Imitation: is cognitive neuroscience solving the correspondence problem?

Anxiety disorders are highly prevalent in elderly persons, and they are associated with functional impairment, poorer quality of life, and adverse long-term consequences such as cognitive decline. Intervention research in late-life anxiety disorders (LLAD) lags behind where it ought to be. Research in cognitive neuroscience, aging, and stress intersects in LLAD and provides the opportunity to develop innovative interventions to prevent chronic anxiety and its consequences in this age group. This paper evaluates gaps in the evidence base for treatment of LLAD and synthesizes recent research in cognitive neuroscience, basic behavioral science, stress, and aging. We examine three intervention issues in LLAD: (1) prevention; (2) acute treatment; and (3) pre-empting adverse consequences. We propose combining randomized controlled trials (RCTs) with mechanistic biobehavioral methodologies as an optimal approach for developing novel, optimized, and personalized treatments. Additionally, we examine three barriers in the field of LLAD research: (1) How do we measure anxiety?; (2) How do we raise awareness?; (3) How will we ensure our research is applicable to underserved populations (particularly minority groups)? This prospectus outlines approaches for intervention research that can reduce the morbidity of LLAD.

Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user’s disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.

Recent research in cognitive neuroscience has begun to uncover the processes underlying increasingly complex voluntary behaviours, including learning and decision-making. Partly this success has been possible by progressing from simple experimental tasks to paradigms that incorporate more ecological features. More specifically, the premise is that to understand cognitions and brain functions relevant for real life, we need to introduce some of the ecological challenges that we have evolved to solve. This often entails an increase in task complexity, which can be managed by using computational models to help parse complex behaviours into specific component mechanisms. Here we propose that using computational models with tasks that capture ecologically relevant learning and decision-making processes may provide a critical advantage for capturing the mechanisms underlying symptoms of disorders in psychiatry. As a result, it may help develop mechanistic approaches towards diagnosis and treatment. We begin this review by mapping out the basic concepts and models of learning and decision-making. We then move on to consider specific challenges that emerge in realistic environments and describe how they can be captured by tasks. These include changes of context, uncertainty, reflexive/emotional biases, cost-benefit decision-making, and balancing exploration and exploitation. Where appropriate we highlight future or current links to psychiatry. We particularly draw examples from research on clinical depression, a disorder that greatly compromises motivated behaviours in real-life, but where simpler paradigms have yielded mixed results. Finally, we highlight several paradigms that could be used to help provide new insights into the mechanisms of psychiatric disorders.

Recent research in cognitive social psychology and social cognitive neuroscience has powerful implications for lawyers and other advocates in situations where stereotypes are at work. The science suggests that there are, indeed, few advocacy situations where stereotypes are not at work. Legal scholars have attempted to bring some of this science to the attention of courts, with limited success. Meanwhile, lawyers and other advocates have continued their work based on implicit theories about stereotyping and prejudice that we now know to be wrong. As a result, advocacy is often considerably less effective than it might be. In this Article, I provide a brief overview of the rapidly developing science regarding stereotypes and prejudice, and then sketch some of the implications for lawyers and other advocates.

Many long-standing predominant theories of cognition have viewed higher levels of processing, such as language and cognition, as free from the influence of lower levels of processing, such as action and perception. However, many recent experiments have found evidence that the delineation between traditional modules is not so clean or precise, with motor output and language comprehension interacting much more fluidly than traditional theories predict. Evidence for this account includes findings of systematic activation of motor cortex while processing action words, as well as functional consequences of language on action and of action on language. It is worth noting that this recent spate of interest in the embodiment of cognition is not without historical precedence. Here, we will review such evidence coming from previous decades of research on the interaction between language and action, in addition to exploring the empirical results of these more recent experiments and methodologies. It seems to be the case, as evidenced from research in cognitive neuroscience and modeling, that motor representations and actions can influence language processing in predictable ways. Not only is there continuous competition between simultaneously active representations in language processing, with multiple sources of information interacting immediately, this competition is apparent in the motor output produced as a response to language.

Recent research in cognitive and developmental neuroscience is providing a new approach to the understanding of dyscalculia that emphasizes a core deficit in understanding sets and their numerosities, which is fundamental to all aspects of elementary school mathematics. The neural bases of numerosity processing have been investigated in structural and functional neuroimaging studies of adults and children, and neural markers of its impairment in dyscalculia have been identified. New interventions to strengthen numerosity processing, including adaptive software, promise effective evidence-based education for dyscalculic learners.

Despite the recent proliferation of scientific, clinical, and narrative accounts of auditory verbal hallucinations (AVHs), the phenomenology of voice hearing remains opaque and undertheorized. In this article, we outline an interdisciplinary approach to understanding hallucinatory experiences which seeks to demonstrate the value of the humanities and social sciences to advancing knowledge in clinical research and practice. We argue that an interdisciplinary approach to the phenomenology of AVH utilizes rigorous and context-appropriate methodologies to analyze a wider range of first-person accounts of AVH at 3 contextual levels: (1) cultural, social, and historical; (2) experiential; and (3) biographical. We go on to show that there are significant potential benefits for voice hearers, clinicians, and researchers. These include (1) informing the development and refinement of subtypes of hallucinations within and across diagnostic categories; (2) “front-loading” research in cognitive neuroscience; and (3) suggesting new possibilities for therapeutic intervention. In conclusion, we argue that an interdisciplinary approach to the phenomenology of AVH can nourish the ethical core of scientific enquiry by challenging its interpretive paradigms, and offer voice hearers richer, potentially more empowering ways to make sense of their experiences.

Jeanne S. Bentzen explores how cognitive neuroscience and cognitive neuropsychology can contribute to the development of the field of entrepreneurship and, specifically, the understanding of what influences an individual’s propensity to become an entrepreneur. Bentzen builds on research in cognitive neuroscience on autobiographical memories, defined as memories of past events from one’s own life, and their role in decision-making, as an interesting perspective with potential for developing the neuroentrepreneurship approach. She maintains that autobiographical memories are used not only to recall past events but also to imagine, simulate, and predict future events. Bentzen also discusses methodological challenges in studying autobiographical memories and identifies interesting future research directions in memory-related areas in cognitive neuroscience, for example, in areas such as priming, procedural learning, and making of an entrepreneur.

Research in embodied and grounded cognition is defined by the types of hypotheses researchers pursue, with specific emphasis on the ways in which the body, brain, and environment interact to give rise to intelligent behavior. In cognitive neuroscience, it is hypothesized that simulations in modality‐specific representations, situated and embedded in a behavioral context, underlie our ability to plan actions, discuss our thoughts, and coordinate our activities with each other. In this framework, representations are powerful, predictive constructs, and integrate the brain (i.e., neural networks), body, and immediate environment. Overall, the grounded perspective encourages us to think deeply about our cognitive ontology, cognition's relation to the brain, and the dynamic processes that underlie our most complex behaviors. In this way, embodied and grounded perspectives promise to shape research in cognitive neuroscience in highly productive ways.

Neuroscience of art might give us traction with aesthetic issues. However it can be seen to have trouble modeling the artistically salient semantic properties of artworks. So if meaning really matters, and it does, even in aesthetic contexts, the prospects for this nascent field are dim. The issue boils down to a question of whether or not we can get a grip on the kinds of constraints present and available to guide interpretive behavior in our engagement with works of fine art. I argue that biased competition models of selective attention can be used to solve this problem, generalize to the affective content of our responses to artworks, and so show that research in cognitive neuroscience is germane to the types of problems of interest within the philosophy of art.

Spontaneous thinking is a ubiquitous aspect of our mental life and has increasingly become a hot topic of research in cognitive neuroscience. To date, functional neuroimaging studies of spontaneous thought have revealed general brain recruitment centered on a combination of default mode network and executive regions. Despite recent findings about general brain recruitment, very little is known about how these regions are recruited dynamically over time. The current research addresses this gap in the literature by using EEG to investigate the fine-grained temporal dynamics of brain activity underlying spontaneous thoughts. We employed the first-person reports of experienced meditators to index the onset of spontaneous thoughts, and examined brain electrical activity preceding indications of spontaneous thought onset. An independent component analysis-based source localization procedure recovered sources very similar to those previously found with fMRI (Ellamil et al. in NeuroImage 136:186–196, 2016). In addition, phase synchrony analyses revealed a temporal trajectory that begins with default network midline and salience network connectivity, followed by the incorporation of language and executive regions during the period from thought generation to appraisal.

Over the last two decades, research in cognitive neuroscience has revealed that insight problem-solving is characterised by a temporally structured sequence of neural events. Preparatory brain states – marked by anterior cingulate engagement and reductions in visual sampling, including increased blinking – appear to prime the cognitive system for the restructuring that insight later requires. As the moment of solution approaches, a second period of sensory gating further limits interference from the external environment, facilitating weakly activated associations to cohere and break into awareness, a transition associated with right anterior superior temporal gyrus activity and pupillary dilation. Moreover, insight has been found to be related to the dopaminergic system, a finding that helps explain the characteristic affective ‘Aha!’ response as well as why insights are accompanied by a feeling of pleasure and are better remembered. Taken together, the evidence points to insight being supported by an evolutionarily shaped system that promotes exploratory behaviour and strengthens effective problem-solving responses.

This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive neuroscience and related fields. It spans from single‐subject data preprocessing to advanced multisubject analyses. This handbook contains four chapters: Preprocessing Single‐Subject Data, Basic Python Data Operations, Multiple‐Subject Analysis, and Advanced EEG Analysis. The Preprocessing Single‐Subject Data chapter provides a standardized procedure for single‐subject EEG data preprocessing, primarily using the MNE‐Python package. The Basic Python Data Operations chapter introduces essential Python operations for EEG data handling, including data reading, storage, and statistical analysis. The Multiple‐Subject Analysis chapter guides readers on performing event‐related potential and time‐frequency analyses and visualizing outcomes through examples from a face perception task dataset. The Advanced EEG Analysis chapter explores three advanced analysis methodologies, Classification‐based decoding, Representational Similarity Analysis, and Inverted Encoding Model, through practical examples from a visual working memory task dataset using NeuroRA and other powerful packages. We designed our handbook for easy comprehension to be an essential tool for anyone delving into EEG data analysis with Python (GitHub website: https://github.com/ZitongLu1996/Python‐EEG‐Handbook; For Chinese version: https://github.com/ZitongLu1996/Python‐EEG‐Handbook‐CN).

Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in several applied areas, ranging from medical applications to entertainment industry, and creates new conditions for basic research in cognitive neuroscience. The BCIs of today, however, offer only crude online classification of the user's current state of mind, and more sophisticated decoding of mental states depends on time-consuming offline data analysis. The present paper addresses this limitation directly by leveraging a set of improvements to the analytical pipeline to pave the way for the next generation of online BCIs. Specifically, we introduce an open-source research framework that features a modular and customizable hardware-independent design. This framework facilitates human-in-the-loop (HIL) model training and retraining, real-time stimulus control, and enables transfer learning and cloud computing for the online classification of electroencephalography (EEG) data. Stimuli for the subject and diagnostics for the researcher are shown on separate displays using web browser technologies. Messages are sent using the Lab Streaming Layer standard and websockets. Real-time signal processing and classification, as well as training of machine learning models, is facilitated by the open-source Python package Timeflux. The framework runs on Linux, MacOS, and Windows. While online analysis is the main target of the BCI-HIL framework, offline analysis of the EEG data can be performed with Python, MATLAB, and Julia through packages like MNE, EEGLAB, or FieldTrip. The paper describes and discusses desirable properties of a human-in-the-loop BCI research platform. The BCI-HIL framework is released under MIT license with examples at: bci.lu.se/bci-hil (or at: github.com/bci-hil/bci-hil).

Moral dilemmas engender conflicts between two traditions: consequentialism, which evaluates actions based on their outcomes, and deontology, which evaluates actions themselves. These strikingly resemble two distinct decision-making architectures: a model-based system that selects actions based on inferences about their consequences; and a model-free system that selects actions based on their reinforcement history. Here, I consider how these systems, along with a Pavlovian system that responds reflexively to rewards and punishments, can illuminate puzzles in moral psychology.