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In recent decades, the neuroscientific community has moved from describing the neural underpinnings of mental phenomena—as characterized by experimental psychology and philosophy of mind—to attempting to redefine those mental phenomena based on neural findings. Nowadays, many are intrigued by the idea that neuroscience might provide the "missing piece" that would allow philosophers (and, to an extent, psychologists, too) to make important advances, generating new means that these disciplines lack to close knowledge gaps and answer questions like "What is Free Will?" and "Do humans have it?." In this paper, we argue that instead of striving for neuroscience to replace philosophy in the ongoing quest to understanding human thought and behavior, more synergetic relations should be established, where neuroscience does not only inspire philosophy but also draws from it. We claim that such a collaborative coevolution, with the two disciplines nourishing and influencing each other, is key to resolving long‐lasting questions that have thus far proved impenetrable for either discipline on its own. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Our intuitive concept of the relations between brain and mind is increasingly challenged by the scientific world view. Yet, although few neuroscientists openly endorse Cartesian dualism, careful reading reveals dualistic intuitions in prominent neuroscientific texts. Here, we present the "double-subject fallacy": treating the brain and the entire person as two independent subjects who can simultaneously occupy divergent psychological states and even have complex interactions with each other--as in "my brain knew before I did." Although at first, such writing may appear like harmless, or even cute, short hand, a closer look suggests that it can be seriously misleading. Surprisingly, this confused writing appears in various cognitive-neuroscience texts, from prominent peer-reviewed articles to books intended for lay audience. Far from being merely metaphorical or figurative, this type of writing demonstrates that dualistic intuitions are still deeply rooted in contemporary thought, affecting even the most rigorous practitioners of the neuroscientific method. We discuss the origins of such writing and its effects on the scientific arena as well as demonstrate its relevance to the debate on legal and moral responsibility. [ABSTRACT FROM AUTHOR]

The two-thirds power law, postulating an inverse local relation between the velocity and cubed root of curvature of planar trajectories, is a long-established simplifying principle of human hand movements. In perception, the motion of a dot along a planar elliptical path appears most uniform for speed profiles closer to those predicted by the power law than to constant Euclidean speed, a kinetic-visual illusion. Mathematically, complying with this law is equivalent to moving at constant planar equi-affine speed, while unconstrained three-dimensional drawing movements generally follow constant spatial equi-affine speed. Here we test the generalization of this illusion to visual perception of spatial motion for a dot moving along five differently shaped paths, using stereoscopic projection. The movements appeared most uniform for speed profiles closer to constant spatial equi-affine speed than to constant Euclidean speed, with path torsion (i.e., local deviation from planarity) directly affecting the speed profiles perceived as most uniform, as predicted for constant spatial equi-affine speed. This demonstrates the dominance of equi-affine geometry in spatial motion perception. However, constant equi-affine speed did not fully account for the variability among the speed profiles selected as most uniform for different shapes. Moreover, in a followup experiment, we found that viewing distance affected the speed profile reported as most uniform for the extensively studied planar elliptical motion paths. These findings provide evidence for the critical role of equi-affine geometry in spatial motion perception and contribute to the mounting evidence for the role of non-Euclidean geometries in motion perception and production. [ABSTRACT FROM AUTHOR]

Rational, value-based decision-making mandates selecting the option with highest subjective expected value after appropriate deliberation. We examined activity in the dorsolateral prefrontal cortex (DLPFC) and striatum of monkeys deciding between smaller, immediate rewards and larger, delayed ones. We previously found neurons that modulated their activity in this task according to the animal's choice, while it deliberated (choice neurons). Here we found neurons whose spiking activities were predictive of the spatial location of the selected target (spatial-bias neurons) or the size of the chosen reward (reward-bias neurons) before the onset of the cue presenting the decision-alternatives, and thus before rational deliberation could begin. Their predictive power increased as the values the animals associated with the two decision alternatives became more similar. The ventral striatum (VS) preferentially contained spatial-bias neurons; the caudate nucleus (CD) preferentially contained choice neurons. In contrast, the DLPFC contained significant numbers of all three neuron types, but choice neurons were not preferentially also bias neurons of either kind there, nor were spatial-bias neurons preferentially also choice neurons, and vice versa. We suggest a simple winner-take-all (WTA) circuit model to account for the dissociation of choice and bias neurons. The model reproduced our results and made additional predictions that were borne out empirically. Our data are compatible with the hypothesis that the DLPFC and striatum harbor dissociated neural populations that represent choices and predeliberation biases that are combined after cue onset; the bias neurons have a weaker effect on the ultimate decision than the choice neurons, so their influence is progressively apparent for trials where the values associated with the decision alternatives are increasingly similar. [ABSTRACT FROM AUTHOR]

• Participants ascribed slightly higher but similar levels of freedom, free will, and control for arbitrary compared to easy deliberate decisions. • Participants ascribed greater freedom, free will, and control for easy decisions compared to difficult deliberate decisions. • Participants ascribed high levels of freedom, free will, and control while denying the ability to do otherwise in easy deliberate decisions. • Whether decisions were highly consequential or mundane had little impact on participants' intuitions about freedom, free will, control, and the ability to do otherwise. 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. [ABSTRACT FROM AUTHOR]

• Data augmentation (DA) is increasingly used with deep learning (DL) on EEG. • It enhances decoding accuracy left unexplained by 29 % on average on the datasets we review. • We analyze which specific DA techniques appear to work best for which EEG tasks. • We tested various DA techniques on an open motor-imagery task. • We propose guidelines for reporting parameters for different DA techniques. 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. 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? 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. 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. DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis. [ABSTRACT FROM AUTHOR]

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)., Competing Interests: UM, GY, CK, LM No competing interests declared, (© 2019, Maoz et al.)

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.

One long-established simplifying principle behind the large repertoire and high versatility of human hand movements is the two-thirds power law-an empirical law stating a relationship between local geometry and kinematics of human hand trajectories during planar curved movements. It was further generalized not only to various types of human movements, but also to motion perception and prediction, although it was unsuccessful in explaining unconstrained three-dimensional (3D) movements. Recently, movement obeying the power law was proved to be equivalent to moving with constant planar equi-affine speed. Generalizing such motion to 3D space-i.e., to movement at constant spatial equi-affine speed-predicts the emergence of a new power law, whose utility for describing spatial scribbling movements we have previously demonstrated. In this empirical investigation of the new power law, subjects repetitively traced six different 3D geometrical shapes with their hand. We show that the 3D power law explains the data consistently better than both the two-thirds power law and an additional power law that was previously suggested for spatial hand movements. We also found small yet systematic modifications of the power-law's exponents across the various shapes, which further scrutiny suggested to be correlated with global geometric factors of the traced shape. Nevertheless, averaging over all subjects and shapes, the power-law exponents are generally in accordance with constant spatial equi-affine speed. Taken together, our findings provide evidence for the potential role of non-Euclidean geometry in motion planning and control. Moreover, these results seem to imply a relationship between geometry and kinematics that is more complex than the simple local one stipulated by the two-thirds power law and similar models.