Psychology & Methods
In our daily life, we are continuously required to learn new motor skills and adapt these skills to new situations, such as during tool-use. Tool-use as one of the hallmark skills in humans serves to functionally extend our body to overcome physical limitations to interact or manipulate other objects or organisms in an environment.
In the present dissertation, I used behavioral and neural oscillation correlates of body schema plasticity during virtual tool-use in young and older adults to investigate the embodiment of virtual tools into the body schema, body image and the association between body ownership and agency as well as their mutual dependency on action-related sensory feedback.
To do so, an arm-shaped virtual tool-use paradigm was employed in order to study forearm sensorimotor body schema, and its level of plasticity in young and older individuals during a sequential motor learning task.
Overall, our findings suggest that virtual tools can be incorporated into the existing body schema of the forearm in younger adults but not in older adults, while showcasing how future work may further disambiguate the contributions of tactile and visual feedback. Additionally, resting-state beta power and task-related theta, alpha and beta power predicts stronger practice effect during virtual tool-use in younger adults compared to older adults.
All together, I conclude that a sense of agency may strongly relate to improvement in tool-use in older adults dependent on practice effect but independent of alterations in the body schema, while ownership did not emerge due to a lack of body schema plasticity. Additionally, I concluded that the stronger practice effect during virtual tool-use training is positively related with resting-state relative beta power, higher task-related relative theta power and lower task-related alpha across frontal, parietal and occipital regions in younger adults but not in older adults.
The COVID-19 pandemic as a crisis has been associated with changes in daily interaction, social connectedness, mental health, support structures, and the provision of (mental) health care treatment. This thesis aims to examine 3 main targets: psychological well-being during crises, psychological mechanisms related to reactions and consequences of individuals and systems, and the effectiveness of digital interventions to support mental health.
In 5 studies the following were examined: (1) triggers of preventable adverse events (pAEs) through evaluating the psychometric properties of a questionnaire; (2) hand hygiene behavior along the Health Action Process Approach (HAPA) through structural equation modeling; (3) differences in worries and mental health of the general population and psychosomatic rehabilitation patients; (4) evaluation of the intercorrelation between psychological variables along the Evolutionary Theory of Loneliness via a serial mediation model; (5) evaluation of the effectiveness of digital (psycho)therapeutic interventions.
In study 1, 5 areas of triggers of pAEs were defined and the questionnaire showed good psychometric properties. Study 2 has shown that hand hygiene behavior could be explained along the HAPA with the pattern being invariant for mental health. Study 3 has demonstrated higher mental health-related symptoms for psychosomatic rehabilitation patients who also reported different worries than the general population. Study 4 has shown a serial and individual mediation effect of loneliness and anxiety between distress and depression. Adding digital interventions to the traditional therapy approach supported the reduction of psychological symptoms as shown in study 5.
The findings of this thesis contribute to a greater understanding of psychological mechanisms evaluated by reactions and consequences associated with the COVID-19 pandemic and allow for several practical, theoretical, and methodological conclusions to be drawn.
As children develop, they learn to copy from others, interpret their intentions, and evaluate their reliability as sources of information (social learning). Today, these ‘others’ include not only children's peers, parents, or teachers, but also technological devices with which they can interact, including in educational settings. Social robots occupy a particular niche among such technologies, with the ability to embody specific social behaviours such as gaze, gesture, and verbal communication. Nonetheless, the technology underlying the design of social robots remains far from perfect, and opportunities for failures are rife. Understanding how the social capabilities inherent with social robots interact with these inevitable failures is therefore necessary for the design of robust educational interactions with robots. Consequently, the goal of this thesis is to develop an understanding of how errors impact children's social attitudes and behaviour towards social robots. First, a meta-analysis on children's trust in social robots was conducted, creating a theoretical baseline from which to study robot errors. Second, a learning task and measurements for use in child-robot-interaction (cHRI) were developed, through which robot errors could be manipulated and their effect on constructs such as trust, liking, and perceived agency captured. Third, the role of robot errors in a real-world implementation of the learning task was examined. Finally, how current social cognition paradigms can be used to explain children's perceptions of robot errors was explored. Throughout these studies, a theme emerged towards errors not being detrimental to children's perceptions of social robots, at least for more short-term interactions. The significance of robot errors and, more generally, social behaviour when evaluating social robots as tutors is discussed. The thesis concludes with recommendations for how cHRI research can draw from psychology in the design of future cHRI studies.
The present dissertation aimed to contribute to the attempt of decomposing interindividual aging trajectories and to provide empirical evidence about the potential role of brain aging in emotion processing by using different methodological approaches.
Study 1 revealed attenuated arousal-modulated BOLD signals in older adults with low (vs. high) levels of executive functioning, for both negative and positive emotional stimuli in different brain areas, including bilateral premotor area (BA 6), dorsolateral prefrontal cortex, inferior parietal lobule, and left putamen. Functional connectivity of amygdala and visual cortex with various other brain regions was as well found.
Study 2 revealed that brain functioning related to executive functioning moderates the relation between subjective arousal and level of executive functioning. Moderation effects were found for brain activity in several brain regions including lateral and medial frontal cortex, medial temporal cortex, occipital cortex, insula, and cerebellum. Older adults with brain functioning associated with brain aging and low executive functioning showed high levels of positive as well as negative arousal.
Study 3 investigated changes in subjective negative arousal after a 12-month aerobic intervention training. It revealed that, overall, older adults decreased in negative arousal, most likely due to improvements in emotion regulation. However, one subgroup increased in negative arousal. This subgroup showed high levels of executive functioning and compensatory brain activity at T0.
The preliminary results of study 4 revealed that lower white matter in the frontal cortex went along with higher negative arousal.
Background: Digital physical activity interventions can be effective, but how their components influence complex health behavior change processes has rarely been investigated in older adults.
Objective: Applying principles from social cognitive theory, missing value treatment and person-centered analyses, this thesis aims to tackle three research gaps in the form of barriers to physical activity behavior change in digital interventions targeted at older adults.
Methods: Study 1 covers social-cognitive mechanisms in the effect of tailored, theory-based digital interventions on movement in the physical activity stage of change. In study 2, lifestyle profiles consisting of six self-reported, health-related behaviors were researched using latent profile analysis. Adjusted risk ratios were calculated to identify dropout-vulnerable risk profiles. In Study 3, latent class growth analysis was used to determine trajectory subgroups regarding physical activity and sedentary behavior.
Results: In study 1, the hypothesized positive effects on stage of change were partly mediated by social-cognitive predictor changes. There were heterogenous intervention mechanisms. Four latent health-related lifestyle profiles were identified in study 2. Membership of the “socially inactive lifestyle” profile was associated with an elevated risk of dropping out. Study 3 identified two latent physical activity and sedentary behavior change trajectories, respectively. Significant positive trajectories were only observed in the highly sedentary.
Discussion and Conclusion: This thesis lays out a theoretical and methodological basis of how areas of heterogeneity in the physical activity behavior change process of older adults participating in digital interventions can be analyzed. Characterizing distinct subgroups and their needs can advance tailoring of intervention components and behavior change strategies, and ultimately improve acceptance, retention, and long-term intervention effectiveness.
Despite numerous attempts to reduce socioeconomic disparity in education, the gap in educational attainment and expectations among students with different socioeconomic backgrounds persists. This thesis is an attempt to extend our understanding of the social-psychological mechanisms that could explain this gap and inform solutions that would promote greater equality in education. It presents three manuscripts, which together propose that (1) the link between socioeconomic background and educational attainment can be explained by self-efficacy beliefs, and (2) childhood socioeconomic status and self-efficacy bias the process of judgement that precedes achievement-oriented behaviour. Building on the existing literature and research, in the first study I assess the roles of self-efficacy antecedents in the relationship between socioeconomic background and educational expectations. The findings demonstrate that self-efficacy antecedents fully explain the effects of income, social class, and primary caregiver’s education on educational expectations of students. Further, in a theoretical piece, I propose that pre-existing self-efficacy beliefs guide the selection and interpretation of the immediate information relevant in the process of appraisal of problem-focused coping potential. Finally, building on the results of the first study and the proposed theoretical framework, I test the effects of childhood status on the appraisal of coping potential and attribution of the outcome when people solve cognitive tasks. The findings demonstrate that the effect of childhood status varies across tasks with different difficulty and among people who succeeded and failed. I situate these findings within broader research on socioeconomic disparity in education and discuss their implications for theory, research, and practice.
As insomnia is recognized as a globally prevalent mental disorder with a high comorbidity rate, attempts to cure insomnia have been increasing. Primary insomnia is not attributed to other medical conditions or substance usage but may be due to impaired brain network and dysregulated brain activities. Normally, a bottom-up thalamocortical pathway is targeted with pharmaceutical interventions for neurological and psychiatric disorders, but possibilities to modulate brain mechanisms with a top-down corticothalamic pathway are re-gaining attention and being considered as a potential treatment option. This study focuses on identification of hallmarks of EEG patterns in insomniacs and effects of a combination of two neuromodulation methods: Neurofeedback (NFT) and transcranial direct current stimulation (tDCS). The 12 participants diagnosed with primary insomnia first received 20 sessions of NFT, which was followed by additional 10 sessions of tDCS, and their EEG patterns were measured at baseline and after the completion of the training. The EEG power and coherence in each of theta (1-4 Hz), delta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), gamma (30-50 Hz) frequency bands was analyzed and compared with the EEG of healthy control group. Throughout the research, I found that primary insomniacs tend to have elevated beta and high beta relative power and excessive coherence compared to the norm of healthy population saved in the NeuroGuide database and the healthy control group, respectively. After the intervention, a noticeable reduction in beta power and coherence was observed.
Models of cognition-based brain networks and functions are enormous in number and intricate in function. However, it is possible to compare modules of hierarchically segregated models against one another and model them based on a range of empirical data.
It is essential to elucidate the neurobiological contribution of executive functions (EFs) to cognitive functions such as decision making (DM) and creative thinking.
The prefrontal cortex (PFC) is associated with several EFs. The PFC is part of a deliberate inhibitory control (IC) network; it is also a central node for problem-solving and the creative ideation process. Nevertheless, several cognitive domains for inhibition and flexibility correspond to distinct cortico-frontal networks. An integrated computational–experimental framework could provide insight into the root of EFs.
This PhD thesis aims to explore the role of models of cognition-related brain networks and functions. The thesis addresses 1) the computational perspective on decision-making and creative thinking and 2) the experimental perspective on creative thinking, supported by relevant peer-reviewed publications.
The computational perspective on DM and creative thinking is concerned with validating computational modeling through spiking neural network (SNN) and connectionist models.
The experimental perspective on creative thinking is concerned with the relationships among creative cognition, creative drive, and their associated neuromodulator systems––a subject on which information has, until now, remained scattered and elusive. The experimental study used a non-invasive brain stimulation method (transcranial direct current stimulation [tDCS]) to examine the role of the inferior frontal gyrus (IFG) in divergent thinking (DT) and whether changes in the activity of the IFGs and IC would mediate variations in DT.
Autism Spectrum Disorder is a neurological and developmental disorder that starts early in adolescence and lasts throughout a person’s life affecting information flow in the brain leading to secondary problems for the patient. Early detection of ASD is vital in enhancing the efficiency of the treatment. Current diagnostic approaches for autism are time-consuming, to accelerate this process of diagnosing the disease as early as possible with fewer efforts and better accuracy machine learning methods have been proposed recently. This paper presents the diagnosis of ASD based on resting-state eyes-closed EEG signals using machine learning algorithms. The research study population consists of 100 children with ASD (82 male and 18 female) between 5-19 years and 88 healthy developing children in the same range of age with equal proportion of males and female. Power spectrum analysis was used for the analysis of EEG as feature extraction. In addition, feature selection is applied based on principal component analysis for dimensionality reduction. The Logistic Regression, K-Nearest Neighbours, Decision Trees, Random Forest, Extra Trees and Extreme Gradient Boosting classifiers are used for the classification of autistic versus typically developing children. Evaluating the performance of the best classifier among the baseline models results in a classification accuracy of 67.7%, AUC 0.74, 83.3% recall, 61% precision and 54.3% specificity using Extra Trees Additionally, the results revealed that the children with ASD showed convincingly higher power in theta delta and beta bands and low power in alpha than normal group.
Diverse adaptive visual processing mechanisms allow us to complete simple discrimination and more complex visual search tasks in a wide visual photopic range (> 0.6 cd/m2). Despite extensive research ranging from the psychophysical to neurophysiological disciplines, none has examined how these processes behave in the scotopic and low - mesopic luminance ranges, even though we still utilize these processes daily albeit facing considerable decreases in performance. Characterization of perceptual and environmental limitations are needed. Furthermore, visual performance is often enhanced by innate visual - tactile mechanisms, and if patterned properly, application of additional tactile information could attenuate observed decreases in the low luminance spectral ranges. This dissertation will first demonstrate novel behavioural performance efficiency functions for visual discrimination and visual search as one traverses the scotopic to low - mesopic luminance ranges. Second, will demonstrate how various properties of tactile encoding can either attenuate behavioural deficiencies or even facilitate normal performance. Third, these results are supported from both behavioural (eye tracking) and neurophysiological (event related potential) analyses, isolating critical temporal gating windows with more efficient search patterns mediated via a central - parietal network.
