In everyday life, decisions to act are often initiated spontaneously, without any specific external imperative indicating whether or when to perform an action. The neural origins of spontaneous self-initiated actions and their relation to conscious intentions pose a challenging problem for basic neuroscience research as well as for the engineering of neuro-prosthetic devices (brain-computer interfaces, or BCIs). How do spontaneous decisions-to-act emerge from the tangled complexity of the brain, and what determines whether and when an action is initiated - especially in the face of absent, incomplete, or noisy evidence?
Recent work has introduced formal computational models to the study of self-initiated action, and used them to account for the slow buildup of neural activity that is known to precede self-initiated actions. This buildup, primarily found in pre-motor areas of the brain, evolves over the last one second or so before movement and is called the Bereitschaftspotential or “readiness potential” (RP). The RP has always been presumed to reflect a process of “planning and preparation for movement”. The RP became a source of controversy when, in the 1980s, Benjamin Libet argued that the conscious decision to act came about 300 ms or more *after* the onset of the RP. This was widely taken to imply that the brain had already “decided” to initiate the action well before one became conscious of having decided, casting doubt on our intuitions about conscious volition (the conscious decision should come first).
The introduction of computational models to this field of research exposed a very different interpretation of the RP: According to the “stochastic decision model”, when the external imperative to produce a movement is weak, as is the case in the kind of experiment that Libet performed, then the precise moment at which the decision threshold is crossed leading to movement is partly determined by ongoing sub-threshold fluctuations in brain activity. Time locking to movement onset ensures that these slow fluctuations are recovered in the event-locked average in the form of a gradual buildup. By this account, the real decision may come about near the end of the RP (quite close to the onset of movement) rather than at the onset of the RP, allowing for the possibility that the decision to initiate movement and the conscious feeling of having decided come about at the same time. This new interpretation of the RP is important in large part because of the significance that attaches itself to the topics of “free will” and personal responsibility, but also because it gives us a promising new vantage point from which to approach the study of volition and self-initiated action.
The overall objectives of the project are the following:
• Develop a new variant of the stochastic decision model that accounts for the spectral properties of neural data.
• Test specific divergent predictions that emerge depending on whether the early tail of the RP is modelled as the output or the stochastic input to the accumulator.
• Use machine-learning to map the time course of movement-preceding neural activity that is specifically and causally related to the initiation of movement.
• Refine the brain-behavior forecasting methodology and use it to determine the causal relationship between known antecedents of uncued movement and the fact of performing the movement.
• Determine the precise relationship between the readiness potential, the lateralized readiness potential, spontaneous fluctuations in cortical activity, and cortical mechanisms of evidence accumulation.
• Empirically probe deeper questions of causality and spontaneity through two high-risk / high-gain experiments.
