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Publication Metadata only Are tutor robots for everyone? the influence of attitudes, anxiety, and personality on robot-led language learning(Springer, 2022) Kumkale, G. Tarcan; Department of Psychology; Department of Psychology; Department of Psychology; N/A; Department of Psychology; Küntay, Aylin C.; Kanero, Junko; Oranç, Cansu; Koşkulu, Sümeyye; Göksun, Tilbe; Faculty Member; Researcher; Researcher; Master Student; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; 178879; N/A; N/A; N/A; 47278Do some individuals benefit more from social robots than others? Using a second language (L2) vocabulary lesson as an example, this study examined how individual differences in attitudes toward robots, anxiety in learning L2, and personality traits may be related to the learning outcomes. One hundred and two native Turkish-speaking adults were taught eight English words in a one-on-one lesson either with the NAO robot (N = 51) or with a human tutor (N = 51). The results in both production and receptive language tests indicated that, following the same protocol, the two tutors are fairly comparable in teaching L2 vocabulary. Negative attitudes toward robots and anxiety in L2 learning impeded participants from learning vocabulary in the robot tutor condition whereas the personality trait of extroversion negatively predicted vocabulary learning in the human tutor condition. This study is among the first to demonstrate how individual differences can affect learning outcomes in robot-led sessions and how general attitudes toward a type of device may affect the ways humans learn using the device.Publication Open Access Children's reliance on the non-verbal cues of a robot versus a human(Public Library of Science, 2019) Verhagen J.; Van Den Berghe R.; Oudgenoeg-Paz O.; Leseman P.; Department of Psychology; Küntay, Aylin C.; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; 178879Robots are used for language tutoring increasingly often, and commonly programmed to display non-verbal communicative cues such as eye gaze and pointing during robot-child interactions. With a human speaker, children rely more strongly on non-verbal cues (pointing) than on verbal cues (labeling) if these cues are in conflict. However, we do not know how children weigh the non-verbal cues of a robot. Here, we assessed whether four- to six-year-old children (i) differed in their weighing of non-verbal cues (pointing, eye gaze) and verbal cues provided by a robot versus a human; (ii) weighed non-verbal cues differently depending on whether these contrasted with a novel or familiar label; and (iii) relied differently on a robot's non-verbal cues depending on the degree to which they attributed human-like properties to the robot. The results showed that children generally followed pointing over labeling, in line with earlier research. Children did not rely more strongly on the non-verbal cues of a robot versus those of a human. Regarding pointing, children who perceived the robot as more human-like relied on pointing more strongly when it contrasted with a novel label versus a familiar label, but children who perceived the robot as less human-like did not show this difference. Regarding eye gaze, children relied more strongly on the gaze cue when it contrasted with a novel versus a familiar label, and no effect of anthropomorphism was found. Taken together, these results show no difference in the degree to which children rely on non-verbal cues of a robot versus those of a human and provide preliminary evidence that differences in anthropomorphism may interact with children's reliance on a robot's non-verbal behaviors.Publication Metadata only Competing with or against cozmo, the robot: influence of interaction context and outcome on mind perception(Springer, 2021) N/A; Department of Business Administration; N/A; Department of Business Administration; Department of Psychology; Lefkeli, Deniz; Özbay, Yağmur; Canlı, Zeynep Gürhan; Eskenazi, Terry; Teaching Faculty; Master Student; Faculty Member; Faculty Member; Department of Business Administration; Department of Psychology; College of Administrative Sciences and Economics; Graduate School of Social Sciences and Humanities; College of Administrative Sciences and Economics; College of Social Sciences and Humanities; N/A; N/A; 16135; 258780With the rise of integration of robots in our daily lives, people find their own ways of normalizing their interaction with artificial agents, one of which is attributing mind to them. Research has shown that attributing mind to an artificial agent improves the flow of the interaction and alters behavior following it. However, little is known about the the influence of the interaction context and the outcome of the interaction. Addressing this gap in the literature, we explored the influence of theInteraction Context(cooperation vs. competition) andOutcome(win vs. lose) on the attributed levels of mind to an artificial agent. To that end, we used an interactive game that consisted of trivia questions between teams of human participants and the robot Cozmo. We found that in the cooperation condition, those who lost as a team ascribed greater levels of mind to the agent compared to those who won as a team. However, participants who competed with and won against the robot attributed greater levels of mind to the agent compared to those who cooperated and won as a team. These results suggest that people attribute mind to artificial agents in a self-serving way, depending on the interaction context and outcome.Publication Metadata only Guidelines for designing social robots as second language tutors(Springer, 2018) Belpaeme, Tony; Vogt, Paul; van den Berghe, Rianne; Bergmann, Kirsten; de Haas, Mirjam; Kennedy, James; Oudgenoeg-Paz, Ora; Papadopoulos, Fotios; Schodde, Thorsten; Verhagen, Josje; Wallbridge, Christopher D.; Willemsen, Bram; de Wit, Jan; Hoffmann, Laura; Kopp, Stefan; Krahmer, Emiel; Montanier, Jean-Marc; Pandey, Amit Kumar; Department of Psychology; Department of Psychology; Department of Psychology; Department of Psychology; Department of Psychology; Department of Psychology; Göksun, Tilbe; Kanero, Junko; Küntay, Aylin C.; Geçkin, Vasfiye; Mamuş, Ayşe Ezgi; Oranç, Cansu; Faculty Member; Researcher; Faculty Member; Researcher; Researcher; Researcher; Department of Psychology; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; 47278; N/A; 178879; N/A; N/A; N/AIn recent years, it has been suggested that social robots have potential as tutors and educators for both children and adults. While robots have been shown to be effective in teaching knowledge and skill-based topics, we wish to explore how social robots can be used to tutor a second language to young children. As language learning relies on situated, grounded and social learning, in which interaction and repeated practice are central, social robots hold promise as educational tools for supporting second language learning. This paper surveys the developmental psychology of second language learning and suggests an agenda to study how core concepts of second language learning can be taught by a social robot. It suggests guidelines for designing robot tutors based on observations of second language learning in human-human scenarios, various technical aspects and early studies regarding the effectiveness of social robots as second language tutors.Publication Metadata only Learning deep temporal representations for fMRI brain decoding(Springer International Publishing Ag, 2015) Firat, Orhan; Aksan, Emre; Fatos T. Yarman; Department of Psychology; Öztekin, İlke; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; N/AFunctional magnetic resonance imaging (fMRI) produces low number of samples in high dimensional vector spaces which is hardly adequate for brain decoding tasks. In this study, we propose a combination of autoencoding and temporal convolutional neural network architecture which aims to reduce the feature dimensionality along with improved classification performance. The proposed network learns temporal representations of voxel intensities at each layer of the network by leveraging unlabeled fMRI data with regularized autoencoders. Learned temporal representations capture the temporal regularities of the fMRI data and are observed to be an expressive bank of activation patterns. Then a temporal convolutional neural network with spatial pooling layers reduces the dimensionality of the learned representations. By employing the proposed method, raw input fMRI data is mapped to a low-dimensional feature space where the final classification is conducted. In addition, a simple decorrelated representation approach is proposed for tuning the model hyper-parameters. The proposed method is tested on a ten class recognition memory experiment with nine subjects. Results support the efficiency and potential of the proposed model, compared to the baseline multi-voxel pattern analysis techniques.Publication Metadata only Robo2Box: a toolkit to elicit children's design requirements for classroom robots(Springer-Verlag Berlin, 2016) Barendregt, Wolmet; Department of Mechanical Engineering; Department of Media and Visual Arts; N/A; Department of Psychology; Obaid, Mohammad; Yantaç, Asım Evren; Kırlangıç, Güncel; Göksun, Tilbe; Undergraduate Student; Faculty Member; Master Student; Faculty Member; Department of Mechanical Engineering; Department of Media and Visual Arts; Department of Psychology; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); College of Engineering; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; 52621; N/A; 47278We describe the development and first evaluation of a robot design toolkit (Robo2Box) aimed at involving children in the design of classroom robots. We first describe the origins of the Robo2Box elements based on previous research with children and interaction designers drawing their preferred classroom robots. Then we describe a study in which 31 children created their own classroom robot using the toolkit. We present children’s preferences based on their use of the different elements of the toolkit, compare their designs with the drawings presented in previous research, and suggest changes for improvement of the toolkit.Publication Open Access Second language tutoring using social robots: a large-scale study(Institute of Electrical and Electronics Engineers (IEEE), 2019) Vogt, Paul; van den Berghe, Rianne; de Haas, Mirjam; Hoffman, Laura; Mamus, Ezgi; Montanier, Jean-Marc; Oudgenoeg-Paz, Ora; Garcia, Daniel Hernandez; Papadopoulos, Fotios; Schodde, Thorsten; Verhagen, Josje; Wallbridge, Christopher D.; Willemsen, Bram; de Wit, Jan; Belpaeme, Tony; Goksun, Tilbe; Kopp, Stefan; Krahmer, Emiel; Leseman, Paul; Pandey, Amit Kumar; Department of Psychology; Kanero, Junko; Oranç, Cansu; Küntay, Aylin C.; Faculty Member; Department of Psychology; Graduate School of Social Sciences and HumanitiesWe present a large-scale study of a series of seven lessons designed to help young children learn english vocabulary as a foreign language using a social robot. The experiment was designed to investigate 1) the effectiveness of a social robot teaching children new words over the course of multiple interactions (supported by a tablet), 2) the added benefit of a robot's iconic gestures on word learning and retention, and 3) the effect of learning from a robot tutor accompanied by a tablet versus learning from a tablet application alone. For reasons of transparency, the research questions, hypotheses and methods were preregistered. With a sample size of 194 children, our study was statistically well-powered. Our findings demonstrate that children are able to acquire and retain English vocabulary words taught by a robot tutor to a similar extent as when they are taught by a tablet application. In addition, we found no beneficial effect of a robot's iconic gestures on learning gains.Publication Metadata only Second language tutoring using social robots: a large-scale study(Institute of Electrical and Electronics Engineers (IEEE), 2019) Vogt, Paul; van den Berghe, Rianne; de Haas, Mirjam; Hoffman, Laura; Montanier, Jean-Marc; Oudgenoeg-Paz, Ora; Garcia, Daniel Hernandez; Papadopoulos, Fotios; Schodde, Thorsten; Verhagen, Josje; Wallbridge, Christopher D.; Willemsen, Bram; de Wit, Jan; Belpaeme, Tony; Kopp, Stefan; Krahmer, Emiel; Leseman, Paul; Pandey, Amit Kumar; Department of Psychology; Department of Psychology; Department of Psychology; Department of Psychology; Department of Psychology; Göksun, Tilbe; Kanero, Junko; Küntay, Aylin C.; Mamuş, Ayşe Ezgi; Oranç, Cansu; Faculty Member; Researcher; Faculty Member; Researcher; Researcher; Department of Psychology; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; 47278; N/A; 178879; N/A; N/AWe present a large-scale study of a series of seven lessons designed to help young children learn English vocabulary as a foreign language using a social robot. The experiment was designed to investigate 1) the effectiveness of a social robot teaching children new words over the course of multiple interactions (supported by a tablet), 2) the added benefit of a robot's iconic gestures on word learning and retention, and 3) the effect of learning from a robot tutor accompanied by a tablet versus learning from a tablet application alone. For reasons of transparency, the research questions, hypotheses and methods were preregistered. With a sample size of 194 children, our study was statistically well-powered. Our findings demonstrate that children are able to acquire and retain English vocabulary words taught by a robot tutor to a similar extent as when they are taught by a tablet application. In addition, we found no beneficial effect of a robot's iconic gestures on learning gains.Publication Metadata only Second language tutoring using social robots: L2TOR-the movie(IEEE Computer Society, 2019) Vogt, Paul; van den Berghe, Rianne; de Haas, Mirjam; Hoffman, Laura; Montanier, Jean-Marc; Oudgenoeg-Paz, Ora; García, Daniel Hernández; Papadopoulos, Fotios; Schodde, Thorsten; Verhagen, Josje; Wallbridge, Christopher D.; Willemsen, Bram; de Wit, Jan; Belpaeme, Tony; Kopp, Stefan; Krahmer, Emiel; Leseman, Paul; Pandey, Amit Kumar; Department of Psychology; Department of Psychology; Department of Psychology; Department of Psychology; Department of Psychology; Kanero, Junko; Mamuş, Ayşe Ezgi; Oranç, Cansu; Göksun, Tilbe; Küntay, Aylin C.; Researcher; Researcher; Researcher; Faculty Member; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; N/A; 47278; 178879This video illustrates the large-scale experiment of the L2TOR project that will be presented at the HRI 2019 conference. The experiment aimed to investigate how 192 Dutch 5-year-old children could learn 34 English words from a NAO robot in 7 lessons. The experiment compared 4 conditions: 1) robot using iconic gestures, 2) robot without iconic gestures, 3) tablet only, and 4) a control group. The results revealed that children could learn more English words in all experimental conditions compared to the control group. The three experimental conditions did not show any significant differences regarding the learning outcomes.Publication Open Access When even a robot tutor zooms: a study of embodiment, attitudes, and impressions(Frontiers, 2021) Kanero, Junko; Tunalı, Elif Tutku; Oranç, Cansu; Department of Psychology; Göksun, Tilbe; Küntay, Aylin C.; Faculty Member; Department of Psychology; College of Social Sciences and Humanities; 47278; 178879This study used an online second language (L2) vocabulary lesson to evaluate whether the physical body (i.e., embodiment) of a robot tutor has an impact on how the learner learns from the robot. In addition, we tested how individual differences in attitudes toward robots, first impressions of the robot, anxiety in learning L2, and personality traits may be related to L2 vocabulary learning. One hundred Turkish-speaking young adults were taught eight English words in a one-on-one Zoom session either with a NAO robot tutor (N = 50) or with a voice-only tutor (N = 50). The findings showed that participants learned the vocabulary equally well from the robot and voice tutors, indicating that the physical embodiment of the robot did not change learning gains in a short vocabulary lesson. Further, negative attitudes toward robots had negative effects on learning for participants in the robot tutor condition, but first impressions did not predict vocabulary learning in either of the two conditions. L2 anxiety, on the other hand, negatively predicted learning outcomes in both conditions. We also report that attitudes toward robots and the impressions of the robot tutor remained unchanged before and after the lesson. As one of the first to examine the effectiveness of robots as an online lecturer, this study presents an example of comparable learning outcomes regardless of physical embodiment.