Feel, Don\u27t Think Review of the Application of Neuroscience Methods for Conversational Agent Research

Conversational agents (CAs) equipped with human-like features (e.g., name, avatar) have been reported to induce the perception of humanness and social presence in users, which can also increase other aspects of users’ affection, cognition, and behavior. However, current research is primarily based on self-reported measurements, leaving the door open for errors related to the self-serving bias, socially desired responding, negativity bias and others. In this context, applying neuroscience methods (e.g., EEG or MRI) could provide a means to supplement current research. However, it is unclear to what extent such methods have already been applied and what future directions for their application might be. Against this background, we conducted a comprehensive and transdisciplinary review. Based on our sample of 37 articles, we find an increased interest in the topic after 2017, with neural signal and trust/decision-making as upcoming areas of research and five separate research clusters, describing current research trends

Conversational AI Agents: Investigating AI-Specific Characteristics that Induce Anthropomorphism and Trust in Human-AI Interaction

The investment in AI agents has steadily increased over the past few years, yet the adoption of these agents has been uneven. Industry reports show that the majority of people do not trust AI agents with important tasks. While the existing IS theories explain users’ trust in IT artifacts, several new studies have raised doubts about the applicability of current theories in the context of AI agents. At first glance, an AI agent might seem like any other technological artifact. However, a more in-depth assessment exposes some fundamental characteristics that make AI agents different from previous IT artifacts. The aim of this dissertation, therefore, is to identify the AI-specific characteristics and behaviors that hinder and contribute to trust and distrust, thereby shaping users’ behavior in human-AI interaction. Using a custom-developed conversational AI agent, this dissertation extends the human-AI literature by introducing and empirically testing six new constructs, namely, AI indeterminacy, task fulfillment indeterminacy, verbal indeterminacy, AI inheritability, AI trainability, and AI freewill

Towards Computer-Assisted Regulation of Emotions

Tunteet ovat keskeinen ja erottamaton osa ihmisen toimintaa, ajattelua ja yksilöiden välistä vuorovaikutusta. Tunteet luovat perustan mielekkäälle, toimivalle ja tehokkaalle toiminnalle. Joskus tunteiden sävy tai voimakkuus voi kuitenkin olla epäedullinen henkilön tavoitteiden ja hyvinvoinnin kannalta. Tällöin taidokas tunteiden säätely voi auttaa saavuttamaan terveen ja menestyksellisen elämän. Väitöstyön tavoitteena oli muodostaa perusta tulevaisuuden tietokoneille, jotka auttavat säätelemään tunteita. Tietokoneiden tunneälyä on toistaiseksi kehitetty kahdella alueella: ihmisen tunnereaktioiden mittaamisessa ja tietokoneen tuottamissa tunneilmaisuissa. Viimeisimmät teknologiat antavat tietokoneille jo mahdollisuuden tunnistaa ja jäljitellä ihmisen tunneilmaisuja hyvinkin tarkasti. Väitöstyössä toimistotuoliin asennetuilla paineantureilla kyettiin huomaamattomasti havaitsemaan muutoksia kehon liikkeissä: osallistujat nojautuivat kohti heille esitettyjä tietokonehahmoja. Tietokonehahmojen esittämät kasvonilmeet ja kehollinen etäisyys vaikuttivat merkittävästi osallistujien tunne- ja tarkkaavaisuuskokemuksiin sekä sydämen, ihon hikirauhasten ja kasvon lihasten toimintaan. Tulokset osoittavat että keinotekoiset tunneilmaisut voivat olla tehokkaita henkilön kokemusten ja kehon toiminnan säätelyssä. Väitöstyössä laadittiin lopulta vuorovaikutteinen asetelma, jossa tunneilmaisujen automaattinen tarkkailu liitettiin tietokoneen tuottamien sosiaalisten ilmaisujen ohjaamiseen. Osallistujat pystyivät säätelemään välittömiä fysiologisia reaktioitaan ja tunnekokemuksiaan esittämällä tahdonalaisia kasvonilmeitä (mm. ikään kuin hymyilemällä) heitä lähestyvälle tietokonehahmolle. Väitöstyön tuloksia voidaan hyödyntää laajasti, muun muassa uudenlaisten, ihmisen luonnollisia vuorovaikutustapoja paremmin tukevien tietokoneiden suunnittelussa.Emotions are intimately connected with our lives. They are essential in motivating behaviour, for reasoning effectively, and in facilitating interactions with other people. Consequently, the ability to regulate the tone and intensity of emotions is important for leading a life of success and well-being. Intelligent computer perception of human emotions and effective expression of virtual emotions provide a basis for assisting emotion regulation with technology. State-of-the-art technologies already allow computers to recognize and imitate human social and emotional cues accurately and in great detail. For example, in the present work a regular looking office chair was used to covertly measure human body movement responses to artifical expressions of proximity and facial cues. In general, such artificial cues from visual agents were found to significantly affect heart, sweat gland, and facial muscle activities, as well as subjective experiences of emotion and attention. The perceptual and expressive capabilities were combined in a setup where a person regulated her or his more spontaneous reactions by either smiling or frowning voluntarily to a virtual humanlike character. These results highlight the potential of future emotion-sensitive technologies for creating supportive and even healthy interactions between humans and computers

Designing Adaptive Instruction for Teams: a Meta-Analysis

The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams

Overcoming foreign language anxiety in an emotionally intelligent tutoring system

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning. Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct and less regulated by the learner, but it is uncomfortable and requires the learner to be in the lab. Employing self-reports is scalable because it is easy to administer in the lab and online. However, it interrupts the learning flow, and people sometimes respond inaccurately. Using sensor-free human behavioral metrics is a scalable and practical measurement because it is feasible online or in class with minimum adjustments. To overcome FLA, researchers have studied the use of robots, games, or intelligent tutoring systems (ITS). Within these technologies, they applied soothing music, difficulty reduction, or storytelling. These methods lessened FLA but had limitations such as distracting the learner, not improving performance, and producing cognitive overload. Using an animated agent that provides motivational supportive feedback could reduce FLA and increase learning. It is necessary to measure FLA effectively with minimal interruption and then successfully reduce it. In the context of an e-learning system, I investigated ways to detect FLA using sensor-free human behavioral metrics. This scalable and practical method allows us to recognize FLA without being obtrusive. To reduce FLA, I studied applying emotionally adaptive feedback that offers motivational supportive feedback by an animated agent

Towards an Integrative Information Society: Studies on Individuality in Speech and Sign

The flow of information within modern information society has increased rapidly over the last decade. The major part of this information flow relies on the individual’s abilities to handle text or speech input. For the majority of us it presents no problems, but there are some individuals who would benefit from other means of conveying information, e.g. signed information flow. During the last decades the new results from various disciplines have all suggested towards the common background and processing for sign and speech and this was one of the key issues that I wanted to investigate further in this thesis. The basis of this thesis is firmly within speech research and that is why I wanted to design analogous test batteries for widely used speech perception tests for signers – to find out whether the results for signers would be the same as in speakers’ perception tests. One of the key findings within biology – and more precisely its effects on speech and communication research – is the mirror neuron system. That finding has enabled us to form new theories about evolution of communication, and it all seems to converge on the hypothesis that all communication has a common core within humans. In this thesis speech and sign are discussed as equal and analogical counterparts of communication and all research methods used in speech are modified for sign. Both speech and sign are thus investigated using similar test batteries. Furthermore, both production and perception of speech and sign are studied separately. An additional framework for studying production is given by gesture research using cry sounds. Results of cry sound research are then compared to results from children acquiring sign language. These results show that individuality manifests itself from very early on in human development. Articulation in adults, both in speech and sign, is studied from two perspectives: normal production and re-learning production when the apparatus has been changed. Normal production is studied both in speech and sign and the effects of changed articulation are studied with regards to speech. Both these studies are done by using carrier sentences. Furthermore, sign production is studied giving the informants possibility for spontaneous speech. The production data from the signing informants is also used as the basis for input in the sign synthesis stimuli used in sign perception test battery. Speech and sign perception were studied using the informants’ answers to questions using forced choice in identification and discrimination tasks. These answers were then compared across language modalities. Three different informant groups participated in the sign perception tests: native signers, sign language interpreters and Finnish adults with no knowledge of any signed language. This gave a chance to investigate which of the characteristics found in the results were due to the language per se and which were due to the changes in modality itself. As the analogous test batteries yielded similar results over different informant groups, some common threads of results could be observed. Starting from very early on in acquiring speech and sign the results were highly individual. However, the results were the same within one individual when the same test was repeated. This individuality of results represented along same patterns across different language modalities and - in some occasions - across language groups. As both modalities yield similar answers to analogous study questions, this has lead us to providing methods for basic input for sign language applications, i.e. signing avatars. This has also given us answers to questions on precision of the animation and intelligibility for the users – what are the parameters that govern intelligibility of synthesised speech or sign and how precise must the animation or synthetic speech be in order for it to be intelligible. The results also give additional support to the well-known fact that intelligibility in fact is not the same as naturalness. In some cases, as shown within the sign perception test battery design, naturalness decreases intelligibility. This also has to be taken into consideration when designing applications. All in all, results from each of the test batteries, be they for signers or speakers, yield strikingly similar patterns, which would indicate yet further support for the common core for all human communication. Thus, we can modify and deepen the phonetic framework models for human communication based on the knowledge obtained from the results of the test batteries within this thesis.Siirretty Doriast

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills