DOI: 10.31038/PSYJ.2020214

Introduction

One might not think that the ubiquitous availability of cheap, easy, powerful computing power combined with storage and retrieval of information would produce in its wake better students, better minds, and the benefits of better education. The opposite is the case. As the increasing penetration of consumer electronics continues apace, it is becoming increasingly obvious that students have neither the patience to pay attention, nor the ability to think critically.  A good measure of that loss of student capability comes from the popular press, where blog after blog decries the loss of thinking and, in turn, the power of education. Not to be outdone, the academic press as signaled by Google Scholar ® provides us with a strong measure of this electronics-driven loss of thinking and withering of education. Table 1 shows the year by year data.

Table 1. Concern with the loss of critical thinking – a 10-year count (Source: Google Scholar^®)

How then can we make education more interesting?  This paper is not a review of attempts to make education more involving, more interesting, but rather presents a simple, worked approach to making learning more interesting, but really far deeper. It is obvious that children like to talk to each other about what they are doing, to present information about their discoveries, their developments, themselves. Children like experiences which resemble play, in which they are somewhat constrained, but not very much. And when a child discovers something new, something that is his or hers, the fun is all the greater because the discovery can be shared. The ingoing assumption is that if learning can be made fun, a game, with significant outputs of a practical nature, one might stimulate a love of learning which love seems to have disappeared.

Experiential learning and discovery of the new – A proposed approach based upon the emerging science of Mind Genomics

The emerging science of Mind Genomics can be considered as a hybrid of experimental psychology, consumer research, statistics, and mathematical modeling. The objective of Mind Genomics is the study of how people respond to the stimuli of the ordinary, the every day. We know from common experience that people go about their daily business almost without deeply thinking about things, in a way that Nobel Laureate Daniel Kahneman called System 1, or ‘thinking fast’ [1]. We also know that there are many different aspects to the same experience, such as shopping. People differ, consistently so, in when they shop, why they shop, how they get to the place where they will make a purchase, the pattern of shopping, what they shop for, and why.  The variation is dramatic, the topics not so interesting, the exploration of the topics left to mind-numbing tabulations, which list the facts, rather than penetrating below, into the reasons.

Mind Genomics was developed to understand the patterns of decision making, not so much in artificial laboratory situations to develop hypotheses for limited situations, but rather to understand the decision rules of daily life.  Instead of mind-numbing tables of statistics from which one gleans patterns, so-called ‘connecting the dots,’ Mind Genomics attempts to elicit these individual patterns of decision making in easy-to-do experiments.  The output, once explained, fascinates the user, converting that user into an involved explorer, looking for novella, insights, and discoveries that are new to the world, discoveries ‘belonging to the student’. The day to day worlds, the ordinary, quotidian aspects of our existence, become grist for the mill of discovery. The result is that discoveries about the ordinary, discoveries that when harnessed by teachers and appreciated by students, combine experiential learning, and learning how to think critically

As noted above, Mind Genomics derives historically from psychology, consumer research, statistics, and modeling. The objective of Mind Genomics is to uncover the specific criteria by which people assign judgments. The topics are unlimited.

The empirical portion of this paper will show how experiential learning and critical thinking may be at the fingertips, with the use of simple computer programs, specifically BimiLeap, freely accessible at www.BimiLeap.com.  In this paper we look at how a 14-year-old student can learn about laws and ethics, as well as the issues of daily life. The topic is taken from the way young students in a Yeshiva, a rabbinic school, can learn about the issues underlying property, specifically borrowing property and what happens when the property is somehow ‘lost.’

Experiential learning and discovery of the new

A proposed approach based upon the emerging science of Mind Genomics.  Mind Genomics is an emerging science combining experimental psychology, consumer research, experimental design, and statistical modeling. The objective is to explore decision making in the everyday world.

In terms of commercial and social practice, Mind Genomics has been applied in by author HRM to issues as varied as it applies to topics as different as decision making about what we choose to eat, legal cases, and communications in medicine to improve the outcomes during and after hospitalization, respectively. These practical applications along with the ongoing stream of studies suggest Mind Genomics as a simple-to-use but powerful knowledge-creation tool. With Mind Genomics, virtually anyone can become a researcher, explore the world, classify the strategies of decision making, and discover new-to-the-world mind-sets, groups of people who think about the topic in the same way, and who differ from other groups of people thinking about the topic in a different way.

The Mind-Genomics technology has been embedded in easy-to-use computer programs, making the typical Mind Genomics study fast, affordable, and structured. The same simplicity of research, studying what is, may thus find application to educate the non-researcher, the novice, the younger student. Rather than the researcher exploring a topic area with the point of view of a person interested in the specific topic, the notion emerged that the same tool can be used to each a novice how to think, using research as a the tool, and the information and accomplishment as the reward for using the tool.

The Yeshiva approach: Havrutas (groups) studying a topic in depth

This paper presents one of the early attempts to use Mind Genomics to teach legal reasoning to a teenager, helping the teenager to make a specific topic ‘come alive’ as well as imbue experiential learning and critical thinking into the process.  The paper will present the approach step by step, as a ‘vade mecum’ or ‘guide’ for the interested reader.

The objective of Mind Genomics is to understand the decision making in a situation.  The deliverable is a simple table, which one can adorn in different ways, but which at its heart shows questions and answers about a situation.

We take our approach from the way students in Jewish religious schools study the corpus of Jewish law, commentary and discussion.  The notion of using the Bible as a source for teaching modern concepts is not new [3]. created a course on economics, based upon biblical tests, as described in the following paragraph

The author describes a course designed to build the critical thinking skills of undergraduate economics students. The course introduces and uses game theory to study the Bible. Students gain experience using game theory to formalize events and, by drawing parallels between the Bible and common economic concepts, illustrate the pervasiveness of game-theoretic reasoning across topics within economics as well as various fields of study.

We take our source for the course in the way the Talmud is study. The Talmud comprises more than 2700 of pages of explication of basic Jew practice.  The origins of the Talmud are, according to Jewish sources, founded in the Oral Law, the law of Jewish practice based in the Old Testament, the Jewish Bible, but expanded considerably.  For the reader, the important thing to know is that the Talmud comprises two portions, the Mishna, a short, accessible compilation of Jewish Law and practice, finalized by Rabbi Judah the Prince around the second century CE, and then discussions of that compilations, attempting to find discrepancies to reconcile them, done by Rabbis and their students for about 300–400 years hundred years after the Mishna was finished. This section, discussion and reconciliation among sources, is embodied in difficult, occasionally tortuous material known as the Gemara, the word in Aramaic for‘completing.’

Talmud students who spend years learning the Talmud end up thinking critically [4]. The method is to pair off students with each other, havrutas, usually comprising two students, who read, decipher, debate, and struggle to understand the section together. The approach leads, when successful, to logical thinking, and ability to formulate problems ina way worthy of a lawyer.  In the words of [5].

when examined closely, havruta study is a complex interaction which includes steps, moves, norms and identifiable modes of interpretative discussion [5].

Havruta learning or paired study is a traditional mode of Jewish text study. The term itself captures two simultaneous learning activities in which the Havruta partners engage: the study of a text and learning with a partner. Confined in the past to traditional yeshivot and limited to the study of Talmud, Havruta learning has recently made its way into a variety of professional and lay learning contexts that reflect new social realities in the world of Jewish learning [6].

With this very short introduction, the notion emerged from a number of discussions with psychology researchers and with students of the Talmud that perhaps one might use technology inspired by Talmudic style thinking and discussion to teach students to think critically, whether these be Talmud students embedded in the Jewish tradition, or students who could take a topic of the Talmud in an ‘edited’ form, and work with that topic in the way a yeshivastudent might.

Adapting the approach, making it accessible, challenging, interactive, and fun

The situation for this study is simple, based upon a legal case well known to many students of the Talmud, but presented in secular terms. The case concerns an item, the nature of which is unstated but the implication is that the item is something portable.  The information available is:

1. Who initiated the interaction?
2. Why was the interaction initiated?
3. Where was the interactioninitiated (viz., request made)?
4. What happened to the item?

In order to make the system easy, but keep the tone serious, the design for the computer interface was created to be simple. 

Figure 1 shows the three key screen shots.

Figure 1. The setup showing the three panels which force the student to think in an analytic yet creative and participatory fashion

The left panel shows the requirement that the student(s) select a name for the study. It may seem simple, but it will be the name of the study which drives much of the thinking.

The middle panel shows the requirement to create four questions dealing with the topic, with the questions ‘telling a story.’ It is here, at this second stage, beyond the name of the topic, where students encounter problems, and must ‘rewire’ their thinking. Students are taught to understand, to remember, to regurgitate. Students are not taught to ask a series of questions to elucidate a topic.  Eventually, the students will learn how to ask these questions, but it will be much later, when the student is introduced to research in the upper grades, and when the student becomes a professional, especially a lawyer.  We are creating the opportunity to bring that disciplined thinking to the junior high school or even to grade school.

The right panel shows the requirement to provide four answers to one of the questions. There is no hint, no guidance, about what the answers should be, but simply the question repeated to guide the student.  Typically, this third panel is easy to complete once the student has gone through the pain of thinking through the four questions. That is, the questions are hard to formulate; the answers are easy to come by after the hard thinking has been done.

Engaging the student in the creation of questions and answers

The key to the approach is the set of questions, and secondarily the set of answers. As noted above, the demand on the participant is to conceptualize the topic at the start, rather than being trained to deconstruct the topic when it is fully presented.  The approach thus is synthetic, requiring imagination on the part of the student. 

Table 2 presents the four questions and the four answers to each question.  The questions and the answers do not emerge simply from the mind of the student, at least not at first. There are the inevitable false steps, the recognition that that the questions do not make sense, do not tell a story, do not flow to create a sequence, etc.  These false steps are not problems, but rather part of the back and forth learning how to reason, how to tell a coherent story, and how to discard false leads.

Table 2. The four questions, and the four answers to each question

The answers in Table 2 are simple phrases, with the introduction to the answer being a phrase to reinforce the story.  Various efforts at making the approach simply continue to reveal that for those who are beginning a topic, it is helpful to consider the answers as continuations of the questions. That specification, such as ‘initiated by’ will also make the respondent’s effort easier.

It is important to keep in mind that the process of topic/question/answer will become smoother with practice, the topics will become more interesting, the questions will move beyond simple recitation of the order of events, and the answers will become more like a literary sentence, and less like a menu item.

In the various experiences with this system, it is at this point, the four questions, that most people have difficulties. Indeed, even with practice, people find it hard to organize their thinking to bring a problem into sufficiently clear focus that they can make it into a story. When the researcher finally ‘understands the task’ the response is often a statement about ow they feel their ‘brains have been rewired.’ Never before did the researcher have to think in such a structured, analytic way, yet with no guidance about ‘what is right.’

One of the more frequent questions asked at this point is ‘Did I do this right?’  Most people are unaccustomed to structured thinking.  After creating the questions, and on the second or third effort, after the first experiment, the researcher begins to feel more comfortable, and is able to move around the order of questions, changing them to make more sense. This flexibility occurs only after the researcher feels comfortable with the process.

Creating ‘meaningful’ stimuli by means of an underlying experimental design

As students, we are typically taught the ‘scientific method,’ namely to isolate a variable, and understand it.  Our mind is attuned to dissecting a situation, focusing on one aspect.  We lose sight of the fact that the real world comprises mixtures, and that an understanding of the real world requires us to deal with the way mixtures behave.  An observation of our daily actions quickly reveals that virtually all of the situations in which we mind ourselves comprises many variables, acting simultaneously.  Indeed, much of the problem of learners is their experienced difficulties in organizing the multi-modal stimuli impinging on themand learning to focus and to prioritize.  It is to this skill we now turn as we look at the student experience.

The computer program combines alternative answers to these four questions, creating short vignettes, presents them to respondents, gets a rating of ‘must repay’ vs ‘does not need to repay’.’

The above-mentioned approach seems, at first glance, to be dry, almost overly academic. Yet, the Mind Genomics approach makes the ‘case’ into something that the students themselves can create, investigated, and report, with a PowerPoint® presentation of the study, something that will be part of their portfolio for life, and can be replicated on many different topics.

Each respondent evaluates a unique set of vignettes, created by a systematic permutation of the combinations. The mathematical rigor of the underlying experimental design is maintained, but the different combinations ensure that across all the respondents a wide number of potential combinations are evaluated.

The underlying experimental design and indeed all of the mathematics for the analysis are shielded from the respondent, who is forced to ‘think’ about the topic, and the meaning of the data, rather than getting lost in deep statistics. 

The composition of the vignettes is strictly determined by an underlying specification known as an experimental design [7]. Each vignette comprises either two, or three or four answers, at most one answer from a question, but sometimes no answer from a question.  The experimental design ensures that the 16 elements appear as statistically ‘independent of each other,’ so knowing that one answer appears in a vignette does not automatically tell us whether another answer will appear or not appear.

Figure 2 shows an example of a vignette created according to the underlying design.  The respondent does not know that the computer has systematically varied the combinations.The vignette is created by an underlying experimental design which prescribes the composition of each vignette.  Each respondent evaluates 24 different vignettes, or combinations of answers, and answers only. No questions leading to the answers are presented directly, although for this case the question is embodied at the beginning of the answer.

Figure 2. Example of a vignette comprising three answers or elements, one answer from three of the four questions. Most vignettes comprise four answers, some comprise three answers, and a few comprise two answers.

An important challenge in Mind Genomics is to come up with a meaningful rating question. The rating question links the test stimuli, our vignettes, and the mind of the respondent.  Without a meaningful test question all we have are a set of combinations of messages.  The test question focuses the respondent’s mind on how to interpret the information in the vignette.

The test question is posed simply as either a unipolar scale (none vs a lot) or a bipolar scale (strong on one dimension vs strong on the opposite dimension, such as hate/love).  To the degree that the researcher can make the rating question meaningful, the researcher will have added to the power of the Mind Genomics exercise.

Our topic here concerns the loss of property occasioned by one person giving property to another person, after being asked to do so, or after being motivated to do so, that motivation coming from within.  A reasonable rating question is whether the person in whom the property is placed, for whatever reason, is required to ‘make good byreplacing the property’ or ‘not required to replace the property.’  Rather than requiring a yes/no answer, we allow the respondent to assign a graded value, using a Likert Scale:

1  =   The person who asked/borrow is not really to blame and doesn’t
          have to pay ….

5  =   The person who asked/borrowed should ‘make good.’

The phrasing of the question and the simple 5-point rating scale make the evaluation easy, and remove the stress from the respondent. By allowing the student a chance to assign a graded rating, the student can begin to understand gradations of guilt and innocence. Furthermore, the answers are not so clear cut, so straightforward that they prevent the student from thinking.  The requirement to take the facts of the case into account and rate the feeling of guilt vs innocence on a graded scale forces the student to think.  The very ‘ordinariness’ of the case encourages the student to become engaged, since the case is something that no doubt the student has either experienced personally, or at least has heard about at one or another time.

Obtaining additional information from the respondent

Quite often, those who teach do not pay much attention to WHO the respondent actually is, or even the different ways that people think. The academies where the Talmud was created did pay attention to the way people think, coming up with different opinion about the same topic. These different opinions were enshrined in discussions. The intellectual growth coming from thinking about the problem was maintained over the millennium and a half through the discussions of students about different points of a topic, and the study of those who commented on the law, and gave legal opinions about cases. The back and forth discussions about why the same ‘facts on the ground’ would lead to different opinions became a wonderful intellectual springboard for better thinking.  Few people, however, went beyond that to think, in a structured way about how ordinary people might think of the problem, people who were not trained as legal scholars, nor empaneled in a judicial panel.

Part of the effort of the Mind Genomics project is to show to the student the way different people think about the topic, and how there is not necessarily ‘one right answer.’  Thus, at the start of the experiment, before the evaluation of the 24 vignettes by the respondent, the respondent is asked three questions:

1. Year of birth, to establish age
2. Gender
3. A third question, chosen by the researcher. Here is the third question for this study.

How do you feel about mistakes that are made in everyday life, by ‘accident’

1=I believe that the law is the law 2=I believe in being lenient 3=I want to know the facts of the case more

Running the study for educational purpose – Mechanics

During the past several decades, and as the Mind Genomics technology evolved and was refined, a key stumbling point, i.e., a ‘friction point’ in today’s language, continued to emerge. This was the deployment of the study in a way that could be quick, inexpensive, and thus have an effect within a short time. Two decades ago as the Internet was being developed, a great deal of the effort of a Mind Genomics study was expended getting the respondents to participate, typically by having them come into a central location, such as a shopping mall, and spending ten or 15 minutes.  The result was slow, and the pace was such that it would not serve the purposes of education. The process was slow, tedious, expensive, and not at all exciting to anyone but a serious researcher.

During the formative years of the technology, 2010–2015, efforts were put against making the system fast, with very fast feedback.  The upshot was that a study could be set up in 30 minutes or faster and deployed on the internet with simply a credit card to pay for the cost of respondents, usually about 3.00$ per respondent for what turned out to be a 4 minute study.  A field service, specializing in on-line ‘recruiting’ would provide the appropriate respondents, sending them to the link, and obtaining their completed, and motivated answers. The respondent motivation was there because they were part of the panel.

The mechanics were such that the entire study in the field would come back in less than one hour and one minute. The total analysis, including the preparation of the report in PowerPoint(r), ready for the student presentation, took less than one minute from the end of the field.  Figure 3 shows an example of the PowerPoint(r), expanded to the slide sort format, showing the systematic presentation of the results.

Figure 3. Example of the PowerPoint^® report for the study, shown inlide-sort format. The respondent receives the PowerPoint^® report and the accompany Excel^® data sheet one minute after the end of the data acquisition.The report is in color, and ‘editable.’

1. The title page, showing the study name, the researcher, the date
2. Information about the BimiLeap vision
3. The raw material – elements, rating question
4. How the data are analyzed, showing the transformation from a rating scale to a binary rating, as well as introducing the concept of ‘regression modeling’ …. This explanatory information is presented in a short, simplistic manner, yet sufficient to show the nature of mathematical (STEM) thinking
5. Tables of data showing the results from the total panel, key subgroups
6. Mind Sets – a short introduction to how people can think differently about the topic, and a short introduction to the calculations. Once again, the focus is on the findings, not on the method
7. Results from dividing the respondents into two mind-sets and three mind-sets
8. IDT – Index of Divergent Thought – showing how many elements have strong positive coefficients,   when the data are considered in terms of total panel, two mind-sets and three mind-sets, respectively. The IDT can be used to understand how well the researcher has ‘dived in’ to the topic, to uncover different ways of thinking about the topic. The IDT can be used to ‘gamify’ the research process, by providing an operationally defined, objective measure, of ‘winning ideas.’
9. Introduction to ‘response time’ – how long it takes the respondent to ‘process’ the ideas
10. Tables of data showing the results from the total panel, key subgroups, and mind-sets
11. Screen shots of the respondent experience

The report is accompanied by an Excel book with the data, set up for further analysis, if the student wishes.

The results, embedded in a pre-formatted PowerPoint® presentation, and with supporting full data in Excel®, are immediately dispatched by email and can always be retrieved from the researcher’s account, when, for example, the data are ‘updated’ with the ratings assigned by new panel participants. This process compresses the entire time, from set-up to data reporting, so that the results can be discussed within 1–3 hours after the official start the entire process, viz., entering the questions and answers into the BimiLeap programming and getting the panel provider to provide respondents.

The vision behind the PowerPoint® report is that it provides a tangible record of the student’s effort, a measure of what the student has achieved in the growth to learning.  By creating different groups of students who work together, and working together for 90 minutes in the afternoon, once per week, the typical student can generate 20–30 or so PowerPoint® in a year, a collection the quality of whose contents, study by study, will demonstrably improve as each study is designed, and executed with real people, the respondents.

What the student receives and how the student learns

Table 3 presents the unadorned results, a table.  To interpret the table is straightforward, despite the fact that it has simply numbers.  The researcher who is doing the study need not know the mechanics of how the numbers are computed, at least not in the beginning, in order to enjoy the benefits of Mind Genomics, and in order to participate in experiential learning.

Table 3. Output from a Mind Genomics on a simple legal case. The data is from the total panel

The question what is the initiator (Question A) required to do.  At the low end, the initiator does not have to ‘make good’ (innocent, i.e., not pay.)  At the top end, the initiator has ‘make’ good (guilty, i.e., pay.)

Base Size: The table shows us that there are 30 different people who participated in the study. This is the base size.

Additive Constant: This is the expected number of times out of 100 people that the verdict will be ‘guilty’, i.e., must repay. What is special is that the additive constant is a baseline, corresponding to the likelihood that a person will say ‘guilty’ even in the absence of information. Our additive constant is 60. It means that when someone requests from another to do something with the item, 3 out of 5 (60%) the onus is on the person who does the requesting to take responsibility and pay if something happens to the item.

Coefficient: Ability of Each Element to drive Guilty (must repay). Let’s look at the power of Question A: Who initiated.  The four answers or alternatives, which appeared in the vignette, are either 0 (nothing) or negative. The negative means that when we know who initiated, we are likely to forgive. We forgive most when the action is initiated by a young neighbor or, or school friend in high school. The value is -10. That value -10 means the likelihood of guilty (must make good) goes from 60 (no information about the initiator) to 50, when the one piece of information about the initiator is presented as part of the case (specifically,  initiated by: School friend in high school.)  Of course, when the initiator is an older neighbor (29 years old), the coefficient is no longer -10 as it was before, but now 0. We (and of the course the student) have just discovered that it makes a difference WHO the initiator is. 

The same thinking can be done for the other questions and answers. Some, like D2 (What eventually happened: It was destroyed in accident) increase the likelihood of being judged responsible, and forced to make good, i.e., to repay.

The process from the vantage point of the researcher (e.g., the student)

As presented above, the process is simple, although quite uninteresting, except perhaps to the subject matter expert interested in lawor in thinking processes. How can the student be engaged to participate?  Otherwise, what we have here is simply another ‘dry process’ with some interesting but unexciting results.

The three aspects of the process are involvement, ease, and fast/clear results.  Absent those, and the process will remain in the domain of the expert, to be used occasionally when relevant, and otherwise to be relegated to process. The excitement of thinking and learning will not be experienced.

Let us proceed with the process.  The process follows a series of steps designed to make the researcher think. The process is structured, not particular hard after the first few experienced, but sufficiently challenging at the start so that the researcher realizes the intellectual growth which is taking place at the time of the research set up.

Experimental Designs – mixtures of answers

The actual test stimuli comprise mixtures of answer, without the questions. One can go to Table 2, and randomly pick out one answer from each of the four questions, present these answers together, on separate lines, and instruct the respondent to read the vignette, the combination, and rate the combination as a single entity.  The task may seem ‘strange’ to those who are accustomed to reading properly constructed paragraphs in their native language, but to those who are selected from random individuals to be study participants, there is no problem whatsoever. People follow instructions.   The problem with evaluating a few unconnected combinations is that there are no explicit patterns waiting to be discovery, and a discovery in turn which can teach systematic and critical thinking.

As noted above several times, the Mind Genomics paradigms works with an explicitly developed experimental design, which makes it easy for the equipped research to discover the pattern. Table 4 shows an example of the experimental design for a single respondent. There is a total of 16 answers and 24 vignettes, or combinations. The number and arrangement of the vignettes are not accidental, nor haphazard, and certainly not random, although it is tempting to say that the combinations are randomly arranged. Nothing could be further from the truth. The design, i.e., the combinations are precisely defined ahead of time , so that each of the 16 elements or answers appears exactly five time, that any vignette of combination has at most four answers (without their questions), that some combinations comprises two, others comprise three answers, and that the answers are systematically varied.  Finally, each of the experimental design is mathematically identical to every other design, but the specific combinations are different.

Table 4. The experimental design for 7 of the 24 vignettes for a respondent, as well as the rating, the binary expansion of the rating, and the consideration time

Different points of view for the same argument – Mind-Sets and exciting discoveries

Students who ask questions to engage discussion are generally appreciated by their teachers. The questions show involvement.  Sometimes the questions spark lively discussions, especially when they are offered in the spirit of ‘looking at the problem in different ways.’

A foundation stone of Mind Genomics is the recognition and elicitation of different points of view about the same topic. The ingoing rationale is that in matters of everyday life there are facts, but often radically different opinions. Sometimes more learning emerges from the discussion of the topic from these different viewpoints than could ever emerge from rote learning of the ‘facts.’

One of the computations of Mind Genomics is the discovery of different mind-sets, groups of people with these individuals in one group looking at the topic differently from individuals in another group. The computational machinery of Mind Genomics enables these mind-sets to be discovered rapidly and inserted into the PowerPoint® report as simply new groups to consider and to discuss.

Table 5 shows the additive constant and coefficients for total (already discussed), gender, age, and two mind-sets. The students who participate in the study, indeed those who may have come up with the elements, can now see how the simplest of facts can be interpreted in different ways.

Table 5. The pattern of coefficients for ‘make good’, generated by key demographic groups (total, gender, age) and newly uncovered mind-sets.

What should emerge from Table 5 is the charge to the students to ‘tell a story about the mind of each group,’ about whether the mind of the group seems to hold strong views or weak views about what makes a person guilty.  There is no right nor wrong answer, but simply the requirement that the student abstract from these data some narrative of how the group thinks.  The difference in coefficient is most dramatic for the two mind-sets, but what is the MEANING of the difference?  In that short question lies a great deal of opportunity for the students to think creatively, to see patterns, and indeed perhaps even to make new-to-the-world discoveries.  Furthermore, the excitement can be maintained by challenges to the students to create personas of the mind-sets, and to suggest and executed follow-up experiments with Mind Genomics to explore hypotheses about these mind-sets.

Learning to think even more deeply – Is justice blind, and how can the student prove or disprove it?

We finish off the data section by a new way of thinking, and two exercises can be done manually with available statistical programs, planned to be programmed into the next generation of the Mind Genomics report.  These are called ‘scenario analyses.’ The logic behind them is simple. The thinking emerging from them is far from simplistic, however. The struggle to understand the new patterns from this level of analysis helps to move the motivated student into a more profound way of thinking, in a subtle, easy, virtually painless way.

When people look at the ‘facts of the case’ they are often cautioned not to pay attention to the nature of the individuals, but simply the ‘facts on the ground,’ on what happened.  Such caution is easy to givebut may or may not be followed.   One of the opportunities afforded to the student of Mind Genomics is to understand clearly the interaction between WHO the person IS in the case, and the response.  For example, in our case we have four people who initiate the request, ranging from a young neighbor, an old neighbor, the uncle, school friend in high school.

We can learn a lot by sorting the data into five different strata, depending upon who does the requesting, and ten building the model. We don’t know what exactly happened to the item, but we do know who did the requesting.  We build the model based upon the five strata. Each stratum corresponds to one person who requested.  The independent variables are the three other aspects (action: where request was made; what happened.).

The original set up of the Mind Genomics process was to create four questions, and for each question develop four answers.  As described above, the underlying experimental design mixed and matched the combinations according to a plan. Each respondent, i.e., test participant, evaluated 24 different vignettes. Furthermore, unknown to the respondents, an underlying system created different sets of 24 vignettes, a unique set of combinations for each respondent.

It makes no difference to the respondent about the way the combinations are created. Whether the same 24 combinations are tested by 30 different people (the ordinary way), or whether the systemic variation produces 720 different combinations (24 different combinations x 30 different people), is irrelevant to the individual respondent. What happens, however, to the judgments when we look at five different groups of vignettes, varying, say, by WHO DOES THE INITIATION.  We have vignettes with no mention of who does the initiation, as well as vignettes specifying the initiation by the younger neighbor (14 years old), the older neighbor (29 years old), by a school friend in high school, and by the person uncle.

The question which emerges, one provoking a great of discussion, is whether justice is blind.  That is, the intended actions can be the same, the place where the request was made can be the same, and the outcome can be the same. Presumably, it does not matter WHO initiates the request. Justice should be blind. Is it?

We can sort the data into five strata, five groups, with each group comprising one of the five alternatives of ‘initiated by.’  There are five groups or strata because on group has NO mentionof ‘Initiated by:’ The next step is to estimate the coefficients, this time using only the remaining 12 elements. A1-A4 are absent from the regression because the regression is done on a stratum-by-stratum basis, where the element ‘Initiated by:’ is held constant.

After this effort the excitement increases when the students realize how strongly the initiator ‘drives’ the rating of ‘make good’ (i.e., rating of 4–5 converted to 100.)  Table 6 shows this analysis when the strata are based upon Question A (Who Initiated?). Table 7 shows the comparable analysis when the strata are based upon Question B (Action or Purpose).  In both cases there is plenty of space for discovery and for an ah ha experience, as the student uncovers truly new findings, itself motivating, and struggles to explain what she or he has revealed to the world.

Table 6. Coefficients for the models relating presence/absence of elements to the binary transformed rating ‘make good’ (i.e., guilty). The table shows the contribution of each of the elements to ‘make good’ when the ‘Initiated by’ was constant.

Table 7. Coefficients for the models relating presence/absence of elements to the binary transformed rating ‘make good’ (i.e., guilty). The table shows the contribution of each of the elements to ‘make good’ when the ‘Action’ or purpose was constant.

IDT – Index of Divergent Thinking: Making the Mind Genomics into a game

As of this writing (early 2020), the world of students is awash with games, with fun, with a shortened attention span, and with the competition of different forms of entertainment. How do we convert Mind Genomics to entertainment or at least to that over-used neologism ‘edu-tainment?’

The notion of converting critical thinking to ‘games’ requires that there be criteria on which people can complete, and that these criteria be objective, rather than subjective.  That is, to make Mind Genomics into a ‘game’ with points means to create an easy-to-understand scoring system, and specifically a system within which everyone can compete.  Furthermore, in the spirit of critical thinking and experiential learning, the system should reward creative thought.

During the past three years author H Moskowitz has worked on criteria to ‘measure’ critical and creative thought within the framework of Mind Genomics. A key aspect of Mind Genomics is that it automatically estimates the degree of linkage between each of the 16 elements and the rating scale, after the rating is converted to a binary score, 0 or 100.  This linkage is the coefficient from the model relating the presence/absence of the elements to the binary transformed rating.  It will be the linkage, the coefficient, which provides the necessary data to create a gaming aspect to the Mind Genomics exercise.

Consider the tabulation of coefficients in Table 8. Table 8 presents the distribution of POSITIVE coefficients for six different groups which always appear in the Mind Genomics reports. The six groups are total panel, Mind Sets 1 and 2 from the two Mind-Set solutions, and then Mind Sets 1, 2 and 3 from the three Mind-Set solution.  We saw the total panel and the results from the two mind-sets, but not from the three mind-sets.

We tabulate the frequencies of coefficients between 0 and 5, 5 and 10, 10 and 15, 15 and 20, and finally higher than 20.  This tabulation generates a distribution of coefficients. We can either work with the absolute number of coefficients of a certain size (Computation 1) or weight the number of coefficients by the relative size of the subgroupsshowing the particular magnitude of coefficients (Computation 2.)

The summaries in Table 8 provide a quantitative, objective measure of ‘how good the elements are’ as they drive the response. When a student produces elements that score well across the different mind-sets, this is evidence of good thinking on the part of the study, thinking which is sufficiently powerful and expansive as to appeal to different-minded groups,

Table 8. Computation for the IDT, Index of Divergent Thought, a prospective gamifying metric to make Mind Genomics more interesting by being ‘gamified.’

A one-year educational plan for Mind-Genomics to develop the student mind

1. Goal: A one-year plan to create massive intellectual development among students through a once/week exercise using Mind Genomics through the BimiLeap program. The outcome… for each person, an individual portfolio of 20–30 studies showing topics investigated by the student … a portfolio to be shown proudly at interviews, and in school to be shared with fellow students, creating a virtuous circle of learning & knowledge
2. The benefit to the education system: Create a school system which produces first rate creative thinking in younger students ages 7–13, high school students, and university students, each developing far beyond who they are today. Use the BiMiLeap program in the classroom or after school, once/week, to do a study in a topic area of intellectual interest, making it a social process which combines learning, true discovery, and competition,
3. Process:  Four students work together. The younger students work with an older ‘docent.’ The docent records the material, prepares input for BimiLeap, ensures that the input is correctly submitted to the APP, and reviews the automated report with the students in the group after the data are obtained.  Each week, the composition of the group changes, allowing different students to collaborate.
4. Mechanics:  The actual mechanics of the approach are presented in this paper. Mind Genomics studies concern a single topic area and collects data by obtaining reactions through an experiment, albeit an experiment which looks like a survey, but is not. Each group will get a topic from school, create the materials, run the study, get the PowerPoint® report, discuss, add insights to the PowerPoint,  present it in class to the other groups, incorporate the report into one’s personalized portfolio, and then repeat the process the following week with a reconstituted group.
5. Specifics – Number of topics:  We believe that one good policy is to select a set of 10 topic areas, so each topic is treated 2–3 times a year by the students. Each time, the group addresses one of the topics afresh, encouraged to think critically about it and not just accept or replicate prior knowledge. They are freed to create new knowledge on the topic by re-using, updating, or adding new content.
6. Specifics – The ‘sweet spot’ for users: The older students focus on different aspects of a single general topic, with each student creating 8–12 reports on research about the mind of people responding to different aspects of the topic …  Worthy of a PhD at the age of say 15, all while having fun, learning to think, collaborate, present.
7. The BimiLeap program can be found at www.BimiLeap.com

References

1. Kahneman D (2011) Thinking, Fast and Slow. Macmillan.
2. Moskowitz HR, Gofman A, Beckley J, Ashman H (2006) Founding a new science: Mind genomics. Journal of sensory studies 2: 266–307.
3. Mc Cannon BC (2007) Using game theory and the Bible to build critical thinking skills. The Journal of Economic Education 38: 160–164.
4. Lehman M, Kanarek J (2011) Talmud: Making a case for Talmud pedagogy—the Talmud as an educational model. In International Handbook of Jewish Education, Springer, Dordrecht, 581–596.
5. Kent O (2006) Interactive text study: A case of havruta learning. Journal of Jewish Education 72: 205–232.
6. Holzer E, 7 Kent O, (2011) Havruta: What do we know and what can we hope to learn from studying in havruta? In International handbook of Jewish education, Springer, Dordrecht, 404–417.
7. Box GE, Hunter WG, Hunter JS (1978) Statistics for Experimenters, New York, John Wiley

DOI: 10.31038/PSYJ.2020213

Abstract

We present a new way to understand how people perceive situations involving other people, situations that could be considered part of the everyday. The approach is Mind Genomics, which assesses the response of people to short, systematically varied vignettes about situations and other people. The responses to these vignettes are deconstructed into the part-worth contribution of the component elements that the vignette comprises, showing the ‘algebra of the mind.’ The deconstruction also is done on response time to the vignettes, showing the ability of the elements to engage attention when the respondent makes a judgment. When Mind Genomics is applied to descriptions of family life under stress, the data suggest that some elements are linked to predicted violence, others are not. Women appear to be more sensitive than men to the individual elements. Three different mind-sets emerged with different perceived ‘triggers’ to predicted family violence, with each mind-set encompassing both men and women: Mind-Set 1 – no specific warning; Mind-Set 2 – Sensitive to the economy; Mind-Set 3 – Family has problems. We present the PVI (personal viewpoint identifier) as a technique to assign new people to these mind-sets.

Introduction

Violence against the other sex, especially in marriage, is not new. Stories of murder and abuse fill the newspapers, the magazines, and the Internet news of today (late 2019.) Before today’s overwhelming plethora of news, violence by males against females, especially spouses and other family members, occupied a great deal of attention, from those in the news, but of course even more telling, from writers and poets. One cannot read the famous poem, My Last Duchess, by the 19^th Century British poet, Robert Browning without a shudder when one realizes how easy it was to kill one’s spouse. And of course, the popular 1965 Rock n Roll song by Herman’s Hermits, hints at England’s royal lady-killer, King Henry VIII, transformed to a 1960’s idiom of a man with a broken heart.

What is popular in literature only reflects what is the common situation in everyday life. The literature in sociology and psychology is replete with studies about violence and anger. Violence against one’s spouse is dealt with in many publications, with the aspects dissected, studied, statistically analyzed and reports issued. Violence seems to be endemic to the relations, starting even in courtship [1]. The spousal violence continues, even into the 60’s [2]. Violence emerges when the woman ends up supporting the man [3]. Of course, alcoholism plays a role [4], but so does religion [5]. Violence comes from many quarters, but many studies have focused on gender and marriage [6, 7, 8].

The foregoing represents just a bit of the available material on violence in the home. These studies focus on both surveys and discussions with individuals. What is lacking is a sense of the richness of the family life through discussion, an absence promoted by the rigidity of the scientific method, but the absence filled by clinicians and social workers. The key issue is to make this topic come alive by merging the rigor of science with the immediacy of storytelling.

 Violence in the home is especially relevant because it is common and riveting to those involved. Although there seems to be very little academically oriented literature recounting the actual ‘story’ of the abuse, the Internet provides a repository of such personal studies in a number of websites, such as:

1. https://www.getdomesticviolencehelp.com/domestic-violence-stories
2. www.hiddenhurt.co.uk/domestic_violence_stories.html
3. https://www.domesticshelters.org/articles/true-survivor-stories

It may be that websites are more conducive to people ‘telling their story’ in their own language. In contrast, the scientific community has made its information almost unobtainable, except to those schooled in the scholastic tradition, and able to cut through the jargon and statistics to understand what exactly is happening

Exploratory studies through Mind Genomics

This study explores the mind of ‘people’ by having them evaluate different vignettes about violence, vignettes that have been systematically varied, with the components of the vignette, the element, having a richness that is missing from surveys

A review of the scientific literature suggests that many of the studies involving human judgment are done in a manner which is slow, expensive, requiring teams of researchers, and extensive, rigorous statistical analysis. The statistical analysis is often of the type known as ‘inferential,’ with the objective to confirm or to falsify an ingoing hypothesis, with the hypothesis developed from theory.

Mind Genomics presents to the world of science a different approach, not grounded in theory and confirming or falsifying hypotheses [9]. Rather, Mind Genomics can be liked to an exploration of decisions, using cognitively meaningful stimuli, and dealing with issues of the every day. Mind Genomics can be likened to a new cartographical exercise of a land. Mind Genomics works by presenting vignettes to the respondents, with these vignettes comprising combinations of elements or messages to which a respondent can relate. The respondent reads the vignette and responds to the combination. The research approach is analogous to the MRI, which takes multiple pictures of tissue from different vantage points, and then combines these into a picture of the tissue.

The research in this study embodies the Mind Genomics paradigm, dealing with the very important issue of family violence. The objective is to understand a third-party’s estimate of either violence or peace at home occurring when a specific situation is presented, and then to assess the likelihood that each specific element is correlated either with violence or with a peaceful home, respectively, two opposite sides of the scale.

Mind Genomics combines the person with emotion and meaningful description of behavior, i.e., cognitively rich test stimuli. Mind Genomics obtains ratings from the response of people to vignettes about a situation, similar that presented in literature, storytelling, or song. The vignette paints a picture of a situation. The respondent is then asked to judge some aspect of the situation, such as projected violence or projected happiness, based upon what is read. Through this approach it now becomes possible to understand the mind of the person, either the one who is undergoing the experience, or the one who is hearing/reading about the experience. Both points of view differ dramatically from the almost lifeless array of statistics describing a situation. Mind Genomics combines the vividness of experience with numbers, probing the inner mind of the person exposed to the situation, first-hand or second-hand.

The Mind Genomics approach

The Mind Genomics approach is designed to be exploratory, affordable, iterative, and scalable. This set of objectives in the design means that there are certain simple aspects of the study:

1. Exploratory. As suggested above, Mind Genomics does not work by confirming or disconfirming a hypothesis extant in the scientific literature. Rather, the exploration means taking new ideas from every-day experience and exploring them to find out the degree to which people respond positively or negatively to them.
2. Affordable. Mind Genomics is set up to be a so-called DIY, Do it yourself system. The researcher needs access to an APP on the proper machine (Android or Kindle), the ideas (for the researcher), and a convenient source of respondents.
3. Iterative. Mind Genomics is set up to return the data in easy-to-read formats (PowerPoint® for presentation, Excel® for data analysis. The data return in a matter of a few hours. A new study can be launched a few hours later, after the results from the first study are digested. Furthermore, the results are easy to understand, and set up to promote further exploration with the same tool. With the iterative approach the researcher can do as many as 4–6 studies in a 24-hour period, each study building upon the previous study.
4. Scalable. Almost anyone can use Mind Genomics to explore problems. The system is scalable across people, but also across different aspects of a topic, by the same researcher. Within a matter of a week or two, the enterprising researcher can conduct 10–20 studies, exploring the different facets of a topic.

Raw materials

The origin of this study was the focus by author Peer on the causes of violence against women, the fact that so much is known, yet so little. When random people were asked by author Moskowitz about the topic ‘What do you think causes spousal violence,’ very few people could provide an answer quickly. There was no sense of a well-recognized phenomenon, violence, connected with the daily life of people, other than general statistical compilations, available in the literature.

The benefit of a Mind Genomics study is the degree to which it takes any topic and reduces that topic to a set of common aspects, experienced in the everyday. Thus, the elements shown in Table 1 represent the way a person might conceive of the nature of spousal violence. A Mind Genomics is not meant to be exhaustive, but rather introductory, approachable, and in some ways focuses on a very specific topic. When this notion of ‘cartography’ is recognized and accepted, the position of Mind Genomics advances to a useful, early-stage way of understanding a topic from the mind of people.

Table 1. The raw materials for the study, comprising four questions about the conditions of a family, and the four answers to each question.

The reader will see the approach in Table 1, showing the four questions (which tell a story), and the four answers to each question. As we read the answers or elements, we should keep in mind that the answers are concrete and simple. When exploring a topic, we can learn a great deal from four simple questions which tell a story, and from the pattern of responses to the 16 answers. The results in this study should reveal a variety of new-to-the-world patterns about domestic violence, based simply on the different ways that people respond to these unambiguous stimuli.

With the inputs shown in Table 1, Mind Genomics creates combinations of answers, so-called vignettes. An example of a vignette appears in Figure 1.

Figure 1. Example of a vignette as presented to the respondent.

Each respondent evaluated 24 vignettes. The vignettes were constructed according to an experimental design, with the property that a vignette comprised at most one answer from each question, but often had no answers from either one or two of the questions. Thus, the vignettes comprised either two, three, or four answers, the so-called elements. Furthermore, each respondent evaluated a unique set of combinations. The underlying structure of the combinations was maintained, but the specific combinations differed from one respondent to another.

To the respondent, the combinations might seem to be random, but the reality is the exact opposite. The experimental design prescribes the combinations. The objective is to present combinations of elements or answers (without the questions), obtain ratings from the respondents who evaluate these combinations, and then deconstruct the ratings into the separate contribution from each element. In this way the respondent is unable to ‘game’ the system by providing politically correct answers. It is virtually impossible to detect the underlying pattern. As a result, the respondent simply relaxes, and gives responses which are more intuitive, and fundamentally less ‘edited.’ In the words of experimental psychologist Daniel Kahneman, the Mind Genomics approach calls into play ‘System 1’ thinking, the fast, almost automatic thinking that we use daily in our lives, when we don’t have to make rational calculations [10].

A sense of the underlying experimental design can be gotten from looking at the schematic in Table 2, which presents the structure of the first eight vignettes for Respondent #1. The respondent does not, of course, see the underlying structure, but rather the actual combinations, presented on the computer as in Figure 1, or restructured to fit on the screen of a smartphone.

Table 2. Structure of the first eight vignettes for Respondent #1, the conversion to binary for statistical analysis, and the deconstruction of the ratings and response time.

Executing the study

Each respondent receives the invitation to participate, and is instructed to read the vignette, and to rate it on the 9-point scale.

Here is a set of snapshots of families. Please read the full snapshot and tell us what will happen within the foreseeable future. Read the whole snapshot. Is it going to be peaceful or do you sense some family violence brewing?

What will happen in the foreseeable future with this family?

1=peace and love … 9=some violence

The respondent then read each of 24 unique vignettes. The respondent rated vignette on the above 9-point scale. The respondent was then instructed to fill out an open-ended question about violence (results not presented here.) The entire process took approximately 4–5 minutes.

Basic data transformation

The experimental design itself must be transformed to a binary no/yes, as shown in Table 2. Only with a binary scale (absent/present) is it feasible to understand the part-worth contribution of every element. In turn, the 9-point scale can be used as a dependent variable, but experience has shown that most people, researchers included, have a difficult time understanding what the scale points mean. Sometimes this difficulty in understand is addressed by labelling each of the nine scale points, a task which itself is fraught with difficulties. An easier way, taken from the world of consumer research, converts the nine-point scale to a binary scale, 0 or 100. Managers find it easy to understand the binary scale and know what to do with a ‘no’ or a ‘yes’ answer.

The conventional way to divide the scale creates three regions for the scale; 1–3, 4–6, and 7–9, respectively. Then the following conventions is invoked:

Ratings of 7–9 are assumed to represent ‘violence,’ and ratings 1–6 are assumed to reflect the lack of violence. For this new variable, ‘violence’, we convert ratings of 1–6 to 0, and ratings of 7–9 to 100. We then add a small random number (<10^–5). The small random number ensures that that the regression analysis will ‘run’ on the binary-transformed data, even when the respondent confines all of the ratings either to the lower portion of the scale (1–6, transformed to 0), or confines all of the ratings to the upper portion of the scale (7–9 transformed to 100, 1–6 transformed to 0). The small random number provides just enough variability in the dependent to ensure that the OLS (ordinary least=squares) regression ‘does not crash,’

Analysis – What drives violence versus happiness – total panel?

The basic analysis in Mind Genomics is OLS (ordinary least-squares) regression, made possible by the ingoing structure of the vignettes for each individual respondent. Every respondent evaluated 24 carefully constructed vignettes, ensuring that at the individual level all 16 elements or answers to the questions, are statistically independent of each other. Most of the vignettes are different from each other, so that the combination of all the vignettes covers a great deal of the ‘design space.’

We combine all the data from the 50 respondents, creating a database of 1200 vignettes (50 × 24 = 1200). We run two OLS regressions. The first relates the presence/absence of all 16 variables to the binary value of ‘violence’, corresponding to the ratings 7–9 on the original 9-point scale, but now becoming the value 100 on the binary scale for violence. The second OLS regression relates the presence/absence of all 16 variables to the violence of ‘happiness’ corresponding to the ratings of 1–3 on the original 9-point scale.

Table 3 shows the coefficients for the two equations. The equation is expressed as (Binary Rating) = k₀ + k₁(A1) + k₂(A2) + … k₁₆(D4).

Table 3. Parameters of the model for the Total Panel relating the presence / absence of the 16 elements to predicted violence (Ratings of 7–9 converted to 100), and to predicted happiness (Ratings of 1–3 converted to 100).

The additive constant, k₀, is the estimated value of the binary response in the absence of elements. All vignettes comprised a minimum of two and a maximum of four elements. Consequently, the additive constant is an estimated parameter. Nonetheless, the additive constant has value in because it gives a sense of baseline interest or baseline feeling, in the absence of elements.

As noted above, the experimental designs ensure that all 16 elements or answers are statistically independent of each other, allowing the absolute coefficients to be estimated. That is, the values of the coefficients are all relative to 0. A coefficient of 10 is twice as high as a coefficient of 5. Furthermore, the transformation of the scale to binary strengthens the mathematic property. The coefficient of 10 means that in the absence of elements, 10% of the responses will be suggest ‘violence’ (7–9). The coefficient of 5 means that in the absence of elements, 5% of the responses, half the number as before, will suggest ‘violence.’ The absolute value of the coefficient means that the coefficients can be compared from study to study, with different topics and different respondents. The ratio scale properties generated by the binary transformation means that one can relate ratio changes in the coefficients (or properly coefficient + additive constant) to external behaviors. The negative coefficient means that when the element is added to the vignette, the percent of response suggesting ‘violence’ will be removed. Thus, when the coefficient is -10, then adding the element to a vignette will decrease the percent suggesting ‘violence’ by 10%. The coefficients are additive and subtractive.

From thousands of such experiments, a set of rules of thumb have emerged about the value of the coefficients, based upon observations of the data, and knowledge about what happens in the external world. The table below provides these guidelines, which are qualitative in nature. There are no fixed values, but rather a shading of importance, so that the higher the positive number the more important the element.

1. Coefficient of 15 or higher             Extremely important, major signal
2. Coefficient 8–15                           Important to very important
3. Coefficient of 0–8                         From irrelevant to almost important
4. Coefficient 0 to -6                        From irrelevant to almost important
5. Coefficient from -6 to lower           Important

We interpret the parameters of the model for violence (ratings of 7–9 converted to 100).

1. The Additive constant is 27, meaning that there is a low likelihood of predicting violence in the absence of elements. We can compare this to say the purchase intent for pizza on the same type of 9-point scale, albeit with different anchors (definitely not buy … definitely buy). The additive constant for pizza is around 60.
2. The elements for predicted violence are low. There is only one which even approaches potential meaningfulness, C4 (The husband is sad and depressed).
3. We move now to the parameters of the model for happiness (ratings of 1–3 converted to 100.)
4. The additive constant is 12, meaning that there is very little in the way of predicted happiness in the absence of elements.
5. Two elements emerge as strong drivers of predicted happiness, both related to season:
   1. B4 (It’s summer time)
   2. B3 (It’s the middle of winter … Christmas)

Genders react differently when predicting violence, but similarly when predicting happiness

Respondents profiled themselves in term of gender. When we divide the data sets by gender and estimate the two models by gender (predicted violence versus predicted happiness), we find dramatic differences in the models for predicted violence, but similar models for predicted happiness (Table 4).

Table 4. Parameters of the model for males versus females relating the presence / absence of the 16 elements to predicted violence (Ratings of 7–9 converted to 100), and to predicted happiness (Ratings of 1–3 converted to 100).

Table 5. Parameters of the model for the three age groups relating the presence / absence of the 16 elements to predicted violence (Ratings of 7–9 converted to 100), and to predicted happiness (Ratings of 1–3 converted to 100).

Predicted violence

1. Additive constant – lower for females, higher for males (16 vs 39). The difference suggests that the prediction of violence by female respondent occurs for specific situations. In contrast, for males the additive constant is much higher, suggesting that they predict violence without needing to have specifics.
2. Women predict that the violence will occur in different situations, the most surprising of which is the expectation of violence during Christmas time.

   The husband is sad and depressed

   Companies are firing employees

   It’s in middle of winter … Christmas

Predicted happiness

1. Additive constant is very low, 11 for females, 13 for 13
2. Surprisingly, women are divided on winter and Christmas, with females reacting to

   It’s in the middle of winter … Christmas

   The local economy is growing

   It’s summer time

3. Males are happy as well, with both season and a growing economy

   It’s in the middle of winter … Christmas

   Companies are hiring but people are working long hours

   It’s summer time

Predicted Violence

1. The additive constants, prediction of violence without other information, are low, with the additive constant lowest for age 50+ (value = 22), and the additive constant modestly higher for age 19 to 29 (value =31)
2. There are age differences in what drives predicted violence.
3. The oldest respondents, age 50+ predict that violence will occur with the husband sad and depressed, and the companies firing employees.
4. The middle group age predict that violence will occur when the local economy is stressed and in recession
5. The young respondents don’t predict violence will occur in these bad economic times but predict violence will occur when the wife wants to talk but the husband does not.
6. We conclude from this pattern that the older respondents, age 50+, see violence as externally driven, whereas the young respondents, age 19–29 see violence as interaction driven.

Predicted happiness

1. The additive constants, base expectations without elements, vary dramatically across ages. The older respondents (age 50+ and age 30 to 49) see no basic happiness. It’s all a matter of the specifics. The younger respondents, age 19 to 29, in contrast, feel that happiness is all around.
2. The oldest respondents feel that happiness is a function of the time, whether Christmas or the summer.
3. The middle group, age 30 to 39, show some answers which make sense (e.g., companies are honoring, summer time, winter time), but also some answers which don’t make sense (companies are firing employees’ the local economy is stressed and in recession). It could be that this age group feels that the hard times will bring the couple together, rather than eventuate in violence.
4. The youngest group age 19 to 39 feel that happiness will emerge with the Christmas season, but not with the summer season.

Response time and engagement with the elements in the vignette

For more than a century, researchers have searched for ‘objective’ correlates of psychological processes. The notion that the information provided by people was not acceptable to many researchers, who believed, whether correctly or not, that only ‘objective’ physical measures could tell the truth about what a person perceives or thinks. The history of these approaches traces back to the original research on reaction time in the Leipzig laboratory of Wilhelm Wundt [11], and moves on to physiological measures of human reactions, whether GSR (galvanic skin response, electrical conductance of the skin), electromyography (muscle currents), then EEG (electroencephalographs and brain waves), culminating in such methods as fMRI [12, 13]. There are other more recently introduced methods, such as the implicit association test [14].

Response time, the earliest measure and perhaps the most frequently used measure, may shed additional light on the nature of the way people respond to the elements or answers embedded in the vignettes. Mind Genomics has the distinct benefit that the test stimuli, the elements, are themselves cognitively meaningful. It’s not a case of having to infer ‘what about the stimulus’ makes the respondent process it more quickly or more slowly. One can simply look at the response times to the different elements, using deconstruction method below, and ask whether there is something common about those elements taking longer to process, versus those elements processed more quickly.

The Mind Genomics computer program measured the response time to the different vignettes. It then eliminated all vignettes requiring more than 9 seconds to rate, under the assumption that in these Mind Genomics studies, rarely does a respondent stop to consider a vignette for longer than a few seconds. The Mind Genomics program also eliminates all vignettes tested in the first position, with the rationale that at the start of the experiment respondents don’t know what to do.

Figure 2 shows the distribution of response times, with the abscissa spaced logarithmically. The important thing is the relatively large number of vignettes requiring more than four seconds to process. In many comparable studies, albeit with mundane topics like food, we do not see such long response times. There may be a difference in the way people read serious vignettes, such as the vignettes here, versus ‘fun vignettes’ of other topics.

Figure 2. Distribution of response times for the study on predicted family violence. The distribution has been trimmed to eliminate the responses from the vignette evaluated in the first position, and vignettes registering 9 seconds or longer to evaluate.

The analysis of response times follows the standard approach, involving OLS (ordinary least-squares) regression. The equation is written without the additive constant, based upon the ingoing assumption that in the absence of a vignette with elements, there is no response. All vignettes, however, except those tested first, are included in the OLS regression, with all vignettes of response times 9 or more seconds truncated to 9.

The equation is expressed as: Response Time = k₁(A1) + k₂(A2) … k₁₆(D4)

The analysis was performed in the precisely the same way as the regression analyses for the ratings. That is, the relevant group was identified, and all the appropriate vignettes from everyone in the relevant group was put into a single data file, accessed by the OLS regression package.

The coefficients represent the number of tenths of seconds that can be ascribed to each element. The OLS regression deconstructs the response time, estimating the number of tenths of seconds for each element. In the analyses we will look at those response times for individual elements of 1.5 seconds or more. The cut-off of 1.5 seconds is arbitrary, allowing us to get a sense of those elements which strongly engaged the respondents. It is important to keep in mind that these socially relevant topics appear to be generating longer response times than the more typical business and marketing topics run in the same fashion, with the same type of respondents. It may be that respondents pay more attention to socially relevant topics.

The response times for the 16 elements as shown in Table 6 suggest a continuum with response times of 1.0–1.5 seconds. Keep in mind that all response times over 9 seconds or longer were eliminated as suggesting that the respondent might be doing other things. The data do not suggest a pattern. The most engaging elements, those with the longest response times, talk about the couple, about the economy, and about the woman having problems.

Table 6. Response times for the 16 elements, estimated from the data of the Total Panel.

By gender

When we divide the respondents by gender, we see radical differences. The most important result is that men do not find the elements engaging, at least when we operationally define the term ‘engaging’ as a response time of 1.5 seconds (Table 7.)

Table 7. Response times for the 16 elements, estimated from the data broken out by gender.

Table 7. Response times for the 16 elements, estimated from the data broken out by age group.

Males

The most engaging element is

The lady is having problems with finances.

The least engaging elements are

The wife wants to talk but the husband does not

Companies are firing employees

It’s summer time

The family time is shorter together

The husband is having job troubles

Females

There are many engaging elements. The fact that 8 of the 16 elements are engaging to women suggest that women are simply more attentive than men to the topic of violence versus happiness.

The husband wants to talk but the wife does not

The couple are having long term problems

Companies are hiring but people working long hours

The lady is having problems with finances

The local economy is stressed and in recession

The wife wants to talk but the husband does not

It’s in middle of winter … Christmas

Companies are firing employees

Age group

Respondents age 59+

The oldest respondents focus primarily about the issues between the members of the couple, but also react to the economy (companies are hiring but people working long hours). That element might be a signal for problems that emerge between the husband and wife.

Companies are hiring but people working long hours
The husband wants to talk but the wife does not
The couple are having long term problems
The wife wants to talk but the husband does not
The lady is having problems with finances
It’s in middle of winter … Christmas
Companies are firing employees
The lady starts searching for a job to help out

Respondents age 30–49

The most engaging element is the practical issue of finances. The elements are more practical.

The lady is having problems with finances
The family all eat at different times
The local economy is growing

Respondents age -29

None of the elements engaged them. They appear to be disinterested in the topic, or at least don’t pay much attention.

Mind Sets

One of the key tenets of Mind Genomics is that in any topic area involving judgment and decision-making, there are different groups, mind-sets, showing divergent patterns of what is important. The ideal situation, but one quite rare, is that these mind-sets are congruent with some easy-to-define and measure characteristic or set of characteristics of the respondent. Most of psychological and sociological research discovering groups with different points of view, e.g., voting for political parties, attempt to understand these differences within the framework of the standard ways to divide people. Thus, it is not unusual to see voting patterns broken out by age, gender, market, income, education, work, and so forth. Indeed, the world of analytics attempts to predict these mind-set-driven behaviors from some predictive model using easy to measure variables.

In the world of Mind Genomics, the discovery of these basic groups is straightforward, requiring simply one or several studies of the type performed here, and statistical methods to cluster together individuals with similar patterns of coefficients [15]. Individuals with similar patterns are assumed to belong to the same ‘mind genome’ for the topic. The creation of the mind genome is a simple statistical analysis, once the relevant experiment has been run. In this respect Mind Genomics holds the advantage of generating easy to interpret ‘mind genomes’ from simple experiments. The reason for the simplicity is that the experiment deals with the topic itself, and the test stimuli are all relevant. One need not array an analytic armory to discover the ‘mind genomes,’ which emerge readily from these focused experiments.

The procedure for uncovering mind genomes follows these eight steps.

1. Array the vector of all 16 elements for a given respondent as a one line in a data base.
2. Create all the data base, which in our case comprises 16 columns of data (one column per element), and 50 rows (one per respondent).
3. The coefficients tell us the degree to which the respondent would rate that element a 7–9 if the vignette comprised only that element.
4. Apply the method of clustering to divide the set of respondents into two groups, and then again into three groups.
5. Build a model for each of the two groups, and then build a model for each of the three groups.
6. Choose the more parsimonious solution, which is at the same time interpretable.
7. Interpretable means that the strongest positive elements ‘tell a coherent story’.
8. Parsimonious means that the fewer the number of clusters or mind-sets, the better, so long as the mind-sets tell a story which makes sense.

The results from the clustering suggest three mind-sets, as shown in Table 8. The clustering was done on the coefficients after the ratings were converted to the ‘predicted violence scale’ (ratings of 7–9 converted to 100, ratings of 1–6 converted to 0). The mind-sets are named according to the elements which generate the highest coefficients for the mind-set.

Table 8. Parameters of the model for the mind-sets relating the presence / absence of the 16 elements to predicted violence (Ratings of 7–9 converted to 100), and to predicted happiness (Ratings of 1–3 converted to 100.) The mind-sets were generated based upon the predicted violence scale.

When we look at response times for the three mind-sets (Table 9) we see dramatic differences in the pattern of elements which ‘engage,’ i.e., operationally defined as generating a response time of 1.5 seconds or longer. The elements which drive the segmentation also appear to strongly engage only respondents in Mind-Set 3 (family has problems),

Table 9. Response times for the 16 elements, estimated from the separate models, one for each of the three mind-sets.

Mind-Set 3 (Family has problems)

Companies are hiring but people working long hours
The couple are having long term problems
The lady is having problems with finances
The husband wants to talk but the wife does not
Companies are firing employees
It’s in middle of winter are Christmas

Mind-Set 2 (Sensitive to the economy)

The wife wants to talk but the husband does not
The family all eat at different times
The couple are having long term problems
The local economy is stressed and in recession
The husband wants to talk but the wife does not
The local economy is growing

Mind-Set 1 (No specific warning)

No element engages

The nature of people – optimistic versus pessimistic

The original focus of this paper was the pattern of responses of people to vignettes describing a couple who are in a stressful situation. The pattern of responses of our 50 respondents can also show us whether the respondents themselves are typically optimistic, pessimistic, or neither. The analysis is straightforward. We have 24 samples of the respondent’s evaluations of vignettes, with all elements (answers) appearing an equal number of times, and the basic experimental design structure maintained.

In our preparation for modeling we created binary two scales, each 0/100. Each respondent generates an average on each binary scale. When we look at predicted violence, for example, an average of 100 means that 100% of the time, i.e., for all 24 vignettes, the respondent predicts violence will occur. In contrast, if the average if 50, then the respondent predicts that violence will occur on in half the vignettes.

With this way of plotting the data we can look at the respondents, either one at a time or for key subgroups, to determine where the respondent lies on the scatterplot, and what that implies about the respondent. Figure 3 shows the scatterplots for total panel, gender, age, and mind-set, respectively.

Figure 3. Scatterplot of binary transformed ratings. Each point is the average binary rating for a respondent. The abscissa is the average for the respondent for ‘predicted violence’ (rating 7–9 converted to 100). The ordinate is the average for the respondent for ‘predicted happiness’ (rating of 1–3 converted to 100).

The key things to note are:

1. The 45-degree line means that that the respondent is neither pessimistic nor optimistic but predicts violence and predicts happiness an equal number of times.
2. The further out on the abscissa and the ordinate the respondent falls, the more the respondent is judgmental. There respondent either rates the vignette as describing a situation ending in violence, or describing a situation ending in happiness.
3. The closer the respondent falls to 0,0 the less frequently the respondent is judgmental.
4. Respondents falling to the right of the line and high on the abscissa (far right) tend to predict violence
5. Respondents falling above the line, and high on the abscissa (far up) tend to predict happiness.
6. Figure 3 immediately shows the greater negativity of females versus males, Age 50+ versus younger respondents, and Mind-Set 2 versus the other two mind-sets, respectively.

Finding the mind-sets in the population (Attila)

The conventional way to discover different groups in the population is through surveys. When one ‘knows’ the subgroup to which a person belongs, e.g., our mind-sets, it is only nature to believe that there are correlates of membership in the population. If only we could discover those correlates, goes the standard plaint. The ingoing assumption is that people who ‘think similarly’ (our mind-sets) should BE similar on the factors used to measure them. An example is age, another is gender, both of which, of course, are surrogates for various life situations and experiences.

Table 10 suggests that if we are to look to age and to gender as co-variates of segment membership, we are likely to be disappointed. Certainly, as our data suggest, these subgroups exhibit their own general patterns, different from each other, but not suggesting profound differences. In contrast, mind-set segmentation of the type performed with Mind-Genomics data divides people by how they respond, and thus think, in a particular situation.

Table 10. Distribution of the respondents by both the mind-sets (columns) and the more traditional divisions (gender, age, respectively).

The specificity of the mind-set segments to the test stimuli means that we need a way to assign NEW people to one of the three mind-sets. The system must respect the fact that the mind-sets emerged from the elements specific to this topic and this study. Thus, we end up assigning new people to mind-sets based upon a system which is specific to the study. To this end, author Gere has created a PVI, personal viewpoint identifier which uses the pattern of coefficients from the averages for the three segments. The PVI is created by adding ‘noise’ to the basic summary data for the three mind-sets, and then using them to predict mind-set membership. The six strongest predictors in the ‘face of natural noise in data’ are selected as the cohort to be used to assign new people to one of the three mind-sets. Figure 4 shows the PVI for this study, and the three feedback pages which emerge, depending upon the mind-set to which the respondent is assigned. The feedback pages can be used for further scientific study, for clinical purposes, and even for digital and personal marketing. As of this writing (December 2019) the PVI is available this website:

Prediction of Violence: Violence: http://162.243.165.37:3838/TT20/

Prediction of Happiness: http://162.243.165.37:3838/TT21/

Figure 4. The PVI (personal viewpoint identifier) for the spousal violence study, by which new people can be assigned to one of the three mind-sets uncovered in the research.

Discussion and Conclusions

This paper presents the emerging science of Mind Genomics as a way to bridge the gap between the impersonal, quantitative dimension of social science and the qualitative, story-telling, emotion-filled and narrative-rich material provided by qualitative methods, story-telling, and literature.

The scientific literature dealing with marital violence provides us with a sense of the many different contributors to the violence in the home, mainly between spouses and but directed to other members of the family. There is a body of sociological and psychological data looking for correlates of family violence. The range of these correlated variables is extensive, as can be sensed from the small sample the literature cited here.

The problem with studying violence and other factors of the ‘human condition’ is the virtual impossibility of doing experiments. The ethics of science and the moral responsibility of people to act ethically precludes doing experiments. We are left with observations and reports. Mind Genomics steps in with an attempt to go one step further, using the ordinary individual as an observer of a reported situation (the experiment), and reacting in terms of a prediction of the outcome (violence, nothing, happiness, respectively). In this respect we might consider Mind Genomics in these situations to be analogous to the behavioral economics tool of ‘predictive markets,’ or better ‘information markets’ which uses subjective perceptions embedded in a stock market-like game to drive deep insights into the reasons behind choice [16, 17, 18].

The future holds the promise of learning such as we obtained here, not only for violence in the home, but literally for the many dozens, if not hundreds of life situations that do not permit of an experiment, but may yield some of their secrets to Mind Genomics, which combines the rigor of quantitative science with the richness of cognitively meaningful stimuli actually descriptive of normally lived lives.

Acknowledgments

Attila Gere thanks the support of Premium Postdoctoral Research Program of the Hungarian Academy of Sciences.

The authors wish to thank Dr. Gillie Gabay for her help in formulating the problem and placing it into its academic perspective.

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DOI: 10.31038/PSYJ.2020212

Abstract

Prisoners are often thought to harbor thoughts about suicide, with sensationalized stories about the despair in prisons touching the hearts of listeners and readers. We explore the degree to which ordinary people, non-prisoners, feel that there is despair versus hope among prisoners. Through experimentally designed vignettes, we describe who the prisoner IS, what the prisoner FACES, what the OTHER PRISONERS are like, and what preparatory efforts are in place regarding RELEASE. Each respondent read a unique set of 24 vignettes, comprising different elements, and for each vignette rated the degree to which the prisoner would be likely to think of committing suicide versus be hopeful. The analysis reveals the specific contribution of each element in the vignette as a driver of projected suicide versus hopes, and the numbers of tenths of second required to ‘process’ the element before making the decision. The study suggests two mind-sets, one focusing on the prisoner, the other focusing on the surrounding, each as a driver of despair, as represented by the phrase ‘contemplates suicide’.

Introduction

The increasing cost of imprisonment, the increasing number of those imprisoned, and the alternative ways of imprisoning people, have created an entire industry of concern about what to do with the prisoner, how to rehabilitate the prisoner, and how to avoid post-release recidivism, or despair-driven suicide. Certainly, the prisoner, upon release, can be considered as a person with one or many ‘black marks,’ responses by others to his or his misdeeds and punishments. The prospect of a life after prison or a life in prison, one marked with rejection and condemnation by society often leads to severe psychological and social problems. One of these is the possibility of contemplating suicide and then successfully suiciding. The recent literature, both popular and academic, deals with the emotions involved in prison.

The experience of imprisonment is difficult, with detrimental effects on prisoners and their families [1]. The nature of life in prison is particularly noticeable among women whose socialization left them with a lack of voice in the public domain. Female prisoners often referred to their feeling of helpless during their prison experience [2].                                                                                                                                 

Accounts of prison life for males consistently describe a culture of mutual mistrust, fear, aggression and barely submerged violence [3]. Often too, prisoners adapt to this environment by putting on emotional ‘masks’ of masculine bravado hiding their vulnerabilities and deterring the aggression of their peers. Johnson [4] claimed that prisoners’ self-presentations of cool, hard manliness’ often reflect a ‘chronically defensive’ attitude rooted in feelings of moral self-doubt, social rejection and psychic vulnerability. This is a posture against the hurt that imprisonment threatens to expose [5]. Prisoners have a psychological need to re-establish their sense of masculine self-esteem and the need to develop personas to save them from exploitation [3]. De Viggiani [6] emphasized the ‘survival’ functions of prisoners whereas [7] emphasized their jostling for positions of power in their depriving environments.

Interviews with prisoners pointed to the protective functions of emotional self-control to hide fear or hurt which may be interpreted as signs of weakness exposing prisoners to ridicule and exploitation [3]. Prisoners expressed anger, fear, sadness and disgust through facial expressions [8]. Emotional control is an internal defense as means of coping. Many prisoners stressed the need to control their emotions in order not avoid ‘cracking’ especially due to events outside the prison over which they had almost no control [3]

Occasional displays of emotion were deemed acceptable if they were the outcome of bereavements or if they related to children (e.g. serious illnesses, custody issues). Yet to unload one’s emotions on a continuing basis was reported to be unwelcome. In the visiting room, prisoners showed warmth and tenderness that were taboo on the landings, closed to visitors. Visits offered the only opportunity to display authentic feelings and to show warmth. Some were visibly upset as their visitors left, or sat in silent contemplation, their stolidity contrasting with the animated tone of a few minutes earlier.

Although there are many studies about the statistics of prisoners and imprisonment, along with interview accounts with prisoners, there is relatively little in the literature about metric studies on the ‘mind’ of the prisoner from the point of view of those who are not prisoners, i.e., studies of one’s empathic feeling toward prisoners. This study examines the perception of the public regarding emotions of prisoners and the extent of empathy understanding, with empathy being the ability to ‘sense the feeling of the other. This paper introduces a new approach to thinking about a topic, namely the study of empathy of normal people towards people who find themselves in a particularly stressful situation. The worlds of literature and song, stories, novels, poems, ballads, are is filled with descriptions of how one person feels about another or a group of others. Science is not, however, at least with descriptions having depth and tonality. This study begins that new course of research effort.

Method

Mind Genomics is an emerging science of the ‘everyday,’ studying how people make decisions when confront by descriptions of ordinary experience, or at least experiences which could happen to people, experiences with which people are familiar [9, 10]. Mind Genomics moves away from the traditional scientific approach of isolating one variable at a time and studying that variables. Rather, the premise of Mind Genomics is that we are continually confronted by compound situations, comprising many aspects. We, ordinary people, seem to have no trouble coping with these compound situations, making a series of decisions, and moving forward. Often, we are not able to articulate the reason WHY we do what we do. Yet, our behavior is rapid, automatic, appearing considered rather than random.

In its world-view and execution, Mind Genomics differs from most conventional research, which pay a great attention to the test stimuli, and may test a very few stimuli, but offer a variety of conclusions and implications from one study. With Mind Genomics, we look at simple, broad brush strokes of different aspects of prison, and do fast, simple research. Our metaphor is to cover ground, to explore, much like a cartographer explores and maps out an area, without paying very close attention to the minutiae of the area being mapped. The goal is to understand the key points of the topic, what ideas drive strong responses, what ideas drive strong engagements. The responses are measured using rating scales, converted later to a binary scale. The engagements are measured by response time, deconstructed into the number of tenths of second a statement ‘holds the respondent’s attention’ while being processed.

The test stimuli comprise four questions dealing with the person who is in prison, what the person does basis, the nature of other people in the prison, and the preparations, if any, for re-entry. The four questions are used as prompts to drive the researcher to provide four answers to each question. Table 1 show the four questions and the four answers to each question.

Test stimuli created by experimental design

The typical way to understand what people feel about a topic is to ask them questions about the topic, either in discussion (interviews), or through a survey on pencil and paper. An emerging way to understand people is the belief that observing their recorded behavior, e.g., what the person buys or does, gives a sense of who the person is, and what the person believes. This latter approach, is called ‘Big Data.’ The reality is that each of the approaches provides some information about the person, but does not provide the specific information for the topic. Our topic is the empathy of normal people towards their ideas of prisoners, and specifically prisoner despair. There is no way that Big Data can provide this information. Rarely can we find a survey which focuses on this topic because the topic is so specific, and so different from the more mainstream, conventional topics in the world of sociological or psychological research,

Mind Genomics approaches the issue of empathy about prisoner despair by running a simple experiment. The respondent or subject is provided with test combination of the 16 answers, and instructed to rate the combination, the vignette, on an anchored 9-point scale, with the scale focusing on an assessment of estimated feelings. Figure 1 shows the test stimulus.

Figure 1. Example of a 3-element vignette about a prisoner, and the instruction to the respondent to rate.

The underlying experimental design comprises a ‘recipe’ or systematic layout of 24 combinations, vignettes, each vignette comprising 2–4 elements or answers, selected from Table 1. Each respondent evaluated 24 different vignettes, comprising 2–4 elements per vignette, at most one element or answer from one question. The experimental design presents the combinations without concern as to whether the combination ‘makes sense’ [11]. The objective is to present the respondent with a set of test stimuli and force a judgment, that judgment being a rating of the entire vignette. The respondent cannot assign a ‘politically correct rating’ because there is no underlying pattern that the respondent can discern. The respondent may begin by trying to be politically correct and do the task ‘properly’ by paying attention, but soon gets frustrated, and reacts at an almost automatic, intuitive, ‘gut level’. This is the desired state for the respondent. The intuitive response means that the response will be relatively uncontaminated by what the respondent feels to be that which the research ‘wants’.

Table 1. The four questions, and the four answers to each question.

The experiment with 42 respondents generated a data set, comprising 1208 vignettes. Most of the vignettes differed from one another. This structure of testing different combinations by each respondent is known as a permutable experimental design [9, 12]. The structure allows the researcher to ‘cover the space’ of possible test combinations, an approach analogous to the MRI (taking different pictures of the tissue), rather than the way typical scientific research works (repeating the pictures many times to reduce the error of measurement).

Analysis

The ratings from the respondents were transformed to a binary scale, following the approach used by author HRM for 35 years, since the mid 1980’s, and based upon a combination of experience and common industry practice. Experience suggests that most users of scales do not know what the scales mean. Often the user of the data, the ultimate ‘client’ of the results, asks for an interpretation, such as ‘is a 6 a lot or a little better than a 5, or a little or a lot worse than a 7?’. These questions continue to reaffirm the fact that the user of the data does not really understand what to do with the data, other than to make conclusions about ‘better or worse’. A more productive approach, used for decades by market researchers bifurcates the scale, so that there is a top of the scale (e.g., 7–9), and a bottom of the scale (e.g., 1–6). In our case, we would interpret the top of the scale, the ratings of 7–9, as indicating that the prisoner is believed to be thinking of suicide. A rating of 1–6 indicates that the prison is not likely to commit suicide, or to think about committing suicide. We can also look at the scale from the opposite end, hopefulness with a rating of 1–3 indicating that the prisoner is believed to be hopeful,

The underlying experimental design enables us to combine the data from our 42 respondents into a database comprising the 1008 observation. Each observation comes from one respondent, one vignette. The experimental design ensures that the 16 predictor variables, the elements, are statistically independent of each other. The dependent is augmented by the addition of a very small random number, approximately 10^–5.

Table 2 shows the results from the first OLS (ordinary least-squares) regression, focusing on the data from the transformation to the binary scale (1–6 = not thinking about suicide; 7–9 = thinking about suicide.) The regression procedure, colloquially known as curve fitting, deconstructs the 0/100 binary rating into the basic contribution of the 16 elements, the 16 coefficients, and an estimated basic level, the additive constant.

Table 2. Coefficients for ‘thinking about suicide’ (ratings 7–9 converted to binary). Data from the total panel.

The interpretation of the results is straightforward:

1. The additive constant is the estimated probability of a respondent saying that the person described in the vignette will attempt suicide (rating 7–9 on the scale). The additive constant is 24.46, which we interpret to mean that in the absence of elements, the expected proportion of responses 7–9 (thinking of suicide) is 24.46, about 25%.
2. Table 2 shows the 16 elements sorted from highest (believed most likely to think of suicide), to lowest (believed least likely think of suicide).
3. The coefficients have ratio-scale values, so that a value of 10 means believed twice as likely to thinking of suicide than a value of 5.
4. The coefficients can be added to the additive constant to create a sum which provides the estimated probability of a prisoner thinking about suicide. Thus, one needs only the additive constant, and the elements, as well as their coefficients, to estimate the likelihood that one believes that thoughts of suicide will plague prisoner described by the vignette.
5. The coefficients in Table 2 suggest that the two elements co-varying most strongly with likelihood of suicide are C3 (drug addicts, as fellow prisoners), and D3 (the recognition of no support). These two elements talk about two aspects, one who the fellow prisoners happen to be, and second the emotional support in the prison.
6. The coefficients suggest that two descriptions are least likely to covary with the thought of suicide. One is the young inner-city black woman, the other is optional courses to prepare for jobs. These are radically different. The first suggests the appreciation who the prisoner happens to be. The second is the fact that someone is taking care of the prisoner, or at least thinking of the prison to prepare for a job.
7. The T statistics tells us the ratio of the coefficient to the standard error of the coefficient. The higher the T statistic, the more likely it is that the coefficient or the additive constant comes from a distribution whose true value is not 0. The P value, in turn, is the probability that the T statistic comes from a distribution whose true value is 0.

Reversing the perspective – what drives the rating of ‘hopeful’ (1–3 on the 9-point scale)

Our respondents could assign rights on either side of the scale, 1 representing hopeful, 9 representing contemplating suicide at some point. What happens when we focus on the positive aspects, looking at the elements driving the ratings of 1–3. We now convert the ratings on the low end of the scale, 1–3, corresponding to hopeful, so that they become. In turn, the value 100. The remaining six scale points, 4–9, become 0, to denote not hopeful. We perform the same analysis on the data from the total panel, looking at the additive constant, and the coefficient for each element.

1. The additive constant is 43.79, almost 44, meaning that in the absence of elements, almost half of the responses will be between 1 and 3, hopeful. We interpret this to mean that it is basic information (a person is in prison) which conveys some hope. Being in prison does not automatically drive one’s feeling that the imprisoned person will contemplate suicide. Being is prison does, however, drive a sense that the prisoner will be modestly hopeful.
2. Estimated hopefulness is driven by reading about preparations for release, such as ‘optional courses to prepare for job’, and ‘4-hours of forced library’.
3. Lack of hopefulness is driven by who a person is, and the situation in the prison. These are the elements with the lowest coefficients for hopefulness.

21-year-old second conviction for drugs
54-year-old woman convicted for drugs
drug addicts (in prison)
white middle age for theft
no support

Individuals – are they optimistic or pessimistic, based upon their coefficients

Can we classify an individual as optimistic (perceiving the situations in the vignette as ‘hopeful’) or pessimistic (contemplating suicide), both or neither? One way to answer this question computes the average coefficient across 16 elements for each individual, when we look at the equation for ratings of 7–9. Recall that the coefficient shows the believed likelihood that the prisoner being described is likely to commit suicide. Each respondent generates 16 coefficients. The average coefficient for a respondent tells us the proclivity of the respondent to see the described prisoner’s feelings as leading to thoughts of suicide. The second part of the answer is to compute the average for the same respondents for the 16 coefficients dealing with hopeful. The average coefficient for a respondent tells us the proclivity of the respondent to see the described prisoner vignette as leading to hopefulness.

We begin with the scattergram for the total panel, in Figure 2. Each filled point represents one respondent. The abscissa corresponds to the average of the respondent’s 16 coefficients on the top part of the scale, tendency to suicide, i.e., the coefficients of the individual-level regression model run for the respondents when the ratings of 1–6 were converted to 0, and the ratings of 7–9 were converted to 100. The ordinate corresponds to the average of the respondent’s 16 coefficients on the bottom of the scale, hopeful, when the ratings of 1–3 were converted to 100, and the ratings of 4–9 were converted to 0.

When the respondent cluster at 0, 0, we conclude that the respondent does not sense either prisoner despair or prisoner hopefulness in the vignettes. The averages for the latter two scales are both near 0, and thus the respondent falls at the bottom left. The further out to the right on the abscissa lies the respondent’s average, the more the respondent feels that the prisoner will contemplate suicide. The further up the ordinate lies respondent’s average more the respondent feels that the prisoner will feel hopeful. Figure 2 suggests more respondents feel that the prisoner will be hopeful, and fewer respondents will feel that the prisoner will contemplate suicide. Looking more closely at the distribution, we see about five respondents who feel primarily despair in the vignettes, and about five respondents who feel primarily hopefulness in the vignettes.

Figure 2. Distribution of average coefficients for despair/suicide (Ratings 7–9 converted to binary) and average coefficients for happiness/hopefulness (Ratings 1–3 converted to binary). Each filled circle corresponds to a respondent.

As a side note, this format of presenting data suggests a new way to understand the basic mind-set of a person on two opposing dimensions of a feeling. The location of the points gives a sense of how different people think and empathize. Most of the respondents in the large area between 0, 0 and 0.3, 0.3. The location 0, 0 corresponds to a person who is neither pessimistic nor optimistic in the estimation of how the prisoner would feel. The location 0.5, 0.5 corresponds to the location where the person is absolutely decisive, rating the vignettes as either hopeful or despairing (contemplate suicide.) For the person located at 0.5, 0.5, there is no middle ground. For the person located at 0, 0 there is virtually only middle ground.

Subgroups – Gender, Age, Mind-Set – What is expected to drive the prisoner’s though to suicide?

One of the key benefits of the Mind Genomics approach is the use of different combinations of vignettes for each respondent, but at the same time ensure that each respondent evaluates the appropriate set of vignettes in order to create an experimental design. One can do the analysis on key subgroups, both self-defined (gender, age), and defined through analysis (mind-set). The small group of 42 respondents provides sufficient depth into the mind of the respondent to reveal the responses of subgroups, perhaps a bit noisily, but nonetheless powerfully.

Table 4 shows the set of key subgroups. For this study we divided the respondents by gender and by age, respectively, and then created mind-set segments as described in the following section.

Table 3. Coefficients for ‘hopeful’ (ratings 1–3 converted to binary). Data from the total panel.

Table 4. Strongest performing elements by key subgroup of the models relating the presence/absence of elements to the estimate of the prisoner’s contemplation of suicide.

1. Additive constant – all subgroups are approximately the same, showing an additive constant of 22–27.
2. Males feel that simply ‘being bored, having nothing to do’ is a cause for contemplating suicide. Females do not.
3. There is no difference in projected potential of contemplating suicide by younger versus older respondent.
4. We created two mind-sets by clustering the array of 16 coefficients, and extracting two different groups, which are maximally different from each other [13]. Clustering is a purely statistical technique. We extract the fewest number of clusters (parsimony) which tell coherent stories (interpretability).
5. Mind-Set 1 feels that the prisoner will think of suicide if there is no emotional support in the prison, and if the prisoner is simply discharged, released, without any preparation. To Mind-Set 1, it is the sense of aloneness in the prisoner which is distressing. Mind-Set 1 can be called ‘want preparation’.
6. Mind-Set 2 feels that the prisoner will contemplate suicide if there is a sense of nothing to do, and if the prisoner is either a serious drug addict (second conviction) or surround by drug addicts. Mind-Set 2 can be called sensitive to surroundings.

Subgroups – Gender, Age, Mind-Set – What is expected to drive the prisoner’s thoughts to happy

We can follow the same logic, this time looking at gender, age, and newly constructed mind-sets for the low part of the scale, ‘hopeful’.

1. Males show a much higher imputed basic hopefulness for prisoners than do females (54 versus 36). Without any additional information, males believe that that the prisoner will be neither hopeful or not hopeful (additive constant 54), whereas females believe that it’s more likely that the prisoner will not be hopeful (additive constant 36).
2. For males, hopeful is perceived as a matter of preparing for a job and being surrounded by prisoners who are middle class.
3. For females, hopefulness is perceived when the message is about preparing for a job, library, rehabilitation, and surprisingly, with the prisoner has little to do. Males, in contrast feel when the prisoner in bored, and has little to do, the despair is higher, with a greater thought of suicide.
4. Younger respondents (under 30) feel that hopefulness will come from job preparation and from the hours in the library.
5. Older people feel the same way, and also feel that hopefulness will come from being surrounded by middle class prisoners.
6. The previously created Mind Set 1 feels that hopefulness is a matter of the requirement of four forced hours in library, as well as being surround by middle-class prisoners.
7. The previously created Mind-Set 2 feels that hopefulness will come with the preparatory course for jobs, and the requirement of library.

Subgroups – are they optimistic or pessimistic, based upon their coefficients

The previous analysis for the total panel presented a novel way to gauge whether the respondents are optimistic or pessimistic, by plotting the average coefficient from the two models, doing so for each respondent. The coefficient shows the average conditional probability that the respondent would assign the element a rating of 7–9 (top 3, contemplating suicide), versus that the same respondent would assign the element of 1–3 (bottom 3, happy). The two averages come from the coefficients of 16 elements.

When we plot each respondent on a two-dimensional graph, we can sense the respondent’s mind. To review:

1. Each filled circle corresponds to one respondent.
2. The location 0, 0 corresponds to a person who is ‘all middle ground,’ sensing neither despair leading to contemplation of suicide, nor sensing hopefulness. All the ratings for the vignettes lie between 4 and 6.
3. The location 0.5, 0.5 corresponds to a person for whom there is ‘no middle ground’ but not basically optimistic nor basically pessimistic in the estimation of how a prisoner would feel.
4. Plots to the right on the abscissa suggest a person who is more pessimistic, and sees despair leading to the contemplation of suicide.
5. Plots upwards on the ordinate suggest a person who is more optimistic, and sees ‘hopefulness’
6. Figure 3 shows the plots for key subgroups. Each panel (top, middle, bottom) compares two complementary subgroups. The statements below are purely from visual observation and impression, not from a statistical analysis.
7. Females show more respondents closer to the 45-degree line, and further out than men on that line. Qualitatively, females seem to be more judgmental than men, but neither overly optimistic nor pessimistic.
8. Younger respondents aged 30 and younger show more respondents lying close to the non-judgmental region of 0, 0. Older respondents age 31 and older show more respondents as lying further out towards 0.5, 0.5, with a tendency to be more optimistic, and feeling that the prisoner is more hopeful.
9. Mind-Set 1 (want preparation) appears to be less judgmental, and if judgmental then optimistic in terms of rating what the prisoner would feel. Mind-Set 2 (sensitive to surroundings) is more judgmental, with fewer ratings in the 4–6 region of the scale. Mind-Set 2 appears to be slightly more pessimistic.

Figure 3. Distribution of average coefficients for despair/suicide (Ratings 7–9 converted to binary) and average coefficients for happiness/hopefulness (Ratings 1–3 converted to binary). Each filled circle corresponds to a respondent. The three panels show the results for complementary subgroups.

Response time

The previous sections dealt with the analysis of the ratings, specifically what elements are perceived, in one’s opinion to correlate with thinking that would contemplate suicide, at least in the opinion of a non-prisoner respondent reading a vignette about the prisoner. The analysis deals with the conscious assignment of ratings to the test stimuli, even if the decisions tend to be automatic.

 Researchers have been interested in the past few years in possibly deeper mechanisms of decision-making, many of which they put in the grab-bag called neuromarketing, or more correctly non-conscious, physiological correlates of decision-making [14, 15, 16]. Summarize this new area of neuromarketing, or really physiological correlates of messaging. Genco [17] have popularized in a book ‘Neuromarketing for Dummies.’ We now proceed to the analysis of one of one of these measures, response time. The ingoing assumption is that longer response times signal that the respondent is somehow ‘engaged’ in reading and thinking about the particular element in the vignette.

Some of the vignettes constructed were responded to slowly, others were responded to quickly. After removing the first vignette evaluated by each respondent because the respondent was just ‘learning what to do in the experiment,’ and after removing all vignettes responded to after 9 seconds because it was likely the respondent was doing something else, we emerge with a distribution of response times as shown in Figure 4. The time scale, abscissa, is logarithmically spaced, emphasizing the many vignettes responded to faster than 2 seconds. The computer program picked up the response times in tenths of seconds.

Figure 4. Distribution of the response times for the vignettes, after removal of the first vignette and after removal of all response times of 9 seconds or longer.

As stated above, we assume response time to be a correlate of engagement. We operationally define the term as ‘time spent attributed to the element when the vignette is being evaluated’. We cannot, of course, ask the respondent to tell us how engaging each element seems to be, although occasionally the novice researcher might ask that question. The reading and response occur so rapidly, so automatically, that the respondents are not aware of what holds their attention unless there is something so powerfully strong that it ‘stops’ the respondent.

The experimental design enables us to estimate the likely number of seconds in the response time that can be attributed to each element. It is important to note that this assignment is an estimate, only, based upon the application of OLS regression to the response times. Some interesting patterns emerge from Table 5.

Table 5. Strongest performing elements by key subgroup of the models relating the presence/absence of elements to the estimate of the prisoner’s hopefulness.

1. The elements are presented in descending order of response time based upon the results from the total panel. These ratings are from 40 respondents, each evaluating at most 23 vignettes, but a number of vignettes have been removed because they were recorded as being unusually long.
2. We have highlighted and bolded those response times of 1.4 seconds or longer, which can be assumed to be ‘engaging.’ The choice of 1.4 seconds is simply to represent a time that can be thought of as possibly conscious attention.
3. The longest response time for any group is 1.7 seconds (female respondents with the element ‘lower and upper middle class’).
4. The shortest response time for any group is virtually 0 time, ‘optional courses to prepare for jobs’ (0.2 seconds, for Mind-Set 2, who are sensitive to their surroundings, and would be expected not to care about courses for the future).
5. Total panel: The longest response times, i.e., the most engaging, are descriptions of the person, requiring multiple words. The shortest times, i.e., the least engaging, are descriptions of occupation training in prison. It’s all about the people, who they are.

Table 6. Coefficients for response times, by total panel and key subgroups. Coefficients of 1.4 or higher are shown in shaded cells, with bold numbers.

Assigning new individuals to one of the two mind-sets

Conventional research is grounded on the belief that there is an indivisible link between who the person IS and what the person THINKS. This belief motivates the use of large, representative samples of respondents, believing that it is important to measure the correct group of people in order to understand the way the mind works. Thus, good practice in business and political polling is often accompanied by large base sizes and a measure of ‘error,’ or underlying variability.

One of the premises of Mind Genomics is that in virtually any topic area where human judgment comes into play one can discover different points of view, different criteria for judgment. These are called Mind-Sets. Their reality emerges from the analysis of how individuals respond to the different elements or ‘answers’ in the particular study. That is, these mind-sets exist, but are really groups of individuals who behave similarly in a specific situation, as revealed by their patterns of responses, or perhaps as the next paragraph suggests, mind-sets are really combinations of ideas.

Underlying the research in Mind Genomics is the belief that some ideas ‘flow together.’ It is the combination of such ideas which flow together that comprises the focus of interest of Mind Genomics. Individuals, the respondents who participate, are ‘protoplasm’ which in some way embody these basic mind-sets, but the individuals are NOT the mind-sets. The mind-sets are primaries, like the colors red, yellow and blue. Each person comprises a set of mind-sets, with the methods of Mind Genomics both identifying the nature of the mind-sets from clustering, and establishing who in a study embodies each mind-set. Whether these mind-sets represent true primaries like color primaries, red, blue and yellow, is not important. What is important is that they show remarkably different, and interpretable patterns of responses, patterns which make sense, can be interpreted and labelled. These primaries may co-vary with external behaviors, and perhaps even with physiological patterns of responses. What is important is that they represent a new way of looking at individual differences.

With the foregoing accepted, the question is whether there is a natural affinity for the mind-sets established in an experiment to distribute in the way to which we have been accustomed. That is, for our study we have established two mind-sets, those who want preparation, and those who are sensitive to their surroundings, etc.

Table 7 shows that there is no clear relation between mind-set membership and either gender or age. This is typically the case. Mind-sets emerge quite clearly in Mind Genomics studies, but these mind-sets do not distribute in ways that are easy to discern, despite the radical differences in content between or among the mind-sets.

Table 7. Distribution of the two mind-sets by gender and age, respectively.

Given the clear similarity in the patterns of WHO are in the two mind-sets, at least in terms of gender and age, how then do we assign a new person to a mind-set? This is an important question, both for science, and for commerce. For science, we can begin to study the relation between membership in a mind-set for one topic, and both membership in other mind-sets for other topics, and/or external behaviors, and even biological/genetic covariates of membership in different mind-sets.

Our approach uses the average coefficients from each mind-set. We create 1,000 different variations of the average profile by adding ‘noise’ to the coefficients. We then identify the six questions which, in the presence of “noise” can be used to correctly assign the respondents to the correct mind-set. Figure 5 shows an example of the PVI (personal viewpoint identifier), presenting the six strongest questions which, in concert, help us assign a person to the correct mind-set. We also show the feedback page, which can go to the person being typed, or be used to drive the respondent to the right e-commerce website, or perhaps incorporated into the person’s profile for future use. As of this writing (Winter, 2019), the PVI is located at this location: http://162.243.165.37:3838/TT19/

Figure 5. The PVI (personal viewpoint identifier) and the two feedback pages, one for each mind-set segment uncovered in the original study.

Discussion and Conclusions

The sociology and psychology literatures are replete with studies presenting statistics about the backgrounds of prisoners, their environment, and clinical analyses of personalities. There is no end to the fascination with other people, especially those who commit crime. What is lacking, however, is a sense of how the ‘other’ reacts to prisoners. We are aware of the prisoner, but what do we think of prisoners in terms of specifics? The answer may be found in novels, in news clippings, in common discussion, but not particularly in the scientific literature.

Mind Genomics provides a way of understanding how people perceive the ‘other,’ not so much in a clinical sense, but the ‘other’ when represented in a story, that story provided by the vignette. Mind Genomics opens new vistas, probing into the mind, and how the mind reacts to others, the ‘others’ presented in meaningful but manageable descriptions. Simple experiments, of the type presented here, generate the foundations of new knowledge that that hitherto could only be obtained in unstructured form by talking with people, or by reading personal accounts, news commentary, or literature.

Acknowledgements

Attila Gere thanks the support of Premium Postdoctoral Research Program of the Hungarian Academy of Sciences. The authors wish to thank Dr. Gillie Gabay for her help in formulating the problem and placing it into its academic perspective.

References

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DOI: 10.31038/PSYJ.2020211

Abstract

We present two methods-oriented studies on sexuality, one dealing with the discussion of sexuality in the context of a relationship, the second with the societal protection of sex workers. Both studies used consumer respondents to evaluate systematically varied combinations of messages about the topic, the combinations created by experimental design, following the method of Mind Genomics. Study 1 on discussions of sexual intimacy presents Mind Genomics to understand the way people process information, their criteria for decision-making, and the nature of possibly easy-to-understand mind-sets, i.e., different criteria of importance assigned to the same pieces of information. Study 2 on the protection and recourse given to legal workers shows how to assess the interaction between person and situation as drivers of judgments and drivers of engagement. Both studies point to the emerging science of Mind Genomics as an easy, rapid, and cost-effective ways to create archival databases, to introduce new ways of thinking, and to democratize research world-wide, respectively.

Introduction

During the past three decades the focus of researchers has steadily increased on issues involving intimacy, specifically sexual intimacy between consenting partners (love, romance), as well as sexual intimacy as a business (sex workers.) Sexuality in its many manifestations has always attracted research because of its centrality in daily life, but as society has evolved, issues of sexuality have become intertwined with emotions, with public health (e.g., sexually transmitted disease), and finally with issues of the law (e.g., prostitution and the issues revolving around sex workers.)

The topics of love, sexuality, sexual exploitations, and societal reactions each have spawned enormous literatures. Table 1 shows the number of ‘hits’ for Google® and for Google Scholar®, for each of these topics, at the time of this writing, December 2019,

Table 1. Number of citations dealing with sex and its ramifications.

No set of studies can hope to be comprehensive, given the long history of the study of sexuality, the many manifestations in daily life, and the many cultures as well as stages of individual development that must be considered. Rather, we introduce here a new approach to the study of sexuality, the science of Mind Genomics, designed to take small snapshots of a topic, focus in depth on a specific, limited topic, and work with small, affordable samples of respondents.

The worldview of Mind Genomics involves a small, limited topic, investigating the patterns of decision making within that topic. Rather than emerging out of the history of the hypothetico-deductive method, isolating a variable and studying that variable in an experiment, Mind Genomics proceeds in the reverse direction. One might think of the Mind Genomics researcher as a cartographer faced with a new land. The cartographer measures the relevant variables of a topographical area, deduces the nature of the structure below, and maps the land. The cartographer creates maps, not theories. In the case of Mind Genomics, the ‘land’ is the world of sexuality. The cartography of this paper deals with the reactions to issues of sexual intimacy (one set of experiments), and reactions to issues of sex workers (another set of experiments.)

Exploring two topics of sex using Mind Genomics to generate insights and hypotheses

The topic of sexual behavior spans a wide range of topics, from the physical to the emotional to the legal, and to the societal. It is impossible to cover even a very small fraction of the topics with a set of experiments or surveys. The strategy of this paper is to demonstrate how the emerging science of Mind Genomics can generate an affordable, powerful database at the start of a research initiative, using simple ideas, simple thinking, consumer research, and powerful analyses, meaningful even with samples that are traditionally considered ‘small’.

The emerging science of Mind Genomics (Moskowitz & Gofman, 2007) [1], traces its intellectual heritage to the systematized thinking using experimental design to structure the test stimuli, as well as to sociology and consumer research for transforming the ideas into questions to be answered, and finally to the Socratic method to create the system as an inductive knowledge-development technique, easily applied in practice

Experimental design

Experimental design allows a researcher to understand the effects of a variable, either tested along in ‘splendid isolation’ or tested as part of a mixture (Box, Hunter & Hunter, 1978) [2]. Mind Genomics deals with the ordinary situation, wherein a person is presented with a combination of ideas, as the typical situation of daily life. The person responds to the combination, making a decision. But just what specific component of the combination or set of components ‘drive’ that decision? Experimental design sets up efficient combinations of independent variables, messages or elements in the language of Mind Genomics. It is the response to these systematically created mixtures, which, through regression reveals, quite directly the contribution of each Message or Element to the response. The response, in turn, is what the respondent answers.

Sociology and consumer research

These social science disciplines rely upon the responses of people to questions about behavior, or upon the measurement of the behavior of people in situations, i.e., upon attitude versus upon behavior, respectively. Where possible a meaningful behavioral measure may be better than an attitude, although the term ‘meaningful’ is important as a qualifier.

Over almost a century there has been a subtle current of belief that implicit measures are better than explicit ones, e.g., that EEG (brain waves) or GSR (activation) or pupil behavior (dilation, pupil motion) somehow are better than simple attitudinal ratings because the former are more objective, more biological (Boring, 1929) [3].

The foregoing use of ‘meaningful’ is not what is meant here. Rather, the term ‘meaningful’ is used in the sense that the measure to be meaningful must be a direct correlate of the mind of the person, whether person in society or an ordinary citizen faced with a choice. Mind Genomics uses the responses to combinations of messages, i.e., combinations of elements as the meaningful measure, since a great deal of behavior in everyday life is responses to mixtures. Mind Genomics goes the additional step by creating combinations of these messages, presenting them to respondents, measuring the reactions, and then estimating the contribution of each message.

The Socratic Method

The approach is grounded empiricism, not in the hypothetico-deductive method. There is no hypothesis to be tested. Rather, there is a topic to be studied. The topic of interest is presented to the researcher, who must create four questions which ‘tell a story’ about the topic. The questions are not necessarily final, but rather represent the way the topic is thought about, either those who are grounded in the topic, or even novices with no idea at all, so-called ‘newbies’. The four questions each motivate four answers, or a total of 16 answers, as shown in the next sections. The researcher then combines these answers into small vignettes, obtains responses to the vignettes, and shows how the different answers shed light on the topic.

The best way to show the Mind Genomics method is through a case history, dealing with a topic relevant to an individual, or even beyond the individual to a group, and to society. This paper focuses on two aspects of sexual behavior, the first dealing with discussions of sexual intimacy and disease protection between consenting partners, the second dealing with protection of the ‘sex’ worker. These are but two of the perhaps hundreds of topics in the rainbow of topics in sexuality. We show how a one-day experiment can produce data for each topic, making it feasible to explore hundreds of topics about sexuality in the time frame of a year, with affordable, rapid, insightful and archival data.

Study 1 – Discussinag disease prevention between two consenting & emotionally-involved partners

A great deal has been written about sexual relations between consenting partners, from issues to measurements (e.g., Fisher et. al., 2013; Montesi, et. al., 2013; Stephenson, et. al., 2010). [4, 5, 6] The topics range from the emotions felt by the participants to the behavior of adolescents versus older individuals, and on to the issues caused by the ravages of sexually transmitted disease (Harvey et al., 2016; Katz et al., 2000; Peplau et. al., 2007; Widman et. al., 2006). [7, 8, 9, 10] Our focus in this experiment is the couple’s discussion of issues around the prevention of sexually transmitted diseases using methods under their control. The study was motivated by author Ortiz’s plan to sponsor a campaign to reduce sexually transmitted disease.

Method

The Mind Genomics study begins with the creation of the four questions and the four answers to each question. These appear in Table 2 and were created by author Ortiz as part of a campaign against sexually transmitted diseases. The important thing to realize from Table 2 is that the study does not exhaust the topic. Indeed, Mind Genomics studies are not designed as single, exhaustive treatments of a subject, treatments which generate a large volume of disparate information. Rather, Table 2 shows a preliminary attempt to understand four aspects of the topic. The reality is that there may be 40 or 400 aspects of the topic. When one attempts to cover a topic thoroughly, the entire endeavor may collapse because, in common folk wisdom ‘the perfect is often the enemy of the good.’ The Mind Genomics strategy is to create a set of such small studies, accrete the results, and identify emergent patterns ‘from the bottom up.’ Mind Genomics represents the inductive way to learn, i.e., by discovering patterns, rather than by confirming or disconfirming ingoing hypotheses.

Table 2. Sexual Intimacy: Four questions and four answers to each question.

The researcher combines these elements (the answers A1-D4) into small, easy to read combinations, so-called vignettes. The actual experimental design is created as ‘kernel,’ in which the 16 elements are statistically independent of each other, allowing for subsequent analysis by OLS (ordinary least-squares) regression. The kernel, or basic experimental design is permuted so that the design structure remains the same, but the individual combinations changes in a permutation pattern (Gofman & Moskowitz, 2010.) [11] Table 3 shows the experimental design for one respondent (independent variables in the subsequent analysis), and then the ratings, binary transformation (Bin) and Consideration or response Time (CT) (the dependent variables in the subsequent analysis.)

Table 3. Example of the data from one respondent, prepared for statistical analysis.

The structure of the vignettes follows these conventions:

The experimental design, metaphorically a booklet of recipes of the same ingredients to create different dishes. The experimental design specifies the composition of vignettes comprising two elements, three elements, and four elements, respectively.

Each respondent is required to evaluate 24 vignettes, all different from each other. Across the 24 vignettes, each element appears five times and is absent 19 times. A vignette comprises at most one element or answer from any question (Table 3.) This strategy of testing both complete vignettes (one answer from each question) and incomplete vignettes (no answer from either one or two questions) ensures that the analysis of the data by OLS (ordinary least-squares) regression generates coefficients having absolute value, where ratios of coefficients are meaningful.

Each respondent evaluates 24 unique, different vignettes. The underlying experimental design ensures that the 24 vignettes for each respondent differ from the 24 vignettes for any other respondent. The benefit to this permutation scheme is that the Mind Genomics experiment covers a great deal of the so-called ‘design space’. The benefit to the researcher is one need not know ‘what works’ ahead of the study. In contrast, in other research methods using experimental design of messages (so-called conjoint analysis; Green & Srinivasan, 1990), [12] the researcher selects one set of combinations, and tests that set with many people in order to suppress the variation by averaging. Whether averaging out the variation in the typical approach or averaging out the variation by looking at a great deal of the design space ultimately proves to be better is still a matter of dispute.

Table 3 show the 24 vignettes as rows. The 16 elements are shown as A1-D4, corresponding to the four questions and the four answers in each question featured in Table 1.

The column labelled Rat is the 9-point rating assigned to the vignette by the respondent. Table 3 shows the respondent ratings of all 24 vignettes.

The column labelled Top3 is the ‘binary’ transformation of the 9-point ratings, with ratings of 1–6 transformed to 0, and a very small random number added to the transformed number For ratings 7–9 the rating is transformed to 100, and again a very small random number is added to the transformed number.

The addition of the random number is done so that the regression analysis will not ‘crash’ when the analysis creates individual-level models to generate mind-set segments. When a respondent rates all 24 vignettes between either 1–6 or between 7–9, respectively, then transformed ratings will all become either 0 or 100, respectively for the Top 3 measure, and the regression model using the Top3 measure as the dependent variable will ‘crash.’ Adding a small random number prevents that crash, ensuring that the statistical analysis proceeds without incident.

Finally, the column labelled CT is Consideration Time, or Response Time, defined as the number of seconds elapsing between the presentation of the vignette on the screen and the rating assigned by the respondent. The vignettes are short, so that any Consideration Time longer than 9 seconds is assumed to reflect the respondent’s multi-tasking and is brought to the value 9.0. The use of the term Consideration Time makes the number more meaningful to the reader, because the magnitude of the CT can be associated with the time it takes the respondent to consider the element.

The analysis of Mind Genomics data proceeds in a straightforward manner, enabled by the experimental design for the creation of the different vignettes. The experimental design created for a single individual ensures that the 16 elements or answers for that individual appear independently of each other among the 24 vignettes. Putting together a set of such experimental designs, each different from the others simply by a permutation scheme, maintain the statistical independence of the 16 elements.

An easy-to-interpret analysis (OLS Regression) relates the presence/absence of the 16 elements to the binary rating. OLS regression uses the 16 elements as independent variables, and the binary transformation, Top3, as the dependent variable. The regression incorporates the relevant cases, namely the 24 rows from each respondent who belongs to the subgroup. Thus, when it comes to the model or equation for ‘males,’ only the data from the male respondents are used. Each male respondent contributes 24 cases or observations.

The regression model estimates the parameters of this simple equation: Top3 = k₀ + k₁(A1) + k2(A2) … + k₁₆(D4). Top3 is defined as ‘comfortable talking about the topic.

The parameters for the total panel and key subgroups appear in Table 4. The table shows total panel, gender, age groups, relationship status, and the response from all respondents, but broken out into the results from Vignettes 1–12 (Half1) and then from Vignettes 13–24 (Half2). This final comparison shows us whether the respondents ‘change their criteria’ as the study proceeds

Table 4. Parameters (additive constant, coefficients) for equations relating the presence/absence of the 16 elements for binary transformed rating ‘comfortable talking about the topic (prevention of sexually transmitted disease.)’. The table is sorted by the coefficients for the total panel.

The additive constant is a measure of basic comfort talking about the topic, but with no elements in the vignette. The basic comfort for the total panel is 61, meaning that in the absence of any elements, 61% of the responses will be 7–9. That is, about 3 in 5 times the response will be ‘comfortable.’ The only group showing less comfort is the younger respondents (additive constant = 43), whereas their complementary age group, the older respondents, age 25 and older, is more comfortable (additive constant = 73).

There are some elements which ‘stand out’ from the others, topics about which the respondents feel very comfortable discussing. The elements below list the strong performing elements. Although there are strong performing elements, as shown by the coefficient, an underlying theme or story does not appear.

Total – None

Males

Knowing partner’s prior sexual history
Getting tested regularly
Both partners using birth controls
Talking about safe sex during the first conversation about intimacy

Females

Partner says: “Let’s use protection”
Discussing STD precautions planning through texting

Age 25 or older – None

Age 24 or younger

Talking about safe sex before engaging in sexual activity
Both partners using birth controls

Single – None

In a relationship

Both partners using birth controls
Getting tested regularly
Knowing partner’s prior sexual history
Using condoms during sex

First half of the individual’s vignettes (vignette 01- vignette 12)

Both partners using birth controls
Partner says: “Safe sex is the best move”

Second half (vignette 13 – vignettes 24)

Discussing STD precautions planning through texting

An increasing focus of Mind Genomics is upon Consideration Time (CT). In experimental psychology the term Consideration Time may be replaced by either Reaction Time or Response Time. CT is defined as the number of seconds (to the nearest tenth of second) between the presentation of the test stimulus, the vignette, and the rating assigned by the respondent. The term Consideration Time’ is used to underscore that the response is not only the time to perceive and react, but to read and consider.

The computation of response time is straightforward. The Mind Genomics algorithm relates the response time to the presence/absence of the elements, using the same form of equation as done for the Top3 value (comfort, in Table 3). The only difference is that the equation for consideration time has no additive constant. That is, the ingoing assumption is that without any elements in the vignette, the consideration time should be 0.

Table 5 shows the six elements with long consideration times in at least one group of responses or in either the first half or the second half of the Mind Genomics experiment, respectively. In turn, Table 6 shows the Consideration Times for the full set of elements across the different subgroups.

Table 5. The six elements showing long (estimated) consideration times of 1.5 seconds or longer.

To give a perspective, the typical consideration time of a full vignette for less serious topics may be 1–2 seconds. People make up their mind quickly for topics considered to be of minor import, perhaps System 1 in the language of Nobel Laureate Daniel Kahneman in his book Thinking Fast, Thinking Slow (Kahneman, 2011) [13] In contrast, topics of sexual discussion may involve System 2, the slower, more deliberate thinking which is the hallmark of a serious topic.

Table 6. The full set of consideration times for the total panel and key subgroups.

Three emergent mind-sets

One of the ongoing tenets of Mind Genomics is that within any topic where human judgment plays a role, there are usually at least two different groups of people, having different criteria about the same topic. That is, for those topics involving judgment, people disagree. The disagreement may be minor, or major, depending upon the people, the topic, and the information presented.

Researchers have uncovered these differences as a matter of course when studying the criteria for human judgment. The differences themselves exist, but Mind Genomics goes one step further beyond noting the differences. Mind Genomics attempts to uncover, classify and then understand the nature of these differences, creating a set of mind-sets embodying the different criteria for judgment. Mind Genomics can go one step further, creating a tool, the PVI (personal viewpoint identifier), to predict the way new people will respond to the information, i.e., an assignment tool. The analogy is to color science and colorimetry. Mind Genomics creates the ‘color science’ for a topic, and then crafts the tool to identify these mind-sets in the population at large. In the interest of length, the PVI for these data are not presented in this paper.

Mind Genomics follows these steps to identify the emergent mind-sets, with all the information needed present in the data from the basics study:

1. Create the data matrix, with the rows corresponding to the respondents, and the columns corresponding to the elements. For the data presented here, the data matrix comprises 16 columns, one for each element. (The additive constant is not used). The data matrix comprises 50 rows, one row for each respondent.
2. Define the distance between rows (respondents) by a single number. The choice of the number can range from the simple Euclidean distance to a distance between patterns, defined as (1-Pearson correlation between two rows). Mind Genomics uses the latter (1 – Pearson Correlation, or 1-R).
3. The distance metric (1-R) ranges from a low of 0 when two rows are perfectly correlated, to a high of 2 when two rows are perfectly but inversely correlated.
4. The program, k-means clustering (Dubes & Jain, 1980), [14] creates complementary and exhaustive groups, called clusters or segments.
5. Mind Genomics creates two clusters and assigns each respondent to one of the two clusters.
6. Mind Genomics then creates three clusters, and assigns every respondent to one of the three clusters
7. The data from respondents in each cluster are analyzed separately, first for the model for comfort (Top3) and then for the model for Consideration Time.
8. The strongest performing elements for each set of clusters are used to determine whether there is a coherent story (interpretability), and whether the number of clusters is as few as necessary (parsimony). It is important to have as few clusters (mind-sets) as possible, provided that the clusters are interpretable, i.e., make sense.

Table 7 suggests three mind-sets, based upon the clustering using the coefficients for comfortable. Recall that the ingoing coefficients come from the data wherein the response (1–9 scale) was converted to 0 (ratings 1–6) or 100 (ratings 7–9.).

Table 7. Coefficients for ‘Comfortable with talking about the topic of preventing sexually transmitted disease,’ as well as Consideration Time, for three emergent mind-sets.

The three mind-sets can be really divided into one group which feels comfortable with actual conversation as shown by quotation marks (Mind-Set 1), and the remaining two groups, which are less responsive to the elements. We might be satisfied with two mind-sets, not three, one responsive to conversation (Mind-Set 1), and others. On the other hand, the differences between Mind-Set 2 (Discuss safe sex as a prelude to intimacy) and Mind-Set 3 (Safe sex as the responsibility of both partners) points to some key differences between these two groups. That difference between Mind-Sets 2 and 3 is underscored by the differences between the mind-sets in terms of Consideration Time. Mind-Set 2 (discuss safe sex) spends a lot longer than Mind-Set 3 (focuses on responsibility) when reading and rating the vignettes.

Study 2 – Recourse & Protection for the sex worker

The recent literature is replete with discussions of sex trafficking, and other offenses (Van der Meulen, et. al., 2018; Kempadoo & Doezema, 2018) [15, 16] Those stories talk about the system which creates and benefits from the sex worker, and not generally about the sex worker in terms of emotions and personal development (Bekteshi et. al., 2012; McClain & Garrity, 2011.) [17, 18].

This second study was inspired by the interests of marketing students in a graduate course in Bogota, Colombia. The students under the instruction of a8uthor Herrera, investigated the nature and magnitude of the interaction between the WHO (who is the sex worker), the DANGER (what is the danger facing a sex worker in Colombia), as they drive the response of ‘protection of’ and ‘legal recourse available to’ the sex worker. Over the past decades there has been a recognition that prostitution and allied activities constitute a profession with the workers deserving he benefits and protection due to any person who works in a job. The study approach was the same, in terms of creating the four questions, developing four answers to each question (Table 8), and then presenting the vignettes to the respondents. The 24 students themselves offered to be respondents, and so we present this second study as a methodological advancement within the emerging science of Mind Genomics.

Table 8. Sex worker – Four questions and four answers to each question.

Creating scenarios to uncover interactions among answers

The first study presented in the previous sections treated all 16 answers as independent variables, which in fact they are. In this second study, we created the study specifically to comprise a WHO (the sex worker), the danger that the person would face (DANGER), and then two different types of protection (ongoing physical safety, legal recourse, respectively.) Thus, the first two answers are really ‘set-ups’ to frame the information, that information given by protection and recourse. The objective was to identify how different ‘set-ups,’ i.e., combinations of WHO and DANGER, drive the response to protections and to recourse, respectively. The analysis below explicates the approach to study interactions, using two sets of vignettes. The first set comprises a single sex worker exposed to four different dangers. The second set comprises four sex workers, each facing the same danger.

Set 1 – sex worker constant, danger varies

Select one person to study. It does not matter which one, since we are interested in the method. For the sake of simplicity, we study one specific sex worker; an older woman who has gone bankrupt. We create five different strata, varying by the danger to which the individual (older woman) can be exposed. Each stratum thus can be defined as having one type of worker (the older woman), and one type of danger. Each individual danger and ‘no danger’ jointly define the stratum. For each stratum we run a simple model using the eight elements as predictors, the four elements describing physical protection, and the four elements describing legal recourse. Our model has no additive constant, because the rating is ‘agree/disagree.’ The additive constant makes no intuitive sense. We create this model for the rating question, again converted to binary (Top2, for agree), and then for consideration time. Table 9 presents the coefficients for agree (coefficients of 60 or higher shown in shaded cells, bold type.) Table10 presents the coefficients for consideration time (5 seconds and higher shown in shaded cell, bold type.) Both tables also show the average coefficient across all eight elements.

Table 9. Interactions between Sex Worker, Danger as stratifying variables, and legal recourse and protection as variables to be considered when disagreeing or agreeing.

The coefficients are high because two of the variables are not considered in the model. Thus, the binary transformed rating, ‘agree’ (4–5), must be allocated across eight elements, not 16 elements, even though the vignettes still comprised 2–4 elements.

What is remarkable about the table is the dramatic interaction among the ingoing facts of the case, specifically WHO the sex worker is, and the DANGER the sex worker faces, and the specific protections and recourses selected.

1. On average across the eight elements (four protection, four recourse), the level of agreement is similar close across all four Dangers for the single person (older woman)
2. Yet, the specific interactions are dramatic. For example, when the Danger is shame and disgraceful feelings inside’ the sex worker, the strongest Recourse is: Special “shaming” notices for those who hurt sex workers. In contrast, when the Danger is getting beaten up and robbed, the strongest performing else is the legal Recourse: Steep fines for those who cheat sex workers.

When we move to Consideration Time (Table 10), we see that with an older woman who has gone bankrupt, we emerge with dramatically different Consideration Times. The longest Consideration Time comes from the combination of the older woman with ‘not getting paid’ and with ‘shunned as undesirable person’, both an average of 4.6 seconds.

Table 10. Interactions between Sex Worker, Danger as stratifying variables, and legal recourse and protection as variables driving ‘Consideration Time’ when rating disagree vs agree.

There is also a noticeable interaction between the person (older woman who has gone bankrupt), the nature of the danger from the outside (not getting paid / shunned as undesirable), versus from the inside (‘’shame and disgraceful feelings.”). The outside actions / dangers generate longer Consideration Times.

The Consideration Times do not generate as clear a pattern as do the Agreement coefficients. So-called ‘objective measures’ in research may be attractive because of a belief that they are ‘tapping something real,’ but the interpretation of what they are tapping may be harder, and undoubtedly problematic.

Set 2 – danger constant, person varies

Select one danger to study. It does not matter which danger is held constant for purposes of explicating the approach. For simplicity, we focus on an emotional danger from the person’s self-image, ‘shame disgraceful feeling inside.’ As before, we create five different strata anew, varying by the sex worker. Thus, each of five strata has one danger (shame disgraceful feeling inside) and one of four sex workers, as well as the case of ‘no sex worker’.

For each of the five strata we run a simple model using the eight elements as predictors, as we did before, the four for physical protection, and the four for legal protection, respectively Our model has no additive constant. Table 11 presents the coefficients for agree (coefficients of 60 or higher shown in shaded cells, bold type.) Table 12 presents the coefficients for consideration time (5 seconds and higher shown in shaded cell, bold type.) Both tables also show the average coefficient across all eight elements.

1. On average, for a given danger, the average coefficients vary, from a high achieved by vignettes featuring the young woman who is just starting out (average coefficient = 35), to a low achieved by vignettes featuring an older woman who has gone bankrupt (average = 21).
2. When the danger is ‘shame and disgraceful feelings inside’), most of the strong performing elements are plausible, i.e., legal recourse, rather than protection. The shame and disgraceful feelings do not present danger.

Table 11. Interactions between Danger and Worker as stratifying variables, and legal recourse and protection as variables to be considered when disagreeing or agreeing.

Finally, Table 12 shows the how Consideration Time for each of the protection and recourse elements vary with the single fixed danger (shame and disgrace inside), the four different types of sex workers, and the Consideration Time. All Consideration Times are high (4.2- 4.8) except for the older woman who has gone bankrupt (2.0). For the younger sex workers, the focus is protection. For the older sex worker, the focus is legal recourse.

Table 12. Interactions between Danger and Worker as stratifying variables, and legal recourse and protection as variables affecting Consideration Time when assigning a rating of disagree agree for legal recourse and protection.

Discussion – Mind Genomics as a tool to map and to understand relationships

As suggested by the introduction, the field of sexuality, and especially the sexual behavior of intimate couples and the issues involved with sex workers have created in their wake an enormous literature. This paper does not address that literature, and especially does not attempt to answer questions raised by previous studies. Such an effort requires an encyclopedia of papers, not a single short research note. Rather, the objective here is to introduce a way to understand a topic from the inside-out, from the mind of the person, from a combination of psychological ‘thinking’ and consumer research methods.

The tradition of today’s science can be summarized by the term ‘hypothetico-deductive.’ The term means that we create a hypothesis about the nature of behavior, and then perform the requisite experiments either to falsify the hypothesis, or to not-falsify it. Not falsifying a hypothesis does not mean that the hypothesis is correct, but rather that for the time-being the hypothesis may be accepted. The focus of today’s research thus becomes increasingly narrow. The rigors of scientific research demand an almost superhuman concentration to focus the research on the specific problem. Little is left to the exploration of new ideas.

When it comes to the study of human behavior, the many aspects, the nuances, and the impossible-to-remove interactions among the variables make the hypothetico-deductive system interesting, but not particularly productive. One has pieces of information, some convincing than others. Yet, one is missing a narrative, not necessary spun from narratives and stories, but rather emerging from easy-to-do studies. The sheer difficulty of doing inexpensive, comprehensive, focused experiments with people force the researcher either to rely on questionnaires (self-reports), or to weave a story from interviews, or a limited number of experiments.

The approach presented here, Mind-Genomics, demonstrates the opportunity to create a new archival literature on people, personal relations, focusing either on specifics, on limited topics, or on a set of topics which bring into focus a bigger picture. What we see in these two studies is the relative ease of doing computer-aided experiment with messaging in order to identify how the person thinks about a topic. The experiments are short, iterative, yet generate information emerging from the structure of the experiment. The test stimuli are cognitively rich. The richness means that beyond the emergent patterns (what other studies discover) lies the responses to individually, meaningful, relevant, and possible important stimuli. The responses to the individual stimuli teach, rather than having value simply because they are part of an emergent pattern.

Acknowledgement

Attila Gere wishes to acknowledge and thank the Premium Postdoctoral Research Program of the Hungarian Academy of Sciences.

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