DOI: 10.31038/PSYJ.2023553

Abstract

Mind Genomics explored responses for an e-commerce website, focusing on a website with ‘deep knowledge’ of the user’s preferences. To understand the application of Mind Genomics in a real-world setting, the timing of the setup and the fielding were limited to a total of 120 minutes. The data were collected in Spring, 2019. Four years later, newly developed AI analysis further interpreted the results. The initial analysis in 2019 deconstructed the ratings assigned by the respondents to vignettes, combinations of messages, describing the website. The respondents used an anchored 5-point scale, with the anchors ‘buy’ and ‘not buy’, respectively. The deconstruction by OLS regression revealed the contribution of each element to the ‘buy’ rating. Clustering the 46 respondents using the 16 coefficients uncovered three Mind-Sets: MS1-Help the client grow, MS2-Client Consulting, and MS3-Generate Leads. Four years later AI was applied to each group in the population, using six standard AI queries applied to all positive elements which were deemed to be strong drivers of ‘buy.’ This paper shows the possibility of rapid and insightful learning on new topics. Learning is promoted through experimental design coupled with human validation, and AI interpretation.

Introduction

The computer, the Internet, the Internet of Things, and the focus on real-time optimization continue to empower our modern age. You can’t go a single day without encountering a constant stream of ads and requests to buy. These advertisements often feature items that have already been purchased or viewed, as well as items left in a shopping cart. They are based on microsecond analyses of the shopping behavior of consumers. The analytic abilities are now so powerful that big data looks antiquated when compared to the small data generated constantly.

This paper explores what could be considered an important issue, the need to improve the flow of information between the customer and the online retailer. Online retailers function best with a large amount of data about their customers.

For roughly a decade, brands and retailers have been promoting their strategies for being “consumer centric” This means that they want to put the customer at the heart of all the work they do. In effect, this puts the consumer into the driver’s seat, with consumers ‘telling the brands’ what brand experience to create for them. When it comes to analyzing consumer behavior and preferences, retail analysts are tasked with interpreting data from consumers – such as sales figures, consumer trends and satisfaction ratings to develop insights for managing the retail enterprise profitably.

Retailers, the brands they carry, and internet service providers are increasingly criticized by privacy advocates and regulators for using data collected from consumers to develop detailed profiles on each one to tailor the commercial experience and to precisely target their messages. In response, most data collectors now offer opt-out methods to limit the data collection, its uses, and sharing with third parties, but doing so is not obvious. There is a widespread growing belief or, at the very least, a loudly expressed recognition, that data created by an individual belongs to them and should be controlled by them, similar to the requirements set forth in the EU’s 2018 General Data Protection Regulation [1].

Our world is inundated with data. As the speed and volume of data increase, our ability to form scientific questions, track trends, or subject the rapid pace of life to scientific inquiry is lost. Amidst massive amounts of data and massive optimization opportunities, it becomes increasingly hard to ‘think slow’.

We are accustomed to the slow, majestic, ingrained, now entrenched system of hypothetico-deductive reasoning [2]. It is a basic concept that scientists, or even individuals, can ‘advance,’ when they form a hypothesis and test it rigorously, trying to falsify the hypothesis. As technology speeds up the production of data and its acquisition, it is also necessary to accelerate knowledge and thought. In the harder sciences like biology and chemistry it may take exceptional creativity to produce knowledge, but in the human-centered sciences this may not be a major problem. With the advancements in computer technology, some paradigm shifts may already be possible [3].

Recurring Issues in the World of More Knowledgeable Websites

With the increasing excitement and, occasionally, almost manic positive responses about websites ‘knowing people’ comes the issue of privacy at the most obvious, but more deeply the morality of machines which have been programmed to learn about people The topic of this paper is the desire of respondents for websites (viz., provider technology) to ‘know them’, or perhaps the opposite, the fear that the machine may know too much, and the consequent loss of privacy. Some of this issue is one’s own desire for privacy, but some of it is the ‘morality’ of machine knowledge about facts relevant to individuals, and the implications of the wide availability of such knowledge. The issue is not ‘all or not’ either, for with knowledge by websites of people comes the smoother operation of interactions, the reduction of annoying, and occasionally harmful friction.

Mind Genomics: The Promise and Vision for the Future

Mind Genomics is a new, emerging science which traces back to three disciplines [4-8]. These are:

1. Experimental psychology which searches for the causes of human behavior. The specific area of experimental psychology giving rise to Mind Genomics is psychophysics, the discipline which searches for lawful patterns in between what is presented and what we perceive For Mind Genomics that relation is between the words describing a situation and the judgment we say we would make.
2. Statistics, The respondent reads combinations of elements, messages, describing this ordinary situation, and rates the feeling on a scale provided by the researcher.
3. Consumer research, which examines how consumers make decisions in everyday life, in which we live, work, succeed or fail. The goal is not to develop a new theory or to disprove an existing one, though these noble endeavors are possible. The only goal is to make sense of these patterns.

It is important now to keep in mind that the effort is more in the world of ‘hypothesis-generating’ than in the world of ‘hypothesis-testing.’ Quite often researchers really have no hypotheses to test but are constrained to do the study as if it were guided by a hypothesis. Mind Genomics does not care about that. It is simply a tool to discover patterns that may be interesting regarding how people think.

The process of Mind Genomics is a simple one, beginning with the question of what do people do about the information they receive as they are instructed to make a decision? Typically, the respondent is presented with the situation, using a simple story, even a single sentence. It is the goal to find regularities and relations within nature. Such discoveries generate the raw material for understanding how people think. When these discoveries are amalgamated from well-done experiments and when they reach critical mass, they form a coherent database, and in turn these coherent databases become the foundation of technology and science.

This study forms part of a new initiative in Mind Genomics which aims to massively accelerate the acquisition of information and insights for everyday life, including topics such as subjective feelings toward e-retailing, including ‘smart websites.’Another motivation is to show, through a research program integrating artificial intelligence and systematized human testing, if the two can be used to reveal aspects of everyday life or even weak signals about changing attitudes.

This study was done in 2019 before the massive expansion of AI into the world of everyday consumer research analytics. The original study was done without any focus on what AI could add to the research effort, but rather focused on what people would want for a system in which they would give up their privacy. The actual data analysis reflects what people were thinking in 2019. The subsequent AI analysis, in turn, was done in the third week, of May 2024, with a view to what the data might mean for a business issue.

The project itself in each phase ran on an accelerated schedule. For the first part of the project, the actual research, the total time involved was less than three hours from start of the project (create the experiment) to the acquisition of analyzed data. For the second part of the project, the AI analysis of the results obtain four years before, the analysis took 30 minutes, consisting of an automated AI-driven reanalysis of the data tables The actual study itself with people came shortly after the first experience integrating AI and Mind Genomic by hand, with AI providing the raw information first, that information transformed into the test stimuli [9]. The effort presented here moves the process in the opposite direction, with AI providing a second, deep analysis of results already obtained.

The First Part of the Project – The Creation and Execution of the Survey with the Respondents

Mind Genomics is now scripted. It follows a templated process which reduces the “angst” of doing experiments as well as the time and effort required to collect data. Experience over a half century has shown that individuals are anxious when asked to “do science.

Mind Genomics scripts are designed to make sure that researchers can present the information in the correct format. In the actual experiment, relevant messages about a subject (called “elements”) are combined into short, easily readable vignettes. A vignette consists of 2-4 messages. People then rate the combinations using a scale. Each respondent ends up rating 24 different vignettes. The vignettes are different for each respondent, but all the elements remain the same [10].

Mind Genomics’ studies have been scripted to allow them to be run on a computer, smartphone, or tablet, with results available in a very short time. The information in Table comes from the study, text taken from the actual input by the researcher, and put together in the report to document the study. The information in Table 1 is available within five minutes after the end of the study, as is the basic analysis. The AI summarization requires an extra 20-30 minutes after the end of the field work. (Table 1)

Table 1: Key information about the study provided by the Excel report.

The setup begins with naming the study and the instructions to provide four questions relevant to the topic of study. Researchers are asked to structure four questions in a way that they “tell a story,” and then provide four answers to each question. Figure 1 shows the template. In 2019, it took several hours to create the four questions for each question and four answers.

Figure 1: The templated request of four questions (left panel) and four answers as used by respondent to manually provide questions and answers.

Since Mind Genomics became available to the public more than 10 years ago as a DIY (do it yourself) technology, there have been many instances where researchers felt overwhelmed with the task of creating the elements.

It is now possible to use artificial intelligence to generate questions. The researcher only needs to write a paragraph into the Idea Coach box in the Mind Genomics template and the AI will return 30 different questions. The Idea Coach technology, which was launched in 2022, was three years after this study was conducted.

Figure 2 shows three additional set up screen shots from BimiLeap. These include the orientation page (left panel), the rating scale (middle panel), and the researcher’s file information about the rationale for the experiment as well as key words for a later search (right panel).

Figure 2: Three setup screen shots for BimiLeap. These are the orientation page (left panel), the rating scale (middle panel) and the file information (right panel).

The researcher can select the respondents’ source using the screen in Figure 3 following the study’s launch. This is done quickly within the BimiLeap software. Respondents are ‘sourced from’ a panel provider that specializes in online surveys. There are a number of panel providers around the world. These panel providers maintain lists of respondents with their qualifications. They are individuals who have agreed, in exchange for a reward from the supplier, to take part in similar studies. The researcher does not need to know about the agreement. The panel provider only needs to find the right respondent.

Figure 3: Sources of respondents selected by the researcher at the end of the project.

Mind Genomics research can include elements (questions, answers, etc.) in many different languages and alphabets. However, the instructions on how to set up the study as it is done by the researchers are only available in a limited number of languages.

The actual experiment with the subject lasted about 3 minutes. The experiment starts with a brief orientation. The respondent then answers a few self-profiling questionnaires (Figure 4, bottom panel). Finally, the BimiLeap program presents 24 different vignettes that have been systematically created. The vignettes contain 2-4 elements, at most one answer to a single question but often no answer to one or two of the questions (see Figure 4, top panel).. It will be this very incompleteness of the combinations which allows the analysis by OLS (ordinary least-squares), and the estimate of absolute values for the coefficients.

Figure 4: Sample four-element vignette (top), and self-profiling classification (bottom).

The experimental design allows for the analysis of each individual’s ratings, respondent by respondent, as well as analysis of groups comprised of any set of 46 respondents who participate in this study. The vignettes have been set up in a way that each person evaluates 24 unique vignettes. This design structure allows the researcher to explore different aspects of a problem without having to select which combination of elements gives the best chance for discovery.

Database Structure, Analysis, and Reports – Total Panel

It is easy to analyze the data because the experiment design has been preselected in a way that all the 24 combinations of different sets are isomorphs. The vignettes are different, but the mathematical structure is identical. The researcher will have a powerful analytical tool that allows them to explore a large part of the “design space” (the combinations). More respondents, and therefore more people, means that more design space will be covered.

A simple database makes it possible to perform the analysis. The database is divided into 24 rows, each of which corresponds with one of the vignettes that a respondent has tested. The database for the study contains 1104 rows, or 46×24, of data. Each respondent contributes 24 rows to the database, one row for each of the 24 vignettes evaluated by that respondent. The Columns are allocated for bookkeeping (row number, how the respondent profiles herself or himself), a column for the order of rating of the 24 vignettes (1-24), then 16 columns to show absence or presence (value 0 or 1) of an element, and finally the assigned rating and the response time. The response time is the number of seconds between the appearance of a vignette and the answer.

The program then creates two binary variables: TOP (ratings 5, 4 transformed into 100) and BOT (ratings 1, 2 and 4 transformed into 100). BimiLeap adds a vanishingly small number of random numbers for each BOT or TOP value to create needed This prophylactic measure ensures the required variability, even if the respondent rates all the vignettes as either 5 or 4 (all transformed TOP become 100) or 1 or 2 (all transformed BOT become 100).

Table 2 shows the parameters of the equation, expressed as: TOP = k0 + k1A1 + k2A2 .. k16D4 . This equation can be estimated accurately because the OLS regression does not have any correlation issues between variables. The coefficients emerging from the OLS are absolute values, so a 5 has half of the value as a 10 It is crucial to understand this necessary property, which allows Mind Genomics to create a science. The researcher can quickly grasp the dynamics in the data revealed by the experiment when the coefficients show the real magnitude of the effects.

Table 2: Elements for the Total Panel which drive TOP (Sounds interesting). Only elements with coefficients > 1 are shown.

As a side note, this vision of absolute coefficients is often counter-intuitive to ‘experts’ who believe that the respondent needs all the information from the different questions to make a decision. Admirable as that point of view is, which ends up presenting complete vignettes to each respondent, the results data is almost impossible to understand, because the absolute coefficients have no meaning. It is only differences which have meaning. There is no possibility of databasing the results unless the entire study is replicated. Only then do the coefficients have meaning.

Table 2 shows the results from the Total Panel, of 46 respondents, each rating a unique set of 24 vignettes. The Table shows us places for the 16 coefficients, along with the additive constant.

This constant indicates the likelihood of respondents saying ‘buy’ in the absence of any element in the vignette. The vignettes are all designed with a minimum of two and maximum four elements. The additive constant can be thought of as a statistical correction factor. On the other hand, we can use this as a base, or a tendency for respondents to respond “buy”. This will help us gain this insight. Table 2 indicates that 45% of the responses will be 5 or 5, when they know what the system is, even when there are no specific elements to qualify the product. We could have measured this change over the years if we had done the same type of experiment.

In the interests of revealing patterns, the convention in this paper and others is to show only positive coefficients of value 2 or higher. Coefficients of 1, 0 and negative are of no interest. The low coefficients indicate that an element’s presence in a vignette “doesn’t add”. This does not necessarily mean the element detracts or is insignificant.

Eight out of 16 elements have coefficients greater than 1. All the rest generate coefficients that are 0 or negative. . However, only one element is really successful, “create amazing ads” with a coefficient of 6. Subgroups are likely to be hiding strong performers, as we shall soon see.

We now move to the AI interpretation of these results from the total panel. AI analysis should only be viewed as a set of tentative observations by a heuristic. AI can provide a quick answer before taking the time to analyze the entire dataset. The BimiLeap report has been upgraded to provide AI responses to the following six queries, using the coefficients for the key subgroup being summarized.

The following are the six queries.

Interested in

Create a label for this segment:

Describe this segment:

Describe the attractiveness of this segment as a target audience:

Explain why this segment might not be attractive as a target audience

Which messages will interest this segment

These queries primarily consider moderate or high performing elements with coefficients greater than +5. The elements with coefficients below 4 are considered, but not relied upon.

The AI summarization of the data, based on the six queries appears in Table 3.

Table 3: AI first scan and interpretation of the strong performing elements for the Total Panel.

Results from Self-profiling Questionnaire

The BimiLeap program instructed respondents to put themselves into one of four groups, based upon how they feel about an outside website having deep knowledge of oneself. Table 4 shows the pattern of coefficients generated by the two polar opposite groups, the first very excited and positive about the situation, the second bothered. As one might expect, the additive constants are higher for the positive group, and much lower for the negative group. Furthermore, there are more strong-performing elements in the positive group. Table 4 shows the coefficients of the elements. Table 5 shows the AI analysis of the patterns.

Table 4: Elements which drive TOP (Buy) for the two key segments emerging from the self-profiling question: How important is it that the website know my mind to give me offers?

Table 5: Elements which drive TOP (Buy) for the three Mind-Sets, emerging from k-means clustering of all the element coefficients from the 46 respondents.

Dividing Respondents According to Mind-Sets Using the Coefficients

The final analysis of the data will be focused on creating Mind-Sets. These are groups of respondents that have been created by using the K-means Clustering Program based upon the similarity of the patterns made by their coefficients. We use the 16 coefficients whether they are positive or negative for clustering. The additive constant is not used in clustering [11].

BimiLeap. It generates two Mind-Sets at first, then three Mind-Sets. The Mind-Sets encompass all respondents. A person belongs to only one mind-set for the two mind-set solution, and again to only one mind-set for the three mind-set solution. The clustering results in meaningful groups that can be interpreted, despite the fact that the process is mechanical and mathematical.

Table 6 lists the elements that make up the successful performance of the Three-Mind-Solution. In order to save space, the two-mind solution is not included. The AI results are shown in Table 7, emerging after applying the six AI queries to the Mind-Sets.

Table 6: AI interpretation of the strong performing elements) by AI for the three-Mind-Set solution.

Table 7: The IDT, Index of Diverged Thought, showing the performance of the elements, and thus the strength of the thinking behind the specific Mind Genomics study.

Understanding Performance Using an Index Number (IDT, Index of Divergent Thought)

Researchers in many fields ask the same question: “How did we perform?” With Mind Genomics and AI still at an early stage, but available worldwide at the touch of a button, it becomes a matter of whether the research produced anything valuable. It is important to note that the notion of “value” does not refer to personal worth of data for the researcher or to reproducibility of science. Instead, for Mind Genomics we ask whether or not the study produced any high-performing elements. When we find strong elements, there’s a link between the element and rating questions. In these studies, the researchers are looking for this link. In those elements, the issue at hand can be better understood.

We present IDT Index of Divergent Thought as part of our effort to “systematize” the use of Mind Genomics, in an era of simple-to-use AI-powered techniques. IDT’s objective is to determine the effect of elements. IDT produces a simple, indexed result. This is shown in Table 8. When we divide the study into six groups, each sum of positive coefficients for that group is weighted by the relative number of respondents for that group. The six groups are Total, Mind-Sets 1 & 2 for the 2-Mind-Set solution, and Mind Sets 1, 2, & 3 for the 3-Mind-Set solution. The IDT is the weighted sum of positive coefficients. The weight is the ratio between the respondents per group and the total of 138.

The IDT by itself is simply an index number about how well the elements performed. With continuing use of the IDT as a metric, it may become possible to measure the degree to which a person grows in the ability to think creatively. One could imagine charting the IDT value for a person or group of people as they are challenged to think through various problems. The IDT gives us a way of measuring the ‘strength’ of alternative efforts to deal with the same issue, with different issues, after teaching interventions, and so forth. The alternatives may be the same researcher over time, the performance of studies done by individuals vs. those done by collaborating groups, and so forth. The IDT is objective, a simple index quickly calculated as part of the report to the researcher.

Discussion and Conclusions

The Mind Genomics method has been used to investigate the decision-making process for the world of the ‘everyday.’ Rather than focusing on topics of deep significance and with a long history of investigation, the researcher using Mind Genomics investigates simpler problems, such as what does a person want from a website which ‘knows’ the user. It is within that world view that the current study was done.

What emerges from Mind Genomics is far more than a simple snapshot of human decision making at the level of the concrete issue. One can sense from the use of commonplace features as elements that behind these everyday statements lay an entire universe of motivations, a universe that might totally disappear or at least lose its vitality if the statement were couched in the general, rather than in the specific. It is the richness in the meaning of everyday experience which provides deep learning. In other words, Mind Genomics provides emotion-rich, philosophically rich metaphors that ordinary, academic language cannot.

Armed with that point of view the paper no longer talks only about websites which know the person. Rather, the paper talks to the issue of emotional responses to different ways of weighting information to decide. different ways of responding to recommendations from a ‘machine’ and the concern with the nature and trustworthiness of machines which know the person. The issues lose a bit in the translation when they are stated in the form of generalities, but create immediate, palpable internal sensations when they are described by daily, identifiable events, viz., by realistic metaphors.

Beyond the actual data from the Mind Genomics exercise, however, lies the second layer of AI analysis. The AI process was told to look at the elements in general, especially those with coefficients of +6 or higher, along with the additive constant, and summarize the results through six queries. The objective of the exercise was to publicly present the data, and then the AI interpretation of the data, untouched by human hands. No attempt was made to structure the output of the AI, this effort being among the first to attempt a machine-level summarization. It is likely that we will see significant advances in ‘insights and languages, as the ever-evolving AI amplifies the structured outputs of Mind Genomics experiments.

References

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DOI: 10.31038/PSYJ.2023551

Abstract

Through the use of Mind Genomics coupled with AI (artificial intelligence), the researchers explored responses to an almost totally new topic, retail analytics, specifically what might be the applications in the business environment. AI generated four questions about the use of retail analytics, and subsequently four answers (elements) to each question. These AI-generated answers were combined into unique sets of 24 vignettes, one set for each of 50 respondents. The ratings of the respondents in terms of ‘interests me’ were then deconstructed into the contribution of each element to the rating. The deconstruction by regression analysis was done for total panel, for two viewpoints about what a company would do with the data (better prices; better service), and three mind-sets or ways of thinking about the topic (MS1 – Focus on price/profitability; MS2 – Focus on innovation/operations; MS3 – Focus on outside world behavior, MS 3). The paper concludes with AI analysis and summarization of the strong performing elements for each identified group, using the same six queries for summarization. The paper demonstrates the potential for rapid, insightful learning for new topics, a learning promoted by AI and by human ‘validation’ of the ideas generated.

Introduction

This paper presents an exploration into what might be considered a specialized topic, subjective responses to retail analytics.

“Retail analytics involves using software to collect and analyze data from physical, online, and catalog outlets to provide retailers with insights into customer behavior and shopping trends. It can also be used to inform and improve decisions about pricing, inventory, marketing, merchandising, and store operations by applying predictive algorithms.” [1]

As this definition shows, retail analytics involves extensive data collection, complex analysis and presentation so that executives can make decisions for the benefit of their retail operations. For the analytics, there is the analyzed. Today’s marketing savvy consumers know that data is collected from them, and they expect the data to be used for their benefit, viz. personalized services and an improved shopping experience [2]. It’s an implicit contract.

For years now, retailers and most brands, promote their goal to be “consumer-centric,” i.e., putting the consumer at the center of everything a retailer or brand does, in effect placing the consumer in the driver’s seat, a position allowing them to tell brands how to shape brand experiences for them. Oddly, when it comes to retail analytics, the conversations are about executives interpreting consumer data – sales, trends, satisfaction levels to name three, but we could not find examples where the views of consumers about the use of – and how to use – retail analytics, which is derived from their data, to improve their shopping experiences. This gap provided the novel opportunity to contribute new knowledge by turning the research of subjective responses to retail analytics on its head – to explicate the bottom-up perspectives of consumers to executives, leaders who may then become even more consumer-centric in ways that benefit their operations and improve their customers’ experiences.

Our modern age continues to grow in technological capabilities, empowered as it is by the computer, by the Internet, by the so-called Internet of Things, and by the hyperfocus on optimizing in real time. One can scarcely go through a day without being exposed to ceaseless streams of advertisements and calls to take a buying action. Often, the advertisements are for items already purchased, for items recently viewed or for items abandoned in an e-commerce shopping cart, such advertisements and calls to act emerging from rapid, micro-second analysis of people’s shopping behaviors. Indeed, the analytic capabilities are so strong that the previous fad for gaining substantial insight, big data, looks almost antiquated in terms of the ability to deal with small, immediate, personalized data, generated nonstop.

It is no wonder that the world is awash in data. Our ability to formulate scientific questions, to track trends, to subject this rapid life to the slow majesty of scientific inquiry seems to vanish as the speeds and volumes of data feeds increase to an accelerating beat. It is difficult, indeed, to, ‘think slowly’ while in in the throes of massive data, massive opportunities to optimize.

We are accustomed to the slow, majestic, ingrained, now entrenched system of hypothetico-deductive reasoning [3]. The basic idea is that science, or perhaps even people personally, ‘advance’ by forming a hypothesis about something, and rigorously testing that hypothesis, attempting to falsify Whatever is not falsified begins to have the ring of truth to it. At the same time as technology is speeding up data production and acquisition, there is a need to speed up knowledge acquisition and thinking. It may require exceptional innovation to create knowledge in the hard sciences, such as biology and chemistry, but to create valuable knowledge in the softer human-centered sciences may be less of a problem. Indeed, with the advances already made in computers some of the paradigm changes may be within today’s grasp.

Mind Genomics, the Promise of AI, and a Vision of the Future

The study is part of the new effort in Mind Genomics to accelerate the acquisition of knowledge and insight for the world of the everyday, and for topics involving human feelings about situations and activities that we often overlook because of their sheer invisibility, such as our present topic of subjective feelings towards retail analytics. There is another motivation as well, the desire to demonstrate through an ongoing research program, whether the combination of artificial intelligence with systematized testing among humans can reveal new aspects of daily life, and even point to weak signals of attitudes which are evolving.

The underlying science, Mind Genomics, is an emerging discipline with rooms in a number of areas, including experimental psychology to search for causation of behavior, statistics which allow researchers to work with combinations of independent variables in the way which often occurs in nature, and finally consumer research which looks at how people make decisions about the world of the ‘ordinary’, the quotidian reality in which people actually live, function, thrive or fail.

The history of how Mind Genomics emerged or better ‘evolved’ has been told before, in detail. The story is simple, involving the basic question of how people respond when asked to evaluate a compound stimulus comprising a variety of features. The question is simple, and even the thinking is rudimentary. The sole focus is simply to see ‘what happens.’ The effort is simply to find patterns in nature. There is no effort to create a theory, to prove or disprove a theory, although those noble efforts can certainly occur. The worldview underneath searching for patterns in nature come from psychophysics, the branch of experimental psychology, which searches for regularities in nature, such as the perceived sweetness of different concentration of sucrose in a water solution [4], or perhaps more of interest to industrial concerns, the perceived sweetness of different combinations and concentrations of artificial, high-potency sweeteners, in beverages. The effort is to discover relations in nature, regularities. The mass of such discoveries, especially in a defined and coherent field, becomes technological know-how and even the basis of science.

The project itself was run on an accelerated timetable, the total involvement being less than three hours, although spread over two periods, the first being the set up and empirical experiment, the second being the automated analysis using artificial intelligence. These two together constituted the period of less than three hours. The writing of the paper took considerably longer, but plans are underway to make the writing as quick as the design and the field research. In that way the Mind Genomics approach can be a living instantiation of what studies are, namely decision making in real time in the real world of the everyday. It is worth noting that the first use of AI in Mind Genomics goes back four years, with the process being long because it was a handcrafted combination of AI and Mind Genomics [5].

Part 1 – The Creation of the Study, and the Execution with Respondents

Mind Genomics studies are now scripted, following a templated procedure which reduces both the effort to acquire data as well as the ‘angst’ involved in doing an experiment. A half-century of experience by author Moskowitz in the world of science has continued to reveal that anxiety reported by individuals who are not scientists in their daily lives but are asked to ‘do science’ for a specific objective.

The scripting for Mind Genomics is set up to ensure that the researcher can provide the necessary information, in the proper format. The actual experiment involves combining relevant ‘messages’ about a topic (called ‘elements’) into small, easy to read ‘vignettes’, a vignette comprising 2-4 such messages. The combinations are then evaluated by people, using a rating scale. The respondents each end up evaluating 24 different vignettes, the vignettes for one respondent different from the vignettes for another respondent, but the set of elements being the same. Each respondent rates a unique set of 24 vignettes [6].

Mind Genomics studies are now scripted so that the studies can be set up on either a computer or a smartphone, run, and results downloaded with a short period of time. Table 1 provides key information about the study, information that will be explicated. The text in Table 1 comes directly from the report, contained in an Excel workbook, which can be downloaded either immediately for partial analysis (without the artificial intelligence summarizer), or after 30 minutes for complete analysis (with the artificial intelligence summarizer).

Table 1: Key information about the study provided by the Excel report.

The set-up begins with the naming of the study, and then the instruction to provide four questions germane to the study topic. The researcher is prompted to structure the four questions so that they ‘tell a story.’ During more than a decade, from the time that Mind Genomics was opened to the public as a ‘software as a service,’ there has been increasing number of situations where researchers seemed to have been overwhelmed by the task of creating elements. With the advent of artificial intelligence, such as Chat GPT, it has become easy to use AI to create questions. All the researcher has to do is write down a short paragraph in the Idea Coach ‘box’ in the Mind Genomics template, and the AI returns with 30 different questions.

Figure 1 shows the request for four questions (left panel), and the four questions provided by the researcher (right panel). Table 2 shows one run of the Idea Coach, returning with the 30 questions after the AI has been prompted by a short description of the project. The actual time for this first step with the Idea Coach is approximately one 60-90 seconds after the researcher provides the Idea Coach with the small description. The researcher can select 0-4 questions. The questions selected are automatically put into the template. The researcher can then repeat the action to get other answers. The actions are independent of each other, so that each use of the Idea Coach to provide questions is independent of the previous use. In this way a person new to the topic can learn a great deal about the topic, even without selecting questions. It is the sheer number of different questions presented in a short period of time which constitutes an ‘education’ for the researcher on the very topic being studied. It is for that reason that the approach is considered ‘Socratic.’

Figure 1: The templated request for four questions (left panel), and the four questions provided by the Idea Coach (right panel).

Table 2: The 30 questions returned by Idea Coach after being prompted by a small paragraph about the purpose of the study.

The next step consists of providing four answers to each question. Once again the Idea Coach comes into play. This time the information provided to Idea Coach is in the form of a query, that query created directly from the text of the specific question. When the researcher wants to change the query, it becomes a simple task of editing the question.

Figure 2 shows the formatted screen for Idea Coach, with the left panel showing the request for four answers, and the panel showing the completed set of answers used in the study. Table 3, in turn, shows 15 answers generated from one run of the Idea Coach, with the first question. The time elapsed in about 30 seconds.

Figure 2: The templated request for four answers for question #1 (left panel), and the four questions provided by the Idea Coach (right panel).

Table 3: The 15 answers returned by Idea Coach after being prompted by Question #1.

Once the study has been launched with the appropriate respondents, done easily within the BimiLeap program, the researcher selects the source of respondents, using the screen shown in Figure 3.

Figure 3: Source of respondents, selected by the researcher as the last part of the project set-up.

The respondent is ‘sourced’ from a panel provider specializing in on-line surveys. Across the world there are many such panel providers. These providers have lists of respondents and qualifications, individuals who have agreed to participate in similar studies for a reward provided by the supplier. The researcher need not know the agreement. All that is necessary is for the panel provider to source the correct respondent.

The Mind Genomics study can have elements (questions and answers) from different languages, and different alphabets, although the instructions for the actual set-up as done by the research are currently available in just a few languages (e.g., English, Chinese, Arabic Hebrew.

The actual experiment with the participant lasted approximately 3-5 minutes. The experiment begins with a short orientation provided by the researcher, obtains responses to the self-profiling questions (including age and gender, not shown here), and then presents the respondent with the 24 different, systematically designed vignettes. The vignettes comprise 2-4 answers (elements), at most one answer from a question, but often no answers from a question.

The experimental design ensures that the ratings from each respondent can be analyzed separately, as well as being able to be analyzed as part of the group. The vignettes are set up so that each individual evaluates a unique set of 24 vignettes, a design structure which enables the researcher to explore many different aspects of an issue, without having to choose what combination of elements give the best chance of discovery.

It is important at this juncture to keep in mind that the effort is more in the world of ‘hypothesis-generating’ than in the world of ‘hypothesis-testing.’ Quite often researchers really have no hypotheses to test but are constrained to do the study as if it were guided by a hypothesis. Mind Genomics disposes with that, freely representing that it is a discovery tool, to identify patterns which may be of interest in the way people think about a topic. The respondent sees an introduction to the study, usually presented in shortened form so that the specific information must come from the elements selected by the researcher. Figure 4 (top panel) shows the orientation question. Figure 4 (bottom panel) shows the five-point scale used by the researcher. Some of the text is cut off from the scale description. Figure 5 (top panel) shows the additional self-profiling questions as these appear to the respondent at the start of the evaluation by the respondent. Figure 5 (bottom panel) shows a clear reproduction of a vignette, as prescribed by an underlying experimental design. To the respondent there is no ’underlying pattern’ to be discovered. Rather, the vignette itself looks like it was randomly assembled, in virtually a haphazard manner.

Figure 4: Respondent orientation screen in set-up program (top panel), and rating scale specifics in set up program (bottom panel).

Figure 5: Screen shot for the pull-down menu of self-describing questions (top pane), and example of a four-element vignette plus rating question/scale as the respondent would see it on a full screen (bottom panel).

Database Structure, Analysis, and Reports – Total Panel

Almost fifty years ago, it was possible to accelerate the acquisition of data and even the preliminary analysis of such data. One could gather data quickly, and using mark sense cards and later electronic input one could analyze the data by programs then available, usually programs which specialized in descriptive analysis. The objective for this exercise is to move dramatically beyond that simple original analysis, pushing the limits by making use of smart experimental design, clustering, and applied artificial intelligence using GPT.

The actual data analysis is straightforward, made so by the judicious use of the experimental design, pre-selected so that the different sets of 24 combinations are really isomorphs of each other. Although the actual combinations, the vignettes, all differ from each other, mathematically the structure of each set of 24 vignettes is the same. In this way the researcher is assured of having a powerful analytic tool, which explores a great deal of the ‘design space’ (the possible combinations). Clearly, more individuals, more respondents means more of the design space is covered.

The analysis is made possible by the creation of a simple to use database. Each row of the database corresponds to one of the 24 vignettes tested by a respondent. Thus, for this study, comprising as it does the responses of 50 individuals, the database contains 50×24 or 1200 rows of data. The columns themselves are divided not bookkeeping columns (row number, study name, respondent number, test order number for the respondent, via 1-24), then 16 columns devoted to showing absence of element (value 0) or presence of element (value 1), and finally, the rating assigned, and the response time. Response time is defined as the number of seconds from the appearance of the vignette to the actual response assigned.

After the data are collected, the program creates new variables, specifically a binary variable TOP (ratings 5 and 4 transformed to 100; ratings 3, 2 and 1 transformed to 0), and a second binary variable BOT (ratings 1 and 2 transformed to 100, ratings 3, 4 and 5 transformed to 0). The BimiLeap program adds a vanishingly small random number (<10-4) to each BOT and TOP value, in order to ensure variation in the values of TOP and BOT. This prophylactic action ensures that there will be the necessary variability even when the respondent rated all vignettes either 1 or 2, or 3, 4 or 5,.

The first analysis generates an equation relating the presence/absence of the 16 elements to the newly created dependent variable TOP. Table 4 shows the parameters of the equation, expressed as: TOP = k0 + k1(A1) + k2(A2) … k16(D4). . The use of an experimental design, permuted across the 50 respondents to create 1200 vignettes, ensures that the OLS regression will not encounter any problems in terms of variables correlated with each other. The coefficients have absolute value, viz., a 5 is half the value of a 10. This is important. Often it is only differences in coefficient values which are relevant, e.g., when the experimental design calls for each vignette to have exactly one element from each question. That ‘tempting’ requirement substantially weakens the results.

Table 4: Elements for the Total Panel which drive TOP (Sounds interesting).

With the recognition of these properties of the regression results, the researcher can quickly understand the dynamics revealed in the experiment. The results are immediate and obvious once the meaning of the coefficients is understood. The coefficients tell us the proportion of times a response to the vignette will be 5 or 4 (here … ‘sounds interesting’) when the element is put into the vignette.

Note: Other binary dependent variables can be created

BOT = rating 1,2 transformed to 100. ‘Does not sound interesting’

RATE52 = rating 5,2 transformed to 100. ‘Could deliver’

RATE 41 = rating 4, 1 transformed to 100. ‘Won’t deliver’

Table 4 shows us 17 numbers, the additive constant and 16 coefficients. Mind Genomics studies generate a large number of coefficients. We are not interested in the coefficients which are 0 or lower. These low coefficients say that the presence of the element in the vignette ‘does not add’. It does not mean that the element actually detracts, or perhaps just as likely, the element is irrelevant, leading to a rating of 3.

The additive constant tells us the proclivity of the respondents to say ‘sounds interesting’ in the absence of any elements in the vignette. By underlying design, the vignettes all comprise a minimum of two elements and a maximum of four elements. Therefore, the additive constant is simply a correction factor in statistics. ON the other hand, we can use it as a baseline, a proclivity for the respondent to say, ‘sounds interesting’. We will use it to gain that insight. Table 4 shows the additive constant to be 70, meaning that when it comes to knowing that the topic will be store analytics, 70% of the respondents will be strongly positive. Had we done this type of experiment across years ago we could have measured the change in this additive constant to get a sense of how new ideas are accepted.

Only three of the 16 elements generate coefficients of 1 or above. The remaining generate coefficients of 0 or negative. For the sake of clarity, and for the sake of allowing patterns to come through, we do not show any 0 or negative coefficients. If we discard the element with coefficient 1, quite close to 0, we are left with the finding that of our 16 best guesses using artificial intelligence, edited by a human researcher, we end up with only two strong performing elements, and indeed only modest performers at that.

Shown these results and presented with the Mind Genomics approach for the first time, the critic of artificial intelligence might aver, perhaps even strongly aver, that these results contradict the claims of AI proponents that AI can be as good or better than people. The reality is not as positive, however. When a person selects the elements alone, quite often the person does about as poorly, or perhaps slightly better.

C4: Analytics and modeling can help improve transportation management to reduce shipping costs and lead times.

D3: Data analytics can be used to monitor social media activity, helping retailers identify trending products and capitalize on buzz.

Before moving on to an analysis of subgroups and mind-sets, it is instructive to see what AI can provide in terms of a standardized interpretation of the results. The analysis by AI should be considered simply as tentative observations made by a heuristic. It will still take a person to go through the data, but in the interests of speed, one might employ the AI heuristic to get a sense of the answers before spending time with the results.

Table 5 shows the response of AI to six queries. These queries are listed below.

1. Interested in
2. Create a label for this segment
3. Describe this segment
4. Describe the attractiveness of this segment as a target audience:
5. Explain why this segment might not be attractive as a target audience:
6. Which messages will interest this segment?

Table 5: AI first scan and interpretation of the strong performing elements (6 and higher) for the Total Panel, on six queries.

The queries look only at the moderate and higher elements, viz., those with coefficients of +5 or higher. Elements with coefficients of 4 or lower are ignored. For our data in Table 4 using the Total Panel the AI uses only two of the 16 elements to write its analysis.

Results from Self-profiling Questionnaire – What will the Store Do with the New Analytics

Our second analysis parallels the first, this time focusing on how the respondent feels about what the store will do with the analytic information. The actual question and answers appear below. Out of four possible answers, most of the respondents (43 of 50) chose only two, better price and better service, respectively.

If a store knows a lot about customers, do you think the store will

1=Offer better prices

2=Satisfy the customer more

3=Help make a better world

4=Use the information to its own advantage

Two groups or segments emerge, those who feel that the store will offer better prices using the analytics data (15 of 50), and those who feel that the store with use the information to better satisfy customers (29 of 50). Both have similar additive constants (63 and 69) but respondent in radically different ways to the elements.

The data for this new analysis comparing different points of view on what will the store do with the analytics appear in Table 6 for the coefficients, and Table 7for the AI summarization. Once again all coefficients of 1 or lower are not shown. In the interests of simplicity, all elements without at least one coefficient of ‘2’ Table 6 suggests that the response of those in Segment 1 (believe the store will offer better prices) are strong and focused, whereas there are no strong performing elements for Segment 2 (believe that the operations will be better) (Table 7).

Table 6: Elements which drive TOP (Sounds interesting) for the two segments emerging from the question self-profiling question: If a store knows a lot about customers, do you think the store will…

Table 7: First scan and interpretation of the strong performing elements (6 and higher) by for the two segments, based on the self-profiling question of what the store will do with the results from the data analytic.

Results from Dividing Respondents into Mind-Sets Based Upon the Pattern of Coefficients

Our final data analysis will focus on the creation of mind-sets, groups of respondents who are put together by the k-means clustering program [7], based upon the similarity of the patterns made by their coefficients. For the clustering we use all 16 coefficients, whether positive or negative, although we only show the positive coefficients in the results. Clustering does not use the additive constant.

The clustering program is embedded in the BimiLeap program, generating at first two mind-sets, and then totally once again, three mind-sets. Each respondent is assigned to only one of the two or three mind-sets. Furthermore, the mind-sets encompass all respondents.

The objective of the clustering exercise is to discover hitherto unexpected groups of respondents based upon the patterns of their coefficients from thousands of small studies clustering based on coefficients emerges with meaningful, interpretable groups of respondents, even though the process is purely mechanical and mathematical.

Table 8 shows the positive performing elements for the three-mind-solution. The two-mind-solution is not shown in the interest of space. Table 9 once again shows the AI interpretation of the results, obtained by applying six queries to each of the three mind-sets.

Table 8: Elements which drive TOP (Sounds interesting) for the three mind-sets, emerging from k-means clustering of all the element coefficients from the 50 respondents. Only those elements with at least one coefficient of 2 or higher are shown.

Table 9: AI interpretation of the strong performing elements (6 and higher) by AI for the three-mind-set solution.

In contrast to the data from the Total Panel, and the data from the Self-Defined Segments regarding what the company will do with the segments, the division by the pattern of coefficients shows strong performing coefficients, and meaningful stories which repeat from the two-mind-set solution (data not shown in the interest of space.).

Beyond Interpretation to Understanding Performance at a Glance

A continuing issue emerging from these small-scale studies is ‘How did we do?’ The same question is asked by researchers in most areas, with the underlying issue touching on ‘did the experiment work?’ With Mind Genomics coupled with AI at the very early stages, and available world-wide at the press of a key, the issue becomes the degree to which the study generated anything of value. The notion of ‘value’ is not the personal value of the data to the researcher, nor value in terms of scientific reproducibility and validity, but rather did the effort lead to any strong performing elements. We learn that when we have strong performing elements there is a strong link between an element and the rating question. That linkage is what the researcher seeks in these studies. Knowing that some elements get high scores and other elements get low scores tells the researcher she or he can move in the direction of the high scoring elements. It is in those elements that the relevant issue can be further understood, at least in the minds of the people who are the respondents.

As part of the effort to ‘systematize’ the use of Mind Genomics in this era of easy-to-use techniques empowered by AI, we present the IDT, Index of Divergent Thought.

The objective of IDT is to measure the impact of the elements. The IDT generates a simple index, shown in Table 10. Each positive element is weighted by the relative number of respondents when we consider the entire study as six groups (Total, Mind-Sets 1 and 2 in the 2-Mind-Set solution, and Mind-Sets 1, 2 and 3 in the 3-Mind-Set Solution). Each of the six groups has a specific number of respondents, which add up to 3x the base size, or 3×50, viz. 150. The IDT is simply the weighted sum of the positive coefficients, the weight being the ratio of the number of respondents in a group versus the total number of 150. Note that the two-mind-set solution was developed, but not shown in the interest of space.

Table 10: The IDT, Index of Diverged Thought, showing the performance of the elements, and thus the strength of the thinking behind the specific Mind Genomics study.

The ideal use of the IDT at these early days of implementation is to determine how good was the choice of elements. The IDT for this study is 33.05, on the low side. Keeping in mind that the elements were obtained from AI, and only modestly edited, we have with the IDT a way to quantify the strength of the ideas as they are perceived by people.

Discussion and Conclusions

As emphasized above, this study was undertaken as a study of an experience, the experience being the efforts to learn about a topic new to the senior author, viz., sustainability in the world of retail analytics. The original idea for this study came from a call for papers from the newly founded journal ‘Sustainability.’ The fact that the senior author had little experience with the topic of retail analytics, and indeed scarcely, if ever, thought about the topic, made the study more interesting. As a senior scientist, with 54 years of experience after the PhD., the topic was interesting simply because it asked the question ‘what really could be learned by and contributed by a novice, albeit a novice with professional experience.’

In fact, we learned that our respondents, who are also consumers, can easily grasp the features and uses of retail analytics, and that they reveal three distinct Mind-sets towards retail analytics. Mind-set 1: Focus on price/profitability; Mind-set 2: Focus on innovation/operations; and Mind-set 3: Focus on the outside world behavior. Each of these newly discovered mind-sets educates retail executives with knowledge never known to them before, guidance from consumers about how they might leverage retail analytics for their customers’ benefit and, perhaps, unleashing innovation benefitting both the shopper and the retailer. They can break from past tradition of analyzing from on high what they think is best for their shoppers.

The actual study was easy to implement. The use of the Idea Coach made things easier, although it was not clear to whether these were the ‘right questions’ to ask. The answers were not the issue. Rather, it was the questions, leading to the realization that emphasis is research should be put on asking an appropriate, meaningful question. So many of the AI generated questions seemed real until thought through, with the question of ‘what type of answer would this question engender’

The actual process itself revealed some unexpected benefits. The key benefit might be said to be ‘sharpening’ and improving the question, until one reaches a powerful question. The reality of this single powerful question is important. We are not taught important, seminal questions to ask. And all too often, when these questions are asked, their importance is often unrecognized.

To sum up, this paper demonstrates the increasing ease with which a novice can use computer technology to learn by exploration. The templated version of Mind Genomics makes it possible for the novice to move quickly from issue to empirical results. At the front-end AI provides a Socratic way of creating questions, that create an education on the topic. At the back end, the AI allows the researcher to understand the results from a variety of perspectives, provided by the different queries.

If we were to surmise the potential of the approach, we might find its greatest use in the world of education, to teach [8]. Teaching may not be limited to students, but rather ‘teaching’ here is used in the general sense, to instruct a person about a specific topic [9,10]. The unique combination of AI at the front and back, coupled with real human responses, provides a powerful tool to explore many dimensions of being, from the simplest to the profound, rapidly, iteratively, and with the potential of opening entirely new disciplines as the information from related studies is aggregated into a coherent whole with many different facets.

References

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4. Moskowitz HR (1971) The sweetness and pleasantness of sugars. The American Journal of Psychology, 84: 387-405.
5. Zemel R, Choudhuri SG, Gere A, Upreti H, Deitel Y, et al. (2019) Mind, Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive Viewpoint Typing. In Current Issues and Challenges in the Dairy Industry. Intech Open.
6. Gofman A, Moskowitz H (2010) Isomorphic permuted experimental designs and their application in conjoint analysis. Journal of Sensory Studies 25: 127-145.
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DOI: 10.31038/PSYJ.2023544

Abstract

54 respondents from an internet-based panel across the United States each evaluated uniquely different sets of 24 systematically varied ‘vignettes,’ (combinations of messages) about elderberry wine. The messages were created by artificial intelligence (Idea Coach), and afterwards combined into the vignettes according to an underlying experimental design which prescribed the appropriate combinations to use for subsequent regression analysis. Respondents rated each vignette using a two-dimensional rating scale, one dimension representing fit to the respondent (for me vs not for me), the second dimension representing understandability of the message. The data reveal three mind-sets. The study demonstrated the simplicity, speed, and economics of combining artificial intelligence, experimental design, and subsequent human evaluation. The output becomes a scalable bank of subjective information on a topic which is unfamiliar (elderberry wine), with this bank of information combining Socratic learning in a new topic coupled with feedback from real consumers about the information developed through artificial intelligence.

Introduction

The development of new products in the world of commerce is often costly, error-filled, and unduly long. Some of the issues may result from risk-avoidance, a phenomenon rampant in corporations, especially in slow-moving categories such as foods and beverages. When a company in electronics, for example, fails to avail itself of important technology to create new products that company is likely to suffer, often quickly, as its competitors rush to overtake it, doing so at hot speed. Not so in the world of food, even the world-of food start-ups, where the feeling is that there is not really much risk, that the competition moves slowly, and the technology is really not as valuable as the instincts and intuitions of the entrepreneur or the corporate president. The foregoing holds in classic, multi-layer multi-nationals as well as in the starts powered by the ingenue entrepreneur.

At the same time that the world of food development moves cautiously, there is an evolving world of speed, at almost any price. This world has emerged during the past decades due to the confluence of three factors, respectively the computer for processing, the internet for connection, and most recently artificial intelligence (AI) for rapid ‘thinking’ or at least rapid and seeming intelligent processing of text information in a way which seems intelligent. These three factors are making it possible to create ideas, test these ideas, and even expand them in what figuratively be an ‘eyeblink’ in the corporate timeline. What took hours, days, weeks, now can take minutes and seconds.

With the foregoing paragraphs as background, the author has begun a series of studies, small studies to be sure, on topics of daily life. The approach uses experimental design of ideas, mixtures of ideas presented to the respondent, the ratings of these mixtures revealing how each idea or message ‘drives’ the interest of the respondent. Using these tools of computer, internet, and now artificial intelligence, the author has pushed the study of ideas in the food industry down from a pedestal of scientific perfection to an act that even a grade school student can do, and even master after a moderate amount of practice [1-3].

The Mind Genomics Approach – Steps Towards Rapid Ideation

In order to demonstrate the power of new methods for product design, the author conducted a class experiment in April 2023, with students from the University of Florida in Gainesville. The approach was a DIY (do-it-yourself) approach for an advanced version of conjoint measurement, Mind Genomics. The specifics of Mind Genomics have been presented in detail in various papers published since the early days of the 21^st century. The reader is encouraged to look at the different topics covered. This paper will once again present the method, and the new development enabled by popular and available methods for artificial intelligence using the popular Chat GPT [4-6].

The ingoing, perhaps heretical and counterintuitive assumption, was that one could do a study within two hours, a study beginning with little or no knowledge about a field and emerge after those two hours with deep information about a topic. The topic chosen during the active initial back and forth was ‘elderberry wine,’ a wine of Asian origin (No et. al., 1980). The students who designed study had heard of elderberry wine, but were not familiar with the wine, making the exercise a challenge and enjoyable learning experience. The choice of elderberry wine emerges after about an unmoderated, 20-second class ‘discussion’ about ‘a topic, any topic having to do with foods.’

There is a modest-sized literature about elderberry wine, but a growing one, because of due to evolving consumer interest. At the same time, elderberry has received attention by horticulturists as well in part because of the increasing recognition of its health properties [7-11].

Table 1 presents the input information about the study. The table comes from a summarized report of the study automatically generated at the completion of the field work. The table provides the study title, date, purpose of the study as the researcher defines it, keywords for later sorting, self-profiling attitude questions, the respondent orientation (kept very simple), and the rating scale. All of this information is automatically incorporated into the Excel report.

The Mind Genomics process is templated, following choreographed sets which set up the experiment, run the actual experiment, and automatically analyze the data.

Table 1: Information about the study provided by the Excel report returned to the researcher at the end of the field work.

Introduce to the Process and Select the Topic (Elderberry Wine)

The exercise was set up so that the students would be introduced to the Mind Genomics process through a two-minute ‘elevator pitch’. The class was told that they would choose a topic, run a study, get results, and discuss the preliminary results. The students we unprepared, but as noted above, the decision was made to study elderberry wine. It is important to note that in no way was the topic to be focused on a so-called ‘burning issue’ or ‘hole in’ literature. The topic was selected almost randomly. It was at this point that the study had been registered as ‘elderberry wine’, the class as researchers filled out some checklists on using English as the language, and agreeing to not obtain information that could identify the respondents, except with the permission of the respondents.

Create Four Questions through Idea Coach

Mind Genomics works by a Socratic method, posing questions to obtain answers, combining the answers, and having respondents evaluate the combinations. Figure 1 shows the screen requesting the four questions (left panel), and the four questions actually selected (right panel).

Figure 1: The request for four questions dealing with elderberry wine (left panel), the Idea Coach for generating questions through AI (middle panel) and the four questions selected from the AI suggestions (right panel).

To generate the four questions in Table 1 is generally a function of one’s familiarity with the topic, and the predilection of the research group to come to an agreement. Often the group is unfamiliar with the topic, necessitating what ends up being interminable discussion and delay as the individuals in the group grapple with the appropriate questions to ask. The issue becomes even more vexing when the parties feel that they only have ‘one chance’ to run the experiment. It is at that point, the feeling of one-chance-only, that the participants in the research program end up ‘freezing’, often with the unhappy consequence that the project falls apart.

During the past several years users have continued to request, and eventually insist that the set-up of a Mind Genomics experiment be more ‘user-friendly.’ Almost all suggestions have included something about making it easier to generate questions, and to a lesser degree, to generate answers to the questions. As an aside, it appears that instead of trusting their own intuition and thinking, many individuals prefer ideas that have somehow been ‘vetted.’ This desire to have assistance in creating questions and answers, that assistance provided by an electronic ‘third party’, led to the creation of the Idea Coach. The Idea Coach is simply a set of AI prompts, based upon the ‘squib’ written about the topic. The squib is submitted to the AI program embedded in the setup, and generates 25-30 questions. Table 2 shows 60 unique questions emerging from four passes though the Idea Coach. With 30 questions there should have been 120 questions, but only 60 were different from each other.

Table 2: 60 unique questions emerging from four runs of the Idea Coach, each run returning a set of 30 questions about elderberry wine.

Create Four Answers to Each Question through Idea Coach

Idea Coach once again uses artificial intelligence to generate sets of 15 answers to each question. The researcher can select the answers of interest and edit them. Table 3 shows the answers for the first question (flavor profile) from three consecutive iterations of answers. In this case the 45 answers differ from each other. Figure 2 shows the screenshots of the final set of answers to the four questions selected.

Table 3: Three sets of answers to the first question (flavor profile)

Figure 2: Screen shots of the four answers to each question

The mechanism by which Idea Coach provides the questions and answers remains a trade secret of the company providing the AI system. What is important, however, is the rapid ‘learning’ by the Socratic method, question-and-answer, although here the learning might be in from parallel questions and answers, as the squib generates 25-30 questions, and the question generates 15 answers. One might happily imagine the potential of educating oneself on a topic such as elderberry, simply by two, three, four, or even five iterations of squib → 25-30 candidate questions → 15 candidate answers for each candidate question.

Create an Experimental Design Which Specifies the Combinations of Elements (Answers)

Rather than instructing the respondent to rate each of the 16 elements, one element at a time, the Mind Genomics approach presents combinations of these elements, short descriptions of the wine. Short descriptions are easier to judge because they tell a more complete story than do single elements. The respondent will evaluate a set of 24 vignettes, the aforementioned combinations.

In the experimental design element (viz., answer) appears five times in the 24 vignettes and is absent 19 times. No vignette comprises more than one element from any question. As a consequence, 20 of the 24 vignettes contain one answer from a question, whereas four vignettes of 24 are absent answers. Finally, across all 24 vignettes there are combinations comprising two answers, three answers, and finally four answers, but no vignette with only one answer. This specific design ensures that the researcher can analyze the data using standard statistical tools such as OLS (ordinary least squares) regression [12].

A unique, patented aspect of Mind Genomics is that each respondent evaluates a different set of combinations. The mathematical structure of the combination remains the same, but the specific combinations differ from one respondent to another. This difference is created by a permutation scheme described by [13]. The benefit of the permutation scheme is that the researcher need not know anything about the topic. The experiment allows the researcher to explore a great many combinations, analyze the data at the level of the individual respondent, and as a result uncover patterns that might not even have been imagined at the start of the study.

Create an Orientation and then Create the Rating Question that the Respondent Uses to Evaluate the Vignette. The Orientation is Simple: Read this Description about a New Elderberry Wine. How Do You Feel?

The rating scale actually comprises two dimensions. The first dimension is interest (Not for me vs For me). The second dimension is understandability (don’t get it versus get it). The two dimensions allow the researcher to understand the mind of the respondent more deeply, both in terms of emotion (for me / not for me) and intellect (get it / don’t get it).

Tell us what phrase best fits …after you read this.

1=Not for me … AND just don’t get it

2=Not for me … BUT I do get it

3=Can’t answer

4=For me … BUT just don’t get it

5=For me … AND… I do get it.

Create Self-profiling Questions

The Mind Genomics process enables the respondent to profile herself or himself on attitudes that would not be known from knowing who the response IS. At the start of the study the researcher can create up to eight such self-profiling questions. In addition, the Mind Genomics process automatically asks the respondent’s age and gender. The foregoing information (self-profiling as well as age and gender) are attached to the data provided by the respondent when rating the vignette. The self-profiling information will be used to create subgroups. Figure 3 shows an example of a self-profiling question. The Mind Genomics program allows the researcher to create up to eight such self-profiling questions, each with eight answers.

Figure 3: One of the self-profiling questions

‘Field the Study’ with Respondents

Figure 4 shows the information that the researcher provides to the Mind Genomics program. This information includes the number of respondents, how the respondents will be sourced, and whether or not the researcher wants to ‘privatize’ the respondent data. As of this writing (May 2023), the Mind Genomics platform is very low cost for the artificial intelligence (Idea Coach and summarization in the results). The researcher can either provide her or his own respondents at a low cost ($2/respondent for processing), use a third-party group ($2/respondent for processing), or use a Mind-Genomics approved supplier (Luc.id), with an approximate per respondent fee of $4-$6 for recruiting and processing. The researcher can make the data fully private for an extra $2/respondent. In this way the Mind Genomics fees can be kept very low, a boon to students who can explore a topic in depth, and actually run a small (or large study) on elements of interest, if desired.

Not shown is the set of screens which allow the researcher to specify country, age range, gender, education, income, children, and so forth for the respondent. The typical ‘field time’ to execute the experiment is about 60 minutes for 100-200 easy to find respondents.

Figure 4: The researcher specifies parameters about the field execution

Create the Database

Each record of the database corresponds to a vignette. The record has these columns:

Column 1 – Study name

Column 2 – Respondent identification number

Columns 3,4 – Age, Gender Columns 5-6 – Answers to the two self-profiling questions created by the researcher

Columns 8-23 – One column for each of th 16 elements. When the element is absent from the particular vignette, the cell has the value 0. When the element is present in the particular vignette the cell has the value 1. This is called ‘dummy coding’.

Column 24 – Test order of the vignette. Each respondent rated 24 vignettes, so the test order ranged from the first vignette tested (coded 01) to the last vignette tested (coded 24)

Column 25 – Rating assigned by the respondent to the vignette.

Column 26 – Response time in 100^ths of a second, defined as the elapsed time between the presentation of the vignette on the screen and the time that the respondent assigned the rating.

Column 27-30 – Create four new binary variables by a re-code of the rating to a binary valuer 0 or 100 (as well as the addition of a vanishingly small random number to the newly created binary variable)

Column 27 – Create variable ‘For Me.’ Ratings 5, 4 re-coded to 100, rating 3, 2,1 re-coded to 0.

Column 28 – Create variable ‘Not for Me’. Ratings 1, 2 re-coded to 100, 3,4,5 re-coded to 0.

Column 29 – Create variable ‘Get It’. Ratings 5,2 recorded to 100, rating 4, 3, 1 re-coded to 0.

Column 30 – Create variable ‘Don’t Get It’. Ratings 4,1 re-coded to 100. Ratings 5,3,2 re-coded to 0.

The database is set up for dummy variable regression analysis, either at the level of the individual respondent or at the level of the group. The original experimental design specified 24 different combinations, with the combinations being precisely those which ensure that each element appears equally often, and that each of the 16 elements are statistically independent.

The analysis will focus only on the ‘For Me’ ratings.

Create Equations (Models) Relating the Presence/Absence of Elements to Ratings

The equations are estimated using OLS (ordinary least squares) regression. For this analysis we express the equation in the standard way, using an additive constant:

Binary Variable = k₀ + k₁(A1) + k₂(A2) … k₁₆(D4)

The additive constant, k₀, shows the expected percent of responses to be assigned if there were no elements in the vignette. Of course, by design, all vignettes comprise a minimum of two and a maximum of four elements so that the additive constant should be considered a baseline.

Search for the Patterns

The patterns should emerge from the coefficients for the dependent variable ‘For Me’ (ratings 5 and 4 converted to 100, ratings 1,2 and 3 converted to 0). Table 4 shows the coefficients for total panel and for binary gender (male vs female). Mind Genomics returns with a great many coefficients. Table 4 and the remaining tables of coefficients (for mind-sets) show only the positive coefficients, viz, those 2 or higher.

Table 4: Performance of the elements by total and gender using R54 (For me) as the dependent variable. Only positive coefficients, 2 or higher, are shown.

The additive constant gives a sense of the percent of responses that would be 5 or 4 for vignettes that were empty, viz, absent elements. By design this is not possible, but the regression process can estimate this additive constant, which is typically considered to be a correction factor (Burton, 2021). Table 4 shows the additive constant to be about 46-47, suggesting that half of the time we can expect a rating of 4 or 5 for vignette or concept about elderberry wine, even when no elements to deeper detail.

Table 4 suggests that in terms of ‘interest’ (viz., For Me), a few elements perform strongly, and in fact elements that might not have been even thought of without the use of the AI-powered Idea Coach. These are ‘sensory testing’ for males, and ‘restaurant’ and ‘holographic labels’ for females. The benefit of creating elements with the assistance of AI is just this ‘out of the box’ thinking, with the researcher having the power to accept the suggestion or reject the suggestion by simple choice, and indeed to test the suggestion again in an easily run of the study with some new elements, new respondents.

Uncovering New-to-the-World Mind-Sets through Clustering

It is in the DNA of the scientific mind to look for basic causes, fundamentals of a situation. Although scientists and consumer researchers have attempted to develop profiles of archetypes, idealized profiles, these archetypes are too general, and fail to capture the granularity of everyday experience. Indeed, any attempt to divide people from the ‘top down’ is destined to fail because at the level of actual experience there are so many idiosyncratic factors that the archetypes simply do not have the ability to address [14].

The Mind Genomics approach works in the opposite direction, starting at the level of granular for a specific issue or situation, looking at the different dimensions of that granular situation, testing alternatives or expressions of each dimension, and then uncovering parallel groups of individuals or clusters for that situation. The clusters can be thought of as archetypes, not general ones, but archetypes of a specific situation, mind-sets in the language of Mind Genomics.

The statistics of Mind Genomics readily enable the researcher to discover these mind-sets, even without any ingoing knowledge. The approach simply creates individual level models of the type above shown for the total panel, or for any subgroup. Each individual generates a model, a model which is statistically valid because the 24 vignettes for each respondent had been created according to an underlying experimental design. The 24 vignettes are precisely arrayed to allow for OLS regression to be done on the data from each respondent. Each respondent produces 16 coefficients and an additive constant. Afterwards, the respondents are clustered by k-means clustering [15] first into two non-overlapping and exhaustive groups, (2-mindset solution), and then into three non-overlapping and exhaustive solutions (3-indset solution).

Clustering it follows purely mathematical criteria, e.g., minimize the sum of ‘distances’ between people in a cluster while at the same time maximize the distances between the centroids of the different clusters. It is left to the researcher to choose the number of clusters or mind-sets, and to name each cluster. Two good criteria are parsimony (fewer clusters are better), and interpretability (the clusters must tell a reasonably clear story).

For Mind Genomics studies, the measure of distance is the expression (1-Pearson Correlation). The Pearson Correlation coefficient measures the strength and nature of the linear relation between two sets of numbers, in our case the numbers coming from the 16 coefficients. The distance is small, viz., 0, when the Pearson correlation is +1 (1-1 = 0), occurring when the two sets of coefficients are parallel to each other. The distance is greatest, viz., 2 when the Pearson correlation is -1 (1 – 1 = 2), occurring when the two sets of coefficients go in precisely opposite directions.

Mind Genomics clustering usually reveals quite simple groups, the patterns often clear, ‘jumping out’ at the researcher. Table 5 shows the strong performing elements for the two and then the three mind-sets (abbreviated MS). The two mind-sets solution shows a very simple pattern, namely that which is familiar (venue for MS1 versus flavor for MS2). The three-mind-set solution is more intriguing, suggesting Label, Information, Venue, respectively. The three-mind-set solution is not perfect, since there are some strong-performing elements appealing to the mind-set slightly ‘off’ from the main interest of the mind-set.

Table 5 once again shows the ability of the OLS regression to uncover relevant coefficients, often coefficients which ‘make sense’ in their similarity to each other for a specific mind-set.

Table 5: Performance of the elements by total and both two and three mind-sets. gender using R54 (For me) as the dependent variable. Only positive coefficients, 2 or higher, are shown.

How Good are the Results?

Experienced researchers working in the world of inferential statistics and hypothesis testing measure their ‘performance’ by the likelihood that their hypothesis has not been falsified (Sprenger, 2011). The hypothetico deductive system of science is geared toward the creation, testing, acceptance/abandonment of hypothesis as the science moves slowly along. As famed scientist Max Planck opined ‘science advances one funeral at a time’ [16]. an experienced-based aphorism similar to the somewhat longer, more poetic but equally powerful idea from Tennyson’s Le morte d’Arthur “The old order changeth, yielding place to new, … Lest one good custom should corrupt the world “ (Sider, 2013).

With the evolution of research and its introduction into the world of education and application, the introductions often geared to ‘newbies’ (people without research experience), a common question is ‘how did we do?’. These newbies, students, others, do not have the wealth of experience, the years of data analysis, and the know-how about going to the extant ‘literature’ to compare their findings with what has been done. These newbies, aspiring researchers, need reinforcement about their work, e.g., a ‘score’ which tells them just how good their data are. In our over-measured society people use scores as an index of performance and a measure of growth.

One of the developments of Mind Genomics is the IDT, the index of divergent thought. The organizing principal underneath the IDT is that the positive coefficients, or more correctly the weighted positive coefficients, show how strongly the element ‘drives’ the rating. In other words, the IDT show how ‘on target’ the researcher has been by choosing elements to drive a dependent variable, that dependent variable here being ‘For Me.’

The IDT computations appear in Table 6. The total panel results account for 1/3 of the weight; the two mind-sets together account for 1/3 of the weight, and finally the three mind-sets together account for 1/3 of the weight. The stronger the performance of the coefficient for the total panel, the higher will be the IDT because that single high coefficient will in turn be multiplied by the value 0.33 for the total panel. In contrast, consider the value of that same high coefficient, but this time for MS 2 of 3, with 14 respondents, and a weight around 0.10. The contribution will be a lot lower. For this study the IDT is 46, reasonable. Unpublished values for the IDT in other studies have ranged from a high around to a low around 20.

Table 6: The IDT (Index of Divergent Thought)

Were the researcher to systematically vary aspects of the study and then measure the IDT for each aspect, the studies would move beyond informing about the world, and become a measure of the ‘impact’ of the different variables. Perhaps, most important for students is a measure of how well they understand the topic based upon the elements they select, the rating scale they use, the respondents they choose, and their experience as they iterate from one study to the next with clear human feedback. Much remains to be done.

Summarizing the Results Using Artificial Intelligence

The original objective of the study was to demonstrate the speed, power, and cost of Mind Genomics. As such, one of the goals was to see how quickly the key insights could be given to the reader in a format immediately ready for further efforts, including application or follow-on research. The first requirement was that the insight to be presented had to emerge in a robust way from the data, thus linking the insights to the actual experiment. The second requirement was that the insight had to be multifaceted, produced by clearly stated queries. The third requirement is that the insights had to be scalable, emerging from a few to many queries (many being > 10), with the insights emerging automatically. The effort stopped short of automatically creating a preliminary summarization document in the form of a ‘working paper’, but that next step is increasingly within reach. This first step to summarize the results used six queries provided to the AI program, with the instruction to look only at elements scoring +6 or higher for the subgroup.

The six summarization queries were submitted to the AI program, with the summarization done for each defined subgroup in the population, and done twice, once for the ‘TOP” (ratings 5,4 → 100), once for the ‘BOT’ (ratings 1,2→ 100). The defined subgroups were gender, age, response to the various questions in the self-defining questionnaire at the start of the study and finally to the two and the three mind-set solutions. Table 7 shows the summarization of the results for each of the three mind-sets for the TOP values (Rating 5,4, for ME).

Table 7: AI summarization of the three mind-sets

Discussion and Conclusions

The tradition of scientific research has become increasingly professionalized during the past centuries. What started out as the explorations of amateurs into a world hardly known has evolved into the world of science and academe that we know today., replete with societies, with journals, with the inevitable issue of who can publish what, and of course what exact constitutes publishable work. If that is not sufficient, the issues emerging involve the invisible networks of researchers who know each other and give each other help or in some unhappy cases just the opposite. And finally, there is the issue of funding research, funding publication and the need to survive the publish-or-perish world. In the words of an unnamed colleague, ‘we are all fighting for a sliver of the unpredictable funding pie.’

Within this world of discomfort and competitive behavior, the efforts of students, aspiring professionals, end up being crushed more often than not by an invisible college and rules of what makes science valid. All too often, the focus on being safe and correct ends up discouraging the researcher. Within this world, the Mind Genomics effort produces a system which expands the vision and hope of the amateur researcher, providing the potential of systematized, scientific, often even interesting exploration. It is within that spirit that this paper is presented, not so much as the convenient but hardly explored topic of elderberry wine as much as the exploration of what people just might do if given tools to empower their curiosity.

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