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Determining the nature of free will using machine learning
Thesis (MScPhysio)--Stellenbosch University, 2020.
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Stellenbosch : Stellenbosch University
2020
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| _version_ | 1867614115933978624 |
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| access_status_str | Open Access |
| author | Hall, Siobhan |
| author2 | Morris, L. D. |
| author_browse | Hall, Siobhan Morris, L. D. |
| author_facet | Morris, L. D. Hall, Siobhan |
| author_sort | Hall, Siobhan |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MScPhysio)--Stellenbosch University, 2020. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/107821 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:46:55.034Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/107821 Determining the nature of free will using machine learning Hall, Siobhan Morris, L. D. Van den Heever, David Jacobus Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Health & Rehabilitation Sciences. Physiotherapy. Free will and determinism Decision making -- Psychological aspects Subconsciousness Machine learning UCTD Thesis (MScPhysio)--Stellenbosch University, 2020. ENGLISH ABSTRACT : Background: The debate around free will has been topical for millennia. The question around free will is important in assigning agency to our decisions and actions. The definition of free will used in this research is the ability for a person to do otherwise, should the exact circumstances be created. In 1983, the Libet paradigm was developed as a means to empirically investigate the nature of free will. The Libet paradigm resulted in the presentation of a rise in neural activity 350 ms before conscious awareness of a decision to act. This rise in neural activity (known as the readiness potential) was prematurely and incorrectly taken as proof that the subconscious having a prominent role in our decision-making processes and therefore the conscious self has no free will. This result has subsequently faced criticism, particularly its method of averaging out EEG data over all the trials and the readiness potential not being present on an individual trial basis. Another major criticism is the method of retrospectively and subjectively reporting the moment of conscious awareness, termed “W”. Objectives: The aim of this research is to determine the role of the subconscious in our decision-making processes using machine learning. A secondary aim is to determine if eye tracking can be used to objectively mark the moment of conscious awareness of a decision to move. Investigating the role of the subconscious in our decision-making processes not only contributes to the fundamental understanding of our brains’ processes and the nature of free will, but also early detection of intentions to move can aid in the earlier identification of features to classify actions in brain-computer interface (BCI) systems. This earlier classification can improve the real-time nature of thought and then action. This can help improve the functionality of people living with disabilities. Methodology: The data collection involved the recreation of the Libet experiment, with electroencephalography (EEG) data being collected in conjunction with eye tracking. Another addition to the Libet paradigm was the choice between “left” and “right”. 21 participants were included (4 females, all right-handed). The participants were asked to make a decision between moving “left” and moving “right” while observing the Libet clock to subjectively mark the moment of subconscious awareness. Deep learning, a branch of machine learning was used for the EEG data analysis. The deep learning model used is known as a convolutional neural network (CNN). The eye tracking data was used to identify any eye movements (saccades) that occurred 500 ms before the action. Results: The CNN model was able to predict the decision “left” or “right” as early as 1.3 seconds before the action with a test accuracy of 99%. The eye tracking data was analysed and no correlations between an eye movement and the moment of conscious awareness was found. Conclusion: This research has provided evidence to support the hypothesis that there is no free will. Further research is needed to investigate earlier predictions using deep learning as well as research focused on using eye tracking as a means to objectively time-lock the moment of conscious awareness. AFRIKAANSE OPSOMMING : Agtergrond: Die kwessie van vrye wil is duisende jaar oud. Dit is belangrik om bemiddeling aan ons besluite te gee. In hierdie navorsing, beteken vrye wil om iets anders te kan doen of ’n in presies dieselfde omstandighede alternatiewe aksies uit te voer, sou die persoon of persone so besluit. Libet en sy span het in 1983 ’n eksperiment ontwikkel om die kwessie van vrye wil te toets. Hulle het uitgevind dat daar 350 ms voor die persoon bewus geword het van hulle besluit om te beweeg, reeds breinaktiwiteit plaasgevind het. Hierdie aktiwiteit is die ‘readiness potential’, of beruitschaft-potential’ genoem. Libet het tot die verkeerde slotsom gekom dat die ‘readiness potential’ bewys dat ons besluitneming in die onderbewussyn van ons brein begin en as gevolg daarvan is daar geen vrye wil nie. Onlangse navorsing het hierdie resultaat gekritiseer omdat die ‘readiness potential’ ’n gemiddelde teken van breinaktiwiteit is, en is nie beskikbaar voor enkele besluite nie. ’n Ander belangrike kritiek hieroor is oor die subjektiewe wyse waarop bepaal word hoe om die oomblik van bewustheid van ’n besluit te meet. Doelstellings: Die primêre doel van die navorsing is om die rol van die onderbewussyn in ons besluitnemingsproses te verstaan. Die sekondêre doel is om oogbeweging te gebruik om die oomblik van bewustheid van ’n besluit te meet. Hierdie navorsing sal help met die fundamentele begrip van die brein se prosesse en vrye wil. Om hierdie vroeë vasstelling van besluite te kan maak sal ook help met die ontwikkeling van brein-rekenaar-interaksie sisteme. Dit sal help om meer tydelike en natuurlike bewegings te ontwikkel. Dit sal die funksionele potensiaal van mense met beserings beïnvloed. Metodes: Die data-insamelling is gebaseer op die oorspronlike Libet-eksperiment. Daar was twee verskillende data-tipes wat opgeneem is: elektroënselografie (EEG) en oogbewegings. ’n Verandering aan die oorspronklike Libet eksperiment was gemaak: die deelnemers moes tussen “links” en “regs” kies. Een-en twintig regshandige deelnemers is ingesluit van wie vier vrouens was. Die deelnemers het hulle keuses gemaak terwyl hulle die Libet horlosie dopgehou het. Die Libet-horlosie word gebruik om die oomblik van bewustheid van ’n besluit te meet. Masjien-leer algoritmes is gebruik om die EEG data te analiseer. ’n Verwikkelde neurale netwerk is gebruik. Die oogbewegingsdata is geanaliseer om oogbeweging 500 ms voor die aksie te probeer identifiseer. Resultate: Die verwikkelde neurale netwerk kon die besluit “links” of “regs” met 99 % akkuraatheid voorspel. Die voorspelling is 1.3 sekondes voor die aksie gemaak. Daar was geen korrelasie tussen die oogbeweging en die oomblik waarop die besluit bewustelik gemaak is nie. Slotsom: Die navorsing het meer bewyse gelewer ten opsigte van die hipotese dat daar geen vrywilligheid is nie. Verdere navorsing is nodig om vroeëre voorspellings met masjienleer-algoritmes te kan maak en nog meer navorsing is nodig om die korrelasie tussen oogbeweging en die oomblik van besluitneming bewustheid te verstaan. Masters 2020-02-05T18:05:32Z 2020-04-28T12:04:57Z 2020-02-05T18:05:32Z 2020-04-28T12:04:57Z 2020-03 Thesis http://hdl.handle.net/10019.1/107821 en_ZA Stellenbosch University xix, 139 pages ; illustrations, includes annexures application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Free will and determinism Decision making -- Psychological aspects Subconsciousness Machine learning UCTD Hall, Siobhan Determining the nature of free will using machine learning |
| title | Determining the nature of free will using machine learning |
| title_full | Determining the nature of free will using machine learning |
| title_fullStr | Determining the nature of free will using machine learning |
| title_full_unstemmed | Determining the nature of free will using machine learning |
| title_short | Determining the nature of free will using machine learning |
| title_sort | determining the nature of free will using machine learning |
| topic | Free will and determinism Decision making -- Psychological aspects Subconsciousness Machine learning UCTD |
| url | http://hdl.handle.net/10019.1/107821 |
| work_keys_str_mv | AT hallsiobhan determiningthenatureoffreewillusingmachinelearning |