Single trial-based prediction of a go/no-go decision in monkey superior colliculus

Ryohei P. Hasegawa*, Yukako T. Hasegawa, Mark A. Segraves

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

While some decision-making processes often result in the generation of an observable action, for example eye or limb movements, others may prevent actions and occur without an overt behavioral response. To understand how these decisions are made, one must look directly at their neuronal substrates. We trained two monkeys on a go/no-go task which requires a saccade to a peripheral cue stimulus (go) or maintenance of fixation (no-go). We performed binary regressions on the activity of single neurons in the superior colliculus (SC), with the go/no-go decision as a predictor variable, and constructed a virtual decision function (VDF) designed to provide a good estimation of decision content and its timing in a single trial decision process. Post hoc analyses by VDF correctly predicted the monkey's choice in more than 80% of trials. These results suggest that monitoring of SC activity has sufficient capacity to predict go/no-go decisions on a trial-by-trial basis.

Original languageEnglish (US)
Pages (from-to)1223-1232
Number of pages10
JournalNeural Networks
Volume19
Issue number8
DOIs
StatePublished - Oct 2006

Keywords

  • Brain-machine interface
  • Decision-making
  • Monkey
  • Prediction
  • Saccade
  • Superior colliculus

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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