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Computational Neuroscience across the Lifespan: Promises and Pitfalls


Computational Neuroscience across the Lifespan: Promises and Pitfalls

van den Bos, Wouter, Bruckner, Rasmus, Nassar, Matthew R., Mata, Rui and Eppinger, Ben (2017) Computational Neuroscience across the Lifespan: Promises and Pitfalls. Developmental Cognitive Neuroscience . ISSN 18789293 (In Press)

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Official URL: http://dx.doi.org/10.1016/j.dcn.2017.09.008


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.

Divisions:Concordia University > Faculty of Arts and Science > Psychology
Item Type:Article
Authors:van den Bos, Wouter and Bruckner, Rasmus and Nassar, Matthew R. and Mata, Rui and Eppinger, Ben
Journal or Publication:Developmental Cognitive Neuroscience
Date:13 October 2017
Digital Object Identifier (DOI):10.1016/j.dcn.2017.09.008
ID Code:983119
Deposited By: Danielle Dennie
Deposited On:18 Oct 2017 20:49
Last Modified:18 Jan 2018 17:56
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