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.

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.

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.

Cortical networks involved in visual awareness independent of visual attention.

It is now well established that visual attention, as measured with standard spatial attention tasks, and visual awareness, as measured by report, can be dissociated. It is possible to attend to a stimulus with no reported awareness of the stimulus. We used a behavioral paradigm in which people were aware of a stimulus in one condition and unaware of it in another condition, but the stimulus drew a similar amount of spatial attention in both conditions. The paradigm allowed us to test for brain regions active in association with awareness independent of level of attention. Participants performed the task in an MRI scanner. We looked for brain regions that were more active in the aware than the unaware trials. The largest cluster of activity was obtained in the temporoparietal junction (TPJ) bilaterally. Local independent component analysis (ICA) revealed that this activity contained three distinct, but overlapping, components: a bilateral, anterior component; a left dorsal component; and a right dorsal component. These components had brain-wide functional connectivity that partially overlapped the ventral attention network and the frontoparietal control network. In contrast, no significant activity in association with awareness was found in the banks of the intraparietal sulcus, a region connected to the dorsal attention network and traditionally associated with attention control. These results show the importance of separating awareness and attention when testing for cortical substrates. They are also consistent with a recent proposal that awareness is associated with ventral attention areas, especially in the TPJ.

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.

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.

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.

Cortical activity is more stable when sensory stimuli are consciously perceived.

According to recent evidence, stimulus-tuned neurons in the cerebral cortex exhibit reduced variability in firing rate across trials, after the onset of a stimulus. However, in order for a reduction in variability to be directly relevant to perception and behavior, it must be realized within trial--the pattern of activity must be relatively stable. Stability is characteristic of decision states in recurrent attractor networks, and its possible relevance to conscious perception has been suggested by theorists. However, it is difficult to measure on the within-trial time scales and broadly distributed spatial scales relevant to perception. We recorded simultaneous magneto- and electroencephalography (MEG and EEG) data while subjects observed threshold-level visual stimuli. Pattern-similarity analyses applied to the data from MEG gradiometers uncovered a pronounced decrease in variability across trials after stimulus onset, consistent with previous single-unit data. This was followed by a significant divergence in variability depending upon subjective report (seen/unseen), with seen trials exhibiting less variability. Applying the same analysis across time, within trial, we found that the latter effect coincided in time with a difference in the stability of the pattern of activity. Stability alone could be used to classify data from individual trials as "seen" or "unseen." The same metric applied to EEG data from patients with disorders of consciousness exposed to auditory stimuli diverged parametrically according to clinically diagnosed level of consciousness. Differences in signal strength could not account for these results. Conscious perception may involve the transient stabilization of distributed cortical networks, corresponding to a global brain-scale decision.

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.

Performance monitoring for brain-computer-interface actions.

When presented with a difficult perceptual decision, human observers are able to make metacognitive judgements of subjective certainty. Such judgements can be made independently of and prior to any overt response to a sensory stimulus, presumably via internal monitoring. Retrospective judgements about one's own task performance, on the other hand, require first that the subject perform a task and thus could potentially be made based on motor processes, proprioceptive, and other sensory feedback rather than internal monitoring. With this dichotomy in mind, we set out to study performance monitoring using a brain-computer interface (BCI), with which subjects could voluntarily perform an action - moving a cursor on a computer screen - without any movement of the body, and thus without somatosensory feedback. Real-time visual feedback was available to subjects during training, but not during the experiment where the true final position of the cursor was only revealed after the subject had estimated where s/he thought it had ended up after 6s of BCI-based cursor control. During the first half of the experiment subjects based their assessments primarily on the prior probability of the end position of the cursor on previous trials. However, during the second half of the experiment subjects' judgements moved significantly closer to the true end position of the cursor, and away from the prior. This suggests that subjects can monitor task performance when the task is performed without overt movement of the body.

Oscillatory neural responses evoked by natural vestibular stimuli in humans.

While there have been numerous studies of the vestibular system in mammals, less is known about the brain mechanisms of vestibular processing in humans. In particular, of the studies that have been carried out in humans over the last 30 years, none has investigated how vestibular stimulation (VS) affects cortical oscillations. Here we recorded high-density electroencephalography (EEG) in healthy human subjects and a group of bilateral vestibular loss patients (BVPs) undergoing transient and constant-velocity passive whole body yaw rotations, focusing our analyses on the modulation of cortical oscillations in response to natural VS. The present approach overcame significant technical challenges associated with combining natural VS with human electrophysiology and reveals that both transient and constant-velocity VS are associated with a prominent suppression of alpha power (8-13 Hz). Alpha band suppression was localized over bilateral temporo-parietal scalp regions, and these alpha modulations were significantly smaller in BVPs. We propose that suppression of oscillations in the alpha band over temporo-parietal scalp regions reflects cortical vestibular processing, potentially comparable with alpha and mu oscillations in the visual and sensorimotor systems, respectively, opening the door to the investigation of human cortical processing under various experimental conditions during natural VS.

An accumulator model for spontaneous neural activity prior to self-initiated movement.

A gradual buildup of neuronal activity known as the "readiness potential" reliably precedes voluntary self-initiated movements, in the average time locked to movement onset. This buildup is presumed to reflect the final stages of planning and preparation for movement. Here we present a different interpretation of the premovement buildup. We used a leaky stochastic accumulator to model the neural decision of "when" to move in a task where there is no specific temporal cue, but only a general imperative to produce a movement after an unspecified delay on the order of several seconds. According to our model, when the imperative to produce a movement is weak, the precise moment at which the decision threshold is crossed leading to movement is largely determined by spontaneous subthreshold fluctuations in neuronal activity. Time locking to movement onset ensures that these fluctuations appear in the average as a gradual exponential-looking increase in neuronal activity. Our model accounts for the behavioral and electroencephalography data recorded from human subjects performing the task and also makes a specific prediction that we confirmed in a second electroencephalography experiment: Fast responses to temporally unpredictable interruptions should be preceded by a slow negative-going voltage deflection beginning well before the interruption itself, even when the subject was not preparing to move at that particular moment.

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 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.

Reducing multi-sensor data to a single time course that reveals experimental effects.

BACKGROUND: Multi-sensor technologies such as EEG, MEG, and ECoG result in high-dimensional data sets. Given the high temporal resolution of such techniques, scientific questions very often focus on the time-course of an experimental effect. In many studies, researchers focus on a single sensor or the average over a subset of sensors covering a "region of interest" (ROI). However, single-sensor or ROI analyses ignore the fact that the spatial focus of activity is constantly changing, and fail to make full use of the information distributed over the sensor array. METHODS: We describe a technique that exploits the optimality and simplicity of matched spatial filters in order to reduce experimental effects in multivariate time series data to a single time course. Each (multi-sensor) time sample of each trial is replaced with its projection onto a spatial filter that is matched to an observed experimental effect, estimated from the remaining trials (Effect-Matched Spatial filtering, or EMS filtering). The resulting set of time courses (one per trial) can be used to reveal the temporal evolution of an experimental effect, which distinguishes this approach from techniques that reveal the temporal evolution of an anatomical source or region of interest. RESULTS: We illustrate the technique with data from a dual-task experiment and use it to track the temporal evolution of brain activity during the psychological refractory period. We demonstrate its effectiveness in separating the means of two experimental conditions, and in significantly improving the signal-to-noise ratio at the single-trial level. It is fast to compute and results in readily-interpretable time courses and topographies. The technique can be applied to any data-analysis question that can be posed independently at each sensor, and we provide one example, using linear regression, that highlights the versatility of the technique. CONCLUSION: The approach described here combines established techniques in a way that strikes a balance between power, simplicity, speed of processing, and interpretability. We have used it to provide a direct view of parallel and serial processes in the human brain that previously could only be measured indirectly. An implementation of the technique in MatLab is freely available via the internet.

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.

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.

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.