Traditional experimental methodology in neuroscience involves comparing responses to different stimuli to make inferences about the human brain. However, human volition entails making decisions and acting in a manner that is underdetermined by the external environment. Investigations of the neuronal basis of volition have led to new controversies regarding the existence of free will, and offer potential directions for medical treatments of disorders such as addiction, akinetic mutism, and locked-in syndrome. However, because volition experiments leave aspects of responses “up to” participants, neuroscientists must utilize computational techniques such as modeling, simulation, and advanced analyses to progress our understanding of human volition. In this dissertation, I describe four studies in which I applied computational techniques to investigate the neurocognitive basis of human volition. In the first study, I developed a spiking neural network model of self-initiated action that explains movement-related signals at multiple spatiotemporal scales. In the second study, I investigated what kind of process underlies spontaneous action initiation and how that process relates to awareness of motor preparation through a combination of EEG analysis and computational modeling. In the third study, I showed that pupil dilations before spontaneous movements reflect the transition into awareness of motor intentions and are predictive of upcoming movements. Then, in a fourth study, I developed and validated a new paradigm for studying instructed and freely chosen intentions via reaction-time costs associated with changes of mind.
","abstract_html":"<p>Traditional experimental methodology in neuroscience involves comparing responses to different stimuli to make inferences about the human brain. However, human volition entails making decisions and acting in a manner that is underdetermined by the external environment. Investigations of the neuronal basis of volition have led to new controversies regarding the existence of free will, and offer potential directions for medical treatments of disorders such as addiction, akinetic mutism, and locked-in syndrome. However, because volition experiments leave aspects of responses “up to” participants, neuroscientists must utilize computational techniques such as modeling, simulation, and advanced analyses to progress our understanding of human volition. In this dissertation, I describe four studies in which I applied computational techniques to investigate the neurocognitive basis of human volition. In the first study, I developed a spiking neural network model of self-initiated action that explains movement-related signals at multiple spatiotemporal scales. In the second study, I investigated what kind of process underlies spontaneous action initiation and how that process relates to awareness of motor preparation through a combination of EEG analysis and computational modeling. In the third study, I showed that pupil dilations before spontaneous movements reflect the transition into awareness of motor intentions and are predictive of upcoming movements. Then, in a fourth study, I developed and validated a new paradigm for studying instructed and freely chosen intentions via reaction-time costs associated with changes of mind.</p>","abstract_has_math":false,"creators":["Gavenas, Jake"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Uri Maoz","Aaron Schurger","Ueli Rutishauser"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08-01T07:00:00Z","date_published":"2023-08-01T07:00:00Z","updated_at":"2026-07-24T01:38:31Z","subjects":["voluntary action","conscious intention","volition","EEG","neural network","pupillometry","Cognitive Neuroscience","Cognitive Psychology","Cognitive Science","Computational Neuroscience"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/39","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Uri Maoz","Aaron Schurger","Ueli Rutishauser"]},{"key":"dc:creator","label":"Author","values":["Gavenas, Jake"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2024-08-10T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["voluntary action","conscious intention","volition","EEG","neural network","pupillometry","Cognitive Neuroscience","Cognitive Psychology","Cognitive Science","Computational Neuroscience"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/39"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Traditional experimental methodology in neuroscience involves comparing responses to different stimuli to make inferences about the human brain. However, human volition entails making decisions and acting in a manner that is underdetermined by the external environment. Investigations of the neuronal basis of volition have led to new controversies regarding the existence of free will, and offer potential directions for medical treatments of disorders such as addiction, akinetic mutism, and locked-in syndrome. However, because volition experiments leave aspects of responses “up to” participants, neuroscientists must utilize computational techniques such as modeling, simulation, and advanced analyses to progress our understanding of human volition. In this dissertation, I describe four studies in which I applied computational techniques to investigate the neurocognitive basis of human volition. In the first study, I developed a spiking neural network model of self-initiated action that explains movement-related signals at multiple spatiotemporal scales. In the second study, I investigated what kind of process underlies spontaneous action initiation and how that process relates to awareness of motor preparation through a combination of EEG analysis and computational modeling. In the third study, I showed that pupil dilations before spontaneous movements reflect the transition into awareness of motor intentions and are predictive of upcoming movements. Then, in a fourth study, I developed and validated a new paradigm for studying instructed and freely chosen intentions via reaction-time costs associated with changes of mind.
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"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/39"],"dc:source":["J. Gavenas, \"Voluntary action and conscious intention,\" Ph.D. dissertation, Chapman University, Orange, CA, 2023. https://doi.org/10.36837/chapman.000501"],"dc:subject":["voluntary action","conscious intention","volition","EEG","neural network","pupillometry","Cognitive Neuroscience","Cognitive Psychology","Cognitive Science","Computational Neuroscience"],"dc:title":["Voluntary Action and Conscious Intention"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:31Z"}