Bridging Neural and Computational Viewpoints on Perceptual Decision-Making

Redmond O'Connell, KongFatt Wong-Lin, Michael Shadlen, Simon Kelly

Research output: Contribution to journalReview articlepeer-review

96 Citations (Scopus)
53 Downloads (Pure)


Sequential sampling models have provided a dominant theoretical framework guiding computational and neurophysiological investigations of perceptual decision-making. While these models share the basic principle that decisions are formed by accumulating sensory evidence to a bound, they come in many forms that can make similar predictions of choice behaviour despite invoking fundamentally different mechanisms. The identification of neural signals that reflect some of the core computations underpinning decision formation offers new avenues for empirically testing and refining key model assumptions. Here, we highlight recent efforts to explore these avenues and, in so doing, consider the conceptual and methodological challenges that arise when seeking to infer decision computations from complex neural data.
Original languageEnglish
Number of pages15
JournalTrends in Neurosciences
Publication statusPublished online - 13 Jul 2018


  • perceptual decision-making
  • computational modelling
  • sequential sampling
  • lateral intraparietal area (LIP)


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