Libet's legacy: A primer to the neuroscience of volition.

The neuroscience of volition is an emerging subfield of the brain sciences, with hundreds of papers on the role of consciousness in action formation published each year. This makes the state-of-the-art in the discipline poorly accessible to newcomers and difficult to follow even for experts in the field. Here we provide a comprehensive summary of research in this field since its inception that will be useful to both groups. We also discuss important ideas that have received little coverage in the literature so far. We systematically reviewed a set of 2220 publications, with detailed consideration of almost 500 of the most relevant papers. We provide a thorough introduction to the seminal work of Benjamin Libet from the 1960s to 1980s. We also discuss common criticisms of Libet's method, including temporal introspection, the interpretation of the assumed physiological correlates of volition, and various conceptual issues. We conclude with recent advances and potential future directions in the field, highlighting modern methodological approaches to volition, as well as important recent findings.

Slow ramping emerges from spontaneous fluctuations in spiking neural networks.

The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping medial frontal cortex activity that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ∼2 seconds before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses. Highlights: We reveal a mechanism for slow-ramping signals before spontaneous voluntary movements.Slow synapses stabilize spontaneous fluctuations in spiking neural network.We validate model predictions in human frontal cortical single neuron recordingsThe model recreates the readiness potential in an EEG proxy signal.Neurons that ramp together had correlated activity before ramping onset.

What is the intention to move and when does it occur?

In 1983 Benjamin Libet and colleagues published a paper apparently challenging the view that the conscious intention to move precedes the brain's preparation for movement. The experiment initiated debates about the nature of intention, the neurophysiology of movement, and philosophical and legal understanding of free will and moral responsibility. Here we review the concept of "conscious intention" and attempts to measure its timing. Scalp electroencephalographic activity prior to movement, the Bereitschaftspotential, clearly begins prior to the reported onset of conscious intent. However, the interpretation of this finding remains controversial. Numerous studies show that the Libet method for determining intent, W time, is not accurate and may be misleading. We conclude that intention has many different aspects, and although we now understand much more about how the brain makes movements, identifying the time of conscious intention is still elusive.

Free will without consciousness?

Findings demonstrating decision-related neural activity preceding volitional actions have dominated the discussion about how science can inform the free will debate. These discussions have largely ignored studies suggesting that decisions might be influenced or biased by various unconscious processes. If these effects are indeed real, do they render subjects' decisions less free or even unfree? Here, we argue that, while unconscious influences on decision-making do not threaten the existence of free will in general, they provide important information about limitations on freedom in specific circumstances. We demonstrate that aspects of this long-lasting controversy are empirically testable and provide insight into their bearing on degrees of freedom, laying the groundwork for future scientific-philosophical approaches.

Consciousness explained or described?

Consciousness is an unusual phenomenon to study scientifically. It is defined as a subjective, first-person phenomenon, and science is an objective, third-person endeavor. This misalignment between the means-science-and the end-explaining consciousness-gave rise to what has become a productive workaround: the search for 'neural correlates of consciousness' (NCCs). Science can sidestep trying to explain consciousness and instead focus on characterizing the kind(s) of neural activity that are reliably correlated with consciousness. However, while we have learned a lot about consciousness in the bargain, the NCC approach was not originally intended as the foundation for a true explanation of consciousness. Indeed, it was proposed precisely to sidestep the, arguably futile, attempt to find one. So how can an account, couched in terms of neural correlates, do the work that a theory is supposed to do: explain consciousness? The answer is that it cannot, and in fact most modern accounts of consciousness do not pretend to. Thus, here, we challenge whether or not any modern accounts of consciousness are in fact theories at all. Instead we argue that they are (competing) laws of consciousness. They describe what they cannot explain, just as Newton described gravity long before a true explanation was ever offered. We lay out our argument using a variety of modern accounts as examples and go on to argue that at least one modern account of consciousness, attention schema theory, goes beyond describing consciousness-related brain activity and qualifies as an explanatory theory.

First-person experience cannot rescue causal structure theories from the unfolding argument.

We recently put forward an argument, the Unfolding Argument (UA), that integrated information theory (IIT) and other causal structure theories are either already falsified or unfalsifiable, which provoked significant criticism. It seems that we and the critics agree that the main question in this debate is whether first-person experience, independent of third-person data, is a sufficient foundation for theories of consciousness. Here, we argue that pure first-person experience cannot be a scientific foundation for IIT because science relies on taking measurements, and pure first-person experience is not measurable except through reports, brain activity, and the relationship between them. We also argue that pure first-person experience cannot be taken as ground truth because science is about backing up theories with data, not about asserting that we have ground truth independent of data. Lastly, we explain why no experiment based on third-person data can test IIT as a theory of consciousness. IIT may be a good theory of something, but not of consciousness. We conclude by exposing a deeper reason for the above conclusions: IIT's consciousness is by construction fully dissociated from any measurable thing and, for this reason, IIT implies that both the level and content of consciousness are epiphenomenal, with no causal power. IIT and other causal structure theories end up in a form of dissociative epiphenomenalism, in which we cannot even trust reports about first-person experiences. But reports about first-person experiences are taken as ground truth and the foundation for IIT's axioms. Therefore, accepting IIT leads to rejecting its own axioms. We also respond to several other criticisms against the UA.

What Is the Readiness Potential?

The readiness potential (RP), a slow buildup of electrical potential recorded at the scalp using electroencephalography, has been associated with neural activity involved in movement preparation. It became famous thanks to Benjamin Libet (Brain 1983;106:623-642), who used the time difference between the RP and self-reported time of conscious intention to move to argue that we lack free will. The RP's informativeness about self-generated action and derivatively about free will has prompted continued research on this neural phenomenon. Here, we argue that recent advances in our understanding of the RP, including computational modeling of the phenomenon, call for a reassessment of its relevance for understanding volition and the philosophical problem of free will.

Response to commentaries on 'hard criteria for empirical theories of consciousness'.

In consciousness research, we have a very large number of theories, which exceeds by far the number of theories in other fields. We recently presented a set of criteria for evaluating and comparing theories of consciousness, and then applied the criteria to a number of different theories. Our publication sparked strong responses as evident by the many comments published in Cognitive Neuroscience (this issue). Overall, there seems to be consensus that a theory of consciousness (ToC) needs to have an unconscious alternative, but other criteria sparked controversy. The hottest debate is to what extent consciousness needs to work with purely 1st person data, containing information not available in 3rd person reports. We would like to thank all the commentators for their lively input and we look forward to continued dialog as theories evolve and compete.

Hard criteria for empirical theories of consciousness.

Consciousness is now a well-established field of empirical research. A large body of experimental results has been accumulated and is steadily growing. In parallel, many Theories of Consciousness (ToCs) have been proposed. These theories are diverse in nature, ranging from computational to neurophysiological and quantum theoretical approaches. This contrasts with other fields of natural science, which host a smaller number of competing theories. We suggest that one reason for this abundance of extremely different theories may be the lack of stringent criteria specifying how empirical data constrains ToCs. First, we argue that consciousness is a well-defined topic from an empirical point of view and motivate a purely empirical stance on the quest for consciousness. Second, we present a checklist of criteria that, we propose, empirical ToCs need to cope with. Third, we review 13 of the most influential ToCs and subject them to the criteria. Our analysis helps to situate these different ToCs in the theoretical landscapeand sheds light on their strengths and weaknesses from a strictly empirical point of view.

Do readiness potentials happen all the time?

The Readiness Potential (RP) is a slow negative EEG potential found in the seconds preceding voluntary actions. Here, we explore whether the RP is found only at this time, or if it also occurs when no action is produced. Recent theories suggest the RP reflects the average of accumulated stochastic fluctuations in neural activity, rather than a specific signal related to self-initiated action: RP-like events should then be widely present, even in the absence of actions. We investigated this hypothesis by searching for RP-like events in background EEG of an appropriate dataset for which the action-locked EEG had previously been analysed to test other hypotheses [Khalighinejad, N., Brann, E., Dorgham, A., Haggard, P. Dissociating cognitive and motoric precursors of human self-initiated action. Journal of Cognitive Neuroscience. 2019, 1-14]. We used the actual mean RP as a template, and searched the entire epoch for similar neural signals, using similarity metrics that capture the temporal or spatial properties of the RP. Most EEG epochs contained a number of events that were similar to the true RP, but did not lead directly to any voluntary action. However, these RP-like events were equally common in epochs that eventually terminated in voluntary actions as in those where voluntary actions were not permitted. Events matching the temporal profile of the RP were also a poor match for the spatial profile, and vice versa. We conclude that these events are false positives, and do not reflect the same mechanism as the RP itself. Finally, applying the same template-search algorithm to simulated EEG data synthesized from different noise distributions showed that RP-like events will occur in any dataset containing the 1/f noise ubiquitous in EEG recordings. To summarise, we found no evidence of genuinely RP-like events at any time other than immediately prior to self-initiated actions. Our findings do not support a purely stochastic model of RP generation, and suggest that the RP may be a specific precursor of self-initiated voluntary actions.

An integration-to-bound model of decision-making that accounts for the spectral properties of neural data.

Integration-to-bound models are among the most widely used models of perceptual decision-making due to their simplicity and power in accounting for behavioral and neurophysiological data. They involve temporal integration over an input signal ("evidence") plus Gaussian white noise. However, brain data shows that noise in the brain is long-term correlated, with a spectral density of the form 1/fα (with typically 1 < α < 2), also known as pink noise or '1/f' noise. Surprisingly, the adequacy of the spectral properties of drift-diffusion models to electrophysiological data has received little attention in the literature. Here we propose a model of accumulation of evidence for decision-making that takes into consideration the spectral properties of brain signals. We develop a generalization of the leaky stochastic accumulator model using a Langevin equation whose non-linear noise term allows for varying levels of autocorrelation in the time course of the decision variable. We derive this equation directly from magnetoencephalographic data recorded while subjects performed a spontaneous movement-initiation task. We then propose a nonlinear model of accumulation of evidence that accounts for the '1/f' spectral properties of brain signals, and the observed variability in the power spectral properties of brain signals. Furthermore, our model outperforms the standard drift-diffusion model at approximating the empirical waiting time distribution.

The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness.

How can we explain consciousness? This question has become a vibrant topic of neuroscience research in recent decades. A large body of empirical results has been accumulated, and many theories have been proposed. Certain theories suggest that consciousness should be explained in terms of brain functions, such as accessing information in a global workspace, applying higher order to lower order representations, or predictive coding. These functions could be realized by a variety of patterns of brain connectivity. Other theories, such as Information Integration Theory (IIT) and Recurrent Processing Theory (RPT), identify causal structure with consciousness. For example, according to these theories, feedforward systems are never conscious, and feedback systems always are. Here, using theorems from the theory of computation, we show that causal structure theories are either false or outside the realm of science.

Pre-stimulus theta power is correlated with variation of motor evoked potential latency: a single-pulse TMS study.

There has been a growing interest in the role of pre-stimulus oscillations on cortical excitability in visual and motor systems. Prior studies focused on the relationship between pre-stimulus neuronal activity and TMS-evoked motor evoked potentials (MEPs) have reported heterogeneous results. We aimed to assess the role of pre-stimulus neural activity on the latency of MEPs, which might enhance our understanding of the variability of MEP signals, and potentially provide information on the role played by cortical activity fluctuations in the excitability of corticospinal pathways. Near-threshold single-pulse TMS (spTMS) was applied at random intervals over the primary motor cortex of 14 healthy participants while they sat passively, to trigger hand muscle contractions. Multichannel EEG was recorded during spTMS blocks. Spearman correlations between both the variation in MEP onset latencies and peak-to-peak MEP amplitudes, and the pre-stimulus power of EEG oscillations were calculated across participants. We found that the variation in MEP latency was positively correlated with pre-stimulus power in the theta range (4-7 Hz) in a broad time window (- 3.1 to - 1.9 s) preceding the spTMS generating the MEP. No correlation between pre-stimulus power in any frequency band and MEP amplitude was found. Our results show that pre-stimulus theta oscillations are correlated with the variation in MEP latency, an outcome measure determined by fiber conduction velocity and synaptic delays along the corticospinal tract. This finding could prove useful for clinicians using MEP latency-based information in pre- or intra-operative diagnostics of corticospinal impairment.

Mooney face stimuli for visual perception research.

In 1957, Craig Mooney published a set of human face stimuli to study perceptual closure: the formation of a coherent percept on the basis of minimal visual information. Images of this type, now known as "Mooney faces", are widely used in cognitive psychology and neuroscience because they offer a means of inducing variable perception with constant visuo-spatial characteristics (they are often not perceived as faces if viewed upside down). Mooney's original set of 40 stimuli has been employed in several studies. However, it is often necessary to use a much larger stimulus set. We created a new set of over 500 Mooney faces and tested them on a cohort of human observers. We present the results of our tests here, and make the stimuli freely available via the internet. Our test results can be used to select subsets of the stimuli that are most suited for a given experimental purpose.

Specific Relationship between the Shape of the Readiness Potential, Subjective Decision Time, and Waiting Time Predicted by an Accumulator Model with Temporally Autocorrelated Input Noise.

Self-initiated movements are reliably preceded by a gradual buildup of neuronal activity known as the readiness potential (RP). Recent evidence suggests that the RP may reflect subthreshold stochastic fluctuations in neural activity that can be modeled as a process of accumulation to bound. One element of accumulator models that has been largely overlooked in the literature is the stochastic term, which is traditionally modeled as Gaussian white noise. While there may be practical reasons for this choice, we have long known that noise in neural systems is not white - it is long-term correlated with spectral density of the form 1/fß(with roughly 1 < ß < 3) across a broad range of spatial scales. I explored the behavior of a leaky stochastic accumulator when the noise over which it accumulates is temporally autocorrelated. I also allowed for the possibility that the RP, as measured at the scalp, might reflect the input to the accumulator (i.e., its stochastic noise component) rather than its output. These two premises led to two novel predictions that I empirically confirmed on behavioral and electroencephalography data from human subjects performing a self-initiated movement task. In addition to generating these two predictions, the model also suggested biologically plausible levels of autocorrelation, consistent with the degree of autocorrelation in our empirical data and in prior reports. These results expose new perspectives for accumulator models by suggesting that the spectral properties of the stochastic input should be allowed to vary, consistent with the nature of biological neural noise.

Precursor processes of human self-initiated action.

A gradual buildup of electrical potential over motor areas precedes self-initiated movements. Recently, such "readiness potentials" (RPs) were attributed to stochastic fluctuations in neural activity. We developed a new experimental paradigm that operationalized self-initiated actions as endogenous 'skip' responses while waiting for target stimuli in a perceptual decision task. We compared these to a block of trials where participants could not choose when to skip, but were instead instructed to skip. Frequency and timing of motor action were therefore balanced across blocks, so that conditions differed only in how the timing of skip decisions was generated. We reasoned that across-trial variability of EEG could carry as much information about the source of skip decisions as the mean RP. EEG variability decreased more markedly prior to self-initiated compared to externally-triggered skip actions. This convergence suggests a consistent preparatory process prior to self-initiated action. A leaky stochastic accumulator model could reproduce this convergence given the additional assumption of a systematic decrease in input noise prior to self-initiated actions. Our results may provide a novel neurophysiological perspective on the topical debate regarding whether self-initiated actions arise from a deterministic neurocognitive process, or from neural stochasticity. We suggest that the key precursor of self-initiated action may manifest as a reduction in neural noise.

Entrainment of Voluntary Movement to Undetected Auditory Regularities.

In physics "entrainment" refers to the synchronization of two coupled oscillators with similar fundamental frequencies. In behavioral science, entrainment refers to the tendency of humans to synchronize their movements with rhythmic stimuli. Here, we asked whether human subjects performing a tapping task would entrain their tapping to an undetected auditory rhythm surreptitiously introduced in the guise of ambient background noise in the room. Subjects performed two different tasks, one in which they tapped their finger at a steady rate of their own choosing and one in which they performed a single abrupt finger tap on each trial after a delay of their own choosing. In both cases we found that subjects tended to tap in phase with the inducing modulation, with some variability in the preferred phase across subjects, consistent with prior research. In the repetitive tapping task, if the frequency of the inducing stimulus was far from the subject's own self-paced frequency, then entrainment was abolished, consistent with the properties of entrainment in physics. Thus, undetected ambient noise can influence self-generated movements. This suggests that uncued decisions to act are never completely endogenous, but are subject to subtle unnoticed influences from the sensory environment.

Volition and Action in the Human Brain: Processes, Pathologies, and Reasons.

Humans seem to decide for themselves what to do, and when to do it. This distinctive capacity may emerge from an ability, shared with other animals, to make decisions for action that are related to future goals, or at least free from the constraints of immediate environmental inputs. Studying such volitional acts proves a major challenge for neuroscience. This review highlights key mechanisms in the generation of voluntary, as opposed to stimulus-driven actions, and highlights three issues. The first part focuses on the apparent spontaneity of voluntary action. The second part focuses on one of the most distinctive, but elusive, features of volition, namely, its link to conscious experience, and reviews stimulation and patient studies of the cortical basis of conscious volition down to the single-neuron level. Finally, we consider the goal-directedness of voluntary action, and discuss how internal generation of action can be linked to goals and reasons.

Cortical and subcortical mechanisms of brain-machine interfaces.

Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level. Here, we provide a comprehensive and detailed report of the areas active during on-line BMI control. We recorded functional magnetic resonance imaging (fMRI) data while participants controlled an EEG-based BMI inside the scanner. We identified the regions activated during BMI control and how they overlap with those involved in motor imagery (without any BMI control). In addition, we investigated which regions reflect the subjective sense of controlling a BMI, the sense of agency for BMI-actions. Our data revealed an extended cortical-subcortical network involved in operating a motor-imagery BMI. This includes not only sensorimotor regions but also the posterior parietal cortex, the insula and the lateral occipital cortex. Interestingly, the basal ganglia and the anterior cingulate cortex were involved in the subjective sense of controlling the BMI. These results inform basic neuroscience by showing that the mechanisms of BMI control extend beyond sensorimotor cortices. This knowledge may be useful for the development of BMIs that offer a more natural and embodied feeling of control for the user. Hum Brain Mapp 38:2971-2989, 2017. © 2017 Wiley Periodicals, Inc.