**Luciano Gamberini Anna Spagnolli Giulio Jacucci Benjamin Blankertz Jonathan Freeman (Eds.)**

# **Symbiotic Interaction**

**5th International Workshop, Symbiotic 2016 Padua, Italy, September 29–30, 2016 Revised Selected Papers**

# Lecture Notes in Computer Science 9961

Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

# Editorial Board

David Hutchison Lancaster University, Lancaster, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Zurich, Switzerland John C. Mitchell Stanford University, Stanford, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Dortmund, Germany Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbrücken, Germany More information about this series at http://www.springer.com/series/7409

Luciano Gamberini • Anna Spagnolli Giulio Jacucci • Benjamin Blankertz Jonathan Freeman (Eds.)

# Symbiotic Interaction

5th International Workshop, Symbiotic 2016 Padua, Italy, September 29–30, 2016 Revised Selected Papers

Editors Luciano Gamberini University of Padua Padua Italy

Anna Spagnolli University of Padua Padua Italy

Giulio Jacucci University of Helsinki Helsinki Finland

Benjamin Blankertz TU Berlin Berlin, Berlin Germany

Jonathan Freeman Goldsmiths University London UK

ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-319-57752-4 ISBN 978-3-319-57753-1 (eBook) DOI 10.1007/978-3-319-57753-1

Library of Congress Control Number:<PHONE_NUMBER>

LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI

© The Editor(s) (if applicable) and The Author(s) 2017. This book is an open access publication.

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# Preface

The Symbiotic Workshop is the primary venue for presenting scientific works dealing with the symbiotic relationships between humans and computers and for discussing the nature and implications of such relationships.

This volume collects the papers presented at the 5th International Workshop on Symbiotic Interaction, which was held in Padua (Italy) during September 29–30, 2016. Previous editions of the workshop were held in Padua, London, Helsinki, and Berlin. The proceedings are published this year with an open access license by Springer, to improve their accessibility and dissemination.

The workshop was co-sponsored by the MindSee project (http://mindsee.eu, FP7- ICT n. 611570) and by the Department of General Psychology of the University of Padua and connected to the Open Innovation Days, a three-day national event organized by the University of Padua and the leading Italian economic newspaper IlSole-24Ore. It also benefited from the hospitality of Comune di Padova (Padua Municipality) in its historical city hall.

All papers were received in response to a public call and underwent at least two rounds of reviews: In the first round, three members of the scientific committee reviewed each paper double-blind; the 13 accepted papers were then revised by the authors and iteratively checked by the proceedings editors. This led to an acceptance rate of 50%, which is much lower than in the previous editions of this workshop, and to a multiple-step quality check that is the standard for scientific journals more than for conference proceedings. The paper "Prediction of Difficulty Levels in Videogames from Ongoing EEG" by L. Naumann, M. Schultze-Kraft, S. Daehne, and B. Blankertz was awarded best presentation, based on the audience preferences.

In addition to the papers selected through the process described above, the proceedings also include the short abstracts of the presentations by the three keynote speakers (Steve Benford, Hans Gellersen, Sid Kouider), the report of an interdisciplinary panel on the risks of symbiotic systems involving five experts (Mauro Conti, Jonathan Freeman, Giorgia Guerra, David Kirsh, and A. van Wynsberghe), and an introductory note from the two Symbiotic 2016 co-chairs. Some of the posters included in the program were expanded and published in a special issue of the on-line journal PsychNology (www.psychnology.org).

We would like to express our gratitude to the colleagues who attended the conference, submitted their work, and reviewed the submissions, for their ideas, attention, and time.

December 2016 Luciano Gamberini Anna Spagnolli Benjamin Blankertz Giulio Jacucci Jonathan Freeman

# Organization

#### Program Committee

#### Additional Reviewers

Lal, Chhagan Lazzeretti, Riccardo

# Keynote Speakers Abstracts

# Rethinking Eye Gaze for Symbiotic Human-Computer Interaction

Hans Gellersen

Lancaster University, Lancaster, UK

Eye tracking has a long history and its use for human-computer interaction predates the ubiquitous computing era. While the technology has been maturing and become affordable for widespread use, there has not been much innovation in the use of eye gaze for interaction. For over 25 years, gaze pointing has remained the prevailing usage paradigm, although it overloads the sensory role of the eyes with a control function. In this talk I will present work that explores new ways of using eye movement for interaction. I will discuss gaze and touch: how hands and eyes can naturally work together; gaze and motion: how the natural gaze-following of moving stimuli enables new types of interface; and gaze and games: how gaze can be social and fun.

# Neural Markers of Perceptual Consciousness

Sid Kouider

#### Ecole Normale Supérieure LSCP, Paris, France

Consciousness is one the most intriguing phenomenon in contemporary science. After a long period of denial, scientists are now actively trying to understand how the brain gives rise to our conscious experience.  This lecture describes how scientists are tackling this difficult issue and how much progress has been made so far in determining the neural structures and mechanisms that are responsible for consciousness. I will focus on three main issues. First, I will address recent findings showing that attention and consciousness, classically thought to be equivalent phenomena, are subserved by distinct brain functions. Secondly, I will address how much information can be processed by the brain in the absence of consciousness, by looking at neural responses to stimuli presented either subliminally or during sleep. Finally, I will address how neural signatures of consciousness can be used when verbal report is impossible, in animals, vegetative patients and preverbal infants.

# On the Pleasures of Giving Up Control

Steve Benford

University of Nottingham, Nottingham, UK

If there is one thing that rollercoasters teach us it is that giving up control to the machine can be a thrilling experience. I'd like to invite you, the audience, to strap yourselves in and join me on a ride through creative and entertaining examples of relinquishing control to 'the machine'. From the thrills and spills of a breath-controlled bucking bronco, to a meditative brain-controlled movie, to musicians improvising around music recognition systems, I will seek to illustrate the creative possibilities of partial and negotiated control – both of machines and ultimately of our own bodies and thoughts. I will introduce new taxonomies of control as well as the idea of deliberately designing uncomfortable interactions as a route to entertainment, enlightenment and social bonding.

# Contents

XIV Contents

# **Towards a Definition of Symbiotic Relations Between Humans and Machines**

Luciano Gamberini(✉) and Anna Spagnolli

Department of General Psychology & Human Inspired Technologies Research Centre, Padua University, Padua, Italy<EMAIL_ADDRESS>
**Abstract.** What are symbiotic systems? In this short paper, the workshop chairs describe the focus of this workshop and elaborate on the definition of symbiotic human-machine relation.

**Keywords:** Symbiotic system · Human-computer interaction

Symbiotic relationships are probably most known from biology. In a symbiotic rela‐ tionship between animals and/or plants, some organism closely interacts with another type of organism to ensure its own survival, so that both end up sharing the same habitat [3]. This biological phenomenon, exemplifying a very tight kind of relationship, is used metaphorically1 to characterize a growing part of the current human-machine relations, where machines collect users' data that are not even available to the human conscious cognition (e.g. brain signals, social data collected on the web), and then elaborate solu‐ tions and make decisions based on such data. Here machines are so interconnected to humans that the term interaction sounds like an understatement [13].

After the pioneering discussion on men-computer [*sic*] symbiosis stimulated in the early 60 by J.C.R. Licklider [9], the reference to symbiotic human-machine interaction appeared again around 2013 in a European Union ICT workprogram stressing that concept2 and has grown ever since, as is partly documented in the Symbiotic Interaction workshops and proceedings [2, 5]. Surely, in the field of human-computer interaction as well as in philosophy and sociology of technology, the close connection between humans and machines has been emphasized several times: by the concepts of artifacts and embodied cognition, where the propriety of a technology depends on the users' needs and the users' ability depends in turn on the tools s/he is endowed with (e.g., [7]); by the notion of sociotechnical systems, where a technology inevitably includes some symbolic, politic elements as part of the package (e.g., [8]); by the idea of humans as hybrids or cyborgs, which finds real-life incarnations in medical prosthesis and augmen‐ tation devices (e.g., [1]). However, the term symbiotic emphasizes a noteworthy change

1 Of course, a metaphor is able to illuminate some aspects of a domain by similarity with some other domain [12], but not all aspects. Where the biological metaphor stops is in referring to two leaving organisms in partnership, since a symbiotic technology is not credited to share the

same needs (and rights) as the human being to which it is joined. 2 http://cordis.europa.eu/fp7/ict/docs/ict-wp2013-10-7-2013-with-cover-issn.pdf.

© The Author(s) 2017

L. Gamberini et al. (Eds.): Symbiotic 2016, LNCS 9961, pp. 1–4, 2017.

DOI: 10.1007/978-3-319-57753-1\_1

in the interdependence between humans and machines, pointing to an intimate kind of relation that is made possible by the synergic advances in physiological computing, biometrics, sensing technologies, and machine learning, often combined with the ubiq‐ uity of networked devices. Users do not necessarily need to be aware of what is happening while machines help themselves with information, since this process mostly occurs implicitly [6], relying on human biosignals that are out of grasp to the individual user (e.g., [10]) or on billions of traces left by users of networked devices. A symbiotic device does not need to be reprogrammed or fine-tuned: on the contrary, it can learn, sense the context, evolve in close relation with the environment by analyzing the conse‐ quences of processes jointly started with humans.

So, although very different in purpose and tradition, all the "symbiotic advances" converge in technological products that tap directly the user to get input information. and evolve their own intelligence on the basis of their performance. The pervasiveness, ubiquity and consequentiality of such devices in everyday life justify their being singled out, monitored and reflected upon. The adoption of a dedicated label for such devices, such as 'symbiotic', is meant to encourage such process.

Elaborating on the description of symbiotic interaction first proposed in 2014 [4], we can identify *symbiotic systems* as those computerized devices that can autonomously fetch information from the users, interpret such information to model the user and then make decisions with the ultimate goal of simplifying the users' task, clarifying possible issues, or refining a service for them (e.g., recommendations); and finally to evolve and become more efficient by learning from previous interaction. To represent this process in terms of human-machine interaction, we can extend Norman's model of action [11] into something similar to what is shown in Fig. 1.

**Fig. 1.** A modification of Donald Normans action model to include a symbiotic system.

Since this process is meant to release the user from the burden of providing the information necessary to enjoy some improved, tailored service, it is in continuity with HCI principle of usability and intuitiveness. However, this process works so far in the background to become elusive and so peculiar in its interpretation of the users' goals to misjudge them. There is room for reflection then on the risks for users, let alone the clear advantages brought about by symbiotic machines. Advantages will be described by almost all chapters in this book, each one illustrating some applications of symbiotic technology; risks will be highlighted and reflected upon in the next chapter.

**Acknowledgments.** The present reflections benefit from the work carried out within the EU project MindSee (grant agreement n. 611570).

#### **References**

#### 4 L. Gamberini and A. Spagnolli

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Adapting the System to Users Based on Implicit Data: Ethical Risks and Possible Solutions

Anna Spagnolli1(&) , Mauro Conti2 , Giorgia Guerra3 , Jonathan Freeman4 , David Kirsh5 , and Aimee van Wynsberghe6

1 Human Inspired Technologies Research Centre and Department of General Psychology, Padua University, Padua, Italy<EMAIL_ADDRESS>2 Human Inspired Technologies Research Centre and Department of Mathematics, Padua University, Padua, Italy<EMAIL_ADDRESS>3 Departmentof Political Science, Law and International Studies, Padua University, Padua, Italy<EMAIL_ADDRESS>4 Goldsmiths College, University of London, London, UK<EMAIL_ADDRESS>5 University of California at San Diego, San Diego, USA<EMAIL_ADDRESS>6 Unversity of Twente, Enschede, Netherlands<EMAIL_ADDRESS>
Abstract. Symbiotic systems are systems that gather personal data implicitly provided by the user, derive a profile/model of the user from such data and adjust their output/service according to their notion of what would be desirable to the user thus modeled. Because of these three characteristics, symbiotic systems represent a step forward towards facilitated, simplified, user-friendly digital devices, or do they? Here we propose three cases describing realistic applications of symbiotic systems that potentially encapsulate some serious risk to their users. Experts of five different domains (i.e., ethics, security, law, human-computer interaction and psychology) dissect each case to identify the risks to the users and derive some possible minimization strategies. This panel aims at contributing to a beneficial development of symbiotic systems as it can be achieved by increasing users' discernment and awareness of their consequences for society and everyday life.

Keywords: Symbiotic system Implicit data Ethics Security Design Risks Information leakage Privacy Awareness

### 1 Introduction

The increasing pervasiveness and refinement of systems that can acquire personal data from users based on the users' own actions on the system - or actions in the environment where the system sensors are positioned - marks a qualitative, discrete change in the way in which the human interaction with technologies articulates. The label 'symbiotic systems' is meant to mark such a change, to make it visible, to single it out from a continuous line of technological development in physiological computing, machine learning, and sensing devices.

Symbiotic technologies establish a symbiotic relationship with the human users. They extract information about the users' state, behaviors or preferences, and adapt their services to the users' profile thereby created. Figure 1 illustrates the rationale of this process: a human who interprets his/her relation with the environment and acts consequently is tapped onto by a prosthetic system that can interpret the users' state and response and can plan interventions on the environment based on a set of built-in criteria. This model highlights three main characteristics of symbiotic technologies:

This scheme can foresee several variations: the symbiotic system can get information from many human beings instead of just one, thanks to current network technology and sensors; and the users of the system can be different from the humans giving out implicit information. These variations notwithstanding, the core of the system remains centered on the capturing/modeling/deciding process that makes symbiotic systems able to bypass the user to which they are connected.

Fig. 1. A schematic representation of the symbiotic system tapping into a human system to by-pass its detecting/modeling/deciding processes.

On part of the users feeding the system with personal information at low or no cost, the symbiotic relation is a transaction out of which direct or indirect benefits are expected, mostly in the form of having their tasks simplified, their peculiarities acknowledged and their preferences indulged by the system. From this point of view, symbiotic systems bring to the next level the notion of a user-centered system, where the system tailors to the user'state without the user even needing to ask. At the same time, however, the exact notion of what makes a system 'user-centred' is questioned by symbiotic systems, given the possibility that such systems - if incautiously designed create serious security, ethic, legal and psychological risks to the user. While much debate on the risks of technologies merging with humans focuses on the bodily level of such merge (for example, humans with x-ray sight, robots with realistic emotional responses or networked machines taking control of human society [2]), the merge is already occurring pervasively at a more functional, information level. Recently, for instance, concerns have been raised about the ethics risks of merging Virtual Reality analytics and social networks for persuasive purposes [11] or to gather face recognition data in VR peripherals to allegedly customize the users' experience [5]. This chapter reports the reflections on such possible risks made by an interdisciplinary panel of experts gathered during Symbiotic 2016 in Padua. The panel tried to identify concrete ethic risks of current pervasive symbiotic systems from an interdisciplinary perspective, and to have such risks if not solved - at least effectively framed.

To facilitate discussion, three likely everyday life applications of symbiotic systems were presented to the panel's attention, which bore some problematic implications to users and which focused respectively on detecting, modeling and deciding. Each case was examined from five different perspectives: ethics (Aimee van Wynsberghe, President of the Foundation for Responsible Robotics and Assistant Professor at the University of Twente, the Netherlands), information security (Mauro Conti, University of Padua, Italy), law (Giorgia Guerra, University of Padua, Italy), human-computer interaction (David Kirsh, University of California, San Diego, US) and psychology (Jonathan Freeman, University of London, Goldsmith College, UK perspective). Anna Spagnolli organized and chaired the panel. The examination of each of the three cases is reported in the following sections. The main points are summarized in the final section of this chapter.

#### 2 Case 1, Detecting Users: The Unusual Job Interview

The first case describes the use of symbiotic system where the detection of personal data for uses that are counterproductive to the unaware data owner. Indications about the verisimilarity of such a case are provided by [6, 8, 12, 14] showing that the technology and the preconditions for much of the components of the scenario are already in place.

*An international firm is hiring personnel in the accounting department. At the selection interview, the candidates are asked to use a pc and fill in an electronic form, collecting basic demographic information but also data about previous jobs and a description of the reasons why they think they suit the vacant position. Each candidate is also asked to sign an informed consent allowing the firm to*

*acquire the responses to the questionnaire, which will be used for archival reasons as well as to direct the subsequent interview with the human resources manager. The form specifies that the hiring company will use the written responses to the questionnaire as well as data on the pc usage while typing the responses (time, pressure and trajectory of the mouse movements). The questionnaire is mandatory and the firm commits to keep all collected data confidential. None of the candidates is aware that data on typing behavior can be used not just to identify the user but also to detect the probability that s/he is lying.*

#### 2.1 Ethics

RISK: There are multiple risks involved in this case, e.g. lack of informed consent, lack of protection of best interests of the user, risk of deceit, and lack of transparency regarding data collection and data use. I suggest the area of risk involved in case 1 derives from a combination of the above mentioned risks, i.e. the system collects information about a user without informed consent or transparency for a purpose that is detrimental to the user while the benefits from such a collection belong only to the company collecting data through the system. More specifically, this represents a case of treating direct users as a 'means to an end', as mere tools to achieve the company's goal, and this would be an ethically questionable position in which to put a user. Once we can justify the use of individuals for this kind of practice – as a means to an end – the list of ethically questionable activities that may be condoned increases exponentially.

SOLUTION: A possible solution could be to mimic the current practices in academic institutes whereby an ethics committee is established to monitor and approve research practices. In so doing, ethical approval has to be obtained for studies to be conducted and to provide guidance on how to do so as well as in order to protect the rights and interests of the participants involved. Outside the academy this could happen on the form of an Institutional Review Board for companies or an advocacy group that transcends the company as a body independent from it (i.e., not in the form of a department in the company itself). Independence is important as a way of striving for objective decisions and avoiding persuasion of ethics decisions.

#### 2.2 Security

RISK: From a security perspective, the problem in case 1 is that the user provides more information than s/he means to. S/he only means to input text in the form, but in fact s/he provides a lot more information while doing so. This process, i.e., trying to use data to infer some other information from it, is known as information leakage. There are plenty of other examples of information leakage. For example, analyzing the incoming and outgoing network traffic, the energy consumption patterns and the movement of the phone recorded when using a mobile phone, without having access to content stored on the phone, is sufficient to infer information about the user such as: the application installed on the phone, sex, age range, preferred language, text typed etc. Information leakage is a very real technical possibility, and represents a security risk because such information can be exploited in a malicious way.

SOLUTION: Despite the transaction described in this case represents such an infringement to several policies and users' rights that in some domains it would never been accepted (e.g., in academics), it is nonetheless a very pervasive kind of transaction nowadays. The use of data for extracting more information than it intended to convey by the owner of that data is technically possible and this must make us suspicious that such possibility is actually exploited. It would then be good to find technical means to prevent such leakage to occur in the first place. The best protection in this case would be to use one's own interface to use when typing and then avoid typing on other parties' interfaces. And before that I consider it necessary to increase the awareness in the user that information leakage is a likely occurrence, so they can ask for using their own interfaces in cases such as the one described in the scenario (as well as for policies, etc.).

#### 2.3 Law

RISK: The data collected here is used for a purpose different from the one for which it was collected; it can be considered as a deceitful use of data, and in this specific case this kind of use could create a risk of discriminatory behavior during the recruitment process. The informed consent is insufficient, being not transparent about the use of data. The additional complication is that the collected information, in order to be used, has to be interpreted. The reliability information extracted from pc usage is also questionable and still currently under debate. Consequently, this procedure could limit the users' self-determination, since candidates would have behaved differently had they known the actual purpose of the data collection.

SOLUTION: I think the burden for reducing the risk is on the company, which is required (especially in the US legal system) to have a more and more "proactive" role by providing more information in the consent form thereby increasing the transparency of the process. In particular, the user should be informed that - based on evidence law where "a brick is not a wall" - the interpretation of the information about future use collected in this way (movement of mouse etc.) might not be positively used by the company to inform its recruitment decisions: the reliability of interpreted information has always a margin of questionability that the user needs to be aware of (and the company alike).

#### 2.4 HCI

RISK: From a human-computer interaction perspective, this case represents: (1) asymmetric value, where one side stands to gain more than the other side; (2) asymmetric risk where one side stands to lose more than the other side; and (3) asymmetric knowledge where one side knows what is going on while the other side does not. In a symbiotic relation the two sides are called symbiont and host. The symbiont is the company capturing and analyzing the information and the host is the user unwittingly providing the information. The alleged benefit for the candidate (the 'host') would be to have a chance to obtain the vacant position. Since giving data is mandatory for the application to be considered, it can be construed as forced by the company and of value to them. For the candidate, however, it is not clear that there is any direct benefit for giving out the information. Despite this asymmetry of value this still qualifies as a symbiotic interaction. In biology, symbiotic relations do not need to be mutually beneficial to the parties involved. One can be a parasite on the other.

SOLUTION: One solution is to change the values in the asymmetry. This could be achieved by having a policy according to which people have rights over personal information. This requires defining personal information and defining the difference between public and private domain. Personal information could be assumed to have a tier structure. When it is clear to the candidate what s/he is giving up – as determined by the tier level of private information – the company could be required to acknowledge that storing and analyzing that information is worth a certain amount. The exchange of information is then seen as a transaction which they have an obligation to pay for in some way. The candidate then would be in a position to make an informed decision whether to give up the information. If the company does not reveal the value or does not offer an appropriate amount in exchange then the candidate has grounds for a lawsuit, since whether s/he knows it or not there was a transaction and it violates the law or recognized policy concerning transactions in such cases.

A society might also decide to set a cut-off in such asymmetry, establishing for instance that no parasitic asymmetry is acceptable regardless of cost. That would become a law. Another thing to note is that in addition to the right to know a person might have intrinsic rights over future use. Since the value of a piece of information changes once it is aggregated with the information collected from several individuals it has one value in isolation and a rather greater value when part of the aggregation. If the information is now assigned a value as a function of the value of the aggregation the asymmetry of value is potentially reversed. It might even prove to be so counterproductive to the individual company to purchase information for recruitment purposes since now they would have to pay many individuals, when all they wanted was to select a few candidates. On the other hand, if there is general knowledge to be gained from this sort of data capture then a new business might spring up that provides analytic tools to companies to help them made better hiring decisions. In that case, individual companies would no longer need to capture this data and store. They could make judgments about candidates using existing analyses (sold by the new company) and throw away the data they collect themselves right after they make their decision.

#### 2.5 Psychology

RISK: What in this case strikes from a psychology perspective first of all is a deficiency in informed consent. The case also points to the question of the timescale of the use of data: companies collect tons and tons of data which today are not useful, but might turn out to have a high value in the future. Logically the user cannot provide informed consent for the use of their data for a scenario or use case which today is unknown.

SOLUTION: The only approach to solving this risk is to aim for maximum transparency: informing users of how their data might be used today (the basis of all informed consent), and also illustrating how it might be used in future. Where future uses of the data are markedly different from those envisaged at the time users consent to their data being collected, it is clear that the data collector should be required to obtain consent for the new use of the data. There are some interesting developments in this space right now, with a number of UK companies developing 'games' as part of selection and recruitment, where behaviors which are monitored during selection process game-play reveal underlying psychological characteristics of the applicant. Whilst these characteristics may be analyzed with the informed consent of the appli-cant today, it is easy to imagine that other relationships between the game-play behavior and other psychological characteristics are discovered in the future. Without seeking new informed consent from the applicant at this point, user's test results should not be analyzed for the newly discovered characteristics.

## 3 Case 2, Modeling the User: The Tailored Persuasive Message

The second case illustrates a scenario where a symbiotic system is used to Profiling for persuasive purposes. Indications about the realism of such a case are provided by [1, 4, 9], showing how current users' analytics can be exploited for persuasive or manipulative purposes.

*"Users of a popular search engine log in to a set of free services connected to the search engine; once they are logged-in, the computer keeps track of all webpages that are visited from the browser where the log in occurs. The search engine stores a great amount of data about users and elaborates profiles about choices and preferences connected to the user and to similar users. In particular, it runs a free game showing several scenes and characters, recording the users positive or negative response to those characters. The search engine also runs and advertising service, which is used*

*during a major election in one European city. The election candidate, who purchases the advertising service for the occasion, uses the stored preferences to automatically personalize the campaign ads and to make his/her portrait as a person looking as similar as possible to the individual elector. It also sends favorable voting forecasts collected from lay citizens whose profile matches the profile of the user, in terms of tastes, sports, family situation etc. Voters are positively impressed by such declarations and feel that the candidate is very close to them. The advertising firm revenue from this campaign is huge, but no money was paid to the search engine users for disclosing their information in the first place."*

#### 3.1 Ethics

RISK: Despite the similarities this case shares with the first one, it represents more serious threats for several reasons. In the first case the possibility that the system (of data collection and use) will be exploited for purposes that are negative for the user was questionable; in the present case such negative purposes are certain. Moreover, the negative consequences are far reaching and threaten the values at the very core of a democratic society – protection of human rights as well as undermining the democracy process. I would cluster two different groups of risks: 1. privacy violations and, 2. Human rights violations. For the first cluster, these violations would include: collection of data without informed consent, secondary use of data without consent, use of data for a purpose other than the one specified, possibility to de-anonymize, and lack of transparency. In the philosophical and ethical literature the definition of privacy used to focus on the control one had over one's data; deciding who has access, what it is used for, when it is used, and how much of it is used. Now, the definition of privacy has started to evolve into a concept in which the formation of one's identity is the central focus; being able to establish one's identity without having one created for you or becoming just another number. For the second cluster, the threat to human rights exists in the potential to manipulate emotions by targeting preferences and habits of users. This poses a threat to the values of autonomy and dignity of individuals. These serious negative consequences change the nature of the scenario; the potential infringement on human rights adds to the threat of the democratic process.

In deciding what to do about competing conceptions of the good life one may focus on the consequences of an action (the consequentialist approach) or the duties and principles on which the action is based (the deontological approach). Of course it is not so easy to isolate consequences from duties and many ethicists nowadays would go so far as to say that the line dividing one ethical theory from another are blurring; however, it is important in this case to be sure to point that in this case if one can attempts to justify the threat to human rights like autonomy and dignity (by undermining the democratic process) by saying that it could be a "good political candidate that is chosen", then the potential to engage in similar practices in the future with terrible outcomes (e.g. voting for a candidate or policy that is not good) becomes quite real.

SOLUTION: This is a difficult situation to find a solution for as it requires that companies and politicians are honest about their research practices even if it means they lose money. From an ethics perspective it is important to empower people to find their own voice to base their decisions on; this is why I would support education to allow people to inform their decision based on deeper knowledge of the issue. This education may come through the media or through an institution. As a solution to preventing these things from happening I would recommend establishing an advocacy group or ethics committees that work together to monitor and find solutions that make the symbiotic system process at stake ethical.

#### 3.2 Security

RISK: From an information security point of view this case represents an example of user profiling. Currently, most Internet services and social networks collect data from users and try to profile them via the so-called user profiling in order to create target groups which they can target with other services (e.g., advertising) based on their characteristics. When they notify that they might sell such information to third parties, we are talking of buyers in the order of hundreds of companies what will get and use our data. While this information is commonly used in the aggregate form, it could also be exploited maliciously to try and define the profile of a specific user, his/her preferences, location and behavior. In addition the actual term of our decisions about such information is often ambiguous, as is the case when flagging as private an information on certain social media, which will never be private in the same way as we mean it to be.

SOLUTION: Increasing people's awareness of the risks involved in releasing data is nowadays necessary since security risks are chiefly underestimated. Such awareness and ability to imagine possible consequences should also be projected in the future possible use of the data which is light-mindedly disclosed now. To appreciate the importance of prospective thinking we can consider DNA information sent out to Internet services in exchange for knowing something about ourselves; this equals to disclosing core identity information, information that cannot be changed and that regards not just the individual releasing such information but all his/her relatives and progeny. It is easy to imagine that such information might be used in the future for genetic research applications or discriminations we are not even aware of nowadays and that we should me more jealous of such information than other we protect with much more alacrity.

Regarding possible technical solutions, there are several and they should be better known and more pervasively implemented (the anonymous internet browser system TOR, for example). Awareness, however, is the key solutions since it will not only convince people to prefer safer solutions but also to motivate users, regulators, technicians to ask for such solutions when they are not offered.

# 3.3 Law

RISK: From a legal point of view this case is about the indirect and unaware use of personal data. More technically, it is an information security law case, meaning a distinguished concept from concepts such as privacy and data security. [Many laws that purport to encourage cybersecurity are, in fact, designed with a focus on protecting privacy or encouraging data security]. Unlike privacy and data security, cybersecurity is focused not only on the information, but the entire system and network. For this reason, laws that focus only on privacy and data security may not consider all factors necessary to promote cybersecurity.

SOLUTION: Transparency and adoption of best practices.

On the one hand, the popular search engine where users log in to set free services should inform users of future potential uses of their personal data through the connected advertising service.

On the other hand, there are also some best practices for users that can help to reduce the risk of violation of privacy and unforeseeable use of data:

Also we must consider that different kinds of information would have a different consequence in legal terms: the legal "weight" of human values and rights (e.g. religion; sexual orientation etc.) is different from the legal "weight" of choices and preferences (e.g. kind of theatre preferences; food preferences; sympathy or not for domestic animals etc.). The unaware storage of consumers' information about those different aspects (human rights and human preferences) has, obviously a different legal protection in case of breach. Case number 2 is important because it lets stakeholders thinking about these differences.

## 3.4 HCI

RISK: I would frame this case similarly to the first case, namely as one of asymmetry in risk, value and knowledge. Unlike that case, though, this one adds a complication in terms of human-computer interaction, namely that there is a third party involved, and this party is not the one directly involved in the symbiotic relation. That is, the politician purchasing the service is the third party that benefits from the personal data. Presumably there was a transaction between the symbiont – the party gathering the information and the one who should have paid for it – and the politician.

SOLUTION: The system that manages collection and all transactions – the symbiont and its owners – has an obligation to reveal the value of the information that is being collected. This value concerns the transaction occurring between the system and the user disclosing data but especially between the system owners and the customer using the data collected. One thing is to use the collected information internally and another is to sell it. Can this information be sent to any country whatsoever? For any purpose? Its value depends on what others pay for it. So regulation should shift focus, in this case, from the transaction with the system users to the transactions between the system owner and its customers who buy the collected data. Therefore, the solution would be to develop policy to regulate transactions whenever collected data are sold.

Purely from a HCI perspective, transparency can be increased by re-design, improving the comprehensibility by clever visualization of the meaning of terms of usage that otherwise is specified in 20 pages of text. The owner of the original information – the information host – ought to also have the right to expect to re-negotiate or renew the terms of the transaction once it is apparent the value of the information has changed. Information has a continuously changing value, it is not used once and once forever. Once new value becomes apparent there might be appropriate conditions to ask for a renegotiation. At the same time, acknowledging that some information might not be valuable once it is collected but prove valuable afterwards, it might be foreseen that its value should be paid via fee-for-use, namely only when users' data is actually used for some profit or benefit to the system owner.

#### 3.5 Psychology

RISK: I think that in psychological terms this should be understood in terms of identity and individual differences, but I wonder how this case - being enabled by a symbiotic technology - differs from typical political campaigning, where politicians commonly adapt their propositions to the audience or the interlocutor.

SOLUTION: In this case, as in the first one, what is critical is to increase transparency. I think that transparency in transactions - even commercial ones - should be a leading principle, allowing a better understanding of the value of the transaction. Even more so in a political domain, where democracy is at stake and there is arguably a need for even higher standards of fairness in transactions. And to achieve such transparency, I think that education of the citizen and the consumer have a huge role to play (transparency being not possibly in the absence of a user understanding what data a system is acquiring). Also relevant here is that there is a natural pressure for systems to be easy to use (to maximize likelihood of engagement) and it is likely that the user will not realize the importance of transparency of a system until s/he experiences some negative consequence of a lack of transparency.

# 4 Case 3, Deciding for the User: The AR-Guided Surgical Intervention

The third case illustrates a case where a symbiotic system might lead to the user loosing agency due to automatic responses directly provided by the system. Indications about the realism of such a case are provided by [3, 7, 10].

*"A hospital has adopted a laparoscopic surgery equipment which is connected to the apparatus acquiring vital signals from the patient under surgery. During the surgical procedure, the physician wears a pair of augmented reality eyeglasses to receive information about vital signals along with data about success probability for each procedure applied to the patient during the surgery, in*

*order to better inform his/her ongoing decisions. This data is recorded for archival reasons by the hospital and can be used in case of a lawsuit against the hospital after surgeries that do not succeed. It is the hospital policy to avoid conflicts between the choice of the physician during the surgical procedure and the data displayed by the machine, the evidence of the patient status recorded by the equipment. Thus in case of the patient reaching a critical condition, the physician is recommended to only rely on the standard procedure and quit any other attempts which is less likely to succeed. Therefore, to a certain extent, the decisions are embedded in the intelligence of the computer elaborating data and making recommendations. Somehow a moral decision is incorporated into the machine."*

#### 4.1 Ethics

RISK: To me this represents a prototypical symbiotic relation since parts of the decisional powers are externalized by the physician to the machine based on data it receives. It is a whole system including patients' data, physician's decision and systems' elaboration/recommendations. The most important part of this scenario is the fact that the surgeon is responsible for deciding whether or not he/she will take the advice of the machine. The moment this changes and the surgeon must do what the machine tells it (whether this is an explicit formal policy at the hospital or an implicit one) the scenario changes and the relation is no longer ethically acceptable or desirable. The second scenario is one in which the surgeon's freedom to choose has been limited. This limitation threatens the professionalization of medicine as one would have to be concerned about who is taking decisions and who is liable in case of problems, i.e. if the surgeon does what the machine tells it to do then will the machine be liable if someone goes wrong? Further, will we sue or fire the machine for damages? But responsibility from an ethics point of view is more than liability; it requires a moral agent with intentions who is able to reason through the consequences, understanding the consequences of an action; therefore this cannot be delegated to a machine.

Another potential risk is deskilling if the surgeon learns to rely on the technology and its elaboration more than s/he does on his/her own judgment. Moreover, with the use of this new technology the surgeon may not have an instance in which he/she is able to train using conventional methods.

SOLUTION: Using military terms, the surgeon must remain in the loop instead of being put on the loop. This means that the surgeon should be in control of giving commands and making choices. Part of the training with the systems should be to understand how the machine reasons and how to manage disagreements with the machine especially during emergencies. Another part of the training should be to make sure surgeons know how to perform the surgery if/when the machine breaks and the surgeon must rely on his/her own skills without the technology [13].

#### 4.2 Security

RISK: From information security perspective this scenario points on the one hand at the aspects related to data storage in hospitals, and on the other at the possibility that the system is programmed to make decisions, be controlled remotely or being hacked for malevolent purposes.

SOLUTION: Solutions from information security perspective for this scenario are common good practices for storage of confidential information and to make computer systems secure: avoiding unauthorized parties to take control of such systems.

#### 4.3 Law

RISK: This case regards the use of extra-clinical tools to support clinical decisions.

In general the gradual shift towards the use of extra-clinical tools to supplement the informed consent process and support clinical decisionsì could present the risk to consider the tool not simply as a decision-support tool but a decision-replacement tool (instead of the patient-physician's decision).

A second important risk is of blurring the principal role of the physician and interfering with his/her freedom of therapeutic choice whose responsibility (not simply legal liability) is shared with the patient.

Physicians have long faced tort liability for breach of informed consent if a patient is harmed as a result of the physician's failure to provide the information needed to make an informed medical decision. However, with increased reliance on extra-clinical tools, informed consent mechanisms incur an increased risk of malpractice liability.

The physician who simply relies on eyeglasses without reasoning on the bases of his/her knowledge, under even the most traditional tort principles, will be liable for malpractice. (Failure to engage fully in the informed consent process, even if decision support tools are made available, is a clear breach of the standard of care).

What if the pair of augmented reality eyeglasses gave wrong information? Similar problems where already present in the field of medical guidelines application.

Several scenarios could be traced:

SOLUTION: It is clear that medical providers who prescribe or use decision support tools may face tort liability if they misuse the tools or provide negligent counseling. This is a simple and relatively uncontroversial expansion of traditional malpractice liability. But the use of decision support tools also poses a secondary problem - namely, that patients may be harmed if the decision aids they use are faulty, misleading, or biased. If the regulatory or certification process aimed at ensuring the quality of decision aids fails, injured patients will look to tort law to provide a remedy. And since current tort doctrine makes it extremely difficult for such claims to succeed, it is time for policymakers and legal scholars to evaluate the costs and benefits of expanding tort liability in these cases.

The risk could be minimized with a good training and instructions by the producer on real opportunities offered by the eyeglasses. All information about the real help technology will offer to the patient should be exactly represented to the patient before the surgery in order to share "the potential scenarios".

Apart from this, it is still an open legal question in this field to determine whose responsibility it is to minimize this risk. Producers' will have an important role as well as physicians in transfering to the patient the useful information and sharing potential scenarios prior of the intervention (learned intermediary hand role).

Within the personalized medicine era, these eyeglasses have to be seen as a functional instrument of help in critical situation.

The risk of restricting physician's freedom of choice is inherent and is not avoidable. Perhaps, every physician will have to be aware that the liability for the final decision is due to his/her own choice, so it would be important for him/her to know from the producer the risk of error margin of the high tech product.

It has to be underlined that, because transitioning elements of the consent process into extra-clinical arenas is a dramatic change in the practices of protection medical freedom of choice and informed consent, it necessitates a new kind of conversation about liability. First, although product liability law sometimes subjects creators of faulty products to strict liability (that is, liability regardless of fault), decision support tools do not fall within the legal definition of a "product" and so are not subject to strict liability. Their inherent autonomy is currently under analyses.

Second, in the future it will be crucial to re-analyze issues of vicarious liability of the hospital and other involved subjects.

It should be also underlined that if a hospital system requires physicians to use decision aids for particular conditions it will also have a role in the allocation of liabilities, but this element will not be a "safe harbour" for physician who decided.

#### 4.4 HCI

RISK: This case shows that the parties in the symbiotic relation have roles that depend not only on their knowledge but also on external practices such as the legal attribution of responsibility. The same case appeared years ago concerning expert systems for blood diagnosis, which were about 95% as good as a doctor on a good day. That is way better than most doctors on most days. Yet still hospitals ended up not using them because of the risk of legal suits. In normal cases gross failure leads to a law suit of the doctor. But who do you sue when it is an expert system? And what are the standards that one applies? The risk was that the responsibility for imperfection would have been laid at the foot of the programmer. And that risk might be too high given that the same program would be used in many places. The trouble is that when you think like this you give up reliable expertise (the expert system) to defend a general principle of morality or law. And yet the system is often the best way to proceed.

SOLUTION: Responsibility in this case should be allocated as a function of accountability and ultimately of knowledge. But we want humans in the loop. For instance, if an expert system left the final decision to the physician but also had a facility that would allow the physician to: (a) delegate the decision to the system, on a case-by-case basis; or (b) ask the system for its reasons for its suggestion or decisions and to take issue when the reasons are not clear enough, then we manage to keep humans in the loop. The final decision now lies with the physician. And there is the same mechanism used among teams of humans – they talk it out by asking each other for their reasons. The system and doctor now would be a learning team.

#### 4.5 Psychology

RISK: I agree with the other panelists' responses, this case reduces the surgeon's autonomy and decreases the surgeon's skills. This reminds of the same issue currently at stake with self-driving cars, where the driver must be able to deactivate the cars' automatic behavior to get in control of the situation.

SOLUTIONS: The system should be transparent, explaining itself and then allowing the surgeon to make decisions including the decision to delegate decisions.

#### 5 Synthesis and Conclusions

As mentioned in the introduction, and illustrated in the three cases and related discussion, symbiotic systems bring about a set of information transactions and intervene on the human beings and the environment in ways that are not inherently good or bad, but that at any rate mobilize some interests and values. Symbiotic systems, by which we mean here systems that have the three characteristics mentioned in the introduction (i.e., detecting users' implicit information, modeling the user's state based on such information and making decisions based on such model) can represent a resource to empower the human agent but can also end up serving private goals, manipulate and ultimately - alienate human beings from their rights. In particular, the risks - as they have been identified during the panel - are of information leakage and malicious user profiling; deceitful use of data and threat to information security; decrease in self-determination, privacy and dignity; deskilling; asymmetry in value, risks and knowledge. The actual position that a given symbiotic system holds in the continuum between beneficial and harmful applications, between protecting the many and serving the few, depends on the society's ability to set criteria of acceptability for their design and usage and to compel the application of such criteria. Given the scale and the subdued level at which symbiotic systems operate, it seems urgent that society decides what is a legitimate symbiotic system and finds mechanisms to secure the respect of such decisions. The discussion during the panel highlighted some of these criteria and provisions, which are tentatively summarized in Fig. 2.

Fig. 2. A framework to collectively take charge of ethically acceptable symbiotic systems

Both the ultimate goals of the system and the ways in which it operates must be legitimate, making sure that it directly pursues, respects or at least does not compromise society values such as honesty, equity, self-determination, dignity and freedom. At a less abstract level, these overarching ethic values are achieved by way of domain-specific values that decline them into more concrete objectives, such as network security, user-centeredness, transaction fairness, transparency, identity and data protection. These objectives are achieved by way of education, regulation and agents. Education is the process to understand the risks of such systems and to learn risk minimization procedures (such as informed consents, enabling of own interfaces to input data in third party's systems, reporting lack of comprehension, design principles). Regulation includes the definition of what is a private and public information, how and when the value of a given piece of information is to be (re)defined, what transactions are legitimate, who are the beneficiaries, who is responsible for a decision based on symbiotic systems, and what is the legal value of information derived from implicit data. Watches are agencies such as institutional review boards or advocacy groups that specifically monitor ethical risks, certify procedures and represent users' rights by voicing them publicly and promoting regulations and education initiatives.

In conclusion, ethically acceptable symbiotic systems are the results of a collective effort, where the burden of dealing with the risks of releasing personal information is shared with all relevant stakeholders and publicly debated.

Acknowledgments. The present panel was partially funded by the EU project MindSee (grant agreement n. 611570). Individual panelists are responsible for their interventions as they are written here, each one for his/her own area of expertise; the panel organizer is responsible for the introduction and conclusions. We are grateful to Piero Turra for the illustrations of the three cases.

#### References

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# **Total Immersion: Designing for Affective Symbiosis in a Virtual Reality Game with Haptics, Biosensors, and Emotive Agents**

Imtiaj Ahmed1(✉) , Ville Harjunen1 , Giulio Jacucci1,2, Niklas Ravaja1,4,5, and Michiel M. Spapé1,3

1 Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Helsinki, Finland

<EMAIL_ADDRESS><EMAIL_ADDRESS>
2 Department of Computer Science, Helsinki Institute for Information Technology (HIIT), University of Helsinki, Helsinki, Finland

3 Department of Psychology, Liverpool Hope University, Liverpool, UK<EMAIL_ADDRESS>
4 Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland 5 School of Business, Aalto University, Helsinki, Finland

**Abstract.** Affective symbiosis for human–computer interaction refers to the dynamic relationship between the user and affective virtual agents. In order to facilitate a true, immersive experience, we believe that it is necessary to adapt the presentation of an affective agent to the user's affective state. Investigating the experience, behavior, and physiological correlates of affective events, such as winning and losing during a competitive game, therefore might be used to adapt the agent's emotional states and system events. An experimental virtual reality game environment was designed as a stepping stone toward a system to demon‐ strate affective symbiosis. Users were invited to play a game of air hockey with affective agents in virtual reality. We collected the electrocardiography, electro‐ dermal activity, and postural data, as well as self-reports, to investigate how emotional events affected physiology, behavior, and experience. The users were found to be engaged in the competition strongly while only paying limited atten‐ tion to their adversaries' emotional expressions. We discuss how game events are much stronger causes for affective responses, with the physiological effects of winning and losing becoming more enhanced as the game progresses. We discuss how an affective, symbiotic system could implement both game events and dynamic, affective agents to create a system for total immersion.

 Based on a recent review by Jacucci et al. [2], for a human–computer paradigm, symbiotic interaction is possible to achieve by using technological resources to understand users and to make those resources understandable to users. Computation, sensing technology, and interaction design are key aspects to monitoring and affecting users through the systems output in a closed-loop paradigm. Following the notion of symbiotic interaction in human–computer paradigms outlined by Jacucci et al. [2], we affixed an affective agent or system to form affective symbiosis.

An affective agent, in one respect, can be expressed as the embodied agent or screenbased anthropomorphic entity that can display emotional expressions to affect users emotionally. A number of studies have demonstrated the potential of using affective agents in interaction [3–7]. For example, the use of an embodied agent in an interface can enhance social interaction [8] and improve life-like interaction with agents [5]. Of course, not every situation requires affective agents: Embodied, affective agents in util‐ itarian contexts can result in negative user emotions (e.g., the Microsoft search puppy). On the other hand, competitive contexts, such as computer games, might employ these agents more appropriately. In these types of systems, it is also much more likely that a relationship among a user, virtual agents, and the system grows and changes over time. Moreover, earlier studies have shown that the competitiveness of the interaction is posi‐ tively associated with emotional synchrony between human users [9]. In other words, people pay more attention and are more aware of their adversary's emotions as the competitiveness of the interaction increases. In human–computer interaction, this could mean that the agent's affective state becomes more salient for the user when increasing the competitiveness of the game.

However, creating an affective symbiosis between the user and agent requires more than simply integrating emotional expressions into a competitive context. The interac‐ tion with the system should be built on a dynamic loop that adapts the system to a user's body, behavior, and experience [10]. In the present study, we took steps toward creating this type of system in a competitive game scenario, expecting that it will enhance engagement and immersion during interaction. At this point, the system's adaptiveness is limited to the adversary's performance, meaning that the performance of the adversary improves directly with the user's performance. We expect that if a user engages in affective symbiosis, (a) the user's behavior and physiological states should be affected strongly by system events, (b) the emotional state of the user should be affected by the emotional state of artificial agents in the system, and (c) engagement with the system should increase as a function of the cumulative degree of interaction. In the present study, we aimed to gain early insights on the relationship between these critical aspects of affective symbiosis. The hope is that identified shortcomings will inform us adequately to enable a full-scale affective symbiosis system in the future.

We implemented an interaction scenario similar to the table-top game of air hockey, featuring virtual reality, haptic feedback, psychophysiological measurements, and affective agents. The virtual agent displayed emotional expressions during play. We also built a haptic glove to display the different forms of tactile feedback during the game events. The air hockey game was selected because it represents a fast-changing compet‐ itive context in which the user is facing his/her opponent and is allowed to see his/her adversary's facial expressions without disengaging attention from the game.

In order to investigate the user's engagement in affective symbiosis, we measured the effect of emotional expressions displayed by affective agents. We expected that interaction with the agent could result in two types of emotional relationship, which one might consider, in the context of symbiotic interaction, as either parasitic or mutualistic. From a primarily utilitarian point of view, competition might entail a parasitic emotional effect: a negative expression of one's opponent suggesting a gain and therefore resulting in positive affect. However, many studies have suggested that emotional expressions usually result in mirroring behavior, imitating the agent's expression, which, according to some, might cause associated emotions [11]. In other words, positive emotions expressed by the agents might result in either negative (parasitical affective symbiosis) or positive (mutualistic affective symbiosis) affects in the user.

Because emotions are known to affect users unconsciously on occasion [12], if the system is affectively symbiotic, we expect that the more one interacts with the system, the more one becomes affected by game events. This refers primarily to events that happen to oneself (i.e., one wins or loses). To measure the emotional involvement, we complemented traditional self-report questionnaires with physiological data. To inves‐ tigate how users implicitly responded to emotional events, we took measures of electrodermal activity (EDA) and heart rate variability (HRV) during the game.

We think that immersion might improve affective symbiosis, because it might create a stronger impression of the importance of the game and events, thus increasing the salience of the agent's emotions. For this reason, we used virtual reality [13] and haptic feedback [14]. The findings of the study will contribute to understandings of some crucial points to consider before developing an affective symbiosis adaptation and/or game model and will direct the possibilities.

#### **2 Related Work**

Both systems that adapt to user emotions and affective agents that influence user emotions have been popular topics of research in the field of human–computer interac‐ tion (e.g., [15–19]). Work regarding emotionally adapted games has demonstrated how adapting game events to a user's physiological activity can improve the game experience [18]. Kuikkaniemi et al. [19], for instance, used EDA to control the level of difficulty in a first-person shooting game. Results revealed that players who were able to see their biosignal while playing were more immersed and positively affected by the game.

In addition to the adaptive games, affective agents' emotional expressions also have been shown to influence users' emotional states and social behavior [5, 7, 20]. De Melo and colleagues investigated social decision-making between a human and affective virtual agent, demonstrating how users were more willing to cooperate or make conces‐ sions if agents were displaying certain emotions [21, 22]. Besides making different decisions, users also have shown to mimic the expressions of affective agents automat‐ ically and respond to negative expressions with heightened autonomic arousal [23]. In conclusion, affective agents indeed affect users' emotions and behavior.

Rather than using 2-D screen-based virtual reality, one can use head-mounted displays (HMDs) to achieve better immersion [24] by shutting out the outside world. Recently, advancements in HMD technologies have increased the use of virtual reality and its applications, mostly for gaming purposes [25, 26], to provide the experience of immersion and the enjoyment of full engagement. Though HMDs are used for the visu‐ alization of the virtual environment, the realistic feel comes from the feeling of touch and getting a response from the virtual objects [24], which can be achieved through haptic technologies. A haptic feedback technology increases the player's sense of immersion [24] and provides interesting ways to interact with the game [15] while enhancing the entertainment value [27].

To summarize, systems that adapt to a user's emotions and systems in which affective agents influence user emotions have been popular topics of research. However, an immersive system that incorporates adaptive gaming performance into affective expres‐ sions requires more research. The present work aims to pave the way toward affective symbiosis by investigating the changes in users' physiology and emotional state in a competitive affective game environment.

# **3 The Game**

An air hockey game (similar to Pong) was designed to investigate the effects of affective agents in an immersive, competitive context. A virtual puck was served from the center left of the air hockey table either toward the agent or the player, alternatively. Both the player and agent blocked the puck using a virtual mallet. There were slots on either end of the air hockey table that served as goal bars. The angle of the puck-serving direction was randomly generated between 15° to 75° angles toward either the agent or the player.

#### **3.1 Player Interaction**

We used Leap Motion, a tiny device used to sense a user's hand and finger motions using infrared cameras in an interactive 3-D space, to track the user's right-hand movements. This device was positioned underneath a glass table and enabled sight of the player's hand in virtual reality. A transparent plastic sheet was used to help move the user's right hand freely on the glass table. The default hand model, provided as part of Leap Motion's Unity3D package (www.leapmotion.com), was used to enable interaction in virtual reality. In virtual reality, a user could grip the virtual mallet and move it to face the moving puck. A player's left-hand index, middle, and ring fingers rested on the arrow keys of a PC keyboard and were used to provide answers to the self-reported items shown during gameplay (Fig. 3).

#### **3.2 Agent Interaction**

The agent faced the puck using a set of hand-movement animations. Twelve agents' hand-movement animations were designed to reach 12 predefined target points on the game table (see Fig. 1). While the puck moved toward the agent, the agent tried to find the closest target point according to the puck's movement direction, and the agent played the corresponding hand movement animation to bounce the puck. To make the game competitive based on player performance, we designed three levels of difficulty: *easy*, *medium*, and *hard* (Fig. 1). The adaptation of the competing model depended on the player's performance so that players could gradually improve their skill along with the agent and continually enjoy the competition.

**Fig. 1.** Competing models. Yellow lines represent the medium model and its selected target points. Red lines represent the easy model. All target points were selected for the hard model. (Color figure online)

#### **3.3 Tactile Feedback**

A recent work by Ahmed et al. [28] showed that differences in haptic technologies affect how simulated touch in virtual reality is perceived. They found that haptic exciters provided a more intense signal than the commonly used C2 vibrotactile actuators. However, mechan‐ ical actuators were found to make the touch feel more natural. This motivated us to use both a haptic exciter and a mechanical device to provide tactile feedback for different game events.  We attached a micro servo motor (9 g, speed 0, 10 s/60, torque 1.3 kg/cm at 4.8 V) on top of the glove. Three elastic strips of tape were attached to the glove and connected by a thread to the horn of the servo motor. The servo motor rotated up to 180° (c), thereby pulling the thread, creating tension on the elastic tape (b), and applying pressure to the user's hand. This mimicked the sensation of a player gripping the mallet. Two audio haptic exciters (Tectonic's 14 mm, 8 O TEAX14 C02-8, www.tectonicelements.com/audio-exciters) were placed on top of the glove (a) to produce vibration on the user's hand while the user bounces the puck. Because the skin on a human hand is sensitive to vibrations over 20 Hz and a square wave is perceived to be more intense than the commonly used sine wave [29], we selected a 35 Hz square wave of 50 ms vibration as the puck-bouncing feedback.

**Fig. 2.** Exciter-servo motor tactile glove.

# **4 Method/Evaluation**

#### **4.1 Participants**

Seventeen university students and researchers (12 male, five female, 25.47 ± 4.4 years old) volunteered to take part in the study. They signed informed consent agreements prior to the start of the experiment and, afterward, received a movie ticket for their time.

#### **4.2 Test Setup**

The Unity 3-D game engine (version 4.5.4, www.unity3d.com) was used to implement the experimental system, providing the game scenario and questionnaires visually in virtual reality via the HMD, recording user responses, and communicating with the physiological apparatus. Virtual reality was enabled using the Oculus Rift VR HMD (Oculus Rift Developer Kit 2, Resolution 960 × 1080 per eye, Refresh Rate 75 Hz, 100° nominal field of view). The player's head movements were tracked at 1000 Hz using a three-axis accelerometer, gyroscope, magnetometer, and external positional tracker (www.oculusvr.com).

Affective agents in virtual reality were designed by combining a face model and a body model. The face model used was the original one provided by Faceshift (www.faceshift.com), and the body was designed using the FUSE design tool provided by Mixamo (www.mixamo.com). The texture of the face and body were edited in order to build three different-looking agents. The agent displayed three emotional expressions

**Fig. 3.** Test setup and player interaction.

(happiness, anger, neutral control condition). These were recorded prior to the study by capturing a live presentation by a professional actress using Faceshift algorithms. Each expression animation lasted 4 s, ending with neutral expression. Prior to the experiment, a pilot study was conducted in which a sample of human participants was used to classify the expressions based on categories of six basic emotions (disgust, fear, sadness, anger, happiness, and neutral). Expressions of anger, happiness, and neutrality were then selected for the final paradigm based on their relatively higher recognition accuracy (happy: 95 ± 14%, neutral: 90 ± 14%, angry: 90 ± 23%).

#### **4.3 Procedure and Design**

As illustrated in Fig. 4, at the beginning of the game, the player grabbed the mallet. The haptic glove applied tension to the user's hand to mimic gripping the mallet physically. The agent's facial expression was presented as a common starting point for every trial. The same facial animation was played throughout the entire trial. After 3 s, the puck was served toward either the agent or player in alternating order. A short vibration through the glove was displayed on the dorsal of the player's hand while the player bounced the puck. The trial lasted until a goal was scored, after which the scores were updated. After 1 s, a short questionnaire (see Sect. 4.4) was presented.

**Fig. 4.** Trial sequences. Users grabbed a virtual mallet (A). At the beginning of the trial, the agent was displayed and started to show emotional expression (B). After 3 s, the puck was served (C) and the vibrotactile stimulus was presented as the user bounced the puck (D). After each goal, scores were displayed (E). After each block, a self-reported questionnaire was presented (F).

Participants undertook nine blocks of seven trials. The virtual agent was changed between blocks whereas the order of agents was counterbalanced between participants. Trials were randomized fully across the 63 possibilities obtained by orthogonally crossing the 3 repetitions × 3 agents (angry, happy, neutral) × 7 serves.

#### **4.4 Measurements**

User experience was investigated using a quantitative analysis of both explicit selfreported Likert scales and implicit behavioral and physiological measures.

**Table 1.** *Descriptive statistics of self*-*reported questionnaire (N* = *17)*. A 5-point Likert scale (1 = not at all, 5 = very much) was used for item scoring. *Note:* values are means ± SEs

**Self-reported Data.** Self-reported data on emotional interdependence, co-presence, game experience, and emotion-classification items were collected. Scales of emotional interdependence, co-presence, and game experience were single-item measures obtained from the social presence module of the game experience questionnaire (FUGA, [30]). Items are shown in Table 1. Participants filled out the questionnaires using their other (i.e., non-virtual) hand, resting on the arrow keys of a PC keyboard. The questionnaires used 5-point Likert-style scales, while the emotion classification used a three-alternative forced choice.

**Implicit Measures.** The users' electrocardiography (ECG), EDA and right-handed biaxial accelerometer (ACC) were recorded using a Brain Products QuickAmp biosignal amplifier at a sample rate of 1,000 Hz (Fig. 5). ACC data were transformed to movement speed by taking the root mean square of the first derivative over both axes (i.e., the Euclidean distance traveled per ms). Physiological sensors to measure EDA were placed on the index and middle fingers of the left hand, and to measure ECG were placed on the chest. EDA commonly is used as an emotional arousal indicator [31] or to describe the intensity of an experience. Electronically, it represents the potential difference between two areas of the skin. Two key measurements of the EDA are tonic (low frequency baseline conductivity level indicates slow changes in arousal) and phasic (higher frequency indicates rapid changes in arousal). The average, raw EDA was taken as a tonic measure of autonomic activity. Furthermore, the measure was transformed to reflect phasic arousal related to non-specific events [31] by applying a continuous, local, positive peak (of at least 0.5 μS) detection algorithm and calculating the rate of skinconductivity responses per second. Finally, the latency (in ms) between heartbeats, or inter-beat interval (IBI), was used to calculate the average IBI (inversely related to heart rate).

**Fig. 5.** Ten seconds of raw heart rate (ECG), EDA, and 2-D accelerator (ACCX and ACCY) data during gameplay. Red boxes show events of interest: (A) single heart-beat used for calculating heart rate variability, (B) phasic EDA response used for calculating skin conductivity response rate, and (C) movement across X and Y used for calculating movement speed. (Color figure online)

#### **5 Results**

#### **5.1 Emotion Recognition, Game Experience, and Presence Measures**

Participants' judgments concerning emotions, game experience, emotional interdepend‐ ence, and co-presence were investigated using one-way repeated measure ANOVAs

**Fig. 6.** Effect of losing or winning on the first 3 s of movement speed, EDA/10, and skin conduc‐ tivity response rate (SCR\*10).

(RMANOVAs) with the agent's emotional expression (neutral vs. angry vs. happy) as a factor. The agent's emotional expressions were judged correctly in 91% of cases. Furthermore, some of the expressions were recognized significantly better than others, F(1.36, 21.82) = 4.34, p = .04. A post hoc LSD (Least Significant Difference) analysis revealed significantly higher recognition rates (p < .05) for happy (100%) compared to neutral (88 ± 14%) and angry (85 ± 23%) expressions. Moreover, the agent's emotional expression had a significant effect on experienced emotional interdependence, F(2, 32) = 9.01, p = .001, η2 = .36, and co-presence, F(1.43, 22.84) = 15.31, p < .001, η2 = . 49. A post hoc LSD analysis revealed that participants felt more influenced by the agent's mood in happy (2.81 ± 0.78) and angry (2.54 ± 0.77) conditions than in the neutral (2.04 ± 0.59) condition (p < .05). Along similar lines, participants reported higher experiences of co-presence while playing against happy (3.45 ± 0.66) vs. angry (3.05 ± 0.59) agents, rating neutral (2.48 ± 0.70) agents as the least attention-capturing (both emotions > neutral, p < .05). Finally, the agent's emotions affected the partici‐ pant's ratings on how deeply they concentrated on the game, F(2, 32) = 7.17, p = .003, η2 = .31. A post hoc LSD analysis revealed that participants were more concentrated during the happy (3.45 ± 0.66) than the angry condition (3.05 ± 0.59) and that the neutral expression (2.48 ± 0.70) was associated with poorest concentration (p < .01).

#### **5.2 Experience of Game Events: Behavior and Physiology**

Trials were included in the analysis only if both the agent and user successfully blocked or bounced the puck at least once. Seventeen users were tested, although the accelerator recordings were missing for one. Data for this user were omitted from movement-related analyses.

In order to find out whether the agent's emotion had a general effect on behavioral and physiological responses, RMANOVAs were conducted with the agent's emotion as factor and performance, movement, tonic EDA, average heart rate, HRV (standard deviation of the IBI), and skin conductance response rate as measures. Tonic EDA was found to be affected by the agent's emotional expression significantly, F(2, 32) = 4.59, p = .03, but no effect was observed for any other measure, *p* > 0.1. Angry agents increased tonic EDA (883 μS) relative to neutral (829 μS) and happy (802 μS) conditions.

Next, we investigated how winning or losing affected the behavioral (movement data) and physiological (EDA, SCR, IBI) response. RMANOVAs were conducted on measures taken in the first 3 s after the game event of winning or losing with performance (win vs. loss) as factor. Performance significantly affected EDA, F(1, 16) = 15.20, p = . 001, SCR(1, 16) = 17.92, p = .001, and movement, F(1, 15) = 20.15, p < .001. Losing increased EDA, SCR, and the amount of movement.

Finally, we explored emotional investment as the game progressed. We used the average SCR measure evoked by successfully blocking (bouncing) the puck across time. As shown in Fig. 7, the more often the puck was blocked (N bounced), that is, the further the game progressed, the higher the SCR related to the event itself. In other words, the more the user became excited by the event of blocking, the more the game progressed. Interestingly, this upward trend also was visible when the SCR was measured in response to the agent's, rather than the user's, blocking. Thus, this shows that the user is emotion‐ ally involved with both the game and the agent and that this emotional engagement evolves over time, suggesting affecting symbiosis (Fig. 7).

**Fig. 7.** Skin conductance response rate (SCR/s) related to blocking the puck as the game progresses.

#### **6 Discussion and Conclusion**

The emergence of interest in human–computer symbiosis and affective communication calls for examinations into the possibilities of affective symbiosis in the human– computer paradigm. Here, we developed a competitive VR game environment working as a stepping stone toward a system that allows affective symbiosis. Previously, symbi‐ otic interaction has been considered a beneficial aspect of collaborative interaction. However, recent findings from human–human gaming have demonstrated that affective linkage between users is crucial in competitive contexts [9]. Developing a similar affec‐ tive linkage in a human–computer paradigm would require new sources of information, such as biosignals, and a system that is able to adapt the game events to such information. In the present study, we built a simple, behaviorally adaptive, competitive environment in order to investigate how the affective agent's emotional expressions affected the user's emotions and game performance.

The results of the present study showed that the agent's expressions affected emotional interdependence and co-presence, indicating that the adversary's happy and angry rather than neutral expressions caught users' attention and affected their conscious emotional state. Moreover, users reported concentrating more on the game when the agent expressed happiness. However, neither the difficulty of the game nor other gamerelated experiences were influenced by the agent's emotion. Thus, the results suggest that the computer agent's affective expressions modified the user's attention and emotional experience but did not change the degree to which the user found the game difficult or entertaining.

Additionally, the implicit measures supported this conclusion. Users' tonic EDA level was higher when their adversaries were angry, whereas users' game performance was not influenced by the agent's emotion. EDA and other implicit measures also revealed that users were likewise affected by the game events. Losing was related to more amplified tonic and phasic skin conductance responses than winning. Finally, users' EDA responses got stronger as the game progressed, indicating greater personal investment or engagement over time. The findings suggest that affective agents and game events influence the user's emotional state on both the conscious and implicit levels. These effects do not, however, modify the user's performance in the game or its perceived difficulty.

The limited effect of agents' affect could be due to the competitive nature of the task. In a negotiation context, another person's nonverbal cues might be more salient and thus influence the user's behavior more [6]. In turn, in more competitive contexts, the influ‐ ence of social cues is limited because the game events change more quickly and require continuous attention from the user. Nonetheless, the affectivity of the agent influenced users' emotional state regardless of the competitive context. This effect was consistent with earlier findings [20]. The results thus suggest that affective expressions presented in a competitive context do not necessarily make humans perform better in the game but improves the overall social experience.

The findings of this study will help us understand users' experiences while inter‐ acting with affective adversaries in an immersive competitive environment. The results also can be used to build an affectively adaptive system that integrates users' behaviors to their emotional responses when calculating further game events. More knowledge of agents' adaptive emotional responses and their effect on the user is required. In the future, one could, for instance, investigate whether varying the agents' affective state according to game events would intensify the effect of the agent's emotions on the user's emotions and behavior.

**Acknowledgements.** This work was supported by the Academy of Finland. Project number 268999 (HapCom).

### **References**

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# **Analysis of the Human-Computer Interaction on the Example of Image-Based CAPTCHA by Association Rule Mining**

Darko Brodi´c1(B) and Alessia Amelio2

1 University of Belgrade, Technical Faculty in Bor, V.J. 12, 19210 Bor, Serbia<EMAIL_ADDRESS>
2 DIMES University of Calabria, Via P. Bucci Cube 44, 87036 Rende, CS, Italy<EMAIL_ADDRESS>
**Abstract.** The paper analyzes the interaction between humans and computers in terms of response time in solving the image-based CAPT-CHA. In particular, the analysis focuses on the attitude of the different Internet users in easily solving four different types of image-based CAPTCHAs which include facial expressions such as: animated character, old woman, surprised face, worried face. To pursue this goal, an experiment is realized involving 100 Internet users in solving the four types of CAPTCHAs, differentiated by age, Internet experience, and education level. The response times are collected for each user. Then, association rules are extracted from user data, for evaluating the dependence of the response time in solving the CAPTCHA from age, education level and experience in Internet usage by statistical analysis. The results implicitly capture the users' psychological states showing in what states the users are more sensitive. It reveals to be a novelty and a meaningful analysis in the state-of-the-art.

**Keywords:** Image-based CAPTCHA · Statistical analysis · Association Rules · Human-Computer Interaction · Cognitive psychology

#### **1 Introduction**

HCI (Human Computer Interaction) studies the interaction between humans, i.e. computer users and computers as well as the major phenomena surrounding them. Essentially, HCI concerns development, evaluation and implementation of computer systems that are interactive in their core. The interactive design is linked with the feelings, words and their expressions. It represents usability elements that are firmly connected to the user-interface and human factors. Hence, it is deeply involved with the computer science, artificial intelligence and cognitive psychology. From all aforementioned, HCI includes the elements of understanding the computer user needs as well as the usability elements. To be efficient it should be fluent, cognitive, expressive and communicative.

Usability is the main concept in HCI. It is linked with the following elements: (i) easy to learn, (ii) easy to remember how to use, (iii) effective to use, and (iv) c The Author(s) 2017

DOI: 10.1007/978-3-319-57753-1 4

efficient to use. From this point of view, the puzzles such as CAPTCHA, which humans can easily solve, i.e. decrypt in contrast to the computers, represent an element of HCI. CAPTCHA is an acronym for Completely Automated Public Turing test to tell Computers and Humans Apart. It represents a test program with a task. If the user gives the right answer to the task asked by the program, then the program classifies it as a human, i.e. computer user [10]. Hence, it is created to differentiate the computer users from bots (computer program) in order to effectively login into a certain website. The aim of CAPTCHA is to stop the attacks made by bots. Hence, its main function is to: (i) protect web sites, applications, interfaces and services such as Google, Yahoo or similar, (ii) prevent spam in blogs, and (iii) protect email addresses [9].

CAPTCHA involves three different elements: (i) usability, (ii) security, and (iii) practicality [3]. Usability is linked with the process of solving the CAPTCHA by the computer users. It measures the efficiency of solving the CAPTCHA as well as the response time the computer user needs to solve the CAPTCHA. However, it is sometimes linked with the user's acceptance of the CAPTCHA, too. Security is deeply connected to the difficulty of finding solutions to CAPTCHA by computer. Practicality refers to the easiness of the programmers realization of CAPTCHA.

In this study, the image-based type of CAPTCHA will be considered. Among all types of CAPTCHA, the image-based ones are the most related to the elements of HCI, because they are linked to cognitive psychology. Also, the tested CAPTCHA samples are deeply connected to facial expressions, whose solving is influenced by human factors [6]. The main goal of this paper is exploring the usability elements such as user response time, usage, and preferences related to the image-based CAPTCHA which includes the facial expressions. We used four different image-based CAPTCHAs for testing purposes. Each of them consists of five different images and only one is related to the solution of CAPTCHA. In the experiment, the given CAPTCHAs are tested on a population of 100 different Internet users. The tested Internet users are differentiated by the given attributes: (i) age, (ii) Internet experience, and (iii) educational level. Specifically, we investigate (i) users' response time relating to the usability aspects of the CAPTCHA, and (ii) usage statistics connected to the response time of efficiently solving image-based CAPTCHA which includes elements of facial expression. To process tested data, the association rules are used. In this way, the user response time to efficiently solve certain CAPTCHA is analyzed by the association rule method. The obtained results will show the natural dependence and relationship between different variables which are of importance to the way of solving the CAPTCHA. Also, this approach identifies the influence of facial expression to efficiently solve the image-based CAPTCHA. Hence, it is invaluable for choosing a certain type of CAPTCHA to be accustomed to a certain group of Internet users. At the end, it improves the knowledge about the symbiotic system, because it gives information about to what human states the users are more sensitive, determining a speed response in solving the CAPTCHA.

The paper is organized as follows. Section 2 describes the image-based CAPT-CHA. Section 3 clarifies the data mining elements like association rules, which will be used as the main evaluation tool. Section 4 describes the elements of the experiment. Section 5 presents the results and gives the discussion. Section 6 draws conclusions.

# **2 CAPTCHA**

All CAPTCHAs can be divided into the following groups: (i) text-based CAPT-CHA, (ii) video-based CAPTCHA, (iii) audio-based CAPTCHA, and (iv) imagebased CAPTCHA. Text-based CAPTCHA is the most commonly used CAPT-CHA. It usually includes distorted text with different background. To solve this type of CAPTCHA, the distorted characters should be correctly recognized. It is easy to generate, but easily exposed to vulnerable attacks by bot due to efficient Optical Character Recognition (OCR) software. Video-based CAPTCHA uses video to create a puzzle to be solved. YouTube videos are typically used as a basis. To be solved, a CAPTCHA user is asked to tag videos with descriptive keywords. The users can reach an efficiency of solving CAPTCHA up to 90%. Audio-based CAPTCHA usually plays a set of characters or words. The users should recognize and type them in the designated area. This type of CAPTCHA is critically exposed to bot attacks due to rapid development in the speech recognition. The attack rate can reach up to 70%. Image-based CAPTCHA is usually considered as the most advanced and safest one. This type of CAPTCHA requires users to find a desired image inside a list of images. Because it is based on image details, it represents a very difficult task for bots. Furthermore, this type of CAPTCHA can easily integrate HCI elements connected to cognitive psychology. Figure 1 shows some image-based CAPTCHA types.

**Fig. 1.** Image-based CAPTCHA types

Existing image-based CAPTCHAs generally rely on image classification. In that way, the users see a series of images, which have to be identified as well as the relationship between them. Identifying the facial expression is a task accustomed to the humans [6]. Hence, it is easy to be solved by humans, but an impossible task for bots. Accordingly, integrating such HCI elements in an image-based CAPTCHA can create a very successful CAPTCHA in terms of security, usability as well as practicality.

#### **3 Association Rule Mining**

Association Rules (ARs) are the most intuitive tool to find the frequent sets of feature values and to understand the co-occurrence of these values in a dataset [1]. We start by defining the set of items representing all values *n* features can assume as *T* = *T*1 ∪ *T*2 ∪*, ...,*∪ *Tn*, where *Ti* = {*t* 1 *i , t*2 *i , ..., tx i* } is the set of *x* items which are the possible values of feature *i*. Accordingly, a transaction *I* in our case is a subset of *n* items from *T*, such that each item is a value of a distinct feature. This means that ∀*t j i , th k* ∈ *I*, *i* = *k*, and *j* and *h* are two possible values respectively of feature *i* and *k*. From the aforementioned, an Association Rule (AR) is defined as an implication *W* ⇒ *Z*, characterized by *support* and *confidence*, where *W* and *Z* represent disjoint sets of items, called respectively antecedent and consequent. Support quantifies the frequency of *W* ∪ *Z* in the transactions inside the dataset. It is related to the statistical significance of AR. Confidence measures the frequency of *W* ∪ *Z*, given the transactions containing *W*. It evaluates the conditional probability of *Z* given *W*.

The main goal is the extraction of the ARs whose support value is ≥ *minsupport* and whose confidence value is ≥ *minconf idence*, by adopting the well-known *Apriori* algorithm [2]. It is composed of two main phases: (i) finding all the sets of items with frequency ≥ *minsupport* in the dataset; (ii) determining the ARs from obtained sets of items, taking into account the *minconfidence* threshold. The concept underlying the algorithm is based on the *antimonotonicity* property: if a set of items is infrequent, also every its superset will be. Accordingly, unfrequent sets of items are eliminated in each iteration from the algorithm. First of all, the algorithm finds the sets of items of size *k* whose frequency is ≥ *minsupport*. Then, the sets of items are enlarged to size *k* + 1, by inserting the only sets of items of size 1 and frequency ≥ *minsupport*. These two phases are iterated from *k* = 1 to a given value of *k*, such that sets of items of size *k* + 1 with frequency ≥ *minsupport* cannot be detected further. Secondly, rules are created from each frequent set of items *F*. It is performed by detecting all the subsets *f* ⊂ *F* such that confidence value of *f* → *F* − *f* is ≥ *minconf idence*.

Another interesting measure to evaluate the ARs has been introduced in [5], and it is called *lift*. Lift quantifies the number of times *W* and *Z* co-occur more often than expected if they were statistically independent [7]. If the lift assumes a value of 1, then *W* and *Z* are independent. On the contrary, if the lift assumes a value greater than 1, *W* and *Z* will co-occur more often than expected. It indicates that the occurrence of *W* has a positive effect on the occurrence of *Z*.

#### **4 Experiment**

Our experiment measures the usability elements of the proposed image-based CAPTCHAs, which incorporate facial expression elements. In that sense, the time needed to efficiently solve a certain CAPTCHA is measured. Each CAPT-CHA includes five images representing a certain facial expression. One of the images presents the asked facial expression among the other four images which are not. Hence, the users should choose the right image among the five given under consideration. The main goal is to measure and differentiate the influence of facial expressions to efficiently solve the image-based CAPTCHA linked with cognitive elements. We propose four CAPTCHAs representing the following facial expressions: (i) animated character, (ii) old woman, (iii) surprised face, and (iv) worried face. Figure 2 shows the proposed CAPTCHAs.

**Fig. 2.** Image-based CAPTCHAs incorporating facial expressions: animated character, old woman, surprised face and worried face

#### **4.1 Participants**

An experiment is conducted on a population sample of 100 Internet users. The first half of them is composed of women, and the other half is composed of men. The age of participants is from 18 to 52 years. They have a secondary or higher education level. The population sample includes students, engineers, officials, but also unemployed people. Participants have an Internet experience of at least one up to nine years. All users have joined the experiment voluntarily, knowing that their data would be made publicly available for study purposes, but without publishing their names (anonymous poll). The interview of each participant was conducted personally, by asking to solve the image-based CAPTCHAs. In particular, each Internet user is required to solve the 4 different types of the aforementioned image-based CAPTCHAs. For each user, the response times (in seconds) for finding the solution to the CAPTCHAs are registered. Hence, participants are differentiated by their: (i) age, (b) educational level, and (iii) working experience on the Internet. Also, they are characterized by a certain response time in solving the image-based CAPTCHAs.

## **4.2 Dataset Creation**

At the end, a dataset including 7 features (age, education level, number of years of internet usage, response time in solving the animated character, old woman, surprised face, and worried face CAPTCHAs) is created, for a total of 100 instances of 7 features each. The feature corresponding to the age of the Internet users is linearly divided into two groups, which are below 35 years and above 35 years. The education level feature can assume also two values, which are the higher education and the secondary education levels. About the number of years of Internet usage, this feature has integer values from 0 to +∞. In particular, in the dataset it varies from 1 to 9. Consequently, it is discretized by employing an Equal-Width Discretization [8], obtaining 3 different ranges, named as high Internet usage (*>* 6 years), middle Internet usage (from 4 to 6 years), and low Internet usage (*<* 4 years). The features corresponding to the response time in solving the 4 different types of image-based CAPTCHA have decimal values varying from 0.0 to +∞. A test has been conducted for discretization of these features with multiple methods, including Equal-Width Discretization. It demonstrated that most of the methods fail in this task because they obtain a poor discretization. On the contrary, discretization by adopting the K-Medians clustering algorithm [4], whose advantage is finding the natural groups of data based on their characteristics, performed successfully, identifying 3 different ranges. The aim of the K-Medians is to determine *K* ranges from the values of the features, by minimizing a function *J*. It measures the total sum of *L*1 norm between each value in a certain range and its centroid, which is its median value. We run the K-Medians algorithm 10 times with *K* = 3 and selected the set of ranges with the lowest value of *J* as the optimal solution. Hence, K-Medians found 3 ranges corresponding to low response time (*<* 12.34 s), middle response time (from 12.34 s to 27.30 s), and high response time (*>* 27.30 s). Table 1 reports the feature values, eventually discretized.

**Table 1.** Possible values and corresponding ranges for each feature of the dataset

#### **4.3 Association Rules Extraction**

The obtained dataset is subjected to the *Apriori* algorithm for detection of the ARs, by which we investigate on the association among the different feature values. Specifically, we find sets of frequently co-occurring feature values and that are related to a given response time in solving the 4 types of image-based CAPTCHA. Also, by tuning the *minconfidence* and *minsupport* to appropriate values, we are able to evaluate the strength of such a relationship. It determines a psychological analysis of the HCI, because we are able to understand (i) to which human conditions (age, education level, number of years of internet usage) is more likely the image-based CAPTCHA to be easily solved, and (ii) what type of image-based CAPTCHA is easier to be solved by a human subject. It is an interesting psychological analysis, if we think that each type of imagebased CAPTCHA corresponds to a given condition or "state of mind" (worried, surprised, etc.), to which human subjects could be more or less sensitive. For this reason, the analysis is split into 4 parts, each corresponding to detect the ARs for a given type of image-based CAPTCHA. This means that we extract all the ARs by adopting the *Apriori* algorithm. Then, we filter them by selecting those ARs having only the response time feature value of a certain type of image-based CAPTCHA as consequent. Finally, we group the ARs based on the response time of the different types of image-based CAPTCHA, and evaluate them separately.

In our case, *n* is equal to 7 features. All the possible values of each of them are reported in Table 1. An example of transaction *I*, corresponding to a row of the dataset, with eventually discretized feature values, is given in Table 2.

**Table 2.** Example of transaction *I* (row of the dataset)

Also, an example of AR defined on this transaction is given below:

(Educ. level) (Num. years Internet usage) (Resp. time worried face)

*secondary educ., middle Internet usage* → *low response time* (1)

The antecedent *W* of the AR is {*secondary educ., middle Internet usage*}, while the consequent *Z* is {*low response time*}. We can observe that *W* ∪ *Z*, which is {*secondary educ., middle Internet usage, low response time*}, is found inside the transaction in Table 2. The meaning of this AR is that a secondary education level and a middle experience in Internet usage are enough to easily solve the worried face CAPTCHA.

#### **5 Results and Discussion**

Feature discretization, extraction and post-processing of the ARs have been performed in MATLAB R2012a on a notebook quad-core at 2.2 GHz, 16 GB RAM and UNIX operating system.

Tables 3, 4, 5 and 6 show the ARs, sorted by antecedent, detected from the dataset by the *Apriori* algorithm, whose consequent is the response time in solving the 4 different types of CAPTCHA. For each AR, we report the antecedent *W*, the consequent *Z*, the support, the confidence and the lift values. In order to extract ARs of interest, we fix the *minsupport* and *minconfidence* respectively to 5% and to 50%. The differences between the tables corresponding to missing ARs with the same antecedent are denoted as '-'. It is worth noting that the lift is always greater than 1. It indicates that the extracted ARs are statistically meaningful and that the antecedent and consequent are always positively correlated.

**Table 3.** Sorted association rules extracted from the dataset, for which the consequent is the response time in solving animated character CAPTCHA

In particular, we can observe that animated character CAPTCHA is the easiest to be solved among the 4 types of CAPTCHA (see Table 3). It is mainly because in general the ARs with a low response time exhibit higher support and

**Table 4.** Sorted association rules extracted from the dataset, for which the consequent is the response time in solving the old woman CAPTCHA

confidence values than in the other types of CAPTCHA. It indicates that a low response time is likely to occur more often than in the other types of CAPTCHA. In fact, users below 35 years are associated to a low response time, also independently from the education level, which has no meaningful impact in solving this CAPTCHA, and from the level of internet usage, with a support of 0.50, a confidence of 1.00 and a lift of 1.47. The second more difficult type of CAPTCHA to solve is the old woman one (see Table 4). In fact, the pattern for the users below 35 years is maintained with similar but slightly lower values of support and confidence with respect to animated character CAPTCHA. Because in general the support and confidence values of the rules whose consequent is the low response time are smaller in the case of old woman than in the case of animated character, we will have a smaller probability to determine a low response time in solving the CAPTCHA. On the other hand, the ARs extracted for old woman CAPTCHA present the highest number of differences with respect to the other types of CAPTCHA (see the four missing rules denoted as '-' in Table 4). The

**Table 5.** Sorted association rules extracted from the dataset, for which the consequent is the response time in solving surprised face CAPTCHA

third type of CAPTCHA, more difficult to be solved, is the surprised face one (see Table 5), having the same pattern for users below 35 years, but corresponding lower support and confidence values, indicating that the correlation between users below 35 years and low response time is weaker than before. Also, a higher education level is sufficient to obtain a low response time in animated character, while it is able to solve the surprised face CAPTCHA only in a middle response time. The last type of CAPTCHA presenting the highest difficulty level is the worried face one (see Table 6). We can observe that the extracted ARs for this CAPTCHA are very similar to the ARs extracted for surprised face CAPTCHA. In particular, the pattern for the users below 35 years is also visible, but with support and confidence values less than in the surprised face CAPTCHA, and in general in all the other types of CAPTCHA. It indicates that a smaller percentage of transactions contain the co-occurrence of users below 35 years and low response time. This means that users below 35 years are less correlated to a low response time in solving this CAPTCHA. In general, the differences in

**Table 6.** Sorted association rules extracted from the dataset, for which the consequent is the response time in solving worried face CAPTCHA

the 4 types of CAPTCHA represent distinctive characteristics of each type of CAPTCHA that is mandatory to deeply analyze.

Specifically, looking at Table 3, we can observe that users above 35 years and with a middle internet usage are associated to a middle response time in solving the animated character CAPTCHA, also independently from the education level. This pattern is not present for the old woman CAPTCHA (see Table 4), for which users above 35 years and with a middle internet usage are associated to a middle response time only in the case of secondary education level. Furthermore, in animated character CAPTCHA, we find that users above 35 years are related to a middle response time, also independently from education level (hence, also with higher education level) and number of years of Internet usage. On the contrary, for old woman CAPTCHA (see Table 4), the strongest rule in terms of support, confidence and lift (respectively of 0.14, 0.54 and 2.34) involving the users above 35 years indicates that users above 35 years are able to solve the CAPTCHA in a middle time if they have a secondary education level. Again, looking at Table 5 of surprised face CAPTCHA, another difference can be observed with animated character CAPTCHA. In particular, it is required a higher education level to the users above 35 years to solve this CAPTCHA in a middle time. On the contrary, for the animated character CAPTCHA, the users above 35 years are able to solve it in a middle time also when they have a secondary education level. In the case of worried face CAPTCHA in Table 6, we observe a meaningful difference with animated character CAPTCHA for the users above 35 years. In fact, in the case of animated character CAPTCHA, the users above 35 years are associated to a middle response time even if they have a secondary education level, while, in the case of worried face, if they have a higher education level, with similar values of support, confidence and lift. A last difference is visible between the ARs of the surprised face CAPTCHA and those of the worried face CAPTCHA. In fact, in the second ones, a middle internet usage is related to a middle response time in solving that CAPTCHA, with a support of 0.28, a confidence of 0.52 and a lift of 1.18. Differently, this rule is not present for the surprised face CAPTCHA.

Finally, we can make the following observations: (i) Internet users below 35 years have a good attitude in solving the image-based CAPTCHA; (ii) the easiest type of image-based CAPTCHA to be solved is the animated character one, followed by old woman, surprised face and the more complex worried face ones; (iii) the users above 35 years are able to solve the animated character CAPTCHA in a moderate time; (iv) the users above 35 years have an attitude to solve the old woman CAPTCHA in a moderate time if they have a secondary education level; (v) it is required a higher education level to the users above 35 years to solve the surprised face and the worried face CAPTCHAs in a moderate time.

#### **6 Conclusions**

The paper analyzed the human response to the facial expressions given in specific image-based CAPTCHA. Hence, it explored the computer users' response time (reaction rate) to the given CAPTCHA according to cognitive psychology linked with facial expression recognition. The obtained results were statistically processed by the ARs. The analysis by ARs revealed interesting co-occurrences of feature values and their association with a given response time to solve the facial expression image-based CAPTCHA. Although the results were quite similar, the AR methodology proved to be powerful to differentiate subtle variations between processed values. In that way, the results showed that computer users can more easily recognize CAPTCHAs that incorporate animated characters. Also, the facial expressions such as surprised or worried face are the most difficult to recognize and differentiate. It is worth noting that recognition of the old woman among the other images was characterized by different elements to be influenced to than the images presented in other analyzed CAPTCHAs. The obtained results and further research in the given area can help in finding the most appropriate CAPTCHA for a wider range of Internet users. Also, the knowledge about the symbiotic system is improved by showing in what human states the users are more sensitive.

In this context, ARs have not been properly used for prediction, but for unsupervised detection of the natural dependencies and relationships between feature values and response times. Hence, future work will be focused on tree-based methods for prediction of the response time by supervised learning. Analysis will be further enriched by providing a larger dataset and a literature review of cognitive science on how people differentiated by age or educational level react to different image stimuli. It will be useful to formalize hypotheses on the expected correlations between the response time and the other features.

**Acknowledgments.** The authors are grateful to the participants for publicly providing their data. This work was partially supported by the Grant of the Ministry of Education, Science and Technological Development of the Republic Serbia, as a part of the project TR33037.

## **References**

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# **The "NeuroDante Project": Neurometric Measurements of Participant's Reaction to Literary Auditory Stimuli from Dante's "Divina Commedia"**

Giulia Cartocci1(✉) , Anton Giulio Maglione1 , Enrica Modica1 , Dario Rossi1 , Paolo Canettieri2 , Mariella Combi2 , Roberto Rea2 , Luca Gatti2 , Carmen Silvia Perrotta1 , Francesca Babiloni1 , Roberto Verdirosa1 , Roberta Bernaudo1 , Elena Lerose1 , and Fabio Babiloni1

1 Department of Molecular Medicine, University of Rome Sapienza, Rome, Italy<EMAIL_ADDRESS>
2 Department of European, American and Intercultural Studies, University of Rome Sapienza, Rome, Italy

**Abstract.** This work is a pilot study that used neurometric indexes during the listening of selected pieces of Dante's "Divina Commedia" in 20 participants. Half of them had a literary formation (Humanist; university students of literature) while the other half of is attending other university courses (Not Humanist). The study applied the electroencephalographic (EEG) rhythms variations, the heart rate (HR) and galvanic skin response (GSR) during the listening of the excerpts. The neurometric indexes here employed were the Approach Withdrawal (AW), the Cerebral Effort (CE) and the Emotional indexes (EI). Results for the compar‐ isons of the estimated AW, CE and EI related to the perception of the *canticas* showed as the Humanist group reported higher AW and EI values when compared to the Not Humanist sample (p < 0.03 and p < 0.01, respectively). Results suggest that the perception of the aesthetic experience is significantly modulated by the previous specific knowledge experienced by the participants. Finally, results of this kind of research could find application in the implementation of software and devices based on symbiotic relation with the perspective reader or listener of a literature opera, in order to personalize and maximize the fruition of them.

**Keywords:** Approach Withdrawal · Cerebral Effort · EEG · Alpha · Theta · Electrodermal Activity · HR · GSR · Emotion · Russell's circumplex model of affects

#### **1 Introduction**

Reading and listening stories is a very peculiar human activity. In fact, other animal species on earth have not engaged (as far as we know) in such task. On a more basic level, a lot of knowledge has been accumulated in these last three decades of neuro‐ scientific studies on the perception and appreciation of visual stimuli and the "internal" factors able to shape such perception [1]. Moreover, a lot of knowledge has been also obtained on the evaluation of the cerebral activity related to the judgment of syntactic and grammatical violations on speech listening [2]. However, a recent research path moves from the measurements and the relative understanding of the basic characteristic of the sensory perception towards the more complex way in which we, as specie, perceive and enjoy the reading and listening of stories [3]. In this attempt, researchers have started to measures cerebral and emotional activities manipulating some factors that could alter or increase the pleasure in the story perception [4, 5]. Furthermore, the knowledge of the features modulating the emotional involvement in the reader could be also implemented in designing e-books [6], also potentially based on a symbiotic inter‐ action with the user. Additionally, the neurophysiological approach could provide meas‐ urable information on the emotional content of a text, as evidenced by the lowering of the heart rate and the variation of the breathing and the electroencephalographic (EEG) alpha rhythm asymmetry [7].

In this scenario, neuroaesthetics is an emerging disciplines investigating the biolog‐ ical underpinnings of aesthetic experiences, and since its formal birth in 90s by Semir Zeki (for a review [8]) it constitutes a field receiving growing interest by the scientific community. Although traditional topic of neuroaesthetic investigation are visual art and music [9, 10], an enlargement in the horizon of artistic items to be investigated is begin‐ ning and literature and poetry are experiencing a renewed scientific interest from the neuroaesthetics perspective [3]. That is, the question we would like address here is "Which biological underpinnings make classical authors cognitively and emotionally engaging?". Two aspects of poetry may contribute to the emotional responses it may elicit: its lexical content and its structural features (i.e., poetic form). Meter and rhyme constitute the key features in poetry [11], additionally they have been found to be significant contributors to the aesthetic and emotional perception of poetry [4], but it is difficult to investigate the contribution of the content "per se" due to the needing to compare different kinds of texts. A possibility to overcome this limitation is offered by the famous Italian XIV century poem the "Divina Commedia" by Dante Alighieri (1265– 1321). The book is characterized by a repetitive and constant structure, as it is composed by three parts (canticas: Inferno –Hell–, Purgatorio –Purgatory- and Paradiso –Para‐ dise-), each part is composed by thirty-three cantos, for an average length of 142 verses. The verse scheme used, "terza rima", is hendecasyllabic (lines of eleven syllables), with the lines composing tercets relying to the rhyme scheme xyx yzy z. Each cantica, despite the conservation of the metric structure is characterized by a different content. The reader follows Dante's journey through Hell, Purgatory and Paradise, where the poet respec‐ tively meets damned souls, then souls expiating their sins and finally enjoys the vision of God. Studying the reaction to the exposure to stanzas belonging to each of the three canticas would enable to test the effect of the content of the excerpts, maintaining the meter and the rhyme scheme constant. Furthermore, the comparison among different canticas would enable to assess the effect of a potentially differential previous knowledge of the peculiar cantica.

In this work, we described the results of a pilot study that used neurometric indexes during the listening of a selected pieces of the Divina Commedia in a reduced sample of voluntary participants. Noteworthy, half of the participants had a literary formation in their advanced studies (Humanist; they are students of literature at the University) while the other half of the sample are attending other university courses (Not Humanist). The study applied the gathering of the electroencephalographic rhythms variations, as well as the heart rate (HR) and galvanic skin response (GSR) during the listening of the excerpts. The neurometric indexes here employed were the Approach-Withdrawal (AW) [12], the Effort (CE) [13] and the Emotional indexes (EI) [14]. It is worth noticing that different research teams previously validated those indexes in literature. Results are related to the comparisons of the estimated AW, CE and EI related to the perception of the analyzed canticas in the sample population.

# **2 Methods**