Periodic Reporting for period 3 - ACTINIT (Brain-behavior forecasting: The causal determinants of spontaneous self-initiated action in the study of volition and the development of asynchronous brain-computer interfaces.)
Okres sprawozdawczy: 2018-10-01 do 2020-03-31
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.
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.
The project is divided into three main initiatives:
Initiative 1: theoretical and hypothesis-driven studies
Initiative 2: systematic mapping / exploratory studies
Initiative 3: deeper questions of spontaneity and causality
The summary of work performed so far will be grouped into these three initiatives.
Initiative 1: theoretical and hypothesis-driven studies
1.1 Develop a new variant of the stochastic decision model that accounts for the spectral properties of neural data:
This goal has been achieved and the results published in Nature Scientific Reports. This work was done in collaboration with mathematician Ramón Guevara Erra.
1.2 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:
This work is completed and has been published in the journal eNeuro.
1.3 Test other predictions made by the stochastic decision model regarding entrainment:
This work has been completed and published in the journal Nature Scientific Reports.
Initiative 2: systematic mapping / exploratory studies
2.1 Use machine-learning to map the time course of movement-preceding neural activity that is specifically and causally related to the initiation of movement:
This work is completed and is in review at PNAS.
2.2 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:
Work on this objective continues, although a full implementation of the methodology in a Matlab software toolbox has been completed.
Initiative 3: deeper questions of spontaneity and causality
3.1 Brain-body coupling in the initiation of movement:
This project has been completed and we find that our initial hypothesis was neither supported nor refuted by the data.
3.2 Subjectve versus objective spontaneity:
This project has been completed, and, in spite of a valiant and concerted effort, the results were inconclusive.
Initiative 1: theoretical and hypothesis-driven studies
Initiative 2: systematic mapping / exploratory studies
Initiative 3: deeper questions of spontaneity and causality
The summary of work performed so far will be grouped into these three initiatives.
Initiative 1: theoretical and hypothesis-driven studies
1.1 Develop a new variant of the stochastic decision model that accounts for the spectral properties of neural data:
This goal has been achieved and the results published in Nature Scientific Reports. This work was done in collaboration with mathematician Ramón Guevara Erra.
1.2 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:
This work is completed and has been published in the journal eNeuro.
1.3 Test other predictions made by the stochastic decision model regarding entrainment:
This work has been completed and published in the journal Nature Scientific Reports.
Initiative 2: systematic mapping / exploratory studies
2.1 Use machine-learning to map the time course of movement-preceding neural activity that is specifically and causally related to the initiation of movement:
This work is completed and is in review at PNAS.
2.2 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:
Work on this objective continues, although a full implementation of the methodology in a Matlab software toolbox has been completed.
Initiative 3: deeper questions of spontaneity and causality
3.1 Brain-body coupling in the initiation of movement:
This project has been completed and we find that our initial hypothesis was neither supported nor refuted by the data.
3.2 Subjectve versus objective spontaneity:
This project has been completed, and, in spite of a valiant and concerted effort, the results were inconclusive.
One study, headed up by doctoral student Bianca Trovó, looked at parametric variation in the shape of the readiness potential as a function of the amount of time allowed for making a spontaneous self-initiated movement. Normally, in a standard readiness potential experiment, subjects are allowed to take as long as they like to produce a spontaneous movement. In this experiment we impose a range of different time limits, from 2 to 16 seconds, and predict a higher amplitude early RP for trials with a longer time limit. This experiment tests a prediction of the stochastic decision model and is currently in prep for submission.
We completed study 3.2 (above) on brain-body coupling in the initiation of movement. This study asks the profound question of whether or not proprioceptive feedback from muscles is involved in the decision to initiate movement. A positive result would count as strong evidence in favor of the dynamical systems view of action initiation: Decisions to act are not made in the brain and then transmitted to muscles, but rather decisions to act are formed in the brain+body as a whole. Unfortunately, the results of this bold, but risky experiment, were inconclusive.
We also completed behavioral testing of a novel study looking at the neural basis for randomness in choice and its relationship with stochastic variability in brain activity. We aim to shed light on this matter, establishing as the main objectives of this project to pinpoint the spatiotemporal brain dynamics related to randomness in decisions, and to test how learning, memory, and attention affect randomness in humans, and how this is reflected in the associated neural signals.
We completed study 3.2 (above) on brain-body coupling in the initiation of movement. This study asks the profound question of whether or not proprioceptive feedback from muscles is involved in the decision to initiate movement. A positive result would count as strong evidence in favor of the dynamical systems view of action initiation: Decisions to act are not made in the brain and then transmitted to muscles, but rather decisions to act are formed in the brain+body as a whole. Unfortunately, the results of this bold, but risky experiment, were inconclusive.
We also completed behavioral testing of a novel study looking at the neural basis for randomness in choice and its relationship with stochastic variability in brain activity. We aim to shed light on this matter, establishing as the main objectives of this project to pinpoint the spatiotemporal brain dynamics related to randomness in decisions, and to test how learning, memory, and attention affect randomness in humans, and how this is reflected in the associated neural signals.