Bridging Neural and Computational Viewpoints on Perceptual Decision-Making

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

Research output: Contribution to journalReview article

12 Citations (Scopus)

Abstract

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.
LanguageEnglish
Number of pages15
JournalTrends in Neurosciences
DOIs
Publication statusE-pub ahead of print - 13 Jul 2018

Fingerprint

Choice Behavior
Decision Making

Keywords

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

Cite this

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abstract = "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.",
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Bridging Neural and Computational Viewpoints on Perceptual Decision-Making. / O'Connell, Redmond; Wong-Lin, KongFatt; Shadlen, Michael; Kelly, Simon.

In: Trends in Neurosciences, 13.07.2018.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Bridging Neural and Computational Viewpoints on Perceptual Decision-Making

AU - O'Connell, Redmond

AU - Wong-Lin, KongFatt

AU - Shadlen, Michael

AU - Kelly, Simon

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N2 - 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.

AB - 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.

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KW - computational modelling

KW - sequential sampling

KW - lateral intraparietal area (LIP)

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DO - 10.1016/j.tins.2018.06.005

M3 - Review article

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