Neural mechanisms of cue-approach training. Bakkour, A., Lewis-Peacock, J. A., Poldrack, R. A., & Schonberg, T. NeuroImage, 151(September 2016):92–104, 2017. Publisher: Elsevier![Neural mechanisms of cue-approach training [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training.
@article{bakkour_neural_2017,
title = {Neural mechanisms of cue-approach training},
volume = {151},
issn = {10959572},
url = {http://dx.doi.org/10.1016/j.neuroimage.2016.09.059},
doi = {10.1016/j.neuroimage.2016.09.059},
abstract = {Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training.},
number = {September 2016},
journal = {NeuroImage},
author = {Bakkour, Akram and Lewis-Peacock, Jarrod A. and Poldrack, Russell A. and Schonberg, Tom},
year = {2017},
note = {Publisher: Elsevier},
keywords = {Behavioral change, Cue-approach training, Value-based decision making},
pages = {92--104},
}
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