1,721,146 research outputs found
An introduction to model-based cognitive neuroscience
Get PDFTwo recent innovations, the emergence of formal cognitive models and the addition of cognitive neuroscience data to the traditional behavioral data, have resulted in the birth of a new, interdisciplinary field of study: model-based cognitive neuroscience. Despite the increasing scientific interest in model-based cognitive neuroscience, few active researchers and even fewer students have a good knowledge of the two constituent disciplines. The main goal of this edited collection is to promote the integration of cognitive modeling and cognitive neuroscience. Experts in the field will provide tutorial-style chapters that explain particular techniques and highlight their usefulness through concrete examples and numerous case studies. The book will also include a thorough list of references pointing the reader towards additional literature and online resources
An Introduction to Human Brain Anatomy
Get PDFThis tutorial chapter provides an overview of the human brain anatomy. Knowledge of brain anatomy is fundamental to our understanding of cognitive processes in health and disease; moreover, anatomical constraints are vital for neurocomputational models and can be important for psychological theorizing as well. The main challenge in understanding brain anatomy is to integrate the different levels of description ranging from molecules to macroscopic brain networks. This chapter contains three main sections. The first section provides a brief introduction to the neuroanatomical nomenclature. The second section provides an introduction to the different levels of brain anatomy and describes commonly used atlases for the visualization of functional imaging data. The third section provides a concrete example of how human brain structure relates to performance
Model-Based Cognitive Neuroscience: A Conceptual Introduction
Get PDFThis tutorial chapter shows how the separate fields of mathematical psychology and cognitive neuroscience can interact to their mutual benefit. Historically, the field of mathematical psychology is mostly concerned with formal theories of behavior, whereas cognitive neuroscience is mostly concerned with empirical measurements of brain activity. Despite these superficial differences in method, the ultimate goal of both disciplines is the same: to understand the workings of human cognition. In recognition of this common purpose, mathematical psychologists have recently started to apply their models in cognitive neuroscience, and cognitive neuroscientists have borrowed and extended key ideas that originated from mathematical psychology. This chapter consists of three main sections: the first describes the field of mathematical psychology, the second describes the field of cognitive neuroscience, and the third describes their recent combination: model-based cognitive neuroscience
Bayesian models in cognitive neuroscience: A tutorial
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150195.pdf (Publisher’s version ) (Open Access)This chapter provides an introduction to Bayesian models and their application in cognitive neuroscience. The central feature of Bayesian models, as opposed to other classes of models, is that Bayesian models represent the beliefs of an observer as probability distributions, allowing them to integrate information while taking its uncertainty into account. In the chapter, we will consider how the probabilistic nature of Bayesian models makes them particularly useful in cognitive neuroscience. We will consider two types of tasks in which we believe a Bayesian approach is useful: optimal integration of evidence from different sources, and the development of beliefs about the environment given limited information (such as during learning). We will develop some detailed examples of Bayesian models to give the reader a taste of how the models are constructed and what insights they may be able to offer about participants’ behavior and brain activity
Cognitive Control of Choices and Actions
Get PDFWe review model-based neuroscience work on cognitive control of choices and actions. We consider both strategically deployed executive processes and more automatic influences, first in binary choice tasks and then in more complex tasks. These include “conflict” tasks, where automatic and executive control processes sometimes act in opposition; delay discounting tasks, which require self-control to obtain larger rewards; and tasks where routine actions are occasionally interruptedbycuesrequiringdifferentactionortheinhibitionofaction. For all of these tasks, dynamic cognitive models have been developed based on the idea of accumulating evidence. They have also been studied by traditional neuroscience methods, but direct links to the cognitive models have not always been made. We detail the way in which progress has been made with model-based neuroscience methods in some cases and in others highlight how this points the way towards opportunities for progress. We emphasise generative Bayesian estimation methods that are well suited to the complexities of model-based neuroscience and provide exercises with open-source code that allow readers to develop skills with models relevant to cognitive control.</p
An Introduction to Good Practices in Cognitive Modeling
No full textCognitive modeling can provide important insights into the underlying causes of behavior, but the validity of those insights rests on careful model development and checking. We provide guidelines on five important aspects of the practice of cognitive modeling: parameter recovery, testing selective influence of experimental manipulations on model parameters, quantifying uncertainty in parameter estimates, testing and displaying model fit, and selecting among different model parameterizations and types of models. Each aspect is illustrated with examples
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No full textProject: The connectivity fingerprint of the human frontal cortex, subthalamic nucleus and striatum Authors: Isaacs B.R., Forstmann, B.U., Temel, Y., Keuken, M.C.
data: MRI data of sub-01 <br
Cognitive enhancement: toward the integration of theory and practice
Get PDFPlease note this dissertation was updated in 2024. I refer to the Cover Note (2024) for details about these updates. ------------------------------------------------------------------ Cognitive enhancement reflects the use of any (legitimate) means such as for example food supplements to reach one’s personal best, and has gained great interest over the past years. The increasing costs of the welfare offer one explanation, the second is that both Eastern and Western societies are continuously driven towards more individualism pushing the idea that an individual is the director of his or her own life.In this dissertation, I attempted to explain how and why enhancement techniques such as brain stimulation, videogaming, and food supplements (e.g. tyrosine and tryptophan) are promising and inexpensive ways to enhance cognition. That is, clear ideas about the underlying mechanisms of these effects are needed before these techniques can be applied outside the field of science. Our findings have important societal and economic implications and go hand-in-hand with the ideological individualistic trend in society. More research is needed in order to gain better insights into the underlying mechanisms and the role of individual differences in modulating the observed effects. However, the discussed techniques do have promising potential, not only in possibly delaying cognitive decline in elderly, but also enhancing (social) cognitive functioning and mental well-being in healthy humans.NWOAction Contro
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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