Hick-hyman law is mediated by the cognitive control network in the brain

Tingting Wu, Alexander J. Dufford, Laura J. Egan, Melissa Ann Mackie, Cong Chen, Changhe Yuan, Chao Chen, Xiaobo Li, Xun Liu, Patrick R. Hof, Jin Fan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

The Hick-Hyman law describes a linear increase in reaction time (RT) as a function of the information entropy of response selection, which is computed as the binary logarithm of the number of response alternatives. While numerous behavioral studies have provided evidence for the Hick-Hyman law, its neural underpinnings have rarely been examined and are still unclear. In this functional magnetic resonance imaging study, by utilizing a choice reaction time task to manipulate the entropy of response selection, we examined brain activity mediating the input and the output, as well as the connectivity between corresponding regions in human participants. Beyond confirming the Hick-Hyman law in RT performance, we found that activation of the cognitive control network (CCN) increased and activation of the default mode network (DMN) decreased, both as a function of entropy. However, only the CCN, but not the DMN, was involved in mediating the relationship between entropy and RT. The CCN was involved in both stages of uncertainty representation and response generation, while the DMN was mainly involved at the stage of uncertainty representation. These findings indicate that the CCN serves as a core entity underlying the Hick-Hyman law by coordinating uncertainty representation and response generation in the brain.

Original languageEnglish (US)
Pages (from-to)2267-2282
Number of pages16
JournalCerebral Cortex
Volume28
Issue number7
DOIs
StatePublished - Jul 1 2018
Externally publishedYes

Keywords

  • Choice reaction time
  • Cognitive control network
  • Default mode network
  • Hick-Hyman law
  • Information entropy

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

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