Deconstructing insight: EEG correlates of insightful problem solving

Background

Cognitive insight phenomenon lies at the core of numerous discoveries. Behavioral research indicates four salient features of insightful problem solving: (i) mental impasse, followed by (ii) restructuring of the problem representation, which leads to (iii) a deeper understanding of the problem, and finally culminates in (iv) an “Aha!” feeling of suddenness and obviousness of the solution. However, until now no efforts have been made to investigate the neural mechanisms of these constituent features of insight in a unified framework.

Methodology/Principal Findings

In an electroencephalographic study using verbal remote associate problems, we identified neural correlates of these four features of insightful problem solving. Hints were provided for unsolved problems or after mental impasse. Subjective ratings of the restructuring process and the feeling of suddenness were obtained on trial-by-trial basis. A negative correlation was found between these two ratings indicating that sudden insightful solutions, where restructuring is a key feature, involve automatic, subconscious recombination of information. Electroencephalogram signals were analyzed in the space×time×frequency domain with a nonparametric cluster randomization test. First, we found strong gamma band responses at parieto-occipital regions which we interpreted as (i) an adjustment of selective attention (leading to a mental impasse or to a correct solution depending on the gamma band power level) and (ii) encoding and retrieval processes for the emergence of spontaneous new solutions. Secondly, we observed an increased upper alpha band response in right temporal regions (suggesting active suppression of weakly activated solution relevant information) for initially unsuccessful trials that after hint presentation led to a correct solution. Finally, for trials with high restructuring, decreased alpha power (suggesting greater cortical excitation) was observed in right prefrontal area.

Conclusions/Significance

Our results provide a first account of cognitive insight by dissociating its constituent components and potential neural correlates.     

Material Flow and Ecological Restructuring in China

Environmental problems are closely related to society' processing of materials through the entire economy. Because neither traditional environmental nor economic analytical methods can provide sufficient insight into the physical dimension of economies, this article presents an integrated methodology, combining a substance flow analysis (SFA) approach and an ecological restructuring analysis. This approach is applied to phosphorus (P) in China, one of the most rapidly growing industrializing economies, in order to better understand of the economy's material use and its change over time. A static national SFA model is developed with statistical data from 1996. By tracking the national economy's P flows from origins to destinations, the critical P flows with respect to environmental impacts are identified. Based on the regime of national P flows, this article analyzes the degree of ecological restructuring by dynamically describing the structural changes of related critical P flows over the last two decades with a set of ecological restructuring indicators (ERIs). Finally, some potential and desired changes are discussed, with the goal of ecologizing the national P flow regime; that is, reducing the ecological impact of the national P flow regime. The methodology of this article illustrates its applicability and value for presenting an overall insight into the physical dimensions of national economies.     

Cluster randomization and political philosophy

In this paper, I will argue that, while the ethical issues raised by cluster randomization can be challenging, they are not new. My thesis divides neatly into two parts. In the first, easier part I argue that many of the ethical challenges posed by cluster randomized human subjects research are clearly present in other types of human subjects research, and so are not novel. In the second, more difficult part I discuss the thorniest ethical challenge for cluster randomized research--cases where consent is genuinely impractical to obtain. I argue that once again these cases require no new analytic insight; instead, we should look to political philosophy for guidance. In other words, the most serious ethical problem that arises in cluster randomized research also arises in political philosophy.     

Facilitate insight by non-invasive brain stimulation

Our experiences can blind us. Once we have learned to solve problems by one method, we often have difficulties in generating solutions involving a different kind of insight. Yet there is evidence that people with brain lesions are sometimes more resistant to this so-called mental set effect. This inspired us to investigate whether the mental set effect can be reduced by non-invasive brain stimulation. 60 healthy right-handed participants were asked to take an insight problem solving task while receiving transcranial direct current stimulation (tDCS) to the anterior temporal lobes (ATL). Only 20% of participants solved an insight problem with sham stimulation (control), whereas 3 times as many participants did so (p = 0.011) with cathodal stimulation (decreased excitability) of the left ATL together with anodal stimulation (increased excitability) of the right ATL. We found hemispheric differences in that a stimulation montage involving the opposite polarities did not facilitate performance. Our findings are consistent with the theory that inhibition to the left ATL can lead to a cognitive style that is less influenced by mental templates and that the right ATL may be associated with insight or novel meaning. Further studies including neurophysiological imaging are needed to elucidate the specific mechanisms leading to the enhancement.     

Combining Experiments and Simulations Using the Maximum Entropy Principle

A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges.     

新型冠状病毒(SARS-CoV-2)SNV分型检测技术

新型冠状病毒肺炎(COVID-19)的全球大流行对整个人类社会造成了重大影响,人类面临着财政刺激、金融压力、债务重整等挑战。在特效治疗药物与方法出现之前,大规模的人群筛查隔离成为现在疫情治理的最有效方法。然而,这一次的新冠病毒(SARS-CoV-2)展示出了极高的遗传变异性,截至2022年3月31日统计突变率超过了2.3‰,迄今为止高传染性的新病毒株不断出现,被世界卫生组织正式警告的变异株就达到了7个。因此,在接下来的病毒防控与研究中,不但需要检测SARS-CoV-2,更需要精准、实用的单核苷酸变异(single nucleotide variation, SNV)基因分型技术,特别针对大规模人群筛查中,不仅需要获得SRAS-CoV-2的信息,还需要精准快速区分具有更高传染性与毒性的变异株感染。对病毒的感染和突变机制进行了简要介绍,并着重对现有主要的SARS-CoV-2 SNV分型技术进行了分类综述,希望为新型检测技术的开发提供参考。     

Restructuring and Health in Canadian Coastal Communities

Environmental and socioeconomic restructuring has had profound consequences for coastal communities in Canada. The decline of traditional resource-based industries—fisheries, forestry, and mining—and the emergence of new economic activities, such as tourism and aquaculture, compounded by concurrent shifts in social programs, have affected the health of environments, communities, and people. Drawing on research conducted as part of the interdisciplinary major collaborative research initiative Coasts Under Stress, we examined the implications of interactive restructuring for the health of people and communities on Canada’s east and west coasts. The research is guided by a socioecological framework that identifies the pathways from interactive restructuring through health determinants to health risks and health outcomes. The utility of the proposed framework is exemplified by a specific place-based example in Prince Rupert, British Columbia, and a case-based example from coastal communities in Newfoundland and Labrador. A focus on interactive restructuring draws our attention to the many challenges associated with promoting health in a context of rapid and often accelerating environmental and institutional change that is relevant to other areas and contexts.     

Collegiality and careerism trump critical questions and bold new ideas: a student's perspective and solution. The structure of scientific funding limits bold new ideas

Funding agencies (and journals) seem to be discriminating against ideas that are contrary to the mainstream, leading to leading to the preferential funding of predictable and safe research over radically new ideas. To remedy this problem a restructuring of the scientific funding system is needed, e.g. by utilizing laymen--together with scientists--to evaluate grant proposals.     

Applications Of Dl_poly And Dl_multi To Organic Molecular Crystals

Molecular dynamics (MD) simulations are capable of giving considerable insight into the polymorphism of organic molecules, a problem of major concern to the pharmaceutical and other speciality chemicals industries. We illustrate some of the challenges involved in small organic systems, which have complex solid-state phase behaviour, including characterizing rotationally disordered phases, modelling polymorphs with very different hydrogen bonding motifs and explaining the solvent dependence of a polymorphic system. Simulating the dynamics within the organic solid state can be very demanding of the model for the weak forces between the molecules. This has led to the development of DL_MULTI so that a distributed multipole electrostatic model can be used to describe the orientation dependence of hydrogen bonding and π–π stacking more realistically. Once a simulation is correctly reproducing the known crystal structures, there are also considerable system-specific challenges in extracting novel insights from the MD simulations.     

Rejoinder to Discussions on “Approval policies for modifications to machine learning‐based software as a medical device: A study of bio‐creep”

We thank the discussants for sharing their unique perspectives on the problem of designing automatic algorithm change protocols (aACPs) for machine learning‐based software as a medical device. Both Pennello et al. and Rose highlighted a number of challenges that arise in real‐world settings, and we whole‐heartedly agree that substantial extensions of our work are needed to understand if and how aACPs can be safely deployed in practice. Our work demonstrated that aACPs that appear to be harmless may allow for biocreep, even when the data distribution is assumed to be representative and stationary over time. While we investigated two solutions that protect against this specific issue, many more statistical and practical challenges remain and we look forward to future research on this topic.     

From spatially explicit ecological models to mean-field dynamics: The state of the art and perspectives

In this paper, we provide a brief review of the well-known methods of reducing spatially structured population models to mean-field models. First, we discuss the terminology of mean-field approximation which is used in the ecological modelling literature and show that the various existing interpretations of the mean-field concept can imply different meanings. Then we classify and compare various methods of reducing spatially explicit models to mean-field models: spatial moment approximation, aggregation techniques and the mean-field limit of IBMs. We emphasize the importance of spatial scales in the reduction of spatially explicit models and briefly consider the inverse problem of scaling up local ecological interactions from microscales to macroscales. Then we discuss the current challenges and limitations for construction of mean-field population models. We emphasize the need for developing mixed methods based on a combination of various reduction techniques to cope with the spatio-temporal complexity of real ecosystems including processes taking place on multiple time and space scales. Finally, we argue that the construction of analytically tractable mean-field models is becoming a key issue to provide an insight into the major mechanisms of ecosystem functioning. We complete this review by introducing the contributions to the current special issue of Ecological Complexity.     

The Reproductive Ecology of Industrial Societies,Part I

Is fertility relevant to evolutionary analyses conducted in modern industrial societies? This question has been the subject of a highly contentious debate, beginning in the late 1980s and continuing to this day. Researchers in both evolutionary and social sciences have argued that the measurement of fitness-related traits (e.g., fertility) offers little insight into evolutionary processes, on the grounds that modern industrial environments differ so greatly from those of our ancestral past that our behavior can no longer be expected to be adaptive. In contrast, we argue that fertility measurements in industrial society are essential for a complete evolutionary analysis: in particular, such data can provide evidence for any putative adaptive mismatch between ancestral environments and those of the present day, and they can provide insight into the selection pressures currently operating on contemporary populations. Having made this positive case, we then go on to discuss some challenges of fertility-related analyses among industrialized populations, particularly those that involve large-scale databases. These include “researcher degrees of freedom” (i.e., the choices made about which variables to analyze and how) and the different biases that may exist in such data. Despite these concerns, large datasets from multiple populations represent an excellent opportunity to test evolutionary hypotheses in great detail, enriching the evolutionary understanding of human behavior.     

How Can Research on Plants Contribute to Promoting Human Health?

One of the most pressing challenges for the next 50 years is to reduce the impact of chronic disease. Unhealthy eating is an increasing problem and underlies much of the increase in mortality from chronic diseases that is occurring worldwide. Diets rich in plant-based foods are strongly associated with reduced risks of major chronic diseases, but the constituents in plants that promote health have proved difficult to identify with certainty. This, in turn, has confounded the precision of dietary recommendations. Plant biochemistry can make significant contributions to human health through the identification and measurement of the many metabolites in plant-based foods, particularly those known to promote health (phytonutrients). Plant genetics and metabolic engineering can be used to make foods that differ only in their content of specific phytonutrients. Such foods offer research tools that can provide significant insight into which metabolites promote health and how they work. Plant science can reduce some of the complexity of the diet-health relationship, and through building multidisciplinary interactions with researchers in nutrition and the pathology of chronic diseases, plant scientists can contribute novel insight into which foods reduce the risk of chronic disease and how these foods work to impact human health.     

Raptors and primate evolution

Most scholars agree that avoiding predators is a central concern of lemurs, monkeys, and apes. However, given uncertainties about the frequency with which primates actually become prey, the selective importance of predation in primate evolution continues to be debated. 1 - 9 Some argue that primates are often killed by predators, 5 , 6 while others maintain that such events are relatively rare. 2 , 7 , 9 Some authors have contended that predation's influence on primate sociality has been trivial 10 , 11 ; others counter that predation need not occur often to be a powerful selective force. 12 - 14 Given the challenges of documenting events that can be ephemeral and irregular, we are unlikely ever to amass the volume of systematic, comparative data we have on such topics as feeding, social dynamics, or locomotor behavior. Nevertheless, a steady accumulation of field observations, insight gained from natural experiments, and novel taphonomic analyses have enhanced understanding of how primates interact with several predators, especially raptors, the subject of this review.     

Ecological vulnerability indicators

Indicators provide easy and quick information on the status or condition of an object of interest and are therefore widely used in policy-making. In recent years, policy interest in vulnerability research has increased and a growing number of studies have been aimed at developing vulnerability indicators. Some of these studies have been done within the social–ecological system (SES) framework, taking into account both social vulnerability and ecological vulnerability. A particular challenged faced, however, is with regard to the incorporation of indicators to capture the latter, especially as the concept of vulnerability as applied in ecology is relatively new and not yet well-explored. This paper expounds on this problem by answering the following questions: (1) How are the concept of vulnerability and thereby ecological vulnerability currently understood? (2) What are the challenges in the development and use of ecological vulnerability indicators? (3) What are the current efforts to overcome these challenges? One insight gathered is that due to the complexity, nonlinearity, and multiplicity of dynamics of natural systems, development of sufficiently general indicators of ecological vulnerability may not be realizable. Rather, ecological vulnerability assessment and the development of indicators thereof, whether done independent of the human system or within the SES, should be conducted at smaller scales and must be context-specific.     

Scholarly Context Not Found: One in Five Articles Suffers from Reference Rot

The emergence of the web has fundamentally affected most aspects of information communication, including scholarly communication. The immediacy that characterizes publishing information to the web, as well as accessing it, allows for a dramatic increase in the speed of dissemination of scholarly knowledge. But, the transition from a paper-based to a web-based scholarly communication system also poses challenges. In this paper, we focus on reference rot, the combination of link rot and content drift to which references to web resources included in Science, Technology, and Medicine (STM) articles are subject. We investigate the extent to which reference rot impacts the ability to revisit the web context that surrounds STM articles some time after their publication. We do so on the basis of a vast collection of articles from three corpora that span publication years 1997 to 2012. For over one million references to web resources extracted from over 3.5 million articles, we determine whether the HTTP URI is still responsive on the live web and whether web archives contain an archived snapshot representative of the state the referenced resource had at the time it was referenced. We observe that the fraction of articles containing references to web resources is growing steadily over time. We find one out of five STM articles suffering from reference rot, meaning it is impossible to revisit the web context that surrounds them some time after their publication. When only considering STM articles that contain references to web resources, this fraction increases to seven out of ten. We suggest that, in order to safeguard the long-term integrity of the web-based scholarly record, robust solutions to combat the reference rot problem are required. In conclusion, we provide a brief insight into the directions that are explored with this regard in the context of the Hiberlink project.     

Methods to map protein interactions in mammalian cells: different tools to address different questions

In the post-genome era, functional annotation of the predicted gene-sets will be one of the most important upcoming challenges. So-called interactome analysis positions a protein in its subcellular environment by mapping its interaction partners. Such interaction maps are essential for an accurate insight into protein function since many cellular processes are organised to operate in protein complexes. These assemblies have dynamic structures and can interact with each other, two properties which are often controlled by regulated protein expression and modification. Various methods exist to unravel protein interaction circuitries, which can be roughly divided into biochemical and genetic strategies. In this review we focus on the different strategies to study protein-protein interactions in living mammalian cells. Recently developed analytical and screening methods are also addressed.     

Driving engineering of novel antimicrobial peptides from simulations of peptide-micelle interactions

Simulations of antimicrobial peptides in membrane mimics can provide the high resolution, atomistic picture that is necessary to decipher which sequence and structure components are responsible for activity and toxicity. With such detailed insight, engineering new sequences that are active but non-toxic can, in principle, be rationalized. Armed with supercomputers and accurate force fields for biomolecular interactions, we can now investigate phenomena that span hundreds of nanoseconds. Although the phenomena involved in antimicrobial activity, (i.e., diffusion of peptides, interaction with lipid layers, secondary structure attainment, possible surface aggregation, possible formation of pores, and destruction of the lipid layer integrity) collectively span time scales still prohibitively long for classical mechanics simulations, it is now feasible to investigate the initial approach of single peptides and their interaction with membrane mimics. In this article, we discuss the promise and the challenges of widely used models and detail our recent work on peptide-micelle simulations as an attractive alternative to peptide-bilayer simulations. We detail our results with two large structural classes of peptides, helical and beta-sheet and demonstrate how simulations can assist in engineering of novel antimicrobials with therapeutic potential.     

On the structural complexity of a protein

The determination of the configuration of a protein in three-dimensional (3D) space constitutes one of the major challenges in molecular biology research today. A method consists in choosing a protein structure from a database that minimizes an energy function. First, we model the problem in terms of dynamic programming and show that the determination of the order in which the variables must be considered to minimize the time complexity is an NP-hard problem. Second, we propose a new decomposition algorithm of the threading problem that is based on the connectivity of the graph induced by the 3D structure of a protein. Our decomposition could be used to solve the threading problem. The goal in this paper is to evaluate the intrinsic complexity of 3D structure, which can be viewed as information that may be incorporated into a solution method. It provides two indexes of complexity (time and space) and determines in polynomial time complex components of the 3D structure of a protein.