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
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.
Available from: Jiaxiang Zhang
- "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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ABSTRACT: Progressive supranuclear palsy and Parkinson's disease have distinct underlying neuropathology, but both diseases affect cognitive function in addition to causing a movement disorder. They impair response inhibition and may lead to impulsivity, which can occur even in the presence of profound akinesia and rigidity. The current study examined the mechanisms of cognitive impairments underlying disinhibition, using horizontal saccadic latencies that obviate the impact of limb slowness on executing response decisions. Nineteen patients with clinically diagnosed progressive supranuclear palsy (Richardson's syndrome), 24 patients with clinically diagnosed Parkinson's disease and 26 healthy control subjects completed a saccadic Go/No-Go task with a head-mounted infrared saccadometer. Participants were cued on each trial to make a pro-saccade to a horizontal target or withhold their responses. Both patient groups had impaired behavioural performance, with more commission errors than controls. Mean saccadic latencies were similar between all three groups. We analysed behavioural responses as a binary decision between Go and No-Go choices. By using Bayesian parameter estimation, we fitted a hierarchical drift-diffusion model to individual participants' single trial data. The model decomposes saccadic latencies into parameters for the decision process: decision boundary, drift rate of accumulation, decision bias, and non-decision time. In a leave-one-out three-way classification analysis, the model parameters provided better discrimination between patients and controls than raw behavioural measures. Furthermore, the model revealed disease-specific deficits in the Go/No-Go decision process. Both patient groups had slower drift rate of accumulation, and shorter non-decision time than controls. But patients with progressive supranuclear palsy were strongly biased towards a pro-saccade decision boundary compared to Parkinson's patients and controls. This indicates a prepotency of responding in combination with a reduction in further accumulation of evidence, which provides a parsimonious explanation for the apparently paradoxical combination of disinhibition and severe akinesia. The combination of the well-tolerated oculomotor paradigm and the sensitivity of the model-based analysis provides a valuable approach for interrogating decision-making processes in neurodegenerative disorders. The mechanistic differences underlying participants' poor performance were not observable from classical analysis of behavioural data, but were clearly revealed by modelling. These differences provide a rational basis on which to develop and assess new therapeutic strategies for cognition and behaviour in these disorders.
Brain 11/2015; DOI:10.1093/brain/awv331 · 9.20 Impact Factor
Available from: Christopher Glaze
- "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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ABSTRACT: In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals; differentiation to detect unpredictable changes in those signals; or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals.
eLife Sciences 08/2015; DOI:10.7554/eLife.08825 · 9.32 Impact Factor
Available from: Andrei Gorea
- "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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ABSTRACT: In 1885 Adolphe-Moïse Bloch asked the following simple question “Is there a law describing the relationship between the duration of a light and its perceived intensity?” Based on a series of experiments using a Foucault regulator and a candle, Bloch concluded that “when the lighting duration varies from 0.00173 to 0.0518 seconds (…) the [visible] light is markedly in inverse proportion to its duration” – his famous law. As this law pertains to the more general and hotly debated question of accumulation of sensory information over time, it is timely to offer the public a full translation of Bloch’s original paper (from French) and to present it within the context of contemporary research.
i-Perception 08/2015; in press(4). DOI:10.1177/2041669515593043
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