An emotion and memory model for social robots : a long-term interaction

In this thesis, we investigate the role of emotions and memory in social robotic companions. In particular, our aim is to study the effect of an emotion and memory model towards sustaining engagement and promoting learning in a long-term interaction. Our Emotion and Memory model was based on how humans create memory under various emotional events/states. The model enabled the robot to create a memory account of user's emotional events during a long-term child-robot interaction. The robot later adapted its behaviour through employing the developed memory in the following interactions with the users. The model also had an autonomous decision-making mechanism based on reinforcement learning to select behaviour according to the user preference measured through user's engagement and learning during the task. The model was implemented on the NAO robot in two different educational setups. Firstly, to promote user's vocabulary learning and secondly, to inform how to calculate area and perimeter of regular and irregular shapes. We also conducted multiple long-term evaluations of our model with children at the primary schools to verify its impact on their social engagement and learning. Our results showed that the behaviour generated based on our model was able to sustain social engagement. Additionally, it also helped children to improve their learning. Overall, the results highlighted the benefits of incorporating memory during child-Robot Interaction for extended periods of time. It promoted personalisation and reflected towards creating a child-robot social relationship in a long-term interaction

Emotion and memory model for social robots: a reinforcement learning based behaviour selection

In this paper, we propose a reinforcement learning (RL) mechanism for social robots to select an action based on users’ learning performance and social engagement. We applied this behavior selection mechanism to extend the emotion and memory model, which allows a robot to create a memory account of the user’s emotional events and adapt its behavior based on the developed memory. We evaluated the model in a vocabulary-learning task at a school during a children’s game involving robot interaction to see if the model results in maintaining engagement and improving vocabulary learning across the four different interaction sessions. Generally, we observed positive findings based on child vocabulary learning and sustaining social engagement during all sessions. Compared to the trends of a previous study, we observed a higher level of social engagement across sessions in terms of the duration of the user gaze toward the robot. For vocabulary retention, we saw similar trends in general but also showing high vocabulary retention across some sessions. The findings indicate the benefits of applying RL techniques that have a reward system based on multi-modal user signals or cues

The Relationship Between an Affective Instructional Design, Children’s Attitudes Toward Mathematics, and Math Learning for Kindergarten-Age Children

This study explores the relationship between an Affective Instructional Design (AID), children’s attitudes toward math, and math learning. Participants included 15 kindergarten children at a university K-12 laboratory school located in East Tennessee. This quasi-experimental study employed a pretest-intervention (AID)-posttest design. Data, including pretest/posttest attitude surveys, and baseline and intervention non-participant video observations of math learning and math attitudes, during 13 math lessons were coded and analyzed. As hypothesized, a significant positive correlation (r = 0.936, p = 0.000) was found between attitude and math learning. Additionally significant differences were found between the baseline (pre-intervention) mean score and the final intervention lesson for both math attitude, t(14) = -12.39, p = 0.008, and math learning, t(14) = -8.40, p = 0.002.These findings suggest AID could be one route to supporting educators in establishing quality learning environments that promote positive attitudes and meaningful learning in mathematics

Teaching the problem-solving process in a progressive or in a simultaneous way: a question of making sense?

Over the past two decades, the perennial low success rates of elementary students in math problem-solving and the difficulties experienced by teachers in helping their students with this type of task has become quite a hot topic. In response, several instructional interventions aiming to develop an expert and reflexive approach to problem-solving have been designed. However, these interventions are based on two contrasting learning approaches, either teaching the components of the problem-solving process at the same time or teaching them one at the time. The two approaches have never been compared. Moreover, they have mainly been assessed in terms of cognitive outcomes. Yet, recent studies stress the importance of analyzing the cognitive, motivational and emotional processes involved in problem-solving learning together in order to gain a full understanding of the process. Addressing these limitations is essential to enhance our understanding of problem-solving learning and to design more effective interventions. This paper focuses on this issue by investigating whether it is preferable as regards cognitive, motivational and emotional outcomes, to teach the problem-solving process in all its complexity or one component at a time. This issue is handled both for novice and expert solvers. Data were gathered among 267 upper elementary students. Findings showed that both learning approaches support the short- and long-term acquisition of cognitive problem-solving strategies, regardless of the student’s profile. However, beneficial emotional and motivational outcomes occur only when the problem-solving process is taught in all its complexity, i.e., makes sense for the learner. Novice solvers made less use of the help seeking strategy and persisted more

A computational neuroscience perspective on subjective wellbeing within the active inference framework

Understanding and promoting subjective wellbeing (SWB) has been the topic of increasing research, due in part to its potential contributions to health and productivity. To date, the conceptualization of SWB has been grounded within social psychology and largely focused on self-report measures. In this paper, we explore the potentially complementary tools and theoretical perspectives offered by computational neuroscience, with a focus on the active inference (AI) framework. This framework is motivated by the fact that the brain does not have direct access to the world; to select actions, it must instead infer the most likely external causes of the sensory input it receives from both the body and the external world. Because sensory input is always consistent with multiple interpretations, the brain’s internal model must use background knowledge, in the form of prior expectations, to make a “best guess” about the situation it is in and how it will change by taking one action or another. This best guess arises by minimizing an error signal representing the deviation between predicted and observed sensations given a chosen action—quantified mathematically by a variable called free energy (FE). Crucially, recent proposals have illustrated how emotional experience may emerge within AI as a natural consequence of the brain keeping track of the success of its model in selecting actions to minimize FE. In this paper, we draw on the concepts and mathematics in AI to highlight how different computational strategies can be used to minimize FE—some more successfully than others. This affords a characterization of how diverse individuals may adopt unique strategies for achieving high SWB. It also highlights novel ways in which SWB could be effectively improved. These considerations lead us to propose a novel computational framework for understanding SWB. We highlight several parameters in these models that could explain individual and cultural differences in SWB, and how they might inspire novel interventions. We conclude by proposing a line of future empirical research based on computational modelling that could complement current approaches to the study of wellbeing and its improvement

The words of the body: psychophysiological patterns in dissociative narratives

Trauma has severe consequences on both psychological and somatic levels, even affecting the genetic expression and the cell\u2019s DNA repair ability. A key mechanism in the understanding of clinical disorders deriving from trauma is identified in dissociation, as a primitive defense against the fragmentation of the self originated by overwhelming experiences. The dysregulation of the interpersonal patterns due to the traumatic experience and its detrimental effects on the body are supported by influent neuroscientific models such as Damasio\u2019s somatic markers and Porges\u2019 polyvagal theory. On the basis of these premises, and supported by our previous empirical observations on 40 simulated clinical sessions, we will discuss the longitudinal process of a brief psychodynamic psychotherapy (16 sessions, weekly frequency) with a patient who suffered a relational trauma. The research design consists of the collection of self-report and projective tests, pre-post therapy and after each clinical session, in order to assess personality, empathy, clinical alliance and clinical progress, along with the verbatim analysis of the transcripts trough the Psychotherapy Process Q-Set and the Collaborative Interactions Scale. Furthermore, we collected simultaneous psychophysiological measures of the therapeutic dyad: skin conductance and hearth rate. Lastly, we employed a computerized analysis of non-verbal behaviors to assess synchrony in posture and gestures. These automated measures are able to highlight moments of affective concordance and discordance, allowing for a deep understanding of the mutual regulations between the patient and the therapist. Preliminary results showed that psychophysiological changes in dyadic synchrony, observed in body movements, skin conductance and hearth rate, occurred within sessions during the discussion of traumatic experiences, with levels of attunement that changed in both therapist and the patient depending on the quality of the emotional representation of the experience. These results go in the direction of understanding the relational process in trauma therapy, using an integrative language in which both clinical and neurophysiological knowledge may take advantage of each other

To Think and Feel is to Learn: An Investigation of Brief Mindfulness Meditation Training on the Effects of Emotion Regulation and Learning Outcomes

This study investigates the theoretical links between academic stress, emotion regulation, and learning. Scholars conceptualize mindfulness as comprising two distinct features: focused attention on the present moment and nonjudgmental awareness. Research has found that mindfulness is associated with improved emotion regulation skills, cognitive, and academic performance (Bellinger, DeCaro, & Ralston, 2015; Brown, Ryan, & Creswell, 2007; Jha, Stanley, Kiyonaga, Wong, & Gelfand, 2010). Little past work has investigated the potential mechanisms underlying the cognitive benefits, especially related to learning. I tested the effects of a brief mindfulness training on rumination, stress appraisals, and learning outcomes following an academic stress induction in an experimental setting. Undergraduates were randomly assigned to one of three groups: mindfulness meditation (training on focused attention and nonjudgmental awareness); guided attention to music (training on focused attention but not on nonjudgmental awareness); or wakeful rest (no training on focused attention or on nonjudgmental awareness). To the degree that focused attention and nonjudgmental awareness are critical to learning under stress, I expected mindfulness training to have the strongest positive effects—followed by guided attention to music and, lastly, by wakeful rest—on rumination reduction, stress appraisals, and learning. After controlling for individual differences in mindfulness, emotional regulation, worry, math motivation, math anxiety, and prior knowledge, the results did not support these hypotheses. The present work will, thus, address a research agenda for the future that reconceptualizes stress appraisals, assessing individual differences and contextual factors and collecting data from target samples

What do faculties specializing in brain and neural sciences think about, and how do they approach, brain-friendly teaching-learning in Iran?

Objective: to investigate the perspectives and experiences of the faculties specializing in brain and neural sciences regarding brain-friendly teaching-learning in Iran. Methods: 17 faculties from 5 universities were selected by purposive sampling (2018). In-depth semi-structured interviews with directed content analysis were used. Results: 31 sub-subcategories, 10 subcategories, and 4 categories were formed according to the “General teaching model”. “Mentorship” was a newly added category. Conclusions: A neuro-educational approach that consider the roles of the learner’s brain uniqueness, executive function facilitation, and the valence system are important to learning. Such learning can be facilitated through cognitive load considerations, repetition, deep questioning, visualization, feedback, and reflection. The contextualized, problem-oriented, social, multi-sensory, experiential, spaced learning, and brain-friendly evaluation must be considered. Mentorship is important for coaching and emotional facilitation

Academic Performance of Children with Social-Emotional Difficulties: Examining the Role of Self-Regulation

Student with social-emotional difficulties are at increased risk for many adverse outcomes, including school dropout, serious mental health concerns, justice-involved behavior, and decreased quality of life. Thus, considerable attention needs to be directed toward identifying ways to bolster resilience and mitigate these risks for these students. Research suggests that self-regulation skills, including attention, inhibitory control and emotion regulation, are critical for success across a variety of areas including academic performance; however, there are many questions about which specific facets of self-regulation are most critical for academic performance more broadly as well as in specific areas like reading and mathematics. Moreover, most of the prior literature has focused on early childhood, overlooking the critical learning opportunities in early elementary school. Little is also known about the impact of these factors for higher risk students with clear social-emotional difficulties. As such, this study utilized an integrative theoretical framework of self-regulation to examine how self-regulation factors influence academic performance, specifically for early elementary students with social-emotional difficulties. The student sample consisted of 129 first and second grade students nominated by 68 teachers for a self-regulation intervention. Using baseline data collected as part of a federally-funded study, multilevel modeling was used to determine the extent to which various cognitive and emotional mechanisms of self-regulation were associated with teacher-rated academic performance, reading proficiency, and mathematics proficiency, after controlling for gender and socioeconomic status. Results indicated that attention, inhibitory control, and emotion regulation were significant predictors of teacher-rated academic performance. Attention was the only significant predictor of reading proficiency and inhibitory control was the only significant predictor of mathematics proficiency. Results also indicated that attention was the strongest predictor of teacher-rated academic performance and reading proficiency, whereas inhibitory control was the strongest predictor of mathematics performance. Socioeconomic status, one of the control variables, also accounted for significant variance in reading and mathematics proficiency as indicated by report card grades but not teacher-rated academic performance. These results add to the evidence that self-regulation is important for school success, and functions in a manner expected for early elementary students with social-emotional difficulties. Results suggest that both self-regulation and academic performance should be considered in interventions to help foster positive outcomes for students, particularly those with social-emotional difficulties who are most in need.Doctor of Educatio

Exploring the Utility of Mindfulness in the Elementary School Classroom

Current evidence supporting the efficacy of mindfulness training in the K-12 setting is quite limited. In addition there is a lack of theory that is committed explicitly to explaining how a direct mindfulness-achievement effect might appear. This study builds a promising foundation for helping address this gap in the existing literature. Framed within the context of a limited source model of self-regulation, academic achievement and perceptions of third, fourth, and, fifth graders participating in a 3-week mindfulness training program were examined across variables of executive control, and emotional regulation. Mindfulness training produced observed emotional and cognitive benefits, including increased executive control and decreased negative affect, which translated to improved academic performance at the third grade elementary level. The study occurred in an active school environment and results were analyzed through a series of mixed model analyses of variance. In addition, implications for future research are discussed