Positive Technology Journal

In 1999, Joseph Pine and James Gilmore wrote a seminal book titled “The Experience Economy” (Harvard Business School Press, Boston, MA) that theorized the shift from a service-based economy to an experience-based economy.

According to these authors, in the new experience economy the goal of the purchase is no longer to own a product (be it a good or service), but to use it in order to enjoy a compelling experience. An experience, thus, is a whole-new type of offer: in contrast to commodities, goods and services, it is designed to be as personal and memorable as possible. Just as in a theatrical representation, companies stage meaningful events to engage customers in a memorable and personal way, by offering activities that provide engaging and rewarding experiences.

Indeed, if one looks back at the past ten years, the concept of experience has become more central to several fields, including tourism, architecture, and – perhaps more relevant for this column – to human-computer interaction, with the rise of “User Experience” (UX).

The concept of UX was introduced by Donald Norman in a 1995 article published on the CHI proceedings (D. Norman, J. Miller, A. Henderson: What You See, Some of What's in the Future, And How We Go About Doing It: HI at Apple Computer. Proceedings of CHI 1995, Denver, Colorado, USA). Norman argued that focusing exclusively on usability attribute (i.e. easy of use, efficacy, effectiveness) when designing an interactive product is not enough; one should take into account the whole experience of the user with the system, including users’ emotional and contextual needs. Since then, the UX concept has assumed an increasing importance in HCI. As McCarthy and Wright emphasized in their book “Technology as Experience” (MIT Press, 2004):

“In order to do justice to the wide range of influences that technology has in our lives, we should try to interpret the relationship between people and technology in terms of the felt life and the felt or emotional quality of action and interaction.” (p. 12).

However, according to Pine and Gilmore experience may not be the last step of what they call as “Progression of Economic Value”. They speculated further into the future, by identifying the “Transformation Economy” as the likely next phase. In their view, while experiences are essentially memorable events which stimulate the sensorial and emotional levels, transformations go much further in that they are the result of a series of experiences staged by companies to guide customers learning, taking action and eventually achieving their aspirations and goals.

In Pine and Gilmore terms, an aspirant is the individual who seeks advice for personal change (i.e. a better figure, a new career, and so forth), while the provider of this change (a dietist, a university) is an elictor. The elictor guide the aspirant through a series of experiences which are designed with certain purpose and goals. According to Pine and Gilmore, the main difference between an experience and a transformation is that the latter occurs when an experience is customized:

“When you customize an experience to make it just right for an individual - providing exactly what he needs right now - you cannot help changing that individual. When you customize an experience, you automatically turn it into a transformation, which companies create on top of experiences (recall that phrase: “a life-transforming experience”), just as they create experiences on top of services and so forth” (p. 244).

A further key difference between experiences and transformations concerns their effects: because an experience is inherently personal, no two people can have the same one. Likewise, no individual can undergo the same transformation twice: the second time it’s attempted, the individual would no longer be the same person (p. 254-255).

But what will be the impact of this upcoming, “transformation economy” on how people relate with technology? If in the experience economy the buzzword is “User Experience”, in the next stage the new buzzword might be “User Transformation”.

Indeed, we can see some initial signs of this shift. For example, FitBit and similar self-tracking gadgets are starting to offer personalized advices to foster enduring changes in users’ lifestyle; another example is from the fields of ambient intelligence and domotics, where there is an increasing focus towards designing systems that are able to learn from the user’s behaviour (i.e. by tracking the movement of an elderly in his home) to provide context-aware adaptive services (i.e. sending an alert when the user is at risk of falling).

But likely, the most important ICT step towards the transformation economy could take place with the introduction of next-generation immersive virtual reality systems. Since these new systems are based on mobile devices (an example is the recent partnership between Oculus and Samsung), they are able to deliver VR experiences that incorporate information on the external/internal context of the user (i.e. time, location, temperature, mood etc) by using the sensors incapsulated in the mobile phone.

By personalizing the immersive experience with context-based information, it might be possibile to induce higher levels of involvement and presence in the virtual environment. In case of cyber-therapeutic applications, this could translate into the development of more effective, transformative virtual healing experiences.

Furthermore, the emergence of "symbiotic technologies", such as neuroprosthetic devices and neuro-biofeedback, is enabling a direct connection between the computer and the brain. Increasingly, these neural interfaces are moving from the biomedical domain to become consumer products. But unlike existing digital experiential products, symbiotic technologies have the potential to transform more radically basic human experiences.

Brain-computer interfaces, immersive virtual reality and augmented reality and their various combinations will allow users to create “personalized alterations” of experience. Just as nowadays we can download and install a number of “plug-ins”, i.e. apps to personalize our experience with hardware and software products, so very soon we may download and install new “extensions of the self”, or “experiential plug-ins” which will provide us with a number of options for altering/replacing/simulating our sensorial, emotional and cognitive processes.

Such mediated recombinations of human experience will result from of the application of existing neuro-technologies in completely new domains. Although virtual reality and brain-computer interface were originally developed for applications in specific domains (i.e. military simulations, neurorehabilitation, etc), today the use of these technologies has been extended to other fields of application, ranging from entertainment to education.

In the field of biology, Stephen Jay Gould and Elizabeth Vrba (Paleobiology, 8, 4-15, 1982) have defined “exaptation” the process in which a feature acquires a function that was not acquired through natural selection. Likewise, the exaptation of neurotechnologies to the digital consumer market may lead to the rise of a novel “neuro-experience economy”, in which technology-mediated transformation of experience is the main product.

Just as a Genetically-Modified Organism (GMO) is an organism whose genetic material is altered using genetic-engineering techniques, so we could define aTechnologically-Modified Experience (ETM) a re-engineered experience resulting from the artificial manipulation of neurobiological bases of sensorial, affective, and cognitive processes.

Clearly, the emergence of the transformative neuro-experience economy will not happen in weeks or months but rather in years. It will take some time before people will find brain-computer devices on the shelves of electronic stores: most of these tools are still in the pre-commercial phase at best, and some are found only in laboratories.

Nevertheless, the mere possibility that such scenario will sooner or later come to pass, raises important questions that should be addressed before symbiotic technologies will enter our lives: does technological alteration of human experience threaten the autonomy of individuals, or the authenticity of their lives? How can we help individuals decide which transformations are good or bad for them?

Answering these important issues will require the collaboration of many disciplines, including philosophy, computer ethics and, of course, cyberpsychology.

The field of artificial intelligence (AI) has undergone a dramatic evolution in the last years. The impressive advances in this field have inspired several leaders in the scientific and technological community - including Stephen Hawking and Elon Musk - to raise concerns about a potential domination of machines over humans.

While many people still think about AI as robots with human-like characteristics, this field is much broader and include a number of diverse tools and applications, from SIRI to self-driving cars, to autonomous weapons. Among the key innovations in the AI field, IBM’s Watson computer system is certainly one of the most popular.

Developed within IBM’s DeepQA project lead by principal investigator David Ferrucci, Watson allows answering questions addressed in natural language, but also features advanced cognitive abilities such as information retrieval, knowledge representation, automatic reasoning, and “open domain question answering”.

Thanks to these advanced functions, Watson could compete at the human champion level in real time on the American TV quiz show, Jeopardy. This impressive result has opened several potential business applications of so-called “cognitive computing”, i.e. targeting big data analytics problems in health, pharma, and other business sectors. But psychology, too, may be one of the next frontier of the cognitive computing revolution.

For example, Watson Personality Insight is a service designed to automatically-generate psychological profiles on the basis of unstructured text extracted from mails, tweets, blog posts, articles and forums. In addition to a description of your personality, needs and values, the program provides an automated analysis of “Big Five” personality traits: openness, conscientiousness, extroversion, agreeableness, and neuroticism; all these data can then be visualized in a graphic representation. According to IBM’s documentation, to give a reliable estimate of personality, the Watson program requires at least 3,500 words, but preferably 6,000 words. Furthermore, the content of the text should ideally reflects personal experiences, thoughts and responses. The psychological model behind the service is based on studies showing that frequency with which we use certain categories of words can provide clues to personality, thinking style, social connections, and emotional stress variations.

Clearly, many psychologists (and non-psychologists, too) may have several doubts about the reliability and accuracy of this service. Furthermore, for some people, collecting social media data to identify psychological traits may lead to Orwellian scenarios. Although these concerns are understandable, they may be mitigated by the important positive applications and benefits that this technology may bring about for individuals, organizations and society.

The last decade has witnessed a tremendous advance in technological innovations. This is also thanks to the growing diffusion of open innovation platforms, which have leveraged on the explosion of social network and digital media to promote a new culture of “bottom-up” discovery and invention.

An example of the potential of open innovation to revolutionize technology and science is provided by online crowdfunding sites for creative projects, such Kickstarter and Indiegogo. In the last few years, these online platforms have supported thousands of projects, including extremely innovative products such as the headset Oculus, which has contributed to the renaissance of Virtual Reality.

Incentivized competitions represent a further strategy for engaging the public and gathering innovative ideas on a global scale. This approach consists in identifying the most interesting challenges and inviting the community to solve them.

One of the first and most popular incentivized competitions is the Ansari X-Prize, celebrating this year its 10th anniversary. Funded by the Ansari family, the Ansari X-Prize challenged teams from around the world to build a reliable, reusable, privately financed, manned spaceship capable of carrying three people to 100 kilometers above the Earth's surface twice within two weeks. The prize was awarded in 2004 to Mojave Aereospace Ventures and since then, the award has contributed to create a new private space industry. Recently, X-Prize has introduced spin-off for-profit venture HeroX, a kind of “Kickstarter” of X-Prize-type competitions. The platform allows anyone to post their own competition.

Those who think they have the best solution can then submit their entries to win a cash prize. Another successful incentivized contest is Qualcomm Tricorder X Prize, offering a US$7 million grand prize, US$2 million second prize, and US$1 million third prize to the best among the finalists offering an automatic non-invasive health diagnostics packaged into a single portable device that weighs no more than 5 pounds (2.3 kg), able to diagnose over a dozen medical conditions, including whooping cough, hypertension, mononucleosis, shingles, melanoma, HIV, and osteoporosis.

Incentivized competitions have proven effective in supporting the solution to global issues and develop powerful new visions of the future that can potentially impact the lives of billions of people. The reason of such effectiveness is related to the “format” of these competitions.

Open idea contests include clear and well-defined objectives, which can be measured objectively in terms of performance/outcome, and a significant amount of financial resources to achieve those objectives. Further, incentive competitions target only “stretch goals”, very ambitious (and risky) objectives that require very innovative strategies and original methodologies in order to be addressed.

Incentive competitions are also very “democratic”, in the sense that they are not limited to academic teams or research organizations, but are open to the involvement of large and small companies, start-ups, governments and even single individuals.

Thanks to the pervasive diffusion of social media and the increasing affordability of smartphone and wearable sensors, psychologists can gather and analyse massive quantities of data concerning people behaviours and moods in naturalistic situations.

The availability of “big data” presents psychologists with unprecedented professional and scientific opportunities, but also with new challenges. On the business side, for example, a growing number of tech-companies are hiring psychologists to help make sense of huge data sets collected online from their actual and prospective customers.

The job description of a “data psychologist” not only requires perfect mastery of advanced statistics, but also the ability to identify the kinds of behaviours that are most useful to track and analyse, in order to improve products and business strategies. Psychological research, too, may be revolutionized from emerging field of big data. Until recently, online research methods were mostly represented by web experiments and online survey studies.

Example of topic areas included cognitive psychology, social psychology, but also health psychology and forensing psychology (for an updated list of psychological experiments on the Internet see this useful resource by the Hanover College Psychology Department).

However, the emergence of advanced cloud-based data analytics has provided psychologists with powerful new ways of studying human behaviour using digital footprints. An interesting example is CrowdSignal, a crowdfunded mobile data collection campaign that aims at building the largest set of longitudinal mobile and sensor data recorded from smartphones and smartwatches available to the community. As reported in the project’s website, the final dataset will include geo-location, sensor, system and network logs, user interactions, social connections, communications as well as user-provided ground truth labels and survey feedback, collected from a demographically diverse pool of Android users across the United States.

A further interesting service that well exemplifies the scientific potential of social data analytics is the “Apply Magic Sauce PredictionAPI” developed by the Psychometrics Centre of the University of Cambridge. According to the Cambridge researchers, this algorithm allows predicting users’ personality traits based on Facebook interactions (i.e., Facebook Likes). To test the validity of the tool, the team compared the predictions generated by computer algorithms and the personality judgments made by human. The results, which were reported on Proceedings of the National Academy of Sciences (Youyou et al., 2015, PNAS, 112/4, pp. 1036–1040), showed that the computers’ judgments of people’s personalities based on their digital behaviors were more accurate than judgments made by their close others or acquaintances.

However, the emergence of “big data psychology” presents also big challenges. For example, it is the advantages of this approach for business and research should take into account the issues related to ethical, privacy and legal implications that are unavoidably linked to the collection of digital footprints. On the methodological side, it is also important to consider that quantity (of data) is not synonimous with quality (of data interpretation).

In order to create meaningful and accurate models from behavioural logs, one needs to consider the role played by contextual variables, as well as the possible data errors and spurious correlations introduced by high dimensionality.