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.

Freedom from free willDetermined: A Science of Life without Free Will Robert M. Sapolsky Penguin Press, 2023. 528 pp.

A scientist presents a case for a predetermined future.

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.

COVID-Dynamic: A large-scale longitudinal study of socioemotional and behavioral change across the pandemic.

The COVID-19 pandemic has caused enormous societal upheaval globally. In the US, beyond the devastating toll on life and health, it triggered an economic shock unseen since the great depression and laid bare preexisting societal inequities. The full impacts of these personal, social, economic, and public-health challenges will not be known for years. To minimize societal costs and ensure future preparedness, it is critical to record the psychological and social experiences of individuals during such periods of high societal volatility. Here, we introduce, describe, and assess the COVID-Dynamic dataset, a within-participant longitudinal study conducted from April 2020 through January 2021, that captures the COVID-19 pandemic experiences of >1000 US residents. Each of 16 timepoints combines standard psychological assessments with novel surveys of emotion, social/political/moral attitudes, COVID-19-related behaviors, tasks assessing implicit attitudes and social decision-making, and external data to contextualize participants' responses. This dataset is a resource for researchers interested in COVID-19-specific questions and basic psychological phenomena, as well as clinicians and policy-makers looking to mitigate the effects of future calamities.

Diverging lay intuitions about concepts related to free will in arbitrary and deliberate decisions.

Philosophical accounts of free will frequently appeal to deliberate, consequential, and purposeful decisions. However, some recent studies have found that laypeople attribute more freedom to arbitrary than to deliberate decisions. We hypothesized that these differences stem from diverging intuitions about concepts surrounding free will-especially freedom, being in control, and the ability to decide otherwise. In two studies, we found that laypeople attributed high levels of free will, freedom, and control to both arbitrary and deliberate decisions. However, subjects surprisingly attributed reduced ability to decide otherwise when faced with an "easy" decision with one clearly superior option. Furthermore, laypeople attributed greater free will, freedom, and control to "easy" than "hard" decisions with no clearly superior option. Our results suggest that laypeople have diverging intuitions about these different, free-will-related concepts. Therefore, a scientific account of free will may require integrating results from studies on different types of decision-making.

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.

Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I.

Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items into a single scale. The advent of machine learning has made it possible to apply Nonlinear Regression algorithms-such as Random Forest and Deep Neural Networks-to identify potentially complex linear and nonlinear connections between a multitude of predictors (e.g., all individual items from a scale) and outcome (output) variables. Using a national dataset, we tested the extent to which these techniques allowed us to explain a greater share of the variance in body-image outcomes (adjusted R2) than possible with Linear Regression. We examined how well the connections between body dissatisfaction and dieting behavior could be predicted from demographic factors and measures derived from objectification theory and the tripartite-influence model. In this particular case, although Random Forest analyses sometimes provided greater predictive power than Linear Regression models, the advantages were small. More generally, however, this paper demonstrates how body image researchers might harness the power of machine learning techniques to identify previously undiscovered relations among body image variables.

Characterizing human random-sequence generation in competitive and non-competitive environments using Lempel-Ziv complexity.

The human ability for random-sequence generation (RSG) is limited but improves in a competitive game environment with feedback. However, it remains unclear how random people can be during games and whether RSG during games can improve when explicitly informing people that they must be as random as possible to win the game. Nor is it known whether any such improvement in RSG transfers outside the game environment. To investigate this, we designed a pre/post intervention paradigm around a Rock-Paper-Scissors game followed by a questionnaire. During the game, we manipulated participants' level of awareness of the computer's strategy; they were either (a) not informed of the computer's algorithm or (b) explicitly informed that the computer used patterns in their choice history against them, so they must be maximally random to win. Using a compressibility metric of randomness, our results demonstrate that human RSG can reach levels statistically indistinguishable from computer pseudo-random generators in a competitive-game setting. However, our results also suggest that human RSG cannot be further improved by explicitly informing participants that they need to be random to win. In addition, the higher RSG in the game setting does not transfer outside the game environment. Furthermore, we found that the underrepresentation of long repetitions of the same entry in the series explains up to 29% of the variability in human RSG, and we discuss what might make up the variance left unexplained.

An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task.

Objective.Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based MI classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing.Approach.To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, brain-computer interface (BCI) competition IV 2a and 2b. In addition, we collected a new dataset, recorded using high-density EEG, and containing both MI and motor execution (ME) tasks, which we share with the community.Main results.Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning.Significance.Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI.

Dimensionality reduction for classification of object weight from electromyography.

Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for measuring muscle activity. However, multi-muscle EMG is also a noisy, complex, and high-dimensional signal. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and, in particular, to measure the reach-and-grasp motions of the human hand. Here, we developed an automated pipeline to predict object weight in a reach-grasp-lift task from an open dataset, relying only on EMG data. In doing so, we shifted the focus from manual feature-engineering to automated feature-extraction by using pre-processed EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran several classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% F1 score for 3-way classification). Our results, using EMG alone, are comparable to other researchers', who used EMG and EEG together, in the literature. A running-window analysis further suggests that our method captures information in the EMG signal quickly and remains stable throughout the time that subjects grasp and move the object.

Spinal Anesthesia Reduces Myocardial Ischemia-triggered Ventricular Arrhythmias by Suppressing Spinal Cord Neuronal Network Interactions in Pigs.

BACKGROUND: Cardiac sympathoexcitation leads to ventricular arrhythmias. Spinal anesthesia modulates sympathetic output and can be cardioprotective. However, its effect on the cardio-spinal reflexes and network interactions in the dorsal horn cardiac afferent neurons and the intermediolateral nucleus sympathetic neurons that regulate sympathetic output is not known. The authors hypothesize that spinal bupivacaine reduces cardiac neuronal firing and network interactions in the dorsal horn-dorsal horn and dorsal horn-intermediolateral nucleus that produce sympathoexcitation during myocardial ischemia, attenuating ventricular arrhythmogenesis. METHODS: Extracellular neuronal signals from the dorsal horn and intermediolateral nucleus neurons were simultaneously recorded in Yorkshire pigs (n = 9) using a 64-channel high-density penetrating microarray electrode inserted at the T2 spinal cord. Dorsal horn and intermediolateral nucleus neural interactions and known markers of cardiac arrhythmogenesis were evaluated during myocardial ischemia and cardiac load-dependent perturbations with intrathecal bupivacaine. RESULTS: Cardiac spinal neurons were identified based on their response to myocardial ischemia and cardiac load-dependent perturbations. Spinal bupivacaine did not change the basal activity of cardiac neurons in the dorsal horn or intermediolateral nucleus. After bupivacaine administration, the percentage of cardiac neurons that increased their activity in response to myocardial ischemia was decreased. Myocardial ischemia and cardiac load-dependent stress increased the short-term interactions between the dorsal horn and dorsal horn (324 to 931 correlated pairs out of 1,189 pairs, P < 0.0001), and dorsal horn and intermediolateral nucleus neurons (11 to 69 correlated pairs out of 1,135 pairs, P < 0.0001). Bupivacaine reduced this network response and augmentation in the interactions between dorsal horn-dorsal horn (931 to 38 correlated pairs out of 1,189 pairs, P < 0.0001) and intermediolateral nucleus-dorsal horn neurons (69 to 1 correlated pairs out of 1,135 pairs, P < 0.0001). Spinal bupivacaine reduced shortening of ventricular activation recovery interval and dispersion of repolarization, with decreased ventricular arrhythmogenesis during acute ischemia. CONCLUSIONS: Spinal anesthesia reduces network interactions between dorsal horn-dorsal horn and dorsal horn-intermediolateral nucleus cardiac neurons in the spinal cord during myocardial ischemia. Blocking short-term coordination between local afferent-efferent cardiac neurons in the spinal cord contributes to a decrease in cardiac sympathoexcitation and reduction of ventricular arrhythmogenesis.

Data augmentation for deep-learning-based electroencephalography.

BACKGROUND: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. NEW METHOD: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? RESULTS: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. COMPARING WITH EXISTING METHODS: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8 % for recombination of segmentation and 36 % for noise addition and from 14 % for motor imagery to 56 % for mental workload-29 % on average. CONCLUSIONS: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications-if adhering to our reporting guidelines-will facilitate more detailed analysis.

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. METHODS: We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features. RESULTS: Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910-0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598-0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658-0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829-0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917-0.955). CONCLUSIONS: This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

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).

Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm.

BACKGROUND: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression. METHODS: Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014. RESULTS: A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) of 0.91 and 0.89 for detecting depression in NHANES and K-NHANES, respectively. The deep-learning algorithm trained with serial datasets (NHANES, from 1999 to 2012), predicted the prevalence of depression in the following two years of data (NHANES, 2013 and 2014) with an AUC of 0.92. Machine learning classifiers trained with NHANES could further predict depression in K-NHANES. There, logistic regression had the highest performance (AUC, 0.77) followed by deep learning algorithm (AUC, 0.74). CONCLUSIONS: Deep neural-networks managed to identify depression well from other health and demographic factors in both the NHANES and K-NHANES datasets. The deep-learning algorithm was also able to predict depression relatively well on new data set-cross temporally and cross nationally. Further research can delineate the clinical implications of machine learning and deep learning in detecting disease prevalence and progress as well as other risk factors for depression and other mental illnesses.

Does It Matter Whether You or Your Brain Did It? An Empirical Investigation of the Influence of the Double Subject Fallacy on Moral Responsibility Judgments.

Despite progress in cognitive neuroscience, we are still far from understanding the relations between the brain and the conscious self. We previously suggested that some neuroscientific texts that attempt to clarify these relations may in fact make them more difficult to understand. Such texts-ranging from popular science to high-impact scientific publications-position the brain and the conscious self as two independent, interacting subjects, capable of possessing opposite psychological states. We termed such writing 'Double Subject Fallacy' (DSF). We further suggested that such DSF language, besides being conceptually confusing and reflecting dualistic intuitions, might affect people's conceptions of moral responsibility, lessening the perception of guilt over actions. Here, we empirically investigated this proposition with a series of three experiments (pilot and two preregistered replications). Subjects were presented with moral scenarios where the defendant was either (1) clearly guilty, (2) ambiguous, or (3) clearly innocent while the accompanying neuroscientific evidence about the defendant was presented using DSF or non-DSF language. Subjects were instructed to rate the defendant's guilt in all experiments. Subjects rated the defendant in the clearly guilty scenario as guiltier than in the two other scenarios and the defendant in the ambiguously described scenario as guiltier than in the innocent scenario, as expected. In Experiment 1 (N = 609), an effect was further found for DSF language in the expected direction: subjects rated the defendant less guilty when the neuroscientific evidence was described using DSF language, across all levels of culpability. However, this effect did not replicate in Experiment 2 (N = 1794), which focused on different moral scenario, nor in Experiment 3 (N = 1810), which was an exact replication of Experiment 1. Bayesian analyses yielded strong evidence against the existence of an effect of DSF language on the perception of guilt. Our results thus challenge the claim that DSF language affects subjects' moral judgments. They further demonstrate the importance of good scientific practice, including preregistration and-most critically-replication, to avoid reaching erroneous conclusions based on false-positive results.

Theta-burst microstimulation in the human entorhinal area improves memory specificity.

The hippocampus is critical for episodic memory, and synaptic changes induced by long-term potentiation (LTP) are thought to underlie memory formation. In rodents, hippocampal LTP may be induced through electrical stimulation of the perforant path. To test whether similar techniques could improve episodic memory in humans, we implemented a microstimulation technique that allowed delivery of low-current electrical stimulation via 100 µm-diameter microelectrodes. As thirteen neurosurgical patients performed a person recognition task, microstimulation was applied in a theta-burst pattern, shown to optimally induce LTP. Microstimulation in the right entorhinal area during learning significantly improved subsequent memory specificity for novel portraits; participants were able both to recognize previously-viewed photos and reject similar lures. These results suggest that microstimulation with physiologic level currents-a radical departure from commonly used deep brain stimulation protocols-is sufficient to modulate human behavior and provides an avenue for refined interrogation of the circuits involved in human memory.

What does recent neuroscience tell us about criminal responsibility?

A defendant is criminally responsible for his action only if he is shown to have engaged in a guilty act-actus reus (eg for larceny, voluntarily taking someone else's property without permission)-while possessing a guilty mind-mens rea (eg knowing that he had taken someone else's property without permission, intending not to return it)-and lacking affirmative defenses (eg the insanity defense or self-defense). We therefore first review neuroscientific studies that bear on the nature of voluntary action, and so could, potentially, tell us something of importance about the actus reus of crimes. Then we look at studies of intention, perception of risk, and other mental states that matter to the mens rea of crimes. And, last, we discuss studies of self-control, which might be relevant to some formulations of the insanity defense. As we show, to date, very little is known about the brain that is of significance for understanding criminal responsibility. But there is no reason to think that neuroscience cannot provide evidence that will challenge our understanding of criminal responsibility.