Modeling aspects of human memory for scientific study.

This chapter is organized in two main sub-chapters: Cognitive Science Approach and Management Measurement Approaches. Its objectives are to give the reader an overview of the different disciplines that constitute cognitive science and to discuss the state-of-the art of the current measurement approaches in the knowledge management literature. The study of cognitive science is interdisciplinary, and it covers the fields of philosophy, psychology, neurosciences, computational intelligence, linguistics and language, and finally culture, cognition and evolution, all of which will be discussed. For cognitive scientists the objects under study are behavior and cognition in humans, animals and machines. The examination of cognitive science can be seen as a basic explanation of how humans are perceiving, learning, thinking, speaking, and acting. Its main focus lies in the discussion of how individuals reason under conditions of uncertainty and how perceived information is processed in the brain to build knowledge. A detailed explanation of all views, concepts, methods and methodologies in cognitive science is beyond this research.KeywordsCash FlowBayesian NetworkMental ModelStructural CapitalIntellectual CapitalThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The fifth edition of a work that defines the field of cognitive neuroscience, with entirely new material that reflects recent advances in the field. Each edition of this classic reference has proved to be a benchmark in the developing field of cognitive neuroscience. The fifth edition of The Cognitive Neurosciences continues to chart new directions in the study of the biological underpinnings of complex cognition—the relationship between the structural and physiological mechanisms of the nervous system and the psychological reality of the mind. It offers entirely new material, reflecting recent advances in the field. Many of the developments in cognitive neuroscience have been shaped by the introduction of novel tools and methodologies, and a new section is devoted to methods that promise to guide the field into the future—from sophisticated models of causality in brain function to the application of network theory to massive data sets. Another new section treats neuroscience and society, considering some of the moral and political quandaries posed by current neuroscientific methods. Other sections describe, among other things, new research that draws on developmental imaging to study the changing structure and function of the brain over the lifespan; progress in establishing increasingly precise models of memory; research that confirms the study of emotion and social cognition as a core area in cognitive neuroscience; and new findings that cast doubt on the so-called neural correlates of consciousness.

ABSTRACTMathematically gifted children/adolescents have demonstrated exceptional abilities and traits in logical reasoning, mental imagery, and creative thinking. In the field of cognitive neuroscience, the past studies on mathematically gifted brains have concentrated on investigating event-related brain activation regions, cerebral laterality of cognitive functions, functional specialization that is uniquely dedicated for specific cognitive purposes, and functional interactions among discrete brain regions. From structural and functional perspectives, these studies have witnessed both “general” and “unique” neural characteristics of mathematically gifted brains. In this article, the theoretical background, empirical studies, and neurocognitive mechanisms of mathematically gifted children/adolescents are reviewed. Based on the integration of the findings, some potential directions for the future research are identified and discussed.

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

The relationship between brainwave oscillations and Attention-Deficit/Hyperactivity Disorder (ADHD)-related cognitive challenges is a trending proposition in the field of Cognitive Neuroscience. Studies suggest the role of brainwave oscillations in the symptom expressions of ADHD-diagnosed children. Intervention studies have further suggested the scope of brain stimulation techniques in improving cognition. The current manuscript explored the effect of changes in the brainwaves post-sensory entrainment on cognitive performance of children. We calculated each participant’s brainwave difference and ratios of theta, alpha, and beta power after the entrainment sessions. Further, we explored possible correlations between these values and the psychometric scores. The beta resting state showed the strongest association with selective attention performance of all participants. Theta-beta ratio (TBR) showed an inverse correlation with selective attention and working memory performances. The theta frequency was associated with decreased working performance in children without ADHD. Our findings also suggest a predominant role of TBR than the theta-alpha ratio in determining the cognitive performance of children with ADHD. The individual differences in the entrainment reception were attributed to the participant’s age, IQ, and their innate baseline frequencies. The implications of our findings can initiate substantiating brainwave-based entrainment sessions as a therapeutic modality to improve cognition among children.

Activity in the dorsal anterior cingulate cortex (dACC) is observed across a variety of contexts, and its function remains intensely debated in the field of cognitive neuroscience. While traditional views emphasize its role in inhibitory control (suppressing prepotent, incorrect actions), recent proposals suggest a more active role in motivated control (invigorating actions to obtain rewards). Lagging behind empirical findings, formal models of dACC function primarily focus on inhibitory control, highlighting surprise, choice difficulty and value of control as key computations. Although successful in explaining dACC involvement in inhibitory control, it remains unclear whether these mechanisms generalize to motivated control. In this study, we derive predictions from three prominent accounts of dACC and test these with functional magnetic resonance imaging during value-based decision-making under time pressure. We find that the single mechanism of surprise best accounts for activity in dACC during a task requiring response invigoration, suggesting surprise signalling as a shared driver of inhibitory and motivated control.

Erin Rasmussen is an Assistant Professor in the Department of Psychology at Idaho State University where she teaches learning, behavioral pharmacology, senior seminar, and introductory psychology. She received her MS and PhD in experimental psychology (with a minor in behavioral pharmacology and toxicology) from Auburn University. She taught at the College of Charleston for 3 years before joining the faculty at Idaho State. Her past research involved examination of how prenatal exposure to heavy metals affects the behavior of offspring and the role of environmental enrichment in attenuating those effects. Currently, she is examining how prenatal food restriction affects food choices that lead to obesity. Michael Gazzaniga, the David T. McLaughlin Distinguished University Professor at Dartmouth College, is a pioneer in the field of cognitive neuroscience. He received his PhD from California Institute of Technology in psychobiology, where he conducted his famous work with Roger Sperry on the split-brain phenomenon. His research has provided insights on functional lateralization and how the cerebral hemispheres communicate with one another. He has written more than 20 books, many for the lay audience, and has well over 100 publications, including book chapters, interviews, and monographs. Currently, he serves as Director for the Center of Cognitive Neuroscience at Dartmouth. He was elected president of the American Psychological Society in 2004. He manages a summer school program in cognitive neuroscience and conducts many visiting professor seminars.

Transcranial magnetic stimulation (TMS) can modulate a targeted brain region to assess whether that region is involved in a cognitive process. When TMS is employed in cognitive neuroscience, participants are typically healthy volunteers, and the technique is described as noninvasive. However, TMS parameters can be set such that stimulation produces long-lasting effects. Critically, TMS effects that have any possibility of lasting beyond a participant’s time in the lab are potentially harmful. In this editorial, evidence is considered that indicates a 20-Hz multi-day TMS protocol has long-lasting effects, and a continuous theta-burst stimulation protocol needs further testing before it is deemed noninvasive. The following guidelines are provided for TMS protocol evaluation: 1) Effects must be shown to completely dissipate before participants leave the lab by testing well beyond the expected duration. 2) Participants should complete a cognitive test battery before TMS and after the effects are expected to dissipate. 3) Protocols should not be employed that produce effects lasting longer than the time in the lab. 4) The number of participants should ensure error bars are small, and results generalize to the population. 5) Results should be assessed at the group and individual-participant level, and effects should dissipate for every participant. 6) Bayesian analysis should be conducted to evaluate evidence in favor of the null hypothesis. 7) Effects should be assessed in multiple cortical regions. It is hoped that these guidelines will be employed to ensure the continued use of TMS as a valuable tool in the field of cognitive neuroscience.

Goal Programming (GP) is applied to modelling the decision making processes in the well‐known Ultimatum Game and some of its variations. The decision model for a player is a Chebychev GP model that balances her individual desires with the mental model she has of the desires of other relevant players. Fairness is modelled as a universal mechanism, allowing players to differ in their belief of what a fair solution should be in any particular game. The model's conceptual framework draws upon elements considered of importance in the field of cognitive neuroscience, and results from the field of psychology are used to further specify the types of goals in the model. Computer simulations of the GP models, testing a number of Ultimatum, Dictator and Double‐Blind Dictator Games, lead to distributions of proposals made and accepted that correspond reasonably well with experimental findings.

Learning and memory is an essential component of human intelligence. To understand its underlying molecular and neuronal mechanisms is currently an extensive focus in the field of cognitive neuroscience. We have employed advanced mouse genetic approaches to analyze the molecular and neuronal bases for learning and memory, and our results showed that brain region-specific genetic manipulations (including transgenic and knockout), inducible/reversible knockout, genetic/chemical kinase inactivation, and neuronal-based genetic approach are very powerful tools for studying the involvements of various molecules or neuronal substrates in the processes of learning and memory. Studies using these techniques may eventually lead to the understanding of how new information is acquired and how learned information is memorized in the brain.

Combined effects of acoustic, thermal, and illumination conditions on the comfort of discrete senses and overall indoor environment

BackgroundThe study of the involvement of the cerebellum in learning and memory has become one of the recent hot topics in the field of cognitive neuroscience. Transcranial magnetic stimulation (TMS) of the cerebellum has gained increasing interest in the treatment of cognition‐related disorders, making it necessary to determine the optimal parameters for cerebellar TMS. In this study, we aim to explore the effects of different frequencies of cerebellar repetitive TMS (rTMS) on working memory regulation and the associated electrophysiological changes. Through this research, we hope to provide objective bioinformatic guiding evidence for parameter optimization of cerebellar TMS.MethodWe recruited 75 healthy university students or graduate students as participants and divided them into five groups: 1Hz rTMS group, 5Hz rTMS group, 10Hz rTMS group, 20Hz rTMS group, and sham stimulation group. Each group received rTMS at different frequencies (1Hz, 5Hz, 10Hz, and 20Hz) over the cerebellar Crus II region, while a control group received sham stimulation. We assessed working memory performance using a 2‐back task and recorded electroencephalographic (EEG) signals during the 2‐back task and resting state before and after the stimulation. Cluster‐based permutation statistics and One‐way analysis of variance were used for the analysis of both neurophysiological and behavioral data.ResultThe 5Hz rTMS significantly increased the induced ERP activity of N100, P150, and N200, showed greater theta and alpha oscillatory activity, and significantly improved working memory performance. Furthermore, using phase locking value to construct brain networks and conduct analysis, we found that 5Hz rTMS significantly enhanced the whole‐brain local and global efficiency of brain networks in the theta frequency band, while 20Hz rTMS improved the local efficiency of the occipital and parietal lobes in the beta frequency band, with no significant differences observed in other frequency bands.ConclusionOur results demonstrate that 5Hz rTMS stimulation can significantly improve subjects' neurophysiological changes and working memory performance, showing a certain frequency‐dependent effect. These findings contribute to providing scientific evidence for the application of cerebellar TMS in memory improvement and offer valuable references for future related research.

Object recognition is the ability to identify an object or category based on the combination of visual features observed. It is a remarkable feat of the human brain, given that the patterns of light received by the eye associated with the properties of a given object vary widely with simple changes in viewing angle, ambient lighting, and distance. Furthermore, different exemplars of a specific object category can vary widely in visual appearance, such that successful categorization requires generalization across disparate visual features. In this review, we discuss recent advances in understanding the neural representations underlying object recognition in the human brain. We highlight three current trends in the approach towards this goal within the field of cognitive neuroscience. Firstly, we consider the influence of deep neural networks both as potential models of object vision and in how their representations relate to those in the human brain. Secondly, we review the contribution that time-series neuroimaging methods have made towards understanding the temporal dynamics of object representations beyond their spatial organization within different brain regions. Finally, we argue that an increasing emphasis on the context (both visual and task) within which object recognition occurs has led to a broader conceptualization of what constitutes an object representation for the brain. We conclude by identifying some current challenges facing the experimental pursuit of understanding object recognition and outline some emerging directions that are likely to yield new insight into this complex cognitive process.

This chapter shows that the processing power of the hemispheres working together can surpass that of the separate capabilities of each hemisphere added together. The major quest in the field of cognitive neuroscience is to understand how the neural wiring of the brain constrains and shapes the information processing capacity of the human mind. One of the primary features of the neurological organization of the human brain is lateral asymmetry. One could view interhemispheric processing as merely a simple shuttling mechanism allowing for passive transport of information between the hemispheres. The chapter argues that interhemispheric interaction is a means whereby the overall processing capacity of the brain is enhanced. The fact that the distribution model is a better description of performance than the concentration model is important, because it suggests that, indeed, interhemispheric interaction allows processing to be dispersed over as much neural space as possible.

Recent studies of creative cognition have revealed interactions between functional brain networks involved in the generation of novel ideas; however, the neural basis of creativity is highly complex and presents a great challenge in the field of cognitive neuroscience, partly because of ambiguity around how to assess creativity. We applied a novel computational method of verbal creativity assessment-semantic distance-and performed weighted degree functional connectivity analyses to explore how individual differences in assembly of resting-state networks are associated with this objective creativity assessment. To measure creative performance, a sample of healthy adults (n = 175) completed a battery of divergent thinking (DT) tasks, in which they were asked to think of unusual uses for everyday objects. Computational semantic models were applied to calculate the semantic distance between objects and responses to obtain an objective measure of DT performance. All participants underwent resting-state imaging, from which we computed voxel-wise connectivity matrices between all gray matter voxels. A linear regression analysis was applied between DT and weighted degree of the connectivity matrices. Our analysis revealed a significant connectivity decrease in the visual-temporal and parietal regions, in relation to increased levels of DT. Link-level analyses showed higher local connectivity within visual regions was associated with lower DT, whereas projections from the precuneus to the right inferior occipital and temporal cortex were positively associated with DT. Our results demonstrate differential patterns of resting-state connectivity associated with individual creative thinking ability, extending past work using a new application to automatically assess creativity via semantic distance.