What Does Neuroscience Have to Say About Free Will?

Hardly any question could be more important than whether human beings have free will. Of course it is of specific importance for some religions, but also for many non-religious people. Indeed, historically, many of those who rejected religion did so in the name of intellectual freedom and called themselves \u27freethinkers\u27. The question is whether science allows us to believe in freedom of any sort, whether intellectual or moral. In the last half century, there have been claims that neuroscience was yielding evidence against the reality of human freedom. What I will discuss here is whether that is the case. My goal is not to argue either for or against the reality of freedom, but only to examine what neuroscience is at present able to say on the subject — and what it is not able to say

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/f^β (with roughly 1 \u3c β \u3c 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

Paradoxical Interaction Between Ocular Activity, Perception, and Decision Confidence at the Threshold of Vision

In humans and some other species perceptual decision-making is complemented by the ability to make confidence judgements about the certainty of sensory evidence. While both forms of decision process have been studied empirically, the precise relationship between them remains poorly understood. We performed an experiment that combined a perceptual decision-making task (identifying the category of a faint visual stimulus) with a confidence-judgement task (wagering on the accuracy of each perceptual decision). The visual stimulation paradigm required steady fixation, so we used eye-tracking to control for stray eye movements. Our data analyses revealed an unexpected and counterintuitive interaction between the steadiness of fixation (prior to and during stimulation), perceptual decision making, and post-decision wagering: greater variability in gaze direction during fixation was associated with significantly increased visual-perceptual sensitivity, but significantly decreased reliability of confidence judgements. The latter effect could not be explained by a simple change in overall confidence (i.e. a criterion artifact), but rather was tied to a change in the degree to which high wagers predicted correct decisions (i.e. the sensitivity of the confidence judgement). We found no evidence of a differential change in pupil diameter that could account for the effect and thus our results are consistent with fixational eye movements being the relevant covariate. However, we note that small changes in pupil diameter can sometimes cause artefactual fluctuations in measured gaze direction and this possibility could not be fully ruled out. In either case, our results suggest that perceptual decisions and confidence judgements can be processed independently and point toward a new avenue of research into the relationship between them

Effects of Transcranial Direct Current Stimulation on Adults with Post-Acute COVID-19 Syndrome

Since its detection in December 2019, coronavirus disease 2019 (COVID-19), the viral disease caused by the SARS-CoV-2 novel coronavirus, has had prominent effects on human health and mortality. Studies in previous infections of SARS-CoV and MERS-CoV have found evidence of persistent symptoms in recovered patients, such as lethargy and shortness of breath. Similar residual symptoms have also been seen in recovered COVID-19 patients beyond four weeks of the initial onset of symptoms — collectively termed post-acute COVID-19 syndrome (PACS). These symptoms include deficits in working memory. Preliminary studies done in the United States and Europe have shown a significant portion of recovered individuals suffer from PACS. Thus, there is a need to understand the neurophysiological effects of PACS better and develop a systemic approach to treating its symptoms. Transcranial direct current stimulation (tDCS), a non-invasive transcranial electrical stimulation method, has been recently investigated as a possible non-pharmacological intervention in various neuropsychiatric disorders. The most appealing aspects of the intervention have been its safety, portability, and at-home application. tDCS regulates neuronal transmembrane potentials towards depolarization or hyperpolarization via weak electrical currents, resulting in changes in the resting membrane potential and transmembrane proteins. This project aims to investigate the effect of tDCS on working memory in individuals with PACS and its potential for clinical applications. Participants are asked to undergo eight 20-minute tDCS stimulation periods over four sessions. Each participant is tasked with the 2-back task before and after each stimulation period. Parameters related to working memory, such as response time, are recorded for data analysis. We anticipate that the results from this project will help us better understand PACS and enable us to propose new approaches to treating residual symptoms

Reckoning the Moment of Reckoning in Spontaneous Voluntary Movement

One question that naturally arises is: When, if at all, along the time course of the RP does the brain make the final commitment to initiate movement? Is there a point of no return after which the sequence of action potentials becomes “ballistic” and movement, although not yet happening, can no longer be aborted? This is the question that Schultze-Kraft et al. (9) ask through a clever experiment involving a direct brain–computer interface (BCI). On-line detection of the RP allowed them to present a stop signal when the probability of an impending movement was high. This process afforded the authors a unique perspective on the inhibition of voluntary, uncued actions

A brain-constrained deep neural-network model that can account for the readiness potential in self-initiated volitional action

The readiness potential (RP) is a gradual buildup of negative electrical potential over the motor cortices prior to onset of a self-initiated movement. It is typically interpreted as having a goal-directed nature, whereby it signals movement planning and preparation. However, a similar buildup can also be observed by averaging continuous random neural fluctuations aligned to crests in their time series [1]. Therefore, an alternative account of the RP is that it reflects ongoing background neuronal noise that has at least a small influence on the precise time of movement onset [2]. While computational modelling studies were used in the past to adjudicate between these accounts, previous attempts did not employ a fully neuroanatomically and neurobiologically realistic architecture, hence falling short of providing a cortical-level mechanistic validation of either theory. Here, we investigated the stochastic origin of the RP by applying a fully brain-constrained deep neural-network model reproducing real cortical neurons dynamics and the structure and connectivity of relevant primary sensorimotor, secondary and association areas of the frontal and temporal lobes. This model has been previously used to account for the neuromechanistic origins and cortical topography of volitional decisions to speak and act [3]. We used the emergent feature of this neural architecture – its ability to exhibit noise-driven periodic spontaneous ignitions of previously learnt internal representations (cell assemblies, CAs, circuits of strongly and reciprocally connected cells distributed across the entire network) – to mimic spontaneous decisions to act as observed in the classical Libet experiment. Specifically, we recorded the network’s activity for 2,000 trials, each trial beginning with a network reset and lasting until the spontaneous ignition of one of the CAs occurred, and used the time interval between trial start and spontaneous CA ignition as a model correlate of waiting times. We found that the model data accounted well for the experimental waiting-time distribution. Furthermore, in line with the stochastic interpretation of the RP, appropriate calibration of the model parameters resulted in subthreshold reverberation of activity within CA circuits, and averaging across cell assemblies’ ignition episodes produced a curve that closely matched the gradual buildup of activity observed in the experimental RP and its onset time. There are various neurophysiological sources of ongoing noise that result from neural activity. Some of this noise might accumulate and reverberate within previously acquired perception-action circuits, and, hence, produce spontaneous action. The present simulation results, obtained with a fully brain-constrained neural architecture, provide further support for this alternative view, placing the classical explanation of the RP further under scrutiny.Peer reviewe

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

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 \u3c α \u3c 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

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

Theories of Consciousness and a Life Worth Living

What is it that makes a life valuable? A popular view is that life’s moral worth depends in some way on its relationship to consciousness or subjective experience. But a practical application of this view requires the ability to test for consciousness, which is currently lacking. Here, we examine how theories of consciousness (ToCs) can help do so, focusing especially on difficult cases where the answer is not clear (e.g. fetuses, nonhuman animals, unresponsive brain-injured patients, and advanced artificial systems). We consider five major ToCs and what predictions they offer: Integrated information theory, Higher-Order Thought Theory, Recurrent Processing Theory, Global Neuronal Workspace Theory, and Attention Schema Theory. We highlight the important distinction between the capacity and potential for consciousness and use it to explore the limitations in our ability to draw firm conclusions regarding an entity\u27s consciousness on the basis of each theory