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The neural representation of voluntary task-set selection in dynamic environments.

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Tusche,  Anita
Bernstein Center for Computational Neuroscience, Berlin, Germany;
Department Social Neuroscience, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA;

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Citation

Wisniewski, D., Reverberi, C., Tusche, A., & Haynes, J.-D. (2015). The neural representation of voluntary task-set selection in dynamic environments. Cerebral Cortex, 25(12), 4715-4726. doi:10.1093/cercor/bhu155.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0027-BBA8-6
Abstract
When choosing actions, humans have to balance carefully between different task demands. On the one hand, they should perform tasks repeatedly to avoid frequent and effortful switching between different tasks. On the other hand, subjects have to retain their flexibility to adapt to changes in external task demands such as switching away from an increasingly difficult task. Here, we developed a difficulty-based choice task to investigate how subjects voluntarily select task-sets in predictably changing environments. Subjects were free to choose 1 of the 3 task-sets on a trial-by-trial basis, while the task difficulty changed dynamically over time. Subjects self-sequenced their behavior in this environment while we measured brain responses with functional magnetic resonance imaging (fMRI). Using multivariate decoding, we found that task choices were encoded in the medial prefrontal cortex (dorso-medial prefrontal cortex, dmPFC, and dorsal anterior cingulate cortex, dACC). The same regions were found to encode task difficulty, a major factor influencing choices. Importantly, the present paradigm allowed us to disentangle the neural code for task choices and task difficulty, ensuring that activation patterns in dmPFC/dACC independently encode these 2 factors. This finding provides new evidence for the importance of the dmPFC/dACC for task-selection and motivational functions in highly dynamic environments.