant failure in an academic milieu undermines emotional safety and many students did not return to school after lockdown. When the pillars of ‘learning to be’ and ‘learning to live together’ are on offer in schools there is more opportunity for achievement. Social and emotional learning with an appropriately safe and solution-focused pedagogy also has the potential to address issues that undermine safety, such as bullying, addictions, social media use, toxic masculinity, and racism.

Positivity

Young people are increasingly unhappy across the world and since the pandemic are even more at risk of poor mental health [6]. It therefore makes sense to focus on helping young people feel better about themselves, other people and the world around them. Although important to acknowledge negative emotions there is also a wealth of interventions that promote optimism, hope, gratitude, resilience and coping skills. The quality of relationships, especially language makes the most difference. Strengths based conversations focus on what is going well and the qualities that students bring to learning and life.

Inclusion

Many young people experienced a sense of loneliness during the pandemic as they did not have their peer group around to help them explore and develop their sense of identity. Social media use increased and although that enabled positive connections for some it also had a negative impact in teenagers comparing themselves negatively to others and perhaps getting involved in closed groups who promoted conspiracy theories or right-wing ideology. Having a sense of belonging is critical to wellbeing and resilience but this needs to be inclusive of all, not exclusive. A sense of belonging at school means being accepted, being supported and making progress in learning. Prilleltensky [7] writes about ‘mattering‘ which he defines as being valued but also being able to contribute value. As students lost social confidence in the pandemic they need opportunities talk with their peers about things that concern them and regain friendship skills. In Circle Solutions, students are regularly mixed up out of their usual social groups to talk with those they don’t know. This has far reaching impacts for promoting class cohesion, supportive networks and resilience [8-10].

Respect

Respect is for individuals, their ideas, their rights and their culture. It is encapsulated in the Golden Rule-treating others as you would wish to be treated. In some contexts, respect was enhanced in the pandemic as the role of health and key-workers was acknowledged and parents trying to teach their own children at home developed a new respect for teachers. In schools respect is demonstrated in courteous communications, in acknowledging context, in not jumping to judgement and listening well. It means treating everyone with dignity, regardless of their background or position. It also means respecting diversity and the different needs and abilities that students have. When we have a ‘one-size’ fits all education system with homogenous expectations we may lose respect for those that do not ‘fit’. This is having negative repercussions across society and requires a rethinking of what education is for.

Equity

The pandemic exacerbated inequality across the world, not only for those in poverty, but also related to gender and geography. Access to on-line learning was restricted to those who could afford good technology, had space to use this and were living in areas with reliable internet. Girls often found themselves looking after others rather than maintaining their education. Equity is not the same as equality-it refers to fairness and flexibility-being able to offer students the resources and support that give them the same opportunities as others. That is clearly not happening in many countries. The barriers to equity include a lack of investment in state education, a competitive ethos and the inflexibility of the curriculum, focused predominantly on academic knowledge rather than the skills and understanding that facilitate a life lived well. Equity therefore needs to address issues of citizenship, ensuring that everyone is aware of issues of power and influence and what is needed to build a fairer, more cohesive society where everyone has the opportunity to thrive and be valued for what they contribute.

Conclusion

A report for UNESCO notes that the pandemic has not only revealed vulnerabilities across the world, but also human resourcefulness and potential. They ask that world leaders commit to strengthen education as a common good. In education, as in health, we are safe when everybody is safe; we flourish when everybody flourishes. The ASPIRE principles show how education might lead the way.

References

Roffey S (2012) Positive Relationships: Evidence based practice across the world. Springer.
Roffey S (2020) Circle Solutions for Student Wellbeing. Relationships, resilience, responsibility. Sage.
Kern P, Weymeher M (eds) (2021) The Palgrave Handbook of Positive Education. Palgrave
Deci EL, Ryan RM (2017) Self-Determination Theory: Basic Psychological Needs in Motivation, Development and Wellness. The Guilford. Press: New York, NY, USA, 2017.
Mentally Heathy Schools: Pupil Voice (2023).
The World Happiness Report 2023.
Prilleltensky I (2020) Mattering at the intersection of psychology, philosophy and politics. J. Community Psychol 65: 16-34.
Dobia B, Parada R, Roffey S, Smith M (2019) Social and Emotional Learning: From Individual Skills to Group Cohesion. Educational and Child Psychology 36: 79-90.
Martinsone B, Stokenberga I, Damberga I, Supe I, Simões C, et al. (2022) Adolescent social emotional skills, resilience and behavioral problems during the COVID-19 pandemic: A longitudinal study in three European countries. Psychiatry.1: 942692. [crossref]
UNESCO (2023) International Commission on the Futures of Education.

Enhancing Orthodontic Strategies: Evaluating Miniplate Use in Skeletal Class III Malocclusions

Leave a reply

DOI: 10.31038/JDMR.2023613

Abstract

Skeletal Class III malocclusions pose significant challenges in orthodontic treatment, often requiring a multidisciplinary approach for successful correction. Miniplates have emerged as a valuable adjunct in orthodontic therapy, offering enhanced skeletal anchorage and facilitating complex movements. This article aims to evaluate orthodontic habits and practices concerning the use of miniplates in addressing Skeletal Class III malocclusions, analyzing their efficacy, challenges, and current trends.

Introduction

The aim is to discuss the challenges associated with correcting Skeletal Class III malocclusions using traditional methods like facemasks and introduces the use of titanium miniplates by De Clerck as an alternative. The miniplate technique aims to mitigate skeletal effects while advancing the zygomaticomaxillary complex, thereby reducing undesirable impacts on facial aesthetics and dental compensationSkeletal Class III malocclusions are characterized by mandibular prognathism or maxillary retrognathism, leading to functional and esthetic concerns. The complexity of Skeletal Class III malocclusions, arising from maxillary deficiency or mandibular prognathism, influenced by genetic and environmental factors [1,2] occupates many clinicians. Traditional approaches, like the facemask, used at young age, primarily address sagittal disharmony in growing children but often lead to dentoalveolar compensations and potential skeletal effects, with significant relapse in preadolescents [3,4]. Traditional orthodontic approaches often face limitations in achieving optimal results, necessitating supplementary methods like miniplates for reinforcement. The utilization of miniplates as temporary anchorage devices has gained popularity, revolutionizing treatment strategies for Class III malocclusions [3].

Miniplates in Orthodontics

Miniplates, also known as temporary anchorage devices (TADs), are titanium implants placed into bone to provide absolute anchorage for orthodontic tooth movement. Their use in Class III correction involves strategic placement to counteract undesired skeletal growth patterns, aiding in achieving outcomes that are more predictable. De Clerck’s introduction of titanium miniplates for Class III correction represents a significant shift in treatment methodology [2,5,6]. The strategic placement of these miniplates in the maxilla and mandible, coupled with inter-arch elastics, allows for continuous application without extra-oral devices. This technique aims to advance the zygomaticomaxillary complex while minimizing adverse skeletal effects [7-10].

Advantages and Considerations of Miniplate Technique

The miniplate system offers advantages such as continuous wear without extra-oral devices and potential avoidance of undesirable skeletal and dental compensations [10]. However, the timing of mandibular plate placement is contingent upon the eruption of mandibular canines to mitigate anatomical risks [8]. Regular elastics changes and the possibility of incorporating composite bite-ramps to bypass anterior crossbites illustrate the adaptability of this technique to individual patient needs. Assessing factors that hinder the implementation of this technique could shed light on barriers to its widespread adoption and guide improvements in its application.

Evaluation of Orthodontic Habits and Practices

Clinical Efficacy

Numerous studies demonstrate the effectiveness of miniplates in Class III treatment, showcasing favorable outcomes in controlling skeletal discrepancies and facilitating complex orthodontic movements [10-12].

Challenges and Limitations

Despite their advantages, challenges exist, including potential risks of infection, implant failure, or interference with adjacent structures. Moreover, patient compliance and meticulous surgical technique are crucial for successful integration and stability.

Treatment Approaches

Diverse treatment protocols exist, ranging from unilateral to bilateral miniplate placements, depending on the severity and nature of the malocclusion. Combining miniplates with orthognathic surgery or utilizing them solely for orthodontic camouflage influences treatment approaches.

Current Trends and Innovations

Ongoing research aims to refine miniplate designs, improve insertion techniques, and explore novel materials to enhance biocompatibility and reduce potential complications.

The data presented in our recent survey (Friang et al.) sheds light on the preferences and practices of orthodontists regarding the management of Skeletal Class III malocclusions, notably focusing on the utilization of miniplates as a treatment modality. The findings encompass aspects such as practitioner experience, treatment modalities employed, obstacles encountered, treatment duration, satisfaction levels, observed effects, and potential relapses associated with miniplate usage.

Orthodontist Experience and Treatment Choices

The distribution of practitioners based on experience elucidates an interesting trend in treatment preference. Less experienced orthodontists (<5 years) exhibit a propensity towards prescribing miniplates, while those with more experience (>5 years) favor traditional methods like the facemask or orthosurgical treatments. This shift in treatment choices among practitioners with varying experience levels signifies an evolving trend in the orthodontic landscape.

Practitioner Preference and Prescription Patterns

The statistical analysis demonstrates a significant correlation between practitioner experience and the type of treatment prescribed. This includes a contrast in prescriptions between miniplates and facemasks, highlighting a divergence in approach based on experience (p=1.26 x 10^-6). Moreover, practitioners with less experience seem to opt for miniplates more frequently, while those with greater experience tend to prefer facemask treatments or orthosurgical interventions (p=0.03, p=0.02 respectively, α=95%).

Obstacles and Patient-Related Factors

Obstacles hindering miniplate usage predominantly include high financial costs, perceived treatment burden, fear of pain, logistical challenges, and issues related to patient cooperation. These factors impede the widespread adoption of miniplates and warrant attention for enhancing patient acceptance and accessibility to advanced orthodontic modalities.

Treatment Duration, Satisfaction, and Observations

The active treatment duration primarily spans 6 to 18 months, with a majority of practitioners satisfied or very satisfied with the obtained results. Notably, a significant percentage (14%) expresses dissatisfaction. Most practitioners report observing skeletal effects post-miniplate usage, such as maxillary protrusion, mandibular clockwise rotation, and, to a lesser extent, mandibular setback. Additionally, observed dentoalveolar effects and relapse occurrences post-treatment are noteworthy considerations (Figures 1-10).

Figure 1-10: Dentoalveolar effects and relapse occurrences post-treatment

Conclusion and Implications

The study provides valuable insights into the preferences, challenges, and outcomes associated with miniplate usage in managing Skeletal Class III malocclusions. The findings underscore the impact of practitioner experience on treatment choices and highlight the multifaceted challenges influencing the adoption and execution of advanced orthodontic techniques. Addressing these obstacles could enhance patient acceptance and overall treatment outcomes, ultimately contributing to improved patient care in orthodontic practice. Further research and strategies focused on overcoming these hurdles would be instrumental in refining and expanding the use of advanced orthodontic modalities like miniplates.

The utilization of miniplates in correcting Skeletal Class III malocclusions represents a paradigm shift in orthodontic therapy. Despite challenges, they offer valuable benefits in enhancing treatment outcomes. Continued advancements in technology and research promise further refinement and optimization of miniplate utilization, ensuring improved patient care and successful management of Class III malocclusions. Studying Orthodontists’ practices and understanding potential barriers could contribute to refining and expanding the use of this technique, potentially offering more efficient and patient-friendly treatment options for individuals with moderate Skeletal Class III malocclusions.

References

Proffit WR, Fields Jr HW, Sarver DM (2018) Contemporary orthodontics. Elsevier Health Sciences; 2018.
De Clerck H, Nguyen T, De Mot B, Wilson M (2011) A prospective study on the effects of the biscupid intrusion appliance in the treatment of anterior open bite. European Journal of Orthodontics 33: 298-305. [crossref]
Ngan P, Moon W (2015) Evolution of Class III treatment in orthodontics. American Journal of Orthodontics and Dentofacial Orthopedics 148: 22-36.
Sivieri A, Manni A, Bonetti GA, et al. (2020) Treatment of Class III malocclusion with skeletal anchorage: A review of the literature. Prog Orthod 21: 9. [crossref]
Baccetti T, Franchi L, McNamara Jr JA (2002) An improved version of the cervical vertebral maturation (CVM) method for the assessment of mandibular growth. Angle Orthodontist 72: 316-323. [crossref]
De Clerck H, Geerinckx V, Siciliano S (2002) The zygoma anchor. Journal of Clinical Orthodontics 36: 455-459.
Wilmes B, Ludwig B (2011) Uprighting mesially impacted mandibular permanent second molars using orthodontic miniscrew anchorage. Journal of Clinical Orthodontics 45: 443-448. [crossref]
Bae SM, Park HS, Kyung HM, Kwon OW, Sung JH (2022) Clinical application of micro-implant anchorage. Journal of Clinical Orthodontics 36: 298-302. [crossref]
Park HS, Lee SK, Kwon TG (2007) Treatment of Class III malocclusion using a non-surgical approach with microscrew implants. Angle Orthod 77: 1119-1128.
Cha JY, Kim HJ, Hwang CJ (2018) Skeletal anchorage for orthodontic correction of severe Class III malocclusion. Am J Orthod Dentofacial Orthop 153: 321-330.
Ludwig B, Glasl B, Bowman SJ, Wilmes B, Kinzinger GS et al. (2011) Anatomical guidelines for miniscrew insertion: Palatal sites. J Clin Orthod 2011;45: 433-441. [crossref]
Kircelli BH, Pektas ZO, Uckan S (2011) Orthopedic protraction with skeletal anchorage in a patient with maxillary hypoplasia and hypodontia. Am J Orthod Dentofacial Orthop 139: 699-712. [crossref]

Motivating Mammography: Combining AI and Mind Genomics to Discover What a Patient Needs to Hear

Leave a reply

DOI: 10.31038/AWHC.2023645

Abstract

The study focused on the messages which might enhance women’s participation in mammogram screening. The study followed the Mind Genomics process of presenting respondents with different combinations of relevant messages, identifying the strong performing messages for each respondent, and clustering respondents on the basis of the patterns of elements which drive positive responses. The messages themselves were developed using AI, allowing the researcher ahead of the experiment to gain a broad education-type overview of the types of messages that could be used. Two mind-sets of respondents emerged, representing different patterns of messages which are deemed motivating. Mind-Set 1, “Motivated and Informed Women,” seeks improved breast health, values screenings, with the goal to minimize health risk, and maximize well-being. Mind-Set 2, “Concerns and Motivations,” focuses on the mammogram experience itself, the mammogram risks, comfort, and cost, demanding accurate diagnoses. It may be possible to increase mammogram screening by assigning a person to one of these mind-sets. The Mind-Set Assigner (previously called Personal Viewpoint Identifier (PVI)) comprises a set of six questions based upon the study, the patterns of answers to which assign a person to either Mind-Set1 or Mind-Set 2. The Mind-Set assigner offers the promise of a personalized approach to understanding what people need to hear to ensure better health.

Introduction

Despite its significant advantages in understanding human perception and decision-making, contemporary technology involving health messages is confronted at every turn by hosts of drawbacks and challenges. Among these, data privacy and security concerns loom large as the collection and storage of vast amounts of personal data raise questions about safeguarding sensitive information [1]. Moreover, the potential for bias and unfairness in machine learning algorithms remains an ongoing challenge, demanding continual efforts to ensure fairness and reduce [2]. A consequence of the efforts to maintain privacy in a data-intensive world is the focus on making sure that key information about a patient is safeguarded [3]. Those efforts move the focus from advancing by understanding the patient as a human being towards protecting the patient who could be the source of possibly compromising data.

The focus of this paper moves attention from understanding the body and behavior of the patient to the mind of the patient as a consumer of the health care experience. The objective of this paper is to showcase what can be achieved in just a few hours, even with limited prior knowledge of a subject. Our goal is to gain insights into how people perceive the ordinary world, make decisions, and, perhaps most crucially, understand the myriad ways in which everyday individuals view topics in their daily lives using the innovative approach of Mind Genomics. Instead of focusing on exceptional or unusual circumstances, Mind Genomics centers on the daily, commonplace world in which most people live.

For this exercise, we have chosen to examine the issue of mammography, a vital aspect of healthcare, as it pertains to women’s participation in breast cancer screening. The focus will be on the person as a receiver of heath messages, the person being likened to a consumer, the health messages being the motivational communications to get the consumer, the woman, to buy the product.

When healthcare providers screen patients for breast cancer, their primary objective is to encourage greater participation among women in screening mammograms. Increasing participation needs a multifaceted approach which goes beyond simply knowing what to say. Rather, it is critical to understand and interpret women’s responses and, in turn, effectively address their concerns. Communication plays a pivotal role in this process. Providers must effectively convey the importance of regular screenings and underscore the potential to save lives through early detection.

The literature about mammography is large, not surprisingly because of the pivotal role of mammography in detecting breast cancer. The existing literature recognizes the importance of the doctor-patient relationship, and the emotions involved in breast cancer. There is a significant literature on mammograms and mammography. Part of the focus is on the medical aspects of mammograms as a preventive for breast cancer, which belongs in the world of biological and medical science [4,5].

Unfortunately, the existing literature often falls short when it comes to delving into the communicating with patients, even though the literature does recognize the psychological and emotional effects on individuals undergoing mammography, with a predominant focus on clinical and technical facets [6], as well as keeping appointments [7-9]. Whereas some studies do touch upon the significance of communication and addressing patient concerns, there remains the important gap regarding the specific language, words, and customized communication strategies which truly resonate with individuals.

Closing this gap by understanding the unique needs, assessing the psychological impact, and recognizing the decision-making processes of patients is paramount. Such an approach can significantly enhance the effectiveness of breast cancer screening and improve the overall patient experience, ultimately leading to increased participation rates and earlier detection of cancer.

The important thing to keep in mind is that the approach presented here breaks new intellectual ground, lying between the world of the scientific establishment and the world of the practitioner. As will be shown below, the objective is to understand the way the person thinks, letting the patterns which emerge suggest directions for research. In scientific parlance, the traditional way, the hypothetico-deductive system [10] with its effort to create science step-by-step gives way to the more intellectually adventurous but less rigorous effort called ‘grounded theory’ [11]. In a sense the approach presented here will end up being a ‘user’s manual of the everyday mind.’

Mind Genomics Offers a New Perspective on Thinking

Surveys are popular, easy to use, and understand. However, polls have fundamental issues. One of the worst is susceptibility to respondent bias toward giving the “correct answer.” This bias is especially harmful when the topic is emotional, or when the respondent doesn’t know the answer. The desire to appear positive often leads to hesitation to respond negatively [12].

A second, subtle bias is lack of context. The respondent is instructed to rate or to rank relevance of distinct characteristics like price, nutrition, brand, etc. Respondents typically adapt their rating criteria to fit the thing being rated. Interspersing topic examples is better. Instead of asking how important ‘price’ is against ‘availability in store’, a more productive technique may be to present different combinations of features, and afterwards deconstruct the response to the combination into the contribution of the single components of the combinations.

Mind Genomics has been developed to address these biases. Mind Genomics combines experimental psychology (psychophysics), statistics (experimental design), and consumer research (focus on the world as is, rather than put the respondent through an artificial situation, unless that artificial situation is of direct scientific interest). The past history and future opportunities Mind Genomics have been described in previous papers [13-15].

Mind Genomics traces its history to three disciplines:

Psychophysics – The oldest discipline of experimental psychology. Psychophysics examines how humans interpret external inputs. Traditional psychophysics connects the physical and psychological worlds. Traditional psychophysics focuses on ‘measuring’ the intensity of perception, such as beverage sweetness [16].
Experimental design is a discipline in statistics which examines how independent factors combine to create a dependent variable. Mind Genomics adopts that concept but focuses on how thoughts interact. It might be called the ‘algebra of the mind.’
Consumer research explores daily life. Mind Genomics explores everyday scenarios rather than developing contrived ones, in its effort to understand the way people think. Consumer research shows how individuals act in the real world as consumers and humans.

The Mind Genomics approach has evolved from paper and pencil evaluations of vignettes (combinations of ideas or messages, now called ‘elements’) to a DIY (do it yourself) system which allows rapid, inexpensive study creation using a template combined with artificial intelligence to help the researcher think through a topic, along with subsequent automated analysis of the data and reporting in a user-friendly set of tables.

In early versions of the Mind Genomics science, it was up to researchers to develop the raw materials, so-called ‘elements’ or messages. The elements often numbered in the dozens, and occasionally far more. The researcher categorized the elements into like-ideas, manually created the vignettes, viz., combinations of elements, checking that the elements were statistically independent of each other, and that mutually contradictory elements never appeared together. The process was tedious, frustrating, required a great deal of up-front preparation, took a long time, and ended being expensive when the time of the researcher was factored-in to the total cost.

Implementing the early Mind Genomics studies was also a challenge, and expensive. The studies were presented on a local computer, with respondents pre-recruited to participate. All respondents evaluated a randomized set of combinations or vignettes, selected from the original large set of vignettes. During the course of the supervised evaluation on the local computer, the respondent would end up evaluating 50-100 vignettes in 20-30 minutes. The results were pooled and analyzed by total panel and self-identified subgroups, with subgroups formed by a questionnaire about attitudes and practices on the problem. The Internet evolved the Mind Genomics approach to a structured, simple design (four categories or now four ‘questions’, four elements or answers per category), a basic experimental design with 24 statistically appropriate combinations. The underlying design ensured that the 16 elements are statistically independent and set up for immediate analysis. The data could be analyzed by OLS (ordinary least squares) regression at the level of a defined group or respondents, or even at the level of an individual respondent. Furthermore, Mind Genomics ensured that each respondent would evaluate 24 different vignettes, or combinations, a method halfway between the constricting requirements of a single design replicated for each respondent, and the combinations encountered in a cross-sectional study with the combinations of independent variables being uncontrolled. The benefit of the permuted design was that it was no longer necessary to know the answer before doing the study. The study would end up covering many of the possible combinations, using a systematized permutation system [17], reducing a lot of the need for prior knowledge.

A Worked Example – What Goes Through the Mind of a Female Patient When Thinking about Mammography?

The rest of this paper shows study design, implementation, and analysis of a study on communications about why a patient should consider mammography. The study goal was to generate a new topic and advance it via AI. AI becomes a ‘second set of eyes’ for question-and-answer-development at the time of set-up, and for discovery of themes at the time of analysis.

Mind Genomics starts with a query, a brief paragraph to explains the situation and the research objective. The statement is constructed to allow the embedded link to AI (Idea Coach), to generate the appropriate query to Chat GPT 3.5 (Liu et. al., 2023) [18]. For this study, the brief paragraph was: Explain to me exactly how I should talk to women so I can convince them to get their yearly mammogram? I want to talk to them as a doctor who is concerned about their health as their mother or their daughter. please make the question easy to understand, filled with emotion and no more than 12 words. Figure 1 shows a schematic of these first steps of the set-up process.

Figure 1: Schematic process. Panel A shows the space for four questions. Panel B shows a schematic ‘general background prompt’ given to Idea Coach.

Generating Questions to Answer the General Background Prompt

The researcher requests Idea Coach (AI) to provide 15 questions based upon the general prompt (see Figure 1, Panel B). The researcher can vary the general background prompt given to Idea Coach or can re-run the same request several times. The results are provided immediately, as well as recorded in the Idea Book, along with a set of AI based ‘summarizations’ and expansions of the topic (Table 1). These questions may be ‘on target.’ If not, the researcher can tweak the paragraph to guide the AI, run Idea Coach again, and indeed several times, each time learning more about the topic through the questions provided.

Table 1: The first set of questions emerging from the Idea Coach in response to the paragraph written by the research, which generates the query. The text in in Table comes from the Idea Book provided to the researcher after the element creation has been completed, both for questions and for answers.

A user-trend has emerged during the past several months since the Idea Coach was incorporated into Mind Genomics. ‘Newbies’, beginning researchers, find this process with the Idea Coach to be pleasant, entertaining, and anxiety-reducing. Their excitement about research increases. And, as a bonus, after some experiences with Idea Coach, even in one study, the new researcher begins to feel empowered to add in her or his own questions.

Idea Coach now analyzes returned questions instead of just coaching. Table 2 shows how Idea Coach (viz., AI) uses a cue about ‘themes’ to find commonalities in Table 1 queries. Again, the Idea Coach summarizes ‘themes’ for each researcher-generated question set. The researcher could use Idea Coach say 10x to get 10 theme analyses, one for each set of returned questions. One could analyze all themes for all aspects, but the bookkeeping to eliminate similar-but-not-identical issues would take time.

Table 2: Themes emerging from the set of questions returned in the first iteration of Idea Coach

AI then finds ‘holes’ in themes, a major benefit. Table 3 presents the third analysis of the first set of questions, ‘what is missing.’

Table 3: Suggestion of ideas that may be missing from the set of questions returned by Idea Coach

Finally, Tables 4-6 look at possible innovations in products and services.

Table 4: Suggestion of alternative viewpoints, viz., contrary ideas

Table 5: Suggestion of the likely audience

Table 6: Suggestion of innovations in products services, experiences, policies

Creating Answers to Questions

The second step creates four answers to each question selected by the researcher. The researcher must come up with four answers to each question. The same process is followed, with Idea Coach providing sets of 15 answers to a question, and with the researcher selecting the four answers for each study. The final four sets of questions appear below. The substantive part of the question as returned by Idea Coach is shown in italics. The other part of the question, viz., the phrase ‘Elaborate in detail and in 15 words or less’ was added to the Idea Coach query to ensure simple to understand answers to the question.

Question A: Elaborate in detail and in 15 words or less The importance of regular mammograms for women?”
Question B: Elaborate in detail and in 15 words or less Any concerns or fears regarding mammograms?”
Question C: Elaborate in detail and in 15 words or less Prioritize your health together. When can I expect you to schedule your mammogram?”
Question D: Elaborate in detail and in 15 words or less The peace of mind that comes with early breast cancer detection?

Once again the process with Idea Coach ends up educating the researcher as the researcher continues to request the four answers for each question, with each request generating four answers. Many users consider this activity crucial. The shock of seeing 15 answers appear a few seconds after the question is asked is remarkable, and often quite motivating.

Each set of the 15 solutions undergoes the same set of AI analyses, similar in depth to the AI analyses above done for the questions, but with a few different queries. The first 15 answers to the first question appear in Table 7, which also shows the extensive AI summarization.

Table 7: The first set of answers to Question #1: Elaborate in detail and in 15 words or less the importance of regular mammograms for women

Table 8 shows the final set of questions and answers, edited by the researcher. In the actual study, the vignettes will comprise only answers (also called ‘elements’) in combinations comprising a minimum of two and a maximum of four answers, never more than one answer for a question. The questions never appear in the vignette, but rather are used only to guide the creation of the answers.

Table 8: The final set of questions and answers, after creation by Idea Coach and editing by the researcher

The Self-profiling Classification

Mind Genomics allows the researcher to create a set of eight questions which allow the respondent to provide more about the topic from the respondent’s point of view. Table 9 shows the list of questions. Gender and age questions are asked as a matter of course in all Mind Genomics studies.

Table 9: Self profiling questions about the respondent

In addition to the self-profiling classification questions, the respondent was instructed to complete an open-ended question about their own feelings and history regarding mammograms. The respondent completed this open-end question after having completed the Mind Genomics evaluation, and thus were ‘primed.’ Most respondents wrote detailed answers, an unusual occurrence in Mind Genomics studies of simpler, less emotionally tinged topics.

The Test Stimuli

Test stimuli combine answers to questions, with these answers henceforth called ‘elements’, and combinations called vignettes. The underlying experimental design prescribes the specific composition of each of the 24 vignettes. Each set of 24 vignettes can be analyzed in and of itself, to estimate the contribution of the elements to the response. The method of analysis is called OLS (ordinary least-squares) regression [19]. In the experimental design, each element occurs five times in 24 vignettes and absent from 19. Thus, each question contributes an element to 20 of the 24 vignettes and is absent from four of the 24 vignettes.

A key benefit of Mind Genomics is the ability to have each respondent evaluate virtually a totally unique set of vignettes, the aforementioned combinations of elements. The process to create the unique sets is called isomorphic permutation [17]. The benefit is that the researcher ends up testing many combinations, rather than testing a few combinations but many people. The happy consequence is that Mind Genomics empowers researchers to study a topic without having to have an idea of the answer ahead of time. The sheer scope of the combinations tested allow the research to ‘explore’ the unknown rather than having to ‘confirm’ one’s hypothesis.

We can contrast the Mind Genomics approach with the way typical research is conducted in the hypothetico-deductive system. Typical research studies aim to understand nature. Reducing the ‘noise’ around the ‘signal’ helps it emerge more clearly. Typically, this is done by decreasing superfluous variability, or ‘noise.’ In consumer research, dozens or hundreds of respondents evaluating the same vignettes is easy. Average variance, or standard error, diminishes with the square root of replicates. Four times the number of participants are needed to reduce variation around the mean by half. The above technique works when the researcher already ‘knows’ the right vignettes or elements, with the research ending up confirming hypotheses rather than discovering new realities. Mind Genomics focus on these new realities.

One reaction to the Mind Genomics experience is frustration, especially among professionals, far less so among non-professionals with no ‘ego’. Because there is no order or consistency, professionals and beginners alike think these pairings are random. The number of elements in a vignette varies, confusing those who try to outsmart the system. Many Mind Genomics research participants feel like they are guessing because they cannot see a pattern but ‘soldier on’, generally just doing what they have been instructed to do. These respondents participate in a state of relaxation and what might seem to be a lack of involvement, that seeming indifference actually being a good thing because it allows true feelings to emerge. The situation is even more frustrating to professionals, especially consumer researchers, who stop participating in the middle of the study because they become frustrated that they cannot ‘game the system.’

Respondent Orientation and Rating Scale

Every effort is made to lighten the mental ‘load’ of the respondent. The respondent need not read a long paragraph. All the respondent needs to know is that the topic is breast cancer. Everything else makes sense. The attitudes and judgments come from the respondent as she reads the vignette.

When presented with the vignettes, most respondents do not know what to do, since the vignettes are simply lists of phrases. The design of the vignette in that way is deliberate, making it easy for the respondent to ‘graze’ through the vignette and immediately give a rating. There is no effort made to fill the spaces between the elements with connective words, an effort which often backfires because the vignette goes from sparse, easy to scan, to dense, weighty, and simply obstructively boring.

Table 10 shows the very short orientation and the five scale values. The scale has two dimensions. The analysis uses newly created variables, each of which has only two values, ‘0’ to denote ‘no’ and ‘100’ to denote yes. That is, the responses 1-5 are transformed according to specific rules listed at the bottom half of Table 10. After creating these new binary variables, the Mind Genomics program adds a vanishingly small random number (<10^-4) to ensure some minimal variability, a prophylactic step ensuring the variability necessary for subsequent creation of equations or ‘models’ using OLS (ordinary least-squares).

Table 10: The rating question, the two-dimensional scale, and the binary transformation

Creating the Database for Analysis

The previous stages established an analysis-ready database. The Mind Genomics program delivers test vignettes and gathers 5-point ratings and response time, the number of seconds elapsing between stimulus presentation and rating. After responding to the current vignette, the program automatically proceeds to the next one. Figure 2 shows a screen shot of the database returned automatically to the researcher in an Excel booklet after the Mind Genomics study has been completed and the result automatically analyzed. The first set of columns shows information about the respondent. The second set of columns shows information about the structure of the vignettes. The third set of columns shows the rating information and some of the transformed data. The last column shows the open-ended questions.

Figure 2: Example of the first vignette evaluated by 10 respondents, with some of the transformations already created.

Creating ‘Models’ (equations) Which Relate the Presence/Absence of Elements to the Binary Ratings

A hallmark analysis of Mind Genomics reveals, though regression, the magnitude of ‘driving power’ of each element for the group of newly developed binary variables. OLS (ordinary least squares) regression is appropriate here, either for the data from each individual, or the data from a defined group. The term ‘appropriate’ is used because the 24 vignettes evaluated by each respondent were created through experimental design, enabling the subsequent regression analysis at the level of the individual respondent.

The OLS regression estimates the 16 parameters for this simple equation, doing so for all the newly created dependent variables (DV): DV = k₁(A1) + k₂(A2) … k₁₆(D4)

The independent variables A1-D4 take on the value ‘1’ when the element appears in the vignette, and, in turn, take on the value ‘0’ when the element is absent from the vignette. This coding is called ‘dummy variable [20]. Nothing is known about the element except its presence or absence, hence the term ‘dummy’. The equation shows the best estimation of the 16 coefficients. The coefficients themselves are easy to understand. Let us consider the dependent variable ‘R54’, viz., ‘motivates.’ A coefficient of 10, for example, means that when the element is present in the vignette,10% of the vignettes will be rated ‘5’ or ‘4’. When the coefficient is 20, two times as many vignettes will be rated 5 or 4. Thus, if we were to come upon four elements, each generating a coefficient of 25, then 100% of the vignettes with those four elements would be rated 5 or 4. Statistical analysis suggest that coefficients around 15 are ‘significant’ at the 95% confidence level. Our focus is not so much on significant elements, but rather on the patterns which reveal themselves.

We can immediately identify patterns of respondent thinking from the patterns of the strong performing coefficients. In ordinary research these patterns would go undetected because the test stimuli have no ‘cognitive richness.’ Thus, the researcher would have to search deeply to find a story behind the pattern. The situation is easier in Mind Genomics because the text of each element has meaning so that the results become easier to interpret. A ‘story’ ends up emerging more readily.

Table 11 shows the pattern of coefficients for the binary variables developed in Table 10, and for response time. The form of the equation is always the same, as described above. No equation has an additive constant or ‘intercept’, simply because the intercept adds a complicating parameter which prevents the coefficients from being compared directly in terms of both magnitude and real meaning. The elements in Table 11 are sorted by the coefficient for R54 (motivates), the key dependent variable.

Table 11: Coefficients for the total panel, for response time (RT) and for five binary variables

The strong performing elements are shaded.

Response time (RT)-In most Mind Genomics studies the respondents rush through the study, with many elements almost skipped over, since they have RT coefficients of 0.2 to 0.5. Not so with mammography. It is worth noting that the respondent stopped to read and think about virtually all of the elements, since many elements have coefficients of 1.3 or higher for RT. These 101 respondents focused on the messages.
Motivates me (R54). The convention for Mind Genomics studies is to shade coefficients of magnitude 21 or higher. Once again the results are startling. Nine of the 16 elements have coefficients of 21 or higher, a ‘first’ in Mind Genomics, and a signal that the topic is exceptionally important. Despite the strong performance of the elements, however, there is no apparent pattern in terms of what makes an element perform strongly.
Emotional reaction (R52) also shows many strong performing elements, but again no clear patterns.
The binary variable, R41 (no emotional reaction) shows one moderate performing element. The one which may not drive an emotional reaction is C2 (It’s time to prioritize your heath make that mammogram appointment). There is no clear story for binary variable R41.
The binary variable R21 (does not motivate) show no elements which fail to motivate.
The binary variable R3 (don’t know) shows no strong performing elements.

If we were to sum up the results, we would conclude that the elements hold the attention of the respondent, motivate quite strongly, drive an emotional reaction. We also should conclude that there is no clear pattern.

One of the benefits of Mind Genomics is the ability for AI to summarize the patterns among the strong performing elements, much as AI does when creating the Question Book at the set-up of the project. Table 12 shows the AI summarization based upon the results from the total panel.

Table 12: The summarization of performance of strong element(s) for the Total Panel by AI embedded in www.BimiLeap.com, the Mind Genomics platform.

The next level of analysis in the search for meaningful patterns focuses on the respondent’s age. Table 13 shows a richer pattern of results, focusing only on R54, Motivates. There are many coefficients which exceed the cut-off value of 21, and thus the pattern is still elusive. The 21 respondents age 65+ show the lowest values for the coefficients.

Table 13: Strong performing elements for Total Panel and four subgroups defied by the respondent’s age

Recall that at the start of the Mind Genomics experiment, the respondent was asked to select answers to two questions, one asking about knowledge about breast cancer, and the other asking about how breast cancer would affect her life. Table 14 shows the remarkable effects of knowing about breast cancer and expectations about life with breast cancer. The patterns across groups become quite clear. For those who either do not know about breast cancer or who have not thought about it, the elements are moderate, but most do not reach the threshold of 21 to be shaded. One group in particular deserves a note. That group of respondents comprises those who when asked about their knowledge of breast cancer selected the answer: I know a lot because I have a relative who has it. They ended up saying that they are strongly motivated by all of the elements except the last set, about peace of mind from early detection. That pattern makes a great deal of sense and is not immediately intuitive. This group has gone beyond peace of mind into the personal angsts accompanying experience with breast cancer, whether breast cancer has struck oneself or a family member, or friend.

Table 14: Strong performing elements for Total Panel and two sets of subgroups, defined by knowledge of breast cancer, and by selected reactions to the prospect of coming down with breast cancer.

Focusing Opportunities by Uncovering Mind-sets

Mind Genomics searches for intuitively obvious patterns of coefficients. Without dancing around the data, spinning theories, and creating stories, the researcher should be struck by ‘ocular trauma’—that is, the patterns should ‘hit one squarely between the eyes.’

The coefficients for the different subgroups identified through the self-profiling classification may be strongly positive, but there are no clear patterns. Tables 13 and 14 simply fail to reveal a pattern. One might strain to develop a credible explanation, but that goes against the worldview embodied in the phrase ‘ocular trauma’. Hypotheses and stories of what may be happening do not constitute science, but simply conjectures. We need a quantitative method which advances our thinking.

The answer to the issue of ‘ocular trauma’ and ‘self-evident patterns’ may emerge from a well-accepted class of statistical procedure known as ‘clustering’. Clustering methods sort items into mutually exclusive, exhaustive groups using mathematical criteria. The researcher specifies the number of groups, intragroup coherence, and intergroup distance, and calculation follows. The researcher names the groups (interpretability) after the exercise.

Numerous statistical methods have been developed to cluster objects. The approach currently used by Mind Genomics is known as k-means clustering [21]. The objects are our 101 female respondents, each of whom generates 16 coefficients for R54, ‘motivates’. The individual-level modeling is valid for these data because each respondent evaluated a unique set of 24 vignettes laid out by experimental design, and because the newly created binary variables, such as R54, were guaranteed to exhibit some minimal level of variable by the prophylactic addition of a vanishingly small random number.

K-means clustering calculates the “distance” between each pair of people (1-Pearson Correlation). The strength of the linear relation relationship between two comparable data points is shown by the Pearson Correlation. Two study respondents are separated by zero distance (1-1 = 0) when the Pearson Correlation is 1. Two study respondents are separated by the maximum distance (1–1 = 2) when the Pearson Correlation is-1, and the patterns of coefficients go in opposite directions.

Table 15 shows the coefficients for the two-cluster solution. These clusters are called ‘mind-sets’ in the language of Mind Genomics. They are more readily interpretable. Mind-Set 1 is clearly most strongly motivated by the appeal to taking control of one’s health. Mind-Set 2 is clearly more strongly motivated by information, by knowing the benefits and negatives of regular mammograms.

Table 15: Strong performing elements for Total Panel and the two emergent mind-sets (MS 1 and MS 2). The labels are assigned by the researcher.. All coefficients of 21 or higher are shown in shaded cells. The equations are estimated without an additive constant (viz., forced through the origin).

Table 16A shows the full set of coefficients for Mind-Set 1, Take control of your health through mammograms. These are the set of newly created binary dependent variables. Table 16B shows the same full set of coefficients for Mind Set 2, Benefit and negatives of mammograms Tables 16A and 16B show shaded cells for Response Times of 1.6 seconds or longer, highlighting those elements which engage attention. For R54 (Motivates) the elements which motivate are also highlighted, with values of 21 or higher. Finally for other rating variables, the threshold level has been lowered to 16+. Elements with ‘0’ or negative coefficients are presented with a blank cell.

Table 16 A: Coefficients for key dependent variables for Mind-Set 1 – Take control of your health through mammograms

Table 16 B: Coefficients for key dependent variables for Mind-Set 2 – Benefits and negatives of mammograms

Measuring the Performance of the Research Results Using the IDT (Index of Divergent Thought)

One of the critiques levelled against Mind Genomics is that the simple-to-use templated form makes the research available to anyone, and that the automated analyses done routinely after the data has been collected allows anyone to do powerful research of the type called ‘conjoint analysis’ [22]. The criticism is valid. Indeed, following the approach laid out by Mind Genomics enables a school child to do a study, and most certainly a high school student [23]. With this simplicity of process, and with the widespread available of computation, even on the smart phone, how does the world of science evaluate the contribution of the research? If the world of daily issues can be investigated with profound tools by inexperienced researchers, then can we measures the strength of the research instead of relying on the reputation of the researcher?

The answer to the foregoing can be ‘yes’ if we create an objective system to measure the strength of the research. One way to do this is the IDT, Index of Divergent Thought. The IDT uses the coefficients, or more properly the squares of the coefficients, to assess the strength of the research. Table 17 shows the computational formula. The IDT is operationally defined. For the computation of the IDT the researcher first must create the two-mind-set solution and then the three-mind-set solution, respectively, no matter which mind-set solution ends up being accepted.

Table 17: The IDT (index of divergent thought) for the mammogram study, based upon the coefficients for R54 (motivates)

Typically, novice researchers with no really strong ideas end up with IDT values around 50-60. Good research, the type with meaningful patterns, ends up with IDT values of 65-80. Really strong data, viz. studies with high coefficients, end up with IDT values of 80+, although in such cases experience suggests that all of the mind-sets respond strongly to the elements, and thus the segmentation may not be clear. These numbers for the IDT are not engraved in stone, but rather are preliminary numbers after six months of studies addressing a variety of topics with Mind Genomics. Whether the high IDT value comes from an interesting topic or comes from powerful research is not a question that can as yet be answered.

What We have Learned-AI Summarization Elements Which ‘Motivate’ Mind-Sets 1 and 2, Respectively

A key benefit of Mind Genomics is the ability to summarize the data using the criteria defined by the researcher. Just as we were able to ‘summarize’ the different questions and answers using Idea Coach, we are now able to summarize the findings for Mind-Sets 1 and 2, respectively Tables 18A and 18B show this summarization, based upon the dependent variable R54, motivates, and based upon the patterns and meaning generated by those elements with coefficients 21 or higher. Table 18A shows the AI summarization of the strong performing elements for Mind-Set 1. Table 18B shows the AI summarization of the strong performing elements for Mind-Set 2.

Table 18 A: AI summarization of strong performing elements for R54 (motivates) for Mind-Set 1

Table 18 B: AI summarization of strong performing elements for R54 (motivates) for Mind-Set 2

Finding Mind-sets in the Population

The Mind Genomics approach works with a relatively few number of respondents, but from those respondents uncovers mind-sets, and through the strong performing elements knows the tonality of the messages and even some of the text of the messages to which the mind-sets will respond. One cannot perform a Mind Genomics study on millions of women, however, to analyze their data, and then assign each woman to the appropriate mind-set for making appointments for a mammography. Such a capability would be wonderful, but not feasible.

To address the issue of assigning a new person to a mind-set, Moskowitz and colleagues developed the PVI, the personal viewpoint identifier. The PVI uses the data from the study, putting it into a simulator, and identifying the best set of elements and their weights in order to assign a new person to one of the two mind-sets.

The PVI system has been embedded in an easy-to-use computer program (www.PVI360.com), linked to the data from a Mind Genomics study.

Figure 3 Panel A shows the first part of the PVI, which gathers information from the respondent. It is here that one can create a registry of individuals, who give their permission to be contacted for further research.

Figure 3 Panel B shows the actual set of six questions, comprising six statements taken directly from the research study, but presented to the respondent as a set of questions with two answers.

Figure 3 Panel C shows the feedback to the researcher or to the respondent about mind-set membership given the responses.

Figure 3 Panel D shows the set-up screen that the researcher uses to transfer summary data from the Mind Genomics results to the input for the PVI. The task takes less than five minutes.

Figure 3: Panel A shows the qualification and background questions for the PVI. Panel B shows the six question PVI and additional questions about mammography asked to the respondents. Panel C shows the feedback information provided immediately by email to either the medical professional or to the respondent, or to both. Panel D shows the first part of the set-up page, done in Excel.

The Mind-sets Differ in the Style of Their Open-ended Responses

Traditionally, the Mind Genomics studies (really experiments) have not been ‘productive’ when it comes to having the response write about the topic. Most respondents are not particularly interested in the topic. This study on mammograms, and perhaps others studies on personal health, may open up an opportunity to learn more from the person, or the patient since the study deals with a topic of interest.

The final analysis subjected the open-end response to AI summarization through an APP ‘QuillBot’ [24] and requested the program to summarize the information contained in the open-end answers. No other request was made. The AI program was given the answers in the form of an excel sheet, one sheet for Mind-Set 1 (Take control of your health), the other sheet for Mind-Set 2 (Benefits and negatives of mammograms). No other information was provided.

Table 19 shows the remarkable difference in the way AI summarized the two sets of open-ended answers. The AI generated summary for Mind-Set 1, take control of your health, appears to be a coherent paragraph. In contrast, the AI generated summary for Mind-Set 2, benefits and negatives of mammograms, appears to be a set of disconnected sentences, rather than a single or small set of paragraphs. This difference in the morphological characteristics of the open-end answers may hint at important ways the different mind-sets organize information and provide new opportunities for understanding the minds of patients.

Table 19: AI summarization of open-end answers for Mind-Set 1 (Take control of your health) and Mind-Set 2 (Benefits and negatives of mammograms).

Discussion and Conclusions

This paper is based upon work originally done as a demonstration project to teach Mind Genomics. The powerful results were unexpected. Most studies using Mind Genomics end up with respondents who are only moderately interested in the topic. These studies usually deal with product features and with messaging about those features. More serious studies, such as those regarding society, law, ethics, and so forth, cannot be said to engage the respondent more deeply. The response times for these other studies are short. The coefficients for RT for these are studies are in the order of 0.3-0.7 seconds for most, suggesting little interest. Rather, it seems for the most part the respondents graze the messages, give a meaningful answer, but are not particularly involved in the Mind Genomics exercise.

The fact that topics of personal medical health produce this degree of interest suggest a rich opportunity to understand the patient at a level not before seen. One can imagine working with patients in a variety of situations, individuals who are motivated to learn about the topic, and learn about themselves at the same time. One could even imagine larger scale initiatives, where patients could participate for 5-10 studies on the different aspects of their condition, join a ‘club’ and keep contributing, with follow-up visits to their doctor, and follow-up studies. Their compensation might be some consideration given to them when they get their drugs and other materials from the pharmacy, a sort of ‘affinity group’ to forward knowledge.

As a final thought, it is worth thinking about the role of Mind Genomics as a way to understand the world. Research has been thought of as a highly disciplined, hypothesis-based procedure done by specialists, researchers, and scientists. Research as an everyday event is rarely discussed, but our intellectual curiosity makes us researchers, whether we realize it or not. Our world exploration includes watching, asking questions, acting on them, and moving on. People learn this way. The Mind Genomics “program” brings science and careful observation to the everyday. We discuss the everyday grind. Mind Genomics practitioners, like the pioneering scientists of the past, can study the globe in hours, discovering new themes. Artificial intelligence combined with natural curiosity is the next instrument for exploring, learning, and comprehending. The science is kept, but childlike love of simplicity, a guide or mentor, quickness, low cost, and most of all learning fun is introduced. Why not an adult serious researcher engaged in important problems where people’s thoughts are studied . If a nine-year-old can do the experiment and have fun, why not adults and more important, why not patients with medical conditions? The result might be a healthier world if one could discover how to have the patients comply, live a healthier lifestyle, and pass on their ideas to others through the systematization afforded by Mind Genomics.

References

Enaizan O, Zaidan AA, Alwi NHM, Zaidan BB, Alsalem MA, et al. (2020) Electronic medical record systems: Decision support examination framework for individual, security & privacy concerns using multi-perspective analysis. Health and Technology 10: 795-822.
Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G (2018) Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine 178: 1544-1547. [crossref]
Li J, Shaw MJ (2012) Safeguarding the Privacy of Electronic Medical Records. In Cyber Crime: Concepts, Methodologies, Tools and Applications. IGI Global 891-901.
Gøtzsche PC, Jørgensen KJ (2013) Screening for breast cancer with mammography. Cochrane Database of Systematic Reviews 2013.
Kerlikowske K, Grady D, Rubin SM, Sandrock C, Ernster VL (1995) Efficacy of screening mammography: a meta-analysis. JAMA 273: 149-154. [crossref]
Brett J, Bankhead C, Henderson B, Austoke J (2005) The psychological impact of mammographic screening. A systematic review. Psycho-Oncology 14: 917-938. [crossref]
Bernstein J, Mutschler P, Bernstein E (2000) Keeping mammography referral appointments: motivation, health beliefs, and access barriers experienced by older minority women. Journal of Midwifery & Women’s Health 45: 308-313. [crossref]
Holm CJ, Frank DI, Curtin J (1999) Health beliefs, health locus of control, and women’s mammography behavior. Cancer Nursing 22: 149-156. [crossref]
Taplin SH, Barlow WE, Ludman E, MacLehos R, Meyer DM, et al. (2000) Testing reminder and motivational telephone calls to increase screening mammography: a randomized study. Journal of the National Cancer Institute 92: 233-242. [crossref]
Lawson AE (2000) The generality of hypothetico-deductive reasoning: Making scientific thinking explicit. The American Biology Teacher 62: 482-495.
Backman K, Kyngäs HA (1999) Challenges of the grounded theory approach to a novice researcher. Nursing & health sciences 1: 147-153. [crossref]
Nederhof AJ (1985) Methods of coping with social desirability bias: A review. European Journal of Social Psychology 15: 263-280.
Moskowitz HR (2012) ‘Mind Genomics’: The experimental, inductive science of the ordinary, and its application to aspects of food and feeding. Physiology & Behavior 107: 606-613. [crossref]
Moskowitz HR, Gofman A, Beckley J, Ashman H (2006) Founding a new science: Mind Genomics. Journal of Sensory Studies 21: 266-307.
Zemel R, Choudhuri SG, Gere A, Upreti H, Deitel Y, et al. (2019) Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive Viewpoint Typing. In Current Issues and Challenges in the Dairy Industry. Intech Open.
Stevens SS (1975) Psychophysics: An Introduction to Its Perceptual, Neural and Social Prospects, New York, John Wiley.
Gofman A, Moskowitz H (2010) Isomorphic permuted experimental designs and their application in conjoint analysis. Journal of Sensory Studies 25: 127-145.
Liu Y, Han T, Ma S, Zhang J, Yang, Y, et al. (2023) Summary of ChatGPT-related research and perspective towards the future of large language models. Meta-Radiology 100017.
Craven BD, Islam SM (2011) Ordinary least-squares regression. The SAGE Dictionary of Quantitative Management Research 224-228.
Hardy MA (1993) Regression with Dummy Variables. Sage 93.
Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognition 36: 451-461.
Marshall D, Bridges JF, Hauber B, Cameron R, Donnalley L, et al. (2010) Conjoint analysis applications in health—how are studies being designed and reported? An update on current practice in the published literature between 2005 and 2008. The Patient: Patient-Centered Outcomes Research 3: 249-256. [crossref]
Kornstein B, Rappaport S, Moskowitz H (2023) Communication styles regarding child obesity: Investigation of a heath and communication issue by a high school student researcher, using Mind Genomics and artificial intelligence. Mind Genomics Studies in Psychology and Experience 3: 1-14.
Fitria TN (2021) QuillBot as an online tool: Students’ alternative in paraphrasing and rewriting of English writing. Englisia: Journal of Language, Education, and Humanities 9: 183-196.

New Thinking on Psychological Health: Finding Purpose and Meaning in Life

Leave a reply

DOI: 10.31038/PSYJ.2023583

Purpose and meaning in life are now vibrant topics in multiple domains of science and practice. We recently published a collection of articles on purpose and meaning in life to showcase the rich content of this emergent work [1]. Contributors brought differing samples, measures, and contexts to the collective inquiry. Most visible in prior studies have been those linking purpose in life to specific aspects of health, such as risk for various disease outcomes and length of life (mortality). Here the special issue carved new territory. Links between purpose in life and waist circumference, an indicator of abdominal obesity, was shown to be mediated by healthy eating [2]. Activist purpose, defined as commitment to engage in social activism, was associated with good health behaviors [3]. Links between religiousness/spirituality and mortality were mediated by purpose in life and social support [4]. Purpose in life moderated relationships between self-rated health and mortality [5]. And low levels of purpose, personal growth, and social connection were linked with increased risk for deaths of despair (due to suicide, addiction, and alcoholism) compared to risk of death due to heart disease [6].

Importantly, other contributors to the special issue probed what precedes these later life outcomes-that is, what early life influences contribute to the emergence of meaning and purpose. Childhood relationships with significant others were examined. Supportive and loving parents in early childhood development had strong impact on the sense of meaning for those in university, with effects mediated by experiences of loneliness [7]. Early influences that shape beliefs such as rigid interpersonal schemas were seen to compromise adult meaning and purpose, however clinical intervention can help with restructuring such schemas to foster improved mental health in adults [8]. Other early-life inquiries focused on educational interventions to promote multiple aspects of eudaimonic well-being in elementary and high school students. Findings showed that preexisting levels of depressive symptoms and anxiety can be obstacles to the development of well-being [9]. Another study examined at-risk adolescents who participated in a sailing experience designed to nurture meaning, identity, commitment, social well-being, and self-acceptance [10]. Together, these contributions offered new insights about child and adolescent experiences that nurture or undermine meaning and purpose.

Another section examined the psychosocial correlates of meaning and purpose. Longitudinal analyses showed how aspects of hedonic and eudaimonic well-being were linked with depressive symptoms, with findings showing reciprocal relationships across time [11]. The connections between meaning in life and character strengths were examined showing that hope, spirituality, zest, curiosity, and gratitude were the strongest predictors [12]. A separate study linked meaning and purpose with sociodemographic factors (age, educational status, work status) as well as with stress, spirituality, optimism, depressive symptoms, social support, and quality of life [13]. Together, these new findings continue mapping of the nomological network of meaning and purpose.

A section on distinct contexts such as work, major public stressors, and the natural environment were considered for understanding what nurtures or undermines meaning in life. The double edge of meaningful work was examined-which can both enhance motivation and performance in organizations, while eroding well-being and increasing the chances of burnout for individuals-with calls to elevate decency as a critical antecedent of meaningful work [14]. A further context pertained to how meaning in life mediates or moderates negative emotion in the face of social unrest and the pandemic [15]. The natural environment was examined as another context wherein nature may play important roles in helping humans find coherence, significance, and purpose [16].

Two final contributions focused on translational science and community action. One called for a stronger reciprocal relationship between research and application with primary emphasis on justice, equity and a commitment to influence public policy [17]. A final article laid out a transdisciplinary approach to meaning-making by describing a set of community-based, context-sensitive and socially responsible interventions designed to be applicable to everyday life including discourse in the public square, intergenerational life stories, and the use of literature, art, and museums to educate for meaning [18].

The special issue concluded with advocacy on two fronts. The first called for new thinking that weaves topics of purpose and meaning together with concern about human virtues and ethics. So doing is necessary to address some of the world’s gravest woes, such as widening inequality, systemic racism, and climate change. A second call is for greater collaboration between researchers and practitioners, so that scientific advances are not sequestered in scholarly journals but are rapidly applied and refined in the real world. Together, we believe that a moral foundation to meaning and purpose research combined with greater collaboration with practitioners will set the stage for the pursuit of meaning and purpose in directions that strive to create a more just, fair, and sustainable world.

Conflicts of Interest

The authors declare no conflict of interest.

References

Ryff CD, Soren A (2023) Introduction to this Special Issue on New Thinking on Psychological Health: Find Purpose and Meaning in Life Int J Environ Res Public Health 20:7168
Berkowitz L, Mateo C, Salazar C, Samith B, Sara D, et al. (2023) Healthy Eating as Potential Mediator of Inverse Association Between Purpose in Life and Waist Circumference: Emerging Evidence from US and Chilean cohorts. Int J Environ Res Public Health 20: 7099.
Hill PL, Rule PD, Wilson ME (2023) Does Activism Mean Being Active? Considering the Health Correlates of Activist Purpose. Soc Sci 12: 425.
Boylan J, Biggane C, Shaffer J, Wilson C, Vagnini K, et al. (2023) Do Purpose in Life and Social Support Mediate the Association between Religiousness/Spirituality and Mortality? Evidence from the MIDUS National Sample. Int J Environ Res Public Health 20: 6112.
Friedman E, Teas E (2023) Self-Rated Health and Mortality: Moderation by Purpose in Life. Int J Environ Res Public Health 20: 6171.
Song J, Kang S, Ryff C (2023) Unpacking Psychological Vulnerabilities in Deaths of Despair. Int J Environ Res Public Health 20: 6480.
Dameron E, Goeke-Morey M (2023) The Relationship between Meaning in Life and the Childhood Family Environment among Emerging Adults. Int J Environ Res Public Health 20: 5945.
André N, Baumeister R (2023) Dysfunctional Schemas from Preadolescence as One Major Avenue by Which Meaning Has Impact on Mental Health. Int J Environ Res Public Health 20: 6225.
Ruini C, Albieri E, Ottolini F, Vescovelli F (2023) Improving Purpose in Life in School Settings. Int J Environ Res Public Health 20: 6772.
Dyrdal G, Løvoll H (2023) Windjammer: Finding Purpose and Meaning on a Tall Ship Adventure. Soc Sci 12: 459.
Joshanloo M, Blasco-Belled A (2023) Reciprocal Associations between Depressive Symptoms, Life Satisfaction, and Eudaimonic Well-Being in Older Adults over a 16-Year. Int J Environ Res Public Health 20: 2374.
Russo-Netzer P, Tarrasch R, Niemiec RA (2023) Meaningful Synergy: The Integration of Character Strengths and the Three Types of Meaning in Life. Soc Sci 12: 494.
Coelho A, Lopes M, Barata M, Sousa S, Goes M et al. (2023) Biopsychosocial Factors That Influence the Purpose in Life among Working Adults and Retirees. Int J Environ Res Public Health 20: 5456.
Soren A, Ryff C (2023) Meaningful Work, Well-Being, and Health: Enacting a Eudaimonic Vision. Int J Environ Res Public Health 20: 6570.
Sun R, Lau E, Cheung S, Chan C (2023) Meaning in Life, Social Axioms, and Emotional Outcomes during the First Outbreak of COVID-19 in Hong Kong. Int J Environ Res Public Health 20: 6224.
Passmore H, Krause A (2023) The Beyond-Human Natural World: Providing Meaning and Making Meaning. Int J Environ Res Public Health 20: 6170.
Burrow A (2023) Beyond Finding Purpose: Motivating a Translational Science of Purpose Acquisition. Int J Environ Res Public Health 20, 6091.
Russo-Netzer P (2023) Building Bridges, Forging New Frontiers: Meaning-Making in Action. Soc Sci 12: 574.

Success in Solving Riddles and Psychometric Intelligence of Students

Leave a reply

DOI: 10.31038/PSYJ.2023582

Abstract

The results of performing the intelligence test were compared with the successfulness of solving Russian folk riddles. Significant correlations between the level of intellectual ability and the number of riddles solved have been discovered. Linear regression relationships between the number of riddles solved and the successfulness of performing the intelligence test have been built. An assumption that riddles may serve as a cognitive model for the investigation of human intelligence was made.

Keywords

Riddle, Intelligence, Metaphor, Language game

Introduction

In the classical psychology of ability, intelligence is understood as the ability to solve well-defined problems, which, as a rule, have a single solution. The limitations of this interpretation have long been the subject of criticism by both domestic and foreign psychologists J. Guilford, E. Torrance and others). In this situation, a number of leading researchers of the problem of cognitive abilities criticize the academicism of the modern psychology of intelligence. For example, R. Sternberg calls the system of diagnosing intelligence a “vicious circle of testing”, in the process of which one test is compared with another [1]. The need to rely on common sense in the study of intellectual phenomena is also emphasized by representatives of the so-called “contextual interactionism.” Referring to IQ as “the kingdom of hobbits,” M. Andersen criticizes the mechanism of the psychometric approach to intelligence [2]. For example, a large number of studies within the framework of cross-cultural psychology are devoted to the so-called “everyday cognition”. In particular, A. D. Schliemann and D. W. Karracher “… call into question cognitive analysis, which is based solely on laboratory research” [3].

Basic Assumptions

Metaphorical ability-the ability to create metaphors-was spoken of as a creative ability by Aristotle [4]. As you know, metaphorization is based on the vagueness, inaccuracy and ambiguity of everyday concepts that a person operates. The assumption that the understanding and construction of metaphor is closely related to the intellectual abilities of a person is expressed by a number of Russian and foreign scientists (E. Cassirer, D. Lakoff, P. Ricoeur, M. A. and others). In particular, J. Ortega y Gasset points out that “… metaphor serves not only to change but also to think”, and that it “… lengthens the arm of the intellect” [5].

In our study, the criterion of metaphor was the level of success in solving Russian folk riddles. The simplest and most succinct definition of the riddle is given by Aristotel, who understood the latter as “a well-constructed metaphor”. I. I. Revzin, assuming the importance of riddles as an integral element of folk pedagogy, defined a riddle as “… a minimal coherent text that stimulates direct activity (finding a denotation)” [6]. Y. I. Levin, interpreting riddles as “… intentionally transformed description of reality”, emphasized the fundamental difficulties of algorithmization and formalization of the semantic procedure for solving a riddle [7].

The important role of riddles as an integral attribute of archaic cultures is noted by J. Huizinga. Calling the riddle a “sacred game” that is on the verge of “serious and non-serious,” this philosopher emphasizes the fundamental role of the play principle in the structure of social consciousness [8]. L. Wittgenstein, illustrating the understanding of the “language game”, along with such classical examples of intellectual activity as the formulation and testing of hypotheses, the solution of arithmetic problems, also cites the solution of riddles as an example [9]. Although the experimental psychology of thought and intellect has a history of more than a century, only a few studies have been devoted to riddles. Such tasks are considered to be the prerogative of developmental psychology, and the object of study is most often children. It should be noted that the riddles are included in the set of test tasks of the popular in the United States test of intellectual achievements by A. S. Kaufman-Kaufman Assessment Battery for Children (KABCsampler.pdf) [10]. When planning the experiment, we proceeded from the hypothesis that the success of solving riddles, which are an example of problem situations used by people for the “spontaneous diagnosis” of ingenuity and ingenuity, is somehow related to the level of psychometric intelligence of adult subjects.

Research Methodology

In the experiments conducted with 2nd year students of the Faculty of Romance and Germanic Philology of the Bashkortostan State University, three selections of Russian folk riddles taken from an academic collection prepared by V. V. Mitrofanova [11] were used. Each experimental series consisted of thematically homogeneous riddles: in the first series (12 tasks) the subjects solved riddles dedicated to natural phenomena (rain, snow, snowdrift, etc.), in the second (10 tasks) riddles related to a person and parts of his body (teeth, tongue, mouth, etc.), and in the last series (10 tasks) students were offered riddles from the animal world. The experiment involved 130 people, 122 girls and 8 boys, aged 17 to 20 years. The experiments were carried out with training groups during laboratory classes on psychology, and no more than 30 minutes were given to solve each cycle. A separate lesson was devoted to the diagnosis of intelligence according to the test of R. Amthauer modified by V. N. Druzhinin [12].

Results of the Study

Describing the results obtained, it should be noted that a number of tasks turned out to be quite difficult for this contingent of subjects. For example, such riddles as “The little horse drank the whole lake”, or “The mother-in-law stands on the current and threatens the daughter-in-law” in general, no one has solved. Although the experimental tasks were organized into thematically homogeneous cycles, only one subject suggested that the riddles were thematic. The average success rate of solving the riddles of the first series was 1.2, the second-3.3, and the third-0. 6 riddles. The results of R. Amthauer’s test are much more stable: the average indices of success in solving the verbal, arithmetic, and geometric subtests of this method are 4.8, 4.1, and 5.8, respectively. A comparison of the success of solving riddles with indices with indicators of intellectual competence according to R. Amthauer using Kendall’s nonparametric correlation coefficient revealed a large number of significant relationships (p<0.05), highlighted in bold in Table 1.

Table 1: Intercorrelation matrix of intellectual competence according to R. Amthauer with the success of solving riddles.

	Verbal subtest	Arithmetic subtest	Geometric Subtest	Total intelligence	Success in solving riddles
Verbal subtest	1,00	0,10	0,19	0,36	0,20
Arithmetic subtest	0,10	1,00	0,40	0,65	0,21
Geometric Subtest	0,19	0,40	1,00	0,74	0,35
Total intelligence	0,36	0,65	0,74	1,00	0,34
Success in solving riddles	0,20	0,21	0,35	0,34	1,00

The normality of the distribution of the total results of R. Amthauer test, checked with the help of the Chi-square test, made it possible to apply the apparatus of linear regression analysis, in which the predictor was the number of solved riddles and the regressor is the success of the R. Amthauer test.

In general, the linear relationship between the overall success of solving riddles (X) and the number of correctly solved tasks according to the R. Amthauer test (Y) is described by the following equation (p<0.01):

Y = 9,6 + X (1)

In the process of analyzing the answers of the subjects, it turned out that for a number of tasks the students managed to find answers that did not literally coincide with the correct ones, but in general were no less successful. For example, the answer to the riddle “A black cat licks the window” is “night.” The answer “wipers on the car window” was assessed as no less successful and was counted as a solution. This circumstance, already described in the literature (Levin, 1973), increased the variability of the subjects’ answers and made it possible to compare the “conditional success” of solving individual riddles with the psychometric intelligence.

With the help of step-by-step regression analysis, it was possible to obtain a linear dependence between the total success of solving individual riddles (X) and intelligence (Y) for only seven experimental tasks: (p<0.01):

Y = 9,5 + 1,6X (2)

Conclusions and Prospects of the Study

A comparison of the productivity of R. Amthauer test with the success of students in solving Russian folk riddles revealed a close relationship between R. Amthauer intellectual competence and the number of correctly solved riddles.
The analysis of the success of solving individual riddles made it possible to construct a number of reliable linear regression dependencies between the total success of individual puzzle selections and the indices of intellectual competence according to R. Amthauer.
As a result of the experiments, it can be concluded that the riddle is a prototype of an intellectual test that was part of the system of folk psychology and pedagogy. The intellectual abilities of students, revealed with the help of riddles, are comparable to the indicators of the classical test of cognitive abilities, and riddles can be used as one of the methods of “non-classical” diagnosis of intelligence.
We believe that on the basis of the results obtained, it is possible to make an assumption about the fundamental importance of a comprehensive psychological and linguistic study of such forms of spontaneous intellectual activity as riddles, proverbs, humor, etc., for understanding such a complex phenomenon as human intelligence. Following L. Wittgenstein (Wittgenstein, 2003), the above phenomena can be considered as a kind of “transcendental normative language games” that play an important role in the process of socialization of the individual. In this regard, such a complex and ambiguous phenomenon as a riddle can be considered as a kind of complex lingvo-psychological model for the study of human intelligence.

References

Sternberg RJ, Kaufman JC, Grigorenko EL (2008) Applied Intelligence. Cambridge:
Cambridge University Press.
Andersen ML (1994) The many and varied social constructions of intelligence. In:
TR Sarbin, JI Kitsuse (Eds.) Constructing the social (pp. 119-138). London: Sage.
Psychology and Culture / Ed. by D. Matsumoto. St. Petersburg, Piter Publ, 2003.
Ortega y Gasset J (1990) Two great metaphors. In: ND Arutyunova, MA Zhurinskaya
(Ed.) Theory of metaphor. Moscow: Progress, pp. 68-81.
Kirby JT (1997) Aristotle on metaphor. American Journal of Philology 118: 517-554.
Revzin I. I. K (1975) obshchesemioticheskogo izpretatsii treh postulatov Proppa
(analiz skazki i teorii svyaznosti teksta) [On the general semiotic interpretation of
Propp’s three postulates (analysis of fairy tales and the theory of text connectivity)].
Collection of articles in memory of V. Y. Propp (1895-1970) Moscow. pp. 77-91.
Levin YI (1973)Semantic Structure of the Russian Riddle // Works on Sign Systems,
Vol. VI. Scientific Articles in Honor of M. I. Bakhtin (To the 75th Anniversary of His
Birth), 166-190.
Huizinga J (2014) Homo ludens ils 86. Routledge.
Wittgenstein L (1953) Philosophical Investigations. New York, NY, USA: Wiley-
Blackwell.
Kaufman AS, Kaufman NL (2004) Kaufman Assessment Battery for Children Second
Edition. Circle Pines, MN: American Guidance Service.
Puzzles. Preparation. Ed. Leningrad: Nauka Publishing House, 1968.
Druzhinin VN (1999) Psikhologiya obshchego sposobnosti [Psychology of general
abilities]. St. Petersburg, Piter Publ.

AI Simulations of What a Doctor Might Want to Hear from a Patient: Mind Genomics, Synthetic Respondents, and New Vistas for Personalizing Medicine

Leave a reply

DOI: 10.31038/MGSPE.2023315

Abstract

The study reported here deals with the creation of questions about what a doctor wants to hear when interacting with a patient, and the evaluation of that question to those questions. The questions, answers and respondents (survey takers) were all generated through artificial intelligence. The results revealed the possibility of AI support in all three areas, and revealed meaningful results when the study was run using the procedure of Mind Genomics. Systematic combinations of messages (elements) according to an experimental design revealed clearly different patterns of responses to the messages based upon who the response personas were designated to be. Three clearly different mind-sets emerged, groups of synthesized respondents whose pattern of coefficients were similar to each other within a mind-set, with the centroids of the mind-sets differing in a way which made intuitive sense.

Introduction

The introduction of artificial intelligence (AI) has created a level of interest perhaps unrivalled in the history of technology, but also spilling over into all areas of human endeavor as well as issues of philosophy [1]. As the use of AI has become easier, more widespread, various uses have emerged, almost beyond counting.

At the same time that technology and society has focused on AI, the author and colleagues have been working with a different, somewhat new way of fathering data about the world of the everyday. The science is called Mind Genomics. The notion is that everyday experience is worth studying for the way it allows us to understand people. Furthermore, rather than studying people by asking them about topics using questionnaires, or by talking directly to them as do qualitative researchers, an intermediate way is to present people with different descriptions, or vignettes, really combinations of phrases to paint a word picture, and then ask the people to rate the vignettes on a scale. The results generate a database of impressions of these vignettes, with the impressions able to be deconstructed into the driving power of each of the element or phrases. The respondent, or survey taker doing this task, cannot ‘game the system’ because the combinations change from person to person, based upon an underlying set of planned combinations, the so-called experimental design [2].

Up to now the test takers in these Mind Genomics studies have been real people, whether of school age or older. The extensive data which has emerged from these studies range from evaluation of descriptions of foods [3,4] and onto education [5] the law [6], social issues [7], and beyond. The Mind Genomics approach has proved fruitful in its ability to allow different ideas to emerge from these studies, as well as uncover new to the world groups of people who think of the world differently. These groups are called mind-sets.

The Mind Genomics Platform and AI as a Generator of Ideas

In the Mind Genomics platform AI has already been used to create questions, and from those questions create sets of answers. It is these ‘answer’s or elements, that Mind Genomics combines into small, easy to read combinations called vignettes. These vignettes, comprise a maximum of four elements and a minimum of two elements, created by an underlying experimental design The vignettes are created in a rigorous fashion, so that:

Each vignette has at most one element or answer from a question, never two or more answers from a question, but occasionally no answer from a question. It is this property of incompleteness that will allow the researcher to use statistical (regression analysis) to show how the elements or answers ‘drive’ ratings
Each vignette is different from every other vignette. The vignettes a systematically changed by a permutation program [8].
Each respondent evaluates a specific set of 24 vignettes, with each element appearing four times. Each set of 24 vignettes is reserved for a specific respondent

During the early part of 2023 the Mind Genomics platform was enhanced by AI, first to provide questions, and then to provide answer to the questions. The enhancement used ChatGPT3.5 [9,10]. The researcher was presented with a screen which requested four questions, and afterwards four screens, each of which requested four separate answers to each question that the research would provide. Though one might not think that the request to provide four questions is particularly daunting, the reality is that it is quite daunting. As a consequence, many nascent uses of Mind Genomics simply abandoned the task. The reality began to become apparent, viz., that people may be good at answering questions, but they are not good at formulating a story in terms of a set of questions to ask which will get at the answer(s). Some may call this a deficit in so-called critical thinking, but for the purposes of this paper it is simply a stumbling block in usability of Mind Genomics.

The creation of a series of built in prompts, provided to the researcher in a non-threatening, rather easy way, ended up producing Idea Coach. We will show the use of Idea Coach in this paper, as part of the specific treatment of the topic, ‘what doctors want in patients’,. We will use Idea Coach to show how the questions are generated, and how data from synthetic respondents are created and analyzed. This paper shows the method, and the nature of the answers that one might get.

The underlying motivation is to see what might emerge from these initial trials with AI acting as a synthetic respondent. The important issue is do the data ‘make sense’ to the reader. The issue about whether the data matches external results must be addressed later, when the approach of creating synthetic respondent has been well worked out. This study is only the first step I that process, not the external validation step. To summarize, the validity considered here is the simplest one of all, namely ‘face validity.’ Do the data generated by AI ‘make sense’

Running the Mind Genomics AI Experiment from Start to Finish

The Mind Genomics process is templated from start to finish. The study presented here deals with what a doctor wants from a patient. The synthesized respondents are going to be medical professionals. The actual study can be found in the website. Much of the set-up of the study has been taken from the senior author’s previous Mind Genomics website, www.BimiLeap.com. The synthetic respondents are created within a new website, Socrates as a Service ™, which uses many of the feature of the Mind Genomics platform, but adds the ability to synthesize respondents simply by describing the way they think, what they do, etc.

Step 1

Give the study a name, select a language for the prompts, and accept the terms for privacy.

Step 2: Create Four Questions Which ‘Tell a Story’

As noted above, it is at this point in the process that many researchers are stymied, and where the researcher can use AI to help formulate questions. The instructions to ‘tell a story’ are simply meant as a help to the research. The underlying idea is that the questions should deal with different aspects of the topic.

Figure 1 shows the screen requesting the four questions, and the next screen invoked when the request is made to use AI in the form of Idea Coach. The ‘box’ in Panel B of Figure 1 is filled out by the researcher. Typically, the request should comprise an introduction (e.g., explain in detail), the issue (the specific request), and then prompts asking the Idea Coach to produce a question of no more than 15 words, and a question understandable to a person of younger age. For this project the age was ‘12’ years, but in other projects the age has been higher (e.g., around 21 years old). Finally, Panel C shows the return of a subset of the 15 questions produced by AI, with the remaining questions requiring the researcher to scroll down. Panel D shows the final set of questions, edited, and in preparation for the next step in AI empower Idea Coach.

Figure 1: Four questions and Idea Coach

The researcher can repeat the request to Idea Coach as many times as desired, with each return by Idea Coach comprising 15 questions, some new, some repeats. At the end of the process, the researcher will have selected four questions, and inserted them into the template, and, if necessary, editing these questions to ensure the proper format of answers to be produced by Idea Coach in the next step. Table 1 presents one set of questions, along with an AI based ‘summarization’ of the questions as well as further extension of the questions into new opportunities. Note that Table 1 is created for every set of 15 questions developed through Idea Coach, as well as for every set of 15 answers to a question produced by Idea Coach (see below). Excel booklet from which Table 1 is extracted is called the project ‘Idea Book.’ Each separate iteration, either to generate questions or answers, generates 15 results. The full Idea Book is available after the project passes the stage of creating questions and answers.

Table 1: The Idea Coach prompt, the first set of 15 questions, and the AI elaboration of those 15

Step 3: Create the Answers

Once the questions have been created and edited (polished to increase the quality of the AI output), it is time to create answers. The same process occurs, with the researcher presented Idea Coach with the edited question, and then 15 answers returned. Again, the researcher has the task of selecting up to four answers and re-running the Idea Coach again for new answers to address the now polished/edited question. During the process it is always possible to revise the question. Figure 2 shows the different steps for the creation of answers. Once again, the Idea Coach can be invoked as many times as desired. Table 2 shows an example of the 15 answers to the first question.

Figure 2: Screenshots showing the process for creating the four answers to a question. Panel A shows the partial output from Idea Coach. Panel 2 shows the four answers actually selected, and then slightly edited for use in the study.

Table 2: First set of answers to question #1

Step 4: Select the Final Set of Questions and Answers

Table 3 shows this selection. All text comes from Idea Coach, but with edits at each step of the way to make sure that the elements can be understood in a meaningful way by people, and presumably in that case by AI as well.

Table 3: The final set of questions and answers

Step 5: Create the Self-profiling Questions

The personas of the synthetic respondents are created from combinations of the self-profiling questions. The underlying process is systematically one randomly selected answer from each question to create the persona. The personas were created by “Socrates as a Service ™,” the next generation of program in the Mind Genomics platform. Figure 3 shows an example of a classification question, with Panel A having no information, and Panel B showing the same template, but filled out to define the respondent. Note that the self-profiling classification allows the researcher to specify anything desired about the to-be-synthesized respondent. Table 4 shows the actual set of self-profiling questions.

Figure 3: Example of one question filled out for the self-profiling classification. Panel A shows the empty placeholder. Panel B shows the first self-profiling classification as filled out by the research. There are up to eight of these questions, each with a possible 2-8 alternative answers.

Table 4: The set of self-profiling questions and answers. The personas were created from combinations of the answers

Step 5: Create an Open-ended Question

As part of the Mind Genomics effort, the platform allows the respondent to complete two open ended questions, one before doing the evaluation of the vignettes the other after doing the evaluation of the vignette. Figure 4 shows the request for the open-ended question to be done after the synthetic respondent has ‘evaluated’ the 24 vignettes comprising combinations of elements or ‘messages’.. The normal human respondent generally has a lackadaisical attitude towards filling out these open-ended questions, unless the topic is deeply emotional, such as breast cancer. The inclusion of the open-ended question was done to explore what might emerge from AI. Those results are discussed below.

Figure 4: Templated screen for the open-ended question, with the question filled out

Step 6: Create the Respondent Orientation and Rating Scale

Figure 5, Panel A shows the very short respondent orientation. The Mind Genomics process has been set up with the guiding vision that the information needed to rate the vignette would be presented in the combinations of the elements or test messages, as well as influenced by who the respondent ‘IS’ and how the respondent ‘THINKS’. Consequently, the very short introduction simply instructs the respondent to read the vignette. Figure 5, Panel B shows the two-sided scale as presented to the researcher during the set-up. Table 5 shows the actual text of the scale, emphasizing the two sides or dimensions embedded in each scale point.

Figure 5: The respondent orientation (Panel 5A), and the rating scale (Panel 5B)

Table 5: The text of the 5-point binary scale used by the synthetic respondent to rate the vignette

Step 7: Select the Source of Respondents

The new Mind Genomics platform, now named SaaS (Socrates as a Service™) has expanded the options to include synthetic respondents using AI. Figure 6 shows the choices. The newest choice is at the bottom, ‘I want to use simulated respondents.’ By making the synthetic respondent simply become another choice, the new Mind Genomics platform has created the opportunity for SaaS to become a simple, affordable teaching tool. The researcher can set up the study in the manner previously done, but ‘explore’ the response using AI, in order to learn. Research now becomes a tool to learn both through the combination of Idea Coach + Question Book at the start of the project, and through iterative explorations using AI in the middle or end of the project.

Figure 6: Screen shot showing how the researcher can source ‘respondents’.

Step 8: Define Respondent

For studies run with people the first step in the actual evaluation consists of a very short ‘hello’ followed by the pull-down menu for the self-profiling classification. Figure 7 shows this pull-down menu, showing the three answers for the question ‘How do you feel about the insurance companies and the medical health holding companies?’ Human respondents find this way of answering the self-profiling questions to be easy and not intimidating. When it comes to the synthetic respondents, there is no need for Step 8. The program automatically creates the personas, the synthesized combinations of the different answers, with each question contributing exactly one answer to the persona being developed.

Figure 7: The pull-down menu for self-profiling classification, used for human respondents, but not for synthetic respondents.

Step 9: Create Test Vignettes by Experimental Design

A hallmark of Mind Genomics is the creation of combinations of messages, these creations being called vignettes. Rather than instructing a respondent to evaluate each of the 16 elements, the Mind Genomics strategy is to combine these elements into small, easy-to-read combinations. There is no effort to link the elements together, an effort which would backfire because the ensuing paragraph of linked elements would contain too much connective material, verbal plaque, as it were.

The actual vignettes are created by an underlying experiment design which ensures that the 16 elements appear equally often (5x in a set of 24 vignettes). Furthermore, no vignette ever has more than one element or answer from a question, but many vignettes have only two or three answers, with other questions failing to contribute to the vignette. Finally, each respondent evaluates a mathematically equivalent set of vignettes by the permutation process, but the actual combinations evaluated by the individual respondents differ from one respondent to another [8].

The foregoing strategy lies at the basis of Mind Genomics. It becomes virtually impossible to game the system because the combinations are overwhelming. The real focus is on the performance of the individual elements. The vignettes are only the way to get the elements in front of the respondent in a way which resembles the seemingly discordant nature of everyday experience. Quite often exit interviews with respondents as well as discussions with professionals end up with the ‘complaint’ that it was simply impossible to figure out the; right answer’, an effect which mildly irritates people, but all too often infuriates academics.

Step 10: Present the 24 Vignettes for a Respondent to the AI and Obtain a Rating on the Five-point Scale

The AI system proceeds by creating a prompt for each vignette. The first part of the prompt defines WHO the respondent is. The respondent is some randomized combinations of answers from the six self-profiling classifications, with each answer appearing approximately equally often across the 801 respondents. This first part of Step 10 will produce a constant persona across the 24 vignettes.

The second part of Step 10 presents the rating question and scale to the AI. This second part of step 10 will produce a constant rating question and set of answers across the 24 vignettes for the synthesized persona.

The third part of Step 10 presents the AI with the vignette. The AI is instructed to assume the persona, to read the scale, and then to rate the vignette on the scale by choosing one of the five answers.

The actual study is now run, the total time for 801 respondents lasting 15-30 minutes. The time may be substantially shorter, but there is extensive back and forth with the AI modules and provider.

Step 11: Uncover the Distribution of the Five-point Scale Ratings across the Set of All Self-profiling Scales

At the end of the process, we can look at the distribution of the ratings across the groups synthetic respondents, these groups defined by the how the synthetic respondent ‘identifies itself’. Table 6 shows a remarkable consistency across the different self-profiling groups. If we were to stop here, we would conclude that there is no discernible difference across the different self-profiling groups, and thus the effort to create synthetic respondents at this stage of AI development has failed. We would, however, be quite wrong in that conclusion, as the further tables will show.

Table 6: Distribution of the five rating scale points for each selection in the self-profiling classificaiton questionnaire

Step 12: Transform the Ratings into Binary Variables

A now-standard practice in Mind Genomics is to transform the rating scale. The rating scale created here provides two dimensions. Our focus here is on simulating the positive response ‘My style’, corresponding to the combination or union of ratings 5 and 4, respectively. The transformation makes ratings of 5 or 4 equal to 100, and in turn ratings of 1,2 or 3 equal to 0. To each newly transformed variable, now called R54x, is added a vanishingly small number (<10^-4). This prophylactic step ensures some minimum level of variation in R54x, which will become a dependent variable in OLS (ordinary least-squares regression), discussed in Step 11. For other analyses, the system or the researcher can create different binary variables, such as R52X, a positive gut feel.

Step 13: Relate the Presence/Absence of the 16 Elements to the Newly Developed Binary Variables

Table 7 shows the coefficients for the equations relating the presence/absence of each element to the following dependent variables, which have been coded 0 or 100.

R1x-Rating of 1 coded 100, ratings of 2, 3, 4 or 5 coded 0
R2x-Rating of 2 coded 100, ratings of 1, 3, 4 or 5 coded 0
R3x-Rating of 3 coded 100, ratings of 1, 2, 4 or 5 coded 0
R4x-Rating of 4 coded 100, ratings of 1, 2, 3, or 5 coded 0
R5x-Rating of 5 coded 100, rating of 1, 2, 3 or 4 coded 0
R54x-Ratings of 5 or 4 coded 100, ratings of 1, 2 or 3 coded 0
R52x-Ratings of 5 or 2 coded 100, ratings of 4, 3 or 1 coded 0
R21z-Ratings of 2 or 1 coded 100, ratings of 5, 4 or 3 coded 0
R41x-Ratings of 4 or 1 coded 100, ratings of 5, 3, or 2 coded 0

RT-Response time-with human being defined as the number of seconds elapsing between the presentation of the vignette and the response. Not definable for AI, although measurable.

It is clear from Table 7 that the coefficients within a column are quite similar to each other. There are some variations, but remarkably little. Furthermore, the answers seem to make intuitive sense. It does not pay to analyze each set of numbers, however, because within a column the numbers are simply too close. Finally, there is a response time emerging, although it is not clear what that means. The RT, response time, is measured in terms of seconds between the presentation of the vignette and the respondent’s rating. All response times are low, around 0.6, but do not know what is occurring.

Table 7: Coefficients for the Total Panel (801 respondents x 24 vignettes each)

Step 14: Show the Linkage between Elements and R54 for Different Levels of Each Persona Variable

A slightly more nuanced picture emerges when the total panel results are broken up into separate persona ‘levels.’ Table 4 shows the six different self-profiling questions, and the answers to each. Tables 8A-8F show the strong performing elements for each persona ‘level’. Each table, Tables 8A-Table 8F, corresponds to one of the six self-profiling questions. The columns correspond to the answers. The coefficients are strong performing values for element, with ‘strong performing’ operationally defined as a coefficient of +14 or higher.

Table 8A: Strong performing elements for persona Q1: How many years have you been a medical professional

Table 8B: Strong performing elements for persona Q2: What makes you dislike a patient?

Table 8C: Strong performing elements for persona Q3: How long is a reasonable time with a patient?

Table 8D: Strong performing elements for persona Q4: What is your feeing about telehealth. No strong performing elements emerged

Table 8E: Strong performing elements for persona Q5: How do you feel about the insurance companies and the medical holding companies

Table 8F: Strong performing elements for persona Q6: How do you feel when you start the day

The development of Tables 8A-8F is straightforward, consisting of the isolation of the vignettes showing the specified persona option, and then running the OLS (ordinary least-squares) regression for all the cases having the appropriate self-profiling answer. Each table has a base size, referring to the number of respondents in the simulated set of 801 who are assigned the particular answer. Thus, in Table 8A, for example, 86 respondents were assigned to the answer 1 of question 1, namely: How many years have you been a medical professional, with the answer 1, ‘I’m a student, planning to start my career.’

The OLS regression [9] returns return with coefficients for each cell, based upon the rating: R54 = k₁A1 k₂A2… k₁₆A16. The result is a wall of numbers. Table 7 suggests that the highest coefficient for R54 for the total panel is 10. Therefore, Tables 8A-AF shows only those coefficients of 14 or higher. Furthermore, Tables 8A-8F shows only those elements which have at least one coefficient of 14 in a row. This stringent criterion substantially reduces the number of data points that need to be considered.

Our initial results here suggest that there are coefficients higher than others, although not many of them. Nor is the underlying story particularly clear. Finally, the highest coefficient is 17, hardly as strong as the results obtained with human beings, but yet suggesting that AI can differentiate among elements based upon the persona created.

Step 15: Create Mind-sets from Synthesized Respondent Data

Our final analysis for this study considers the existence of mind-sets, different ways of looking at the data. When Mind Genomics is executed with human respondents there is an almost universal emergence of mind-sets, with perhaps the exception of ‘murder [6].

When Mind Genomics data are clustered together on the basis of the coefficients, generally the meaning of the mind-sets becomes exceptionally clear, even though the process of creating mind-sets does not use any interpretation of the data. Rather, the process to create mind-sets is clustering, with the process easy to do with conventional data, and now just as easy to do with synthesized data. The process uses k-means clusters [11,12], and a measure of ‘distance’ between two objects (e.g., between two synthesized persons) defined as (1-Pearson R). The Pearson R, the correlation coefficient, shows the degree to which two sets of numbers co-vary. When the 16 coefficients of the two synthesized people co-vary perfectly, they are considered to be in the same mind-set, the Pearson R is 1.00, and the distance is 0. When they 16 coefficients vary perfectly inversely with each other, they are considered to be in different mind-sets, the Pearson R is-1, and the distance is 2.0.

Moving now to the results from the k-means clustering at the top of Table 9, we see coefficients around 9-11 for the total panel, coefficients 0-22 for two clusters or mindsets but not many high coefficients of 21+, and three mind-sets emerging from three clusters, two of the mind-sets being strong, with a number of coefficients 21 or higher. The value 21 has been chosen for simplicity, based upon observations over a two-year period working with human respondents in different topics.

Table 9: Coefficients for the total panel, and for the two and three mind-set groupings. Strong performing coefficients (21 or higher) are shown in shaded cells. Coefficients with negative or 0 values are not shown.

When we apply the criterion of 21 or higher we end up with three mind-sets, two of which show the requisite value of coefficients 21 or higher (Mind-Sets 2 of 3 and 3 of 3, respectively).

Mind-Set 1 of 3-Focus on the process of the visit (but no truly strong elements)
Mind-Set 2 of 3-“Intervention-focused Patients”
Mind-Set 3 of 3-Engaged and collaborative patients.

The three mind-sets can be interpreted more deeply through AI, using the same set of prompts as we used to summarize the ideas on each page of questions (see Table 1) and each page of answers (see Table 2). Table 10 shows the summarization for Mind-Sets 2 of 3 and 3 of 3, respectively. The summarization is based on the commonalities of all elements with coefficients of 21 or higher. Mind-Set 1 of 3 fails to meet that minimum level, and therefore the Idea Coach Summarizer was not applied.

Table 10: Summarization by Idea Coach (AI) of the strong performing elements for Mind-Sets 2 of 3 and 3 of 3. The names of the mind-sets were also suggested by AI.

Step 16: How well does the AI Perform When Synthesizing Respondents?

A continuing effort in Mind Genomics is the attempt to increase critical thinking. How does one measure critical thinking, however, and more importantly, how can one set up criteria to assess the development of critical thinking. One way to assess such thinking is by looking at the set of positive coefficients for the total panel, for the two mind-set solutions, and for the three mind-set solutions. The objective is to create elements with high coefficients, but also elements which are very high in one mind-set, but low in the in other mind-sets. Thus, higher may not be better because the elements do not score differently across the mind-sets. Some preliminary simulation suggest that strong performance occurs with an IDT value of 68-72. The IDT is the Index of Divergent Thought, shown in Table 11. The table shows the relevant parameters to compute the IDT.

Table 11: Computation specifics of the IDT, Index of Divergent Thought. A value between 68 and 72 may be optimal.

The results from this study and from several other parallel studies of the same type (doctor-patient) suggest that the IDT values for synthesized data are lower than what are obtained from people. That is, the synthetic respondents do generate easy-to-interpret mind-sets, but the inner structure is not as strong, based upon the IDT of 47 rather than the IDT’s of 70 often observed in the simplest of these Mind Genomics studies. In other words, synthetic respondents give answers, but the ‘deep structure’ is somehow not quite ‘human’.

Discussion and Conclusions

The appetite for AI as synthetic ‘people’ is increasing daily. Whether the topic be social issues [1], health [13], or politics [14] there appears to be a one-way push towards more sophistication in the application. We no longer question the utility or even the ‘validity’ of synthetic people. Rather, the focus is on the improvement of the application. Towards that goal of improvement, the study reported here suggests a new application, namely the use of AI to explore medical issues involving stated specifics of the doctor-patient interaction. The potential for Mind Genomics is this area is as yet unknown, but one might imagine doctors using Mind Genomics with synthetic patients to learn how to interact with patients. It may well be that the years of experience of a doctor in the so-called ‘bedside manner’ might be quickly learned with AI. Only time will tell, but fortunately the use of Socrates as a Service ™ may well shorten that time for learning.

References

Bryson JJ, Diamantis ME, Grant TD (2017) Of, for, and by the people: the legal lacuna of synthetic persons. Artificial Intelligence and Law 273-291.
Kirk RE (2009) Experimental design. Sage handbook of quantitative methods in psychology, pp. 23-45.
Moskowitz HR (2012) ‘Mind genomics’: The experimental, inductive science of the ordinary, and its application to aspects of food and feeding. Physiology & Behavior 107: 606-613. [crossref]
Moskowitz HR, Gofman A, Beckley J, Ashman H (2006) Founding a new science: Mind genomics. Journal of Sensory Studies 21: 266-307.
Moskowitz H, Sciacca A, Lester A (2018) I’d like to teach the world to think: Mind Genomics, big mind, and encouraging youth. In: Harnessing Human Capital Analytics for Competitive Advantage, IGI Global, pp. 55-90.
Moskowitz HR, Wren J, Papajorgji P (2020) Mind Genomics and the Law. LAP LAMBERT Academic Publishing.
Moskowitz H, Kover A, Papajorgji P(eds.) (2022) Applying Mind Genomics to Social Sciences. IGI Global.
Gofman A, Moskowitz H (2010) Isomorphic permuted experimental designs and their application in conjoint analysis. Journal of Sensory Studies 25: 127-145.
Abdullah M, Madain A, Jararweh Y (2022) ChatGPT: Fundamentals, applications and social impacts. In 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS) (1-8) IEEE. November.
Spitale G, Biller-Andorno N, Germani F (2023) AI model GPT-3 (dis) informs us better than humans. arXiv preprint arXiv: 2301.11924. sci adv. [crossref]
Burton AL (2021) OLS (Linear) regression. The encyclopedia of research methods in criminology and Criminal Justice 2: 509-514.
Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognition 36: 451-461.
Loong B, Zaslavsky AM, He Y, Harrington DP (2013) Disclosure control using partially synthetic data for large‐scale health surveys, with applications to CanCORS. Statistics in Medicine 32: 4139-4161. [crossref]
Sanders NE, Ulinich A, Schneier B (2023) Demonstrations of the potential of AI-based political issue polling. arXiv preprint arXiv: 2307.04781.