Journal
TRENDS IN NEUROSCIENCES
Volume 41, Issue 11, Pages 838-852Publisher
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tins.2018.06.005
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Funding
- U.S. National Science Foundation [BCS-1358955]
- European Research Council Starting Grant [63829]
- Science Foundation Ireland ERC Support Award [15/ERCS/3267]
- Career Development Award from Science Foundation Ireland [15/CDA/3591]
- Science Foundation Ireland (SFI) [15/ERCS/3267, 15/CDA/3591] Funding Source: Science Foundation Ireland (SFI)
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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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