Computational neuroscience across the lifespan: Promises and pitfalls
In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination...
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doaj-433db26e8f9c4280bc8f6f50b8ebad432020-11-24T21:19:21ZengElsevierDevelopmental Cognitive Neuroscience1878-92932018-10-01334253Computational neuroscience across the lifespan: Promises and pitfallsWouter van den Bos0Rasmus Bruckner1Matthew R. Nassar2Rui Mata3Ben Eppinger4Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands; International Max Planck Research School LIFE, Berlin, Germany; Corresponding author at: Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; International Max Planck Research School LIFE, Berlin, GermanyDepartment of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, USACenter for Cognitive and Decision Sciences, Department of Psychology, University of Basel, Basel, SwitzerlandDepartment of Psychology, Concordia University, Montreal, Canada; Department of Psychology, TU Dresden, Dresden, Germany; Corresponding author at: Department of Psychology, Concordia University, Montreal, Canada.In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent variables can help to test hypotheses about age-related changes in behavioral and neurobiological measures at a level of specificity that is not achievable with descriptive analysis approaches alone. This level of specificity can in turn be beneficial to establish the identity of the corresponding behavioral and neurobiological mechanisms. In this paper, we will illustrate applications of computational methods using examples of lifespan research on risk taking, strategy selection and reinforcement learning. We will elaborate on problems that can occur when computational neuroscience methods are applied to data of different age groups. Finally, we will discuss potential targets for future applications and outline general shortcomings of computational neuroscience methods for research on human lifespan development. keywords: Computational neuroscience, Reinforcement learning, Risk-taking, Decision-making, Brain development, Identification, Strategieshttp://www.sciencedirect.com/science/article/pii/S1878929317301068 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wouter van den Bos Rasmus Bruckner Matthew R. Nassar Rui Mata Ben Eppinger |
spellingShingle |
Wouter van den Bos Rasmus Bruckner Matthew R. Nassar Rui Mata Ben Eppinger Computational neuroscience across the lifespan: Promises and pitfalls Developmental Cognitive Neuroscience |
author_facet |
Wouter van den Bos Rasmus Bruckner Matthew R. Nassar Rui Mata Ben Eppinger |
author_sort |
Wouter van den Bos |
title |
Computational neuroscience across the lifespan: Promises and pitfalls |
title_short |
Computational neuroscience across the lifespan: Promises and pitfalls |
title_full |
Computational neuroscience across the lifespan: Promises and pitfalls |
title_fullStr |
Computational neuroscience across the lifespan: Promises and pitfalls |
title_full_unstemmed |
Computational neuroscience across the lifespan: Promises and pitfalls |
title_sort |
computational neuroscience across the lifespan: promises and pitfalls |
publisher |
Elsevier |
series |
Developmental Cognitive Neuroscience |
issn |
1878-9293 |
publishDate |
2018-10-01 |
description |
In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent variables can help to test hypotheses about age-related changes in behavioral and neurobiological measures at a level of specificity that is not achievable with descriptive analysis approaches alone. This level of specificity can in turn be beneficial to establish the identity of the corresponding behavioral and neurobiological mechanisms. In this paper, we will illustrate applications of computational methods using examples of lifespan research on risk taking, strategy selection and reinforcement learning. We will elaborate on problems that can occur when computational neuroscience methods are applied to data of different age groups. Finally, we will discuss potential targets for future applications and outline general shortcomings of computational neuroscience methods for research on human lifespan development. keywords: Computational neuroscience, Reinforcement learning, Risk-taking, Decision-making, Brain development, Identification, Strategies |
url |
http://www.sciencedirect.com/science/article/pii/S1878929317301068 |
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1726005901812301824 |