| Title: | Connectivity of functional brain networks during ostracism : doctoral dissertation |
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| Authors: | ID Lesar, Mateja (Author)
ID Levnajić, Zoran (Mentor) More about this mentor... 
ID Drevenšek, Gorazd (Comentor)
ID Rogelj, Peter (Comentor) |
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| Files: |  DR_Lesar_Mateja_2026.pdf (7,34 MB)
MD5: 59D224D9856151B7797C56D7F77CDA19
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| Language: | English |
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| Work type: | Doctoral dissertation |
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| Typology: | 2.08 - Doctoral Dissertation |
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| Organization: | FIŠ - Faculty of Information Studies in Novo mesto
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| Abstract: | This dissertation investigates two distinct yet interconnected aspects of human cognition: the influence of caffeine on cognitive performance and the neural dynamics of social exclusion. Using electroencephalography (EEG) as the primary methodological tool, this research builds on two neuroscience experiments to explore the brain activity underpinning these processes.
The first experiment examines caffeine's impact on attention and cognitive performance through an auditory oddball paradigm and a mental arithmetic task. Results reveal that caffeine enhances cognitive performance by improving reaction times, accompanied by distinct changes in event-related potentials (ERPs), particularly the P3 component, and the resting-state EEG dynamics. The study demonstrates the ‘ritual dimension’ of coffee: while behavioural effects
appeared after consumption of both caffeine and placebo, specific neural changes (P3 modulation, reduced alpha/beta power) occurred only after caffeine ingestion. Cardiovascular responses further show caffeine's physiological impacts. The second experiment addresses the mental state of social exclusion through the Cyberball
paradigm, focusing on its neural correlates. EEG findings reveal important differences in the brain activity between inclusion and exclusion conditions. Statistical analyses reveal theta oscillations at CP2 and increased activity at ROI dACC during exclusion. Functional connectivity analyses show network-level reconfiguration across multiple frequency bands. Machine learning classification achieved ~99% accuracy (subject-variant) and ~58% (subject-invariant), revealing high inter-individual variability while identifying distributed network patterns. Together, these experiments underscore the utility of EEG, combined with advanced data science methods, in enhancing our understanding of human brain mechanisms behind cognitive
processes and social conditions. The overall findings contribute to both the theoretical framework and the practical applications of cognitive neuroscience. |
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| Keywords: | electroencephalography, cognitive performance, social cognition, auditory oddball, Cyberball, functional brain connectivity, statistical analysis, machine learning |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Place of publishing: | Novo mesto |
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| Place of performance: | Novo mesto |
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| Publisher: | M. Lesar |
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| Year of publishing: | 2026 |
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| Year of performance: | 2026 |
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| Number of pages: | XXIII, 240 str. |
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| PID: | 20.500.12556/ReVIS-13459 |
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| COBISS.SI-ID: | 273363715 |
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| UDC: | 616.8 |
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| Note: | Na ov.: Doctoral dissertation;
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| Publication date in ReVIS: | 31.03.2026 |
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| Views: | 31 |
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| Downloads: | 0 |
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