“Me & My Brain”: Exposing Neuroscienceʼs Closet Dualism

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, shorthand, 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

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

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

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

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

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

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

Measuring neural time series data in a sensory deprivation tank

We are interested in studying the neurological and physiological effects of the float pod, also known as REST therapy, or sensory deprivation tank. Float pods rely on the concept of depriving most senses (from sound and light to temperature and proprioception) in a pool filled with buoyant salt water at body temperature. While float pods are most commonly used in spa environments, we intend to look at the potential benefits of floating under the empirical lens. In this study, we aim to measure neural activity using electroencephalography (EEG). We intend to look at the different levels of relaxation and the brain frequencies are associated with relaxation. Research done in this field has shown that the float pod induced a state of relaxation and heightened introspective awareness in participants with high levels of anxiety sensitivity (Feinstein et al., 2018). Research has also shown that the float pod may be a promising technique for reducing suffering in individuals with anxiety and depression (Feinstein et al., 2018). There is little research on the topic of float pods, and there have been no successful attempts to record EEG inside a float pod, to the best of our knowledge. We are currently able to record 6-channel EEGs on the frontal lobe. However, we are still working on the EEG signal quality and signal to noise ratio (SNR). One of the goals is to overcome those challenges, which were brought about by electrocardiography (ECG) artifacts and moisture in the pod and then adjust our EEG cap and electrodes accordingly. One of the ways we are addressing this problem is by adjusting the position of the referential electrode and fabric materials that we use to make the EEG cap. This has been effective in reducing the ECG artifact, but has so far not eliminated it. We aim to improve the SNR of EEG signal in the float pod and be capable of recording continuously and in a stable fashion. Once stable EEG recordings will be obtained in the float pod, various experimental paradigms will be introduced inside the float pod

How Degrees of Freedom Affects Sense of Agency

Can the rubber-hand illusion be extended to a moving robotic arm in different degrees of freedom (DOF), inducing sense of ownership & agency over the arm? We hypothesize that DOF closer to what humans possess will result in a stronger sense of ownership and agency