Relation between parent feeding and emotional overeating in preschoolers as mediated by emotion regulation

Emotional overeating is defined as eating in response to negative emotions, and the shift from emotional undereating to overeating around the preschool years indicates environmental influences. Parent feeding practices such as using food to regulate emotions and behavior may impede children’s ability to regulate their emotions, leading to emotional overeating. This study analyzed the relation between parent feeding practices, child emotion regulation, and emotional overeating in 4- and 5-year-old children. For study 1, mothers of 4- and 5-year-old children completed online questionnaires through MTurk and Prolific. Questionnaires measured parent feeding practices, emotion regulation, and emotional overeating. Parent use of food to control emotions and behaviors was positively correlated with emotional overeating. Additionally, parent use of food to control emotions and behavior predicted higher levels of emotional overeating, and this was independently mediated by Child Emotion Regulation and Child Lability/Negativity. Study 2 was a pilot study examining the feasibility of an fNIRS emotion regulation task in preschool children. Outcome variables included whether children could complete the task, their accuracy across conditions, and the fNIRS signal quality during the task. Although more data needs to be collected to determine whether both 4 year-old and 5 year-old children can complete the task, these initial data indicate that adequate signal quality can be obtained when using fNIRS in preschool samples. Overall, this study sheds light on potential environmental influences and parenting practices associated with emotional overeating in preschool children

Towards a Neuroscience of Computer Programming & Education:A thesis submitted in partial fulfilment of the requirements of the University of East Anglia for the degree of Doctor of Philosophy. Research undertaken in the School of Psychology, University of East Anglia.

Computer programming is fast becoming a required part of School curricula, but students find the topic challenging and university dropout rates are high. Observations suggest that hands-on keyboard typing improves learning, but quantitative evidence for this is lacking and the mechanisms are still unclear. Here we study neural and behavioral processes of programming in general, and Hands-on in particular. In project 1, we taught naïve teenagers programming in a classroom-like session, where one student in a pair typed code (Hands-on) while the other participated by discussion (Hands-off). They were scanned with fMRI 1-2 days later while evaluating written code, and their knowledge was tested again after a week. We find confidence and math grades to be important for learning, and easing of intrinsic inhibitions of parietal, temporal, and superior frontal activation to be a typical neural mechanism during programming, more so in stronger learners. Moreover, left inferior frontal cortex plays a central role; operculum integrates information from the dorsal and ventral streams and its intrinsic connectivity predicts confidence and long-term memory, while activity in Broca’s area also reflects deductive reasoning. Hands-on led to greater confidence and memory retention. In project 2, we investigated the impact of feedback on motivation and reaction time in a rule-switching task. We find that feedback targeting personal traits increasingly impair performance and motivation over the experiment, and we find that activity in precentral gyrus and anterior insula decrease linearly over time during the personal feedback condition, implicating these areas in this effect. These findings promote hands-on learning and emphasize possibilities for feedback interventions on motivation. Future studies should investigate interventions for increasing Need for Cognition, the relationship between computer programming and second language learning (L2), and the role of explicit verbalization of knowledge for successful coding, given the language-like processing of code

Exploiting physiological changes during the flow experience for assessing virtual-reality game design.

Immersive experiences are considered the principal attraction of video games. Achieving a healthy balance between the game's demands and the user's skills is a particularly challenging goal. However, it is a coveted outcome, as it gives rise to the flow experience – a mental state of deep concentration and game engagement. When this balance fractures, the player may experience considerable disinclination to continue playing, which may be a product of anxiety or boredom. Thus, being able to predict manifestations of these psychological states in video game players is essential for understanding player motivation and designing better games. To this end, we build on earlier work to evaluate flow dynamics from a physiological perspective using a custom video game. Although advancements in this area are growing, there has been little consideration given to the interpersonal characteristics that may influence the expression of the flow experience. In this thesis, two angles are introduced that remain poorly understood. First, the investigation is contextualized in the virtual reality domain, a technology that putatively amplifies affective experiences, yet is still insufficiently addressed in the flow literature. Second, a novel analysis setup is proposed, whereby the recorded physiological responses and psychometric self-ratings are combined to assess the effectiveness of our game's design in a series of experiments. The analysis workflow employed heart rate and eye blink variability, and electroencephalography (EEG) as objective assessment measures of the game's impact, and self-reports as subjective assessment measures. These inputs were submitted to a clustering method, cross-referencing the membership of the observations with self-report ratings of the players they originated from. Next, this information was used to effectively inform specialized decoders of the flow state from the physiological responses. This approach successfully enabled classifiers to operate at high accuracy rates in all our studies. Furthermore, we addressed the compression of medium-resolution EEG sensors to a minimal set required to decode flow. Overall, our findings suggest that the approaches employed in this thesis have wide applicability and potential for improving game designing practices