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

Investigating the impact of early deafness on learned action-effect contingency for action linked to peripheral sensory effects

Investigating peripheral visual processing in individuals with early auditory deprivation is a critical research area in the field of neuroscience, since it helps understanding the phenomenon of sensory adaptation and brain plasticity after sensory loss. Prior research has already demonstrated that the absence of auditory input, which is crucial to detect events occurring out of the central egocentric visual space, leads to an improved processing of visual and tactile stimuli occurring in peripheral regions of the sensory space. Nevertheless, no prior studies have explored whether such enhanced processing also takes place within the domain of action, particularly when individuals are required to perform actions that produce peripheral sensory outcomes. To test this hypothesis, we recruited 15 hearing (31 ± 3.3 years) and 15 early deaf adults (42 ± 2.6 years) for a neuro-behavioral experiment involving: 1) a behavioral task where participants executed a simple motor action (i.e., a button press) and received a visual feedback either in the center or in a peripheral region of the visual field, and 2) the electrophysiological recording of brain electrical potentials (EEG). We measured and compared neural activity preceding the motor action (the readiness potentials) and visual evoked responses (the N1 and P2 ERP components) and found that deaf individuals did not exhibit more pronounced modulation of neural responses when their motor actions resulted in peripheral visual stimuli compared to their hearing counterparts. Instead they showed a reduced modulation when visual stimuli were presented in the center. Our results suggest a redistribution of attentional resources from center to periphery in deaf individuals during sensorimotor coupling.

Using a Veto paradigm to investigate the decision models in explaining Libet-style experiments

The question of whether free will exists or not has intrigued philosophers for centuries. About 40 years ago, cognitive neuroscientists such as Benjamin Libet have joined the discussion by demonstrating that an ERP component, the readiness potential (RP), precedes the reported time of decision to act by a few hundred milliseconds. Libet et al. (1983) argued that our brains unconsciously prepare the movement before we experience any conscious intention, which led some free will skeptics (e.g., Ebert & Wegner, 2011) to argue that free will does not exist. While Libet's interpretation of his findings initiated an intense philosophical debate, alternative interpretations have been put forward more recently (Bode et al., 2014; Brass et al., 2019; Schurger et al., 2012; 2021). Integration to bound models (ITB) of Libet-style experiments suggest that we accumulate information until an intention threshold is reached, which triggers our experience of intention and execution of voluntary behaviors. The RP, from this perspective reflects the decision process itself rather than the consequence of an unconscious decision. To determine if the ITB model better predicts behavioral patterns in Libet-style experiments, we added a whether-component to the classical Libet task (the Veto Libet task) and compared the behavioral measures in the Veto Libet task with the Classical Libet task. We hypothesized that the signal accumulation in the Veto Libet task would be less steep than in the Classical Libet task, resulting in longer wait times and earlier self-reported intentions to act (i.e., the W). The result in general supported our hypotheses. In addition, these behavioral differences between the Classical Libet task and the Veto Libet task established valuable behavioral correlates for future investigations into the vetoing phenomenon. Finally, this study was also the first application of the Libet task in an online setting, and the behavioral parameters were highly comparable to the previous offline studies, further supporting the possibility of using the online platform to study arbitrary decision-making.

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 activity in medial frontal cortex 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 s 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.

Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data

Exoskeleton-based support for patients requires the learning of individual machine-learning models to recognize movement intentions of patients based on the electroencephalogram (EEG). A major issue in EEG-based movement intention recognition is the long calibration time required to train a model. In this paper, we propose a transfer learning approach that eliminates the need for a calibration session. This approach is validated on healthy subjects in this study. We will use the proposed approach in our future rehabilitation application, where the movement intention of the affected arm of a patient can be inferred from the EEG data recorded during bilateral arm movements enabled by the exoskeleton mirroring arm movements from the unaffected to the affected arm. For the initial evaluation, we compared two trained models for predicting unilateral and bilateral movement intentions without applying a classifier transfer. For the main evaluation, we predicted unilateral movement intentions without a calibration session by transferring the classifier trained on data from bilateral movement intentions. Our results showed that the classification performance for the transfer case was comparable to that in the non-transfer case, even with only 4 or 8 EEG channels. Our results contribute to robotic rehabilitation by eliminating the need for a calibration session, since EEG data for training is recorded during the rehabilitation session, and only a small number of EEG channels are required for model training.

Slow ramping emerges from spontaneous fluctuations in spiking neural networks

1. We reveal a mechanism for slow-ramping signals before spontaneous voluntary movements. 2. Slow synapses stabilize spontaneous fluctuations in spiking neural network. 3. We validate model predictions in human frontal cortical single-neuron recordings. 4. The model recreates the readiness potential in an EEG proxy signal. 5. Neurons that ramp together had correlated activity before ramping onset. The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex 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.

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.

Clarifying the nature of stochastic fluctuations and accumulation processes in spontaneous movements

Experiments on choice-predictive brain signals have played an important role in the debate on free will. In a seminal study, Benjamin Libet and colleagues found that a negative-going EEG signal, the readiness potential (RP), can be observed over motor-related brain regions even hundreds of ms before the time of the conscious decision to move. If the early onset of the readiness potential is taken as an indicator of the "brain's decision to move" this could mean that this decision is made early, by unconscious brain activity, rather than later, at the time when the subject believes to have decided. However, an alternative kind of interpretation, involving ongoing stochastic fluctuations, has recently been brought to light. This stochastic decision model (SDM) takes its inspiration from leaky accumulator models of perceptual decision making. It suggests that the RP originates from an accumulation of ongoing stochastic fluctuations. In this view, the decision happens only at a much later stage when an accumulated noisy signal (plus imperative) reaches a threshold. Here, we clarify a number of confusions regarding both the evidence for the stochastic decision model as well as the interpretation that it offers. We will explore several points that we feel are in need of clarification: (a) the empirical evidence for the role of stochastic fluctuations is so far only indirect; (b) the interpretation of animal studies is unclear; (c) a model that is deterministic during the accumulation stage can explain the data in a similar way; (d) the primary focus in the literature has been on the role of random fluctuations whereas the deterministic aspects of the model have been largely ignored; (e) contrary to the original interpretation, the deterministic component of the model is quantitatively the dominant input into the accumulator; and finally (f) there is confusion regarding the role of "imperative" in the SDM and its link to "evidence" in perceptual decision making. Our aim is not to rehabilitate the role of the RP in the free will debate. Rather we aim to address some confusions regarding the evidence for accumulators playing a role in these preparatory brain processes.

Can neuroscience enlighten the philosophical debate about free will?

Free will has been at the heart of philosophical and scientific discussions for many years. However, recent advances in neuroscience have been perceived as a threat to the commonsense notion of free will as they challenge two core requirements for actions to be free. The first is the notion of determinism and free will, i.e., decisions and actions must not be entirely determined by antecedent causes. The second is the notion of mental causation, i.e., our mental state must have causal effects in the physical world, in other words, actions are caused by conscious intention. We present the classical philosophical positions related to determinism and mental causation, and discuss how neuroscience could shed a new light on the philosophical debate based on recent experimental findings. Overall, we conclude that the current evidence is insufficient to undermine free will.

Distinguishing intentional from nonintentional actions through eeg and kinematic markers

How can an intentional movement be distinguished from the same movement done nonintentionally? How can this distinction be drawn without asking the subject, or in patients who are unable to communicate? Here we address these questions, by focusing on blinking. This is one of the most frequent spontaneous actions in our daily life, but it can also be done intentionally. Furthermore, blinking is often spared in patients with severe brain injuries, and for some, it is the only way to report complex meanings. Using kinematic and EEG-based measures, we found that intentional and spontaneous blinking are preceded by different brain activities, even when they are indistinguishable. Unlike spontaneous ones, intentional blinks are characterized by a slow negative EEG drift, resembling the classic readiness potential. We investigated the theoretical implication of this finding in stochastic decision models as well as the practical significance of using brain-based signals to improve the discrimination between intentional and nonintentional actions. As proof of principle, we considered three brain-injured patients with rare neurological syndromes characterized by motor and communicative impairments. Although further research is needed, our results indicate that brain-based signals can offer a feasible way to infer intentionality even in absence of overt communication.

Accounting for endogenous effects in decision-making with a non-linear diffusion decision model

The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.

Implicit mechanisms of intention

High-level cortical regions encode motor decisions before or even absent awareness, suggesting that neural processes predetermine behavior before conscious choice. Such early neural encoding challenges popular conceptions of human agency. It also raises fundamental questions for brain-machine interfaces (BMIs) that traditionally assume that neural activity reflects the user's conscious intentions. Here, we study the timing of human posterior parietal cortex single-neuron activity recorded from implanted microelectrode arrays relative to the explicit urge to initiate movement. Participants were free to choose when to move, whether to move, and what to move, and they retrospectively reported the time they felt the urge to move. We replicate prior studies by showing that posterior parietal cortex (PPC) neural activity sharply rises hundreds of milliseconds before the reported urge. However, we find that this "preconscious" activity is part of a dynamic neural population response that initiates much earlier, when the participant first chooses to perform the task. Together with details of neural timing, our results suggest that PPC encodes an internal model of the motor planning network that transforms high-level task objectives into appropriate motor behavior. These new data challenge traditional interpretations of early neural activity and offer a more holistic perspective on the interplay between choice, behavior, and their neural underpinnings. Our results have important implications for translating BMIs into more complex real-world environments. We find that early neural dynamics are sufficient to drive BMI movements before the participant intends to initiate movement. Appropriate algorithms ensure that BMI movements align with the subject's awareness of choice.

Conscious intention: New data on where and how in the brain

How do we decide to act, and how do those decisions relate to conscious choice? A new study helps dissociate the neuronal mechanisms that choose, prepare, and trigger movement from our explicit reports of conscious intention.

Conscious intention and human action: Review of the rise and fall of the readiness potential and Libet's clock

Is consciousness-the subjective awareness of the sensations, perceptions, beliefs, desires, and intentions of mental life-a genuine cause of human action or a mere impotent epiphenomenon accompanying the brain's physical activity but utterly incapable of making anything actually happen? This article will review the history and current status of experiments and commentary related to Libet's influential paper (Brain 106:623-664, 1983) whose conclusion "that cerebral initiation even of a spontaneous voluntary act …can and usually does begin unconsciously" has had a huge effect on debate about the efficacy of conscious intentions. Early (up to 2008) and more recent (2008 on) experiments replicating and criticizing Libet's conclusions and especially his methods will be discussed, focusing especially on recent observations that the readiness potential (RP) may only be an "artifact of averaging" and that, when intention is measured using "tone probes," the onset of intention is found much earlier and often before the onset of the RP. Based on these findings, Libet's methodology was flawed and his results are no longer valid reasons for rejecting Fodor's "good old commonsense belief/desire psychology" that "my wanting is causally responsible for my reaching.".

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.

Suppress Me if You Can: Neurofeedback of the Readiness Potential

Voluntary movements are usually preceded by a slow, negative-going brain signal over motor areas, the so-called readiness potential (RP). To date, the exact nature and causal role of the RP in movement preparation have remained heavily debated. Although the RP is influenced by several motorical and cognitive factors, it has remained unclear whether people can learn to exert mental control over their RP, for example, by deliberately suppressing it. If people were able to initiate spontaneous movements without eliciting an RP, this would challenge the idea that the RP is a necessary stage of the causal chain leading up to a voluntary movement. We tested the ability of participants to control the magnitude of their RP in a neurofeedback experiment. Participants performed self-initiated movements, and after every movement, they were provided with immediate feedback about the magnitude of their RP. They were asked to find a strategy to perform voluntary movements such that the RPs were as small as possible. We found no evidence that participants were able to to willfully modulate or suppress their RPs while still eliciting voluntary movements. This suggests that the RP might be an involuntary component of voluntary action over which people cannot exert conscious control.

The Readiness Potential reflects the internal source of action, rather than decision uncertainty

Voluntary actions are preceded by a Readiness Potential (RP), a slow EEG (electroencephalogram) component generated in medial frontal cortical areas. The RP is classically thought to be specific to internally-driven decisions to act, and to reflect post-decision motor preparation. Recent work suggests instead that it may reflect noise or conflict during the decision itself, with internally driven decisions tending to be more random, more conflicted and thus more uncertain than externally driven actions. To contrast accounts based on endogenicity with accounts based on uncertainty, we recorded EEG in a task where participants decided to act or withhold action to accept or reject visually presented gambles, and used multivariate methods to extract an RP-like component. We found no difference in amplitude of this component between actions driven by strong versus weak evidence, suggesting that the RP may not reflect uncertainty. In contrast, the same RP-like component showed higher amplitudes prior to actions performed without any external evidence (guesses) than for actions performed in response to equivocal, conflicting evidence. This supports the view that the RP reflects the internal source of action, rather than decision uncertainty.

Differences in Intersubject Early Readiness Potentials Between Voluntary and Instructed Actions

Readiness potential (RP) is a slow negative electroencephalogram (EEG) potential prior to voluntary action and was first described by Kornhuber and Deecke (1965). Recent studies have demonstrated that a few subjects do not exhibit standard RP before voluntary action. In our previous study, we also found that some subjects did not show an early RP preceding instructed action. Although this phenomenon may be meaningful, no studies have yet investigated its origins. In the present study, we designed and implemented an experimental paradigm involving voluntary and instructed actions in the form of hand movements from 29 subjects with concurrent acquisition of EEGs. According to whether the subjects showed a standard RP waveform during instructed action, they were divided into the SHOW and NOSHOW group. Then, the RPs and voltage topographies were plotted for each group. Finally, the slope of each epoch at the early RP phase was estimated. We showed that early RPs were absent in 14 of 29 subjects during instructed actions. Besides, based on the slow cortical potential (SCP) sampling hypothesis, we also showed a decreased proportion in the negative potential for the NOSHOW group. Our results suggested that early RP is absent among approximately half of subjects during instructed action and that the decreased proportion of negative potential shifts may account for the absence of early RP in the NOSHOW group.

Neural precursors of decisions that matter-an ERP study of deliberate and arbitrary choice

The readiness potential (RP)—a key ERP correlate of upcoming action—is known to precede subjects' reports of their decision to move. Some view this as evidence against a causal role for consciousness in human decision-making and thus against free-will. But previous work focused on arbitrary decisions—purposeless, unreasoned, and without consequences. It remains unknown to what degree the RP generalizes to deliberate, more ecological decisions. We directly compared deliberate and arbitrary decision-making during a $1000-donation task to non-profit organizations. While we found the expected RPs for arbitrary decisions, they were strikingly absent for deliberate ones. Our results and drift-diffusion model are congruent with the RP representing accumulation of noisy, random fluctuations that drive arbitrary—but not deliberate—decisions. They further point to different neural mechanisms underlying deliberate and arbitrary decisions, challenging the generalizability of studies that argue for no causal role for consciousness in decision-making to real-life decisions.

Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

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.

Why neuroscience does not disprove free will

While the question whether free will exists or not has concerned philosophers for centuries, empirical research on this question is relatively young. About 35 years ago Benjamin Libet designed an experiment that challenged the common intuition of free will, namely that conscious intentions are causally efficacious. Libet demonstrated that conscious intentions are preceded by a specific pattern of brain activation, suggesting that unconscious processes determine our decisions and we are only retrospectively informed about these decisions. Libet-style experiments have ever since dominated the discourse about the existence of free will and have found their way into the public media. Here we review the most important challenges to the common interpretation of Libet-style tasks and argue that the common interpretation is questionable. Brain activity preceding conscious decisions reflects the decision process rather than its outcome. Furthermore, the decision process is configured by conditional intentions that participants form at the beginning of the experiment. We conclude that Libet-style tasks do not provide a serious challenge to our intuition of free will.

The Neurocognitive Bases of Human Volition

Volition refers to a capacity for endogenous action, particularly goal-directed endogenous action, shared by humans and some other animals. It has long been controversial whether a specific set of cognitive processes for volition exist in the human brain, and much scientific thinking on the topic continues to revolve around traditional metaphysical debates about free will. At its origins, scientific psychology had a strong engagement with volition. This was followed by a period of disenchantment, or even outright hostility, during the second half of the twentieth century. In this review, I aim to reinvigorate the scientific approach to volition by, first, proposing a range of different features that constitute a new, neurocognitively realistic working definition of volition. I then focus on three core features of human volition: its generativity (the capacity to trigger actions), its subjectivity (the conscious experiences associated with initiating voluntary actions), and its teleology (the goal-directed quality of some voluntary actions). I conclude that volition is a neurocognitive process of enormous societal importance and susceptible to scientific investigation.

Neural Oscillations and the Initiation of Voluntary Movement

The brain processes involved in the planning and initiation of voluntary action are of great interest for understanding the relationship between conscious awareness of decisions and the neural control of movement. Voluntary motor behavior has generally been considered to occur when conscious decisions trigger movements. However, several studies now provide compelling evidence that brain states indicative of forthcoming movements take place before a person becomes aware of a conscious decision to act. While such studies have created much debate over the nature of ‘free will,’ at the very least they suggest that unconscious brain processes are predictive of forthcoming movements. Recent studies suggest that slow changes in neuroelectric potentials may play a role in the timing of movement onset by pushing brain activity above a threshold to trigger the initiation of action. Indeed, recent studies have shown relationships between the phase of low frequency oscillatory activity of the brain and the onset of voluntary action. Such studies, however, cannot determine whether this underlying neural activity plays a causal role in the initiation of movement or is only associated with the intentional behavior. Non-invasive transcranial alternating current brain stimulation can entrain neural activity at particular frequencies in order to assess whether underlying brain processes are causally related to associated behaviors. In this review, we examine the evidence for neural coding of action as well as the brain states prior to action initiation and discuss whether low frequency alternating current brain stimulation could influence the timing of a persons’ decision to act.