We experience our actions as under our control. Yet when neuroscience starts to show that this control can be reduced to brain activity that precedes our own awareness of it, our intuitions unravel. The field of volition neuroscience has long uncovered brain signals that precede our awareness of our own decisions, often interpreting the presence of early antecedent activity as evidence of early decisions preceding our subjective experience of deciding. However, the field has overlooked whether those neural precursors were predictive of the upcoming actions, a feature expected of a signal indexing a decision. In this thesis, I present four studies addressing whether results in the neuroscience of volition support claims that early decisions are settled at the neural level prior to our own awareness of them.
In a first study, I show that when classifying neural data that precedes voluntary decisions from that of a matched control, high classification is only achieved very close to movement onsets. I show that previous results achieving earlier classification did so by classifying against a baseline window which can lead to spurious early classification in autocorrelated event-locked data. In a second study, I explore how baseline correction in the neuroscience of volition can distort early signals, modulating both their apparent onsets and amplitudes. I provide guidance on how to apply baseline correction to such signals and how to interpret subsequent results. In a third study, I show that when spontaneous decisions feel spontaneous, early signals that precede them are not outcome specific. In a fourth study, I propose a new method to measure the timing of intention formation by measuring the suggestibility of intention reports and show that it yields onsets closer to that of neural signals. I conclude that the neuroscience of volition has yet to show evidence that decisions are present in the brain data prior to our awareness of them and by encouraging the field of volition research to move away from seeking early signals and instead look for outcome-specific predictors of voluntary actions.
","abstract_html":"<p>We experience our actions as under our control. Yet when neuroscience starts to show that this control can be reduced to brain activity that precedes our own awareness of it, our intuitions unravel. The field of volition neuroscience has long uncovered brain signals that precede our awareness of our own decisions, often interpreting the presence of early antecedent activity as evidence of early decisions preceding our subjective experience of deciding. However, the field has overlooked whether those neural precursors were predictive of the upcoming actions, a feature expected of a signal indexing a decision. In this thesis, I present four studies addressing whether results in the neuroscience of volition support claims that early decisions are settled at the neural level prior to our own awareness of them.</p> <p>In a first study, I show that when classifying neural data that precedes voluntary decisions from that of a matched control, high classification is only achieved very close to movement onsets. I show that previous results achieving earlier classification did so by classifying against a baseline window which can lead to spurious early classification in autocorrelated event-locked data. In a second study, I explore how baseline correction in the neuroscience of volition can distort early signals, modulating both their apparent onsets and amplitudes. I provide guidance on how to apply baseline correction to such signals and how to interpret subsequent results. In a third study, I show that when spontaneous decisions feel spontaneous, early signals that precede them are not outcome specific. In a fourth study, I propose a new method to measure the timing of intention formation by measuring the suggestibility of intention reports and show that it yields onsets closer to that of neural signals. I conclude that the neuroscience of volition has yet to show evidence that decisions are present in the brain data prior to our awareness of them and by encouraging the field of volition research to move away from seeking early signals and instead look for outcome-specific predictors of voluntary actions.</p>","abstract_has_math":false,"creators":["Jeay-Bizot, Lucas"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Aaron Schurger","Uri Maoz","Adina Roskies","Marcel Brass"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05-01T07:00:00Z","date_published":"2026-05-01T07:00:00Z","updated_at":"2026-07-24T01:38:47Z","subjects":["Volition","Cognitive Neuroscience","Cognitive Psychology","Cognitive Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/51","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Aaron Schurger","Uri Maoz","Adina Roskies","Marcel Brass"]},{"key":"dc:creator","label":"Author","values":["Jeay-Bizot, Lucas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2027-06-01T07:00:00Z"]},{"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":["Volition","Cognitive Neuroscience","Cognitive Psychology","Cognitive Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/51"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["We experience our actions as under our control. Yet when neuroscience starts to show that this control can be reduced to brain activity that precedes our own awareness of it, our intuitions unravel. The field of volition neuroscience has long uncovered brain signals that precede our awareness of our own decisions, often interpreting the presence of early antecedent activity as evidence of early decisions preceding our subjective experience of deciding. However, the field has overlooked whether those neural precursors were predictive of the upcoming actions, a feature expected of a signal indexing a decision. In this thesis, I present four studies addressing whether results in the neuroscience of volition support claims that early decisions are settled at the neural level prior to our own awareness of them.
In a first study, I show that when classifying neural data that precedes voluntary decisions from that of a matched control, high classification is only achieved very close to movement onsets. I show that previous results achieving earlier classification did so by classifying against a baseline window which can lead to spurious early classification in autocorrelated event-locked data. In a second study, I explore how baseline correction in the neuroscience of volition can distort early signals, modulating both their apparent onsets and amplitudes. I provide guidance on how to apply baseline correction to such signals and how to interpret subsequent results. In a third study, I show that when spontaneous decisions feel spontaneous, early signals that precede them are not outcome specific. In a fourth study, I propose a new method to measure the timing of intention formation by measuring the suggestibility of intention reports and show that it yields onsets closer to that of neural signals. I conclude that the neuroscience of volition has yet to show evidence that decisions are present in the brain data prior to our awareness of them and by encouraging the field of volition research to move away from seeking early signals and instead look for outcome-specific predictors of voluntary actions.
"]},{"key":"dc:source","label":"Dc Source","values":["L. Jeay-Bizot, \"From intention to action: neural dynamics of voluntary action,\" Ph.D. dissertation, Chapman University, Orange, CA, 2026. https://doi.org/10.36837/chapman.000722"]},{"key":"dc:title","label":"Title","values":["From Intention to Action: Neural Dynamics of Voluntary Action"]}]}],"canonical_facts":{"dc:contributor":["Aaron Schurger","Uri Maoz","Adina Roskies","Marcel Brass"],"dc:creator":["Jeay-Bizot, Lucas"],"dc:date.available":["2027-06-01T07:00:00Z"],"dc:description.abstract":["We experience our actions as under our control. Yet when neuroscience starts to show that this control can be reduced to brain activity that precedes our own awareness of it, our intuitions unravel. The field of volition neuroscience has long uncovered brain signals that precede our awareness of our own decisions, often interpreting the presence of early antecedent activity as evidence of early decisions preceding our subjective experience of deciding. However, the field has overlooked whether those neural precursors were predictive of the upcoming actions, a feature expected of a signal indexing a decision. In this thesis, I present four studies addressing whether results in the neuroscience of volition support claims that early decisions are settled at the neural level prior to our own awareness of them.
In a first study, I show that when classifying neural data that precedes voluntary decisions from that of a matched control, high classification is only achieved very close to movement onsets. I show that previous results achieving earlier classification did so by classifying against a baseline window which can lead to spurious early classification in autocorrelated event-locked data. In a second study, I explore how baseline correction in the neuroscience of volition can distort early signals, modulating both their apparent onsets and amplitudes. I provide guidance on how to apply baseline correction to such signals and how to interpret subsequent results. In a third study, I show that when spontaneous decisions feel spontaneous, early signals that precede them are not outcome specific. In a fourth study, I propose a new method to measure the timing of intention formation by measuring the suggestibility of intention reports and show that it yields onsets closer to that of neural signals. I conclude that the neuroscience of volition has yet to show evidence that decisions are present in the brain data prior to our awareness of them and by encouraging the field of volition research to move away from seeking early signals and instead look for outcome-specific predictors of voluntary actions.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/51"],"dc:source":["L. Jeay-Bizot, \"From intention to action: neural dynamics of voluntary action,\" Ph.D. dissertation, Chapman University, Orange, CA, 2026. https://doi.org/10.36837/chapman.000722"],"dc:subject":["Volition","Cognitive Neuroscience","Cognitive Psychology","Cognitive Science"],"dc:title":["From Intention to Action: Neural Dynamics of Voluntary Action"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:47Z"}