Rats and Humans Can Optimally Accumulate Evidence for Decision-Making

Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.
Science (Impact Factor: 33.61). 04/2013; 340(6128):95-8. DOI: 10.1126/science.1233912
Source: PubMed


The gradual and noisy accumulation of evidence is a fundamental component of decision-making, with noise playing a key role as the source of variability and errors. However, the origins of this noise have never been determined. We developed decision-making tasks in which sensory evidence is delivered in randomly timed pulses, and analyzed the resulting data with models that use the richly detailed information of each trial's pulse timing to distinguish between different decision-making mechanisms. This analysis allowed measurement of the magnitude of noise in the accumulator's memory, separately from noise associated with incoming sensory evidence. In our tasks, the accumulator's memory was noiseless, for both rats and humans. In contrast, the addition of new sensory evidence was the primary source of variability. We suggest our task and modeling approach as a powerful method for revealing internal properties of decision-making processes.

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    • "This model-based approach has proven valuable and has already revealed the critical role of the subthalamic nuclei for motor inhibition under conditions of ambiguity or risk (Cavanagh et al., 2011) and the effect of subthalamotomy on inhibitory behaviour (Obeso et al., 2014). Accurate fitting of the model in studies of animals and healthy participants often requires thousands of trials (Brunton et al., 2013). This would not be tolerated by patients with neurodegenerative disorders. "
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    Brain 11/2015; DOI:10.1093/brain/awv331 · 9.20 Impact Factor
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    • "In some cases, decisions about perfectly stable stimuli appear to involve perfect accumulation, as described by drift-diffusion and related models (Gold and Shadlen, 2000; Roitman and Shadlen, 2002; Brunton et al., 2013; Hanks et al., 2015). Under those conditions, deviations from perfect accumulation in the brain may be considered as inefficient, operating under other constraints "
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    eLife Sciences 08/2015; DOI:10.7554/eLife.08825 · 9.32 Impact Factor
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    • "All studies above focused on sensory detection for which the transfer function and Bloch's curve are—given the linear systems approach—interchangeable empirical descriptors (once the impulse response of the system is known). Nonetheless, starting perhaps with Ratcliff (1978), drift diffusion became the standard modeling approach to information accumulation over time for both threshold and suprathreshold stimuli and thereby to its relation with response time (e.g., Bogacz, Brown, Moehlis, Holmes, & Cohen, 2006; Brunton, Botvinick, & Brody, 2013; Gold & Shadlen, 2001, 2007; Usher & McClelland, 2001). Drift-diffusion (also referred to as bound diffusion or integration-to-bound) models were and still are meant to account for subjects' decision time and, critically, of its stochastic variability over time in the presence of an ongoing stimulation. "
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