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Full-Text Articles in Computational Neuroscience
Voluntary Action And Conscious Intention, Jake Gavenas
Voluntary Action And Conscious Intention, Jake Gavenas
Computational and Data Sciences (PhD) Dissertations
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 …
Probing The Boundaries Of Human Agency, Sook Mun Wong
Probing The Boundaries Of Human Agency, Sook Mun Wong
Computational and Data Sciences (PhD) Dissertations
In this dissertation, I aim to advance our understanding of human agency and how it is computed. First, I probe the limits of what behavior we can and cannot produce agentically. Secondly, I explore microdosing with psychedelic drugs as a method of perturbing and studying changes to the sense of agency. Thirdly, I use transcranial magnetic stimulation to induce body movements and investigate how the sense of agency is affected. Finally, I summarise my findings and synthesize insights to further our understanding human agency.
Causal Inference In Psychology And Neuroscience: From Association To Causation, Dehua Liang
Causal Inference In Psychology And Neuroscience: From Association To Causation, Dehua Liang
Computational and Data Sciences (PhD) Dissertations
In psychology and neuroscience, inferring causality in non-experimental studies is almost taboo, because data in these studies, e.g., survey data and resting-state neuroimaging data, are often contaminated by unmeasured confounders. Psychologists and neuroscientists are often cautious about their results, and reluctant to make false claims about causality in non-experimental studies. Therefore, they adopt less stringent statistical analysis techniques that can only infer associational relations. However, the ambiguity about causality in traditional statistical analysis creates much confusion in interpreting analytical results - some studies make implicit causal claims about their results using words such as “impacts”, “lead to” and “affects”. This …